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2011.13240
|
Blockchain mechanism and distributional characteristics of cryptos
|
We investigate the relationship between underlying blockchain mechanism of
cryptocurrencies and its distributional characteristics. In addition to price,
we emphasise on using actual block size and block time as the operational
features of cryptos. We use distributional characteristics such as fourier
power spectrum, moments, quantiles, global we optimums, as well as the measures
for long term dependencies, risk and noise to summarise the information from
crypto time series. With the hypothesis that the blockchain structure explains
the distributional characteristics of cryptos, we use characteristic based
spectral clustering to cluster the selected cryptos into five groups. We
scrutinise these clusters and find that indeed, the clusters of cryptos share
similar mechanism such as origin of fork, difficulty adjustment frequency, and
the nature of block size. This paper provides crypto creators and users with a
better understanding toward the connection between the blockchain protocol
design and distributional characteristics of cryptos.
|
http://arxiv.org/pdf/2011.13240v2
|
Min-Bin Lin, Kainat Khowaja, Cathy Yi-Hsuan Chen, Wolfgang Karl Härdle
|
cs.CR, q-fin.GN
|
cs.CR
|
Blockchain mechanism and distributional characteristics of
cryptos∗
Min-Bin Lin†Kainat Khowaja‡Cathy Yi-Hsuan Chen§
Wolfgang Karl H ardle¶
Abstract
We investigate the relationship between underlying blockchain mechanism of cryptocurren-
cies and its distributional characteristics. In addition to price, we emphasise on using actual
block size and block time as the operational features of cryptos. We use distributional charac-
teristics such as fourier power spectrum, moments, quantiles, global we optimums, as well as
the measures for long term dependencies, risk and noise to summarise the information from
crypto time series. With the hypothesis that the blockchain structure explains the distribu-
tional characteristics of cryptos, we use characteristic based spectral clustering to cluster the
selected cryptos into ve groups. We scrutinise these clusters and nd that indeed, the clus-
ters of cryptos share similar mechanism such as origin of fork, diculty adjustment frequency,
and the nature of block size. This paper provides crypto creators and users with a better un-
derstanding toward the connection between the blockchain protocol design and distributional
characteristics of cryptos.
Keywords: Cryptocurrency, price, blockchain mechanism, distributional characteristics,
clustering
JEL Classi cation: C00
∗Financial support of the European Union's Horizon 2020 research and innovation program \FIN- TECH: A Finan-
cial supervision and Technology compliance training programme" under the grant agreement No 825215 (Topic: ICT-
35-2018, Type of action: CSA), the European Cooperation in Science & Technology COST Action grant CA19130 -
Fintech and Arti cial Intelligence in Finance - Towards a transparent nancial industry, the Deutsche Forschungs-
gemeinschaft's IRTG 1792 grant, the Yushan Scholar Program of Taiwan and the Czech Science Foundation's grant
no. 19-28231X / CAS: XDA 23020303 are greatly acknowledged.
†International Research Training Group 1792, Humboldt-Universit at zu Berlin, Spandauer Str. 1, 10178 Berlin,
Germany. Email: min-bin.lin@hu-berlin.de
‡International Research Training Group 1792, Humboldt-Universit at zu Berlin, Spandauer Str. 1, 10178 Berlin,
Germany. Email: kainat.khowaja@hu-berlin.de
§Adam Smith Business School, University of Glasgow, United Kingdom; IRTG 1792 High Dimensional Non
Stationary Time Series, Humboldt-Universit at zu Berlin. Email: cathyyi-hsuan.chen@glasgow.ac.uk
¶BRC Blockchain Research Center, Humboldt-Universit at zu Berlin, Berlin, Germany; Sim Kee Boon Institute,
Singapore Management University, Singapore; WISE Wang Yanan Institute for Studies in Economics, Xiamen Uni-
versity, Xiamen, China; Dept. Information Science and Finance, National Chiao Tung University, Hsinchu, Taiwan,
ROC; Dept. Mathematics and Physics, Charles University, Prague, Czech Republic, Grants{DFG IRTG 1792 grate-
fully acknowledged. Email: haerdle@hu-berlin.de
1arXiv:2011.13240v2 [cs.CR] 24 Aug 2021
1 Introduction
Cryptocurrency (crypto) is a digital asset designed to be as a medium of exchange wherein individual
coin ownership is recorded in a digital ledger or computerised database. Its creation of monetary
units and veri cation of fund transactions are secured using encryption techniques and distributed
across several nodes (devices) on a peer-to-peer network. Such technology-enhanced and privacy-
preserving features make it potentially di erent to other existing nancial instruments and has
attracted attention of many investors and researchers (H ardle et al., 2020). Many studies have
investigated the similarity between a pool of cryptocurrencies in order to classify the important
features of digital currencies. For example, Blau et al. (2020) has concluded that the top sixteen
most active cryptocurrencies co-move with bitcoin. Researchers have also focused on describing the
price behaviour of cryptos using economic factors (Ciaian et al., 2016; Sovbetov, 2018). However,
owing to the unique technology of cryptocurrencies, there still exists a gap between the creators of
blockchain mechanism and users operating the nancial market of the crytocurrencies and through
this research, we aim to take a step towards mitigating that gap.
We specialise our research on the following research questions. First, we characterise crypto
behaviour using distributional characteristics of time series data. Also, instead of using the prices
alone, we use actual block time and block size to incorporate the operational features of cryp-
tos. Second, we hypothesise that the blockchain structure that the coin attaches plays a pivotal
role in explaining the behaviour. More explicitly, we investigate the extent to which blockchain
structure leads to explain the distributional characteristics. Using a characteristic based clustering
coupled with spectral clustering technique, we group the selected cryptos into a number of clusters
and stratify the mechanisms that make the coins within the particular cluster showing the same
behaviour in price, actual block time, and actual block size, respectively.
When studying cryptocurrencies, many researchers only focus on crypto price and daily returns
(Trimborn and H ardle, 2018; Hou et al., 2020). While price is important when cryptos are used
as a medium of payment, it is de nitely not the only measure for evaluation of cryptocurrencies.
For example, many low price coins are highly traded and many coins that are not used as medium
2
of payment have low prices, e.g., XPR and Dogecoin. Cryptos were introduced to serve various
purposes and the purpose of the coin does matter. This makes it necessary to use other time series
while studying crypto markets. In this research, we propose to use actual block size and actual
block time alongside price.
Actual block size is the average actual size "usage" of a single block in data storage for one
day. Since a block comprises of transaction data, it can represent the status of how a blockchain
mechanism allocates transactions to a block. We consider it a measure of scalability of the system. A
well-functioning blockchain should be able to level the transaction arrivals. Transaction distribution
within a day for any crypto needs such balancing because it a ects miners rewards and hence the
demand of the coin. An ideal block size would keep con rmation times from ballooning while
keeping fees and security reasonable. Therefore, actual block size of cryptos can provide insight
into the behaviour of cryptos.
Actual block time, on the other hand, measures the consistency and performance of the system.
It is de ned as the mean time required in minutes for each day to create the next block. In other
words, it is the average amount of time for the day a user has to wait, after broadcasting their
transaction, to see this transaction appear on the blockchain. Think of crypto markets as a fast
food franchise and miners as customers who have to wait a certain time to make the purchase. If
the waiting time is shorter on certain days while on other instances, the customers have to wait
much longer, there is a discrepancy in the system. Analogously, the time series of block time, which
is the distribution of waiting time, can be seen as a service level of the whole system and it is
necessary to maintain as the users' expectation or target block time set by the system depend on
it.
The idea of investigating the underlying blockchain mechanism, a cornerstone of crypto technol-
ogy, and its connection to the crypto behaviour is still in its infancy. One of the rst endeavours in
explaining this relationship was made by Guo et al. (2018) who highlight that the the fundamental
characteristics of cryptocurrencies (e.g., algorithm and proof type) have a vital role in di eren-
tiating the performance of cryptocurrencies. They develop a spectral clustering methodology to
3
group cryptos in a dynamic fashion, but their research is limited in the exploitation of blockchain
characteristics. With a similar spirit, Iwamura et al. (2019) start by claiming that high
uctuation
is a re
ection of the lack of
exibility in the Bitcoin supply schedule. They further strengthen their
arguments by considering the predetermined algorithm of cryptos (speci cally, the proof of work)
to explain the volatility in cryptocurrency market. Zimmerman (2020) argue in their work that the
higher congestion in blockchain technology leads to higher volatility in crypto prices. They claim
that the limited settlement space in blockchain architecture makes users compete with one another,
a ecting the demand. In his model, the value of cryptos is governed by its demand, making the
price sensitive to blockchain capacity.
These research results, albeit true, are limited to a particular set of cryptocurrency mechanism
and do not thoroughly explain the dynamics of cryptocurrencies. Also, most of the papers only
use price as a proxy of behaviour. We advance the previous ndings by incorporating a rich set
of underlying mechanisms and connecting them to multiple time series. We take a deep dive into
eighteen cryptos with a variety of mechanisms- concluded in Garriga et al. (2020))- from a technical
perspective to summarise their mechanism and algorithm designs using variables, such as consensus
algorithm, type of hashing algorithm, diculty adjustment frequency and so on.
We investigate a relationship between underlying blockchain mechanism of cryptocurrencies
and the distributional characteristics. Using the a characteristic-based clustering technique, we
cluster the selected coins into a number of clusters and scrutinise the compositions of fundamental
characteristics in each group. We observe that the clusters obtained from these time series indeed
share common underlying mechanism. Through empirical evidence, we show that the cryptos forked
from same origin and same consensus mechanism tend to become part of same clustering group.
Furthermore, the clusters obtained by the time series of block time have same hashing algorithms
and diculty adjustment algorithms. Also, a similar nature (static or dynamic) of block size was
observed within clusters obtained by the time series of actual block size. We conclude with empirical
evidence that the crypto behaviour is actually linked with their blockchain protocol architectures.
The implications of this study are abundant. The creators of cryptocurrencies can manage the
4
impact of blockchain underlying mechanisms on the corresponding distributional characteristics, in
a consideration of adoption rate of invented coins. From the users' perspective, they can make an
optimal decision in which coins should be adopted while concerning the price
uctuation.
This paper proceeds as follows. Section 2 discusses data source and the underlying mechanisms
of the cryptos. Section 3 presents the methodology used for classifying characteristics of time series
and clustering algorithm. Section 4 provides an illustration of analysis results. Section 5 concludes
and provides several avenues for future research.
2 Data Source and Description
According to CoinMarketCap (https://coinmarketcap.com), currently there are over 7,000 cryp-
tocurrencies and their total market capitalisation has surpassed USD $400 billion as of November
09, 2020. Most of studies have focused on the mainstream coins (e.g., Bitcoin, Ethereum), and
little has been investigated on the coins which have been introduced and featured with a diverse
blockchain mechanisms and invented technologies. The work of Guo and Donev (2020) is one of
exceptions. In this study, 18 cryptos with di erent set of blockchain mechanisms have been ex-
amined {Bitcoin, Bitcoin Cash, Bitcoin Gold, Bitcoin SV, Blackcoin, Dash, Dogecoin, Ethereum,
Ethereum Classic, Feathercoin, Litecoin, Monero, Novacoin, Peercoin, Reddcoin, Vertcoin, XRP
(Ripple), and Zcash. We explore an interplay between distributional characteristics of crpytos and
blockchain mechanism. We discuss the key characteristics of blockchain mechanisms and the time
series data in this section.
2.1 Underlying Mechanism
Most of cryptos nowadays apply blockchain-based systems in which transactions are grouped into
blocks and cryptographically interlinked to form a back-linked list of blocks containing transactions.
The transactions are validated using the nodes within the crypto peer-to-peer network through
a majority consensus directed by algorithms instead of a central authority's approval. In such
an operation process, many algorithmic mechanisms are required to govern the performance and
5
outcome of a crypto system. Some key blockchain-based characteristics are discussed below:
Fork: It occurs as user base or developers conduct a fundamental or signi cant software change,
see as in Figure 1. There are two types of forks { soft and hard forks. The former is an update
to the protocol architecture and then all the nodes are enforced to follow in order to proceed with
the operations of a crypto. The latter one creates a duplicate copy of the origin blockchain and
modi es the copy to meet the desired quality (e.g., safety, scalability). In this case, a new crypto
can be generated accordingly. For example, Peercoin network facilitates an alternative consensus
mechanism {proof-of-stake (PoS) to Bitcoin's proof-of-work (PoW) system for reducing dependency
on energy consumption from mining process (King and Nadal, 2012).
Going beyond a digital currency, Ethereum establishes an open-ended decentralised platform
for diverse applications such as decentralised applications (dapps) and smart contracts (Buterin,
2014).
Consensus mechanism: In order to establish an agreement on a speci c subset of the candi-
date transactions, consensus mechanism provides a protocol for a large number of trust-less nodes
in a decentralised blockchain network. For instance, PoW (Proof-of-Work, as adopted by e.g.,
Bitcoin, Litecoin) achieves consensus with a competition among miners on solving computational
puzzles, which consume numerous computational resources; and PoS (Proof-of-Stake, as adopted by
e.g., Peercoin, Blackcoin) randomly assigns a block creator (transaction validator) with probability
proportional to their coins staked.
Hashing algorithm: It is a mathematical algorithm that encrypts a new transaction (or a new
block) into a xed length character string, known as hash value, and later interlinks this string with
a given blockchain to ensure the security and immutability of a crypto. Various hashing algorithms
are implemented in cryptos such as SHA-256, Scrypt and Equihash. These provide di erent degree
of complexity to blockchain operations.
Diculty adjustment algorithm: It is an adaptive mechanism which periodically adjusts the
diculty toward hashrate to target an average time interval between blocks, known as target block
6
Figure 1: Blockchain software forks in cryptocurrency.
time or target con rmation time. It regulates the creation rate of a block and maintains a certain
amount of outputs of a blockchain. Such a mechanism is commonly seen in a PoW framework.
An example from Bitcoin is shown in Figure 2 where its diculty adjustment algorithm, known as
DAA, modi es the diculty every 2016 blocks to meet target block time of 10 minutes.
2.2 Time Series Data
The data applied in this paper are collected from Bitinfocharts which is available at https://bitinfocharts.com/.
These time series are composed of data points observed daily from the genesis date of each crypto.
The lengths of these time series are thus varied coin by coin, but as explained in the section 2.2,
we continue to use the whole time series for each coin.
Price: Much previous literature has been triggered by the substantial
uctuations in crypto
7
Figure 2: Bitcoin's diculty adjustment toward actual block time.
Blockchain mechanism -
plotting
prices. In this study we investigate 18 crypto prices in USD on daily time series. Among these
18 cryptos, Bitcoin has been dominant and Reddcoin has the lowest price on balance as seen in
Figure 3. We characterise these price time series in Table 1. Most of these coins (i.e., Bitcoin,
Ethereum, Bitcoin Cash) have high
uctuations in price; while some coins (i.e., XRP, Blackcoin)
tends to be steady.
Figure 3: Time series of prices of the 18 cryptos
Blockchain mechanism plotting
8
Actual block time: It is the mean time required in minutes for each day to create the next
block. In other words, it is the average amount of time for the day a user has to wait, after
broadcasting their transaction, to see this transaction appear on the blockchain. Some literature
also refers it as con rmation time. It can be considered as a service level indicator for cryptos which
should be maintained by underlying mechanisms. Most of the coins discussed in this paper tend to
have lower block time compared with Bitcoin as seen in Figure 4. Also, many coins show outliers
in observations and this can indicate that the extreme events appear in the blockchain system.
The underlying mechanisms can be ine ective to accommodate the current system demand. The
distributional characteristics for time series of actual block time are presented in Table 2. The data
for XRP are missing but its designed block time is around 5 second per transaction.
Figure 4: Actual block time in minutes.
Blockchain mechanism plotting
9
Actual block size: It is de ned as the average actual size "usage" of a single block in data
storage for one day. Since a block is is comprised of transaction data, it can represent the status
of how a cryptocurrency mechanism allocates transactions to a block. In this study, as introduced
in Section 1, we consider it as an indicator for the stableness of scalability of a crypto. In Figure 5
shows that most of the cryptos under study have smaller block size usage than Bitcoin, except
Bitcoin SV. The plot also depicts that almost all the coins have outliers. These outliers can lead
to the imbalance in transaction fee and reward which can in
uence the ecosystem of a crypto. The
characteristics for block size time series are shown in Table 3. XRP does not have typical blockchain
structure, hence, there is no block size data in the study. The data for Peercoin are missing.
Figure 5: Actual block size in megabytes.
Blockchain mechanism plotting
10
3 Methodology
In order to investigate the relationship between underlying blockchain mechanism of cryptocur-
rencies and the distributional characteristics of cryptos as a proxy of behaviour, we aim to group
them into number of clusters and scrutinise the compositions of features in each group. These
blockchain-based features manifest the underlying mechanism of how the cryptos operate trans-
actions on their chains, and subsequently govern the price, actual block size and block time. As
described in the previous section, we use the time series data of 18 di erent cryptos with a range
of di erent mechanisms.
The time series data available for the cryptos is subject to numerous limitations. The most
important one of them is that di erent coins were introduced at di erent time points, therefore,
the data available for each coin has di erent lengths. For the clustering problems (Aghabozorgi
et al., 2015), de ning the distance metric between points in time series with various lengths is not
conventional. For many analytical problems, this issue is easily tackled by truncating the time series
to the shared sample period. We refrain from doing so because, in the analysis of cryptocurrency
prices, the evolution of the data in time is highly crucial for an investigation in the short term
and long term dynamics and therefore, truncating the time series would lead to loss of important
information. Hence, we deal with the time series data of cryptos with di erent lengths and do not
directly impose a distance metric on the input data points.
Furthermore, characterising the behaviour of a time series in terms of a single quantitative
attribute (such as range based volatility) has its own limitations. The chosen attribute usually
captures the dynamics of time series in one particular aspect, which may not be sucient to
encompass an entire behaviour or introduces a biased assessment. This becomes particularly true
in the problems of crypto classi cation and clustering where these attributes, used as a similarity
measure, are very diverse, resulting in weak robustness in the results.
To cope with these limitations, we resort to the characteristic based clustering method proposed
by Wang et al. (2005). It was recently applied by Pele et al. (2020) for classifying cryptos in order to
11
distinguish them from traditional assets. This methods recommends to incorporate various global
measures describing the structural characteristics of a time series for a clustering problem. These
global measures are obtained by applying statistical operations that best represent the underlying
characteristics. Also, by extracting a set of measures from the original time series we simply bypass
the issue of de ning a distance metric. It's understood that the global measures are domain-
speci c. Employing a greedy search algorithm, Wang et al. (2005) selects the pivotal features in
the clustering tasks. In our case, we import the experts' discretion on the choice of features as
distributional characteristics which best represent the dynamics of cryptocurrencies.
We choose a variety of measures for our analysis. Starting from the rst four moments and
quantiles that characterises the distribution and symmetry of the data, we include the statistics
for concluding the global structure such as global optimum, as well as the measures for long term
dependencies, risk and noise. The selected features are mean, standard deviation, skewness, kur-
tosis, maximum, minimum, rst quartile, median, third quartile, 1% and 5% extreme quantiles
as a measure of downside risk, linear trend, intercept, autocorrelation for long term dependency,
self-similarity using Hurst exponent and chaos using Lyaponav's exponents.
We further extend the methodology by including the power spectrum of time series as an addi-
tional measure. The power spectrum is obtained in this work using Fast Fourier Transform (FFT).
For computational ease, discrete fourier transform (DFT) has been formalised as a linear operator
that maps the data points in a discrete input signal Xfx1;x2;;xngto the frequency domain
f=ff1;f2;fng.
For a given time series Xofntime points, sine and cosine functions are used to get the
coecients !n=e 2i=and the frequencies are calculated using the matrix multiplication:
12
2
666666666664f1
f2
f3
...
fn3
777777777775=2
6666666666641 1 1 1
1!n!2
n!n 1
n
1!2
n!4
n!2(n 1)
n
...............
1!n 1
n!2(n 1)
n!(n 1)2
n3
7777777777752
666666666664x1
x2
x3
...
xn3
777777777775(1)
This matrix multiplication involves O(n2) and makes DFT computationally expensive. FFT is
a fast algorithm to compute DFT using only O(nlogn) operations (Brunton and Kutz, 2019). A
simple tcommand in python computes the FFT of the given time signal. The power spectrum of
this signal is the normalised squared magnitude of the fand it indicates how much variance of the
initial space each frequency explains (Brunton and Kutz, 2019). Including the power spectrum as
a feature for characteristic based clustering allows capturing the variability in the time signal that
is not explained by any other measure.
Accumulating all the aforementioned features in a vector gives in a reduced dimensional rep-
resentation of time series of each crypto. These vectors are then used to cluster the cryptos into
groups using spectral clustering. Spectral clustering exploits the eigenvalues of similarity matrix
to cluster and results in more balanced clusters than other techniques that were employed during
the process. For details related to spectral clustering, the readers are recommended to follow the
tutorial on spectral clustering by von Luxburg (2006). The results of the above methodology are
discussed in detail in the next section.
4 Empirical Evidence
In this section, we showcase the result from the characteristic based clustering individually on the
crypto price and operational features{which are constructed with price, block size "scalability" and
block time "service level" time series. We explore the clustering results and classify them with the
underlying mechanisms of the investigated 18 cryptos. The 18 cryptos are: Bitcoin, Bitcoin Cash,
Bitcoin Gold, Bitcoin SV, Blackcoin , Dash, Dogecoin, Ethereum, Ethereum Classic, Feathercoin,
13
Litecoin, Monero, Novacoin, Peercoin, Reddcoin, Vertcoin, XRP, and Zcash.
We calculate the characteristics for each of these cryptos for prices, block size and block time
separately. The results of all other attributes except the FFT are summarised in Tables 1, 2, 3
correspondingly in Appendix. Note that the data for XRP are not available for the block size and
block time, and for Peercoin block size is missing as described before in Section 3.
After calculating the attributes and FFT power spectrum described in section 2.2, the feature
space is 216 dimensional (200 dimensional vector of power spectrum and 16 characteristics), vi-
sualisation of which is not possible. We project the feature space into a three dimensional space
using principle component analysis (PCA), and the results of which are exhibited for an intuitive
understanding. We discuss each of the clustering in detail below. Moreover, in order to avoid a
monopoly outcome and sustain a certain level of interpretability, we impose the maximum number
of the clusters to avoid a single coin case in each cluster.
4.1 Clustering with crypto prices
Table 1 shows that as expected, Bitcoin has the highest average price and highest standard deviation,
due to high magnitude of its prices. The VaR99 and VaR95 for Bitcoin are, however, very low,
showing a low downside risk of Bitcoin. On the contrary, Bitcoin Cash, Bitcoin SV, Bitcoin Gold and
Zcash all show high value at risk. This could be due to low persistence of risk shocks (de Souza,
2019; Katsiampa et al., 2019). The high positive coecients of self similarity for all the coins
show high dependency on the previous time values. The high autocorrelation further con rms the
presence of long term dependencies of the time series. The Lyaponov exponent as a measure of
chaos is greater than 0 for all the time series which shows unstable dynamics throughout the prices
of cryptos.
The characteristics of Dogecoin in Table 1 assume very low values, unlike any other coin, because
the prices of Dogecoin are very low, despite it being a popular coin. This can be due to high supply
of the coin with no limit on the total number of coins created. The coin also has no technical
innovations, which is considered as one of the reasons why the coin has such small price. Hence,
14
the uncontrolled underlying mechanism of the coin has signi cant impact on the prices, despite the
high trading volumes of the coin. Same can be concluded for XRP and Reddcoin, which also have
a very high maximum supply that is re
ected in their very low prices.
Using characteristic based clustering on price time series, we have the result with 5 clusters as
below:
0. Bitcoin, Dash
1. Bitcoin SV, Zcash
2. Bitcoin Cash, Bitcoin Gold
3. Ethereum, Litecoin, XRP, Monero, Peercoin, Vertcoin, Reddcoin, Feathercoin, Blackcoin
4. Ethereum Classic, Dogecoin, Novacoin
Most of coins are close to each others in a three-dimensional space, as seen in Figure 6. Except
Dash, all the altcoins are in a di erent clusters than Bitcoin. Bitcoin Cash and Bitcoin Gold, which
principally inherit the protocol architecture from Bitcoin, are clustered together, but not centred
around with other coins. However, Bitcoin SV{which is a fork from Bitcoin Cash and mainly
increases the designed block size to lower the transaction fee as a main software change{is not in
the same cluster. This indicates that even as a crypto adopts a similar blockchain mechanism with
the other crypto, it might have di erent price dynamics than its origin.
XRP, Monero, Peercoin, Reddcoin, and Blackcoin which apply signi cantly di erent blockchain
protocols in their governance types and consensus mechanisms are in the same cluster. Speci cally,
XRP, Monero and Peercoin are private based blockchain which possesses a stronger moderator
to control the entrants (users or investors) to their network. Peercoin, Reddcoin, and Blackcoin,
instead of using PoW as their consensus mechanisms, employ PoS which does not depends on miners'
e ort to create a block. So that, coin supply and demand can reach an equilibrium without the
interference of miners, which leads to higher transaction costs. Moreover, the forks from Litecoin{
Vertcoin, Reddcoin and Feathercoin are within the same cluster with Litecoin.
15
Figure 6: Visualisation of ve clusters 0, 1, 2, 3, 4 of cryptos based on the prices
Blockchain -
mechanism clustering
Ethereum Classic is, in fact, the version of Ethereum that existed before the hard fork of
Ethereum resulting after the DAO attack, but it is not within the cluster with Ethereum.
4.2 Clustering with actual block time
The block time here is measured in minutes. Likewise, we apply the characteristic based clustering
on the data and conclude them into 5 clusters as below.
0. Dogecoin, Feathercoin
16
1. Ethereum, Litecoin, Ethereum Classic, Dash, Zcash, Monero, Blackcoin
2. Bitcoin, Bitcoin Cash, Vertcoin
3. Bitcoin SV, Bitcoin Gold, Novacoin
4. Peercoin, Reddcoin
The result is correspondingly visualised in Figure 4. The gure shows that Peercoin and Redd-
coin lie far away from other coins (marked by cyan cluster). They are clustered in the same group
because they both use PoS and their initial block takes the maximum time to be added, as shown
by the maximum and intercept characteristics in Table 2. This shows that even though the coins
have lower actual block time later (with low mean), their behaviour is still the similar, resulting
them in the same cluster. Also, the cryptos using PoS tend to lower the complexity of their hashing
algorithms since it is not required for miners to spend computational e ort on them. The diculty
adjustment algorithms of theirs are purely used as a mechanism for maintaining the certain service
level for users without considering hashrate from miners. Their block time performance is relatively
stable after the initialisation. Here we emphasise that the initial price, block time and block size
that are usually characterised by the underlying mechanism play a pivotal role in determining the
price behaviour of cryptos. This is why we did not truncate the time series, as mentioned in the
Section 4.
Though Bitcoin, Bitcoin Gold, Bitcoin Cash and Bitcoin SV are not completely grouped into
the same cluster, they are close to each others in the three dimensional space as seen in Figure 4.
They apply the same hashing algorithm{SHA-256 and also with the same expected block time for
their diculty adjustment algorithms. Let's call attention to forks again. Dogecoin and Feathercoin
are both forked from Litecoin with the Script-based hashing algorithm and diculty adjustment
frequency after large number of blocks{240 and 504 blocks. Litecoin is in a di erent cluster because
the frequency is much higher as 2016 blocks. Given the cryptos forked from the same origin coins,
their block time can be found in the same group, likewise Ethereum and Ethereum Classic.
17
Figure 7: Visualisation of ve clusters 0, 1, 2, 3, 4 of cryptos based on block time
Blockchain -
mechanism clustering
4.3 Clustering with actual block size
As previously done for price and block time, we use the characteristics based clustering and grouped
these cryptos into 5 clusters according to the characteristics of their time series. The block size
here is measured in bytes for a better data representation. As stated before in Section 3, XRP
and Peercoin data are missing due to the mechanism design and incomplete data from the source,
respectively. The clustering result is shown as below and the corresponding visualisation is in
Figure 5.
18
0. Zcash, Bitcoin Gold, Reddcoin, Novacoin
1. Ethereum, Ethereum Classic, Dogecoin
2. Bitcoin Cash, Bitcoin SV
3. Bitcoin, Dash, Monero, Feathercoin
4. Litecoin, Vertcoin, Blackcoin
Figure 8: Visualisation of ve clusters 0, 1, 2, 3, 4 of cryptos based on block size
Blockchain -
mechanism clustering
The actual block size (usage) of these cryptos does rarely meet their designed block size limit
19
(capacity), except for Bitcoin that it nearly outstretches its limit, 1 megabyte, as seen in Table 3.
In this case, it raises an issue: Can increasing crypto's block size limit improves scalability? For
example, Bitcoin SV enlarges dramatically its limit to 128 megabytes but it is out of the necessity for
such a design. Likewise, Bitcoin Cash, which Bitcoin SV forks from, has its limit as 32 megabytes.
These two coins are, therefore, clustered together. Moreover, instead of having a static block size
limit, Ethereum and Ethereum Classic grouped in the same cluster apply block gas limit, which is
the energy consumption limit for a block, to adaptively regulate its block size. Both Monero and
Blackcoin have a dynamic mechanisms to control the block size, however, it does not represent in
the clustering result.
5 Conclusion
In this paper we investigate the relationship between crypto behaviours and their underlying mech-
anisms. We specify the crypto behaviour with their price and operational features de ned by actual
block time and block size. We calculate the distributional characteristics to de ne the behaviour of
time series. Using a characteristics based spectral clustering technique, we cluster the selected coins
into a number of clusters and scrutinise the blockchain mechanism in each group. We nd that the
underlying mechanism of cryptos are re
ected in the clustering results. We observe that cryptos
forked from same origin and same consensus mechanism tend to become part of same clustering
group. Furthermore, the clusters obtained by the time series of block time have same hashing al-
gorithms and diculty adjustment algorithms. Also, a similar nature (static or dynamic) of block
size was observed within clusters obtained by the time series of actual block size. We conclude
with empirical evidence that the crypto behaviour is indeed linked with their blockchain protocol
architectures. As a result, cryptocurrency users and investors can have a better understanding and
explanation of price and operational features through cryptocurrency mechanism. In the future re-
search, we would elaborate the relation of price and operational features to underlying mechanism
with an economic model and conduct relevant simulations. We would also like to investigate the
impact of versions revisions on the dynamics of cryptos.
20
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22
Appendix
Table 1: Characteristics of prices of di erent cryptocurrencies
Characteristic Bitcoin Ethereum LitecoinBitcoin
CashEthereum
ClassicXRP
mean 2659.127 178.966 34.394 537.723 9.381 0.192
standard deviation 3798.466 222.452 48.645 509.244 7.827 0.302
skewness 1.338 1.950 2.389 2.322 1.491 4.193
kurtosis 0.672 4.654 7.272 6.157 2.239 29.471
maximum 19401.000 1356.000 352.799 3526.000 43.765 3.649
minimum 0.050 0.401 0.032 58.626 0.687 0.003
lowerquant 20.193 7.975 3.153 233.404 4.364 0.007
median 455.892 136.557 8.618 324.646 6.571 0.024
upperquant 5128.000 250.965 53.128 620.947 13.813 0.291
VaR99 0.062 0.578 0.040 107.426 0.809 0.004
VaR95 0.393 0.696 0.072 129.491 1.105 0.005
slope 2.781 0.163 0.032 -0.876 -0.002 0.000
intercept 0.050 2.820 0.033 63.765 0.892 0.006
autocorrelation 0.998 0.998 0.997 0.992 0.994 0.991
selfsimilarity 1.574 1.611 1.596 1.609 1.564 1.551
chaos 0.088 0.093 0.091 0.086 0.087 0.085
CharacteristicBitcoin
SVDash Zcash Monero DogecoinBitcoin
Gold
mean 145.401 113.910 135.596 57.588 0.006 43.167
standard deviation 66.784 187.915 125.654 75.569 0.193 70.420
skewness 0.678 3.126 1.756 2.145 49.692 2.879
kurtosis 0.079 11.777 3.208 5.300 2469.511 8.351
maximum 370.647 1436.000 728.159 439.391 9.608 513.293
minimum 52.683 0.516 23.940 0.233 0.000 5.093
lowerquant 87.323 3.950 50.251 1.100 0.000 9.710
median 135.217 66.508 72.251 44.090 0.001 15.869
upperquant 191.739 133.239 199.807 84.834 0.003 29.706
VaR99 53.377 0.711 27.767 0.272 0.000 5.357
VaR95 62.111 1.833 31.842 0.417 0.000 6.604
slope 0.218 0.083 -0.134 0.053 0.000 -0.147
intercept 111.700 1.380 286.297 1.911 0.000 513.293
autocorrelation 0.990 0.997 0.995 0.997 0.002 0.961
selfsimilarity 1.628 1.642 1.573 1.577 1.024 1.431
chaos 0.077 0.090 0.092 0.091 0.086 0.073
CharacteristicPeer
coinVertcoinRedd-
coinFeather-
coinBlack-
coinNova-
coin
mean 1.004 0.670 0.001 0.062 0.095 2.185
standard deviation 1.238 1.319 0.003 0.102 0.127 2.989
skewness 2.511 3.637 4.175 3.379 3.397 3.102
kurtosis 7.017 14.792 24.526 17.172 15.251 12.916
maximum 9.118 9.386 0.029 1.203 1.108 24.777
minimum 0.110 0.006 0.000 0.002 0.014 0.078
lowerquant 0.291 0.043 0.000 0.008 0.030 0.507
median 0.445 0.237 0.001 0.019 0.045 0.901
upperquant 1.275 0.626 0.001 0.072 0.088 3.301
VaR99 0.125 0.009 0.000 0.003 0.015 0.156
VaR95 0.168 0.015 0.000 0.004 0.020 0.187
slope 0.000 0.000 0.000 0.000 0.000 -0.001
intercept 0.382 6.315 0.000 0.559 0.035 0.078
autocorrelation 0.993 0.992 0.988 0.983 0.993 0.994
selfsimilarity 1.577 1.603 1.548 1.523 1.537 1.596
chaos 0.088 0.085 0.079 0.078 0.084 0.09123
Table 2: Characteristics of Block time of di erent cryptocurrencies
Characteristic Bitcoin Ethereum LitecoinBitcoin
CashEthereum
ClassicXRP
mean 10.453 0.257 2.507 11.167 0.246 NA
standard deviation 8.814 0.045 0.385 11.009 0.032 NA
skewness 21.779 3.098 5.003 11.597 5.144 NA
kurtosis 701.717 11.987 54.589 160.209 61.066 NA
maximum 360.000 0.509 8.521 205.714 0.800 NA
minimum 2.081 0.208 0.149 1.275 0.153 NA
lowerquant 8.623 0.235 2.357 9.664 0.235 NA
median 9.474 0.241 2.474 9.931 0.238 NA
upperquant 10.435 0.268 2.599 10.360 0.242 NA
VaR99 5.923 0.220 1.710 2.331 0.215 NA
VaR95 7.129 0.222 2.111 8.479 0.218 NA
slope -0.001 0.000 0.000 -0.007 0.000 NA
intercept 102.857 0.208 0.149 160.000 0.208 NA
autocorrelation 0.494 0.981 0.705 0.395 0.818 NA
selfsimilarity 1.027 1.522 0.787 0.704 1.249 NA
chaos 0.012 0.070 0.012 0.003 0.068 NA
CharacteristicBitcoin
SVDash Zcash Monero DogecoinBitcoin
Gold
mean 10.195 2.659 2.409 1.686 1.048 9.823
standard deviation 1.639 0.805 0.345 0.541 0.043 0.741
skewness 12.504 19.831 -3.025 3.258 -9.220 -5.375
kurtosis 221.950 409.827 7.261 57.807 222.460 60.686
maximum 40.000 22.500 2.618 10.992 1.288 11.250
minimum 7.310 0.348 1.240 0.829 0.100 0.254
lowerquant 9.600 2.609 2.487 1.025 1.038 9.664
median 10.000 2.623 2.509 1.951 1.044 9.931
upperquant 10.511 2.637 2.531 2.020 1.050 10.141
VaR99 8.361 2.476 1.248 0.947 0.980 7.767
VaR95 9.034 2.571 1.258 0.984 1.031 8.623
slope -0.001 0.000 0.000 0.001 0.000 0.001
intercept 40.000 0.348 2.286 1.627 0.100 0.254
autocorrelation -0.115 0.707 0.982 0.805 0.787 0.378
selfsimilarity 0.367 0.811 1.121 0.922 1.044 0.494
chaos 0.023 0.003 0.010 0.001 0.011 -0.001
CharacteristicPeer-
coinVert-
coinRedd-
coinFeather-
coinBlack-
coinNova-
coin
mean 10.085 2.502 4.646 2.005 1.090 6.819
standard deviation 47.070 0.180 68.175 6.443 0.105 2.295
skewness 30.324 -1.782 20.761 11.521 -4.368 24.326
kurtosis 919.356 30.015 434.280 157.793 18.525 891.281
maximum 1440.000 4.079 1440.000 130.909 1.335 96.000
minimum 1.377 0.151 0.646 0.148 0.442 0.451
lowerquant 7.742 2.412 0.986 1.042 1.111 6.154
median 8.372 2.500 1.007 1.048 1.114 6.606
upperquant 9.057 2.590 1.028 1.171 1.117 7.164
VaR99 5.464 2.144 0.935 1.034 0.551 4.364
VaR95 6.545 2.289 0.957 1.036 0.949 5.390
slope -0.003 0.000 -0.010 -0.002 0.000 -0.001
intercept 1440.000 0.151 1440.000 0.291 1.309 1.765
autocorrelation 0.667 0.154 0.821 0.914 0.976 0.373
selfsimilarity 0.717 0.437 1.051 1.210 1.337 0.697
chaos 0.002 0.008 -0.001 0.032 0.006 0.00924
Table 3: Characteristics of Block size of di erent cryptocurrencies
Characteristic Bitcoin Ethereum LitecoinBitcoin
CashEthereum
ClassicXRP
mean 407162.152 14376.916 12909.684 138173.724 1297.638 NA
standard deviation 363245.372 11337.562 15590.195 284058.956 340.581 NA
skewness 0.241 0.285 4.309 9.176 0.679 NA
kurtosis -1.583 -0.819 31.780 109.791 2.106 NA
maximum 998092.000 58953.000 206020.000 4710539.000 3594.000 NA
minimum 134.000 575.164 134.000 4982.000 575.164 NA
lowerquant 21246.000 1627.750 4004.750 60455.500 1054.750 NA
median 310990.000 17024.000 7016.000 94775.000 1310.500 NA
upperquant 777369.500 23068.750 19366.500 122827.500 1492.250 NA
VaR99 134.548 658.423 561.630 15574.520 653.404 NA
VaR95 134.952 788.678 800.306 27169.700 775.052 NA
slope 266.541 17.464 8.806 -89.253 0.189 NA
intercept 204.000 643.886 199.000 385996.000 643.886 NA
autocorrelation 0.985 0.981 0.872 0.626 0.850 NA
selfsimilarity 1.067 1.310 1.148 1.074 1.131 NA
chaos 0.058 0.058 0.065 0.027 0.045 NA
CharacteristicBitcoin
SVDash Zcash Monero DogecoinBitcoin
Gold
mean 1100149.254 12999.389 23802.102 39874.397 10523.242 25312.953
standard deviation 1278250.457 26340.294 38911.209 47310.430 6607.125 67527.275
skewness 6.673 27.654 8.711 1.703 5.917 6.269
kurtosis 84.455 1040.743 117.847 4.063 68.981 45.828
maximum 20460199.000 1059232.000 687685.000 347816.000 116605.000 739259.000
minimum 5005.000 226.545 379.573 375.434 143.000 133.000
lowerquant 257789.500 3038.000 7189.500 3047.250 6775.000 6512.500
median 996071.500 9240.000 11670.000 20980.000 9510.000 9316.000
upperquant 1573243.000 19193.000 28242.000 62002.000 12022.000 14118.000
VaR99 6435.000 1312.960 2605.530 1058.990 3432.400 2727.870
VaR95 14660.750 1736.200 3103.900 1320.350 4491.000 3983.600
slope 2318.003 14.357 -25.267 26.939 1.018 -67.625
intercept 10871172.000 226.545 379.573 375.434 143.000 133.000
autocorrelation 0.377 0.298 0.836 0.958 0.798 0.618
selfsimilarity 1.004 0.947 1.138 1.214 1.070 1.015
chaos 0.009 0.018 0.030 0.041 0.021 -0.012
CharacteristicPeer-
coinVert-
coinRedd-
coinFeather-
coinBlack-
coinNova-
coin
mean NA 2641.881 772.025 806.556 687.622 539.712
standard deviation NA 3611.409 634.442 1621.154 3441.373 1223.175
skewness NA 3.420 3.613 10.605 28.388 38.218
kurtosis NA 16.189 21.857 158.924 894.526 1712.453
maximum NA 36709.000 7808.000 36789.000 120169.000 57527.000
minimum NA 105.000 105.000 109.625 252.514 110.835
lowerquant NA 682.104 388.361 359.746 286.296 360.352
median NA 1149.000 526.043 460.827 386.251 436.181
upperquant NA 3185.000 937.696 598.841 627.727 542.228
VaR99 NA 248.950 317.797 126.333 255.520 262.588
VaR95 NA 310.697 337.320 247.907 261.297 284.524
slope NA -0.586 -0.475 -0.739 -0.025 -0.204
intercept NA 130.000 175.000 109.625 464.500 141.000
autocorrelation NA 0.894 0.609 0.705 0.360 0.069
selfsimilarity NA 1.129 1.007 1.063 0.959 0.951
chaos NA 0.100 0.034 0.034 0.039 0.01125
IRTG 1792 Discussion Paper Series 2020
For a complete list of Discussion Papers published, please visit
http://irtg1792.hu-berlin.de.
001 ”Estimation and Determinants of Chinese Banks’ Total Factor Efficiency: A New
Vision Based on Unbalanced Development of Chinese Banks and Their Overall Risk”
by Shiyi Chen, Wolfgang K. H¨ ardle, Li Wang, January 2020.
002 ”Service Data Analytics and Business Intelligence” by Desheng Dang Wu, Wolfgang
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by Natalie Packham, February 2020.
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005 ”Targeting Cutsomers Under Response-Dependent Costs” by Johannes Haupt, Ste-
fan Lessmann, March 2020.
006 ”Forex exchange rate forecasting using deep recurrent neural networks” by Alexander
Jakob Dautel, Wolfgang Karl H¨ ardle, Stefan Lessmann, Hsin-Vonn Seow, March
2020.
007 ”Deep Learning application for fraud detection in financial statements” by Patricia
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008 ”Simultaneous Inference of the Partially Linear Model with a Multivariate Unknown
Function” by Kun Ho Kim, Shih-Kang Chao, Wolfgang K. H¨ ardle, May 2020.
009 ”CRIX an Index for cryptocurrencies” by Simon Trimborn, Wolfgang Karl H¨ ardle,
May 2020.
010 ”Kernel Estimation: the Equivalent Spline Smoothing Method” by Wolfgang K.
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011 ”The Effect of Control Measures on COVID-19 Transmission and Work Resumption:
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Treatment Effects” by Daniel Jacob, August 2020.
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Spilak, Wolfgang Karl H¨ ardle, October 2020.
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Feng, Wolfgang Karl H¨ ardle, October 2020.
017 ”Using generalized estimating equations to estimate nonlinear models with spatial
data” by Cuicui Lu, Weining Wang, Jeffrey M. Wooldridge, October 2020.
018 ”A supreme test for periodic explosive GARCH” by Stefan Richter, Weining Wang,
Wei Biao Wu, October 2020.
019 ”Inference of breakpoints in high-dimensional time series” by Likai Chen, Weining
Wang, Wei Biao Wu, October 2020.
IRTG 1792, Spandauer Strasse 1, D-10178 Berlin
http://irtg1792.hu-berlin.de
This research was supported by the Deutsche
Forschungsgemeinschaft through the IRTG 1792.
IRTG 1792 Discussion Paper Series 2020
For a complete list of Discussion Papers published, please visit
http://irtg1792.hu-berlin.de.
020 ”Long- and Short-Run Components of Factor Betas: Implications for Stock Pricing”
by Hossein Asgharian, Charlotte Christiansen, Ai Jun Hou, Weining Wang, October
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021 ”Improved Estimation of Dynamic Models of Conditional Means and Variances” by
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022 ”Tail Event Driven Factor Augmented Dynamic Model” by Weining Wang, Lining
Yu, Bingling Wang, October 2020.
023 ”The common and speci
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024 ”Dynamic Spatial Network Quantile Autoregression” by Xiu Xu, Weining Wang,
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025 ”Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data”
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IRTG 1792, Spandauer Strasse 1, D-10178 Berlin
http://irtg1792.hu-berlin.de
This research was supported by the Deutsche
Forschungsgemeinschaft through the IRTG 1792.
|
{
"id": "2011.13240"
}
|
2301.02734
|
Political, economic, and governance attitudes of blockchain users
|
We present a survey to evaluate crypto-political, crypto-economic, and
crypto-governance sentiment in people who are part of a blockchain ecosystem.
Based on 3710 survey responses, we describe their beliefs, attitudes, and modes
of participation in crypto and investigate how self-reported political
affiliation and blockchain ecosystem affiliation are associated with these. We
observed polarization in questions on perceptions of the distribution of
economic power, personal attitudes towards crypto, normative beliefs about the
distribution of power in governance, and external regulation of blockchain
technologies. Differences in political self-identification correlated with
opinions on economic fairness, gender equity, decision-making power and how to
obtain favorable regulation, while blockchain affiliation correlated with
opinions on governance and regulation of crypto and respondents' semantic
conception of crypto and personal goals for their involvement. We also find
that a theory-driven constructed political axis is supported by the data and
investigate the possibility of other groupings of respondents or beliefs
arising from the data.
|
http://arxiv.org/pdf/2301.02734v1
|
Lucia M. Korpas, Seth Frey, Joshua Tan
|
cs.CY
|
cs.CY
|
Political, economic, and governance attitudes of blockchain users
Lucia M. Korpas
1
, Seth Fr ey
2,3
, Joshua Tan
1,4*
1
The Metagovernance Project, Brookline, MA, USA
2
Department of Communication, University of California
Davis, Davis, CA, USA
3
The Ostrom Workshop, Indiana University , Bloomington,
IN, USA
4
University of Oxford, Oxford, Oxfordshire, UK
* Corr espondence:
Joshua Tan
joshua.z.tan@gmail.com
Abstract
We
present
a
survey
to
evaluate
crypto-political,
crypto-economic,
and
crypto-governance
sentiment
in
people
who
are
part
of
a
blockchain
ecosystem.
Based
on
3710
survey
responses,
we
describe
their
beliefs,
attitudes,
and
modes
of
participation
in
crypto
and
investigate
how
self-reported
political
affiliation
and
blockchain
ecosystem
affiliation
are
associated
with
these.
We
observed
polarization
in
questions
on
perceptions
of
the
distribution
of
economic
power ,
personal
attitudes
towards
crypto,
normative
beliefs
about
the
distribution
of
power
in
governance,
and
external
regulation
of
blockchain
technologies.
Differences
in
political
self-identification
correlated
with
opinions
on
economic
fairness,
gender
equity ,
decision-making
power
and
how
to
obtain
favorable
regulation,
while
blockchain
affiliation
correlated
with
opinions
on
governance
and
regulation
of
crypto
and
respondents’
semantic
conception
of
crypto
and
personal
goals
for
their
involvement.
We
also
find
that
a
theory-driven
constructed
political
axis
is
supported
by
the
data
and
investigate
the
possibility
of
other
groupings
of
respondents
or
beliefs arising from the data.
1
Introduction
As blockchain technology has evolved over more than a decade, cryptocurrencies and
crypto-economic systems have had a growing impact on the world. Millions of people have involved
themselves in crypto
1
: as of 2021, around 15 percent
of American adults have reported owning
cryptocurrency (Perrin 2021), and many other countries have even higher adoption rates (Buchholz
2021). The past few years have seen the growth of decentralized apps and the crypto startup industry .
Correspondingly , governments are beginning to take regulatory actions. Also, even as blockchain
ecosystems move towards less computationally-intensive consensus mechanisms, the ongoing
environmental impact of blockchain use is huge.
Given the impact of crypto-economic activity on individuals and on shared resources, it is
increasingly important to understand how its users are relating to the technology . While the hard data
of cryptocurrency transactions and account balances is often publicly available by design, users’
1
Throughout the text, we use the term “crypto” to
encompass blockchain technologies such as
cryptocurrencies and the communities and ideologies which drive their development and use.
1
motivations for engaging with crypto are more opaque. There is little existing data on the stated
beliefs or attitudes of the variety of people using blockchain technologies. What do blockchain users
believe about the economic, political, and social relevance of crypto? While there has been attention
to the attitudes of the general population towards cryptocurrencies and blockchain technology (Perrin
2021; “Global State of Crypto, 2022” 2022), there is also a need to understand the beliefs of active
participants of blockchain ecosystems.
What do blockchain ecosystem participants believe about how the technology is being – or should be
– developed, used, and regulated? Are there discrete types of crypto contributors, or is there a
spectrum of beliefs? What specific beliefs are most relevant in distinguishing respondents between
types or along axes? This work is a first step in the development of a framework for thinking about
this spectrum or grouping of beliefs in crypto.
We report the results of a lar ge-scale survey of participants in the blockchain economy . The survey
was designed to shed light on respondents’ socioeconomic and sociopolitical beliefs relating to
crypto, economic modes of engagement with crypto, and attitudes towards governance of blockchain
technology . We describe the distributions of these responses and their relationships to self-reported
political ideology and specific crypto ecosystems such as Bitcoin and Ethereum.
We also evaluate the survey instrument itself: are the questions able to assess distinct and relevant
facets of beliefs? Can we identify underlying factors which describe broader groupings of beliefs?
Using factor analysis methods, we find that a political axis and corresponding typology , informed by
the Pew Research Center ’s Political Typology Quiz, meaningfully describes variation between
respondents.
2
Backgr ound
While there is no existing political theory of crypto per se, there are substantial ethnographic studies
of crypto communities (and related digital communities) that address the political dimensions of
crypto. For example, ethnographic studies have informed the creation of a proposed political
typology of blockchain projects (Husain, Franklin, and Roep 2020), reflecting earlier ideas on the
“intrinsic” political values of technical artifacts (W inner 1980). In this vein, cryptocurrencies have
been characterized as realizations of crypto-anarchist values such as privacy and autonomy (Chohan
2017; Beltramini 2021), following in the footsteps of earlier cypherpunk writings (Hughes 1993;
May 1994) as well as the original Bitcoin whitepaper (Nakamoto 2008). Other ethnographies have
described issues of on- and of f-chain governance (De Filippi and Loveluck 2016) and the political
motivations and cultural context of projects such as Bitcoin (Golumbia 2016) and Ethereum (Brody
and Couture 2021).
A previous industry survey , conducted by CoinDesk in 2018, contained several questions related to
politics and governance (R yan 2018; Bauerle and R yan 2018), though the questions focused more
specifically on individual projects and topical questions such as reactions to SEC rulings on the
securitization status of Ethereum.
Distinct from questions about political values, the topic of blockchain governance—including the
relationship between blockchains and traditional governments—is one of the most salient and
polarizing questions in crypto, one that has led to the creation, forking, and dissolution of many
projects. While we cannot recount all the major positions here (some of which are reflected in the
survey itself; see “Methodology”), there is a broad distinction between approaches that emphasize
on-chain governance and those that emphasize of f-chain governance. A number of academic analyses
2
have studied these dif ferent approaches to blockchain governance (Reijers, O’Brolcháin, and Haynes
2016; Liu et al. 2021; van Pelt et al. 2021), along with a vastly greater number of industry manifestos
and opinion pieces (Zamfir 2019; Szabo 1996).
3
Methodology
3.1
Survey questions
The survey consists of 19 questions related to respondents’ crypto-related beliefs and activities, with
three types of questions interspersed: those eliciting opinions about the political dimensions of crypto
activity (“crypto-political”), those eliciting economics opinions (“crypto-economic”), and those
eliciting attitudes about the governance of crypto projects (“crypto-governance”). All questions were
multiple choice, with 2-4 possible selections, and the respondent could opt not to answer . See Table 1
for the full list of questions.
The survey questions and provided choices included both a formal portion drawing from existing
political survey instruments and a more exploratory portion intended to elicit beliefs relevant to a
general crypto-political typology . In particular , a few of the questions selected (Q1 1-13, Q15, Q19),
were based on questions from Pew’ s Political Typology Quiz (Nadeem 2021) and intended to relate
to political sentiment. Other questions (e.g., Q1, Q17) were developed in collaboration with a number
of community members in crypto, drawing on the culture, memes, and references common in crypto.
Altogether , the content was designed to elicit respondents' primary modes of economic engagement
with crypto, their political sentiment, and opinions as to how crypto communities themselves should
be governed.
3.2
Construction of political “types” and identification of “axes” of belief
Our choice to identify separate “axes” of economic, political, and governance beliefs were based on
discussion with community members and in analogy to existing classifications such as the traditional
“left-right” political axis. For one of these, the political axis, we also leveraged our study design to
group and relate questions more directly by defining a continuous construct intended to assess
respondents' crypto-political leanings. We identified a subset of questions as most relevant to political
orientation, and computed a score for each participant by summing the responses to these questions
(coded with values in the range [-1, 2] as described in Table 1) in analogy to the Pew methodology
(Nadeem 2021). The lowest and highest scores on this political “axis” were designed to highlight
extreme positions of collectivist and anarcho-capitalist approaches to using blockchain technology .
Five discrete types were defined by thresholds in the score according to Table 2:
crypto-anarcho-capitalist, crypto-libertarian, centrist, crypto-communitarian, and crypto-leftist; these
types were developed both with definitions from the Pew typologies and with input from the
community .
3.3
Recruitment
We relied on a convenience (self-selected) sample of participants in the crypto community .
Participants were recruited by distributing the survey through blockchain-focused forums and
listservs, conferences (LisCon and ETHDenver), social media posts, and articles published on
blockchain-focused news sites.
We motivated voluntary participant engagement with two strategies. We presented the survey as a
quiz that assigned respondents one out of an entertaining typology of “types” on the basis of their
3
responses (“crypto-leftist,” “cryptopunk,” etc.) immediately upon completion of the survey . Stylized
as “factions”, the crypto-political types corresponded to the political types we defined based on the
Pew typology , while the crypto-economic and crypto-governance types were constructed by using
thresholds to partition respondents into five ad hoc types (for more detail, see Section 1.1 of the
Supplementary Material). We also incentivized survey completion with the opportunity to receive a
non-fungible token (NFT) corresponding to their assigned “type”, contingent upon their provision of
a valid Ethereum wallet address or ENS name.
3.4
Analysis
To survey the overall landscape of crypto-political beliefs, we observed the distribution of choices
selected by respondents. We aggregated these responses for each question, including the null
response of no choice selected, and computed the mar gins of error for a 95% confidence interval,
assuming a random sample of the population.
To investigate how political self-identification and participation in specific blockchain ecosystems
related to beliefs, we grouped participants by their responses to the corresponding questions. We then
determined which questions displayed a statistically significant dif ference in the distribution of
responses between these groups.
We also wanted to understand which questions were most meaningful in dif ferentiating respondents.
To this end, we first performed a check on the extent to which each question measured a distinct
belief by computing the correlation between responses to dif ferent questions as Cramer ’s V (a
version of Pearson’ s chi-squared statistic scaled to provide a measure of association). Then we used
principal component analysis (PCA) to identify which of the 48 choices provided across 17 questions
explained the most variance between respondents. Specifically , we looked at the component loading
for each feature, i.e., contribution to the first principal component.
For use with PCA, we normalized the feature data (shifted to a mean of 0 with unit variance). Note
that for questions where only two choices were provided, the alternative answer contributed with
equal magnitude (though opposite sign). We omitted questions 2 and 19, relating to specific
blockchain ecosystems. We also omitted answers from respondents that did not answer all questions.
We also wished to evaluate to what extent our crypto-political types, delineated from the political
score we defined, corresponded with patterns in beliefs across respondents. From the PCA results, we
can identify to what extent the assigned types or classes directly correlate with any of the first few
components.
4
Results
4.1
Responses and r espondents
Between September 27, 2021 and March 4, 2022, the survey received 3710 responses. In 3418 (92%)
of these, all questions were answered. For questions presented to all respondents, the percentage of
respondents who chose not to answer each question was between 0.5% and 1.5% across all questions.
The survey took on average 8 minutes and 40 seconds to complete.
4.2
Responses to questions: political, economic, and governance attitudes
Overall, respondents were varied in their perceptions of the distribution of economic power in crypto
and their personal attitudes towards crypto. They were also split between the most common
4
Figure 1.
Responses to
(A)
question 11,
(B)
question 12, and
(C)
question 13 on perceptions of the
distribution of economic power in crypto, with 95% confidence intervals. Though optimistic beliefs
about the current state of crypto-economics were slightly more prevalent, dissatisfaction with the fair
distribution and attainability of crypto-economic wealth was nearly as frequent.
responses to two questions on the distribution of power in governance of crypto. There was
somewhat more agreement on broad beliefs towards external regulation of crypto, though
respondents disagreed on some of the specifics and in matters of degree. The lar gest majorities were
observed in questions relating to the social implications of crypto.
Perceptions of the distribution of economic power in crypto were closely split between the two
choices provided for each question (Figure 1). By a few percentage points, a slightly higher
proportion of respondents believed that most crypto teams make “a fair and reasonable amount of
profit” rather than “too much profit” (Q1 1, Fig. 1(A)) and that the economic system in crypto “is
generally fair to most of its participants” rather than “unfairly favors powerful interests” (Q12, Fig.
1(B)). A majority (58%) believed that “most people who want to get ahead in crypto can make it if
they’re willing to work hard” (Q13, Fig. 1(C)).
5
Figure 2.
Responses to
(A)
question 3 and
(B)
question
4 on personal attitudes towards blockchain,
with 95% confidence intervals. Together, these responses show that both a desire for sociopolitical
change and an interest in personal financial gain were common factors in participants’ interest in
blockchain technologies.
Figure 3.
Responses to
(A)
question 5 and
(B)
question
16 on blockchain governance, with 95%
confidence intervals.
Personal attitudes towards crypto were also diverse (Figure 2). Respondents were divided on whether
they regarded crypto as “mainly a political philosophy and/or lifestyle” or “mainly an economic
technology”, with a slight majority favoring the latter (Q3, Fig. 2(A)). There was no majority in
respondents’ goals for their own involvement in crypto: the most common goal was “to create social
change and/or disrupt the industry” (39%), followed by “to make as much money as possible” (29%)
(Q4, Fig. 2(B)).
Normative beliefs about the distribution of power in governance of crypto appear to be in some
tension (Figure 3). Most respondents favored a hands-of f approach to the governance of crypto, with
45% believing that “most or all cryptogovernance should be on-chain” and 30% believing “crypto
does not need (human) governance” (Q5, Fig. 3(A)). However , a majority of respondents believed
that “a wide variety of on- and of f-chain stakeholders” should have decision-making power over a
blockchain (though the next most common response was “the token holders and/or node operators,
i.e., voters, as determined by the protocol”) (Q16, Fig. 3(B)). Note that while the most common
responses to each of these questions are not incompatible, their coexistence indicates a possible
tension in the community between maximizing on-chain governance and empowering of f-chain
stakeholders.
6
Figure 4.
Responses to
(A)
question 7 and
(B)
question
14 on external regulation of blockchain
technologies, with 95% confidence intervals.
Regarding external regulation of blockchain technologies, respondents were somewhat more
consistent (Figure 4). A majority of respondents believed at least some good will come of
government regulation of crypto, though nearly 40% asserted that “government regulation of crypto
will almost always do more harm than good” (Q7, Fig. 4(A)). In line with the above, when asked
what the most important thing the crypto community can do to get more favorable regulation of
cryptocurrencies from national governments, a plurality of respondents sought a cooperative
relationship with government, choosing to “work hand-in-hand with regulators to identify a solution
that works for both government and industry ,” versus adopting an evasive approach to “adapt our
technology and practices in order to minimize potential conflicts with the law” or even an
antagonistic one to “mount a public pressure campaign on politicians” or to “keep doing what we’re
doing, legal or not” (Q14, Fig. 4(B)). Also, more than three-quarters of respondents believed that
“having a central bank run a cryptocurrency is a bad idea” (Q8). Overall, though a majority of
respondents were willing to accept or even collaborate on regulation, lar ge minorities strongly
disagreed, and distaste for direct government involvement in implementations of crypto technology
was common.
On the social implications of crypto, most respondents were in agreement, believing that blockchain
and DeFi are “beneficial technologies that, on balance, will help most members of society” (Q10).
Even so, more than a quarter of respondents believed that crypto “has a gender problem” (Q15).
Also, around a quarter of respondents indicated privacy is “the most important feature of blockchain
and crypto” (Q6).
We asked two additional questions on political orientation and blockchain ecosystem af filiation
(Figure 5). Only 14 percent of respondents considered themselves “conservative or right-wing” (532
respondents) with the remaining participants split equally (with no statistically significant dif ference)
between “liberal or left-wing” (1550) and “neither” (1599; Q18, Fig. 5(A)). Nearly all participants
(97%) stated an af filiation with at least one blockchain ecosystem or community (Q19, Fig. 5(B)),
supporting our use of this dataset to focus on users of blockchain technology (rather than the general
public). In particular , of the 3591 respondents who indicated af filiation with at least one blockchain,
2175 (61%) selected af filiation with Ethereum and 1 120 (31%) with Bitcoin (Fig. 5b). Note that these
are not mutually exclusive groups (789 indicated af filiation with both); furthermore, though a
majority of respondents only specified one af filiation, less than a quarter believe that “there is one
(layer 1) blockchain that is the best” (Q1). In the following two subsections, we discuss the relation
of these distributions with respondents’ beliefs in more depth.
7
Figure 5.
Responses to
(A)
question 18 on political
orientation and
(B)
question 19 on blockchain
ecosystem affiliations, with 95% confidence intervals.
The distribution of responses for the questions not covered in this section are included in the
Supplementary Material (Supplementary Figures S1-S4).
4.3
Differ ences between r espondents by self-r eported political orientation
To examine the dif ferences in opinion between the left-of-center , right-of-center , and unaligned
groups, we compared the distribution of answers selected by respondents af filiated with each group
(Q18). We found that perceptions of economic fairness and gender equity elicited the clearest
differences between the three political orientation groups, with economic fairness especially
differentiating left-of-center respondents from the other two groups. Beliefs about governance,
regulation, and personal goals in crypto dif ferentiated right-of-center respondents from the other two
groups. Dif ferences between political orientation groups were ubiquitous: all but one question had at
least one statistically significant dif ference between the responses groups.
The economic fairness questions (Q1 1, Q12, and Q13) were among those with the greatest
differentiation between the three groups. Somewhat surprisingly , unlike nonaligned and
right-of-center respondents, a majority of left-of-center respondents believe that “most crypto teams
Figure 6.
Responses, grouped by self-reported political
affiliation, to question 12 on crypto-economic
fairness, with 95% confidence intervals. Taken together with questions 11 and 13, this distribution
shows that left-of-center respondents overall held a different set of beliefs about wealth distribution
and economic opportunity than other respondents.
8
make a fair and reasonable amount of profit” (Q1 1) and “the economic system in crypto is generally
fair to most of its participants” (Q12, Fig. 6). Though a majority of both right-of-center and
nonaligned respondents believed instead that “the economic system in crypto unfairly favors
powerful interests”, right-of-center respondents were more likely than nonaligned respondents to
choose this answer (Q12). However , left-of-center respondents were more likely than right-of-center
or nonaligned respondents to believe that “hard work and determination are no guarantees of
success” in crypto (Q13).
Question 12 was one of three questions for which all three groups had a statistically dif ferent
distribution of responses. Another was on gender equity: right-of-center respondents were least likely
to believe “crypto has a gender problem,” nonaligned respondents somewhat more likely , and
left-of-center respondents most likely , with about half of left-of-center respondents selecting this
answer (Q15). This spread shows that self-reported political alignment relates to not only economic
but also social issues in the use of blockchain technology .
Differences also arose between the groups in the most common answer to questions on
decision-making power and how to obtain favorable regulation. When asked who should hold
decision-making power over a blockchain, right-of-center respondents were more likely to choose
“the token holders and/or node operators” than “a wide variety of on- and of f-chain stakeholders”;
the reverse was true for left-of-center and nonaligned respondents, with left-of-center respondents
more likely than other respondents to choose a variety of stakeholders (Q16, Fig. 7). Concerning how
to obtain favorable regulation, left-of-center and nonaligned respondents were most likely to choose
“work hand-in-hand with regulators” out of the available choices, and more likely to do so than
right-of-center respondents; in contrast, right-of-center respondents were, within confidence
intervals, evenly split between three of the four available choices (Q14).
Other statistically significant dif ferences occurred in the distribution of responses, where one of the
three groups dif fered from the other two. Right-of-center respondents were most likely to choose
“make as much money as possible” as their goal and less likely to select “create social change and/or
disrupt the industry”; the reverse was true for left-of-center and nonaligned respondents (Q4).
However , left-of-center respondents were less likely than others to believe crypto needs to prioritize
“building art and community” to grow (Q9). Also, a smaller proportion of left-of-center respondents
than other respondents believed that privacy is “the most important feature of blockchain” (Q6).
Figure 7.
Responses, grouped by self-reported political
affiliation, to question 16 on decision-making
power, with 95% confidence intervals. Together with question 14, this distribution indicates that
right-of-center respondents were more likely than other respondents to hold beliefs aligned with
minimizing external influence on blockchain governance and development.
9
Left-of-center respondents were less likely to believe that “crypto does not need (human)
governance,” while nonaligned respondents were less likely to believe “however crypto governs
itself, it should also be regulated by the government” (Q5). Left-of-center respondents were also
more polarized on government regulation: they were less likely to believe it “can do some good,” and
more likely to believe it is either “critical to protect the public interest” or “will always do more harm
than good” (Q7).
4.4
Differ ences between r espondents by Bitcoin and Ether eum affiliation
At present, dynamics in the crypto community are lar gely driven by actors in two ecosystems:
Bitcoin and Ethereum. To examine dif ferences in opinion between the 61% of respondents af filiated
with Ethereum and the 31% (non-exclusive) af filiated with Bitcoin, we compared the distribution of
answers selected by respondents af filiated with each of the two blockchains. We found an overall
quite similar distribution of responses regardless of af filiation, with a few statistically significant
differences arising in beliefs about cryptogovernance, the semantics of the term crypto, personal
goals in crypto, and stated political orientation.
Governance and regulation of crypto were a key topic distinguishing Bitcoin af filiates from Ethereum
affiliates (Figure 8). Bitcoin af filiation was associated with a higher likelihood of believing that
“crypto does not need (human) governance” (Q5, Fig. 8(A)) and that “token holders and/or node
operators” should have decision-making power over a blockchain, whereas Ethereum was associated
with “a wide variety of on- and of f-chain stakeholders” (Q16, Fig. 8(B)). Somewhat surprisingly ,
Bitcoin af filiation was also associated with a higher likelihood of believing that government
regulation of crypto “can do some good” (Q7), although there was no statistically significant
difference in opinions on how to obtain favorable regulation (Q14). Thus, it appears that Bitcoin
affiliation is associated with a higher rate of wanting to maximize on-chain governance but also of
tolerance of external regulation, perhaps in particular that which “can help force blockchains to
become more decentralized,” as is included in the wording of question 7.
Respondents’ semantic conception of crypto and their personal goals for their involvement also had
some relation to blockchain af filiation: Bitcoin af filiation was associated with a higher likelihood of
believing “crypto is mainly an economic technology” (Q3) and identifying with the statement “my
goal in crypto is to make as much money as possible” (Q4). Ethereum af filiation was associated with
a higher likelihood of believing that “the economic system in crypto unfairly favors powerful
interests” (Q12) and that “crypto has a gender problem” (Q15).
Figure 8.
Responses, grouped by blockchain affiliation,
to
(A)
question 5 and
(B)
question 16 on
blockchain governance, with 95% confidence intervals. These distributions indicate that Bitcoin
affiliates were more likely to favor a narrow definition of governance and its participants.
10
Figure 9.
Responses, grouped by blockchain affiliation,
to question 18 on self-reported political
affiliation, with 95% confidence intervals. While there was a statistically significant difference
between affiliates of the two blockchains in identifying as right-of-center or nonaligned, Cramer’s V
indicates that the strength of association between blockchain affiliation and political orientation was
low.
For question 18 on political orientation, Bitcoin af filiation correlated with a higher likelihood of
selecting “conservative or right-wing” and lower likelihood of selecting “neither” (Figure 9). There
was no statistically significant dif ference between the proportions of respondents who chose “liberal
or left-wing”. Given that we were interested in analyzing blockchain af filiation separately from stated
political orientation, we additionally checked for the strength of association between Q18 and a
reduced version of Q19 with the options “Bitcoin”, “Ethereum”, and “Neither” (not mutually
exclusive). Cramer ’s V was low (less than 0.15) for all combinations of responses, indicating at most
very weak association between the two questions (Supplementary Figure S5). This gives us
confidence that Bitcoin and Ethereum af filiation were not strongly associated with stated political
orientation.
4.5
Validation of survey instrument
To assess any correlations between responses to dif ferent questions, we computed the correlation
matrix for all pairs of questions (Supplementary Fig. S6). Of the 153 unique pairs, most showed little
if any association (V < 0.1); the strength of association was weak for 52 questions (0.1 <= V < 0.3),
and one question pair related to wealth distribution (Q1 1-Q12) showed a moderate strength of
association (0.33). The prevalence of weak or no association between distinct questions supports our
assertion that each question addresses a distinct facet of a respondent’ s beliefs or actions. This allows
us to assess the relative importance of the specific statements provided in the answer choices to
explain dif ferences between respondents.
4.6
Featur e selection and factor analysis
To identify the beliefs which most contributed to explaining variance between respondents and to test
our hypothesis, we computed the PCA vectors for individual choices (features) and examined the
first principal component. Beliefs above a threshold of magnitude 0.18, corresponding to the loading
each response would have if all questions contributed equally to the component, were labeled as
important. The features with the lar gest contributions to the first principal component were the
following (listed in descending order of importance):
11
-
“The economic system in crypto unfairly favors powerful interests.” (Q12)
-
“Crypto has a gender problem.” (Q15)
-
“Government regulation of crypto will almost always do more harm than good.” (Q7)
-
“[I consider myself] liberal or left-wing.” (Q18)
-
“Crypto teams make too much profit.” (Q1 1)
-
“In crypto, hard work and determination are no guarantee of success for most people.” (Q13)
-
“However crypto governs itself, it should also be regulated by the government.” (Q5)
-
“Blockchain and DeFi are predatory technologies that, on balance, will harm most members
of society .” (Q10)
All three questions relating to wealth distribution and economic fairness (Q1 1-13) contributed more
to explaining variance than most other questions. Polarized opinions on government regulation (Q5
and Q7) and one specific political af filiation (Q18) also featured here. Altogether , 5 of the 8
questions that we had coded as defining an axis of political belief had a lar ge contribution to this
leading component.
The same analysis can be done for the remaining principal components. The features with the lar gest
component loading for the next two principal components are “Privacy is the most important feature
of blockchain and crypto” (Q6) for the second principal component and “Crypto is mainly a political
philosophy and/or lifestyle” (Q3) for the third. These choices, and their corresponding questions, are
therefore among the more salient in explaining variance between respondents.
Altogether , however , the variance explained by only the first few components was relatively low
(21% for the first three components) and less than 10% was explained by the first component alone.
Taken together with weak associations between questions as described in Section 4.5, this implies
that the number of latent variables required to describe respondents' beliefs is lar ge. Indeed, factor
analysis using PCA and feature agglomeration yielded a null result, meaning that features did not
cluster into a few interpretable groupings (see Section 1.3 of the Supplementary Material). Even so,
we find that the first principal component axis corroborates a theory-first constructed axis, as
described in the following section.
4.7
Validation of constructed crypto-political axis
For each respondent, a political score was calculated using the values in Table 1 and a type was
assigned according to the score thresholds described in Table 2. The feature selection and factor
analysis results can be used to evaluate the validity of this constructed crypto-political axis.
The distribution of scores and types assigned to participants who completed the survey is shown in
Figure 10. On the left-of-center side, 20% of respondents were identified as “crypto-communitarian”
or “crypto-leftist”, while 9% of respondents were given the “crypto-centrist” label. The most
commonly assigned type was the “crypto-libertarian” types, with nearly half of respondents receiving
this designation; overall, right-of-center types (“crypto-libertarian” and “crypto-anarcho-capitalist”)
dominated with 71% of respondents. This distribution is unimodal, low skewness, and centered
around the median possible score. However , because the range of possible scores was not centered
around zero, we find that a majority of respondents were labeled as crypto-politically
“right-of-center”. For a summary of how this distribution dif fered with political self-identification
and blockchain af filiation, Section 1.2 of the Supplementary Material.
12
Figure 10.
Distribution of assigned crypto-political
scores and corresponding sentiment types.
Despite a 14% minority of respondents identifying as ideologically conservative or right-wing, our
measure placed 71% in the right-of-center libertarian and anarchocapitalist categories.
Figure 11.
Box plots showing the distribution of values
for each political type along each of the first
three principal components produced by PCA, with the mean scores indicated by a white triangle.
There is essentially no overlap between crypto-leftist and crypto-ancap types for component 0.
Partitioning the respondents by the types we identified for them, and plotting them in the first three
PCA components, we find, again, that the first principal component succeeds at capturing the
political dimension of respondent variation, while the next two components are less informative
(Figure 1 1). The low overlap between the interquartile ranges for adjacent types indicates that the
continuous construct we defined and the defined types which discretize it help to explain dif ferences
between respondents’ beliefs. Thus, the constructed political axis seems to reflect true variation in the
population and may be of use in future work characterizing the ideological structure of the crypto
community .
5
Discussion
Though optimistic beliefs about the current state of cryptoeconomics were slightly more prevalent,
the survey responses indicate nearly as much dissatisfaction with the fair distribution and attainability
of cryptoeconomic wealth. Both a desire for sociopolitical change and an interest in personal
financial gain were common factors in participants’ interest in blockchain technologies. Respondents
13
generally were optimistic about the social potential of blockchain technology , with some having
reservations about its gender equity and some focusing on its privacy implications. Overall, though a
majority of respondents were willing to accept or even collaborate on regulation, lar ge minorities
strongly disagreed, and distaste for direct government involvement in implementations of crypto
technology was common.
Despite low rates of respondents’ self-identification with “conservative or right-wing” politics, we
observed a prevalence of right-of-center crypto-political types. A broadly similar distribution was
observed in a CoinDesk report published in 2018 (R yan 2018). The discrepancy between general
political self-identification and our crypto-specific labeling bears further investigation. It may relate
to an association of the term “conservative” with social conservatism, whereas crypto-libertarianism,
the crypto-political type we found to be most common, emphasizes a form of economic
libertarianism. Furthermore, the connotations of “conservative” and “liberal” vary significantly by
geographic region, so the question may have been interpreted dif ferently across respondents based on
their country of residence.
The correlation of the constructed political axis with the first principal component–a commonly-used,
well-validated axis–suggests a primacy of political variation in explaining patterns of responses.
Furthermore, the existence of dif ferences in the distribution of beliefs between self-identified
political orientations indicates that traditional political ideologies have some bearing on how
participants relate to blockchain technology . For example, the “left-of-center” group articulated
distinct beliefs about economic opportunity , fairness of wealth distribution, privacy , and the growth
of crypto (Q1 1-13, Q6, and Q9), suggesting that left-of-center respondents are more likely to apply
more community-oriented multi-stakeholder values to the blockchain ecosystem. The “nonaligned”
group, on the other hand, articulated distinct beliefs about gender equity and government regulation
(Q15 and Q5), suggesting this group is more clearly defined by lower trust in existing government
institutions
2
. This lower approval of government regulation
suggests that those who identified as
neither left-of-center nor right-of-center are more likely to position themselves as separate from
existing political and governance systems entirely .
We are interested in understanding the extent to which the characteristics of developers and users of
specific blockchains are distinctive of each blockchain. Critics like David Golumbia have ar gued that
Bitcoin, both in its design and ideological constitution, is principally a conservative movement
interested strictly in Bitcoin’ s record of gaining value (Golumbia 2016). Those observations were not
made in opposition to Ethereum or any other blockchain, although Ethereum had been live for a year
at the time of Golumbia’ s writing. Our findings indicate that in fact, there are few dif ferences
between Bitcoin and Ethereum users. However , differences in technical implementation between
Bitcoin and Ethereum may relate to dif ferences in opinion on their governance. Unlike Bitcoin,
which has limited support for transactions other than money transfers, Ethereum as an infrastructure
enables the developing and building of various applications and projects. The broader set of use cases
for Ethereum may lead its users to believe a broader set of stakeholders should be involved in its
governance. Furthermore, Ethereum af filiation was associated with a greater sensitivity to perceived
socioeconomic inequity , which may relate to dif ferences in how the blockchains are used alongside
other technology . In Ethereum ecosystems, users linking their own blockchain activity to other
2
We refer in this work to respondents who chose to
identify as neither left-of-center nor
right-of-center as “nonaligned”. We choose this term in contrast to a term such as “apolitical” in a
nod to ideas of political agnosticism developed by
ethnographers in observing open-source
communities
(Gabriella Coleman 2013)
.
14
personally-identifying information, such as Discord handles or Twitter accounts, is not uncommon;
more research is needed to understand whether lower rates of anonymity relate to greater awareness
of actual or perceived social demographics.
Communities or ganized around crypto are proving to be a laboratory for new ways that humans can
organize collective action, but are not operating in a historical vacuum: it appears that some patterns
observed in early users of other internet technologies have arisen or continue to appear in the
blockchain context as well. To complement this sociological work, further anthropological research
could shed some light on the extent to which the economic and political beliefs held by participants
in crypto echo the ideologies of two earlier movements: the cryptographic hacker and open-source
software communities. The distribution of responses relating to fair rewards for developer teams and
the utility of hard work and in crypto indicates that meritocratic values are prevalent; meritocracy
may play a similar role in blockchain ecosystems, themselves often open source, as it has in prior
open source and hacker communities (Gabriella Coleman 2013; Dunbar -Hester 2019). Privacy has
been at the forefront of concerns in the development of internet technologies since the cypherpunks
(Hughes 1993) and remains prevalent in blockchain (Brunton 2020). Further research is needed to
understand how these values compare to those of open-source software communities and early
adopters of the internet or how they may have changed over time as cryptocurrencies become more
mainstream.
6
Limitations
6.1
Survey methodology
Selection bias may arise given that the random sample assumption is limited by how the survey was
distributed. In particular , since the survey was opt-in, people with stronger and potentially more
extreme opinions may have been more motivated to complete the survey . Also, the survey was made
available only in English, and so is likely not representative of the full geographic distribution of
users of blockchain technologies. Presentation of some preliminary findings prior to finalizing data
collection may also have influenced some respondents. Additionally , we do not have a guarantee of
uniqueness of each respondent; moreover , the two recruitment strategies we used may have
motivated respondents to provide multiple responses.
In choosing the wording of each question and answer choice, we made an ef fort to mitigate response
bias. Still, we have identified some limitations in interpreting questions based on the wording of the
questions. Q5 may have had an insuf ficient distinction between the two most commonly-selected
choices. In answering Q14, respondents who selected “Keep on doing what we’re doing” may have
rejected the premise of the question rather than believed this was a way to achieve the stated goal.
Additionally , while we intended Q15 to refer to perceptions of gender inequity in participation or
compensation within crypto, the wording of the choices may have been too vague.
Further demographic information would be valuable context for interpreting some questions. Future
work could include a question on the geographic location of respondents, where local regulations and
political attitudes could inform a more detailed analysis of questions on national government
regulation and political af filiation. Interpretation of question 18 on political self-identification may be
similarly limited by dif ferences in how terms such as “liberal” and “conservative” are understood
across the world.
15
6.2
Analysis
To be able to use PCA for the discrete data, we one-hot encoded specific choices. While PCA is
generally better suited to continuous data than boolean data, we find that in this context the results
were cleanly interpretable. We also chose not to include null responses as an additional coded choice
for feature selection or factor analysis. While this does result in using only a subset of the responses
and potentially removing relevant information about respondents’ beliefs, it prevents the null
responses from receiving artificially high importance due to their relative rarity and bypasses the
difficulty in interpreting the null response.
7
Conclusion
In this work, we have introduced a new survey of blockchain users’ political, economic, and
governance opinions with respect to crypto. Based on 3710 survey responses, we find that users were
spread across a variety of perceptions of the distribution of economic power , normative beliefs about
the distribution of power in governance, and opinions on the role of external regulation of crypto,
though they were broadly in agreement that crypto has a net-positive impact on the world. Equal
numbers of respondents self-identified as liberal or non-aligned, while only about a third as many
respondents self-identified as conservative; this self-reported political af filiation was associated with
differences in opinions on most questions, but especially on economic fairness, decision-making
power , and how to obtain favorable regulation. In contrast, we observed few dif ferences in opinions
between respondents af filiated with Bitcoin and with Ethereum, on issues of blockchain governance
and regulation and on personal attitudes towards crypto. While the full field of beliefs elides neat
interpretation in terms of underlying factors, we found that the existence of a political dimension was
supported both by a theory-driven construct and by a common, well-validated analytical method
(PCA).
While this dataset is an important step towards understanding the distribution of crypto users’ beliefs
about blockchain technology and its utility , open questions remain as to why users believe what they
do about crypto and how their beliefs match up with reality . For example, considering the question of
who should have decision-making authority over a blockchain: Is the lar ge-minority opinion that
token-holding voters should control a blockchain underlied by a belief that minimizing human input
to governance will make it more ef ficient and less flawed? Is there a disconnect between the common
normative beliefs of what should be happening in cryptogovernance and which types of stakeholders
actually can and do participate in governance of major blockchains?
Although our research found only a few instances where af filiation with a specific blockchain was
associated with dif ferences in beliefs, further research is needed to better understand whether specific
architectures or ecosystems within crypto dif fer in the values or goals embedded in them. Future
interdisciplinary work could shed some light on the extent to which participants have common
understandings of core signifiers such as decentralization and autonomy .
Given that our work takes inspiration from the long-running Pew political survey , we see the need for
a regular survey of cryptopolitical sentiment, with an added demographic panel. This could facilitate
the identification and comparison of ideologies and modes of participation within newer chains such
as Solana, L2s such as Polygon, and even lar ge DAOs.
16
8
Conflict of Inter est
The authors declare that the research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest.
9
Author Contributions
LMK conducted data analysis and wrote manuscript. SF advised on methodology and advised on
manuscript. JT designed and deployed the survey instrument and advised on manuscript.
10
Funding
Lucia Korpas and Joshua Tan were supported by grants from the Filecoin Foundation and from One
Project.
11
Acknowledgments
The authors would like to acknowledge Michael Zar gham for technical discussion, Ann Brody for
qualitative discussion, and Tyler Sullber g and Nathan Schneider for feedback on the manuscript.
12
Data Availability Statement
The dataset generated and analyzed for this study can be found in the Metagovernance Project’ s
Govbase repository on Airtable
(
https://airtable.com/shr gnUrj0dqzZDsOd/tblvwbt4KFm8MOSUQ/viw82nVNrdHFrowoo
).
The
Python code used to conduct the analysis and produce the figures can be found in the GitHub
repository for this work (
https://github.com/metagov/cryptopolitics-paper
).
17
13
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d0f3d3827.
19
14
Tables
Table 1
Question
number
Question text
Choice text
Contribution
to political
score
1
Which statement
comes closest to
your views?
There is one (layer 1) blockchain that is the best.
There is no one best blockchain.
2
Which blockchain
is the best?
Bitcoin
Ethereum
Solana
Cardano
Polkadot
Other
3
Which statement
comes closest to
your views?
Crypto is mainly an economic technology.
Crypto is mainly a political philosophy and/or lifestyle.
4
Which statement
comes closest to
your views?
My goal in crypto is to have fun.
My goal in crypto is to make as much money as possible.
My goal in crypto is to create social change and/or disrupt the
industry.
My goal in crypto is to earn a living and/or build my career.
5
Which statement
comes closest to
your views?
Most or all cryptogovernance should be on-chain.
Most or all cryptogovernance should be off-chain.
Crypto does not need (human) governance; let the algorithms run as
they were designed.
However crypto governs itself, it should also be regulated by the
government.
6
Which statement
comes closest to
your views?
Privacy is the most important feature of blockchain and crypto.
2
Privacy is nice, but it’s not the most important feature of blockchain
and crypto.
0
7
Which statement
comes closest to
your views?
Government regulation of crypto will almost always do more harm
than good.
1
Government regulation of crypto can do some good, e.g. it can help
force blockchains to become more decentralized.
Government regulation of crypto is critical to protect the public
interest in these technologies.
-1
8
Which statement
comes closest to
your views?
Having a central bank run a cryptocurrency is a good idea.
Having a central bank run a cryptocurrency is a bad idea.
9
In order to grow,
the crypto
ecosystem should:
Build art and community.
-1
Help people around the world earn a living.
-1
Build useful tech that solve real problems for a set of users.
1
20
Provide financial instruments for maximum wealth creation.
1
10
Which statement
comes closest to
your views?
Blockchain and DeFi are beneficial technologies that, on balance,
will help most members of society.
Blockchain and DeFi are predatory technologies that, on balance,
will harm most members of society.
11
Which statement
comes closest to
your views?
Most crypto teams make a fair and reasonable amount of profit.
1
Crypto teams make too much profit.
-1
12
Which statement
comes closest to
your views?
The economic system in crypto is generally fair to most of its
participants.
1
The economic system in crypto unfairly favors powerful interests.
-1
13
Which statement
comes closest to
your views?
Most people who want to get ahead in crypto can make it if they're
willing to work hard.
1
In crypto, hard work and determination are no guarantee of success
for most people.
-1
14
To get more
favorable
regulation of
cryptocurrencies
from national
governments, the
most important
thing the crypto
community can do
is:
Adapt our technology and practices in order to minimize potential
conflicts with the law.
Work hand-in-hand with regulators to identify a solution that works
for both government and industry.
Hire lawyers and lobbyists; organize the community to mount a
public pressure campaign on politicians.
Keep on doing what we’re doing, legal or not.
1
15
Which statement
comes closest to
your views?
Crypto has a gender problem.
0
Crypto does not have a gender problem.
1
16
Who should have
decision-making
power over a
blockchain?
The public, elected representatives, and/or national leaders
A wide variety of on- and off-chain stakeholders including token
holders, node operators, application developers, foundations, and
users
The token holders and/or node operators, i.e. voters, as determined
by the protocol
The core developers and technical staff of a blockchain
17
I'm here for...
the memes
the jobs
the tech
the airdrops
18
I identify as:
Liberal or left-wing
-1
Conservative or right-wing
1
Neither
0
21
19
OPTIONAL: Do
you affiliate with
any of the
following
ecosystems or
communities?
Bitcoin
Ethereum
Solana
Cardano
Polkadot
Other
Table 2
Politics score
Assigned type
[-7,9]
Overall possible range of
scores
>= 5
Crypto-anarchocapitalist
0 < x < 5
Crypto-libertarian
x = 0
Crypto-centrist
-3 < x < 0
Crypto-communitarian
<= -3
Crypto-leftist
22
Supplementary Material
1
Supplementary Information
1.1
Recruitment strategy: Ad-hoc typology
Based on their responses to the survey , each respondent was assigned a political faction, an economic
faction, and a governance class. These were computed immediately upon completion of the survey
according to numerical weights assigned to each answer choice and formulas defined for each
faction. The faction definitions and names were developed with input from the community , and
served lar gely as a way to (1) recruit participants and (2) help participants interpret their results.
Depending on the response to Question 3 – whether the respondent considered crypto to be primarily
a political philosophy or an economic technology – each respondent was correspondingly presented
with either their political faction or economic faction as their overall assigned faction. The five
political factions (“crypto-leftist”, “DAOist”, “true neutral”, “crypto-libertarian”, and
“crypto-ancap”) were an alternate naming scheme for our constructed political axis. The five
economic factions (“earner”, “cryptopunk”, “NPC”, “techtrepreneur”, “degen”) were intended to
represent the respondents’ primary mode of economic engagement with crypto. Additionally , we
defined four “classes” (Szabian, Gavinist, Zamfirist, and Walchian) that were intended to capture
respondents’ beliefs about governance and government regulation, inspired by the positions
articulated by Nick Szabo, Gavin Wood, Vlad Zamfir , and Angela Walch.
The mechanism for assigning the factions based on respondents’ answers was a weighted sum of
their responses to each question; every answer choice in every question adds points to one or more
factions, and the faction assigned is the one with the most points, with some thresholding to account
for respondents who did respond to all questions. In cases where not enough responses were given,
the assignment defaulted to “true neutral” or “NPC”. Questions 6, 7, 9, 1 1, 12, 13, 14, 15, and 18
were used in computing the political faction, questions 4, 9, 17 in computing the economic faction,
and questions 5, 7, 8, 14, and 16 in computing the governance class.
The political faction names and definitions were conceptualized and iterated through a
community-based ef fort at crypto conferences and online, especially within the Metagovernance
Project Slack community . The names of the factions reflected both popular slang in crypto (degen,
cryptopunk, crypto-libertarian, crypto-leftist, DAOist, crypto-ancap) as well as a few inventions of
the authors when there was no existing slang or word for that archetype (earner , techtrepreneur , true
neutral, NPC). The visual representations of the factions, which were used as the underlying images
of a series of NFT s, played of f various memes (e.g. shiba inu / doge representing degens) and cultural
icons (e.g. Shrek representing crypto-libertarians) common in crypto and on the internet more
broadly .
Within the main text, the political “factions” were renamed to the described “types”.
1.2
Differ ences in assigned political types by self-r eported political orientation and
blockchain ecosystem affiliation
There was a statistically significant dif ference in the overall distribution of assigned cryptopolitical
factions across the left-of-center , right-of-center , and nonaligned groups. Right-of-center respondents
were the only group more likely to be assigned the crypto-anarcho-capitalist faction than the
crypto-libertarian faction, and less likely than the other two groups to be assigned true neutral,
23
DAOist, or crypto-leftist. Unlike the other two groups, left-of-center respondents were more likely to
be assigned any of crypto-centrist, crypto-communitarian, or crypto-leftist than to be assigned
crypto-anarcho-capitalist. Nonaligned respondents generally split the dif ference between the
proportions of left-of-center and right-of-center respondents assigned each faction except for
crypto-libertarian, which they were more likely to be assigned than either of the other two groups.
Note that while Q18 was used directly in assigning the political faction, it was one of nine such
questions, each with similar weights in the overall cryptopolitical score.
A higher percentage of Bitcoin-af filiated respondents than Ethereum-af filiated respondents received
the “crypto-anarcho-capitalist” label.
1.3
Are ther e clusters of r espondents or featur es?
We were interested in understanding whether the range of responses would be better captured by the
idea of “types” (groupings of respondents, consistent with data-driven clustering methods) or “axes”
(grouping of questions, consistent with factor analysis techniques). We investigated this by extending
our PCA analysis with feature agglomeration, based on a feature set of 48 choices provided across 17
questions.
We used feature agglomeration to hierarchically generate clusters of features, where each feature
corresponded to the selection of a provided choice. Given that these features are boolean in nature,
we used the Dice distance metric to determine distances between features; this can be understood as
the fraction of the sample for which the two features intersect or overlap, as a representation of the
shared information between them. To determine which clusters should be mer ged and when, we used
the complete linkage criterion, which relates to the maximum distances between all features of the
two clusters.
From the feature agglomeration results, we ask whether the assigned political types directly correlate
with any of the identified factors (i.e., to what extent does our categorization of beliefs as political,
economic, or governance-related meaningfully describe joint variations in the choices selected?). At
the threshold where three clusters were present, the clusters contained A=31, B=10, and C=7 choices.
The cluster that shared the most features in common with the important principal component features
listed above was the cluster with 10 features: the specific choices listed above for beliefs on
economic fairness, gender , and political af filiation (Q1 1-Q13, Q15, and Q18) appeared in this feature.
The two specific choices relating to government regulation (Q5 and Q7) were present together in the
cluster with 7 features Although cluster B aligned fairly well with the first (politically-focused)
principal component, our exploration of the other clusters, and the hierarchy that produced them, did
not indicate much qualitative support for this approach. This null assessment is supported by our
mapping of respondents into feature space: both factor analysis methods shown in
Supplementary
Figure S7
shows responses or ganized around a single
centroid, not the multiple clusters that would be
expected in the presence of clear respondent types.
Given that the feature agglomeration method allowed us to traverse the entire hierarchy of feature
clusters, we also looked at the agglomerated clusters to see whether three stable feature clusters arise
from the full feature set. Based on the distances required for clusters to mer ge, it appears instead that
five distinct latent variables may best describe the distribution of respondents’ beliefs, as shown in
Supplementary Figure S8
. That said, the smaller two
out of the three clusters identified above
persisted unchanged at the five-cluster threshold, suggesting that the underlying factors for each of
those two clusters have more explanatory power .
24
2
Supplementary Figur es and Tables
2.1
Supplementary Figur es
Supplementary Figur e S1
. Distribution of responses
to questions 1, 2, and 5. Note that question 2
was presented only to respondents who selected the response “There is one (layer one) blockchain
that is the best” for question 1, and that respondents were given the option to select Ethereum,
Bitcoin, Solana, Polkadot, Cardano, or a fill-in-the-blank “Other”.
25
Supplementary Figur e S2
. Distribution of responses
to questions 6, 8, and 9.
26
Supplementary Figur e S3
. Distribution of responses
to questions 10, 15, and 17.
27
Supplementary Figur e S4
. Distribution of responses
to question 19, and distribution in the number
of those multiple-select choices that respondents who answered the question chose. Note that
respondents were given the option to select Ethereum, Bitcoin, Solana, Polkadot, Cardano, or a
fill-in-the-blank “Other”. For plot legibility , only the ecosystems listed by at least 10 respondents are
shown here.
28
Supplementary Figur e S5.
Correlation (Cramer ’s V)
between choices to questions 18 and 19. The
low correlation between choices belonging to dif ferent questions indicates that political
self-identification and blockchain af filiation are lar gely independent.
29
Supplementary Figur e S6
. Correlation (Cramer ’s V)
between all questions. Gray indicates that the
correlation between questions was not computed; this is used when one question was only presented
to the respondents upon a particular selection for the other question. No clusters are clearly
observable: the questions are not systematically related in macro groupings.
30
(A)
(B)
Supplementary Figur e S7.
(
A
) Projection of the respondents
onto the first two principal
components. (
B
) Projection of the respondents onto
the feature clusters B (10 features) and C (7
features). In both cases, there is a continuous (rather than discrete) distribution of respondents along
the two axes. The unimodal distribution of respondents along the two axes does not suggest the
presence of clusters of respondents by their responses.
31
Supplementary Figur e S8.
Dendrogram describing the
hierarchical agglomeration of feature
clusters. The colors delineate the first five clusters that branch of f from the one-cluster case (i.e., the
one containing all features). The three-cluster case involves grouping some of these together .
32
|
{
"id": "2301.02734"
}
|
2012.02147
|
The Application of Blockchain-Based Crypto Assets for Integrating the
Physical and Financial Supply Chains in the Construction & Engineering
Industry
|
Supply chain integration remains an elusive goal for the construction and
engineering industry. The high degree of fragmentation and the reliance on
third-party financial institutions has pushed the physical and financial supply
chains apart. The paper demonstrates how blockchain-based crypto assets (crypto
currencies and crypto tokens) can address this limitation when used for
conditioning the flow of funds based on the flow of products. The paper
contrasts the integration between cash and product flows in supply chains that
rely on fiat currencies and crypto assets for their payment settlement. Two
facets of crypto asset-enabled integration, atomicity and granularity, are
further introduced. The thesis is validated in the context of construction
progress payments. The as-built data captured by unmanned aerial and ground
vehicles was passed to an autonomous smart contract-based method that utilizes
crypto-currencies and crypto tokens for payment settlement; the resulting
payment datasets, written to the Ethereum blockchain, were analyzed in terms of
their integration of product and cash flow. The work is concluded with a
discussion of findings and their implications for the industry.
|
http://arxiv.org/pdf/2012.02147v1
|
Hesam Hamledari, Martin Fischer
|
cs.CR, cs.AI
|
cs.CR
|
The Application of
Blockchain -Based Crypto Assets
for Integrating the Physical
and Financial Supply Chains in the
Construction & Engineering
Industry
By
Hesam Hamledari & Martin Fischer
CIFE Technical Report #TR 245
Decem ber 2020
STANFORD UNIVERSITY
The Application of Blockchain -Based Crypto Asset s for Integrating the Physical
and Financial Supply Chains in the Construction & Engineering Industry
Hesam Hamledari1, Martin Fischer2
1 PhD Candidate, Stanford University, Department of Civil & Environmental Engineering, Stanford, CA,
United States, hesamh@stanford.edu
2 Kumagai Professor of Engineering and Professor in Civil & Environmental Eng ineering, Stanford
University, Stanford, CA, United States, fischer@stanford.edu
ABSTRACT
Supply chain integration remains an elusive goal for the construction and engineering industry. The high
degree of fragmentation and the reliance on third -party financial institutions has p ushed the physical and
financial supply chains apart . The paper demonstrates how blockchain-based crypto asset s (crypto
currencies and crypto tokens) can address this limitation when used for conditioning the flow of funds based
on the flow of products. The paper contrasts the integration between cash and product flows in supply
chains that rely on fiat currencies and crypto asset s for their payment settlement. Two facets of crypto as set-
enabled integration, atomicity and granularity, are further introduced. The thesis is validated in the context
of construction progress payments . The a s-built data captured by unmanned aerial and ground vehicles was
passed to an autonomous smart contract -based method that utilizes crypto -currencies and crypto tokens for
payment settlement ; the resulting payment data sets, written to the Ethereum blockchain, were analyzed in
terms of their integration of product and cash flow . The work is concluded with a discussion of findings
and their implications for the industry.
1 INTRODUCTION
The construction and engineering industry has long been in the pursuit of supply chain integration
(Howard et al. 1989; O'Brien and Fischer 1993) . Most effort s have focused on increasing the strategic
collaboration and partnership between the construction supply chain partners (Briscoe and Dainty 2005;
Cheng et al. 2010; Dainty et al. 2001) . A goal that is equally importan t and often neglected is the integration
of flows (Rai et al. 2006) and in particular the cash and product flows (Blount 2008; Hofmann 2005) . In an
integrated supply chain, the flow of products (physical supply chain) and funds (financial supply chain)
need to be integrated throughout the life cycle of a project (Rai et al. 2006) ; they should not be separate .
Integrating the physical and financial supply chain enables supply chain optimization (Kifokeris and Koch
2020) and creates a single source of truth, key to successful delivery of projects (Barbosa 2017; Fischer et
al. 2017) .
Despite the desired benefits of such integration, t he physical and financial supply chains have been
pushed apart due to the high degree of fragmentation in the industry (Bilal et al. 2016; Maria João
Ribeirinho et al. 2020) . This fragmentation stems from the high number of stakeholders (Briscoe and Dainty
2005; Cheng et al. 2010) and the siloed data development in the industry’s information technology
infrastructure (Barua et al. 2004; Chen et al. 2018) .
To manage its financial supply chain , the fragmented construction industry has to rely on trusted third
parties, banks and financial institutions (Bitran et al. 2007; Feldmann and Müller 2003) . These institutions
play a role that is key to achieving integration (Blount 2008) , yet their involvement as an external
stakeholder has hurt the integration between the physical and financial supply chains (Blount 2008; Fellenz
et al. 2009; John Mathis and Cavinato 2010) . This lack of integration has led to misalignments between the
documentations of flows across different project information sources (Čuš -Babič et al. 2014) .
This paper demonstrates how blockchain -based crypto asset s can address this limitation when used for
conditioning payments based on the flow of products. The underlying mechanisms contributing to crypto
asset -enabled integration are elaborated by contrast ing the flow of cash in today’s supply chains, reliant on
fiat currency , and those using crypto assets. Two facets of crypto asset -enabled integration, atomicity and
granularity, are further introduced. To validate this work’s thesis, a series of experiments are conduc ted
where crypto asset s are used for payment processing and lien right management on two commercial
construction projects. The r obot-capture d observations of two job sites are passed to a smart contract -based
method that uses crypto currencies and crypto tokens for p rocessing payments to subcontracto rs. T he
resulting payment datasets, written to a public blockchain, are examined in terms of their integration of
cash and product flow . Th e paper is concluded with a discussion of finding s, limitations, and the
implications for the AEC industry.
2 POINT OF DEPARTURE
Blockchain and smart contract are the underlying technologies that empower crypto asset s. This
section first reviews the studies around the use of these two technologies in managing cash flow and its
integration with product flow (section 2.1) . This is followed by a review of crypto asset classes (section
2.2). In this work, the terms “ financial supply chain ”, “cash flow” , “the flow of cash ”, and “ the flow of
funds ” are used interchangeably.
2.1 Applications of Smart Contract for Cash Flow Management
Cash flow management is currently governed by inefficient contracts that rely on manual workflows
and third party interference (Salleh et al. 2020) ; these limitations make traditional contracts a major source
of delay (Odeh and Battaineh 2002) , distrust (Gabert and Grönlund 2018; Manu et al. 2015) , and
information asymmetry between supply chain partners (Xiang et al. 2015) .
Blockchain -enabled smart contract s are believed to provide cost and time savings by reducing the
administrative work (Sreckovic and Windsperger 2019) and to promote collaboration by increasing
transparency (McNamara and Sepasgozar 2018) . Therefore , smart contract s can reduce the cost of
contracting (Qian and Papadonikolaki 2020) . Others argue that smart contract s’ true value proposition in
cash flow management is twofold: 1) they increase the confidence in the output of the computational
systems (De Filippi et al. 2020) and the input data (Penzes et al. 2018) used in payment processing; and 2)
they add reliability to payment automation due to their elimination of centralized control mechanisms and
providing guarantee of execution (Hamledari and Fischer 2020a) . While alternative technologi es may
support automation, they lack such reliability. An example is internet -based payment applications ; they are
proven to decrease the processing time by 84% compared with paper -based payment applications (Barrón
and Fischer 2001) , but they still rely on the same workflows that hurt traditional contracts .
Despite these potential benefits, practitioners have doubts about the technology’s capabilit ies (Mason
and Escott 2018) . The adoption will remain slow (Sharma and Kumar 2020) until th e industry adapt s its
policies (Hamma-adama et al. 2020; Li et al. 2019) , addresses the legal hurdles (Badi et al. 2020) , and
develops new business models (Tezel et al. 2020) . Others argued that semi -automated contracts (Altay and
Motawa 2020) and modular construction (Owusu et al. 2020) provide a more feasible path to adoption due
to their relatively simpler contract structure.
One study (Elghaish et al. 2020) propose d a framework for the execution of financial transactions in
integrated project delivery (IPD) projects using smart contracts. The method , based on Hyperledger F abric
(Androulaki et al. 2018) , keeps track of achieved profit in comparison with planned profit. Others argue d
that the alignment between IPD and blockchain’s incentive structure can enhance collaboration (Hunhevicz
et al. 2020) .
To improve the security of payments, a method was proposed (Ahmadisheykhsarmast and Sonmez
2020) to lock funds in a smart contract account for a period of 30 days, reducing the trades’ exposure to the
insolvency of clients. To enable the sharing of payment records at project -level, a key management strategy
was proposed (Das et al. 2020) to provide “ selective- transparency ”, keeping sensitive information only
visible between two contracting parties. A semi -autonomous solution (Luo et al. 2019) proposed the use of
a consensus mechanism in the place of today’s payment workflows; contracto rs submit data regarding the
applications for payment which is processed in a decentralized peer -to-peer network of stakeholders .
Payments are executed by a smart contract if the relevant stakeholders reach consensus.
The integrations with BIM is key to successful applications of the smart contract s (Mason 2017) , and
this has been the focus of recent work on payment automation. A framework was introduced for using 5D
BIM in the context of automated billing (Ye and König 2020; Ye et al. 2020) , extracting the bill of quantities
from the project models and enhancing transparency ; the framework needs to be implemented , validated,
and imporve its design to include payments to subcontractors.
Two studies took a more integrated approach , focusing on the manag ement of the cash flow in
relationship with the physical supply chain: a framework was proposed (Chong and Diamantopoulos 2020)
for conditioning the smart contract’s payments based on the sensor feed and the BIM used for tr acking the
on-site installation of building façade panels. This work created a link between off - and on-chain realities
by its direct use of product flow for payment processing. The smart contract design was not detailed
however . A smart contract -enabled solution (Hamledari and Fischer 2020b) was introduced to
autonomously translate the on-site reality captures to direct payments to general contractor and
subcontractors ; it eliminated the need for payment applications . The progress data and as-built BIMs are
stored off -chain on a private InterPlantary File Sharing (IPFS) (Ben et 2014) with their cryptographic
summary stored on the Ethereum blockchain (Buterin 2014) and used for on-chain payment settlement .
It was argued (Hamledari and Fischer 2020a) that such autonomous conditioning of cash flow on
produc t flow , as described in the two studies above, cannot be achieved with other technological
alternatives. The C ash and product flows are respectively institutional and brute facts; the former is a soc ial
reality, relying on the collective agreement of stakeholders . The transition from brute to institutional facts
necessitates an elimination of single points of failure and centralized control mechanisms that are present
in payment applications ; smart contracts make this possible (Hamledari and Fischer 2020a) . A conceptual
blockchain-based business model (Kifokeris and Koch 2020) was introduced for use by construction
logistics consultants and in support of integrating supply chain flows. The process flows and the the lo gistics
set up was motivated through a review of literature and empirical findings in the context of sweedish construction industry.
The review of the literature reveals a need for increased attention on the integration between physical
and financial suppl y chains (Kifokeris and Koch 2019) . The research landscape has remained mostly
theoretical (Darabseh and Martins 2020; Hunhevicz and Hall 2020; Kasten 2020) and lack s validation of
usability (Hijazi et al. 2019) . While a handful of studies focus ed on the transition from product flow to cash
flow, it is not clear whether this transition leads to integration and how such integration compares with that
of today’s construction supply chain. In addressing this challenge, this work focuses on the role of crypto
asset s as enabler s of integration between physical and supply chain. The section 2. 2 provides a brief
overview of crypto asset classe s, a point of departure used in this work.
2.2 Crypto asset
The term “crypto asset ” is used herein to refer to both crypto currencies and crypto tokens. At their
core, crypto asset s provide a decentralized governance model for reaching agreement on a shared notion of
reality in trust-less environment s.
2.2.1 Crypto Currency
In the case of crypto currencies, th is shared notion is the concept of money and its exchange
between a network of peers. Money is an institutional fact (Searle 1995) , and it require s the active
participation of trusted third -part y institutions such as banks who define and back this shared notion of
value . The invention of Bitcoin (Nakamoto 2008) and its Nakamoto consensus allowed for independent
parties, with competing objectives, to collectively execute and agree on the exch anges of monetary value
(bitcoin) without reliance on trusted third -party intermediaries .
In their early writings, the Bitcoin inventor Satoshi Nakamoto referred to the blockchain as “ proof -
of-work chain ” (Champagne 2014) . This further emphasizes the critical role of the consensus/ governance
model as the core contribution of Bitcoin (Tschorsch and Scheuermann 2016) , and not the use of a shared
distributed ledger . This decentralization reduces the cost of transaction verification and networking
(Catalini and Gans 2016) , and it is the innovation that distin guishes Bitcoin and other crypto currencies
from their unsuccessful predecessors (Narayanan et al. 2016; Narayanan and Clark 2017) such as B-Money
(Dai 1998) , and Bit Gold (Szabo 2005) .
Bitcoin ’s shortcomings gave rise to alternative coins ( “alt-coin ”) (Antonopoulos 2014) . The
Litecoin (Lee 2011) was announced as a “lite version of Bitcoin ”, and it replaced the Bitcoin’s SHA-256
with the Scrypt algorithm . This made its mining more accessible since the Scrypt algorithm is less
susceptible to custom -hardware solutions and the resulting high consolidation of miners. Privacy is a
concern due to the Bitcoin ’s use of pseudonymous transactions (Conti et al. 2018) . The study of transaction
patterns may jeopardize users’ spending habits and their identity . For e xample, one study successfully
distilled millions of transactions into a few thousand superclusters, each representing a business entity
(Tasca et al. 2018) . This need for privacy gave rise to Monero (Van Saberhagen 2013) , Dash (D uffield and
Diaz 2015) , and Zerocash (also Zcash) (Sasson et al. 2014) . For example, Zcash provides zero -knowledge
proofs for transactions without revealing the ir source, amount, and destination. This is achieved with the
combined use of an anonymous and a base non-anonymous c urrency. In addition to privacy, there are
concerns with respect to scalability and the li mited tr ansaction throughput (Eyal et al. 2016) .
While both permission-less and private chains use shared distributed legers to manage the
exchanges of their native coin s, their functionality and security stem from the design of their consensus
mechanism (Antonopoulos 2017a) . The mere use of a distributed database for exchange of crypto currencies
does not guarantee that transactions are irreversible, and that consensus can be emerged. The anti-trust risks
associated with consortium blockchains (Schrepel 2019; Schrepel and Buterin 2020) are one example of
such challenges; this can posit difficulties for distributed leger technology (DLT) -based solutions (Mills et
al. 2016) .
While a lt-coins face difficulties in bootstrapping (Böhme et al. 2015) , they continue to gain
adoption and constitute a bigger portion of the market . As a result, the support for Bitcoin has slightly
declined , dropping from 98% in 2017 to 90% in 2020 (Blandin et al. 2020) . Regardless of which crypto
currencies survive, their innovation in the decentralized exchange of money is here to stay (Lo and Wang
2014) .
2.2.2 Crypto Token
The invention of Bitcoin made decentralized money a reality , yet it fell short of supporting
decentralized applications (DApp) due to its limited stack -based scripting language and its lack of turning
completeness (Swan 2015) . This motivated the invention of the Ethereum blockchain (Buterin 2014; Wood
2014) and its quasi -Turning complete Ethereum Virtual Machine (EVM). Ether (ETH) is the native coin of
the Ethereum blockchain. This innovation enabled computerized algorithms to be executed in a
decentralized manner , making earlier visions such as smart contract (Szabo 1994; Szabo 1997) a reality.
Crypto token s are one example of smart contracts. A crypto token contract is executed on the EVM ,
and it manages its supply of tokens and their exchanges by updating a set of variables written to the
underlying Ethereum blockchain. For smart contracts to transact with one another and for trading to take
place, these token contracts and their functionalities needed to be standardized. This gave birth to the
Ethereum Request for Comments (E RC) documents that describe a token standard. The two notable token
standards are the ERC20 standard (Vogelsteller and Buterin 2015) and ERC721 standard (Entriken et al.
2018) , respectively used to represent fungible and non-fungible digital assets.
ERC20 tokens are fungible; each token can be swapped with another . In contrast to fungible crypto
currencies, however, these tokens can have additional functionalities, acting as “ programmable money”
(Antonopoulos 2017b) . For example, they can represent a share in a venture; this has given rise to initial
coin offerings (ICO), the crypto world’s parallel to initial public offerings and a means of fund raising
(Howell et al. 2020) . The ERC20 ’s popularity a nd utilization has made ETH the second most common
crypto currency (Rauchs et al. 2018) trailing bitcoin .
ERC721 tokens are unique and cannot be swapped with one another. This enables tokenization, wit h
each non-fungible token (NFT) representing a unique asset. This concept , however, has origins in the earlier
writings by Nick Szabo on the decentralized exchanges of secure property titles (Szabo 1998) and the efforts
in the Bitcoin ecosystem for the development of “ colored coins ” (Rosenfeld 2012) . The application of NFTs
has been proposed f or infrastructure financing (Tian et al. 2020) , the lien right management in construction
industry (Hamledari and Fischer 2020b) , life cycle management of information in AEC (Succar and Poirier
2020) , and tracking goods across supply chains (Westerkamp et al. 2020) .
3 CRYPTO ASSET -ENABLED INTEGRATION OF PHYSICAL AND FINANCIAL
SUPPLY CHAINS
This work argues that the application of crypto assets in the place of fiat currencies and for
conditioning payments on the updates in the product flow status can increase the integration between
physical and financial supply chains . The authors first elaborate the underlying mechanisms that create
such integration by contrasting the flow of cash in supply chains that are rely on fiat currencies and those
using crypto assets (section 3.1). Two types of integration enabled by crypto asset s are further introduced
in section 3.2.
3.1 The Movement of Money: Seeming Versus Actual
Financial institutions such as banks have become indispensable parts of today’s construction supply
chain (Fig 1), responsible for processing payments in fiat currencies throughout a project’s life cycle and
between all its stakeholders. Banks operate based on the concept of liability . An account balance represents
the sum owed to an account holder by a bank, and not reserves of cash. Banks process payments by adjusting
their liability and the amounts they owe to project stakeholders on the receiving and sending end of a
payme nt. A bank owes respectively more and less to the payee and payer; these changes in liability are
respectively called credit and debit operations. This process is more straightforward when both stakeholders
hold accounts with the same bank (Fig. 1b), where the financial institution debits one account by the exact
amount it credits the other. The bank sees no change in its net liabilities, but instead changes in its liability
to each individual stakeholder.
Fig. 1. The payment workflows in today’s AEC industry: a) the lack of integration between physical and
financial supply chains, b) payment settlement when parties hold accounts with the same bank or c) with
different banks
In the more complex situations where stakeholders hold accounts with different banks (Fig. 1c).
Correspondent banking is a model used in the past, where each bank holds accounts with other banks
(Nostro account) and hosts accounts for them (Vostro account) . Payments are settled by changes in the net
liability of banks on the receiving and sending end of a transaction, reflected in their Vostro and Nostro
accounts. More recently, payments between banks are handled using central banking models and batch
processing approaches (Fig. 1c), where a network receives and batches transactions from the originating
depository financial institutions (ODFI) and reports them to the receiving depository financial institutions
(RDFI); the resulting net debit and credit positio n of a bank is determined by the aggregate effect of
outgoing and incoming transactions and is reflected in the bank’s Nostro account with a central reserve
(Fig. 1c). Examples of batch processing include the automated clearing house (ACH) (McAndrews 1994)
in USA and the clearing house automated payment system (CHAPS) in UK (Bech er et al. 2008) .
In instances explained above , the bank does not move money; the payment is settled by flowing
information , arithmetic operations on the banks’ books , and not by flowing cash. This is a key factor that
distinguishes current supply chains from those empowered by crypto asset use (Fig. 2):
Fig. 2. Integration of physical and financial supply chains in the crypto asset -enabled supply chains
In a crypto asset -enabled system, th e payment can be settled using native crypto currencies or
overlay crypto tokens, with transactions formed between externally owned accounts (EOA), between
contract accounts and EOAs, and between contract accounts; the first two are illustrated in Fig. 2a .
Regardless of the crypto asset and the account type: 1) each account’s balance represents the existence of
actual reserves of crypto currencies, crypto tokens, or unique unspent transaction outputs; these funds are
under the control of the party with access to the private key of the account address; 2) each payment changes
the ownership of the aforementioned funds; this change represents a movement of money. This movement
is written to and can be tracked on the blockchain. The payment moves crypto asset s from one account to
the next, such that the funds can be controlled only by the private key of the payee.
This actual movement of funds is what crypto asset -enabled supply chain in terms of integration;
each movement of money provides an opportunity for integrating the product flow data with the payments
(cash flow) they trigger. The updates in the status of product flows such as material deliveries, installation s,
and the construction of building elements at job sites can be directly tied to the crypto asset s’ change of
ownership.
3.2 Two Facets of Crypto Asset-Enabled Integration: Granularity and Atomicity
The application of crypto asset s for payments enhances 1) the granularity and 2) the atomicity of the
integration between physical and financial supply chains :
3.2.1 Granularity
This work characterizes granularity from three perspectives: 1) time , 2) trade, and 3) product.
Therefore, a more g ranular integration between cash and product flows is herein characterized by payments
that are more frequent, are payable to fewer parties, and are associated with smaller amount of work or
fewer products (Fig. 3b).
• Temporal granularity refers to the peri od of time for which the construction work is documented
in the payment application s and for which trades are entitled to a compensation. Increased temporal
granularity equals more frequent payments.
• Trade -level granularity refers to the number of trades included in a payment. For example, a
payment made to general contractor for indoor finishing work can include compensation for multiple trades such as insulation installer, drywaller, painter, and electrician. A more granula r
trade-level payment would involve fewer trades per transaction, resulting in more direct payments
to each trade for their share of the work during a billing period.
• Product -level granularity refers to the number of products and building elements for which a party
is compensated. For example, a payment to a general contractor for the concrete pour on one floor
has higher product -level granularity than a payment for multiple floors.
Fig. 3. A comparison between low and high granularity in payments: a) longer payment cycles,
compensation to multiple trades, and for bigger scope of work; b) high temporal, trade -level, and
product -level granularity
The increased granular ity in the integration of cash and product flows makes it easier to align
various sources of project data. The information, product, and cash flows are currently documented at
different granularity and across sources such as 3D models, construction progress data, invoices, payment
applications, and lien waivers, among others (Fig. 4) . For example, the product documentation has the least
granularity in the payment application submitted to owner , medium granularity in subcontractor invoices,
and most granularity in the 3D models. These discrepancies make it difficult to connect these data sources;
this problem is compounded when matching data using other features such as trade and time .
The time-consuming workflows associated with today’s fragmented payment systems (Fig.1) hampers
project stakeholders from achieving more granular integrations. Increased temporal, trade -level, and
product -level granularity would result in exponentially higher number of payment applications and
interactions with outside financial institutions ; the resources required for preparation, review, approval, and
the enforcement of these applications make such high granularity impossible to achieve. In the crypto asset -
enabled system, on the other hand, the exchange of monetary value is decentralized a nd processed using an
overlay protocol that benefits from the underlying blockchain’s autonomy and consensus mechanism. This
autonomy allows for direct use of product data for payment processing, increasing the granularity of
integration. The third -party i nstitutions are not involved in processing these granular payments; they instead
are located at the periphery of the supply chain (Fig. 2) , where they provide financial services to
stakeholders.
Fig. 4. The level of granularity significantly varies bet ween different sources of project information
3.2.2 Atomicity
Atomicity is herein defined as both the product and cash flows documented within the same space;
an atomic integration is characterized by a grouping of product flow status updates and the corresponding
flow of cash into a single transaction. Increased atomicity contributes to the creation of a single source of
truth and integrated information.
The construction supply chain currently lacks atomicity because the physical and financial supply
chains are pushed apart from each other due to the heavily intermediated nature of the supply chain and
reliance on external financial institutions (Fig. 1). This has resulted in a siloed data development ecosystem;
the product flow is stored with project members and scattered through virtual models and remote captured
progress data, whereas banks hold onto t he corresponding cash flow data. The product flow status updates
that trigger payments cannot be retrieved from t he records of payments; the latter appear as a series of credit
and debit operations on the bank’s books and in aggregate form.
The applicatio n of crypto asset s pushes financial institutions to the periphery (Fig. 2). This changes
the role of banks in the construction supply chain, giving project stakeholders the ability to settle payments and agree on the concept of money without reliance on third -party intermediaries. As a result, the flow of
funds is executed and documented within the supply chain network and not outside its boundaries. In this
crypto asset -enabled supply chain, both the cash and product flows are documented in the same transactions,
stored on the underlying blockchain (Fig. 2).
4 EXPERIMENT SET UP
A series of experiments were conducted in the context of construction progress payments to explore
the role of crypto asset s in enabling integration between product and cash flows. Fig. 5 illustrates the design
of experiments, where the instances of crypto asset s were used for payment processing. To validate this
work’s thesis, the resulting payment data was assessed in terms of the atomicity and granularity of
integration between cash and product flows.
The product flow data (Fig. 5a) consists of two sets of 1) on-site reality captures, 2) as-built BIMs , and
3) artificial intelligence -enabled progress assessments for two commercial construction projects in
California (USA) and Ontario (Canada). The first data set (Law et al. 2020) is captured by an unmanned
ground vehicle (UGV) equipped with a laser scanner, where three subcontractors were compensated for
work on plumbing, indoor partitions, and heating, ventilations, and cooling. The second da taset (Hamledari
et al. 2017a; Hamledari et al. 2017b) is captured by a camera- mounted unmanned aerial vehicle (U AV),
where four subcontractors performed work on indoor partitions (framing, insulation, installation and
plastering of drywalls, and painting).
The product flow data is passed to an autonomous smart contract -based payment method
(Hamledari and Fischer 2020b; Hamledari et al. 2018) (Fig. 5b) which uses three instances of crypto assets:
ERC721, ERC20, and ETH . The former is used for the transfer of lien rights; each to ken represent s the
right to physical property for which a payment is made. T he ERC20 and ETH are used for on -chain payment
settlement (see section 2. 2 for an overview of these crypto asset classes). The smart contract -based solution
stores product flow off -chain on a private IPFS network; the updates in the status of product flow are
communicated to a smart contract on the Ethereum virtual machine (EVM) which performs the on -chain
payment settlements and directly writes to the Ethereum blockchain.
Table 1. The two levels of granu larity defined for product, temporal, and trade features of the payment
data
Level of granularity
Low High
Product all elements of the same type one building element
Time monthly weekly
Trade general contractor subcontractors
Fig. 5. The experiments designed to evaluate the effectiveness of crypto asset s in enabling integration
between cash and product flows: a) product flow data on two commercial construction projects, 2) smart
contract -based method used for payment processing, c) the eight testing scenarios defined based on
variations in the product -level, temporal, and trade -level granularit y, and d) the resulting 18 data sets of
payment data
To evaluate the effectiveness of crypto asset s in enabling granularity, the low and the high level of
granularity were defined for product -level , temporal, and trade -level features of the payment data (Table
1). For example, the low and high trade -level granularity respectively correspond to payments from the
owner to general contractor (GC) and the direct payments from the owner to subcontractors.
Fig. 6 illustrates these levels of granularity for product-level, temporal, and trade -level features.
The combination of the low and high granularity for these three features resulted in a total of 8 testing
scenarios (Fig. 5c). Each of the UGV and UAV dat asets was used to generate 8 sets of payment data,
ranging from the lowest granularity (i.e., monthly payment from the owner to GC for work on all building
elements) to highest (i.e., weekly payment from the owner to a subcontractor for work on one particu lar
building element). These tests were run in parallel and for a period of one month.
Fig. 6. The low and the high levels of granularity for product, time, and trade
5 RESULTS & DISCUSSION
The experiments resulted in 16 sets of payment data (Fig. 5d), c onsisting of transactions written to the
blockchain. Tests 1 -8 only vary in terms of their granularity and otherwise correspond to the same scope of
work on the first project. This is also the case for tests 9 -16, corresponding to payments on the second
project.
The payment datasets were examined to verify whether granularity and atomicity were achieved in the
integration between cash and product flows; the results are detailed in sections 5 .1 and 5.2 respectively.
The implications of these findings are further discussed in section 5 .3.
5.1 Granularity of Integration
The integration is successfully achieved if the payments have the intended level of granularity as
specified in the testing scenarios (Fig. 5c). According to the on-chain transactions for the UGV dataset (Fig.
5a), payments in the tests 4, 5, 7 and 8 failed to materialize; these unsuccessful tests all correspond to high
product -level granularity. As for the UAV dataset (Fig. 5a), all eight tests (9 -16) were successfully executed
and achieved the intended granularity.
Further analysis of the tests’ input data reveals that this discrepancy is due to the differences between
the product flow’s level of detail (LoD) in the two datasets. In both projects, the BIM provides design and
construction data for each building element, available per globally unique identifiers (GUID). The progress
assessments (i.e., as-built condition at the jobsites), on the other hand, are provided in aggregate form for
the UGV dataset and per GUID in the UAV dataset . As a result, the high product -level granularity failed to
materialize in payments associated with the former.
Fig. 7 illustrates four transactions achieved using ETH and ERC20 for payment settlement. These
transactions vary i n terms of their levels of granularity and are respectively part of the transaction pools in
tests 1 (low granularity), 4 (high product granularity), 8 (high product, temporal, and trade granularity), and
6 (high temporal and trade granularity). They respe ctively correspond to 1) payment to the GC for the
framing, insulation, drywall installation, plastering, and painting performed during the month of June on
104 partitions (Fig. 7a); 2) payment to the GC for the framing, insulation, drywall installation, a nd
plastering performed on one particular partition during the week of June 4- 11 (Fig. 7b); c) payment to the
framing subcontractor for the work on one particular partition during the week of June 4-11 (Fig. 7c); and
d) payment to the insulation subcontrac tor for the insulation work performed on 42 partitions during the
week of June 12 -19.
Fig. 7. Different levels of granularity achieved using ERC20 and ETH in payment settlement: a) low
granularity, b) high product granularity, c) high product, temporal , and trade granularity, and d) high
temporal and trade granularity
This increased granularity is not limited to the payment settlement and is extended to the transfer
of lien right. As shown in Fig. 7, the smart contract -based method (Hamledari and Fischer 2020b) uses the
ERC721 -based LIEN token to transfer the lien right to the owner for the scope of work for which the trades
are compensated in the ERC20/ETH transaction. The se two transactions are broadcasted simultaneously by
the smart contract . As the construction progresses, instances of LIEN token are minted and transferred to
the owner, with each having its own unique ID. Fig. 8 shows the LIEN token corresponding to the payment
depicted in Fig. 7c; it represents the right to the framing work on the partition shown in Fig. 8b.
Fig. 8. The application of ERC721 in the transfer of lien rights: granular definition of scope of work in the
LIEN token and the atomic integration between product data (off-chain) and financial data (on-chain)
5.2 Atomicity of Integration
The transactions in all tests were observed to achieve atomicity in their integration of cash and
product flows: the product flow updates corresponding to each flow of cash were directly retrieved from
the payment transactions and vice versa. These flows are stored within th e same space (Fig. 9) and not in
separate siloes.
Fig. 9 illustrates how the application of the crypto assets enables this atomic integration: the EVM
is a singleton state machine, and the transactions processed by its miners result in changes to the state of
the Ethereum blockchain. This change is reflected in the state trie (also “state tree”) (Fig. 9c). It is a
modified Merkle Patricia trie, a global tree -like data structure that maps key -value pairs, where the key and
value are respectively an account address and the encoding of its nonce , balance , StorageRoot , and
CodeHash (Fig. 9d). These changes are recorded for both EOAs and contract accounts.
Fig. 9. Crypto asset application enables atomic integration of cash and product flows, connecting the off -
and on-chain realities
The StorageRoot is the 256 -bit hash of the account storage trie , where all the smart contract data
and the mapping between product and cash flow i s stored . This includes the content identifiers (CID)
corresponding to the product flow updates stored off -chain and on the project’s IPFS private network (Fig.
9e); the off-chain data include , among others, the construction progress, as-built BIM, and the schedule of
values. Each transaction, stored in the transaction trie and the body of a block (Fig. 9a), can be linked with
the corresponding CID that initiated that transaction, creating an atomic integration where both flows are
stored within the same sp ace. The CIDs cannot be interpreted by individuals outside the IPFS network and
hence do not jeopardize the privacy of the data.
As shown in Fig. 8, this atomicity is also extended to the transfer of lien rights . As part of the ERC721
standard, each LIEN token has a uniform resource identifier (URI) which stores its metadata. A LIEN
token’ s URI store s the CID of the product flow updates for which the right is transferred. For example, the
token shown in Fig. 8a stores the CID referencing the framing work on the partition shown in Fig. 8b. This
atomicity makes it possible to retrieve the monetary value and the product flow associated with each transfer
of lien right directly from the LIEN token.
5.3 Implications for the AEC Industry
The application of crypto asset s was observed to enhance the atomicity and the granularity of the
integration between cash and product flows. In contrast with today’s payment systems, the crypto asset use
provides a means of payment processing and the transfer of lien rights for the scopes of work that have
varying levels of granularity. This enables the flow of funds on projects to occur more frequently, to
compensate smaller scopes of construction work, and to be more directly payable to the trades.
For instance, the application of ETH and ERC20 made it possible for a project to compensate a
subcontractor on a weekly basis for the framing work on a single interior partition. The ERC721 use allowed
for the transfer of lien right corresponding to the same granular scope of work. These transactions were recorded in an immutable manner and timestamped on the Ethereum blockchain, creating a permanent and
auditable link between the flow of funds and the corresponding product flow updates. The record of each
paym ent provides access to the construction progress data triggering that payment along with project
information such as BIM, schedule of values, and the data analytics tools used in the valuation of the work.
This level of granularity and atomicity is not pos sible in today’s supply chains .
Based on the results, it is evident that the product flow’s LoD plays a critical role in achieving
increased granularity. The use of crypto asset alone does not guarantee an increase in granularity. This
incentivizes the dev elopment of robust product flow management systems using robotics and artificial
intelligence. Stakeholders that invest in such data -driven solutions across their supply chain can benefit
from an increased granularity in their financial supply chain, enabl ed by crypto asset use. These same
investments do not currently translate to enhancements in the payment systems.
The p ayment applications currently used in the industry have the same granularity regardless of how
technology is used in man aging the physic al supply chain; the end result is blind to improvements in the
on-site use of reality capture technologies, the robust tracking of construction material, and the use of as-
built BIMs. In the crypto asset -enabled system, on the other hand, the financial supply chain can be as
granular as the physical supply chain, and so can their integration. Increases in the frequency of data capture,
the LoD of progress data, and the detailed modeling of trades in BIM directly translate to enhancements in
payment’s granularity.
Achieving atomicity, on the other hand, appeared to be independent of the product flow’s LoD. The
application of crypto asset increases atomicity even when the payment granularity is as its lowest and
matches that of today’s payment applications. Both the physical and financial supply chain data can be
retrieved directly from the on-chain transactions or the instances of the crypto assets. T he stakeholders do
not need to run full nodes in the public blockchain to access this data.
Integrating information is key to successful project delivery and cr eating a single source of truth
(Fischer et al. 2017) . The combination of atomicity and granularity, enabled by crypto asset use, can
enhance the alignment between project data stored across different data sources (Fig. 4); this is because
information can be stored in a single space, rather than in siloes, and it can be highly granular, allowing for
successful matching across different sources such as BIM, activities, schedule of values, and on -site
observations. Increased transparency is another potential impact of su ch improvements . In the blockchain
data structures such as state trie, storage trie, and transaction trie, the changes propagate upwards; any alteration of the project data results in inconsistencies that can be automatically detected. This has potential
to both detect and prevent fraud, one of the common form s of corruption in the construction industry (Le
et al. 2014) .
6 LIMITATIONS & RISKS
The experiments conducted in this work used datasets that were collected at different construction
projects, described work conducted on a variety of building elements, and were collected using different
sensors. However, they both focused on the on-site installation and construction of elements. They did not ,
for example, include off-site construction and the delivery of materials . That said, t he on- and off -chain
documentation of flows occurs independent of the type of product flow status updates, the type of sensor,
and the source of the product flow data. Therefore , it is expected that crypto asset use enabl es the same
integration for those scopes of wo rk. Future work should validate this by extending the experiments to a
broader range of activities.
In this work, t he experiments were conducted on the Ethereum blockchain, a permission-less chain.
While it is expected that the findings extend to private or consortium blockchains , this was not validated .
In private chains, t he integrity of the transactions depends on the design of the consensus algorithm and the
possibility of collusion between parties. It is not certain that the integration of physical and financial supply
chain, once achieved on a private chain , will remain immutable and secure. On the other hand, the
applicat ion of permission-less chains in this work necessitates a closer look at privacy concerns; this was
not part of the scope and needs to be addressed in future.
The application of crypto asset s for conditioning the cash flow on product flow may expose proje cts
to risks associated with 1) the regulat ions and 2) the price volatility of c rypto currencies:
The crypto asset space is fast evolving , and so are the regulation s surrounding their use. Crypto asset s
may be considered securities (De Filippi and Wright 2018) ; this can make them subject to scrutiny by
regulatory bodies such as the Securities and Exchange Commission (SEC) and the Federal Trade
Commission (FTC). While there is debate as to whether more regulation is needed, some case studies
suggest that the current regulation s are sufficient in addressing the risks of crypto asset s to financial stability
and monetary policy (Manaa et al. 2019) . The regulatory bodies need to provide clarity as to which tokens
are considered security (Edwards et al. 2019) . This reduces the uncertainties for the industry applications.
Crypto currencies have higher price volatility compared with fiat currency. While stablecoins
(Calcaterra et al. 2019) can offer a feasible alternative, the increased adoption of crypto currencies can
reduce the volatility in future . The price of a crypto currency is a function of its utilization and the market’s
speculation about its potential value as an investment asset. As more industries adopt a crypto currency, its
utilization increases, and the changes in the speculative component will have smaller influence on the
overall price of that crypto currency . Increased adoption also means the availability of more buyers and
sellers; this increases market liquidity , further reducing the price volatility.
7 CONCLUSION
In response to recent technological disruptions, t he construction and e ngineering industry is
projected to undergo consolidations across its value chain; this can particularly benefit the financial
supply chain and its integration with the physical supply chain. The transition from product flow to
payments is currently heavily intermediated; the product flow is scattered across several supply chain
partners, and the cash flow sits with external financial institutions. There is a disconnect between the two.
This wo rk demonstrated how the application of blockchain-based crypto assets (crypto currencies
and crypto tokens) can enable integration when used in the place of fiat currency and for conditioning the
payments on the flow of products. The thesis was validated i n a series of experiments where crypto assets
were used for processing payments to subcontractors on two commercial construction projects. The
findings indicate that supply chains empowered by crypto asset s can benefit from increased granularity
and atomic ity in their integration of flows. The increase in the granularity , however, is dependent on the
product flow’s LoD . This motivates further attention automated and data -driven approaches toward the
management of product flow. The atomicity, on the other ha nd, can be achieved regardless of the LoD for
product flow. Projects can benefit from crypto asset -enabled integration regardless of the LoD used in
their product flow management. The investments in the latter, however, can directly translate to
improvements in the financial supply chain.
While this work examined the mechanisms contributing to the integration of flows, it is crucial to
analyze the impact of such integration on the supply chain visibility and the performance of supply chain
partners. It is n ot currently clear how enhancements in the granularity and atomicity of integration affects
the experience of stakeholders retrieving and using the data. This necessitates the design of experiments
where the acc uracy of the data retrieval, the level of effort, and other measures of supply chain visibility
can be quantified in supply chains using fiat currency and crypto assets.
8 ACKNOWLEDGEMENT
This work is financially supported by the Center for Integrated Facility Engineering (CIFE) at Stanford
University (grants 2020- 09, 2018-06 , 2017-06). The authors are grateful to Swinerton, PMX Construction,
Perkins+Will, Inc., for their support during the d ata collection phase and granting access to project data.
The first author extends his gratitude to Eric Law and Tristen Magallanes (Swinerton); Dr. Kincho Law,
Dr. Michael Lepech, Dr. Forest Flager, Alissa Cooperman, Parisa Nikkhoo, and Tulika Majumdar (S tanford
University); Dr. Brenda McCabe, and Pouya Zangeneh (University of Toronto) for their immense role in
his PhD journey and for their invaluable intellectual companionship.
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|
{
"id": "2012.02147"
}
|
2309.12322
|
The Rise and Fall of Cryptocurrencies: Defining the Economic and Social
Values of Blockchain Technologies, assessing the Opportunities, and defining
the Financial and Cybersecurity Risks of the Metaverse
|
This paper contextualises the common queries of "why is crypto crashing?" and
"why is crypto down?", the research transcends beyond the frequent market
fluctuations to unravel how cryptocurrencies fundamentally work and the
step-by-step process on how to create a cryptocurrency.
The study examines blockchain technologies and their pivotal role in the
evolving Metaverse, shedding light on topics such as how to invest in
cryptocurrency, the mechanics behind crypto mining, and strategies to
effectively buy and trade cryptocurrencies. Through an interdisciplinary
approach, the research transitions from the fundamental principles of fintech
investment strategies to the overarching implications of blockchain within the
Metaverse. Alongside exploring machine learning potentials in financial sectors
and risk assessment methodologies, the study critically assesses whether
developed or developing nations are poised to reap greater benefits from these
technologies. Moreover, it probes into both enduring and dubious crypto
projects, drawing a distinct line between genuine blockchain applications and
Ponzi-like schemes. The conclusion resolutely affirms the continuing dominance
of blockchain technologies, underlined by a profound exploration of their
intrinsic value and a reflective commentary by the author on the potential
risks confronting individual investors.
|
http://arxiv.org/pdf/2309.12322v1
|
Petar Radanliev
|
cs.CY, q-fin.CP, q-fin.TR
|
cs.CY
|
University of Oxford
Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
1
The Rise and Fall of Cryptocurrencies:
Defining the Economic and Social Values of
Blockchain Technologies, assessing the
Opportunities, and defining the Financial
and Cybersecurity Risks of the Metaverse.
Dr Petar Radanliev
Department of Engineering Science, University of Oxford, United Kingdom:
petar.radanliev@eng.ox.ac.uk
Abstract:
The study examines blockchain technologies and their pivotal role in the evolving
Metaverse, shedding light on topics such as how to invest in cryptocurrency, the
mechanics behind crypto mining, and strategies to effectively buy and trade
cryptocurrencies. Through an interdisciplinary approach, the research transitions
from the fundamental principles of fintech investment strategies to the overarching
implications of blockchain within the Metaverse. Alongside exploring machine
learning potentials in financial sector s and risk assessment methodologies, the study
critically assesses whether developed or developing nations are poised to reap
greater benefits from these technologies. Moreover, it probes into both enduring and
dubious crypto projects, drawing a distinct l ine between genuine blockchain
applications and Ponzi -like schemes. The conclusion resolutely affirms the
continuing dominance of blockchain technologies, underlined by a profound
exploration of their intrinsic value and a reflective commentary by the auth or on the
potential risks confronting individual investors.
Keywords: Blockchain Technologies, Cryptocurrencies, Metaverse, Decentralised
Finance (DeFi), Crypto Regulations, Blockchain Standards, Risk, Value.
This paper contextualises the common queries of "why is
crypto crashing?" and "why is crypto down?", the research
transcends beyond the frequent market fluctuations to
unravel how cryptocurrencies fundamentally work and the
step -by-step process on how to create a cryptocurrency.
"
"
University of Oxford
Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
2
Note about the author :
Dr Petar Radanliev began his career as a penetration tester for the military and the
defence industry. Subsequently, he transitioned to cyber risk management in the
finance sector. After a decade in defence and finance, he re -joined the academic
realm, obtaining hi s Ph.D., MSc, and BA (Hons) from the University of Wales. Before
joining Oxford, Petar undertook postdoctoral research projects at Imperial College
London, the University of Cambridge, MIT, and the University of North Carolina. He
held positions as a Princ e of Wales Innovation Scholar at the University of Wales
and as a Fulbright Scholar at both MIT and the University of North Carolina. His
research areas encompass Artificial Intelligence, Generative Pre -trained
Transformers (GPT), Cybersecurity, Blockchain Technologies, Cryptocurrencies, and
the Internet of Things. Recent research specialisations include the Software Bill of
Materials (SBOM) and the Vulnerability Exploitability Exchange (VEX).
Funding : This work has been supported by the PETRAS National Cen tre of
Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC
[under grant number EP/S035362/1]; the Software Sustainability Institute [grant
number: EP/S021779/1]; and by the Cisco Research Centre [grant number
CG1525381].
Acknowl edgements : Eternal gratitude to the Fulbright Commission.
University of Oxford
Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
3
1. Introduction .
In recent years, cryptocurrency has emerged as a significant and often contentious
component of the financial landscape. This article delves deep into the complex
world of dig ital currencies, clarifying the methods of investing in these volatile assets
and the intricate mechanisms of crypto mining vs crypto staking . We explore the
various platforms and methodologies for purchasing cryptocurrency, whilst also
casting insights on the frequent price fluctuations observed in the market, analysing
both the technical and external factors leading to periodic crashes and downturns.
To provide a comprehensive understanding, the underlying technology that powers
these digital tokens is br oken down, offering insights into the decentralised nature of
blockchain -based assets. This study navigate s the steps and challenges involved in
creating a new cryptocurrency and guide readers through the intricacies of trading
and maximising potential ret urns in the crypto market.
This article provides an up -to-date assessment of the current state of
cryptocurrencies, examining both their values and associated risks within the realm
of blockchain technology. With the exponential growth of over 20,000 crypto projects,
the study offers a snapshot of the landscape in 2023, tracing the historical trajectory
from Satoshi's ground -breaking paper on decentralised blockchains to the present
day. The study clarifies the distinctions between cryptocurrencies and blockchain
technologies while exploring pertinent research questions: Is blockchain technology
an innovation or an obsolete concept? What significant risks do cryptocurrencies
entail? Are the potential societal and economic benefits worth pursuing?
In lig ht of the immense growth observed in the crypto market, encompassing over
20,000 projects, this investigation offers a timely snapshot of the landscape in 2023
while providing a historical account from the seminal work of Satoshi on
decentralised blockchai ns to the present day. The study objectives extend beyond
merely examining values and risks associated with cryptocurrencies; we also aim to
delineate the nuances distinguishing cryptocurrencies from blockchain technologies.
Guided by a series of research questions, we seek to ascertain the nature of
blockchain technology as either an innovation or an outdated concept, identify
significant risks posed by cryptocurrencies, evaluate their potential value to society
and the economy, and determine which countri es are likely to benefit the most from
these technologies. Additionally, we endeavour to shed light on the surviva bility
prospects of various blockchain projects, thereby addressing whether the allure
surrounding them is hype or a substantial opportunity. While acknowledging the
presence of numerous fraudulent crypto ventures, often functioning as Ponzi
schemes, our focus p rimarily lies in exploring the genuine use cases of blockchain
projects. Ultimately, this research study culminates in a resolute affirmation that
blockchain technologies are here to stay, substantiated by an extensive discussion
of their intrinsic value. Furthermore, we re-evaluate vital risks, including a personal
statement from the author regarding the perils faced by individual investors.
1.1. Historical context
This article has been around 13 years in the making. I started writing this article back
in 2009 , with the emergence of Bitcoin [1]. The report is influenced by points of view
that existed before 2009 and have long been forgotten, such as the fear of Bitcoin
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POSTDOCTORAL RESEARCH ASSOCIATE
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(BTC) legality, the ethics of decentralised control, and the ‘ethical impact of
cryptocurre ncies as morally beneficial, detrimental, and ambiguous’ and the
‘antisocial use for shadow banking and transactions in the ‘dark net’ and
cryptocurrencies’ effect on inflation and deflation’ [2]. Although some of these
viewpoints have subdued, and crypt ocurrencies are legal to own and operate in
many countries, many of these fears remain among early adopters. The article
discusses the values and risks of a few selected Blockchain projects based on real-
world value and their potential to contribute to the future of society.
The main research areas reviewed in this study are included in Figure 1, which also
helps visualise the main areas of interest in the year 2023 in the Metaverse concept .
Figure 1: Overview of the research topics discussed.
1.2. Historical ov erview of Blockchain technologies
The first whitepaper on Bitcoin emerged at the peak of the financial crisis in 2008 [1]
and it promoted the idea of a different economic system that is not dependent on a
trusted third party. In 2009, the main concern w as the legality of such currencies and
projects. Bitcoin was considered a mechanism for criminals and drug dealers to
bypass the legal banking system. Between 2020 and 2022, investment in
cryptocurrencies (crypto) exploded. Despite the collapse of Terra Lu na, the FTX
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Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
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exchange in 2022, and the overall downturn in the market for crypto, crypto
investments have continued.
Cryptocurrencies, or digital assets, serve as virtual currencies underpinned by
cryptography, offering heightened security and privacy meas ures. They often
operate decentralised , distinguishing them from traditional forms of money . Notably,
a defining characteristic of cryptocurrencies is their purported immunity to
governmental or institutional control. However, the validity of this claim is subject to
debate, as the potential for a single government, such as the United States, or a
dominant institution, like BlackRock, to acquire a controlling stake in a
cryptocurrency undermines this principle. It is crucial to recogni se that while Bitcoin
remains the most widely recognized cryptocurrency, numerous other
cryptocurrencies exist in the market. Cryptocurrencies can facilitate the purchase of
goods and services and are actively traded on various online platforms. It is worth
noting, however, that none of these platforms are subject to comprehensive
regulation. This regulatory vacuum presents a unique opportunity for countries like
the UK, which seek to establish their presence internationally , particularly in light of
recent challenges stemming from the impacts of Covid -19 and Brexit.
One argument for pursuing Cryptocurrencies as a solution to financial risk is ‘that the
main indicators to improve financial development should enhance the process of
bank lending and equity mark et development’ [3] Second argument is that smart
contracts and metaheuristic can help with securing the quality -of-service and even
help with the ‘ cost-efficient scheduling of medical -data processing’ [4], which seems
of crucial importance for the med ical systems in the UK – after the Covid shock to
the National Health Service ( NHS ) [5]–[7].
In summary, pursuing cryptocurrencies as a solution to financial risk is supported by
the potential to improve economic development indicators, such as bank lending and
equity market development, while simultaneously addressing crucial challenges in
the healthcare sector. By harnessing the capabilities of smart contracts and
metaheuristic techniques, cryptocurrencies offer opportunities for enhancing the
quality-of-service and cost -efficiency of medical data processing, which is of utmost
importance for the UK's healthcare systems in the aftermath of the Covid -19
pandemic. These advancements can potentially bring about transformative changes,
promoting financi al stability and bolstering the resilience of the healthcare sector.
1.3. Brief History of Cryptocurrencies .
Cryptocurrencies (crypto) are digital assets, or more precisely , a set of digital
currencies that emerged with the release of Bitcoin in 20091. As of January 2021,
there are over 4,000 in circulation, some cryptos with minimal trading volume (or not
at all). Cryptocurrencies are traded as digital ‘tokens’ or ‘coins’ on a distributed and
decentralised ledger. Bitcoin leads on market capitalisatio n, but other cryptos are
trying to break into the market by providing different and improved services to
Bitcoin. Some of the other cryptos - also known as ‘altcoins’ , i.e., all cryptocurrencies
other than Bitcoin – are used to create a decentralised finan cial system , e.g.,
1 https://bitcoin.org/en/
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POSTDOCTORAL RESEARCH ASSOCIATE
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Ethereum2, with the ability to handle more transactions , e.g., Dogecoin3, or just use
different consensus algorithms , e.g., Cardano4. Cryptocurrencies remain highly
volatile, and without central control , a single statement by Elon Mus k has been
sufficient to trigger a spike in search trends (as seen in Figure 2) and attract
significant interest in news media.
Figure 2: Comp arison of search trends on some of the most popular cryptos (data from 2020 – before the last
prominent bull run in crypto)
Although the search trends can spike about specific cryptos , i.e., Bitcoin and
Dogecoin in Figure 2, a dynamic equicorrelation [8] shows a contagious correlation
(a mutual relationship or connection) effect between cryptocurrencies (i.e., when
Bitcoin crashes, altcoins follow) , and the exact relationship is also connected
between Bitcoin and NASDAQ . This disincentivises diversification into multiple
cryptocurrencies, but a more robust analysis with a value -at-risk [9]–[13] and
expected shortfall must be computed to confirm this. Looking at t he spikes in search
trends after the announcement from Elon Musk on Bitcoin purchase and comparing
them to the price cap of Bitcoin and Dogecoin, it almost resembles a specific
behaviour in crypto markets.
The user behaviour in some crypto (e.g., Ethereum ) appears more stable . In
contrast, the behaviour of users in Bitcoin seems more speculative, with fluctuations
based on market trends, followed by more considerable sell-outs when the market is
down [14]. This is not to say that influential people canno t manipulate the stock
2 https://ethereum.org/en/
3 https://dogecoin.com/
4 https://cardano.org/
University of Oxford
Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
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market, but the point here is that stock market investors are protected by regulations
which don’t yet exist in crypto markets.
Figure 3 shows that ‘interest over time’ has generally changed for Bitcoin and
Cryptocurrencies – including Altcoins. While the interest in Google Trends for Bitcoin
peaked in the 2018 bull run, the interest in cryptocurrencies , as a search trend, has
increased (to a new 100%) in the 2021 bull run.
Bitcoin: Interest over time – 1st of January 2004 until 7th of March 20 23
Cryptocurrencies: Interest over time – 1st of January 2004 until 7th of March 2023
Figure 3: Interest over time: Bitcoin vs Cryptocurrencies (including Altcoins)
Crypto users can be categorised according to their actions and resources , e.g.,
fortune, hunter, and idealist [15]. Still, the bigger question is, can we classify
cryptocurrencies into taxonomies and forecast individual Altcoin categories' future
success or failure ? Another important topic discussed in t his article is related to
cryptocurrencies , the idea of Central Bank Digital Currencies (CBDCs) [16]–[18], and
Friedrich von Hayekʼs theory of private money , which some experts argue could lead
to national currencies being replaced by ‘the currency of th e digital platform’ [19].
Another point on centralisation is that even cryptocurrencies that are meant to be
very decentralised, such as Ethereum (ETH), and stablecoins , such as UDSC , can
be (in reality) very centrali sed and potentially controllable.
1.4. Research questions and structure .
The research aims, objectives, questions, and goals motivating this article can be
defined in one sentence as the current and future values and risks associated with
blockchain projects . The areas of focus encouraging this study include :
University of Oxford
Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
8
1. To assess the potential of blockchain technology as a catalyst for innovation
in future iterations of the internet and Web3 and to determine whether it
represents a cutting -edge tech nology or an outdated concept in the context of
financial invention .
2. To comprehensively analyse and evaluate the risks associated with
cryptocurrencies, including but not limited to market volatility, security
vulnerabilities, regulatory challenges, and po tential illicit activities, to
understand the risk landscape within financial innovation better .
3. Investigating and identifying the intrinsic values derived from cryptocurrencies,
considering their potential impact on financial systems, economic growth,
financial inclusion, transaction efficiency, and transparency, thus contributing
to understanding their value proposition within financial innovation.
4. Conduct a thorough analysis of different blockchain projects and their
underlying technologies to identify critical factors that contribute to their long -
term viability and survival in the dynamic landscape of financial innovation,
thereby providing insights into the sustainability of blockchain projects.
5. To explore and identify blockchain projects that have the potential to further
the development and advancement of already developed countries' financial
systems, addressing specific areas such as efficiency, security, transparency,
financial inclusion, and regulatory compliance, thus providing valuable insights
into the role of blockchain in enhancing financial innovation in advanced
economies.
6. To examine and identify blockchain projects that can foster development and
progress in developing countries, taking into consideration their unique
challenges and needs, including financial inclusion, access to capital,
remittances, land registries, supply chain management, and government
services, thereby contributing to the understanding of how blockchain can
drive financial innovation in developing economies.
7. To critica lly evaluate the feasibility and implications of regulating
cryptocurrencies and blockchain projects, including both national and
international regulatory frameworks, to assess the potential benefits,
challenges, and trade -offs associated with effective re gulation in the context
of financial innovation. This research objective seeks to contribute to the
ongoing discourse on appropriate regulatory approaches for cryptocurrencies
and blockchain technology.
The article follows a traditional (standard) structure , starting with chapter one (1)
Introduction, chapter two ( 2) Methodology, then (3) the research engages with a
Case study review of secondary data sources , and compliments the case study with
a new chapter ( 4) that comprises a s urvey r eview . The article ends with a chapter ( 5)
discussion and (6) Conclusion.
1.5. Crypto regulations .
Given that the US has already started crafting new regulations on cryptocurrencies
[16] and after the markets collapsed in 2022 , the European Union also started
catching up with rules [20] the UK recently started initial consultation plans to
regulate cryptocurrencies [20]. It is difficult to determine if the proposed methods are
positive or negative . Still, the consultation generally builds upon previous HM
University of Oxford
Petar Radanliev, BA Hons., MSc., Ph.D.
POSTDOCTORAL RESEARCH ASSOCIATE
9
Treasury proposals on stablecoins . If the plan is to develop a UK stablecoin (USD
and GBR), the talk could prove positive for ensuring stability in cryptocurrency
markets. However, the proposed regulation sugg ests that cryptocurrenc ies should be
overseen by the Financial Conduct Authority (FCA), and not all crypto is just a simple
cryptocurrency . Ethereum (for example) is a virtual computer and can perform far
more tasks than just transact payments. The consult ation also suggests that all UK-
based cryptocurrency firms should have anti -money laundering and KYC processes .
This can be managed in the UK, but the regulation ignores that cryptocurrencies are
designed to bypass such rules . The effectiveness of this reg ulation remains
questionable , but it could help stabilise and legalise the trade of cryptocurrencies in
the UK . In other words, these regulations could apply to large companies compliant
with many different rules. Still, it’s difficult to see how such laws would prevent
money laundering on non-UK-based cryptocurrency firms.
1.6. Structure and novelty of the research study
This study offers a multifaceted exploration of blockchain technologies, investigating
their economic, social, and wider implications within the Metaverse. The research
highlights key themes from blockchain's impact across sectors to the fintech
revolution . Contrasting with existing literature, this work broadens the discussion
from the mechanics of cryptocurrency trading to the wider ramifications of
blockchain, emphasising its financial and cybersecurity risks. The novelty of the
research is further under scored by its interdisciplinary approach, merging the
fundamental principles of fintech investment strategies, blockchain, and the
metaverse. Such a comprehensive perspective positions this study uniquely,
complementing and enriching the current academic d iscourse on cryptocurrency
trading, machine learning potentials in financial sectors, and financial risk
assessment methodologies. The research not only provides insights into the evolving
digital landscape but also sets a new benchmark for future investig ations at the
crossroads of finance, technology, and societal constructs. Then, the study
investigates whether developed or developing countries stand to gain more from
these technologies and which blockchain projects are likely to endure in the long
term. This review acknowledges the prevalence of dubious crypto projects, delving
into the realm of Ponzi schemes yet ultimately shifting the focus towards the practical
applications of blockchain projects. The study concludes with a resolute assertion
that blo ckchain technologies are here to stay, supported by a comprehensive
discussion of their inherent value. Moreover, the article reassesses critical risks,
including a personal reflection from the author on the potential risks’ individual
investors face.
2. Meth odology .
In this article, we present a robust research methodology, employing a combination
of the case study method, survey review, and literature review to delve into the
dynamic realm of financial innovation and its social implications. Drawing inspirat ion
from established and time -tested guidelines such as Eisenhardt's 'Building Theories
from Case Study Research' [21], Yin's recommendations on case study research,
design, and methods [23], and the principles of Grounded theory [22], and the
Grounded theory [23]–[25] Figure 4, provides a visual representation of the
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POSTDOCTORAL RESEARCH ASSOCIATE
10
structured methodology, delineating areas of interest and focus. Our research
methodology is thoughtfully divided into a dual emphasis on economic and social
values. Recognising that assessing values, risks, and impact extends beyond purely
economic metric s, we aim to elucidate the interplay between financial and social
considerations.
A prominent concern driving the exploration of Web3 arises from the challenges
posed by Web2, particularly regarding diminishing personal privacy. Web2 sceptics
advocate for a transition to Web3 not solely for its decentralised financial framework
but also for its potential to enhance individual privacy. Embracing Blockchain
technologies in the Web3 design, along with the integration of virtual and augmented
(mixed) reality in education, fosters opportunities for improved personal privacy,
decentralised social media, and global community building of like -minded individuals.
By adopting this methodological approach, we aspire to offer a coherent and
understandable analysis of th e intricate landscape of financial innovation, elucidating
the role of Web3 in addressing critical issues and shaping the future of finance,
privacy, and social interactions. Through a multidimensional lens, we aim to
contribute valuable insights into the potential benefits and challenges of embracing
Web3, shedding light on its transformative impact on the digital landscape.
Figure 4: The case study method and the study objectives
Since the initial search on the Web of Science and Scopus didn’t result in many
records on the topics discussed in Figure 4, the search was expanded into a broader
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POSTDOCTORAL RESEARCH ASSOCIATE
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review of Google Scholar, IEEE, and other libraries. Although the initial stages
focused on new technologies , the study expanded into the literature on ethics ,
trading, gambling, decentralised finance, and the Metaverse after the initial search .
2.1. Data sources .
The primary data collection included 20 case study interviews and three workshops.
The secondary data collection included n ew and emerging forms of data (NEFD) that
present new records of value in IoT-based intelligent contracts, including open data –
(e.g., Open Data Institute); spatiotemporal data; high -dimensional data; time -
stamped data; real -time data and big data. To ana lyse NEFD, t he data collection
strategy in this article applied stratified sampling and random sampling for
comparative analysis of collected NEFD.
2.2. Contextualising this study with the existing body of literature
This section is focused on contextualisatio n of the findings and the novelty of the
work in this study. This section compares the differences and similarities, including
the findings and novelty, of this work, with eight articles closely related to the
research area investigated in this study.
This study started with a systematic review of existing studies that used data recor ds
obtained from the Web of Science Core Collection [26], focusing on the broad
landscape of blockchain's impact across sectors, highlighting key themes from
economic benefits to the fintech revolution. Within this body of literature, this study
on "The Rise and Fall of Cryptocurrencies" offers a complex and interesting
exploration, delving deep into the intricacies of cryptocurrency and the broader
implications in the metaverse. This article not only complements the identified
themes but goes a step furt her by elucidating the financial and cybersecurity risks
associated with blockchain technologies. This in -depth focus on the metaverse and
its associated challenges underlines the novelty of this work, positioning it as a
pivotal piece that bridges gaps in the current literature and offers fresh insights into
the evolving digital realm.
This study delves into the intricacies of blockchain technologies, highlighting their
economic and social values while scrutinising opportunities and risks inherent to the
metaverse. In juxtaposition to the comprehensive survey on cryptocurrency trading
by Fan Fang et al., [27] this work holds significance. While Fang and colleagues
extensively map out the landscape of cryptocurrency trading, from sentiment -based
research t o the public nature of blockchain technology, our study on exploration
underscores broader implications, particularly in relation to the metaverse's
opportunities and challenges. This positions this new article uniquely, as it extends
the dialogue beyond t he mechanics of trading to the ramifications of blockchain
technologies in shaping our digital futures. This new investigation into the metaverse,
an area not deeply touched upon by Fang et al., accentuates the novelty and
contemporaneity of this research, presenting a holistic view that complements and
augments existing literature.
In the broader discourse surrounding the implications of cryptocurrencies, this study
delineates the multi -dimensional societal and economic repercussions of blockchain
innovat ions, setting an expansive stage to understand their systemic influences. By
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POSTDOCTORAL RESEARCH ASSOCIATE
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contrast, the research on "Forecasting and trading cryptocurrencies with machine
learning under changing market conditions" [28] offers a deep dive into the predictive
and profi t-centric paradigms of these digital assets, specifically focusing on machine
learning's applicability in this domain. The convergence of these studies is
particularly enlightening. While the former casts a wide net, encompassing the
multifaceted socio -economic interplays and the emergent realm of the metaverse,
the latter drills down, examining the nuances of cryptocurrency trading amidst
volatile market conditions. The overarching narrative of "The Rise and Fall of
Cryptocurrencies," when viewed in tandem with the in -depth findings on ML's
potential and challenges in deciphering cryptocurrency markets, offers a synergistic
understanding. It enriches the academic conversation, providing a holistic and multi -
layered exploration of the cryptocurrency ecosyste m, from its foundational
technologies to the Metaversal futures they portend.
In the contemporary milieu of group decision -making processes, the study titled "Soft
consensus cost models for group decision -making and economic interpretations"
[29]provide s a robust foundation for understanding the nuances and costs of
consensus -building among experts. This work unravels the complexity of striking a
balance between achieving consensus and incurring associated costs with the
introduction of the novel concept of soft minimum cost consensus. Their findings not
only advance previous models but also delve into the economic underpinnings of
such consensus mechanisms. Within this context, this study on "The Rise and Fall of
Cryptocurrencies" emerges as a novel cont ribution to the discourse. This
investigation transcends the traditional realms of consensus -building in decision -
making contexts and dives deep into the multifaceted world of cryptocurrencies and
the burgeoning metaverse. It not only seeks to define the i ntrinsic economic and
social values underpinning blockchain technologies but also aspires to critically
assess the opportunities and challenges presented by the metaverse. The novelty of
this work lies in its ability to contextualise and intertwine the fou ndational principles
of consensus models, as observed in the prior study, with the intricate dynamics of
blockchain technologies. By comparing the core tenets of group decision -making and
the rapidly evolving world of digital assets, this study offers fres h insights and sets a
new precedent for interdisciplinary research in this domain.
In the dynamic world of financial technology, the pivotal research "Fintech
investments in European banks: a hybrid IT2 fuzzy multidimensional decision -
making approach" [30] pedantically evaluates the essence of Fintech investments in
European banking services, elucidating key criteria and offering indispensable
insights into optimal investment alternatives. Notably, the emphasis on payment and
money -transferring systems a s paramount Fintech investment alternatives presents
a foundational benchmark in the industry. Within this rich tapestry of Fintech
investigations, "The Rise and Fall of Cryptocurrencies: Defining the Economic and
Social Values of Blockchain Technologies a nd Assessing the Opportunities, and
Risks of the Metaverse" offers an innovative and integrative exploration of blockchain
technologies and their interplay with the metaverse. By seamlessly weaving the
threads of cryptocurrencies' economic and social value s, this research transcends
the conventional boundaries of financial technology discussions and ventures into
the evolving realms of the Metaverse. The novelty of this study is underscored by its
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multidisciplinary approach, integrating the core tenets of Fintech investment
strategies with the multifaceted nuances of blockchain and the metaverse. In doing
so, it not only complements existing works but also carves a distinct niche in
contemporary financial and technological scholarship, presenting fresh vistas and
research trajectories in the interplay of Fintech, blockchain, and virtual realms.
In comparison with the research on the "Integrated Cluster Detection, Optimization,
and Interpretation Approach for Financial Data," [31] which concentrat es on the
identification and interpretation of clusters within financial data to enhance decision -
making processes, this study on cryptocurrencies offers a broader perspective.
Where the former employs sophisticated methodologies to discern patterns in vas t
financial datasets, emphasising adaptivity, speed, and interpretability, our exploration
into the realm of cryptocurrencies and the metaverse endeavours to contextualise
these innovations within the larger socio -economic canvas. The novelty of our
invest igation lies in its interdisciplinary approach, bridging the gaps between finance,
technology, and social constructs and presenting a holistic understanding of the
current digital transformation. Thus, while the study offers invaluable tools for
immediate financial data interpretation, our research equips readers with a
comprehensive understanding of the evolving landscape of blockchain technologies
and the metaverse, forecasting potential trajectories and their implications on our
collective future.
This study casts a discerning eye on the emergent dynamics of blockchain
technologies, pivoting on their socio -economic implications and the opportunities and
risks they introduce in the digital realm of the metaverse. When compared with the
study titled " Machine Learning Methods for Systemic Risk Analysis in Financial
Sectors," [32] which delves into the potent intersections of machine learning and
systemic risk within the financial sphere, our research offers a more expansive vista,
encompassing not jus t the financial but also the societal reverberations of digital
currencies and virtual spaces. While the referenced article meticulously maps the
deployment of machine learning techniques in gauging financial systemic risk,
underscoring the power of big da ta, network, and sentiment analyses in this quest,
our study pivots on the broader implications and trajectory of blockchain and the
metaverse. The novelty of our research resides in its comprehensive purview,
encapsulating not just financial but also soci o-cultural and technological dimensions.
While the surveyed work offers invaluable insights into the detection and modulation
of systemic risks through machine learning, our article contextualises
cryptocurrencies and the metaverse within the evolving tape stry of our digital age,
seeking to foretell and shape the contours of this transformation.
For a final contextualisation of the findings and novelty of the work in this study, we
need to emphasise that in this study, the multifaceted impacts, and prospect s of
blockchain technologies, with a particular focus on cryptocurrencies, are scrutinised
within both socio -economic dimensions and the expanding horizon of the metaverse.
Contextualising this work against the study titled "Evaluation of clustering algori thms
for financial risk analysis using MCDM methods," [33] which meticulously dissects
the application of multiple criteria decision -making methods in ranking clustering
algorithms for financial risk analyses reveals notable distinctions. While the latte r
delves deep into the computational methodologies employed to enhance precision in
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financial risk evaluation, our investigation spans a broader spectrum, exploring the
underpinnings of blockchain's socio -economic repercussions and the metaverse's
potentia l. The innovative aspect of our work lies in its amalgamation of technological,
financial, and social dynamics within the realm of cryptocurrencies and the
Metaverse. Conversely, the compared article offers a deep technical dive into the
optimisation of cl ustering algorithms for targeted financial outcomes. In essence,
while both pieces contribute valuably to the discourse on financial systems and risk,
they illuminate different facets — one the technical intricacies and the other a
comprehensive socio -econo mic and technological panorama.
3. Academic literature review .
Decentralised finance is at present isolated to Blockchain projects . Still, most of the
existing financial instruments can be transferred into an alternative economic
infrastructure with the help of Blockchain Oracles [34]. Decentralised finance
transactions can be cleaner than traditional finance and support ‘local climate
adaptation planning and implementation’ [35]. This was a natural progression, given
that digital finance and renewable energy consumption have already been studied
for economic growth and technological progress, with evidence from China [36].
Blockchain and digital finance have also been the subject of other studies [38].
Especially the ‘risk of blockchain technology in Internet finance supported by
wireless network’ [37].
It has been almost half a decade since the Blockchain was suggested as the new
‘secure, decentralised , trusted cyber infrastruc ture solution for future energy
systems' [38]. One of the solutions proposed in 2019 was the new concept for
improving industry with private capital in China, with renewables finance and
investment [39]. However, these concepts are not immune to the earlier problems
related to ‘the governance of local infrastructure funding and financing’ [40],
especially the role of mitigating the effect of the recession. As the authors stated in
2015, ‘austerity and th e fiscal consolidation of public finances have reinforced
government efforts to reduce expenditure and debt, and secure private sector
engagement and resources [40].
Blockchain values are also studied in decentralised electricity access developed with
private investment as a sustainable development finance business model [41]. There
are various platforms for decentralised autonomous organisations [42], even in just
one of the existing blockchain s, such as Ethereum. Many new Blockchains , such as
Algora nd, Cardano, Solana, and Avalanche, are emerging daily . The most
interesting are the new Blockchains with cross -chain operability, such as Cosmo,
Polkadot, and Chainlink. There are also numerous ‘automated market makers and
decentralised exchanges’ [43], some even for cryptocurrency trading [44]. The
question is, do the new regulations ‘provide legal certainty’ [45]?
The proper form of decentralised finance will alway s be based on private investment .
Still, solid regulations and guidance also support centralisation in financial
instruments. Before transferring traditional instruments into a new system, we must
analyse ‘ the return –volume relationship in decentralised fi nance ’ [46]. Although
decentralised banking is not an entirely accepted model yet, even in the present
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adoption stage , digital banks ‘can learn from decentralised finance’ [47]. The
European Union has recently advanced into new Blockchain regulations based on
the ‘Regulation on Market in Crypto -Assets' and 'Decentralised Finance - MiCA’ [48].
The United States has also produced new regulations [16], [17], [49] , and the United
Kingdom is slowly catching up [20]. However, the countries that can benefit the most
from new financial instruments are not the EU, the US, and the UK.
Developing African countries have been very flexible in adopting Blockchain projects,
as recorded in the recent study on ‘Decentralised Finance and Cryptocurrency
Activity in Africa’ [50]. The values from Blockchain projects and new Metaverses do
not have to be purely financial . For example, Blockchain projects have been
‘empowering school -based managem ent through decentralised financial control’
since 2008 [51]. Despite these best efforts, even in 2023, we are still working on the
‘conceptualisation and outlook’ of ‘decentralised finance platform ecosystems’ [52].
This is predominately because Blockchain projects, and decentralised finance , have
been advancing and developing in various areas . One example is creating an asset -
backed decentralised finance instrument for food supply chains in the livestock
export industry [53].
Another ex ample is the ‘decentralisation on health -related equity ’ for the
‘decentralised governance of health care ’ [54]. The most critical example is from the
Monetary and Economic Department , which discusses the ‘potential benefits and
challenges of the new sys tem and presents a comparison to the traditional system of
financial intermediation’ [55]. This signals that decentralised finance is becoming a
mainstream topic.
4. Case Study Review : Case studies of existing Blockchain
solutions.
4.1. Blockchain 3.0 and Web 3.0
Blockchain technology, initially introduced through Bitcoin, combines cryptography
with distributed computing, both of which have existed for several decades.
Blockchain 1.0 repre sents a distributed secure database where a network of
computers collaborates and shares data. In this architecture, individual computers,
acting as network nodes, validate transactions and propagate them to other network
nodes, creating a blockchain . Blockchain relies on a distributed consensus algorithm,
requiring agreement among different nodes before any alteration can be made. The
interdependence of blocks is achieved through hash values, making it virtually
impossible to delete a block without affecti ng the entire chain.
The evolution of blockchain technology has led to Blockchain 2.0, exemplified by
Ethereum, which introduces the capability to execute any computer code on the
system, essentially creating a distributed virtual computer. This advancemen t opens
the doors to various applications, envisioning a decentralised Turing -complete virtual
machine. Blockchain 2.0 enables the creation of decentralised ledgers for asset
registries, encompassing physical and intangible assets, such as property,
curren cies, patents, votes, identity, and healthcare data. It replaces the need for
multiple private databases with a shared, trusted database accessible by all relevant
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parties. The immutability of data stored on a blockchain enhances its credibility, as it
is incredibly challenging to alter or corrupt.
Despite the potential of existing blockchain solutions, they are often considered
inefficient and face scalability issues, leading to the emergence of the third
generation, Blockchain 3.0. Prominent examples of t his generation include IOTA and
Dfinity. Blockchain 3.0 introduces "The Distributed Cloud," a pivotal infrastructure
supporting the development of the next generation of the internet, also known as
Web 3.0 or the decentralised web. A broader ecosystem of t echnologies is required,
including advanced web data analytics and the Internet of Things (IoT). This
integration of technologies enables the storage and analysis of vast amounts of
sensitive data, unlocking new value and insights through cross -correlation s from
diverse IoT data sources integrated into the blockchain.
The actual value of IoT data lies not in making individual devices or systems bright ,
but in enabling seamless processes across domains, organisations , and procedures .
This necessitates open n etworks capable of communicating and coordinating
components on demand. Blockchain 3.0 and Web 3.0 mark a paradigm shift from
ownership to "Servitization," where assets are used as services rather than owned
outright. This shift requires the establishment of frictionless markets and automated
exchanges, with the blockchain acting as the trust machine, ensuring secure and
transparent transactions.
In summary, the case study review demonstrates the evolution of blockchain
technology from its initial implement ation in Bitcoin (Blockchain 1.0) to Ethereum and
other platforms (Blockchain 2.0) and the ongoing development of Blockchain 3.0
solutions. These advancements present new opportunities for decentralisation ,
enhanced data analytics, and the integration of I oT, culminating in Web 3.0. By
embracing these technologies, we can transition towards a future where assets are
utilised as services, supported by seamless processes and trust -enabled exchanges
facilitated by the blockchain.
4.2. IoT-based Blockchain solutions .
The Internet -of-Things (IoT) is already used in Blockchain 3.0 as an open -source
distributed ledger (e.g., IOTA, NEO, EOS) and has presented many unique
alternatives for storing transactions with a potential for higher scalability (by using
Tangle) over Blockchain 1.0 based distributed ledgers (such as Bitcoin). However,
the interest in some of the early crypto projects seems to be dying down. In Figure 5
we can see the main research interest from the pre -2018 bull run .
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Figure 5: Trends in interest for early (pre -2018) crypto projects vs interest in cryptocurrencies in general.
One solution that seems to be under consideration is to rename existing projects
(e.g., NEO is renamed to N3 – with a promise of a better Blockchain)5. IOTA is
keeping its name but evolving into a new blockchain called Crysalis6. Although the
idea of IoT-based crypto is undeni ably valuable, further research is required to
determine how IoT technologies would resolve the main problem of Blockchain 1.0,
namely the (i) speed of transactions , and the (ii) security risk from quantum
computers [56]–[62]. For the first part of the objective, the IOTA project can process
around 500 -800 transactions per second. Many faster blockchains like Solana can
process 50,000 transactions per second. However, Solana and other Blockchain 2.0
are run by validators or en tirely by volunteers . In other words, random people making
impossible to guarantee that the network is altogether reliable. IOTA and other
Blockchain 3.0 technologies are run by IoT systems, which can be considered
reliable if the devices are secure. Secur ity and reliability are necessary for
technological adoption in critical infrastructure, and NHS supply chains can benefit
from improved security that comes with Blockchain technologies . Still, scalability is a
significant concern for the adoption of Block chain 1.0. In other words, at present,
Blockchain 1.0 can process up to seven cryptographic hash function computations
per second and has a confirmation time of 15 minutes.
In comparison, Visa processes around 1,700 transactions per second on average
(base d on a calculation derived from the official claim of over 150 million daily
transactions ). The potential for Blockchain adoption is there but is currently
bottlenecked by scalability. Another obstacle to the adoption of blockchain is the
energy demand and the cost (fees).
Most Blockchain 1.0 (Bitcoin) and 2.0 (Ethereum) come with heavy fees, while
Blockchain 3.0 (e.g., IOTA) can have zero fees. Zero fees are possible because IoT
devices can communicate autonomously and send verified messages, enabling full
automation of decentralised crypto, and future versions can make nano payments
possible. The main problem with IoT Blockchains is the security of IoT devices. Until
5 https://neo.org/
6 https://chrysalis.iota.org/status
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these devices are secure, it is difficult to see mass adoption of IoT-based crypto. The
IOTA project has been in the cryptocurrency markets for a long time (in crypto terms)
and still hasn’t picked up in price or mass adoption.
4.3. IoT for healthcare .
The increased adoption of IoT in health services seems inevitable [63]–[68] because
IoT offers increased efficiency at a reduced amount of time spent, which appears to
be what the NHS needs , but the value of the low cost of IoT comes at risk [69]–[71].
To reduce costs and provide the required support , the NHS must automate some
essential data collection and monitoring, freeing skilled staff to focus on patient
safety and patient service. This IoT automation in national critical infrastructure must
come with increased (or at least matched) security to the traditional legacy IT
systems [72]–[74], and there are various solutions for improved cybersecurity [75]–
[81].
One of the leading crypto projects that could benefit NHS is the ‘VeChain’ project,
which resolves many of the existing supply chain issues [82]–[123] , especially the
software supply chain, which has many areas of current work, including the Software
Bill of Materials (SBOM) [124] –[128] , the Vulnerability Exploitability Exchange (VEX)
[129] –[131] , and many other research efforts. The ‘VeChain ’ project addresses many
of these long-standing areas of concern and is already operational . Still , it would be
interesting to have even more automated, machine -controlled supply chains in the
future. For that, we have a very different set of Blockchain so lutions – based on IoT
systems [97], [132] –[138] .
Using existing IoT Blockchain solutions in the NHS -specific requirements, multiple
healthcare functions can be performed simultaneously . The main concern is the lack
of interoperability. Designing specific solitons individually and rolling out such
solutions piecemeal could result in a lack of interoperability between one NHS trust
and another, or even one hospital and another in the same trust. The expected
outcome of su ch blockchain solutions is an interoperable of-the-shelf solution
explicitly tested for compatibility with existing NHS systems.
In addition, by using established IoT Blockchains solutions, the expected outcomes
are resolutions to some challenging philoso phical questions around the use of IoT in
NHS, such as issues around data ownership. Since IoT Blockchains operate as
decentralised systems, with cryptographic hashing of data in blocks, this will resolve
whether the data belongs to patients, the NHS, and the technology providers. By
storing personal data in Blockchain Cloud solutions, such as the NuNet Cloud, a
centralised authority won't own the data , and sharing of the data, or alternative uses
and analysis, could only be achieved by a consensus of the B lockchain validator
nodes. Depending on what Blockchain is used, this could be many validator nodes.
4.4. Decentralised finance – DeFi .
Decentralised finance (DeFi) is built upon automated protocols that offer financial
services. DeFi solutions bring numerous advantages compared to traditional finance,
including faster transaction speeds, improved transparency, interoperability, and
immutability. However, these strengths also give rise to specific weaknesses. In
other sections of this review, we explore the cha llenges of ensuring compliance with
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anti-money laundering processes when conducting cross -border transactions at high
speeds. The rapid pace of DeFi operations often comes at the expense of non -
compliance . Although this issue is expected to be resolved in the future, it poses a
significant hurdle for widespread adoption, particularly on a national level, as
observed in the case of El Salvador.
In 2023, several significant DeFi protocols have emerged, such as Aave, Compound,
Curve Finance, Synthetix, PancakeSwap, Kyber Network, Uniswap, and others.
While new protocols are gaining momentum, Uniswap remains the most popular
decentralised exchange. The collapse of centralised exchanges, including FTX, has
created new market potential for decentralised exchanges. Nevertheless, questions
persist regarding the level of decentralisation achieved by some of these platforms.
As DeFi continues to evolve, striking a balance between speed, compliance, and
decentralisation becomes crucial for sustainable growth. Overcoming the challenges
associated with regulatory compliance will facilitate the broader adoption and
integration of DeFi into existing financial systems. Addressing concerns about the
degree of decentralisation within decentralised exchanges will foster greater trust
and confidence among users. By navigating these complexities, the DeFi ecosystem
can unlock its full pot ential and reshape the accessibility of financial services,
empowering individuals worldwide with greater financial inclusivity and autonomy.
4.5. Centralised exchanges .
Centralised exchanges faced significant challenges in 2022, and without
comprehensive regul atory oversight, they appear poised to repeat past mistakes.
While surviving centralised exchanges like Binance, Coinbase, Kraken, and KuCoin,
among others, claim to adhere to compliance standards, the absence of effective
oversight is evident, with limite d exceptions in the United States, Australia, and New
Zealand. In Australia and New Zealand, certain major exchanges are subject to
regulatory measures that encompass ATO (Australian Taxation Office) and AML
(Anti-Money Laundering) compliance, as well as K YC (Know Your Customer)
protocols. Coinbase in the United States operates under a similar regulatory
framework. However, the effectiveness of such compliance measures warrants
further examination in a separate article. In this piece, we focus on the level of
compliance necessary for crypto assets to be fully compliant.
The issue of partial compliance can be perplexing, as classifying inherently risky
assets as compliant creates opportunities for fund managers to include these assets
within seemingly secure financial products, such as pension funds. This scenario
raises concerns about a potential future parallel to the subprime mortgage crisis
2008. Thus, it is crucial to thoroughly assess and understand the actual compliance
status of crypto assets to avoid misleading categorisations that may inadvertently
contribute to the creation of unstable financial products.
Implementing robust regulatory measures and ensuring effective oversight are
imperative steps towards fostering transparency and safeguarding inves tors'
interests within the cryptocurrency landscape. Striking a balance between innovation
and regulation will be critical in building a sustainable and resilient financial
ecosystem that mitigates risks and instils confidence among market participants.
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4.6. Layer 1s and 2s , Non-Fungible Tokens (NFTs), and the Metaverse s.
Blockchains such as Bitcoin and Ethereum are called Layer 1 in decentralised
terminology . Alongside these Blockchains, there are Layer 2 protocols that can be
utilised in conjunction, enhancing the capabilities of the underlying Blockchain.
Examples of Layer 2 protocols include Arbitrum and Optimism. Within the Metaverse
landscape, many platf orms operate as Layer 2 protocols built upon existing
Blockchains. For instance, Decentraland (MANA), ApeCoin (APE), Axie Infinity
(AXS), the Sandbox (SAND), Enjin Coin (ENJ), Gala (GALA), Render (RNDR), and
Metahero (HERO) are all powered by the Ethereum (ETH) blockchain. Theta
(THETA) initially started as an ERC -20 token but subsequently transitioned to its
native THETA token. Theta utilises two tokens: THETA for governance and TFUEL
for utility. Stacks (STX) is one of the few layer -1 blockchains in the M etaverse
ecosystem.
Non-Fungible Tokens (NFTs) represent unique pieces of art, digital content, or
media within the Metaverse. These tokens enable trading and serve as a means of
storing value. However, it is essential to note that not all NFTs have proven to
possess the anticipated value that some investors had hoped for.
To summarise the information presented:
Blockchain/Protocol Layer
Type Notable Tokens
Bitcoin Layer 1 -
Ethereum Layer 1 MANA, APE, AXS, SAND, ENJ, GALA,
RNDR, HERO
Arbitrum Layer 2 -
Optimism Layer 2 -
Theta Layer 1 THETA (governance), TFUEL (utility)
Stacks Layer 1 STX
The Metaverse ecosystem encompasses a diverse range of blockchains, protocols,
and tokens, providing unique opportunities for participation and engagement within
virtual environments.
4.7. Crypto Bridges and Oracles .
Blockchain bridges enable the movement of assets from one Layer 1 to another
Layer 1 or from Layer 1 to Layer 2 and the reverse. Some of the most famous
bridges in 2023 include Hop Exchange, Orbiter, Rango Exchange, cBridge ,
xPollinate, AllBridge, and many others . Many of the major hacks that resulted in a
significant loss of Crypto in 2022 were based on cyber -attacks on Crypto bridges.
While Bridges resolve interoperability issues across chains , Oracles (such as
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Chainlink) enable cross -chain communication and intelligent contracts to execute on
different Blockchains.
4.8. Crypto Wallets .
Cryptocurrency digital wallets can be classified into three main categories. The first
category comprises safe storage tricky wallets, where cryptocurrencie s are securely
stored on a personal device. Examples of such wallets include Ledger and Trezor.
The second category consists of hot wallets, such as Metamask and TrustWallet,
software -based wallets typically accessed through internet -connected devices.
Lastly, there are exchange wallets, such as Coinbase Wallet, provided by
Centralised exchanges.
Considering the recent collapses of Centralised exchanges, it becomes challenging
to understand why individuals still choose to store their cryptocurrencies in exchange
wallets. Users may prefer the convenience of having someone else manage the day -
to-day operations of their savings and finances. Howev er, this reliance on
Centralised exchanges highlights the importance of implementing robust regulations
to ensure the security and integrity of these platforms.
To summarise the information presented:
Wallet Type Examples
Safe Storage Ledger, Trezor
Hot Wallet Metamask, TrustWallet
Exchange Wallet Coinbase Wallet
Classifying cryptocurrency wallets into these categories provides users with various
options to suit their preferences for security, accessibility, and management of their
digital assets. It is crucial to weigh the advantages and risks associated with each
type of wallet to make informed decisions regarding the storage and protection of
cryptocurrencies.
5. Lessons to be learned from the past errors: the two Cases of
FTX and Terra Luna
FTX was a centralised cryptocurrency exchange, providing crypto derivatives and
leverage trading services . Still, the primary use for centralised exchanges is to
enable customers to buy and exchange different cryptocurrencies. The main
problems resulting from the collapse of FTX also apply to all other centralised
cryptocurrency exchanges currently in operation. Cryptocurrency exchanges are not
regulated, which leads to individuals taking risks without the approval of the asset
owners , which is an oversimplified description of why centralised cryptocurrency
exchanges should not be allowed to operate without regulations. Individual savers
are not always keen on keeping their assets on a USB drive or writing the private
keys on a piece of p aper, which , if lost, would result in the loss of their savings.
Hence, many small crypto savers use centralised cryptocurrency exchanges (such
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as FTX) to store their crypto savings and earn interest – similarly to the traditional
banking system. Despite a ll the warning signs, individual savers are still locking their
crypto savings in unregulated centralised exchanges.
This presents three options to the UK government and all other governments
worldwide .
1. The first is to create standards and regulations fo r cryptocurrencies because
as of today (08 Jan 2023), we have 22,228 different cryptos (i.e., crypto
projects) and 534 crypto exchanges, with a market cap of $824,468,428,103
and 24h trade volume of $16,374,071,351 [139] . None of the trades are
regulated in the UK, nor most other countries.
2. The second is to ban all use of cryptocurrencies, including ownership and
trading, but this is unlikely to be effective because most crypto projects are
run from outside of the UK, and some (e.g., Bitcoin) are decentralised. Hence,
even if a global task force could be created to track and trace cryptocurrency
projects and exchanges, it would be ineffective against decentralised crypto
and will only push trade and ownership into the dark econ omy. In addition, it is
unlikely that the legal mechanisms can cope with persecuting all
cryptocurrency projects and exchanges because , as we can see from the
case of the XRP legal proceedings, just one point can take years to resolve.
The US Government ha s proven that it can effectively ban crypto projects . In
August 2022, the U.S. Treasury sanctioned the virtual currency mixer Tornado
Cash [140] . The Tornado Cash DAO was shut down , and its lead developer
Aleksey Pertsev was arrested, but what this trans lates to is that the mixer's
code itself is banned for use, and it does not mean that the code has been
disabled and cannot be used. It means that the Tornado Cash U.S. crypto
customers are not allowed to use the mixer, at least not without permission
from the U.S. Treasury. The mixer is blacklisted in the US because of its use
in money laundering. However, the Tornado Cash app will continue to operate
on the Ethereum blockchain exists . The critical point is that it is impossible to
shut down such technolog y without shutting down the entire Blockchain. Since
some Blockchains are decentralised, this will prove difficult, and even, if
possible, many new Blockchains are constantly emerging . Hence, sanctioning
and banning are unlikely to be valid for completely closing all operations.
3. The third option is to create fully centralised Government run Blockchains,
upon which open crypto projects and exchanges can be built. In this scenario,
Governments could control the type of projects and impose regulations and
standards upon the developers and the user community. In such fully
centralised Blockchains, the government could allow the development of
centralised and decentralised crypto exchanges and fund or encourage the
development of CBDCs (Central Bank Digital Curr encies) and regulated
Stablecoins (cryptocurrency with a pegged value to another currency,
commodity, or financial instrument). By enabling the development of a fully
regulated Stablecoin, the UK Government would prevent one of the main risks
for individua l crypto savers : the collapse of another Stablecoin , which
happened to the UST Algorithmic Stablecoin in 2022. Many of the current
stablecoins are highly speculative, and at present most stablecoins are not
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audited or regulated – at least not in any meanin gful way. Although Tether
(USDT) has announced that it is preparing to be audited by a large accounting
firm to prove the transparency of Tether, at present , USDT market reserves
are not audited . As of today, Tether's market cap is $66,268,895,618. Around
$11,106,992,770 of the cryptocurrency stablecoins traded in the last 24 hours
alone. Tether (USDT) is just one of many stablecoins on the many current
crypto exchanges . In the top 10 cryptocurrencies by market cap, apart from
USDT, we also have the USDC (m arket cap of $43,922,152,193) and BUSD
(market cap of $16,377,185,225) .
4. In contrast, in 11th place , we have DAI (market cap of $5,790,436,026) . In the
41st place , we have USDP (market cap of $876,254,775) . On the 43rd place is
TUSD (market cap of $846,271, 617), in the 52nd place is USDD (market cap
of $707,743,989) and so on – data from the 8th of January 2023 [139] . From
the above -listed stablecoins, USDC has reserves regularly attested but not
audited . None of the stablecoins are audited. This creates a systemic risk for
all cryptocurrencies , and regulating the stablecoins will not only prevent future
loss of savings for individual users and savers (hodlers), but it would increase
the confidence in the market . Combined with a regulated crypto exchange, it
would provide security and quick exit for investors during times of volatility. In
the final comment on CBDCs, we must point out that t he view emerging from
this article is not sympathetic to the values of society and economy from
CBDCs. Although CBDCs would resolve many issues related to fluctuations in
the price of all cryptocurrencies, the stablecoin solution could be a preferred
versi on of a Blockchain -based currency, specifically, decentralised
stablecoin s. However, the collapse of UST – LUNA has exposed
vulnerabilities in some of the decentralised algorithmic stablecoins . We need
new solutions to address some of the vulnerabilities disclosed in 2022.
5. The main lesson we must learn from FTX is that without taking regulatory
action, corporate malfunction and malfeasance cases will continue to
dominate the cryptocurrency ecosystems. Even if governments worldwide
embrace the concept of co mplete monetary decentralisation (which seems
highly unlikely), some crypto market elements still need to be regulated to
ensure that self -governance is not replaced again with malfeasance. The
collapse of FTX (which was considered one of the safest exchan ges because
of the public display of approval from various high -profile politicians), has
proven that corporate malfeasance exists in cryptocurrencies on a much
greater level than we are aware. To put this into perspective, if users start
withdrawing large volumes from any of the above -listed stablecoins, it seems
questionable if they will survive. That is not to say that the concept of
stablecoins should be abandoned or that the currency should be pegged to
gold and not to the USD. Stablecoins provide cruc ial services in the crypto
markets, and USD is the most traded currency. The concept seems sound,
but the regulations, standards and accountancy audits are missing.
These three scenarios could be seen as opportunities for the UK Government to
intervene and take advantage of the situation to establish the UK as the leading
country in the world, that is providing a highly demanded service (which is also highly
profitabl e), but also highly regulated, standardised, and audited according to
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POSTDOCTORAL RESEARCH ASSOCIATE
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international standards. Would this expose the UK economy to unnecessary risk?
That depends on how this process is undertaken.
Suppose the UK develops a new Blockchain that provides the services that
companies use to build crypto projects . In that case, the risk to the UK is minimal,
even if the crypto markets collapse . The value of Bitcoin dropped to its all -time lows
from year 2009 . The Blockchain would still charge transaction fees un til the market
cap goes down to zero, at which point, there won’t be any transactions, and there
won’t be any cost, because there won’t be any need for maintaining the new
Blockchain. Similar arguments can be made about the development of a UK crypto
excha nge. Regardless of the level of centralisation, the code for Uniswap and many
other exchanges are open and can be copied to create a new exchange without
building a new code. That is precisely what SushiSwap did, gaining a significant
market cap and tradin g volume almost instantly.
6. Survey of Crypto use cases .
The use case for crypto projects depends mainly on the specifics of the project
features and characteristics. Some of the most popular use cases come with
questionable motives. For example, the idea that bitcoin can be considered as digital
gold, or as a store of value, and that bitcoin can be used to preserve wealth and
hedge against inflation . This use case is very debatable. There are many use cases
for crypto, and below I list some realistic use c ases, but the idea that a digital asset
with no other purpose or a use case can replace gold, is not very convincing. It
seems more likely that Bitcoin will need to be wrapped and transferred to a different
chain, where the cost of transactions is much low er, and be used as a payment
system, similar to SWIFT.
That could be a real -world use case, and we already have the Algorand Blockchain,
which is capable of handling wrapped Bitcoins, and the cost of transactions is very
low, while security is high. Algor and could even enhance the security of Bitcoin.
There will be many other Blockchains that can do the same function. Hence, the
bitcoin community needs to start innovating because back in 2009, Satoshi
presented the most innovative and secure technology, bu t surely, he didn’t expect
this to remain the same forever.
Some of the real -world use cases include:
1. Borderless payments without any centralised entities acting as an
intermediary,
2. Decentralised finance for lending and borrowing, accessible to anyone an d
everyone that has an internet connection and knows how to use the specific
blockchain –crypto project,
3. Security and privacy of products (and services) as they move through different
supply chains,
4. Authenticity verification for products and services,
5. Ensuring payments are processed somewhat in supply chains - with the use of
smart contracts,
6. Buying and owning digital assets, such as gaming and collectables , can be
held as non -fungible tokens (NFTs).
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7. Crypto transactions can be designed to help with priva cy and anonymity,
creating added value for users that prefer to keep their finances private.
8. Crypto can be used for crowdfunding, where new funds can be raised by
issuing initial coin offerings (ICO) or token generation events (TGEs).
9. Creatives can use c rypto to monetise their work . For example, digital content
creators can accept payments for premium content, opening a safer and
cheaper environment for various artists, from dancers, tutors, and painters to
fitness instructors, digital consultants and edu cation providers.
10. Charitable donations, crypto can be used as a fast and secure method for
transferring wealth to people in need.
New use cases will continue to emerge with the increased adoption of decentralised
blockchain technology. One way to compare the current state of crypto is to think of
web one vs web two and the emergence of web three. Web one was just a collection
of data an d information made available for free online.
6.1. Evolution and Uncertainties of the Internet: From Web1 to Web3
The internet has undergone significant transformations over the years, from the
emergence of Web1 as the "information highway" to the interactive capabilities of
Web2. However, Web2 has raised concerns regarding privacy exploitation, paving
the way for the anticipated arrival of Web3, which is expected to be built on
blockchain technology. The blockchain's primary use case has already found its
place in adopting blockchains within Web3. This section explores the evolution of the
Internet and the uncertainti es surrounding the dominance of specific platforms and
cryptocurrencies.
The transition from Web1 to Web2 can be likened to the shift from AOL (the initial
popular web browser) to Hotmail. Similarly, Ethereum has emerged as the dominant
force in the crypto world, particularly with the introduction of layer two projects like
Optimism and Arbitrum. Ethereum's upgraded blockchain, transitioning from proof of
work to proof of stake, became a prominent player in 2023. However, whether
Ethereum will sustain its p opularity in the coming years or if another cryptocurrency
will surpass it. This draws parallels to Hotmail's decline despite experimenting with
features similar to its competitors.
The similarity between the current version of WhatsApp and the older MSN c hat is
quite puzzling. While WhatsApp remains popular, MSN lost many users and was
eventually abandoned. Although some argue that WhatsApp relies on mobile
signals, it uses personal mobile numbers as usernames rather than a communication
method. In contras t, MSN offered the advantage of creating new and random
usernames, making it seemingly more secure. This example highlights the
unpredictability of platform preferences among users.
As the emergence of Web3 unfolds, multiple platforms aim to provide simila r
services. Identifying the platform that will prevail in the long run is challenging.
Ethereum may be the MSN in this scenario, but who will be the WhatsApp? It's not a
single platform; projects like NEAR and SOL, among others, offer comparable
functional ities to Ethereum. The long -term viability of crypto projects depends on
their ability to deliver unique services and align with user preferences.
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The question arises whether Bitcoin will experience a revival with the Lightning
network. When transmitted vi a the Lightning network, there is no real distinction
between layer one and layer two solutions. Additionally, Bitcoin has been striving to
increase the use of renewable energy and improve speed and user -friendliness for
everyday transactions. Perhaps, the re is potential for new blockchain innovations to
emerge from Bitcoin.
In summary, the Internet has evolved from Web1 to Web2, and the anticipated
arrival of Web3 brings new uncertainties regarding platform dominance and the
emergence of innovative cryptoc urrencies. The future landscape of the internet and
blockchain technologies remains unpredictable, as users' preferences and
advancements in various projects play pivotal roles in shaping the future of the digital
landscape.
6.2. The Buterin's trilemma .
This re view article would not be complete without discussing the Buterin's trilemma
and how that fundamentally captures the trade -offs between security,
decentralisation, speed, and the attendant risks. Some blockchains (e.g., Sol) go for
speed but are more centr alised; others are less centralised but often more secure.
One study suggested a solution called ‘The Blockchain Quadrilemma’ but also
recognised that for basic Blockchain operations ‘, Algorand can often be the right
choice’ , but Ethereum is recommended for ‘more sophisticated computations’ [141] .
Another research study recommended ‘a dichotomy of algorithms between leader -
based and voting -based consensus algorithms’ based on ‘tradeoffs … for a given
distributed system’ [142] It is worth mentioning t hat some experts recommend an
‘incentive -based role in the governance of DeFi as opposed to an enforcement -
oriented role’ because we need to build new tools that enable new ‘ policy options in
a transnational environment hostile to formal state intervention ’ [143] .
7. Discussion .
Crypto is still subject to many risks for investors and users. One of the main risks for
investors is that the value is highly volatile and fluctuates significantly in short
periods . This makes crypto price and value extremely difficult to predict , leading to
significant losses when the value drops significantly. Since crypto is also a
speculative asset, investors cannot be confident that the value of their investment
will ever recov er or go to zero.
Another risk is that no one, a government, or any financial institution regulate crypto .
This is the clearest indicator that there won’t be any protection or oversight from
fraud, mismanagement, or financial malfeasance. In other words, crypto investors
need to be aware that they will have very little recourse if something goes wrong .
Apart from these risks, one commonly discussed risk is the safety and security of
blockchain technology. The common topic in media is that the underlying blockchain
technology is not as secure as it was thought . The rationale for this assumption is
that in the past, we had some very high-profile hacks that resulted in the theft of
large amounts of crypto from exchanges and wallets. Although the latest is cor rect,
the first is not. Bitcoin and other secured blockchains have never been hacked, the
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hacks happen on crypto exchanges and digital wallets that do not apply appropriate
security, and most of the theft has been on crypto bridges, pools, and other
instru ments unrelated to the blockchain technology itself.
To explain this , in other words, the Blockchain is as secure as it has been described
in the first paper written by Satoshi, but the new projects are bypassing the security
requirements, often because t here are no cybersecurity standards . The hacks are
increasing, but this does not mean that blockchain technology is not secure . It
means that we need cybersecurity standards for projects that use blockchain
technology.
7.1. The good .
Traditional finance has a lso been slowly developing new , faster and more secure
solutions. The SWIFT network is very slow compared to some of the crypto
solutions, and the idea of tokenised USD does seem appealing to many users,
specifically traders. Cryptocurrencies can also solv e many of the banking problems
in developing countries. The ability to make payments and transfer tokens pegged
with the value of USD could provide solutions to much of the developed world still
lacking essential banking services. Since existing payment me thods like Visa or
Mastercard charge service fees, it is reasonable to expect that some small fees
would be acceptable to users. However, there is no evidence of crypto being
adopted as a payment method in developing countries. Although some countries like
El Salvador have adopted Bitcoin, its use for everyday payment has not been
adopted. Another point to make here is related to the value of Bitcoin (BTC) and
private money and wallets. Although decentralised cryptocurrencies don’t hedge
against short -term inflation, Bitcoin has massively outperformed gold over a decade
despite monetary easing/printing.
7.2. The bad .
Bitcoin emerged from the financial chaos in 2008, and it was presented as a solution
to the centralised banking system and the high -risk pract ices of a few greedy
financial firms that only care about profits. The cryptocurrencies only replaced one
centralised set of intermediaries that are strictly regulated, with another set of
centralised intermediaries that are not regulated. Despite the fall of FTX, crypto
exchanges do not want to be controlled, and they are challenging to regulate,
because they can be run from anywhere in the world.
One of the reasons for this is that consumers prefer the safety and service of third
parties looking after their money and rarely prefer keeping their savings in a cold
wallet that can be lost, damaged, or hacked, along with all the money saved. The
shift from decentralised money to centralised exchanges just shows that people
prefer the convenience of a third party looking after their money.
The long -term economic utility is also questionable. Why would one coin created out
of code be valued in thousands of pounds and another coin created out of code be
worth zero. It is often compared with traditional securities such as stock shares, but
stocks generate cash flow, and we can discount the cash flow to the present time to
come up with a valuation. Another e xample is that a fiat currency is valued relative to
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other fiat currencies based on GDP, inflation, interest rates, and other data from
different countries. None of these valuations apply to cryptocurrencies.
This means that cryptocurrencies cannot be val ued because they do not have any
trade fundamentals. Instead, cryptocurrencies trade purely on sentiment, and most
of the price spikes are created by influence rs and social media. If this trend
continues, we can argue that even decentralised cryptocurrenci es are just
decentralised Ponzi schemes and rely on a supply of new buyers to buy new tokens
at higher prices . The supply of new buyers will run out eventually. Worth
emphasising here is that from the five most famous Ponzi schemes of the 2020s, are
alloca ted to cryptocurrencies (QuadrigaCX, Terra Luna, and FTX7).
What is really striking in this scenario is that the poorest usually learn last about the
scheme and tend to lose the most. A clear example of this is the case of El Salvador,
where they adopted Bitcoin as a legal tender, followed by credit rating agencies
downgrading their sovereign credit rating . The IMF has started to cut off funding,
nobody is using Bitcoin there to buy anything, and it turned out to be a disaster.
Other developing countries have found effective fintech solutions that do not require
crypto, such as the M -Pesa in Africa, which is based on SIM card payment from a
mobile phone, or We Chat Pay , a QR Code payment. Both examples have proven
success in adoption and financial inclusion, with billions of users and almost no
banking infrastructure investment. If we compare this to Bitcoin, we are still not able
to walk into a shop and buy things with Bitcoin or any of the cryptocurrencies that we
have at present . It is questionable if we will ever be able to do that because of the
cost of validatin g the transaction – it might simply not be viable to process so many
transactions with the current Bitcoin mechanisms. It seems more likely that Bitcoin
would need to be wrapped as a token on a different – less secure Blockchain just to
be used as a curren cy for payment of everyday things, like coffee or beer.
For crypto to be seen as a long -term value, it must provide economic utility, and at
present, crypto is not providing such utility. Another problem is that in technological
terms, crypto is old techn ology, the first block was created in 2009, and we have
seen a rapid technological change since then. Most of the mobile phones from 2009
are now considered old and almost obsolete. We are still waiting for some
extraordinary use case for crypto, but since it hasn’t materialised until now, the
question is when it will, and would it ever happen ?
7.3. The ugly .
Currently , the crypto market is not connected to traditional markets and is relatively
small. If the crypto market relates to the traditional finance, t he spill -over effect must
be considered. The main concern is that if the crypto market is regulated in a way
that it would get supercharged and it’s allowed to create the connections between
regulated finance and the crypto system, then crypto problems can become much
bigger problems. This would mean that people that never invested in crypto are
affected by price fluctuations and crypto market risks, in the same way that investors
were affected by the mortgage -backed securities in 2008. Hence, the focus in
7 https://en.wikipedia.org/wiki/List_of_Ponzi_schemes
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regulations should be on minimising the connections between the crypto market and
the regulated financial system.
One significant risk from cryptocurrencies to our society is gambling, including
leveraged and other forms of trading. I n this article, we fo cused on the crypto
ecosystem and the risk and values of the technology for society and the economy –
from a perspective of improved wealth of a nation. But we also noticed that in the
literature , we seem to have ignored that trading and gambling are addic tions that
humans have been subjected to since the early age of humanity, and th e gambling
market is booming in crypto. Wherever there is a demand, there will be a supply, and
this needs to be regulated because gambling is a severe addiction.
One of the more concerning discoveries in this area is that Crypto gambling is
‘distinct with regard to higher novelty seeking, higher gambling tendencies, and
unique investmen t patterns ’ [144] and this conclusion has been reached with the use
of established and new Crypto specific methods, including ‘Fear of Missing Out
(FoMO) scale, Temperament and Character Inventory -Revised -Short (TCI -RS),
Mood Disorder Questionnaire (MDQ) , trait anxiety part of the State -Trait Anxiety
Inventory (STAI -T), and the Korean version of the Canadian Problem Gambling
Index (K -CPGI) ’.
Cryptocurrency trading and gambling have also been associated with mental health
problems , including depression an d anxiety , with the main findings confirming a
direct similarity between the demographic and personality characteristics of
cryptocurrency traders and gamblers [145] . However, the study recognises that there
could be ‘differences between long -term investors and short -term traders of
cryptocurrency’, although, given the market uncertainty, some of the long-term
investors in Terra Luna of FTX might disagree . Another recent study found that
cryptocurr ency trading results in ‘rise to excessive or harmful behaviour including
over-spending and compulsive checking’ , and although they identified many
‘similarities between online sports betting and day trading’, there are also some even
more concerning facto rs, like ‘the continuous 24 -hour availability of trading, the
global nature of the market, and the vital role of social media, social influence and
non-balance sheet related events as determinants of price movements’ [146] .
These examples illustrate tha t we have reviewed some of the leading and most
recent journal papers on this topic, but this topic needs much deeper research and
understanding from a mental health perspective .
8. Conclusion .
In conclusion, this article has comprehensively examined the cu rrent state of
cryptocurrencies and blockchains, aiming to enhance coherence, structure, and
comprehensibility within the domain. We have shed light on the values and risks
associated with these digital assets by presenting an up -to-date snapshot of the
crypto landscape in 2023 and tracing its historical development from Satoshi's
pioneering work. Moreover, we have endeavoured to clarify the distinctions between
cryptocurrencies and blockchain technologies, addressing pertinent research
questions regarding the innovative nature of blockchain, the significant risks posed
by cryptocurrencies, and the potential societal and economic benefits of pursuing
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these technologies. Furthermore, we have explored the varying impacts on
developed and developing countries and contemplated the longevity of different
blockchain projects. While acknowledging the prevalence of fraudulent schemes, we
have shifted our focus to the practical applications of blockchain projects, ultimately
affirming the enduring presence of blockcha in technologies. Our comprehensive
discussion on the value derived from blockchain projects underscores their
significance while re -evaluating key risks, including a personal reflection from the
author on the potential risks.
The question that emerges from this review paper is, if we fast forward ten years,
would all the transactions that we perform in our society be in fiat currencies, and the
answer is that most probably they won’t. With the emergence of Web3, assets,
money and marketplaces will become interconnected, and some of the
cryptocurrencies will form part of these new digital assets, but would that be Bitcoin,
or some of the other 22,250 cryptos that are in circulation today, that is difficult to
predict.
What also becomes clear is that regulation would eliminate many of the cases of
corporate malfeasance. Regulations will most likely also remove many current use
cases for crypto. Much of the recent hype around crypto is around the lack of
regulations, and when regul ations are applied, the promise of getting rich from crypto
will certainly start to weaken. With regulations, crypto projects will have to start
making checks (KYC) on who their customers are, and this argument for regulations
killing the crypto is especia lly strong for cryptocurrencies that are created with no
actual use case and based purely on the promise of making a great deal of money
for early investors. Once regulations are created, these cryptos will be out of the
picture. Most of the crypto project s are almost certainly not compliant with the
derivatives or either security regulators.
Crypto has been operating in a very grey area, where different crypto project is
considered as commodities, and because of that, crypto exchanges do not need to
regis ter with the federal government and be subject to regulation. This debate has
been ongoing for far too long . The issue of whether crypto is a commodity or crypto
is a security is not the main point of concern.
The main concern is not the naming but whethe r crypto is subject to regulation, and
from this perspective, it makes sense to call all crypto assets securities, which will
mean that all crypto is subjected to robust oversight. The issue is that, if that
happens, most crypto projects won’t be able to c omply, which will hurt not only the
crypto industry but also the crypto investors. Given that regulations are designed to
protect investors, it is uncertain if such robust approach would serve the purpose it
intended to , or would it lead to a significant l oss for crypto investors. A more realistic
approach would be to regulate crypto exchanges and ensure that exchanges are
registered as investment dealers.
This seems realistic and reasonable because if a crypto investor engages in a
contract with a crypto exchange, where the exchange would promise a very lucrative
return or some exceptional benefits that are not very realistic, there are small crypto
investors that might fail for such advertisements. This makes crypto exchanges
investment deadlier, and they need to be regulated. Regulators need to engage in
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how these exchanges keep their assets, how they get the returns, and ensure that
exchanges are not taking unnecessary risks that expose investors to risks they are
unaware of or do not fully understand. T he old saying in crypto is ‘not your keys, not
your crypto’, and regulating the centralised exchanges, won’t even come with any
disagreement from the crypto community.
8.1. Final comments .
A final comment on investing in crypto, despite the remarkable returns some
investors have benefited from, the crypto market has not matured yet, and the
market – including the technology – is still evolving. There are risks, and investing in
crypto comes with serious risks, and investors should approach crypto with caution.
In addition, we still cannot use Bitcoin or any other Crypto to buy coffee in Costa or
Starbucks, we cannot buy food in Tesco, and transactions come at a cost, while
using fiat doesn’t cost.
Until we can use crypto as a fiat currency in every aspect of t he use cases, we won't
be able to claim that retail and commerce adoption is increasing . Even if the price of
crypto goes up (or down ), the use case doesn’t change much, which triggers
concerns. On the other hand, the claim that Bitcoin is used for crime and money
laundry has been contradicted by blockchain analysis company Chainalysis, which
reported that only a very small percentage ( 0.15% total of $14bl ) of known
cryptocurrency transactions conducted in 2021 were inv olved in illicit activities [147] .
9. Abbreviations
Bitcoin – the first decentralised blockchain .
Terra Luna – collapsed crypto project.
FTX – collapsed crypto exchange .
Solana (SOL) – crypto project that got affected by the FTX collapse .
Ethereum, Cardano, Dogecoin, Litecoin, Algorand, NEAR – crypto projects that
remained popular with investors in the 2021 bull run .
IOTA, NEO, EOS – crypto projects that were popular in the previous bull runs, and
are still present in the crypto market in 2023 .
NEFD - new and emerging forms of data .
CBDCs - Central Bank Digital Currencies .
Tornado Cash DAO – crypto mixer that has been prohibited for use by the USA
UST Algorithmic Stablecoin – collapsed stablecoin .
USDT, USDC, DAI, BUSD, USDP, USDD, TUSD - Stablecoins still in existence in
2023 (as of 25th of January 2023) .
NHS – National health Service .
Uniswap / SushiSwap – decentralised exchanges .
NFTs - non-fungible tokens .
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ICO - initial coin offerings .
TGEs - token generation events .
ChatGPT – AI based chat designed to replace the Google search engine .
M-Pesa - SIM card payment from a mobile phone .
We Chat Pay - a QR Code payment .
KYC – know your customer .
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|
{
"id": "2309.12322"
}
|
1912.10105
|
Dissecting Ethereum Blockchain Analytics: What We Learn from Topology
and Geometry of Ethereum Graph
|
Blockchain technology and, in particular, blockchain-based cryptocurrencies
offer us information that has never been seen before in the financial world. In
contrast to fiat currencies, all transactions of crypto-currencies and
crypto-tokens are permanently recorded on distributed ledgers and are publicly
available. As a result, this allows us to construct a transaction graph and to
assess not only its organization but to glean relationships between transaction
graph properties and crypto price dynamics. The ultimate goal of this paper is
to facilitate our understanding on horizons and limitations of what can be
learned on crypto-tokens from local topology and geometry of the Ethereum
transaction network whose even global network properties remain scarcely
explored. By introducing novel tools based on topological data analysis and
functional data depth into Blockchain Data Analytics, we show that Ethereum
network (one of the most popular blockchains for creating new crypto-tokens)
can provide critical insights on price strikes of crypto-tokens that are
otherwise largely inaccessible with conventional data sources and traditional
analytic methods.
|
http://arxiv.org/pdf/1912.10105v1
|
Yitao Li, Umar Islambekov, Cuneyt Akcora, Ekaterina Smirnova, Yulia R. Gel, Murat Kantarcioglu
|
cs.SI, cs.LG, q-fin.ST
|
cs.SI
|
Dissecting Ethereum Blockchain Analytics: What We Learn from
Topology and Geometry of Ethereum Graph
Yitao LiUmar IslambekovyCuneyt AkcorazEkaterina Smirnovax
Yulia R. Gel{Murat Kantarcioglu{
Abstract
Blockchain technology and, in particular, blockchain-based
cryptocurrencies o er us information that has never been
seen before in the nancial world. In contrast to at
currencies, alltransactions of crypto-currencies and crypto-
tokens are permanently recorded on distributed ledgers
and are publicly available. As a result, this allows us to
construct a transaction graph and to assess not only its
organization but to glean relationships between transaction
graph properties and crypto price dynamics. The ultimate
goal of this paper is to facilitate our understanding on
horizons and limitations of what can be learned on crypto-
tokens from local topology and geometry of the Ethereum
transaction network whose even global network properties
remain scarcely explored. By introducing novel tools based
on topological data analysis and functional data depth into
Blockchain Data Analytics, we show that Ethereum network
(one of the most popular blockchains for creating new
crypto-tokens) can provide critical insights on price strikes
of crypto-tokens that are otherwise largely inaccessible with
conventional data sources and traditional analytic methods.
1 Introduction
Past few years marked the beginning of a new era of
technology { the era of Blockchain. Blockchain has
already revolutionized many elds, from e-payments
to digital asset ownership management. Undoubtedly,
one of the primary magnets of the Blockchain craze is
to take advantage of the unprecedented opportunities
to invest (and to lose!) in the largely unregulated
crypto-markets via various forms of digital instruments
such cryptocurrencies and crypto-tokens. Recent sharp
soars and miraculous comebacks of crypto-assets only
continue to further heat the investment mania due to
unprecedented chances to make a quick, and strikingly
high pro t, which in turn goes hand in hand with high
investment risk. Naturally, one of the most momentous
Purdue University, USA.
yBowling Green State University, USA.
zUniversity of Manitoba, Canada.
xVirginia Commonwealth University, USA.
{University of Texas at Dallas, USA.questions nowadays are whether ,howandto what extent
we can forecast trading dynamics of crypto-currencies
and tokens.
Despite many novel analytic challenges associated
with blockchain-based nancial instruments, the crypto-
market o ers us highly informative and novel data that
have never been available before due to data protection
and privacy policies in banking { that is, all transactions
are permanently recorded and publicly available. This
in turn allows us, for the rst time in the history of
nance, to construct a global transaction graph and
relate its properties to price dynamics. The intuition
behind this approach is multi-fold.
First, as various patterns of retail shopper activity
provide a foundation for assessment of the current state
of the economy and form the basis of many economic
indicators [32], it is natural to hypothesize that patterns
of the transaction graph may also o er a glimpse into
crypto-market health.
Second, in contrast to retail shopper data that are
both heavily aggregated and delayed in time, informa-
tion on the transaction graph is available in real time
and on a transaction-level basis.
Third, availability of the transaction graph allows
to study speculative and even malicious behavior of
crypto-assets' users which, as often happens in the -
nancial world, involves multiple players { such behavior
remains largely inaccessible with conventional analytics
and requires tools of complex network inference.
We show that geometry and local topology of the
transaction graph contains a wealth of information on
crypto-token market, ranging from price prediction and
price anomalies to hidden co-movement of multiple
instruments.
In contrast, we nd that both conventional variables
of nancial time series and global network features of
the transaction graph are not capable to glean a deeper
insight into crypto price dynamics.
Why Ethereum? Ethereum is one of the the
most popular blockchain platforms. It allows creating
smart contracts and, hence, enables everyone to create
a crypto-asset on it. Ethereum tokens are sold through
Initial Coin O erings (ICOs). Such token ICOs have
Copyright c
2020 by SIAM SDM 2020
Unauthorized reproduction of this article is prohibitedarXiv:1912.10105v1 [cs.SI] 20 Dec 2019
Figure 1: Betti pivot of the token Powerledger (shown
in red).
already enabled many start-ups and organizations to
raise capital by selling digital coins which allow recip-
ients to use a promised service if and when available.
Only in 2018 and 2019, Ethereum ICOs raised billions of
dollars. As such, analysis of Ethereum networks might
be even more acute than cryptocurrency price predic-
tion. However, despite this high token activity, net-
work structure of Ethereum transaction graph remains
largely understudied [12, 18]. Furthermore, to the best
of our knowledge, there exist no studies of Ethereum
that link crypto-token price analytics with the underly-
ing Ethereum transaction graph.
Why Not Simpler Methods? Ethereum data
poses several challenges. First, the transaction graph
is very sparse and dynamic. Nodes (i.e., account ad-
dresses) appear and disappear (i.e., no future transac-
tion) daily, while the number of transactions widely
uc-
tuates across days. Hence, conventional graph analytic
tools such as global clustering coecient and k-core [15]
analysis may not be feasible indicators of token activ-
ity. Second, crypto-tokens may exhibit very di erent
responses to external positive and negative shocks, and
the signal on such a response to shocks which is con-
tained in the crypto-token price and global network fea-
tures, is weakened due to aggregation. These challenges
require development of novel robust graph theoretic ap-
proaches that are suitable for analysis of time-varying,
highly irregular, and very sparse networks .
Proposed Approach. We address the above-
mentioned challenges by introducing the arsenal of topo-
logical data analysis (TDA) tools into Ethereum data
analytics. TDA allows us to systematically and robustly
assess a local geometric and topological structure of the
Ethereum transaction graph. Our approach is based on
the premise that any abnormal situation, for instance
price anomaly, viewed as a response to a negative or
positive shock (e.g., announcement of a new crypto-
currency regulation) is likely to be re
ected in the un-
derlying topology and geometry of a transaction graph.To study the local network geometry and topology of the
Ethereum transaction graph, we blend concepts from al-
gebraic topology and functional data analysis.
The important methodological distinction of our
new approach is that while TDA has been applied
before to nancial time series, including time series of
cryptocurrencies [20], TDA has never been yet applied to
complex networks of nancial transactions on account-
based blockchains such as Ethereum . Moreover, to the
best of our knowledge, the only other paper discussing
utility of TDA on nancial networks, including both
traditional nance and blockchain , is our earlier study
of Bitcoin graph [1] which belongs to the unspent
transaction output (UTXO) based blockchains. Since
UTXO based blockchain graphs have transactions with
multiple inputs and outputs, the techniques developed
for UTXO blockchains cannot be directly applied to
account-based blockchains.
As such, the importance of our methodology and
ndings can be summarized as follows:
We o er a novel perspective to risk analysis of crypto-
assets, particularly, Ethereum tokens, by dissecting
hidden linkages between the token price dynamics and
local geometry of the Ethereum transaction graph.
While the paper focuses on blockchain data analytics,
the proposed novel methodology to risk analysis based
on geometry and topology of the transaction graph is
applicable beyond crypto instruments. For instance,
subject to data availability on transactions and other
nancial interactions, the proposed analytic tools can be
used for analysis of systemic risk in interbank networks
as well as for optimizing strategies in algorithmic stock
trading.
We propose a new measure of the most illustrative,
or \normal" behavior on the Ethereum transaction
network: a Betti pivot . Betti pivots, based on
analysis of network persistent homology and functional
data depth, allow us to quantify and visually assess
di erences between normal and anomalous transaction
activity, as we show in Fig. 1 for the PowerLedger token.
We develop an innovative ltering approach that
signi cantly reduces the (prohibitively high) computa-
tional costs of TDA. We report the rst results where
TDA tools can be adopted in large networks while pre-
serving the performance.
We report the rst results for crypto-token price
anomaly prediction, and show that token networks
are likely to contain adequate information to develop
arbitrage trading strategies in the real world. As the
crypto-token ICOs have reached $12B in the rst half
of 2018 [25], our prediction results have important real-
life implications for start-up funding.
Copyright c
2020 by SIAM SDM 2020
Unauthorized reproduction of this article is prohibited
2 Related Work
We outline four relevant research areas: Ethereum
graph analysis, Blockchain price prediction and
anomaly detection, as well as TDA.
Ethereum graph analysis. Di ering from crypto-
currencies (e.g., Bitcoin) where each transaction can
have multiple inputs and outputs [33], Ethereum trans-
actions transfer ether or tokens from one address to an-
other. As such, Ethereum lends itself to traditional net-
work analysis. For instance, [4] studied empirical prop-
erties of Ethereum and [36] explored token networks, in
terms of degree distribution, power laws and clustering.
However, there are yet no results that employ network
tools for Ethereum price analytics.
Cryptocurrency price prediction . Analyzing trans-
actions and addresses to track the Bitcoin economy has
become an important research direction. A time se-
ries prediction approach by [29] uses a Bayesian opti-
mized RNN and LSTM network with varying degrees
of success. Blockchain features, such as average trans-
action amount, are also shown to exhibit mixed perfor-
mance for cryptocurrency price forecasting [22]. Var-
ious blockchain graph characteristics, such as average
degree, can be used as prediction features. Recently, [2]
employed blockchain motifs, termed chainlets, as fea-
tures to predict Bitcoin price.
However, all the mentioned approaches are carried
out to track a single cryptocurrency. In contrast, our
goal is to track multiple cryptoassets at the same time.
Blockchain anomaly detection. Blockchain ad-
dresses can be linked to identify people behind suspi-
cious transaction patterns in cryptocurrencies [37]. The
pattern is usually de ned as a repeating shape that in-
volves moving coins from a (black) address to an online
exchange, where the coins can be cashed out without be-
ing con scated by authorities. The black address that
starts the transaction chain may be related to money
laundering [30], blackmailing [34] and ransomware pay-
ments [24]. There exists ample evidence of these anoma-
lies in the transaction network [5]. A more recent ap-
proach found anomalies in Bitcoin price by linking ad-
dresses to transactions in time [23]. In contrast, we do
not assume any prior knowledge about pattern shapes or
addresses; our unsupervised data depth approach tracks
token networks for price anomalies.
Topological Data Analysis. TDA is an emerging
eld at the interface of algebraic topology, statistics, and
computer science. The rationale is that the observed
data are sampled from some metric space and the
underlying unknown geometric structure of this space
is lost due to sampling. The key idea is to recover
the lost underlying topology [39]. Persistent homology
(PH) is one of the tools to characterize a topologicaldata structure under varying scales of dissimilarity. The
most widely used topological summaries of persistent
features are the Betti numbers, barcode plots, persistent
diagrams, and persistent landscapes [19]. However,
barcode plots and persistent diagrams cannot be easily
used in machine learning models [7]. Di ering from
these approaches, we propose Betti pivots, which can be
directly integrated with functional data analysis tools.
3 Methodology
Problem Statement: Given the transaction network
of an Ethereum token and time series of the token prices
in at currency, predict whether the token absolute
price return R t= (Pricet Pricet 1)=(Pricet 1) will
change more than jj>0, in the next hdays. Further-
more, identify the maximum horizon value hsuch that
the prediction accuracy is at least . That is, the ulti-
mate goal is to predict whether a window of the next
hdays,h >0, will contain a price return anomaly. In
return, an informed and reliable answer to this question
allows to optimize investment strategies in algorithmic
trading, and higher his preferred.
The key idea behind our approach is the following:
rst, armed with TDA, extract multi-resolution topo-
logical summaries of the Ethereum network and then in-
corporate the resulting geometric information into anal-
ysis of token prices. As the primary TDA methodolog-
ical engine, we employ the tool of persistent homology
due to its
exibility in integration with machine learning
models.
We start by detailing persistent homology and
associated topological summaries.
3.1 Persistent Homology and a New Look at Its
Summaries via Functional Data Analysis { Betti
Limits LetG= (V;E;! ) be a weighted graph, where
VandEare the set of nodes and edges, respectively;
!:E!R[f1g is a weight function encoding
similarity between two nodes connected by an edge.
To account for dissimilarity between two disconnected
nodes, we introduce the weight ~ !:VV!R[f1g
~!uv=
!uv(u;v)2E;
1 (u;v)=2E:
where
!uv=h
1 + Auv Amin
Amax Amini 1
:
HereAuvis the amount of transferred tokens by
transactions between nodes uandv;AminandAmax
are the smallest and largest transaction amounts, re-
spectively. That is, the larger the transferred amount,
the smaller the inter-nodal dissimilarity. We set = 9
to map weights to the interval [0 :1;1].
The most important aspect of Persistent Homology
Copyright c
2020 by SIAM SDM 2020
Unauthorized reproduction of this article is prohibited
(PH) is that it allows us to analyze data at multiple spa-
tial resolutions in a uni ed way, bypassing a subjective
selection of the dissimilarity parameter or searching for
its optimal value. However, to be able to extract topo-
logical information from a point cloud, it needs to be
equipped with a structure of a topological space. In the
context of PH, this is commonly achieved by construct-
ing an abstract simplicial complex on the top of data
points.
De nition 1. (Abstract simplicial complex) Let
Xbe a discrete set. An abstract simplicial complex is
a collectionCof nite subsets of Xsuch that if 2C
then 2Cfor all . Ifjj=p+ 1, thenis called
ap-simplex .
Intuitively, a simplicial complex can be viewed as a
higher dimensional generalization of graphs which rep-
resents a structure consisting of points, edges, triangles
and their higher order counterparts. Vietoris-Rips is a
widely used simplicial complex due to its easy construc-
tion and fast computational implementation [8].
De nition 2. (Vietoris-Rips complex) LetXbe a
discrete set in some metric space. A Vietoris-Rips
complex on Xat dissimilarity scale 0, denoted
byVR, is an abstract simplicial complex whose p-
simplices,p= 0;:::;d , consist of points which are
pairwise within distance of . Here,dis called the
dimension of the complex.
Remarkably, simplicial complexes can not only be re-
garded as topological spaces from which topological
information is derived, but also as combinatorial ob-
jects which are convenient for computational purposes.
Hence, this dual nature of simplicial complexes turns the
task of extracting topological information into a compu-
tationally feasible combinatorial problem [13].
Now, we x a sequence of scale resolutions 1<2<
:::<nand form a chain of nested VR complexes called
a( nite) VR ltration VR1VR2:::VRn,
whereVRk,k= 1;:::;n , is a VR complex on Vsuch
thatVRk=
Vj~!uvk;8u;v2
.
Armed with the VR ltration, we now get a for-
mal multi-resolution glimpse into the Ethereum network
topology and geometry and track topological features
that appear and later disappear as the scale param-
eter increases. Evolution of such topological features
sheds light on organization of the Ethereum transac-
tion network. That is, we can expect that features with
a longer lifespan, i.e. persistent features , have a higher
role in explaining functionality of the Ethereum network
than features with a shorter lifespan. These short term
features are regarded as topological noise . Persistent
features are instrumental for distinguishing anomalous
dynamics in token transaction activities. We extractdescriptors of such topological features at a multi level
in the form of sequences of Betti numbers .
De nition 3. (Betti number) Betti-pnumber of a
simplicial complex Cof dimension d, denoted by p(C),
is de ned as the rank of the p-th homology group of C,
p= 0;1;2;:::;d .
Fortunately, for applied data analysis Betti- pnumber
has a simpler practical interpretation, i.e. Betti-0 is the
number of connected components, Betti-1 is the number
of loops (or holes), Betti-2 is the number of voids (or
cavities), etc.
In this paper, we consider features up to dimension
2 and takeCto be a VR complex. Following the PH
methodology, we compute sequences of Betti numbers
of a chain of nested VR complexes and thereby track
the counts of di erent topological features at increasing
scales of complexity. Note that the resulting topological
descriptors in the form of Betti numbers over a VR
ltration depend on kand are intrinsically in nite
dimensional. As such, an intuitive approach to analyze
their dynamic properties is via functional data analysis
(FDA) [31, 38]. In this context, we introduce a novel
concept of Betti limits which relates these counts to
the scale parameter viewed as continuum .
De nition 4. (Betti limit) LetfCkgn
k=0be a ltra-
tion of simplicial complexes where fkgn
k=0is an increas-
ing sequence of scales such that 0= 0 andn=Lfor
someL > 0. Then, the Betti- plimitBp: [0;L]!
Z[f0g,p= 0;:::;d , is de ned as
Bp() = lim
max k!0 p(Ck)
where the max is taken over all k= 0;:::;n ,k=
k k 1andkis the index such that 2[k 1;k)
The Betti limits can be regarded as functional sum-
mary statistics of the network's topological structure
and o er multi-fold bene ts. First, the Betti limits pro-
vide a systematic linkage with the tools of functional
data analysis (FDA). For instance, underlying nonlin-
ear dynamics of the Betti limits can be then assessed
with derivatives and associated manifold learning and
empirical di erential equations. In turn, relative posi-
tions of individual trajectories of the Betti limits can
be quanti ed using a concept of functional data depth .
Furthermore, Betti limits can be viewed as generalized
descriptors of network topology for a class of continu-
ous latent space models, particularly, including distance
models and graphons [9, 35]. We leave this more funda-
mental mathematical hypothesis on characterizing ge-
ometry of the continuous latent space network models
via Betti limits for future research.
Copyright c
2020 by SIAM SDM 2020
Unauthorized reproduction of this article is prohibited
3.2 Functional Data Depth of Betti Limits Let
f(Gt;~!t)gT
t=1be a time series of weighted graphs and
fBp;tgT
t=1be the associated sequence of Betti limits.
To assess which topological descriptors (or equivalently
which transaction networks) signal towards anomalous
patterns relative to others, we employ the notion of data
depth .
De nition 5. (Data Depth) Informally, data depth
is a function that measures how closely a given mul-
tivariate observation is located to the \center" of the
observed point cloud. That is, data depth extends the
concept of quantiles from univariate to multivariate dis-
tributions. Formally, let Fbe a set of probability dis-
tributions on a Banach space X(e.g.,X=Rn). A
data depth is a function D:XF! [0;1]such that
D(jF)is a center-outward ordering of elements of X
with respect to F. (Here, by a center-outward order-
ing, we mean that each element of Xis assigned a score
from 0 to 1 such that a higher score implies that the
element is more centrally located within a point cloud
and a lower score implies that the element is likely to be
an outlier in respect to the remaining elements.) The
depth ofy2X with respect tofyigm
i=1X , denoted
byD(yjy1;:::;ym), is de ned as D(yj^Fm), where ^Fmis
the empirical distribution of fyigm
i=1.
Since we focus on Betti limits, we resort to func-
tional data depths (i.e., where Xis a space of func-
tions). Among such functional depths, the modi ed
band depth (MBD) [26] is particularly well-suited for
detecting anomalies as MBD accounts for both the
shape and magnitude of the function graphs. In ad-
dition, MBD is robust and enjoys fast computational
implementation. However, our framework is suciently
general and can be integrated with any functional data
depth function.
De nition 6. (MBD) LetB(I)be the Banach space of
bounded functions on interval Iandbe the Lebesgue
measure. Given Y=fy1;y2;:::;ymg B(I). The
MBD ofy2B(I)with respect toYis
MBD (yjY) =m
2 1
(I) 1X
1i1i2m(A(y;yi1;yi2))
whereA(y;yi1;yi2) =fx2I:min
r=i1;i2yr(x)y(x)
max
r=i1;i2yr(x)g
Intuitively, MBD (yjY) measures the extent to which
the graph of a function ylies within the bands deter-
mined by the graphs of all possible pairs from Y=
fy1;y2;:::;ymg. MBD enables us to order a set of func-
tions in [0;1]-scale, where the depth values closest tozero and one correspond to the most anomalous and
central functions, respectively. We introduce a concept
ofBetti pivots which is de ned as the deepest or most
central Betti limit.
De nition 7. (Betti pivot) For a given collection of
Betti limitsfBp;t1;Bp;t2;:::;Bp;tmg, their Betti pivot is
de ned as
Bs
p=argmax
Bp;t2fBp;t1;:::;Bp;tmgMBD (Bp;tjBp;t1;:::;Bp;tm)
To measure how the Betti limits change over time and
compare with the ones prior to them, we calculate the
MBD depth of each day's Betti limit with respect to
those of the past wdays. We introduce a notion of
rolling depth (RD) on Betti limits
RDw(Bp;t) :=MBD (Bp;tjBp;t;:::Bp;t w+1): (3.1)
Note that RD2(0;1) and shows the position of the
Betti limit on any given day t, relative to the past w
days. In turn, the Betti pivot yields the most central,
or the "baseline" behavior of Betti limits over a subset
of dayst1;:::;tm. The concept of RD echoes the rolling
window approaches used to detect signals of short and
long term trends in algorithmic trading and to construct
stock price indicators such as percentage price oscillator
and moving average convergence divergence [40].
3.3 Anomaly Detection with Topological Fea-
tures We label a day tas anomalous in Ethereum to-
ken trading, if there is a price shock on day t, that is, if
jRtj, where>0 is a trader-de ned threshold (i.e.,
magnitude of a price shock) (see Problem Statement in
Section 3). We combine new graph topological features
with traditional network summaries and build one pre-
dictive model for each token. We then examine model
performance for di erent prediction horizons h>0.
Our token-based price anomaly detection methodol-
ogy for Ethereum crypto-tokens problem is summarized
as follows. For each day, t, with available token data, we
calculate the binary
ag variable with values equal to
true if price strike in terms of the absolute token price
return (jRtj), has been detected in at least one of
the nexthdays (i.e., days t+ 1;t+ 2;:::;t +h) and
false otherwise. Here, t= 1;:::;Tkis the set of histor-
ical dates for which we have the k-th token data. For
dayt, we compute the token's normalized open price,
PNt=Pricet=maxfPrice 1;:::;Price Tkg. Next, we
construct the user transactions network Gfork-th to-
ken on day t. FromG, we calculate the number of user
transactions E.
Model validity: In our prediction models we use
past information up to and including day tto predict
anomalies for day t+ 1 (i.e., prediction horizon hof 1)
or dayst+h(i.e., longer prediction horizons h,h>1).
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Table 1: Model descriptions
Model Description F: Model Inputs
M1 Baseline PN,nE,nV,GC
M2 Betti 0PN,nE,nV,GC,
RD7(B0)
M3 Betti 0, 1PN,nE,nV,GC,
RD7(B0);RD 7(B1)
M4 Full modelPN,nE,nV,GC,
RD7(B0),RD7(B1); RD 7(B2)
Hence, these experimental settings ensure that no data
leakage occurs.
Filtering. Although Betti numbers provide a non-
parametric solution to combine information on edge dis-
similarity with node connectedness, the computational
complexity of Betti calculations prohibits their usage in
large networks. For example, for 2-simplicial complexes,
\currently no upper bound better than a constant times
n3is known" [16]. To decrease complexity, we induce
a sub-network G0by selecting Kusers who have the
most edges in the network G. This ltering not only re-
duces the network size, but also removes network order
uctuations across time. Di erences in Betti numbers
of daily token networks can now be attributed to edges
and their weights directly. From G0, we then calculate
7-day rolling depth values (3.1) RD7(B0),RD7(B1) and
RD7(B2), respectively.
Rationale behind our modeling approach is that
network topological features, summarized in terms of
RD of Betti limits, add an important layer of infor-
mation that can be missed by the traditional net-
work summaries. Hence, to test the improvement in
anomaly prediction due to adding the network topo-
logical features, we evaluate predictive performance of
the four models listed in Table 1, using normalized
token price (PN), graph based (edge count nE, node
countnV, average clustering coecient GC) and topo-
logical variables (rolling depth values of Betti limits
RD7(B0);RD 7(B1);RD 7(B2)). Models are tted using
Random Forest (see Section 4).
4 Experimental Settings
Dataset. We created our dataset by installing the of-
cial Ethereum Wallet and downloading all blocks. We
used the EthR ( github.com/BSDStudios/ethr ) library
to query Ethereum blocks through the Go Ethereum
Client (i.e., Geth). Our set contains all Ethereum data
during 07/2015-05/2018, with a total of 5.5 million
blocks. Our data and code are available at github.
com/yitao416/EthereumCurves .
By parsing the data, we discovered 1.7K ERC20
tokens which had more than 10K transactions. We
Figure 2: Ethereum token start dates.
included an ERC20 token in our analysis if it had
more than $100M in market value, as reported by the
EtherScan.io online explorer. This choice has resulted
in 31 tokens and is motivated by a goal of developing
veri able prediction results on valuable tokens which
likely will not fail and disappear in a short time.
On average, each token has a history of 297 days,
with minimum and maximum of 151 and 576 days,
respectively. The rst dates of tokens on the Ethereum
blockchain are reported in Figure 2.
Betti Descriptors. We compute the Betti limits for up
top= 2 (i.e.,B0;:,B1;:andB2;:) by using GUDHI (i.e.,
a generic open source C++ library for TDA [28]). We a-
priori setKof 150 in the ltered network approach (see
Section 3.3), as even for the most traded tokens such as
Tronix and Bat, top 150 nodes in daily networks form
75% and 80% of all edges, respectively. The ltered
node approach e ectively removes 20 25% of edges in
Betti calculations, which reduces computational costs.
Prediction Models. We set the rst 2 =3 and 1=3 of a
token's timeline period as training and testing sets, re-
spectively. We report our results based on Random For-
est models which consistently outperform Box-Jenkins
models for all prediction horizons. For example, at 2-
day ahead forecasting, the best Box-Jenkins autoregres-
sive integrated moving average (ARIMA) model with
all predictors (M4) yields a prediction accuracy of 89%,
whereas our results for Random Forest model (M4)
reach 94%.
For space limitations, we detail ARIMA settings
and results in the supplementary material. Each Ran-
dom Forest model uses 500 trees, and sampling all rows
of the dataset is done with replacement. Number of
variables used at each split for all the four models is
the
oor of number of features. The models are imple-
mented using the randomForest package in R.
Finally, for illustrative purposes we set the magni-
tude of the price shock = 0:25, following the guidelines
on the trading cost perspectives by [14]. (For the de-
tailed overview on the trader-de ned choice of and
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associated investment strategies see [3, 10, 6].)
5 Experimental Results
We now illustrate what practical insights the resulting
extracted information on the local geometry and topol-
ogy of the Ethereum transaction graph can bring into
crypto-token analytics.
5.1 Hidden Cointegration of Price and Graph
Topology: How Do Tokens Co-move? Cointegra-
tion refers to a phenomenon when two economic or -
nancial time series follow a common stochastic trend
which is represented as a linear combination of system
shocks [17] { that is, the two time series exhibit a simi-
lar response to shocks. In contrast, hidden cointegration
analysis, as a variant of nonlinear cointegration, allows
to assess a response of the two time series to various
asymmetric system shocks, i.e. upward and downward
movements due to, for example, positive and negative
media news [21].
To develop the best arbitrage trading strategy based
on multiple assets [11], the primary interest of many al-
gorithmic trading platforms is to gain an insight on:
which nancial instruments exhibit joint co-movement
trends?, and what can serve as a sign for future co-
movement patterns? Intuitively, pairs of instruments
that have exhibited co-movements in the past, are like-
lier to show co-movements in the future [27]. Our study
is then motivated by the following queries: Can cointe-
gration in the currently observed local topological struc-
tures of crypto-tokens be a sign for future cointegration
in crypto-token prices? Does this information contain
an additional utility, compared to the cointegration of
the currently observed crypto-token prices?
To address these queries, for each pair of tokens,
we nd their common trading time interval and equally
divide it into two periods. The hidden cointegration
tests [17, 21] are then conducted in both periods for
pairs of crypto-tokens in terms of their i) prices and ii)
Betti descriptors.
As Figure 3 shows, only 9 pairs of crypto-tokens
are cointegrated in price in both training and testing
periods. In contrast, in 15 cases a cointegration in Betti
descriptors in the training period is also re
ected in a
crypto-token price cointegration in the testing period.
Hence, we can conclude that previous cointegration in
Betti descriptors of crypto-tokens might be a stronger
sign for future cointegration in the prices of these
crypto-tokens .
Furthermore, price and Betti cointegrations found
in the training period among the 31 considered tokens
are almost disjoint, with the exception of the civic -
qtum pair. These ndings suggest that local topology
of crypto-token graphs is likely to contain importantcomplementary information to more traditional data
sources such as prices .
(a) Price co-integration in to-
kens.
(b) Betti co-integration in to-
kens.
Figure 3: Cointegrated tokens that are also cointegrated
in future price. An edge denotes cointegration.
Figure 4: Model Accuracy
5.2 Performance in Crypto-Token Price
Anomaly Forecasting We predict price anomalies
in 31 token networks, where a total of 9,042 days
are predicted as anomalous (anomaly:true) or non-
anomalous (anomaly:false). On 145 of these days, a
true price anomaly occurs, as de ned by a change in
the absolute price return of more than 0.25. Mean and
median numbers of anomalies are 6.59 and 2 per token,
respectively. The Veros token had a maximum of 46
anomalies. Nine tokens do not have any price anomalies
in their test period (the last 1/3 of their timeline).
In the days leading up to 2018 January, token prices
exhibit substantial increases; on some days more than
20 tokens show >0:25 absolute price returns. In this
period (Oct-Dec 2017) price of the Ethereum currency,
ether, increased from $305 to $1,389. In 2018 Jan
we see token prices decreasing sharply, but unlike the
increase period, we observe fewer ( 7) anomalies in
tokens on the same day.
Fig. 5 depicts the number of anomaly/true predic-
tions by models. Models M2, M3 and M4 (Betti models)
predict the same 138 days as anomalous. Additional 13
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Figure 5: A Venn diagram for the number of predicted
anomalies in all token networks for h= 2. Intersecting
regions indicate agreement on predictions
days are predicted anomalous by either only Betti model
M2 (6 days) or Betti model M3 (7 days). Betti models
make a lower number of anomaly:true predictions com-
pared to the baseline M1 model, which uses traditional
features such as price and number of edges. For exam-
ple, there are 186 true anomalies ( h= 2, i.e., anomalies
in either of the next two days) in the considered token
networks. M4 makes 146 anomaly/true predictions, and
86 of them are indeed true anomalies. In M1 these val-
ues are 238 and 94, respectively. Compared to M4, M1
predicts 92 more days as anomalous, but only 8 of them
are true anomalies.
Table 2 shows model accuracy values for the top
ten tokens, ordered by average edge counts in daily
networks. Models have high accuracy values, but for
some tokens, such as icon, we reach high accuracy (i.e.,
0.9) with the full model only. We show the accuracy
improvement over the baseline model M1 in Fig. 4. For
up to 7-day horizons, all Betti models have a positive
gain over the baseline model M1. Compared to other
models, the M4 (full) model has the best performance as
horizon increases from 1 to 7. The accuracy results o er
evidence that Betti models are more conservative in
making anomalous day predictions, and their accuracy
is better than the baseline model M1.
The recall results in Fig. 6a show that M3 delivers
the highest gain in recall for all horizons. Fig 6b
depicts the precision results. For hof 1, recall values
are the highest but precision gains are negative. We
achieve the best performance for hof 2, where both
precision (in M4) and recall (in M3) gains are over
20%. As M4 di ers from M3 in its use of B2, we nd
the di ering performance of M3 and M4 in Figures 6a
and 6b interesting. In particular, the results indicate
that the use of B2in M4 decreases the number of
correctly predicted true anomalies, but increases the
number of true anomalies in predictions.
Although predicting true negatives (non-anomalous
days) is useful, the most important task of anomaly
(a) Recall (Sensitivity)
(b) Precision
Figure 6: Performance for increasing horizon values.
detection is to predict true anomalies well in advance.
The unbalanced nature of our dataset complicates this
task; only 1.58% of all days are true anomalies, limiting
the training cases to a few days per each token. For h
of 2, we achieve the highest average precision of 0.393
per token in M4.
6 Conclusions
We have introduced the concepts of persistent homol-
ogy and functional data depth to analysis of a yet un-
tapped source of information on cryptocurrency dynam-
ics: Ethereum transaction graph, and we have inves-
tigated such phenomena as price anomaly forecasting
and hidden co-movement in pairs of tokens (Please see
appendix for more details). Furthermore, we have pro-
posed new functional summaries of topological descrip-
tors, namely, Betti limits and Betti pivots. Our ndings
indicate that Betti pivots of the Ethereum transaction
graph deliver up to 40% improvement in precision over
baseline methods in price anomaly prediction. Based
on our analysis, we advocate that local geometry and
topology of the transaction graph has a high utility in
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Table 2: Accuracy for h= 2 for the top-10 (by edge
count E) tokens.
token M1 M2 M3 M4 E
tronix 0.861 0.962 0.962 0.975 5198.2
omisego 0.890 0.970 0.940 0.990 3027.7
mcap 0.887 0.904 0.913 0.913 1502.1
storj 0.933 0.971 0.952 0.962 1224.3
bnb 0.927 0.969 0.979 0.979 1089.5
zrx 0.955 0.966 0.966 0.978 905.4
cybermiles 0.922 0.961 0.961 0.961 872.7
vechain 0.954 0.966 0.920 0.954 851.7
icon 0.754 0.877 0.877 0.908 783.5
bat 0.965 0.965 0.965 0.965 773.5
such important research directions on blockchain data
analytics as health of the crypto-token ecosystem and
identi cation of malicious trading activities. Further-
more, the newly proposed concepts of Betti limits and
Betti pivots and, more generally, a systematic linkage
of TDA and FDA o er new perspective in data shape
analysis way beyond blockchain applications.
7 Acknowledgments
Gel has been partially supported by NSF DMS 1925346,
IIS 1633331 and ECCS 1824716. Kantarcioglu has been
partially supported by NIH 1R01HG006844, NSF CICI-
1547324, and IIS-1633331.
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A Supplementary Material
This supplementary material consists of two parts. We
provide the generation algorithm for Betti pivots and
rolling depths (RD) on Betti limits, and discussion of
performance of conventional time series models. Sym-
bols are listed in Table 3.
A.1 The Pivot generation algorithm Algo-
rithm 1 details the generation process for Betti pivots
and rolling depths (RD) on Betti limits.
Figure 7 depicts discrete realizations of Betti limits
(i.e., Betti numbers) for the Tronix token on 4 consecu-
tive days in February 2018.
Figure 7: Betti numbers of the Tronix token.A.2 Temporal Models To advocate the use of Ran-
dom Forest classi ers, in this subsection we o er a
glimpse into performance of traditional time series mod-
els based on conventional information sources as well as
Betti pivots of the transaction graph.
In time series analysis and forecasting, Autore-
gressive Integrated Moving Average (ARIMA) model
with exogeneous regressors is a conventional benchmark
choice [ ?]. For each token, we divide the data set into
training and test by ratio 2:1. The ARIMA model is
constructed in the training set to predict anomalies in
the test set.
The optimal ARIMA model is selected based on
the Hyndman-Khandakar algorithm [ ?] which considers
unit root tests, the minimization of the corrected Akaike
Information Criterion (AICc) and maximum likelihood
estimator (MLE). We consider ve models based on the
employed features: the price autoregressive model and
four dynamic regression models with di erent sets of
lagged predictors. The lagged period is experimented
from 1 day to 7 days; the lagged 3 days' predictors have
the best price prediction. The calculation of prediction
intervals is under the conventional assumption that
the residuals are a white noise and follow a normal
distribution.
As Figure 8 shows, all dynamic regression models
outperform the price autoregressive model. Compared
with the benchmark (M1), the models with topological
inputs have much narrow con dence intervals. Espe-
cially, for the shown Bat token, there exists a strong
alignment between the full model prediction (M4) and
the actual price movement.
Copyright c
2020 by SIAM SDM 2020
Unauthorized reproduction of this article is prohibited
Figure 8: ARIMAx performance with topological models in the BAT token. The shaded region shows the vertical
con dence interval ( = 0:05) around the predicted price. The M4 model contains all Betti predictors, and its
predicted con dence interval gives the closest prediction to the actual price.
Table 3: Symbols and notations.
Symbol Explanation
~! extension of !
F feature matrix
h prediction horizon (in days)
min price change for anomaly
scale parameter
C simplicial complex at scale
Rt Price return for day t
nE,nV number of edges, nodes
GC average clustering coecient
PN Normalized price
VR Vietoris-Rips complex at scale
p,Bp,Bs
p Betti-pnumber, limit and pivot
RD and MBD rolling and modi ed band depthAlgorithm 1 Betti Pivots and Rolling Depths (RD) on
Betti Limits Generation
1:procedure number (G: token graph, K: lter,d:
Betti dimension max, w: window)
2: induce graph G0fortop Knodes
3: compute ~!uvfor eache= (u;v)2G0
4: forBetti dimension p = f0,. . . ,dgdo
5: foreach dayG0
t2G0do
6: computeBp;t
7:Bs
p argmax
Bp;t2fBp;t1;:::;Bp;tmgMBD (Bp;tjBp;t1; : : : ;Bp;tm)
8: Ft
p RDw(Bp;t)
9: end for
10: end forreturn feature matrix F
11:end procedure
Copyright c
2020 by SIAM SDM 2020
Unauthorized reproduction of this article is prohibited
|
{
"id": "1912.10105"
}
|
2209.09832
|
Disorder Unleashes Panic in Bitcoin Dynamics
|
The behaviour of Bitcoin owners is reflected in the structure and the number
of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of
Bitcoin traders is reflected in the formation of bullish and bearish trends in
the crypto market. In light of these observations, we wonder if human behaviour
underlies some relationship between the Blockchain and the crypto market. To
address this question, we map the Blockchain to a spin-lattice problem, whose
configurations form ordered and disordered patterns, representing the behaviour
of Bitcoin owners. This novel approach allows us to obtain time series suitable
to detect a causal relationship between the dynamics of the Blockchain and
market trends of the Bitcoin and to find that disordered patterns in the
Blockchain precede Bitcoin panic selling. Our results suggest that human
behaviour underlying Blockchain evolution and the crypto market brings out a
fascinating connection between disorder and panic in Bitcoin dynamics.
|
http://arxiv.org/pdf/2209.09832v1
|
Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, Raffaele Cristodaro, Claudio J. Tessone, Luciano Pietronero
|
physics.soc-ph, cond-mat.stat-mech
|
physics.soc-ph
|
Disorder Unleashes Panic in Bitcoin Dynamics
Marco Alberto Javarone,1, 2,Gabriele Di Antonio,1, 3, 4, Gianni Valerio
Vinci,3, 5,Ra aele Cristodaro,6Claudio J. Tessone,6, 7and Luciano Pietronero1
1Centro Ricerche Enrico Fermi, Rome, Italy
2University College London - Centre for Blockchain Technologies, London, UK
3Istituto Superiore di Sanit a, Rome, Italy
4Universit a degli Studi Roma Tre, Rome, Italy
5Universit a Roma Tor Vergata, Rome, Italy
6Blockchain and Distributed Ledger Technologies,
Institute of Informatics, University of Zurich, Z urich, Switzerland
7UZH Blockchain Center, University of Zurich, Z urich, Switzerland
(Dated: September 21, 2022)
The behaviour of Bitcoin owners is re
ected in the structure and the number of bitcoin transactions
encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is re
ected in the formation
of bullish and bearish trends in the crypto market. In light of these observations, we wonder if
human behaviour underlies some relationship between the Blockchain and the crypto market. To
address this question, we map the Blockchain to a spin-lattice problem, whose con gurations form
ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach
allows us to obtain time series suitable to detect a causal relationship between the dynamics of the
Blockchain and market trends of the Bitcoin and to nd that disordered patterns in the Blockchain
precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain
evolution and the crypto market brings out a fascinating connection between disorder and panic in
Bitcoin dynamics.
Introduction - Blockchain [1, 2] is a distributed
ledger technology introduced by Satoshi Nakamoto [1],
rapidly expanding in many sectors of our society, the
economy and industry. Among the several applications,
cryptocurrencies such as Bitcoin represent the most
successful ones. Bitcoin is a digital currency whose
transactions get managed by a fully decentralised system
that hinges on a blockchain. The latter has a data
structure composed of a chain of blocks. Each block
stores a set of transactions commonly veri ed by block
creators termed miners in this context. Within the
block size limit, the miners can receive an incentive to
add as many transactions as possible. Nevertheless, the
chain of blocks keeps growing no matter the amount of
executed transactions since, in principle, even blocks
with no transactions can be mined and added to the
chain. Cryptographic protocols protect the Blockchain
from double-spending [3] and other risks. Remarkably,
while at money requires third-party authorities, such
as banks, to verify transactions, Blockchain does not
need any additional authority. Over the years, many
blockchains based on new tokens, such as Bitcoin (BTC),
have been implemented. These tokens are also called
cryptocurrencies, or cryptos, due to the underlying
cryptographic mechanisms supporting and securing
transactions. Nowadays, a crypto ecosystem [4{7] which
includes, for instance, Ethereum (ETH), XRP (XRP),
Cardano (ADA), Bitcoin Cash (BCH), Solana (SOL),
Dogecoin (DOGE), Bitcoin Satoshi Vision (BSV), and
These authors contributed equally to this work.
marcojavarone@gmail.commany other tokens, continuously grows. Many cryptos of
such an ecosystem get exchanged in the crypto market
and accessed by several trading platforms. Like in
nancial markets, the crypto market shows positive (i.e.
bullish) and negative (i.e. bearish) trends resulting from
the behaviour of traders. In summary, the behaviour
of Bitcoin users, i.e. wallet owners, traders, and so
on, is relevant for the evolution of the Blockchain
and the crypto market. But several questions remain
unanswered in this complex socio-technical system: Does
human behaviour underlie some relevant relationship
between the Blockchain and the crypto market? The
goal of this investigation is to face this question. To
this end, we map the Blockchain to a spin model, which
allows assessing and measuring interactions with the
crypto market. Before moving to the details of the
proposed model and related results, we remark that the
Blockchain and cryptocurrencies constitute a modern
and expanding research area. Just to cite a few, previous
investigations studied the Bitcoin price dynamics [8{11],
the crypto network of transactions [12{18], the predic-
tive signals [19], using social data [20{23] and machine
learning-based approaches [24, 25], and the interplay
between the network of Bitcoin transactions and the
crypto market [26].
From Data to Model - Datasets used in this inves-
tigation refer to a time interval from 2013 to 2022,
including about 518643 blocks and 730662636 transac-
tions. Blockchain data can be accessed at [27] and crypto
market data at [28]. Blockchain data describe blocks and
contain Bitcoin transactions and other parameters such
as the Timestamp and the Blockheight. For instance, thearXiv:2209.09832v1 [physics.soc-ph] 20 Sep 2022
2
Timestamp corresponds to the time a block gets 'mined'
(i.e. generated), whereas the Blockheight identi es the
position of a block along the chain.
As above-mentioned, we de ne a spin model by map-
ping blocks to vectors (see also [29]). In particular, we
consider the following parameters: the number of trans-
actions, the number of inputs, and the number of out-
puts per block. The number of transactions per block
has a self-explanatory meaning, while the other parame-
ters, which refer to the structure of transactions [2], need
further details. To this end, we describe a simple transac-
tion between Alice and Bob. Alice owns 3 BTC, collected
from previous transactions, and wants to send 2 :5 BTC to
Bob. She previously received: 1 :0 BTC, 0:35 BTC, 0:45
BTC, 0:9 BTC, and 0 :3 BTC, each constituting an 'un-
spent transaction output' (UTXO) for a motivation later
clari ed. To send 2 :5 BTC to Bob, she has to compose a
transaction using a combination of UTXOs, e.g. choos-
ing 1:0 BTC, 0:9 BTC, 0:45 BTC, and 0 :35 BTC, whose
summation equals 2 :7 BTC. The chosen UTXOs consti-
tute the inputs of the new transaction. Then, noting
that the UTXO summation is greater than the amount
of Bitcoin Alice wants to send to Bob, the transaction
has two outputs. The rst output is addressed to Bob's
wallet (i.e. 2 :5 BTC), while the other is to Alice's wallet
(i.e. 0:2). These two outputs, in turn, become UTXOs
that the respective receivers (i.e. Bob and Alice) can
use for future transactions. Detailed information about
the microstructure of the Blockchain, i.e. the content
of its blocks, can be accessed by anyone, albeit the Bit-
coin owners' identity remains preserved. Coming back
to our model, using three parameters, each block gets
represented by a 3-dimensional normalised vector B, and
the Blockchain gets mapped to a one-dimensional lattice.
The resulting structure resembles an n-vector model [30]
withn= 3. Now, we highlight that the content of blocks
cannot change over time, as the Blockchain is an im-
mutable ledger. Therefore, although new spin vectors
add to the chain, those added in the past do not modify
their con guration. Also, spin vectors forming the cur-
rent chain do not a ect spin vectors that will add in the
future. In summary, the Blockchain does not evolve as an
Ising-like model. However, that does not prevent de ning
a Hamiltonian function, for instance, by xing an instant
of time to consider a limited number of spin vectors. In
addition, we may assume that the spin con guration we
observe at a given time represents an equilibrium con g-
uration obtained at some temperature. In general, the
Hamiltonian of a spin model minimises at low tempera-
tures as ordered spin patterns emerge. Similarly, it in-
creases its value at high temperatures as disordered spin
patterns show up.
As detailed below, the formation of ordered and disor-
dered spin patterns o ers valuable information to analyse
the evolution of the Blockchain. Then, the Hamiltonian
of the obtained spin model (see also [31]) reads
H(B;T) =X
i;jJi;j(1 (B|
iBj)) (1)
FIG. 1. Blockchain evolution derived from local Hamiltonian
(H) and its time derivative (dH). The small
uctuations in-
dicate blocks are usually very similar to each other. In July
2015, a clear peak shows up.
withJi;jinteraction weight whose value is set to 0 if
i>j , andBispin vector corresponding to the i-th block
of the chain (the index irepresents the Blockheight and
goes from 0 to T). The scalar products in equation 1 get
close to 1 when consecutive vectors, i.e. blocks, are simi-
lar, otherwise get close to 0. Note that the scalar product
usually can range from 1 to +1. However, according to
the range of values of the selected block parameters, the
scalar product can span the interval [0 ;+1]. Eventually,
to include long-range interactions in the Hamiltonian,
whose amplitude decays with the distance Jt k;te k
,
Equation 1 gets re-written as follows:
H(B;T) =TX
t=01 tX
k=1e k
ZtB|
t kBt (2)
withZt=Pt
k=1e k
. In doing so, each block interacts
with all previous ones. However, the exponential term
weights the interactions between blocks, decaying over
long distances. Such a decay gets controlled by the
parameter . Using the single components which sum
over in equation 2, we obtain a collection of spin con-
gurations that form ordered and disordered patterns.
Lastly, we emphasise that the formation of ordered
and disordered patterns in the 3-vector model can get
exploited for studying the relationships between the
Blockchain and trends of Bitcoin in the crypto market.
Results - The Hamiltonian de ned in 2 can be decom-
posed in single contributions H(B;T) =PT
t=0Ht(B)
forming a time series, to which we refer to as H. The
latter, shown on the top of Figure 1, gets computed
by setting a small enough to include only signi cant
long-range interactions limited to the previous 90 days.
In addition, after applying an exponential moving aver-
age (EMA) to H, we compute its time derivative. The
resulting time series, i.e. dH, is shown at the bottom
of Figure 1. Interestingly, signals in Figure 1 have a
3
FIG. 2. Causality test between HandATH .A) The p-value for Granger causality (using F-test) is shown as a function of
time lags used in the t. Increasing the knowledge of the past of Hin forecasting ATH leads to higher signi cance in the test
(always below p=0.05), whereas the opposite is found when trying to forecast Husing ATH .B) The results of the CCM test
for the same variables. The convergence to an asymptotic value of the reconstruction of the variables, increasing the library
length, indicates causal relation in both directions. The test was performed, for each L, using 400 random points or contiguous
segments. The straight lines are median values, and the error is computed with the 95-th percentile.
prominent peak temporally located around July 2015.
After looking for the possible sources of such a peak, we
found it corresponds to the Flood attack [32], a stress
test performed for testing the Bitcoin network. Both H
anddHcan get used for studying causal relationships
with the crypto market and related phenomena. To
this end, we focus on the BTC=USD ratio (i.e. Bitcoin
in American Dollars) and work on the time series
composed of samples of the percentual drawdown from
the All-Time-High of the BTC=USD ratio. Notably,
these samples equal 1 every time Bitcoin overcomes its
previous historical maximum. Accordingly, the time
series related to the BTC=USD ratio, which we refer
to asATH , andHhave the same range. To study
the causal relationship between the Blockchain and the
crypto market, we use only the Htime series, as H
anddHare strongly related. However, we anticipate
that thedHtime series becomes particularly relevant
in the subsequent analysis. Accordingly, we now aim
to infer a causal relationship between HandATH ,
whose task constitutes a complex and old problem [33].
For this purpose, we consider approaches relying on
statistics and dynamical systems theory. For the rst
case, i.e. approaches based on statistics, we perform the
Granger causality test [34]. Given two variables, xand
y, the Granger causality test compares the forecasting
quality of future values of y, of a standard ARMA
model (Null-hypothesis), with the same ARMA having
additional information on previous values of the variable
x. More in detail, xis said to be Grange-cause of y
whether the quality of the forecasting using information
overxis signi cantly higher (p-value <0:05) than the
quality of the forecasting obtained without x. The
plotAin Figure 2 shows the result of this test, whosevariables are HandATH . In that gure, we report the
p-value as a function of previous data points used in the
t of the ARMA model. Interestingly, the quality of the
t improves as the forecasting of ATH exploits more
information on H, i.e. more historical data, suggesting
a strong causal e ect of HoverATH . The reverse is
not the case since, according to the p-value, we have to
accept the Null-Hypothesis, i.e. ATH has no Granger
causality over H. For the sake of completeness, we also
perform a Cross Convergent Mapping (CCM) [35, 36]
test, which relies on dynamical systems theory and is
deeply related to the Takens theorem and embedding
theory [37]. In this case, we look for a deterministic
causality, which means that if two variables belong
to the same dynamical system, one of them could be
reconstructed by using the other (and vice versa) via
a delayed embedding. Here, we consider the quality
of the reconstruction of a variable ythrough a second
variablex, which we refer to as yjMx. If such a quality
increases with the number of data samples (de ned
as library length) the variable xcausally in
uences y.
Moreover, the faster the convergence to an asymptotic
value of the CCM test, the stronger the dependence
between the considered variables. A critical point of the
CCM test lies in the sample selection to compose the
library. So, following [38], we perform the CCM test
composing the library of samples by a random selection
of contiguous segments and by a random selection of
samples. Results are reported in the plot Bof Figure 2.
Here, we observe that both sampling strategies suggest
a causal relationship between the two variables, i.e. H
andATH , in both directions.
We deem that the di erence between the results
obtained by the CCM test and the Granger causality
4
FIG. 3. A) The trend of the dHtime series. Coloured lines indicate positive (red, above the 98 :5 percentile) and negative
(green, below the 1 :5 percentile)
uctuations. B) Percentual drawdown from the ATH in time. Colored dots identify events
of large
uctuations in dH. Red arrows highlight events followed by a decrease of at least 10% in the next 90 days. Rapid
increases in the Htime series can predict collapses in the BTC price, while fast relaxation may indicate a local market recovery.
C) Sentiment level after dHpositive large
uctuation. In the 83% of cases, these events predate interval in which the BTC
price remains below the initial price most of the time (taken a 90-day time window). D) Boolean of the exceeding the minimum
price change threshold ( 10%, respectively OTH =) after dHpositive large
uctuation. 90% of these signals gets followed
by a price reduction of at least 10% (in a 90-day time window), while only the 29% gets followed by a 10% price growth
highlighting a clear downtrend.
test, i.e. a bi-directional causal relationship and a
one-directional causal relationship, respectively, might
be motivated considering that the Granger causality
test can only detect linear causal relationships. In
light of the above result, we study whether the Htime
series contains information to forecast Bitcoin trends
in the crypto market. Remarkably, rapid variations
ofHpredate large
uctuations of ATH . Therefore,
thedH time series becomes particularly relevant
for quantifying such phenomenon (Figure 3). More
in detail, we observe that rapid increases in the H
time series (i.e. large positive
uctuations of dH)
can predict collapses in the BTC value, while fast
relaxations may indicate a local market recovery |see
plotBin Figure 3. In addition, the 90% of positive
large
uctuations of dHare followed by a reduction of
theBTC value of at least 10% (see plot Din Figure 3).
Conclusion - In summary, this work unveils relevant
relationships between the dynamics of the Blockchain
and the crypto market, focusing on the Bitcoin price.
The investigation, motivated by observing that human
behaviour a ects both the dynamics of the Blockchain
and those of the crypto market, exploits tools from
statistical physics. More speci cally, we generated time
series describing the evolution of the Blockchain via a
spin-lattice model. Such time series allowed us to obtain
the following results. Firstly, we detected a causal rela-
tionship between the Blockchain and the crypto market,
and then we found Blockchain contains information
to forecast some trends in Bitcoin price. Remarkably,disordered patterns in the Blockchain, identi ed via the
spin model, predate the phenomenon of Bitcoin panic
selling, suggesting a fascinating connection between
disorder and panic. Before concluding, let us report a
few observations about some previous investigations.
In [10], authors highlight the potential role of Bitcoin
transactions in driving the Bitcoin trading volume and
price. That is con rmed by our results, as we show
that the number of Bitcoin transactions plays a role
in forecasting the Bitcoin market trends. In addition,
some ideas and outcomes of our investigation remind
works [39, 40], which aimed at forecasting nancial
market trends by looking at Wikipedia and Google
Trends analytics, respectively. Likewise, here we aim to
foresee relevant phenomena in the crypto market, e.g.
panic selling, by exploiting analytics data related to an
external system, i.e. the Blockchain. Finally, we deem
the proposed model sheds light on relevant aspects of
Bitcoin dynamics. Therefore, future works based on
this investigation could address the behaviour of other
cryptocurrencies and assess whether related results can
support the design of trading strategies for the crypto
market.
ACKNOWLEDGEMENT
MAJ wishes to thank Marco Corradino for stimulating
discussions and Mario Bortoli for helpful suggestions.
5
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|
{
"id": "2209.09832"
}
|
2205.08087
|
An Empirical Study of Blockchain Repositories in GitHub
|
Blockchain is a distributed ledger technique that guarantees the traceability
of transactions. Blockchain is adopted in multiple domains like finance (e.g.,
cryptocurrency), healthcare, security, and supply chain. In the open-source
software (OSS) portal GitHub, we observe a growing adoption of Blockchain-based
solutions. Given the rapid emergence of Blockchain-based solutions in our daily
life and the evolving cryptocurrency market, it is important to know the status
quo, how developers generally interact in those repos, and how much freedom
they have in applying code changes. We report an empirical study of 3,664
Blockchain software repositories from GitHub. We divide the Blockchain
repositories into two categories: Tool (e.g., SDKs) and Applications (e.g.,
service/solutions developed using SDKs). The Application category is further
divided into two sub-categories: Crypto and Non-Crypto applications. In all
Blockchain repository categories, the contribution interactions on commits are
the most common interaction type. We found that more organizations contributing
to the Blockchain repos than individual users. The median numbers of internal
and external users in tools are higher than the application repos. We observed
a higher degree of collaboration (e.g., for maintenance efforts) among users in
Blockchain tools than those in the application repos. Among the artifacts,
issues have a greater number of interactions than commits and pull requests.
Related to autonomy we found that less than half of total project contributions
are autonomous. Our findings offer implications to Blockchain stakeholders,
like developers to stay aware of OSS practices around Blockchain software.
|
http://arxiv.org/pdf/2205.08087v1
|
Ajoy Das, Gias Uddin, Guenther Ruhe
|
cs.CR, cs.SE
|
cs.CR
|
An Empirical Study of Blockchain Repositories in GitHub
Ajoy Das
ajoy.das@ucalgary.ca
DISA Lab, University of Calgary
Calgary, CanadaGias Uddin
gias.uddin@ucalgary.ca
DISA Lab, University of Calgary
Calgary, CanadaGuenther Ruhe
ruhe@ucalgary.ca
University of Calgary, Canada
Calgary, Canada
ABSTRACT
Blockchain is a distributed ledger technique that guarantees the
traceability of transactions. Blockchain is adopted in multiple do-
mains like finance (e.g., cryptocurrency), healthcare, security, and
supply chain. In the open-source software (OSS) portal GitHub, we
observe a growing adoption of Blockchain-based solutions. Given
the rapid emergence of Blockchain-based solutions in our daily life
and the evolving cryptocurrency market, it is important to know
the status quo, how developers generally interact in those repos,
and how much freedom they have in applying code changes. We
report an empirical study of 3,664 Blockchain software repositories
from GitHub. We divide the Blockchain repositories into two cat-
egories: Tool (e.g., SDKs) and Applications (e.g., service/solutions
developed using SDKs). The Application category is further divided
into two sub-categories: Crypto and Non-Crypto applications. In
all Blockchain repository categories, the contribution interactions
on commits are the most common interaction type. We found that
more organizations contributing to the Blockchain repos than indi-
vidual users. The median numbers of internal and external users in
tools are higher than the application repos. We observed a higher
degree of collaboration (e.g., for maintenance efforts) among users
in Blockchain tools than those in the application repos. Among the
artifacts, issues have a greater number of interactions than commits
and pull requests. Related to autonomy we found that less than
half of total project contributions are autonomous. Our findings
offer implications to Blockchain stakeholders, like developers to
stay aware of OSS practices around Blockchain software.
CCS CONCEPTS
•Software and its engineering →Software libraries and reposi-
tories .
KEYWORDS
Blockchain, GitHub, Repositories, Bitcoin, Cryptocurrency
ACM Reference Format:
Ajoy Das, Gias Uddin, and Guenther Ruhe. 2022. An Empirical Study of
Blockchain Repositories in GitHub. In The International Conference on Eval-
uation and Assessment in Software Engineering 2022 (EASE 2022), June
13–15, 2022, Gothenburg, Sweden. ACM, New York, NY, USA, 10 pages.
https://doi.org/10.1145/3530019.3530041
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https://doi.org/10.1145/3530019.35300411 INTRODUCTION
Ginni Rometty, CEO, 2019, IBM “What the internet
did for communications, Blockchain will do for trusted
transactions. ”
Blockchain is a distributed ledger technology that stores records
as identical copies on multiple computers to ensure security and
transaction traceability [ 31]. It was invented in 2008 to serve the
public transaction ledger of the Bitcoin cryptocurrency. In 2014,
Marc Andersson wrote a widely cited article in New York Times by
comparing the influence of bitcoin with those of internet during
1993 [ 7]. He predicted that prominent industry vendors would soon
develop critical solutions using Blockchain. Indeed, by 2016, Block-
chain funding overtook Bitcoin, i.e., Blockchain-based solutions
became more diverse than simply offering cryptocurrency-based
solutions [13].
Many Blockchain projects are open-sourced to promote rapid
growth and adoption. As such, we find an increasing number of
Blockchain-based software repositories (denoted as ‘repos’ from
hereon) in the open-source software (OSS) portal GitHub. As of July
2021, GitHub hosts more than 200M repos from over 65M develop-
ers worldwide. Several studies investigated the source code of the
Blockchain software in GitHub, e.g. [ 8]. The studies so far focused
on a specific and small number of Blockchain repos like healthcare
operationalization of three cryptocurrencies [ 27], or the analysis
of 481 Bitcoin repos to learn the Bitcoin-based cryptocurrency as-
pects [ 9]. However, we are aware of no studies that analyzed the
diverse states and user interactions in the GitHub Blockchain soft-
ware ecosystem. Such insights can complement existing studies
with insights into the overall Blockchain software ecosystem.
This paper reports the results of an empirical study to learn
about the Blockchain OSS ecosystem in GitHub. An open-source
Blockchain project tends to be more secure as many people can
verify security. People can trust such OSS projects more, which is a
crucial factor to ensure the success of a Blockchain-based solution.
Given that Github is the biggest platform to host OSS projects, the
Blockchain repos from Github can offer a comprehensive overview
of the states of the Blockchain OSS ecosystem. In our empirical
study, we analyze total 3,664 Blockchain repos from GitHub. Our
goal is to learn about the states of entities (e.g., users) and interac-
tions among the users as observed in the GitHub Blockchain repos.
Our empirical study has two major phases:
•Phase 1. We manually label the 3,664 Blockchain repos un-
der three categories: Tool, Application (Cryptocurrency vs.
non Cryptocurrency-based). This categorization ensures that
we have a better understanding on the type of Blockchain
OSS software systems being developed in GitHub.
•Phase 2. We use the categories and combine those with the
GitHub repo-based metrics (e.g., star rating, forks, interac-
tions, users) to conduct our empirical study with a focus toarXiv:2205.08087v1 [cs.CR] 17 May 2022
EASE 2022, June 13–15, 2022, Gothenburg, Sweden Ajoy Das, Gias Uddin, and Guenther Ruhe
understand the status quo of user engagement and interac-
tions for this special class of software systems.
Our study offers several findings as summarized below:
•We find that Ethereum and Bitcoin Blockchain platforms have
the highest number of projects. The Blockchain tools show more
development activities than the Blockchain applications. Even
though crypto applications are more popular, the development
activity of the crypto and non-crypto applications are mostly
similar. This finding corroborates with a sustained focus on cryp-
tocurrencies and the use of Blockchain to create those. Similar
development activities across crypto and non-crypto indicate
the growing emphasis on applying Blockchain-based solutions
across diverse domains (details in Section 3.1).
•Organizations are contributing more to the Blockchain repos
than the individual users. Many organizations (like Ethereum
with mostly tool-based repos and IBM with mostly application-
based) own multiple Blockchain repos (see Section 3.2).
•We see higher degrees of collaboration among the users in Block-
chain tool repos than those in the application repos. The interac-
tions of commit contribution are higher in number than almost
all other types of interactions. Developers interact more via is-
sues than via the other artifacts (commits and pull requests). This
finding indicates that Blockchain OSS repos hosted in GitHub
are being used by users and the users report their issues of using
the OSS by logging or commenting on issues (see Section 3.3).
•We also find that the contributing users of the Blockchain repos
are not autonomous. This means that many internal developers
generally face restrictions in merging code changes. Therefore,
future measures can be taken to improve the autonomy of the
internal developers in these projects (see Section 3.4).
Our study findings offer implications to diverse stakeholders in
the Blockchain ecosystem: (1) Blockchain vendors to improve auton-
omy in their Blockchain repositories, (2) Blockchain developers to
learn about current trends in Blockchain ecosystem, (3) Blockchain
researchers to offer new tools and techniques for quality assurance
of the Blockchain software and the interactions among users.
Replication Package https://github.com/disa-lab/
BlockchainEmpiricalEASE2022
2 STUDY DATA COLLECTION
2.1 Project Selection
Picking Blockchain OSS repos from GitHub is a non-trivial task
due to the following reasons: (1) There is no standard label/tag/i-
dentifier in a GitHub repo that can be used to decide whether it is a
Blockchain repo or not. (2) GitHub topic (which is introduced as a
tag) is not universally applied to tag each GitHub repo. (3) A GitHub
topic may use any Blockchain-related label other than simply the
keyword ‘Blockchain’. (4) Searching by the keyword ‘Blockchain’
using GitHub search API will not provide all GitHub repo (e.g.,
when the description does not have Blockchain as the keyword) or
may contain false positives (e.g., when the repo simply mentions
Blockchain while referring to a non-Blockchain feature). We, there-
fore, adopted the following process (see Figure 1) to pick a list of
Blockchain repos. We discuss the steps below.
Build a keyword list containing Blockchain related
topics, top Cryptocurrency and Blockchain names,
and popular Blockchain -related keywords
Search for repositories in GHTorrent dataset
containing any of the keywords in the repository
name, description, or topics
Filter out repositories based on metrics such
as popularity measure, activity periodBlockchain -related keywords for search
List of GitHub repos that are potentially Blockchain OSS
Filter out non source code repositoriesList of Relevant Blockchain Repos in GitHub
3,664 Blockchain RepsFigure 1: Steps in Blockchain repos selection process
First, we collect a set of 86 keywords that we can use to search di-
verse Blockchain repos. We produce the keywords using the follow-
ing sources: (1) Top 100 Blockchains-based cryptocurrencies from
CoinMarketCap [ 2] (Bitcoin, Ethereum, Polkadot, etc), to include
top Blockchain platforms in terms of cryptocurrency market capi-
talization; (2) Top permissioned Blockchain names (Hyperledger
Fabric, Corda, etc.), as permissioned Blockchains generally do not
have cryptocurrencies, we need to add these names so that we don’t
miss them while searching for the Blockchain projects; (3) Popular
Blockchain keywords (smart contract, solidity, dapp, etc.), so that
we can include Blockchain-related tools and applications that do not
have the Blockchain names directly in their descriptions. However,
we needed to exclude some Blockchain names from the keyword
list because they were very common words (e.g., icon, waves), and
they produced a substantial amount of false positives in the project
selection step. But, as we also included popular Blockchain-related
keywords (e.g., blockchain, dapp, smart contract), the projects re-
lated to these Blockchains came up in the project selection even
though the Blockchain platform names were filtered out. Second,
we searched the GHTorrent dataset [ 18] with the above keywords.
GHTorrent is an offline database that provides a snapshot of the
GitHub database. We used the latest GHTorrent database that was
available during the time of our analysis (January 2021). For each
repo in GHTorrent, we searched the keywords in the project name,
description, and topic fields. This returned 802K GitHub repos.
2.2 Project Filtering
We then filtered the 802K repos to find relevant and informative
Blockchain repos. Specifically, we followed the same sampling cri-
teria used by Gonzalez et al. [ 17], who previously investigated the
ecosystem of Machine Learning (ML) repositories in GitHub. To fil-
ter out unused, inactive, and non-source code repositories, these cri-
teria have been selected following the best practices [ 19,21,24,26].
The filtering criteria are as follows: (1) Size: A repo must have a size
greater than 0 (KB) (2) Popularity: Must have ≥5 stars (3) Activity:
The last commit of the repo must be on and after January 2019
(4) Data Availability: Project data must be accessible by the GitHub
API and GHTorrent (5) Content: Must be a software project and not
An Empirical Study of Blockchain Repositories in GitHub EASE 2022, June 13–15, 2022, Gothenburg, Sweden
a tutorial, homework assignment, coding challenge, or ‘resource’
storage. This step returned around 5200 GitHub repos. Not all the
repos returned after the third step are Blockchain-related projects
because few non-Blockchain projects also matched with some key-
words that we used to select the projects initially, we also found
non-source code repositories (such as course lectures) after the
above filtering. So, we manually analyzed each of the 5200 repos to
discard irrelevant repositories. This step resulted in our final list of
3,664 Blockchain repos.
3 EMPIRICAL STUDY
In this section, we answer four research questions (RQ) by analyzing
the data of Blockchain software repositories collected in Section 2.
RQ1. How do the popularity and activity across the Blockchain
repos vary? (Section 3.1)
RQ2. How do different users in the Blockchain repositories vary?
(Section 3.2)
RQ3. How do the different users collaborate in the Blockchain
repositories? (Section 3.3)
RQ4. How autonomous are the internal users in the Blockchain
repositories? (Section 3.4)
Like any OSS project in GitHub, a Blockchain project in GitHub
can have two major entities: (1) Repository (repo), (2) User. The
two entities can show diverse status (e.g., user growth). Therefore,
our RQ1 attempts to offer an understanding of the popularity of
the Blockchain repos in GitHub based on their growth and user ac-
tivity. Given the Blockchain stakeholders can be diverse in GitHub,
RQ2 offers an empirical evidence of the distribution of the different
users observed in the GitHub Blockchain repos and RQ3 investi-
gates how the users collaborate while RQ4 offers insights into how
autonomous the internal users to a repo are, i.e., whether they can
make progress without much help from external users to a repo.
3.1 RQ1. How do the popularity and activity
across the Blockchain repos vary?
3.1.1 Approach. First, we divide the Blockchain repos into two
categories: (1) Tool (i.e., offering a stand-alone API/SDK with Block-
chain algorithm/model), and (2) Application (i.e., offering software
to address specific use case scenario). The definition of the cate-
gories is taken from Gonzalez et al. [ 17]. Such categorization can
also offer deeper insights into our Blockchain projects. We thus
attempted to categorize our Blockchain repos into two categories
(i.e., Tools and Applications). We can divide the blockchain appli-
cations into two subcategories. Applications that focus on cryp-
tocurrencies that have direct monetary value, applications that
focus on encompassing blockchain features in other various do-
mains (health, business, law, entertainment, etc). Given the current
focus on Blockchain-based cryptocurrencies, we divided the ‘App-
lication’ category into two sub-categories: (1) Application Crypto,
and (2) Application Non-Crypto/Others , to find out whether these
application categories are in fact separate in terms of different met-
rics (e.g., development activities), or we can consider them as a
single group in future studies.
We labeled each Blockchain repo to one of the three categories
in two steps. (1) Automatic categorization: We attempted to label
a repo automatically using a suite of keywords (see Table 1). TheTable 1: Keywords to categorize the Blockchain repos
Category Keywords
Tool tool, library, stack, client, node, protocol,
helper, utility, , evm, scripts, package
framework, sdk, service, ide, api, miner
Applications exchanges, trading platform, trading bot,
(Crypto) arbitrage, mining pool, payment channels,
crypto/coin/currency/token related,
wallet, wallet extensions
Applications not categorized in tool or crypto application
(Others)
Table 2: Agreements between coders
Iteration ID # of Repositories Cohen 𝜅
1 40 0.63
2 40 0.80
Table 3: Summary of studied 3,664 repos in our dataset
Project Type #Repos # Orgs # Users
Blockchain Tools 2,470 1,536 934
Applications (Crypto) 546 253 293
Applications (Others) 648 358 290
Total Blockchain Projects 3,664 2,147 1,517
list of keywords is picked based on our manual analysis and by
consulting the keywords for the ‘Tool’ category from Gonzalez et
al. [17]. The automated approach was able to categorize about only
25% of the dataset. (2) Manual labeling: Even though we used an
automated approach, we checked each repo manually to determine
its label. This helped us to fix any misclassification that happened at
the automatic categorization step as well as classify the repos that
the automated approach was not able to classify. A total of three
human coders participated in the labeling process. First, two of the
authors consulted in two iterations to create a labeling guide. In the
first iteration, both authors separately labeled 40 repos. They then
computed the agreement between them using Cohen 𝜅value and
resolved disagreements by discussing them together. The Cohen 𝜅
value was 0.63 which is considered substantial (see Viera et al. [ 37]).
This process also helped them update the labeling guide. In the
second phase, both authors again separately labeled 40 repos and
repeated the process of agreement calculation and disagreement
resolution. The Cohen 𝜅value was 0.80. Therefore, after this stage,
any of the authors could continue with the manual labeling of the
rest of the repos without introducing any individual bias. The first
author then completed the rest of the manual labels. Nevertheless,
we further validated the manual labels by consulting with a third
coder, who is not an author of this paper.
In a Github repository, the owner of the repository can set multi-
ple words describing the summary of a repository, these are called
EASE 2022, June 13–15, 2022, Gothenburg, Sweden Ajoy Das, Gias Uddin, and Guenther Ruhe
topics of the repository. Topics generally mean what the repository
is about. For example, Blockchain-based projects use blockchain,
cryptocurrency, etc. as their topics. We also find these topics in the
GHTorrent [ 18] dataset that we used to calculate the most common
topics across different repository types.
3.1.2 Results. During the manual labeling step (described in Sec-
tion 2.1), we categorized each repository in the dataset into the
Blockchain platform it belongs to. We found in total 71 Blockchain
platforms in our 3,664 repos (e.g., Ethereum). Figure 2 shows the top
10 of these platforms based on the total number of repos. Ethereum
wins the race, followed by Bitcoin, then follows the “others” cat-
egory (which denotes the rest of the Blockchain-based projects
that we have not explicitly monitored during the classification of
Blockchain platforms). Then comes the multi-Blockchain projects;
these projects are those projects that used or targeted more than
one Blockchain platforms in their development phase.
ethereumbitcoin othersmultieos
hyperledgerfabricstellarbinance monerotezos051015202530Percentage # of all repositories
Figure 2: Top Blockchain platforms by # of projects
Top four topics per project category based on all the repos are:
(a) Tools: blockchain, ethereum, bitcoin, solidity; (b) Applications
(Crypto): bitcoin, ethereum, cryptocurrency, wallet; (c) Applications
(Others): ethereum, blockchain, bitcoin, smart-contracts. Overall,
we can see some cross-cutting topics like Ethereum and Bitcoin
which are mainly Blockchain platform names and these platforms
are used in Blockchain-based application development. We also
see specific topics like ‘wallet’ in Application Crypto, which is to
develop a virtual wallet for the cryptocurrencies.
In Figure 3, we show the violin plot distribution of two popularity
metrics (# of stars and # of forks) per category. Overall, the median
values are similar across the three categories. Still, the differences
in quartiles are greater between Tools and Applications Others,
which are also two categories with the most repos.
In Figure 4, we show the distribution of the commits, issues, and
pull requests per repository type. We can see that, just like the
above popularity measures, tool repos have a higher number of
commits, issues, and pull requests than the application repos.
We performed Kruskal-Wallis tests on both the popularity and
activity measures and found that in both cases distribution of
the measures is not the same across the repository types (p <
0.001). But from the Dunn’s tests (Bonferroni adjustment), we see
both for the # of stars and # of forks, the distributions of Tool
repositories are not significantly different than the Application
Tools Applications
(Crypto)Applications
(Others)020406080100Stars
Tools Applications
(Crypto)Applications
(Others)01020304050ForksFigure 3: Distribution of the repos by # of stars and # of forks
Crypto repositories (p >0.05). On the other hand, we found from
the Dunn’s tests that the distribution of the activity measures (all
of the commits, issues, and pull requests) of the crypto applications
are not different from the non-crypto ones (p >0.05). The rest of
the distributions are significantly different from each other. Hence,
we can say, though the popularity of the crypto applications is
similar to the popularity of tools repos, the development activities
of the crypto applications are primarily identical to non-crypto
applications.
RQ1. How do the popularity and activity across the
Blockchain repos vary? (1) Ethereum (31.7%) and Bitcoin
(15.9%) projects occupy a good portion of the total repositories.
(2) The tools based Blockchain repos show more dev activities
(e.g., issue creation) than the application based Blockchain
repos. (3) Though the crypto applications are more popular,
the development activity of the non-crypto applications are
mostly similar to crypto applications.
3.2 RQ2. How do different users in the
Blockchain repositories vary?
3.2.1 Approach. We got the ownership data of the repositories
from the dataset itself. But finding out the internal and external
users was not a straightforward task. Following the steps discussed
by Gonzalez et al. [ 17], we calculated and summarized each reposi-
tory user’s contribution such as how many issues, pull requests, or
commits the user created, how many of them are closed/merged by
the user himself/herself, how many of them have been processed by
other users instead, etc. After summarizing the degree of a user’s
contribution in a repository, we categorized them into internal and
external users. We marked the internal users with significant par-
ticipation in processing other users’ issues or pull requests as the
maintainers of the repository.
3.2.2 Results. The distributions of project owners by three project
categories are shown on Table 3. We also indicate the number of
total repos, the total number of contributing organizations, and
the total number of users that created those repositories for each
project category. The number of contributing organizations is 2,147
among the total 3664 repositories demonstrating a significant focus
on Blockchain OSS in GitHub from the software vendors.
An Empirical Study of Blockchain Repositories in GitHub EASE 2022, June 13–15, 2022, Gothenburg, Sweden
Tools Applications
(Crypto)Applications
(Others)100
0100200300400500600700Commits
Tools Applications
(Crypto)Applications
(Others)0255075100125150175Issues
Tools Applications
(Crypto)Applications
(Others)020406080100Pull Requests
Figure 4: Violin plots for # of Commits, # of Issues, and # of Pull Requests per repo category
Tools Applications
(Crypto)Applications
(Others)05101520Contributing Users
(a) Distribution of internal users
Tools Applications
(Crypto)Applications
(Others)0510152025External Users (b) Distribution of external users
Figure 5: Distribution of (a) Internal users and (b) External
users (outliers omitted)
As we have shown in Table 3, organizations own most of the
Blockchain projects of our analysis (58.6%), the rest are owned by
individual user accounts. In Blockchain repositories, 501 accounts
own more than one repositories (20.9% of the dataset), and 86 ac-
counts own at least 5 repositories (3.6% of the dataset). In fact, the
majority of GitHub accounts with more than five Blockchain repos
are organizational accounts. The top 3 accounts with most Block-
chain repositories are Ethereum (46 repos), IBM (32 repos), and
input-output-hk (30 repos). Here we see, Ethereum is the topmost
contributor, followed by IBM. The organizations are working on
multiple Blockchain-based solutions in GitHub, which signifies
the significant focus on the Blockchain OSS ecosystem from the
industry. Some of these organizations (e.g., Ethereum, Stellar) are
dedicated to support specific Blockchain platforms, while others
offer Blockchain-based solutions (e.g., IBM). The top organization
Ethereum, has mostly tool-based repositories as it works on sup-
porting the infrastructure of the Ethereum Blockchain platform.
On the other hand, IBM works on multiple Blockchains with most
of its repositories are non-crypto applications (56.3%).
Figures 5a and 5b show the distribution of internal and external
users across Blockchain project categories. We see that tool reposi-
tories has higher number of both external and internal users than
the application repositories.
Among the Blockchain tool repos, bitcoin [ 1] has the most in-
ternal users (614) and go-ethereum [ 3] has the most external users
(4402). Among the crypto application repos, gekko [ 36] has the
most internal users (199) and metamask-extension [ 4] has the mostexternal users (3013). Among non-crypto application repos, con-
denser [ 6] has the most internal users (82) and monero-gui [ 5] has
the most external users (560).
The performed Kruskal-Wallis tests denote that the distribution
of the users is significantly different across repo types. But the
Dunn’s tests with Bonferroni adjustment show that the distribution
of the internal users for crypto applications are not significantly
different than the non-crypto repos (p >0.05). On the other hand,
for the tools and crypto applications, the distributions of the
external users do not differ significantly (p >0.05).
RQ2. How do different users in the Blockchain reposi-
tories vary? (1) We find more organizations contributing to
the Blockchain repos than individual users. (2) Organizations
like Ethereum and IBM own multiple Blockchain repos. While
Ethereum repos are mostly tool-based, IBM repos are mostly
application-based. (3) The median numbers of internal and
external users in tools are higher than the application repos.
3.3 RQ3. How do the different users collaborate
in the Blockchain repositories?
3.3.1 Approach. We quantify the collaboration among users in a
repo based on five types of interactions defined by Gonzalez et
al. [17]: (1) Contribution, (2) Maintenance, (3) Process, (4) Review,
and (5) Discussion. Description of each of these interaction types
have been provided in Table 4. The goal then is to compute how
frequently users show the above interaction types when interacting
with each other in a repository.
3.3.2 Results. We find that median number of the user per artifact
per repository for (a) Tools are Commits (15.5), Issues (10), Pull
Requests (7); (b) Applications (Crypto) are Commits (10), Issues
(8), Pull Requests (4); (c) Applications (Others) are Commits (12),
Issues (9), Pull Requests (7). Overall, users’ median per artifact is
higher in the Blockchain tools (across all three categories) than the
Blockchain application repos.
In Figures 6, 7, and 8, we quantify the collaboration among
different users in a repo based on the five interaction types defined
in Table 4: (a) Code contribution (blue bar) denotes the frequency
of interaction between the author of a commit and the committer
of that commit. We see that this interaction is the most prevalent
in both tools and non-crypto applications. (b) Maintenance
EASE 2022, June 13–15, 2022, Gothenburg, Sweden Ajoy Das, Gias Uddin, and Guenther Ruhe
Table 4: Interaction types used to measure Collaboration
Type Description
Contribution Interaction that happens between the author
and the committer of a commit.
Maintenance Interaction that happens between two users that
initiate any event (i.e. approve, except
commenting) for the same artifact (issue/
pull request), and any user who is not the
creator (opener/reporter) of the artifact.
Process Interaction that happens between the creator
(opener/reporter) of the artifact and another
user who initiates a maintenance event.
Review Interaction that happens between a user
who comments on an artifact and it’s creator
(author/reporter/opener).
Discussion Interaction that happens between two users
who comments on an artifact and the users
who are not the creator of the artifact.
(brown bar) denotes the frequency of interaction between two
users to approve/comment artifacts like pull requests. We see
issue maintenance interactions are higher than the pull request
maintenance in all repo types. (c) Process (green bar) denotes the
frequency of interactions between the creator and a maintainer
of an artifact. In all repo types, interaction in issue processing
is higher than the pull request processing. (d) Review (red bar)
denotes the frequency of interactions between the creator and a
commenter of an artifact. For tools and non-crypto applications,
reviews in pull requests are higher than the reviews in issues.
(e) Discussion (purple bar) denotes the frequency of interactions
between two users who are not the artifact’s creator but who
nonetheless commented on the artifact. Discussion is the most
prevalent interaction for crypto applications. We see that almost all
of the collaborative interaction types are higher in tool repositories
than the application repos (note that the scale is different in each
graph).
commits issues pull_requests020406080100120140Interaction per Artifactcontribution
maintenanceprocess
reviewdiscussion
Figure 6: Collaborative interactions per artifact in Block-
chain Tool repositories (outliers omitted)
commits issues pull_requests020406080100Interaction per Artifactcontribution
maintenanceprocess
reviewdiscussionFigure 7: Collaborative interactions per artifact in Block-
chain Crypto Applications repositories (outliers omitted)
commits issues pull_requests020406080Interaction per Artifactcontribution
maintenanceprocess
reviewdiscussion
Figure 8: Collaborative interactions per artifact in Block-
chain Others (Non-Crypto) Applications repositories (out-
liers omitted)
RQ3. How do the different users collaborate in the
Blockchain repositories? (1) We observe higher degrees
of collaboration (e.g., for maintenance efforts) among users
in Blockchain tools than those in the application repos. (2)
The amount of interactions that happened in commit contri-
bution are higher in number than almost all other types of
interactions. (3) Among the artifacts, issues has overall greater
number of interactions than commits and pull requests.
3.4 RQ4. How autonomous are the internal
users in the Blockchain repositories?
3.4.1 Approach. To measure the autonomy of a repository, Gon-
zalez et al. [ 17] divided the users of a repo into (1) Maintainer,
(2) Autonomous contributor, and (3) Dependent contributor. The
definition of these user types is provided in Table 5. The goal is to
find the distribution of the above user types in a repository. Based
on the percentage of users that belong to each group, the autonomy
of the whole project is calculated.
3.4.2 Results. In Figure 9, we show the proportion of three user
types (maintainer, autonomous and dependent contributors as
defined in Table 5) across the Blockchain repo categories. We
see that: (1) The proportion of maintainers in a repo is higher in
crypto applications than both tools and non-crypto applications.
An Empirical Study of Blockchain Repositories in GitHub EASE 2022, June 13–15, 2022, Gothenburg, Sweden
Table 5: User types used for Autonomy calculation
Type Description
Maintainer A user who has merged or closed artifacts
(issues and/or pull requests) opened by others.
Autonomous A user who committed majority of his/her
Contributor commits and also and/or self-merged
majority of his/her pull requests.
Dependent A user whose majority of the commits were
Contributor not committed by others, and/or other users also
merged/closed majority of his/her pull requests.
(2) The proportion of autonomous users are the same across all
of the repo types. (3) The proportion of dependent contributors
are also the same across all types of repositories. Overall, we see a
lower proportion of autonomous users but a higher proportion of
dependent contributors across all Blockchain repo categories. This
makes the Blockchain repos less autonomous, indicating more
restriction in the Blockchain repos to make quick code changes.
On the other hand, we can find some inherent reasons for this less
autonomy in the Blockchain repositories. Blockchain networks
are generally distributed networks, and no central authority has
control over them. So, updating the network for fixing a bug
becomes difficult because all the relevant parties need to update
the software simultaneously across the network. So making the
systems fault-tolerant and bug-free early on is very important
for Blockchain-based projects, as merging buggy code into the
codebase can be fatal. Hence multiple reviews are generally
performed before the code changes are approved and merged,
which in turn lessen the autonomy of the developers.
RQ4. How autonomous are the internal users in the
Blockchain repositories? (1) There are no significant dif-
ferences in the autonomy of the teams across all repo types.
Less than half of total project contributions are autonomous,
that means that the development teams in Blockchain reposi-
tories are not autonomous in terms of pushing changes to the
code base. (2) The proportion of autonomous and dependent
contributors are equal.
Tools Applications
(Crypto)Applications
(Others)0.00.20.40.60.81.0Proportion of Total Project ContributorsMaintainers Dependent Autonomous
Figure 9: Distribution of user types per project typeTable 6: Summary of Blockchain (BC) vs Non-Blockchain
(Non-BC) Metrics in the studied 7,328 repos in our dataset
MetricsBC Non-BC
Avg Median Avg Median
Commits 107 53 32 18
Issues 30 12 9 4
Pull Requests 19 8 5 3
Internal Users 5 4 3 3
External Users 3 1 3 1
Maintainers (%) 43 44 54 50
Autonomous Contrib (%) 32 33 35 40
Dependent Contrib (%) 34 33 24 25
4 DISCUSSIONS
4.1 Blockchain Vs Non-Blockchain
Repositories
Blockchain is a relatively new technology; it is important to know
whether and how the findings we observed could differ from the
non-Blockchain projects. We, therefore, randomly pick an equal
number of non-Blockchain repos (i.e., 3664) from GitHub to com-
pare these two categories. In this subsection, we compare both the
states and the interactions of different entities in Blockchain vs.
non-Blockchain projects using total 7,328 GitHub repos.
Data Collection. At first, we sampled 10k repositories from
Github using Github API after applying the same filtering crite-
ria as of those applied for the Blockchain repositories (described
in Section 2.2). Then we randomly selected an equal number of
non-Blockchain repos (i.e., 3664) for the non-Blockchain GitHub
repository set from the filtered set of 10k repos after removing any
Blockchain repositories from this list.
We show the summary of the calculated metrics of the Block-
chain vs. non-Blockchain repos in Table 6. We can see in Table 6,
the Blockchain repos have a higher number of commits, issues,
and pull requests than the non-Blockchain repos in our dataset,
denoting that Blockchain-based repos in our database are more
actively developed than the non-Blockchain repos. There could be
several reasons for this:
•Larger investment in Blockchain projects in terms of both
time and resources;
•The domain is still actively growing, so rapid development
is going on;
•Blockchain industry’s one of the key selling points is fault
tolerance of the systems, and to catch the bugs in the de-
velopment phase, the artifacts (such as commits and pull
requests) go through a lot of processing;
Users. We find that organizations own 2,147 (58.6%) repos among
3664 Blockchain repos. In comparison, only 913 (24.9%) repos of
3664 non-Blockchain repos are owned by organizations which de-
note that organizations have a significant focus on the Blockchain
software ecosystem. From Table 6 we see, the number of both in-
ternal and external users is higher in the Blockchain repos.
EASE 2022, June 13–15, 2022, Gothenburg, Sweden Ajoy Das, Gias Uddin, and Guenther Ruhe
commits issues pull_requests0510152025Interaction per Artifactcontribution
maintenanceprocess
reviewdiscussion
Figure 10: Collaborative interactions per artifact in Non-
Blockchain repositories (outliers omitted)
Interactions. Comparing Figure 10 with Figures 6, 7, and 8 we
see that: (1) The median of Contribution (blue bar) frequencies for
Blockchain repos are between 15-20, but it is around 4 for Block-
chain repos. (2) The median of Maintenance (brown bar) is around
5-10 for Blockchain repos and 1-2 for non-Blockchain repos. This
indicates that more non-authors collaborate in the Blockchain arti-
fact maintenance process. (3) The median for Process (green bar) is
around 5 for Blockchain repos and 1-2 for non-Blockchain repos.
(4) The median of Review (red bar) is around 5-10 for Blockchain
repos and 1-2 for non-Blockchain repos. (5) The median for Dis-
cussion (purple bar) is around 10 for Blockchain repos and 2-3 for
non-Blockchain repos. Thus, across all the five interaction types,
we observe higher degrees of collaboration among the users in
Blockchain repos than in non-Blockchain repos.
Autonomy. In Table 6 we see that: (1) The proportion of main-
tainers in a repo is similar between Blockchain and non-Blockchain
repos. (2) The proportion of autonomous users is more in non-Block-
-chain repos (40% vs. 33% in Blockchain repos). (3) The proportion
of dependent contributors is more in Blockchain repos (33% vs. 25%
in non-Blockchain repos). Therefore, the lower proportion of au-
tonomous users but a higher proportion of dependent contributors
make the Blockchain repos less autonomous, which indicates more
restriction in the Blockchain repos to make quick code changes.
4.2 The Case of Archived Blockchain Repos
In software teams, obsolete projects are generally archived. Given
Blockchain is a rapidly evolving paradigm, it is expected that inno-
vative ideas are rapidly implemented in the Blockchain software
ecosystem, with little or no guidance and previous expertise of
developing such solutions. As such, it may happen that some ideas
are less successful than others, resulting in their archival. We in-
vestigated whether a repo is archived using two approaches:
(1) We used Github API to search for the repositories that are
marked as archived (2) We searched for relevant keywords (e.g.,
deprecated, obsolete, archived, no longer maintained, etc) in the
Readme file of the repo. We find total 332 archived Blockchain
repos and 316 archived non-Blockchain repos. Thus, we find a
slightly higher proportion of archived repos in Blockchain than in
non-Blockchain repos (9.1% vs 8.6%).
In Figure 11a, we show the distribution of the archived repos over
time based on their creation time. For the archived Blockchain repos,
2010 2012 2014 2016 2018 2020020406080100120# of New Archived Repositories
Blockchain
Non Blockchain(a) Trend of project archival
2008 2010 2012 2014 2016 2018 20200200400600800100012001400# of New Repositories
Blockchain
Non Blockchain
(b) Trend of project creation
Figure 11: Trend of (1) Archiving Projects (2) New projects
we see a notable upswing between 2017-2018. No such upswing
is visible for the non-Blockchain archived repos. To investigate
the reasons of the upswing in 2017-2018 for the Blockchain repos,
we checked the evolution of all the repos based on their creation
time (see Figure 11b). For both the Blockchain and non-Blockchain
repos (archived + non-archived), we see an upswing between 2017
- 2018, where the upswing is more prominent for Blockchain repos.
This denotes that the more Blockchain repos got created in 2017 -
2018, the more Blockchain repos also got archived at the time. This
indicates rapid evolution and implementation of new ideas in the
Blockchain GitHub repos, which led to the creation of more new
repos as well as the closure (i.e., archival) of old repos compared to
the Blockchain repos.
We analyze the reasons of archival in the repos using 200
randomly sampled repos, 100 each from Blockchain and non-
Blockchain types. The sample size for each type is statistically
significant with a 95% confidence level and 10 confidence interval.
We then checked the description (i.e., Readme) file of each sampled
repo, where the repo owners could provide their archival reasons.
The first two authors analyzed the reasons together. We observed
total of four archival reasons: (1) New repo migration denotes
when a project development has been moved to a new repo due to
change of ownership/name, rebuilding of the project from scratch,
etc. (2) Resource unavailability denotes the lack of resources
(e.g., developers, funding) to continue a repo. (3) Obsolete depen-
dency denotes that an essential dependency (e.g., a Blockchain
platform or an API/SDK) for a repo became obsolete, which led the
project owner closing the project. (4) Obsolete usecases denotes a
project is no longer needed because its use cases are no longer rele-
vant in the evolving Blockchain ecosystem. In Table 7, we present
the distribution of five archival reasons categories across the 200
sampled repos. The archival reasons for more than 50% repos are
An Empirical Study of Blockchain Repositories in GitHub EASE 2022, June 13–15, 2022, Gothenburg, Sweden
Table 7: Distribution of the archival reasons (in %) of repos
Reason #Blockchain #Non Blockchain
New repo migration 16 10
Resource unavailability 3 3
Obsolete dependency 4 4
Obsolete usecases 26 24
Not mentioned 51 59
not provided (see ‘Not Mentioned’ in Table 7). Except that, the two
other major archival reasons are ‘Obsolete usecases’ and ‘New repo
migration’, both of which are observed more in the Blockchain
repos.
4.3 Implications of Findings
Our study findings can be instrumental to following stakeholders.
(1)Block chain Vendors. We find that majority of Blockchain re-
pos in our dataset are owned by organizations (e.g., IBM, Ethereum),
which is more than the user-owned repos (58.6% vs. 41.4%). Com-
pared to the non-Blockchain repos, users in Blockchain repos show
less autonomy while pushing changes in the codebase. This could
indicate that the Blockchain OSS ecosystem is closely guarded by
few users and industry vendors, which then can create a bottleneck
while making rapid progress. Therefore, Blockchain repo owners
in GitHub can create an environment with less restriction so that
users can enjoy more autonomy while creating features.
(2)Block chain Developersmay expect more development activ-
ities and collaborative interaction in tool repos than the application
repos. As the development teams of the repos are not autonomous,
the developers may expect some restrictions (i.e., multiple review
cycles) in applying code changes to the repos.
(3)Block chain Career Enthusiasts can take note of the growth
of Blockchain repos to stay aware of this rapidly evolving ecosys-
tem. Blockchain repo creation peaked in 2016 - 2018, while we a
high growth rate in the Blockchain repos in 2017 - 2018 (Figure 11b).
Such insights can help career enthusiasts to make decisions like
when to make a career change and when to invest in the ecosystem.
(4)Block chain Researchers. We observed much more interac-
tions of Blockchain users around issues in GitHub (see Figures 6, 7,
and 8). This denotes that quality assurance of Blockchain repos is
getting attention from Blockchain users. While significant research
in Blockchain-based research has focused on improving security
(e.g., smart-contracts [ 10,14]), our results show that research can
develop tools to improve the autonomy in the repos. Autonomy
can be improved by improved collaboration and the generation
of automatic documentation for the Blockchain repos/APIs, e.g.,
by automatically expanding the documentation from online re-
sources [ 33,34], where discussions and reviews about new and
emerging techniques are readily available [12, 32]).
5 THREATS TO VALIDITY
Internal validity threats relate to the author bias while conducting
the analysis. We mitigated the bias in our manual labeling of Block-
chain repos by using three human coders. We report the Cohen 𝜅agreement between the first two coders in two iterations, which
show the substantial to a perfect agreement (according to Viera et
al. [37]) between the coders. Construct Validity threats related
to the potential errors in the study methodologies. To identify the
Blockchain-related projects, we used both topics[ 17] and the project
description metadata and followed best practices to analyze GitHub
repos (e.g., [ 17,19,26]).External Validity threats relate to the
generalizability of the findings. We study Blockchain projects that
are hosted publicly in GitHub. We followed an extensive sampling
process to collect the Blockchain and non-Blockchain repos (see
Section 2.1). As we noted in Section 2, we picked our Blockchain
repos based on a suite of 86 Blockchain-specific keywords. This
approach was necessary because there is no other way to find the
list of Blockchain repos in GitHub. Although GitHub offers the
‘topic’ feature to tag a repo, not all repos are tagged by the topics.
Many of the Blockchain repos we found do not have any topic tag.
Our keyword-based approach returned 802K GitHub repos. This
large number of repos indicates that our keyword-based approach
is sufficient to find a good number of Blockchain repos.
6 RELATED WORK
Related work can broadly be divided into two types: Studies to
understand the trends in Blockchain software and Techniques de-
veloped to address problems in Blockchain-based solutions.
Studies.In this research, we study the evolution of Blockchain
technology from the perspective of OSS development in GitHub.
Several recent empirical studies on Blockchain used GitHub data.
There have been several systematic literature reviews that dis-
cussed the ongoing software engineering-related research regard-
ing blockchain-based software development [ 16,35]. This includes
testing and analysis of smart contracts, performance and security
of DApp, analyzing architecture types used during the blockchain-
based software development, etc. However, no previous studies
performed an empirical analysis of blockchain-based repos in the
wild. Trockman et al. [ 8] report one year of development activity of
around two hundred cryptocurrencies and observe that the popu-
larity of the repos is associated with a higher market capitalization
of the corresponding platforms. Zheng et al. [ 39] conducted an ex-
tensive survey on Blockchain taxonomy, consensus algorithms, and
technical challenges. Reyna et al. [ 29] investigate the challenges and
potential applications for block-chain related IoT applications but
do not analyze the development activity of the Blockchain projects.
Tech niques. Analysing and finding vulnerabilities in Block-
chain is an active research area [ 23,28]. There are also research
opportunities in Blockchain scalability, i.e., improving throughput
and reducing latency for cryptocurrencies [ 38,40]. Researchers
have also presented tools such as Vandal [ 10], ContractFuzzer [ 20],
TEETHER [ 22] to automatically detect security vulnerabilities in
Blockchain smart contacts.
While the above studies limit to either cryptocurrencies or spe-
cific domains (e.g., healthcare), we study the overall Blockchain
OSS ecosystem in GitHub based on a large sample of Blockchain
repos. Similar to our study, many of the recent studies [ 11,15,17]
study Collaboration among developers. Some recent analysis by
Google [ 25] and Microsoft [ 30] showed that Autonomy is a crucial
factor for productivity. We thus study both collaboration and au-
tonomy of the users in the Blockchain repos. While they studied
EASE 2022, June 13–15, 2022, Gothenburg, Sweden Ajoy Das, Gias Uddin, and Guenther Ruhe
the states on the ML universe in GitHub, we study the Blockchain
OSS ecosystem in GitHub.
7 CONCLUSIONS
The promise of complete transparency, traceability and data im-
mutability has made Blockchain a promising architecture to use
in software for diverse domains like finance (e.g., cryptocurrency),
supply chain management, security, etc. Given that this is a new
but rapidly evolving ecosystem, it is important to understand the
states of the Blockchain ecosystem. We conduct an empirical study
by analyzing 3,664 Blockchain repositories in GitHub. We find that
this domain is growing rapidly just after the release of the first
Blockchain project in 2010 that peaked up in 2016 - 2018, when
Blockchain-based solutions are adopted in diverse domains besides
cryptocurrency. We find significantly more presence of large orga-
nizations in the Blockchain repos, who own multiple Blockchain
repos in GitHub. Moreover, we also see less autonomy of users in
the Blockchain repos. With a view to foster further innovation in
Blockchain OSS ecosystem, in our future work, we aim to study
more the causes of low autonomy in the Blockchain repo and de-
velop guidelines and tools to improve the user autonomy.
ACKNOWLEDGMENTS
Gias Uddin and Ajoy Das were supported by NSERC Grant (RGPIN-
2021-02575).
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|
{
"id": "2205.08087"
}
|
2405.19762
|
The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention
|
Current methods to prevent crypto asset fraud are based on the analysis of
transaction graphs within blockchain networks. While effective for identifying
transaction patterns indicative of fraud, it does not capture the semantics of
transactions and is constrained to blockchain data. Consequently, preventive
methods based on transaction graphs are inherently limited. In response to
these limitations, we propose the Kosmosis approach, which aims to
incrementally construct a knowledge graph as new blockchain and social media
data become available. During construction, it aims to extract the semantics of
transactions and connect blockchain addresses to their real-world entities by
fusing blockchain and social media data in a knowledge graph. This enables
novel preventive methods against rug pulls as a form of crypto asset fraud. To
demonstrate the effectiveness and practical applicability of the Kosmosis
approach, we examine a series of real-world rug pulls from 2021. Through this
case, we illustrate how Kosmosis can aid in identifying and preventing such
fraudulent activities by leveraging the insights from the constructed knowledge
graph.
|
http://arxiv.org/pdf/2405.19762v1
|
Philipp Stangl, Christoph P. Neumann
|
cs.CR, cs.DC
|
cs.CR
|
The Kosmosis Use-Case of Crypto Rug Pull
Detection and Prevention
Technical Reports: CL-2024-01, February 2024
Philipp Stangl and Christoph P. Neumann
CyberLytics-Lab at the Department of Electrical Engineering, Media and Computer Science
Ostbayerische Technische Hochschule Amberg-Weiden
Amberg, Germany
Abstract —Current methods to prevent crypto asset fraud are
based on the analysis of transaction graphs within blockchain
networks. While effective for identifying transaction patterns in-
dicative of fraud, it does not capture the semantics of transactions
and is constrained to blockchain data. Consequently, preventive
methods based on transaction graphs are inherently limited. In
response to these limitations, we propose the Kosmosis approach,
which aims to incrementally construct a knowledge graph as
new blockchain and social media data become available. During
construction, it aims to extract the semantics of transactions
and connect blockchain addresses to their real-world entities by
fusing blockchain and social media data in a knowledge graph.
This enables novel preventive methods against rug pulls as a
form of crypto asset fraud. To demonstrate the effectiveness and
practical applicability of the Kosmosis approach, we examine a
series of real-world rug pulls from 2021. Through this case, we
illustrate how Kosmosis can aid in identifying and preventing
such fraudulent activities by leveraging the insights from the
constructed knowledge graph.
Index Terms —blockchain; cyber fraud; rug pull; security;
knowledge graphs; discovery; pseudonymity; untraceability.
I. I NTRODUCTION
Crypto assets are digital assets that use distributed ledger
technology, such as blockchain, to prove ownership and main-
tain a decentralized and public ledger of all transactions. There
are distinct types of assets, each with unique characteristics and
use-cases. Cryptocurrencies, like Bitcoin [1], are the most well-
known form. They function as digital currencies and are used
for storing or transferring monetary value. Fungible Tokens
(FTs), another type of crypto asset, are interchangeable units
representing various utilities or assets within a blockchain
ecosystem. These tokens often play a vital role in Decentralized
Finance ( DeFi) protocols and can represent anything from
voting rights to a currency within a project ecosystem. Lastly,
Non-Fungible Tokens ( NFTs) are unique digital assets that
prove ownership and authenticity of digital or real-world
assets [2]. Unlike cryptocurrencies and FTs, each NFT has
a distinct value and cannot be exchanged on a one-to-one
basis with other tokens. In the rapidly evolving landscape
of crypto assets, the incidence of illicit activities has surged.
Chainalysis, a leading blockchain analytics firm, reported that
illicit transaction volume rose for the second consecutive year
in 2022, reaching an all-time high of $20.6 billion in illicit
activity [3]. Since the rise of DeFi in 2020, followed by NFT sin
2021, Rug Pulls have become a major fraud scheme in termsof amount stolen and frequency [4]. Thus, rug pulls pose a
significant risk to investors and undermine the integrity of the
crypto asset sector.
The predominant approach for identifying patterns indicative
of fraudulent activity is the transaction graph analysis within
blockchain networks [5, 6], [7, pp. 21–24]. However, this
approach presents two key challenges. Firstly, the transacting
parties are pseudonymous and only their blockchain addresses
are publicly known. This means that, although the transactions
of a specific address can be tracked, linking that address to
a real-world entity can be challenging since this approach is
limited to information or patterns observable in blockchain data.
Secondly, this approach is only concerned with the following
aspects of a transaction: 1) The transferred asset, 2) the quantity,
and 3) the sender and receiver. However, the semantics of a
transaction, such as what happened in a transaction that caused
the assets to get transferred, is not covered. Thereby limiting
the depth of analysis that can be conducted on crypto asset
movements.
Knowledge Graphs ( KGs) [8] are increasingly recognized
as a powerful means to integrate fragmented knowledge from
heterogeneous data sources into a graph of data to facilitate
semantic querying (e. g., [9, 10]) and reasoning (e. g., [11]).
AKGprovides a holistic view for identifying patterns and
hidden connections indicative of fraudulent activities in a
highly connected dataset [12]. The KGconsists of semantically
described entities, each with a unique identifier, and relations
among those entities using an ontological representation [13,
14]. Their open world assumption allows for the continual
integration of new data. By leveraging these capabilities, KGs
can enhance crypto asset fraud analysis and aid in predicting
future fraudulent activities.
The remainder of this paper is organized as follows. We first
outline the Kosmosis objectives in Section II. Next, we provide
a background on the Ethereum blockchain and graph-based
blockchain data mining methods in Section III. Subsequently, in
Section IV we propose Kosmosis, our incremental KGconstruc-
tion pipeline. In Section V we provide essential background on
rug pulls before we demonstrate the effectiveness and practical
applicability of the Kosmosis approach for the use case of NFT
rug pull prevention in Section VI. Finally, we outline future
work in Section VII and conclude the paper with a discussion
of our findings.
cb This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Please cite as: Philipp Stangl and Christoph P. Neumann. The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention .
Technical Reports CL-2024-01. Ostbayerische Technische Hochschule Amberg -Weiden, CyberLytics -Lab at the Department of
Electrical Engineering, Media and Computer Science, Feb. 2024.arXiv:2405.19762v1 [cs.CR] 30 May 2024
II. K OSMOSIS OBJECTIVES
This section outlines the objectives of Kosmosis beginning
with the primary objective that investigates the potential of a
KGin identifying and alerting users before they interact with
projects linked to known scammers, addressing a critical need
for security and trust in blockchain ecosystems. Following that,
we explore the technical implications of the primary objective.
Objective 1: How can the KGidentify and aid in alerting
users before interacting with a rug pull project?
With the rise in illicit activities in the crypto asset market,
especially rug pulls, there is a pressing need for effective
means to detect and prevent fraudulent activities. Kosmosis
aims to integrate fragmented knowledge from blockchains
like Ethereum, social media like X1, and potentially other
knowledge graphs into one unified KG, enabling semantic
querying and reasoning over a graph of entities and the
relationships among them. The KGcould serve as a knowledge
base for a real-time alerting system, warning users of potential
risks associated with certain projects or individuals.
Objective 2: How to incrementally construct the KGfrom
heterogeneous data sources?
It is imperative to establish a pipeline capable of integrating
updates into the KGin both batch- and streaming-like manner.
Thereby, maintaining high data freshness by ensuring that
the KGconsistently reflects the most up-to-date information
from the blockchain and other sources. This approach should
not entail a complete reconstruction of the KG, but rather
concentrate on integrating new information, avoiding the
reprocessing of data that is already incorporated.
Objective 3: How to extract the semantics of blockchain
transactions?
Transaction graphs commonly only display transactions with
asset transfers and provide answers to questions such as “what”
assets were transferred and “where” were they transferred to.
Understanding transactions semantically is vital in uncovering
sophisticated fraudulent schemes that might otherwise go
unnoticed. Kosmosis addresses this gap by extracting the
semantics of transactions, providing answers to “why” and
“how” assets were transferred in a transaction. This extraction
of semantic information is primarily achieved through decoding
the input data of a transaction using the Application Binary
Interface ( ABI) of smart contracts a transaction interacts with.
III. B ACKGROUND
In this section we provide background2on blockchain tech-
nology, specifically the Ethereum blockchain, in Section III-A .
Subsequently, we outline related graph-based blockchain data
mining methods in Section III-B.
1X is the platform formerly known as Twitter.
2As additional background, we described general security challenges for
cloud applications in [15] and we did some previous work on correlating
Reddit data with traditional stock market data in [16] as well as analyzing
Twitter data with SPARQL [17].A. The Ethereum Blockchain
Blockchain technology is based on the principles of im-
mutability, decentralization, transparency, and cryptographic
security and has seen various applications in recent years.
For instance, in the financial sector (e. g., [1, 18]), or supply
chain management (e. g., using a single blockchain [19], or
using multiple, interoperable blockchains [20, 21]). Smart
contract platforms represent a subset of blockchains that enable
the development of decentralized applications through smart
contracts. This section outlines the key concepts of Ethereum,
as an example for smart contract platforms, that are essential
for the following sections of this work, such as smart contracts,
their execution environment, and account-based accounting.
1) Blockchain Data Structure: A blockchain is a data
structure whose elements called blocks are linked together
to form a chain of blocks [22]. Each block comprises two
parts: a body and a header. The body of the block contains a
set of transactions. A transaction typically involves the transfer
of assets between a sender and a receiver. These participants
are represented by addresses, which are unique alphanumeric
strings that clearly specify the origin and destination of each
transaction. Further, the block body is used to generate a unique
identifier called the block hash. The block header contains a
reference to the unique identifier of its immediate predecessor,
known as the parent block.
2) Smart Contracts: Through smart contacts, which are
executable source codes that enforce the terms and conditions of
particular agreements, a smart contract platform like Ethereum
facilitates the development of decentralized applications [23].
Once deployed on the blockchain, the smart contract is assigned
an address where the code resides and cannot be altered or
tampered with. By writing custom smart contracts, developers
can create and manage tokens that adhere to the ERC-20 (FT) [24]
orERC-721 (NFT) [25] standard [23]. An ABIspecifies the
functions and data structures exposed by a smart contract,
allowing external applications to understand the capabilities
of the contract. Further, an ABIdefines a format for encoding
and decoding data that is passed between smart contracts and
external applications. This ensures a consistent and standardized
way to exchange information.
The Ethereum blockchain manages Ether ( ETH) as the native
cryptocurrency of the platform. It operates with the Ethereum
Virtual Machine ( EVM) as a fundamental building block, serving
as the execution environment for smart contract code. Smart
contracts, primarily written in a high-level language such as
Solidity, undergo compilation into EVM bytecode. This bytecode
is the executable format used by the EVM to enact smart
contract functions. To interact with this bytecode, a contract
ABIis utilized, which acts as a bridge between the high-level
language and the low-level bytecode. In this context, an EVM
disassembler plays a crucial role; it reverses the bytecode back
into a more readable format, aiding developers in understanding
and analyzing the code deployed on the Ethereum blockchain.
Figure 1 shows the processes involved in deploying smart
contracts to the Ethereum blockchain and reading contract
data from it, including compilation and deployment steps, and
the interaction between a web application and the Ethereum
blockchain. The left side shows the compilation and deployment
of a smart contract, and the right side depicts an interaction
with the contract (e. g., from a web application).
IDE/
Front-end
Ethereum
VM
Ethereum
Blockchain1. CompileSolidit y Source Co de
ABI
2. Deplo yByteco de Opcodes
Block
nWeb Application
4. Deco de
ABI Byteco de
3. Receive
Block
n+iDeploying Contracts to Ethereum Reading Contract Data from Ethereum
Figure 1: Schematic representation of deploying and reading
from smart contracts. Adapted from [26].
3) Externally Owned Account: Unlike smart contracts,
Externally Owned Accounts ( EOA s) are controlled by real-
world entitys through private keys, enabling them to initiate
transactions, such as transferring crypto assets or executing
functions of a smart contract. When an EOA sends a transaction
to a smart contract, it triggers the code of the contract to
execute according to its predefined rules.
4) Account-based Accounting: For the record-keeping of
transactions, blockchains utilize an accounting model. Com-
pared to other blockchains, such as the equally well-known
Bitcoin [1] blockchain that uses the Unspent Transaction Output
(UTXO ) model, or its successor the extended UTXO [27] utilized
by the Cardano [28] blockchain, Ethereum [18] employs the
account-based accounting model. The account-based model
can be best understood through the analogy of a bank account.
This approach mirrors how a banking account operates. Like
a bank account that tracks the inflow and outflow of funds,
thereby reflecting the current balance, the account-based model
in Ethereum maintains a state that records the balance of Ether.
Thus, it is inherently stateful. Each transaction results in a direct
adjustment to this balance, akin to a deposit or withdrawal in
a bank account. This model’s stateful nature ensures that at
any given moment, the system can accurately reflect the total
amount of Ether held in each account, offering an up-to-date
view of account balances within Ethereum.
5) Token Minting: The process of creating new tokens is
called token minting. FTsare typically minted by the creator
either at the inception of the project or progressively over
time. This process is often governed by predefined rules or
algorithms embedded within the smart contracts of the project.
In contrast, NFTminting involves other individuals besides
the token creator, commonly termed as token minters. They
engage by invoking a specific function within a smart contract,
in the ERC-721 token standard, called mint. This action results in
an increase in the supply of the NFT sand simultaneously assigns
these minted tokens to the blockchain address of the minter. Themechanism of minting NFT soften involves utilizing a dedicated
minting website. Here, prospective minters or investors are
required to invest a predetermined amount, as set by the creator,
to initiate the minting process. This investment grants them
the ability to mint one or multiple NFTs, depending on the
terms set forth in the smart contract. This process not only
facilitates the creation of new NFT sbut also serves as a means
of transferring ownership directly from the creator to the NFT
minter.
B. Rug Pull Detection Methods
This section explores two primary methodologies that have
been employed in the past to detect rug pulls: smart contract
code analysis and graph-based methods. Smart contract code
analysis involves a thorough examination of the contract’s code
to extract and analyze the semantic behavior of transactions.
For instance, [29] utilizes smart contract code analysis to
reveal potential vulnerabilities and fraudulent patterns within
the contracts. By dissecting the code, their proposed method,
dubbed "Tokeer”, can identify suspicious patterns and functions
that might indicate a predisposition to rug pull scams. Graph
theories and graph-based data mining methods are applicable
for discovering information in blockchain network graphs,
because blockchain transactions can be easily structured into
graphs [7]. Elmougy and L. Liu [30] identified three types
of graphs, applicable to any blockchain network: money flow
transaction graphs visualize the asset flow over time, address-
transaction graphs showing flow of an asset across transactions
and addresses, and user entity graphs that clusters the graph
for potential linking of addresses controlled by the same user,
to deanonymize their identity and purpose. To detect rug pulls,
graph-based approaches use network embedding methods to
automatically extract features from the blockchain network
(e. g., [31]) using a graph convolutional network
IV. T HEKOSMOSIS APPROACH TO INCREMENTAL
KNOWLEDGE GRAPH CONSTRUCTION
To incrementally construct a knowledge graph that integrates
data in a continuous and periodic way, we propose a multi-
stage pipeline, as illustrated in Figure 2, that consists of three
stages: Data ingestion, data processing, and knowledge storage.
The initial stage, data ingestion, captures the raw data from the
primary data sources (blockchain and social media) as well as
enrichment data sources (e. g., another knowledge base). This
phase is characterized by its versatility in the frequency of
data acquisition: it can be 1) continuous , to capture real-time
updates from sources such as blockchain nodes, 2) incremental
for new posts via the X Streaming Application Programming
Interface ( API), 3) periodic , to capture new entries in structured
data sources like relational databases at regular intervals, or 4)
event-based , responding to events that are emitted upon new
entity additions to the KG.
Following the ingestion stage, the data processing stage is
initiated, which is partitioned into distinct workflows tailored
to handle each type of ingested data. This segmentation allows
for specialized processing depending on the structure of the
Legend: Data Flo w Knowledge Extraction Step Load SubgraphTriplesto reAddress Relation
ExtractionKnowledge Sto rage
Attributions
Database
(structured data)Enrichment Data Sources
Golden Kno wledge Graph API
(structured data)Primary Data SourcesData Ingestion
Address T aggingWebsocket
(continuously)
Text
Entity ResolutionRelation
ExtractionNamed Entit y
Recognition
Attributions
Entity Resolution
Golden Entit y
ResolutionBlockchain
Entity Resolution
X Filtered Stream API
(unstructured data)HTTP Requests
(incrementally)
RDBMS Client
(periodically)
HTTP Requests
(event-driven)
Connecto rData Pro cessing
Blockchain No de
(semi-structured
data)
Knowledge Pro cessing StepFigure 2: A high level overview of the Kosmosis pipeline.
raw data. For instance, for text data sources, natural language
processing techniques, such as named entity recognition [32],
can be used to ensure that the data is accurately interpreted,
and contextual relationships are discerned.
In the third and final stage, the refined data is loaded into
the knowledge storage, where it is systematically organized
within a triplestore, a type of database optimized for storing
and retrieving data in Resource Description Framework ( RDF)
format. The triplestore can then be used for semantic querying
capabilities to extract actionable insights from the knowledge
graph for downstream processes. For the KGwe use the EthOn
[33] ontology that formalizes the concepts and relations within
the domain of the Ethereum network and blockchain. EthOn
is written in RDF and Web Ontology Language ( OWL).
A. Blockchain Data Processing
The blockchain data processing workflow continuously
ingests new transactions from the blockchain via websocket con-
nections. Websockets enable open, interactive communication
sessions between a client and a server, facilitating real-time data
transfer without the need for repeated polling. Upon receiving
these transactions, the workflow processes and integrates them
into the KGby first extracting the address relationship, followed
by tagging the addresses, and finally fusing the addresses with
the entities of the KG.
1) Address Relation Extraction: In order to provide answers
to “why” and “how” assets were transferred in a transaction,
Kosmosis implements a pipeline module titled Address Relation
Extraction . The responsibility of this module is to extractthe semantic information in a transaction through decoding
the input data of a transaction using the ABIof the smart
contract a blockchain address is interacting with in a blockchain
transaction.
First, the ABIis requested from Etherscan [34] and Sourcify
[35] via their respective REST APIs. If the ABI cannot be
successfully fetched from one of the aforementioned sources,
the module resorts to reconstructing the ABIfrom the smart
contract byte code, which is available at any time since the
bytecode is deployed on the blockchain. This operation enables
the decoding of transactions and the interaction with smart
contracts beyond their compiled state. The pseudocode is given
in the appendix as Algorithm 1, it outlines our systematic
approach to this extraction process.
The initial step involves the disassembly of the bytecode
of the smart contract. This process, referred to as DISASM ,
decomposes the bytecode into a series of readable opcodes and
associated data. Disassemblers (e.g., pyevmasm [36]) facilitate
this step by translating the bytecode back into a form that
represents the original instructions and operations defined
within the smart contract.
Following disassembly, the algorithm initializes by creating
an empty array intended to store the ABIand defining lists of
opcodes that either change the state (stateChangingOpcodes) or
read from the state (stateReadingOpcodes) of the blockchain.
These opcodes include SSTORE ,CREATE ,CREATE2 for state-changing
operations, and SLOAD for state-reading operations, reflecting the
fundamental actions a smart contract on the EVM can perform
[18].
The core of the algorithm iterates over selector/offset pairs
within the disassembled bytecode. Selectors serve as identifiers
for functions in the EVM, facilitating the mapping to the
corresponding functionality. If a given offset does not match any
destination within the program’s destinations, the iteration skips
to the next pair, ensuring only valid functions are considered.
Upon finding a valid function destination, the algorithm
retrieves the function definition and assigns tags based on its
behavior. This tagging process involves analyzing the opcodes
contained within the function and any related jump destinations.
The purpose is to categorize functions according to how they
alter the blockchain state, using a depth-first search algorithm
to navigate through the function call graph.
An AbiFunction object is then created for each valid function,
with its payable status determined inversely by the presence of
a notPayable marker at the corresponding offset. The algorithm
next assigns mutability attributes (nonpayable, payable, view,
or pure) based on whether the function alters state, reads state,
or neither. This classification is crucial for understanding how
functions interact with the blockchain and their implications
on transaction costs and permissions.
Finally, the algorithm decides on the inclusion of inputs and
outputs in the function signature, informed by the presence
of specific tags. For instance, tags indicating data retrieval or
state mutation influence whether parameters are classified as
inputs or outputs. This granular control ensures that the ABI
accurately reflects the interface of the smart contract, allowing
for effective transaction decoding.
2) Address Tagging: Since the exact identity of a real-world
entity controlling a blockchain address is often times unknown,
it can still be categorized and tagged accordingly. The address
tagging module tags the sender and receiver address based on
their extracted relationship from the preceding address relation
extraction module. For instance, an EOA deploying a smart
contract is tagged as deployer in case of a contract creation
transaction. Likewise, if an EOA is sending Ether to an NFT
contract Tvia a contract function containing the word “mint”,
the EOA is tagged as is tagged as NFTminter of T. Tags are
subclasses of EOA sand contract accounts, extending the address
concept of the EthOn ontology.
3) Blockchain Entity Resolution: The blockchain entity
resolution module is responsible for resolving blockchain
addresses to either new entities or existing ones in the KG,
by using the extracted information from preceding steps. It
begins with mapping the result data from the preceding steps
into the RDF format, adhering to the ontology defined by the
knowledge graph. This ensures that the data is structured in
a way that is compatible with the knowledge graph’s existing
schema, facilitating seamless integration.
Following the mapping to RDF, the next phase involves fusing
this RDF data with the knowledge graph. This is accomplished
through a two-step process. Initially, a subgraph that is relevant
to the processed data is loaded into the system. This step,
commonly referred to as “blocking,” narrows down the scope
of the resolution process to the most relevant segments of theknowledge graph, thereby enhancing efficiency in the entity
resolution process.
Subsequently, the system proceeds to match the newly
processed data with the corresponding entities within the knowl-
edge graph. This matching process is crucial for identifying
where the new data fits within the existing structure and for
ensuring that it is integrated in a meaningful way. In certain
cases, the fusion process may also involve the clustering of
entities. This is particularly relevant for blockchain data, where
unique characteristics of the data can be leveraged to enhance
the integration process.
For instance, when dealing with blockchains that utilize an
account-based accounting model, address clustering heuristics
can be employed to further refine the fusion process. One such
heuristic is the deposit address reuse, as proposed by Victor
[37]. Kosmosis capitalizes on the deposit address reuse pattern
for blockchain data from Ethereum to resolve entities more
effectively.
B. Text Processing
The workflow starts with the input of unstructured data from
the X Filtered Stream API[38], which is incrementally streamed
and parsed via a long-lived HTTP request into the pipeline for
processing. The first step in processing this data is named
entity recognition, where the system identifies and classifies
named entities present in the text into predefined categories,
such as the names of persons, organizations, and locations.
The next step is relation extraction. This process involves
identifying and extracting relationships between the named
entities that were previously recognized. For instance, it could
determine that a person named “Alice” works for a company
named “Acme.”
The final step in the text processing workflow is the entity
resolution, achieved through blocking and matching. For each
new entity, the system identifies all other entities within the
KGthat need to be considered for matching. Considering the
growing size of the KG, through the incremental updates, it is
important to limit the matching process to as few candidates
as possible [14]. The method of limiting candidates is known
as blocking, which confines the matching process to entities of
the same or most similar entity type. Following the blocking
that serves as a preliminary filtering step, the matching is
performed. This involves a pairwise comparison of the new
entities with those existing entities in the KGidentified during
the blocking phase. Its objective is to identify all entities
that are sufficiently similar and, therefore, potential candidates
for matching. This pairwise comparison relies on a nuanced
assessment of similarity that encompasses both the properties
of the entities and their relational connections within the KG. By
evaluating both property values and the nature of relationships
to other entities, the system determines the degree of similarity
between entities.
C. Enrichment Data Processing
Enrichment data enhances the data obtained from primary
data sources with supplementary context regarding real-world
entitys. Attributions involve the mapping of blockchain ad-
dresses to their corresponding real-world entities. This task is
largely dependent on data sourced from a network of experts,
such as team members from blockchain projects. The input
data for the attribution process is typically not consistent in
its timing, as it depends on when the experts provide updates
or when new information becomes available. As a result, the
enrichment data processing workflow is designed to operate at
regular intervals, ensuring that the KGis updated systematically
and remains as up-to-date as possible.
To further enrich the KG, data from external knowledge
bases is integrated. In our case, we use the Golden Knowledge
Graph due to its concentrated information on tech startups
and cryptocurrencies. This external graph offers a wealth
of information about crypto projects, including details about
their founders, team members, and project descriptions. Such
depth of data provides a valuable context that can significantly
improve the understanding of entities in the constructed KG.
The workflow for integrating knowledge from an external
Knowledge Graph is event-driven, activated once the knowledge
storage indicates the addition of new entities from the social
media platform X. Then, the workflow triggers a process to
pull in additional background information from the Golden
Enrichment API[39]. It uses the X username that has been
newly included in the KGas unique identifier to fetch relevant
data.
V. R UGPULLS AND SERIAL FRAUDSTERS
A rug pull can be categorized as a scam, i. e., the victim
authorizes the transaction. This type of scam is typically carried
out in five stages, according to [4]: (1) Project creation with
roadmap and total supply of tokens (optional), (2) pre-mint
hype, (3) set token mint price, (4) token mint, accumulation
of more capital and increase in popularity, and finally (5) the
creators cash out, abandon the project, and leave the investors
defrauded. To attract users and investments for rug pulls,
Sharma, Agarwal, and Shukla [4] suggest the involvement
of individuals or groups that possess substantial technical skills
and knowledge of blockchain technology and demonstrate a
proficiency in marketing techniques. This specific use case
is particularly relevant given the findings in [4] and [40]:
Mazorra, Adan, and Daza, who analyzed ERC-20 tokens listed on
decentralized exchanges in their 2022 study, labeled 97.7% out
of 27,588 analyzed tokens as rug pulls [40]. Likewise, Sharma,
Agarwal, and Shukla analyzed NFT sand identified a cluster of
168 NFT sassociated with what they termed the “Rug-Pull Mafia,”
a group of creators responsible for orchestrating multiple and
repeated rug pulls [4]. There is a growing trend in both the
frequency and the financial impact of crypto rug pulls and
scams [41], illustrated in Figure 5, provided as supplementary
material in Appendix A. Notably, the year 2021 marks a peak
in the amount stolen, while 2022 shows a sharp rise in the
frequency of these fraudulent activities and remains elevated
since.VI. T HEUSECASE OF RUGPULL PREVENTION
To illustrate the vision of Kosmosis-enabled rug pull preven-
tion methods, this section introduces a hypothetical user story3
centered around a character we name Bob, a crypto market
participant. The Kosmosis user story is designed to provide a
relatable perspective on how individuals like Bob are affected
by such fraudulent activities. The story of Bob, while fictional,
is grounded in a series of real-world rug pulls that took place
in 2021. All rug pulls were carried out by the same fraudulent
NFTcreator and Twitter user known as Homer_eth. In Section
VI-C , we outline how the series of rug pulls experienced by
Bob might have unfolded differently had he been equipped
with a Kosmosis-enabled fraud prevention mechanism at the
time.
A. Past User Story
In the span of two months, from October to November 2021,
a fraudulent NFT creator and X user known as Homer_eth
executed five different NFTproject rug pulls within two months,
accumulating over $2.8 million in profits. Table I provides an
overview of Homer_eth’s rug pull projects, each with launch
date and the estimated profit. The basis of this user story
is the transaction graph depicted in Figure 3 that shows the
blockchain addresses (depicted as EOA nodes in the graph)
that the rug puller used and how they are connected through
transactions.
Table I: Rug Pull Projects by Homer_eth
Project Name Launch Date Estimated Profit
Ether Bananas 10/07/2021 $125k
Ether Monkeys 10/11/2021 $1.77m
Zombie Monkeys 10/15/2021 $413k
Ether Reapers 10/20/2021 $282k
ETH Banana Chips 11/23/2021 $208k
Bob’s story begins with a common enthusiasm for the
burgeoning world of NFT s. His journey into the NFTmarket is
marked by excitement and optimism, spurred by the success
stories he sees online. Homer_eth, an NFTcreator and X user,
has caught the attention of many like Bob by sharing his NFT
projects on X. His first NT collection was Ether Bananas ,
consisting of 750 NFTs, was launched on October 7, 2021.
Only four days later, on October 11, Homer_eth continued
with the release of Ether Monkeys , followed by the release
ofZombie Monkeys . The buzz around Homer_eth’s projects,
especially Ether Monkeys , which promised additional utility
through a casino to gamble and a decentralized autonomous
organization to govern the NFT s, according to [44], draws Bob
into the fray. Being relatively new to the NFT market, Bob
views this as an opportunity not to be missed. Bob bought his
first NFT from Homer_eth, an Ether Reapers , and with that
purchase, he was no longer just a bystander; he was now an
active participant in Homer_eth’s growing community.
3The user story method of use case illustration is adopted from our previous
work in [42, 43].
3.5 ETHEther
Monk eys
2.5 ETH 0x2b ddLegend:
Token Account
Single T ransaction499.85 ETH36.25 ETH
499.85 ETH
0x872dEther
Bananas
36.25 ETHDepositors
1.1 ETH 0xc3b c106.95 ETH
Zombie
Monk eys 106.95 ETHETH
Banana
Chips
0xe396
50.4 ETH 50.4 ETH
EOA
Aggregated T ransactionsEther
Reap ers
0xc8a6
65.61 ETH 65.61 ETH
1.5 ETH 0xf580Figure 3: Simplified transaction graph of Homer_eth’s NFTrug pulls.
Bob’s involvement in the community deepened over time.
He engaged in discussions, shared his excitement with fellow
members, and reveled in the rumors of more NFTlaunches in
the future. His commitment paid off when he earned himself a
whitelist spot that allows Bob to mint the upcoming NFTproject
ETHBanana Chips by Homer_eth. Convinced of its potential,
Bob didn’t hesitate to mint an ETHBanana Chips NFTwhen
the opportunity arose. With a click to confirm the transaction
in his browser wallet (e. g., MetaMask [45]), Bob became the
proud owner of an ETH Banana Chips NFT, unaware of the
underlying risks associated with his investment.
However, the reality of the situation was far from the
optimistic scenario Bob had envisioned. Unknown to him, since
Bob had a limited understanding of blockchain transactions, the
proceeds from the Ether Reapers mint were not being locked in
the smart contract for future development as promised. Instead,
they were directly funneled into Homer_eth’s deployer address.
From there, Homer_eth will later transfer those mint proceeds
either to his next deployer address or to exchanges in order to
pay out his profits made from rug pulling the projects.
After the launch of ETH Banana Chips , a tense silence
enveloped the community. For months, there was no news
from Homer_eth, no updates on the project, leaving everyone
to wonder about the future. It wasn’t until March 2022, that
Homer_eth broke the silence with the announcement of one last
NFTproject, dubbed Froggy Frens . However, due to backlash
from the community, Homer_eth deleted his X account and
vanished [44].
B. Kosmosis Extension
The basis of the extended user story is the Kosmosis KG,
depicted for this specific user story in Figure 4. This KGis
a direct enhancement over the basic transaction graph from
Figure 3 as it was discussed in the previous subsection. The
enhancement comprises semantically annotated edges and the
incorporation of data from the social media platform X.
The data integrated from platform X enriches the KGwith
detailed information about user accounts, labeled as X Account ,and specific announcements or posts, referred to as X Post . This
integration facilitates a deeper understanding of the context
and relationships surrounding these rug pulls. For instance, it
enables the KGto establish a connection between previously
unrelated entities, such as the deployer address 0xc8a6 , the
user Homer_eth, and his associated blockchain addresses.
A key feature of this KGis its semantically annotated edges,
particularly for blockchain data, made possible through the
address relation extraction module. The edges semantically
describe value transactions, such as mintMonkey andTransfer ,
and non-value transactions, such as the deployment of a smart
contract, denoted as Deploy . These semantics allow describing
(i. e., tagging) sender and receiver addresses as NFT minter
(previously depositor) and deployer (previously EOA) addresses.
C. Future User Story
In an alternative scenario where Bob would have had access
to Kosmosis-enabled rug pull prevention, his journey in the
NFTmarket would have been safer, beginning with his initial
transaction to purchase an Ether Reapers NFT.
As soon as Bob initiated his transaction, the rug pull
prevention mechanism would have accessed the KG, to analyze
the rug pull risk of the contract. Based on the integrated
knowledge from X, the system would have been able to link the
contract, Bob is about to interact with, to all of Homer_eth’s
prior blockchain activity. The KGwould have revealed a critical
anomaly. Instead of the mint proceeds being transferred to the
contract address of the project for future development, they
were being diverted to the Ether Reapers deployer address
via the MintReaper function. With smart contracts acting as
an automated and trustless intermediary, where the code of
the contract dictates the flow of funds according to predefined
rules, this pattern of fund diversion is absent in legitimate
projects. When funds are sent directly to a team member’s
address, in this case the deployer address, the funds can be
moved to exchanges or other addresses with ease (i. e., pulling
liquidity from the project without fulfilling the promises). This
is a common tactic in rug pulls, where the developers abandon
p osted
postedHomer
_eth
T ransfer
3.5 ETHEther
Monk eys
Transfer
2.5 ETH
0x2bddLegend:
Token Account
X Account
Transaction
X RelationshipDeplo y
mintMonkey
499.85 ETHMintBanana
36.25ETHmintMonk ey
499.85 ETH
Deployer
0x872dEther
BananasDeployMintBanana
36.25 ETHNFT Mintersannounces
announcesannounces
Transfer1.1 ETH0xc3bcMintMonk ey
106.95 ETHZombie
Monk eysDeployMintMonk ey
106.95 ETHDeplo yEther
Reap ersmintReap er
65.61 ETH0xc8a6mintReap er
65.61 ETH
ETH
Banana
Chips
0xe396 Deplo y
MintBananachips50.4 ETH MintBananachips
50.4 ETHp ostedannouncesT ransfer
1.5 ETH0xf580
EOAAnnouncement
Announcement
Announcement Announcementposted
X PostFigure 4: Knowledge graph of Homer_eth’s NFTrug pulls, constructed using Kosmosis.
the project and disappear with the investor funds. Therefore,
signaling a potential rug pull behavior. Upon detecting this
anomaly, the system would have immediately issued a rug
pull warning to Bob, prompting Bob to make an informed
decision by asking whether he wishes to proceed with the
transaction despite the identified risk. This proactive approach
empowers Bob to reconsider his decision with full awareness
of the potential danger, offering him a chance to opt-out before
potentially falling victim to a rug pull.
VII. F UTURE WORK
The initial findings of our research on Kosmosis have shown
promising results, indicating the potential of our approach in
identifying and preventing rug pulls. However, recognizing
the early stage of our project, there are ample improvement
opportunities for Kosmosis in future work.
The generalization, from the exemplary use case to a
sophisticated general rug pull classification method, covering
various data patterns in the KG, is open research. Our subsequent
endeavor involves the development of an algorithm capable of
discerning rug pull warnings at varying confidence levels. This
pursuit commences with the formulation of an intricate SPARQL
query. Furthermore, an alerting system that utilizes the KG,
constructed with Kosmosis, to alert users before interacting
with a potential rug pull project, as described in the user story
of Section VI, requires future efforts.It will also be necessary to refine the filters used in
the ingestion of data from the X Filtered Stream API. The
current process of data ingestion depends on the presence of
direct links to blockchain addresses in social media posts.
For instance, the ability to link the user Homer_eth with
theEtherReapers smart contract (Section VI-C ) was solely
facilitated by the explicit mention of the smart contract address
in Homer_eth’s announcement post on X. This example
underscores the limitations of the current approach, which
may overlook relevant connections in the absence of direct
references. Consequently, a more sophisticated approach is
required to ensure a broader and still relevant dataset is captured
to associate X users with their respective blockchain addresses.
Additionally, the implementation of knowledge fusion, the
process of identifying true subject-predicate-object triples [46],
sourced from the blockchain and social media stands out as a
critical next step. By fusing multiple records representing the
same real-world entity into a single and consistent representa-
tion [47], knowledge fusion would allow for a more accurate
representation of real-world entitys in the KG.
Moreover, our current software prototype is limited to
blockchains utilizing the account-based accounting model,
such as Ethereum. Recognizing the diversity in blockchain
architectures and their unique features, we aim to allow for the
integration of blockchains using a different accounting system
(e. g., Bitcoin). This expansion is essential for broadening the
applicability and utility of Kosmosis across different blockchain
platforms.
VIII. C ONCLUSION
In this paper, we demonstrated how to build a knowledge
graph from blockchain and social media data using the
Kosmosis approach to incremental knowledge graph construc-
tion. Kosmosis becomes the basis for semantic querying and
reasoning over a graph of entities and the relationships among
them, facilitating analyses for cybercrime and fraud prevention,
with the current focus on rug pulls as a major fraud scheme.
The Kosmosis pipeline supports the ingestion of unstructured,
semi-structured, and structured data, as well as the ingestion
of new data at different time intervals. Continuously in a
stream-like fashion, incrementally, periodically, or event-based.
During construction, the semantics of blockchain transactions
are extracted to address “why” and “how” crypto assets were
transferred.
A threat actor known as Homer_eth executed five NFTproject
heists within two months, accumulating over $2.8 million
in profits. We outlined our user story, in which Kosmosis
provides a knowledge graph that improves the detection of such
fraudulent schemes carried out through sophisticated patterns
that might otherwise go unnoticed.
APPENDIX
The pseudocode in Algorithm 1 outlines our systematic
approach to extract the ABIfrom a smart contract based on its
byte code. This algorithm is important for the Address Relation
Extraction module to be able to decode incoming transactions,
even when no publicly accessible ABIfor the contract that is
interacted with can be found.
Figure 5 shows a growing trend in both the frequency and
the financial impact of crypto rug pulls and scams using data
from [41].
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Algorithm 1 Extract ABIfrom Bytecode
Input: the bytecode of a smart contract on the Ethereum blockchain.
Output: the reconstructed ABI.
1:procedure ABIFROM BYTECODE (bytecode)
2: program←DISASM (bytecode )
3: abi←empty array
4: stateChangingOpcodes ←[SSTORE ,CREATE ,CREATE2 ]
5: stateReadingOpcodes ←[SLOAD ]
6: foreach(selector ,offset)inprogram.selectors do
7: ifoffset not in program.destinations then
8: continue
9: end if
10: fn←program.destinations [offset]
11: tags←FUNCTION TAGS(fn,program.destinations )
12: abiFunction←new AbiFunction with payable =¬program.notPayable [offset]
13: mutability←”nonpayable ”
14: ifabiFunction.payable then
15: mutability←”payable ”
16: else
17: hasStateChangingOps ←false
18: hasStateReadingOps ←false
19: foropcode instateChangingOpcodes do
20: ifopcode intags then
21: hasStateChangingOps ←true
22: break
23: end if
24: end for
25: ifnothasStateChangingOps then
26: foropcode instateReadingOpcodes do
27: ifopcode intags then
28: hasStateReadingOps ←true
29: break
30: end if
31: end for
32: ifhasStateReadingOps then
33: mutability←”view”
34: else
35: mutability←”pure”
36: end if
37: end if
38: end if
39: abiFunction.stateMutability ←mutability
40: iftags hasRETURN ormutability = ”view”then
41: add output to abiFunction.outputs
42: end if
43: iftags hasCALLDATA [LOAD/SIZE/COPY ]then
44: add input to abiFunction.inputs
45: end if
46: addabiFunction toabi
47: end for
48: return abi
49:end procedure
Jan 2017
Jul 2017
Sep 2017
Oct 2017
Nov 2017
Dec 2017
Jan 2018
Feb 2018
Mar 2018
Apr 2018
May 2018
Jun 2018
Aug 2018
Oct 2018
Nov 2018
Dec 2018
Jan 2019
Feb 2019
Apr 2019
May 2019
Jun 2019
Jul 2019
Oct 2019
Dec 2019
Mar 2020
Apr 2020
Jun 2020
Jul 2020
Aug 2020
Sep 2020
Oct 2020
Nov 2020
Dec 2020
Jan 2021
Mar 2021
Apr 2021
May 2021
Jun 2021
Jul 2021
Aug 2021
Sep 2021
Oct 2021
Nov 2021
Dec 2021
Jan 2022
Feb 2022
Mar 2022
Apr 2022
May 2022
Jun 2022
Jul 2022
Aug 2022
Sep 2022
Oct 2022
Nov 2022
Dec 2022
Jan 2023
Feb 2023
Mar 2023
Apr 2023
May 2023
Jun 2023
Jul 2023
Aug 2023
Sep 2023
Oct 2023
Nov 2023
Dec 2023
Month-Year-Abbr01020304050CountRug Pull
ScamNumber of Rug Pulls & Scams by Month and Year
2017
2018
2019
2020
2021
2022
2023
Year0123456Amount Stolen (USD)1e9
Amount Stolen at Time of Rug Pull by Year (USD)
2017
2018
2019
2020
2021
2022
2023
Year050100150200250Count Number of Rug Pulls by Year
Figure 5: Token and NFTrug pulls and scams since 2017. (a) depicts the number of rug pull and scams by month and year
since 2017, highlighting a far great number of rug pulls compared to scam, (b) shows the amount stolen at time of rug pull
with a peak in 2021, and (c) shows the number of rug pulls per year with a sharp rise in 2021. Adapted from [41].
|
{
"id": "2405.19762"
}
|
2109.01047
|
Crypto Currency Regulation and Law Enforcement Perspectives
|
This paper provides an overview of how crypto currency and blockchain
engineering interacts with the law enforcement. We point out that a large
proportion of crypto users are amateur investors and the dominant and the
largest segment in crypto crime are simply investment scams (!). We look at
various questions of criminal use and misuse of technology, especially in the
areas of money laundering or cashing out the profits originating from illicit
activities. The aim of the paper is to raise a set of concerns arising in the
criminal justice and policing circles, based on the interviews with law
enforcement practitioners, and to see how cryptos could be reconciled with
public security and safety. We propose a simplified classification of crimes
related to crypto currency. We study the development of blockchains in a
broader context of applied cryptography and payment technology. Ransomware is a
big threat but we also need protection against corporate misconduct or
negligence, with untested financial services breaching customer trust or
government regulations. Not paying taxes is illegal, but there is more at
stake: exposing crypto holders to losing all their savings in scams or thefts.
Interestingly, privacy helps to defend on multiple fronts: against social
engineering, targeted crime, scams, and also against cybersecurity thefts and
hacks.
|
http://arxiv.org/pdf/2109.01047v1
|
Nicolas T. Courtois, Kacper T. Gradon, Klaus Schmeh
|
cs.CR, cs.CY, cs.DC, K.4; K.5; C.3
|
cs.CR
|
arXiv:2109.01047v1 [cs.CR] 1 Sep 2021Crypto Currency Regulation and Law
Enforcement Perspectives
Nicolas T. Courtois1, Kacper T. Grado´ n2,3, and Klaus Schmeh4
1Department of Computer Science, University College London , UK
2Faculty of Law and Administration, University of Warsaw, Po land
3Department of Security and Crime Science, University Colle ge London, UK
4Cipherbrain.net, Gelsenkirchen, Germany
Abstract. This paper provides an overview of how crypto currency and
blockchain engineering interacts with the law enforcement . We point out
that a large proportion of crypto users are amateur investor s and the
dominant and the largest segment in crypto crime are simply i nvestment
scams (!). We look at various questions of criminal use and mi suse of
technology, especially in theareas ofmoneylaunderingor c ashing outthe
profits originating from illicit activities. The aim of the p aper is to raise a
set of concerns arising in the criminal justice and policing circles, based
on the interviews with law enforcement practitioners, and t o see how
cryptos could be reconciled with public security and safety . We propose a
simplifiedclassification ofcrimesrelatedtocryptocurren cy.Westudythe
development of blockchains in a broader context of applied c ryptography
and payment technology. Ransomware is a big threat but we als o need
protection against corporate misconduct or negligence, wi th untested
financial services breaching customer trust or government r egulations.
Not paying taxes is illegal, but there is more at stake: expos ing crypto
holders tolosing all their savings in scams or thefts. Inter estingly, privacy
helps to defend on multiple fronts: against social engineer ing, targeted
crime, scams, and also against cybersecurity thefts and hac ks.
Keywords: Blockchain,Cryptocurrencies,AppliedCryptography,Sma rt
Cards, Payment, Blockchain Forensics, Crime Science, Nati onal Secu-
rity, Law Enforcement, Criminal Justice, Investment Scams , Organized
Crime, Crypto Currency Thefts, Crypto Mining, Money Launde ring,
Ransomware.
1 Introduction
Blockchain and associated payment methods are the emerging technolo-
gies, which have 220 million of users [24]. In our view, the cu rrent crypto
landscape and ecosystem must be considered to be more than ju st an
evolution of an electronic payment system invented by a cert ain Satoshi
Nakamoto [56]. It has then in fact “diversified” from the init ial intended
function of transferring coins between peers. Cryptoandbl ockchain tech-
nology is now becoming a major disruptor of how our economy an d our
society functions. The challenge goes far beyond small nich e sectors such
as trans-border foreign currency activity. It is now enteri ng substantially
larger parts of the economy: like competing with banks and po tentially
also stock markets, on attracting investor deposits and inv estment activ-
ity fees. It is a powerful programmable toolset capable of do ing so many
things in finance and beyond [34]. Many of these are new and ent irely
impossible to do without cryptography and some form of decen tralized
or “trustless” consensus. New ideas and new forms of distrib uted coop-
erative social and economic order are proposed.
This paper is organized as follows. In Section 2, we introduc e the main
directions and we show that there exists more than one point o f view
about the role of privacy. In Section 3, we elaborate on the ce ntral role
played by crypto exchanges and discuss related risks. In Sec tion 4, we
discuss themajor emerging risksatthecenterstage ofcrypt ocurrency.In
Section 5, we look at the concerns emanating from the law enfo rcement
sector. InSection6,weinvestigate thedangersofopensour ceandexplain
how much engineers are responsible for the crime and theft la ndscape,
which is also simply part of the DNA of our systems. In Section 7, we
explain how cryptographers have been trying to improve the p rivacy of
the original bitcoin in the last 10 years. In Section 8, we add ress certain
misconceptions, which exaggerate the purpose andthe typeo f anonymity
we obtain with privacy-enhanced crypto currencies. In Sect ion 9, we look
at how law enforcement and regulators can cope with new emerg ing
challenges. In Section 10, we look at three main types of crim e by volume
of activity: thefts, scams and ransomware. In Section 11, we look at the
idea of a criminal battlefields. In Section 12, we look at how t he emerging
crypto economy can be better protected rather than banned. I n Section
13, we study how investors and asset holders may need legal pr otection
and crime policing. Then comes our conclusion section with s ome public
policy recommendations.
2 Main Directions and Historical Development
of Crypto Currency
2.1 Legal and Technical Qualifications of Crypto
Currency
There existvariouslegal, regulatoryandtechnicalqualifi cations ofcrypto
currency systems. These qualifications are not exclusive. W hile courts of
law or the United States CFTC recognized bitcoin as a “curren cy”, the
SEC has fined many companies for improperly issuing a “securi ty”, the
police have been chasing bitcoins related to major crimes an d recognizes
that crypto currency poses unique challenges around increa sed privacy.
Then, national tax authorities e.g. IRS decided to consider bitcoin as
“property”, imposing truly excessive transaction reporti ng obligations
and discouraging mainstream adoption.
This list is not exhaustive. For example, almost no one is cal ling bit-
coin a lottery, even though the current proof of work (POW) me chanism
creates a winner every 10 minutes, see [51], and the winner is system-
atically awarded a substantial amount of 6.25 freshly creat ed bitcoins.
Moreover we could also claim that bitcoin is a voting protoco l. In fact it
is a self-governing financial cooperative: the entities mak ing the winning
blocks are able to co-decide which transactions are accepte d and also
which version of the protocol and software is dominant. This aspect of
self-governance is substantially more developed in Ethere um and other
blockchain systems where the smart contract functionality seems to be
the main one. Most crypto currency systems are essentially a lso tools
of ”disintermediation”: they provide secure methods of hol ding and con-
trolling property and financial assets in ”direct” ways, wit hout trusted
intermediaries, banks, notaries etc. This is achieved with help of crypto-
graphic keys stored on secure devices controlled by users. L ikewise, they
provide ways of controlling assets in completely new or dece ntralized
ways. Blockcahin and crypto currency systems are an ongoing challenge
to our ideas. They force us to revisit our ideas about questio ns such as
how the focus of crime policing should change with new techno logy. We
probably need to think twice about what is socially desirabl e to do in a
market economy, or how financial services regulation in the 2 1st century
should look like.
2.2 Waves of Disruption
The story begins with users who needed bitcoins for their par a-banking
ability: to store and transfer money. An anarchic system ini tially, oper-
ating without too much consideration for national boundari es and regu-
lations. Such users will be, for example, families who send t heir children
to study abroad or small businesses which operate across bor ders. All
this is however far from being the primary function of bitcoi n as of 2021.
Bitcoin has yet failed so far to become a champion of small pay ments or
to be adopted in the Internet retail commerce. In 2020-21 we fi nally have
the lightning network, a major enhancement in this directio n. Yet elec-
tronic commerce has not changed yet, it is working primarily with Visa
and Mastercard, as it did for decades. Nevertheless a very su bstantial
disruption is here. We see it in particular in the world of inv estment.
If the first somewhat primary function of bitcoin is trans-bo rder money
transfer, thesecondwouldbethestoreofvalue.Bitcoin has for morethan
a decade managed to establish itself as a reliable store of va lue with a
peculiar ultra deflationary monetary policy [23,51]. Since late 2014, there
have been very substantial press blogosphere and mass-medi a coverage
related to the crypto currencies and how they disrupt the wor ld of banks
and businesses. For example, in 2019, twenty years after the creation of
the Eurosingle currency,Google search showed more searche rs aboutbit-
coin than about Euro. This is happening, even though we buy ou r food
in Euro 100 % of the time, and 0 % of our house loans are denomina ted
in bitcoins. As of 2021, bitcoin is and remains a curiosity fr om the main-
stream economy point of view. Half-way between offshore para -banking,
it is also a super-famous club of cyber-punk innovators and d isruptors.
More recently we hear about institutional adoption and the a bility of
bitcoin to go up faster than inflation, while governments are creating
excessive quantities of fresh money in order to alleviate th e pandemic-
driven recession. Investors have eventually diversified th eir portfolios and
bitcoin became a mainstream financial asset in the recent yea rs and hun-
dreds of millions of people own bitcoins, cf. [24].
It is totally incorrect to believe that crypto is just an inve stment bubble.
It is way more. If it is a “bubble”, it involves an incredibly l arge amount
of coder, developer, R&D and cryptographic engineering act ivity, open
source software. We have a plethora of hardware and business infrastruc-
ture projects developed by expert firms. Bitcoin adoption ha s increased
by 880 % in 2021, according to Chainalysis [58]. It also invol ves a very
substantial amount of academic research activity, which is not less sig-
nificant than some of to the very major intellectual endeavou rs of the
human civilisation, such as sending a man into space, or tack ling the
Covid pandemics. It is also a “bubble” which leads to shortag e of cer-
tain types of GPU computing hardware, as much as bitcoins are in short
supply also. Hackers, cryptographers and coders have creat ed a monu-
mental new technology development movement, which is self- funded. It
is somewhat funded precisely, by inflated investment expect ations... and
the unreasonable crazy ride which it offers to investors. Cry pto economy
is poorly integrated with mainstream economy, yet it has cre ated a huge
economic and startup company activity, which is unpreceden ted.
Bitcoin is a brand, a cultural movement, and it is religious a t times. Bit-
coin maximalists believe that bitcoin prevails no matter wh at. Bitcoin is
an epic story, at thecrossroads of “geek mythology”and libe rtarian anar-
chist cyberpunk ideas. Blockchains are distributed electr onic ledgers run
with peer-to-peer cooperative consensus. They allow keepi ng money out-
side of the presumably “evil” establishment and banks. As a r esult, law
enforcement is not facilitated. It is more difficult, for exam ple, to seize
assets or to undo a transfer. Beneficiaries can be anonymous, transac-
tion flows can be obfuscated, and income can be possibly conce aled for
taxation purposes.
2.3 Privacy - A Two Sided Coin
There is a substantial amount of ambiguity about whether pri vacy is
socially desirable, and to what extent privacy enhancing me thods and
techniques are legal or ethical. Privacy, is a tool of choice which can be
used to obscure your financial history for wrong reasons or an illegiti-
mate purpose. However there are also many strong arguments i n favor
of privacy.
Privacy is a basic and constitutional human right. People ha ve a legiti-
mate desire to escape the prying eyes and mass surveillance. The Article
8 of the European Convention on Human Rights (ECHR) establis hes the
Right to Privacy. Initially this seems to have a limited scop e, with focus
on family life and protection of private correspondence and relationships
with other human beings. However, many courts and lawyers gi ve it a
much broader interpretation, like to be free from “unlawful ” indiscrim-
inate searches done at a large scale. A recent ruling of the Eu ropean
Court of Justice CJEU of 6 August 2020 makes mass surveillanc e pro-
grams run by the UK, French and Belgian governments simply il legal,
cf. [47].
There are “public benefits” from increased anonymity, for ex ample we
have reasonable expectations not to reveal to your spouse, y our employer
or neighbor which political or social cause we support financ ially [41], for
example with political donations in bitcoins. Lack of priva cy in financial
transactions greatly exposes individuals to be easy targets for numerous
serious crimes such as “identity theft, mugging, kidnappin g and black-
mailing” [41]. For example, we consider investment scams wh ich account
for some 54 % of bitcoin criminal activity. Here we observe th at pri-
vacy and law enforcement are also at stake. The more all sorts of data
items pertaining activity are exposed, collected, stored, used, re-used,
compromised, shared or sold, the more criminals can operate sophisti-
cated targeted scams. Or they will simply succeed in hacking into our
accounts. A nice example of how the law can positively impact cyberse-
curity is Texas law HB 3222 from 2009 which forces businesses to comply
with payemnt card industry security standards which in part icular forbid
storing certain items such as 3 digits printed at the back of c redit card,
cf. PCI DSS section, cf. slide 99 in [21]. We see that orderly a nd legal
business activity also requires privacy. Data collection a nd storage needs
some form of regulation. Bruce Schneier has as early as 2005 a dvocated
that we need to “reduce” and “limit” data collection and “how it can be
used”, see slide 92 in [21]. In general large-scale indiscri minate bulk data
collection, or retention, or negligence when information i s leaked out, is
against the law in most jurisdictions. Large companies have been fined
hundreds of millions of dollars or euros, for failing to secu re personal
information under the new GDPR regulations in the EU, or olde r laws
such as the U.S. GLBA regulations [41].
2.4 Privacy vs. Law Enforcement - A Balancing Act
Privacy is not only a political and legal question. It is also a technical
question where we can try to achieve a certain equilibrium. P rivacy can
help to evade taxes or circumventbusiness or financial secto r regulations.
However, poor privacy of financial transactions is precisel y a key factor
which makes it easier for hackers and thieves to operate. If w e have too
much privacy, criminal activity is facilitated. Then also i f we have too
little privacy, crime is also facilitated. In order to achie ve a reasonable
balance between privacy and law enforcement we need toconsi der several
key questions.
Even though blockchains, in theory, are self-policing for c ertain questions
such as theft of funds in transit, they are not very good on pro tecting
customer privacy. Initially, the whole idea of “public” led gers is corrosive
for our privacy: they expose our financial and personal data. Then we
add privacy to these systems, for example with some cryptogr aphic tech-
niques, or simply through mixing and obfuscation. At the end privacy
becomes potentially too strong or so it seems, cf. Section 8, and criminal
activity is potentially facilitated. A question is not quit e to see who wins:
the policemen of the thieves. It is a classical problem in tec hnology and
in law enforcement. Both sides follow a certain curve: secur ity and safety
improves, and criminals and thieves also evolve. The questi on is really
about achieving a better more detailed understanding of how technology
actually impacts our lives.
We need to put things in proportion. Expert studies show that the crimi-
nal segment is small in bitcoin, or small in relative terms; i t is about 0.17
% on average and yet smaller in many jurisdictions, for examp le it is only
0.06 % in Germany, cf. [18]. Very similar statistic were obse rved in credit
card payments in the last two decades: the fraud is 0.15 % typi cally and
significantly smaller in well-policed jurisdictions [21]. Yet nobody says
that credit cards are best friends of criminals. In both case s, we see that
the legit payment activity is substantially larger. To summ arize, criminal
activity is small in bitcoin and elsewhere, and 99.9 % of cryp to currency
activity is legitimate trading or commercial activity. Thi s part arguably
should get more attention than the relatively small illicit activity.
2.5 Investment: Major Not Minor
A major angle to consider is gambling and investment questio ns, which
are very hard to tell apart. On the one hand, we have a legitima te desire
of the public to support and invest in crypto currency startu ps, as such
businesses which are not listed on the stock market. On the ot her hand,
we have the gambling question. In the eyes of millions of smal l investors,
crypto currency markets are an offshore-operating casino wi th less strin-
gent regulations. Wehavethe popular“rich-kid”dream ofma kingmoney
through the crazy appreciation of bitcoin and other crypto a ssets [25].
Here bitcoin competes with the stock market, Internet casin os, and ama-
teur Forex trading to offer some thrills and hopes to millions of investors,
frequently millennials.
According to [48] as many as 11 % of Americans own bitcoins. Ev ery
single day, 1 million bitcoin wallets are active. There are m ore than 25
million bitcoin traders worldwide and “speculating is the m ost popular
use of bitcoin” cf. [48]. In addition, according to a detaile d industry
report, the rate at which the number of users of crypto curren cy has
been growing faster than exponentially. In just four months between
March 2021 and July 2021, the global number of crypto currenc y users
has doubled from 100 to 200 million approximately, cf. page 9 in [24].
3 Main Business Players - Crypto Exchanges
In theory, Satoshi have imagined a utopian peer-to-peer eco nomy where
we exchangecoins against goods andservices. Inrealitywe a lreadyhavea
way more sophisticated economy, with intangible assets and major extra
features: debt and leverage, network effects, business comp etition, and
non-linear behaviour. The central players where the old and new worlds
meet are the crypto exchanges.
3.1 Geography of Crypto Exchanges
It is not true that bitcoin operates outside of the scope of la w and order.
In fact, as much as 20 % of all bitcoins sent to exchanges globa lly are
sent to exchanges based inside the U.S. At the same time, 59 % o f the
volume goes to the so-called ‘high-risk’ exchanges [17]. Th ese exchanges
are notorious for being lax on KYC (Know Your Curstomer) and A ML
(Anti-Money Laundering). At the same time, they are also sim ply small
independent offshore companies where customers are at risk o f losing ev-
erything. It is important to see that these companies themse lves can be
scams, and they can just disappear tomorrow with funds belon ging to
na¨ ıve crypto currency holders. Users do however use them on a massive
scale. Following [17] about 24 % of volume from US-based exch anges
goes to these ‘high-risk’ exchanges, while for Russian exch anges this per-
centage goes up to 69 %.
3.2 On Neeed for Investor Privacy
We have seen that for all sorts of reasons, which could includ e tax eva-
sion, individuals use and trust riskier crypto exchanges wi th their coins.
A major reason for that is that these companies ask less quest ions and
it is easier to pass various verification levels required and to remain
anonymous. We hypothesize that it is NOT in order to escape go vern-
ment surveillance that crypto currency privacy is aiming at . Regardless
of whether we like it or not, amateur trading is a big industry and in-
vestors need to be protected. First of all against losing all they have in
investment scams and magical get-rich-quick schemes. Some 16 billion
dollars were lost in some 132 major crypto currency scams cf. [11] and
[40].
In fact, there is a balancing act to make: tax evasion deprive s govern-
ments from some revenue, but there are substantially larger losses at
sight. Would these amateur investors lose all their savings , governments
worldwide will be facing larger bills for healthcare, socia l housing etc.,
overextendedperiods oftime. Governmentsare theultimate risk takers
for amateur investors, for example assuring minimal income for pension-
ers robbedfrom their savings, which normally should allow t hemtoretire
comfortably. Thus, very clearly, investor privacy can indi rectlyprotect
the governments , against future liabilities at an astronomical scale.
This is given our aging population and the immense popularit y of ama-
teur investing. We need to defend small investors against pr ivate sector
predatory surveillance. Activities meant to deprive inves tors of their fun-
damental rights such as freedom to buy and sell with some priv acy and
without being “gamed” or manipulated. We have the right to pa rticipate
in a fair and open market economy. One which is not rigged by cu rrent
omni-scient monopolistic big data and AI players, which ten d to abuse
their dominant position.
3.3 On the Neeed to Police the Investment Platforms
Crime andlawenforcement occurwhenpeopleavoidpayingtax es. Larger
crimes occur when companies do help tax evaders and organise the tax
evasionatalarge scale. InNovember2020theU.S.Departmen tofJustice
pressed criminal charges for BitMex, an exchange based in th e Seychelles
for ”serving U.S.-based customers” making it very clear tha t U.S. law
will be applied in such cases [61]. Law enforcement also occu rs when SEC
fines companies that raised money in an ICO, selling a token wh ich is
considered to be a security. Several businesses have been in structed to
return vast sums of money to investors by the policeman of the financial
markets, the powerful SEC.
Market manipulation is also criminal. Policing this crime c an arguably
involve increasing citizen security against predatory ban ks. Banks have
beenfinedhugesumsinbillions ofdollars inthelast decade. Oneresearch
question is the one ofadversarial trading andfront-runnin gcf. [61]. A key
problem is that blockchains has so far focused on how to solve consensus
problem, which is mainly about blocks that are eventually ap proved and
final. Now there is a larger space for adversarial traders: the question
of incentives and selection of transactions accepted withi n one block [61].
Then we have the questions of adversarial AI: predicting the behavior of
humans, in order to play games against users in the marketpla ce. This
does not seem illegal per se, but in fact it will clearly be une thical and
fraudulent, if you pay a bank or the exchange some fees or subs cription,
in order to advise you about investment opportunities.
4 Major Risks and Threats at the Center
It is estimated that hundreds millions of dollars per year ar e made by
criminals in extortion of ransom payments, which have plagu ed countless
businesses worldwide, small and large [42] and Section 10.2 . Yet the
illicit activity amounts to just 0.1 % of crypto deposits typ ically, and
this figure went down by 60 % in two years [18]. Millions of peop le go to
crypto currency markets not in order to acquire illegal good s, but rather
to simply acquire bitcoins, which for most users are perfect ly legal to
own. Then we sell them at profit or at a loss. Individuals can al so mine
bitcoins at home, see Section 6.2.
4.1 Is Bitcoin Itself a Financial Scam?
For more than 10 years, bitcoin has been traded. The value if b itcoin
did not go to zero. Bitcooin holds hundreds of billions of dol lars in funds
belonging to hundreds of millions of users [24]. The public p erception
of the evils and dangers of bitcoin has been distorted by the i dea, that
certain incumbentsinthefinancial industry,andsome gover nmentsalike,
would like to make something like bitcoin or blockchain ille gal. However.
The interestsofsmall investorsandthemonopolistic estab lished industry
incumbents are not the same. Here we present both points of vi ew.
In some sense, bitcoin potentially IS an investment scam, or we need to
consider that it could be one, for example as a precautionary safety mea-
sure to protect investors. It is a fact that banks are losing t heir deposits,
which go to a para-banking offshore space, with less regulati ons and no
borders. This makes laws harder to enforce. The world of inve stment is
extremely old-fashioned slow and rigid: for example it is ve ry hard to
buy and sell stocks from Asia for an investor who lives in Euro pe. New
blockchains, currently under development, promise to allo w anyone to
trade any asset freely. We see that banks and stock exchanges are losing
some of their sources of income and the competition is increa sing.
This ongoing process of disrupting a multi trillion-dollar global financial
industry increases the risks due to bad choice of a financial p artner or
broker and due to the new technology itself such as hacking. I t creates
countlessopportunitiesfor negligence, misconduct,care less or“frivolous”
behavior cf. [22]where asmall individualinvestorwill find it verydifficult
to know which companies and which technical standards they c an trust.
Investors can lose everything when hackers steal private ke ys. Open-
source software can be malicious [46,22] or just insecure. C urrent laws
and regulations favor the incumbents, and maybe only very la rge com-
panies can apply. The financial industry remains highly regu lated and is
a subject of intense government oversight and scrutiny. Law enforcement
must simply expand and regulate as much it can. We need to extend
the safety net, allow smaller business to operate, and prote ct individual
investors. Law enforcement needs to follow the investor mon ey, and con-
tribute to defining how the crypto industry should be run as a w hole.
We also need to forbid or allow and regulate more clearly.
As already explained, the criminal or illicit segment is ver y small essen-
tially 0.1 % [18]. The next level will be “Joe the Plumber” sta shing some
cash for his retirement at an offshore cryptoexchange, which income pos-
sibly he forgot to declare with IRS, which raises serious que stions [19].
Here the source of funds is not illegal itself. It is not illeg al either for Joe
to avoid his bank, which pays very little dividend or/and cha rges him
large commissions on his retirement investment. We live in a free market
economy, and it is not a crime at all for anyone, to seek a place to deposit
money, where they can generate a better income. When local ec onomy
and or the established investment providers suffer some setb acks, in-
vestors do and will go to exotic places. One day it was gold, or some tech
companies, today bitcoin is a popular asset for 1 million ama teur traders
each day [48] worried about inflation or maybe a Covid-induce d reces-
sion. The question is now simply, that startups and offshore fi rms which
do not have a banking license, and yet are offering financial se rvices to
Joe. Potentially they are incompetent, cheating, or just to o easygoing
optimistic startups relying on untested cryptography and s oftware, or
neglecting public safety for the sake of new exciting functi onalities. The
risks for small investors are simply enormous, andbanks are regulated for
a reason. The main idea is that small or amateur investors des erve accu-
rate information and some protection against fraud, misrep resentation,
or more ordinary ”preventable technical incidents”.
5 Anarchy Meets Law Enforcement
Our sources of information are academic research, the chaot ic world of
Internet forums and collaborative platforms, the abundant press cover-
age of crypto industry, and interviews with law enforcement profession-
als. Throughout the duration (2014-2017) of the European Co mmission
FP7 PRIME Project (Preventing, Interdicting and Mitigatin g Extrem-
ist Events) we have interviewed 132 front-line investigato rs and law en-
forcement officers with security merit from Australia, Austr ia, Canada,
Germany, Georgia, Hungary, India, Italy, Japan, Mexico, Ne w Zealand,
Poland, Portugal, Spain and United States, cf. [36]. We also asked our
interlocutors for their opinion on the abuse of modern techn ologies by
violent extremists. If this paper contains certain stateme nts which are
a matter of opinion, this will not be necessarily ours: they c ome from
interviews with law enforcement professionals.
We found that the awareness of blockchain technology among o ur target
audience (specialists with a wide array of law enforcement b ackgrounds:
police, border guard, intelligence, counterintelligence , military, govern-
ment and special services) is very low and vague. In 2017 only a small
number (less than 10 %) of our interlocutors understood the s ignificance
of blockchain and appreciated the potential for the crimina l abuse of
technology. It needs to be stressed that these officers were ex perts in
financial analysis and represented top-level law enforceme nt institutions
in their respective countries. Those specialists indicate d that the finan-
cial analysis techniques can and are used to retroactively study the
cash flows and money transfers (including crypto currency su ch as bit-
coin) in the cases of known lone-actor extremists, in order t o search for
patterns that could be later applied to investigate radical ized individuals
[30]. However this approach is potentially ineffective and i nefficient for
the purpose of regulating ordinary crypto market activity. It does not
prevent thefts and scams, and does not help customers to reco gnise re-
liable providers and reliable investment opportunities. O n the contrary.
Many people are lured by exotic places where maybe the taxman or their
spouse are no longer watching, and take substantial risks fo r no reason
other than, a quite naive expectation of better privacy. It i s naive, be-
cause unregulated offshore service providers are in fact MOR E likely to
sell data to criminals than reputable crypto companies. Arg uably we
need more ”legit” providers of financial privacy. A UK govern ment re-
port explains that Suspicious Activity Reports (SARs) do no t help a lot
[65]. They are filed in ”huge numbers” and are typically just p ersonal
living expenses. The safeguards which help to catch terrori sts or totackle
organized crime, are potentially not that useful in regulat ing a broader
para-banking activity. A broader population of legitimate participants in
these markets lacks sufficient protection against poor secur ity standards
or negligent service providers.
5.1 Risk Awareness, Blockchain Education, Trends and
Geography
When consumer protection and technology fail, or when regul ating or
banning does not help, or when policing does not reduce crime and
fraud, we need to work on public awareness and education. Fir st of all,
we should not underestimate the educational benefits of cryp to currency
markets. Instead of unemployed youngsters depressed by ant i-capitalist
propaganda and gloom, we have a game of building a new world or der
by geeks, coders and hackers. A dream, of becoming a banker, c reating
money out of nothing, being a stakeholder participating in t he economy
of the future in active ways. The term “cryptographer’s drea m” is some-
times used [49]. A new form of cooperative decentralized and inclusive
economy is possible. Middle class peopleworldwide learn ab out newtech-
nology, maths and cryptography, consult various blogs and w ebsites, and
develop a feeling of being autonomous players and exploring the world
of alternative finance. Coders and designers are empowered t o build fi-
nancial systems, which work the way theythink is ethical and socially
desirable.
Even though blockchains were invented more than 12 years ago [56], it
is still – in 2021 – a new and emerging phenomenon and the knowl -
edge about it is poor. This is clearly indicated by the patter ns in search
queries indicated by Google Trends analytics. We have seen a 10-fold
increase in searches about the word “blockchain” in 2017, th en it goes
down 3-fold but never reaches the pre-2017 levels, and again a 3-fold
increase occurs at the end of 2020. The curve has two sharp pea ks which
correspond exactly to moments when bitcoin price has peaked at the end
of 2017 and again at the end of 2020. The pattern is similar amo ng the
law enforcement and intelligence/security communities. G oogle trends
also show that peripheral or emerging countries do more sear ches than
Western countries. The ability of bitcoin to offer alternati ve international
money transfer services in various countries was just the st arting point.
Blockchains have now become an important piece of the libera l global-
ized English-speaking economy. Among 78 countries Ghana an d China
are those, where most searches about blockchain originate f rom, with
a scores of 100 and 92/100. Most EU countries and the United St ates
are way more conservative: they arrive at positions 30-50 ou t of 78 with
scores between 12/100 to 20/100. Another detailed research report found
that Vietnam followed by India and Pakistan lead the crypto c urrency
adoption in 2021, [58], when adjusted w.r.t. the purchasing power per
capita. We discover that new tech such as blockchain cataliz es progres-
sive pro-business and pro-education forces in emerging cou ntries. This is
great news.
6 On Critical Technical Choices
6.1 Trusting the Cryptographers
There is a level of disconnection between traditional crypt o research and
development of industry standards (e.g. in telecom, smart c ards, and at
NIST, or in EU-funded R&D projects) and the nascent crypto in dustry
since 2009. Developers in bitcoin and other crypto currenci es have made
many deliberate choices in their specifications which are pe culiar, risky
or non-standard features. This contrasts a lot with the domi nant culture
in crypto research, which have been for 50 years very cautiou s, assuming
that the attackers are more powerful than they really are, an d engineer-
ing in truly paranoid ways. Traditional cryptographers hav e been very
cautious and always considered that when something is very s pecial it
should be banned.
For example AES is the most important standardized encrypti on algo-
rithm today. For this reason alone it is also used by the bad gu ys, e.g.
in ransomware [60]. When the AES was standardized in 2000, th ere was
some controversy around the special choice of S-box used, wi th New
Scientist title “Cipher Crisis” in 2003 [45]. Bitcoin and cr ypto currency
developer communities have ignored many traditional rules of careful
security engineering, and are in general very easy-going: s hip it first,
tell users this payment system is experimental, and hope tha t hackers
and thieves do not have sufficient expertise to steal coins and exploit
numerous vulnerabilities in existence. Bitcoin does not us e AES, it uses
SHA256,RIPEMD-160andECDSAwithsecp256k1. Thelast parti spar-
ticularly controversial cf. [23]. In fact secp256k1 is not w hat other people
in the industry use for securing Internet commerce with TLS ( secp256r1
is used 90 % of the time). The crypto currency community has de veloped
its own set of incompatible crypto standards. Similar but not quite the
same or not exactly the same, as found in more traditional sec urity in-
dustry such as in smart cards or in TLS. Bitcoin does not obey g lobal
de-facto crypto standards such as defined by the U.S. NIST, ra ther it de-
fines its own standards used primarily in crypto currencies a nd virtually
nowhere else. They differ from security protocols used by maj or com-
panies which define the technical standards in major operati ng systems
and Internet commerce (Microsoft, Apple, Google etc.).
Manyadvancedcryptographicprotocols specifyfurthernew ellipticcurves
with further very special properties. For example, Ethereu m, ZeroCash,
Algorand and Chia network use a special curve called BLS12 381. Inter-
net Computer (a.k.a. DFINITY) uses also more exotic pairing -friendly
curves such as BN254N, BN SNARK1, BN382M, BN462, see [66]. In
general the more advanced is the crypto market functionalit y or privacy
enhancement functionality we find in blockchain technology , the more
risky and untested is the cryptography used there, see [9] an d how re-
searchers are “cavalier about proposing untested cryptosy stems”, see [39]
and [57]. Innovation requires some “riskier than usual” sec urity assump-
tions, and this is normal.
However. New cryptography standards are made and broken eac h year.
History knows numerous examples of faulty cryptography sta ndards,
which has nevertheless dominated the market very heavily. F or example,
the Data Encryption Standard (DES) was developed in the mid 1 970s
and is still used in 2021. Hundreds of millions of people use D ES every
day, when they use their Chip and Pin bank card, and also in tod ay’s
contactless payments. This cryptographic algorithm is far from being
perfect, see in particular page 6 in [7]. For example in the ke y scheduling
in DES there ”is no interaction across the two halves”, plus c ountless
other known problems with DES, cf. [62,50]. Yet DES has thriv ed for
50 years and remains massively used in financial systems. Mor e recently
the cryptographic hash function SHA-256 is used to secure th e integrity
of bitcoin transactions and to secure the chain of events in e ssentially
all current blockchain systems. If this cryptographic hash function was
faulty, it would be extremely difficult to upgrade.
Current systems do not consider that cryptographic algorit hms could be-
come obsolete overnight, and we would need to switch and use a different
crypto algorithm. This question of the “strategic” choice o f the crypto
hash function is also related to feasibility of specific type s of attacks and
crimes, as we will see below.
6.2 How Specific Crypto Engineering Choices Influence
Fraud and Crime
An interesting example to learn from, is a story of an electri c scooter
one of the authors purchased in 2020. Then he visited a large b ike store
and found that no lock currently on the market had the dimensi ons and
shape, which would allow one to attach this scooter reliably . The market
does not provide a solution. As result the scooter was stolen 3 days after
purchase. In addition, it did not have at all any unique seria l number
which could be communicated to the police. How does it relate to crypto
currency market? It is a question of market regulation : potentially
certain models of electric scooters should be banned from th e EU or
US markets, because they lack consumer protection or any pro tection
against theft whatsoever. It is a problem of industry standa rds, holistic
crime prevention and reconciling government and private se ctor industry
regulation when new technology is sold in the marketplace.
Going back to crypto currencies, there is no doubt that sever al technical
features of bitcoin facilitate specific types of crime, cf. l ast point in Ben-
efits section page 9 in [43]. Is it possible to claim that some features of
bitcoin could be banned? Yes without any doubt, and the key pr oblem
is that bitcoin failed so far to become decentralized, on the contrary, cf.
p.17 in [43]. One can argue that bitcoin developers could hav e, in early
days, mandated a different hash function in bitcoin. One, whi ch could be
mined with GPUs which are readily accessible in every comput er store.
Sticking with current NIST crypto standard, which however i s GPU-
incompatible, namely SHA-256 for mining, has immense conse quences.
In some sense bitcoin mining is not a mainstream system, beca use bit-
coin developers decided not to go this way. When crypto miner s started
making specialist ASIC bitcoin mining equipment, such devi ces were not
sold in any of traditional trusted merchants of electronics . They were
just sold by mail/Internet order by extremely few small comp anies in
remote countries. As a result, there were multiple scams, an d compa-
nies who failed to deliver these machines. Thus, bitcoin min ing has been
monopolized by larger investors. They were the only ones abl e not to
fall victim of dishonest providers (a repeated game). This i s NOT possi-
ble for individuals, who just order one unit from a foreign ma nufacturer
advertised on the Internet. All this is a consequence of a que stionable
choice of a crypto hashing algorithm, which has led to a large degree of
centralization of bitcoin mining. Criminals have sold non- existing bitcoin
miners to na¨ ıve investors, and a key problem was that there w as no reli-
able supply chain whatsoever for small individual buyers, c f. [51]. There
are additional problems which also lead to centralization. The reward
from one block is very high which leads to mining with pools, w hich are
a centralizing force and which have heavily distorted the Sa toshi original
dream of a peer to peer anarchic cooperative system. Then, th e incred-
ibly large electricity consumption by bitcoin creates seri ous problems.
One problem is that for many years critics claimed that bitco in is a de
fact Chinese crypto currency: as it is controlled by a few lar ge Chinese
miners1. Why this matters? It is easy tosee that miners can influence t he
future of bitcoin, such as adoption of upgrades. They can als o potentially
censor individual bitcoin transactions, or simply privile ge transactions
originating from China.
Looking beyond China, there is a group of countries which offic ially are
recognized as supporters of terrorism. These countries are then logically
subject to international sanctions which attempt to preven t them from
selling their oil and gas. It follows logically that they may use bitcoin
in order to re-sell their energy production surplus. We see t hat bitcoin
hash function is an unfortunate choice upheld by generation s of devel-
opers in 2009-2021. It makes that terrorists, or just ordina ry authori-
tarian regimes, are likely to mine bitcoins, which makes wid er adoption
of bitcoin problematic. However, due to the choice of the has h function,
this was never a problem with Ethereum, which can be mined wit h ev-
ery GPU available in most computer stores worldwide. More re cently
in 2021 we have seen GPUs which are decidated for gamers, and w here
crypto mining is disabled and its performance is degraded. W e have also
seen the emergence of Chia crypto currency which make extens ive use
of computer hard drives. We see that a bad crypto hash functio n choice
can be toxic (in bitcoin) and a good choice can be beneficial to some
segments of the computer industry.
This consideration was here for a long time, and countless ot her crypto
currencies hadin contrastmandated ahash function, whichc an bemined
even with ordinary PCs. However, this choice can backfire on t he crime
front. There are hackers who break into computers of million s of people,
in order to run networks of zombie machines, which are sold on the black
market in order to be rented to other criminals. We also have s cammers
and spammer activity. It appears that the most frequently mi ned crypto
currency by such zombie networks is Monero. This is precisel y because,
1This has started changing in 2018-2021 when Chinese authori ties have taken numer-
ous decisive steps in order to ban or discourage bitcoin mini ng.
it can be mined on more or less any PC. This has serious consequ ences
[26].
There is yet another side to this debate about the choice of a h ash func-
tion. GPU mining with any PC is what we need if we want privacy-
enhanced tech to protect us against mass surveillance, or ju st say bur-
glars, who will use the Internet to find out that when we are not at home.
Privacy can only improve, if millions of users can effectivel y conceal our
precious transaction data in a larger cloud of user activity . Thus, it is
preferable that a privacy enabling coin can be mined on any de vice. With
more users, we obtain better privacy for our financial transa ctions, and
credentials used to access them (password, cookies, etc). P rivacy is a
basic right [41], and also a necessity, in order to prevent va rious forms
of crime and theft, such as targeted phishing attacks agains t banks and
trading platforms, or targeted kidnapping cf. Section 10.3 .
7 Privacy Enhanced Payments
It is important to see what kind of privacy enhancing techniq ues are
available today in blockchain and bitcoin space. For bitcoi n we refer to
Section V in [43], dated 2017. A high level overview of variou s bitcoin
anonymization techniques and main privacy altcoins can be f ound inside
[43,41,9] and many others. In this section we also briefly rev iew these
techniques. Our approach is to look at the history of ideas an d crypto
technology adoption. The legality of privacy-enhanced coi ns and AML
regulations are covered in [41].
Bitcoin was a first very approximative creation of Satoshi Na kamoto [56].
It is easy to see that later bitcoin is NOT at all as Satoshi has imagined
it [23]. Nakamoto describes a system with a large number of pa rticipants
where everybody is mining, connecting to peers and engaging in trans-
actions. He did not anticipate that these 3 groups of people a re going
to become almost entirely disjoint, and that we will have hug e for-profit
mining farms using huge amount of energy, millions of wallet s connecting
through trusted gateways and vast amounts of activity, whic h are not at
all recorded on the bitcoin blockchain. Moreover the number of people
who actually run the P2P network remains very low, between 5, 000 and
11,500 in recent 5 years, source: www.bitdes.io. Bitcoin ha s many serious
problems [23,43] and this is precisely the challenge for the next genera-
tions of crypto engineers. It has led to creation of hundreds of “altcoins”
with distinct technical features trying to fix some of these p roblems like
cost, speed [53], number of transactions per second, resist ance to attacks
by powerful entities, etc. Among others, anonymity is not as good as it
initially seemed.
Initially, bitcoins are attributions of certain amounts of coins to pub-
lic keys. A possibility inherent to the so-called public key cryptography.
The owner of the private key can spend the coins. We have a pair of
cryptographic keys which match each other. To a large extent bitcoin
is an evolution of the French invention of the Chip-and-Pin b ank card.
A card has a private cryptographic key, which allows to certi fy that one
specific transaction was approved by the owner; the date the a mount and
other details are digitally signed. This is in fact done more than once,
with DES and with a public key algorithm, and at several stage s ever
before the transaction is completed [21]. Then, with bitcoi n the card is
no longer issued by a bank but by a community of peers, which cr eates
their own coins and attribute them to each other. Bitcoin is s imply the
next logical step in an evolution of commerce underpinned by public key
cryptography. The reliance on security and secrecy of crypt ographic keys
increases tremendously, and bitcoins which are not stored o n exchanges
are typically stored on secure hardware devices. These devi ces are evolu-
tions of a Chip and Pin banking smart card. More than 1 million Ledger
Nano devices have been sold in the last 5 years (source: ledge r.com).
Bitcoin can already offer pseudonymity: a user can use many di fferent
public keys, which donot identify him easily. Coins from diff erent sources
can be mixed at will. This works, if we do one transaction, how ever the
more activity we do, themore information is leaked toattack ers. Weneed
to use more advanced cryptography to do better. Interesting ly, almost all
known ways to enhance privacy are simply and directly linked to more
advanced types of public key cryptography. For example, wit h stealth
address techniques cf. [23], coins can be sent to a recipient and we are not
able to link this recipient to the address the recipient hims elf advertises
or makes public. It is simply a more private form of PK cryptog raphy
with additional tricks, where it is the sender not the receiv er, who creates
ephemeral bitcoin public keys used to receive incoming paym ents. This
is used in Monero crypto currency, one of the most frequently cited as
used or mined by criminals [19]. As a result, Monero is now an o bject
of sophisticated de-anonymization efforts for law enforcem ent purposes
[18].
Then ringsignatures, also inMonero, are anonymousdecentr alized group
signatures. A signer can be any of say 5 signers selected in on e trans-
action. The power of this technique is that the other 4 signer s do not
need to cooperate. Anyone is by default involved or can parti cipate in
anonymous transactions without their approval, enhancing the privacy
for everyone. ThenZero-Knowledge proofs can be used tocrea te methods
of generating transactions with a far larger degree of ambig uity about
who the signer is. It is a type of advanced public key cryptogr aphy, which
is notoriously difficult to make.
We see that crypto currency privacy innovation is primarily technical.
Possibly and arguably the market and the economy does not yet require
such high levels of privacy, however tomorrow it will. Cyber security is
a never-ending quest of enhancing privacy and anonymity and security
against fraudandtheftandstandards are always improvedan dextended,
in order to eventually do more than necessary, and they rarel y get sim-
pler. There is no end to this process. We need to understand th at ad-
vancedcryptographic techniquesare primarily here in orde r topolice bad
actors who cheat or steal the funds in various ways. They prev ent crime
and fraud as much as they might help criminals. When anonymit y weak-
ens the certitudes, new types of thefts and attacks can emerg e. There is
a genuine expectation to enhance privacy, with intention to build bet-
ter, fairer, and more secure payments systems. However, unq uestionably
there exist evil geniuses of crime, enabled by new opportuni ties such as
with ransomware and investment scams. Then we have rogue eng ineers
with dubious goals, or the fact that cryptography and comput er systems
fail most of the time and good security is very hard to achieve . Which
brings again the question of the quality of the source code [4 6] and of
regulating or just better organizing (self-regulation) th e chaotic payment
and investment innovation world.
8 The Myth of Crypto Anonymity
A popular misconception in the existing literature press an d blogosphere
istheallegedfull-anonymity(oratleast pseudonymity)of differentcrypto
currencies. In effect, the services responsible for maintai ning public se-
curity and safety fall victim to the misconceptions and fear -mongering
related to blockchain and cryptocurrencies, which is furth er amplified by
the pressures of the governmental agencies supervising the work of law
enforcement, with some countries going as far as endorsing t he idea of
making blockchain “illegal” or supporting the all-inclusi ve state control
over it [55]. In recent years we had multiple events of crypto currency
exchanges in regulated territories such as US or South Korea officially
de-listing several privacy coins due to regulatory pressure. It seems t hat
they have been asked by law enforcement agencies to produce e xcessive
quantities of SARs, which imply a certain cost burden, and ye t are not
the useful in crime prevention, cf. pages 58 and 66 in [65]. Ac cording
to the aforementioned interviews completed throughout the duration of
the European Commission PRIME Project, as well as the furthe r stud-
ies performed during the preparation of this paper, the most serious
concern among law enforcement community is the potential us e of the
crypto currencies as a vehicle for the financing of terrorism [36,30,65]
and specifically, transacting with countries subject to int ernational sanc-
tions. This is confirmed by the existing (and scarce) literat ure, where
the authors explicitly refer to blockchain or bitcoin (or ma ybe Monero
and similar privacy coins [19]) as an obvious method of provi ding funds
to extremist and terrorist organizations and radicalized i ndividuals (so-
called lone-actor terrorists or “lone wolves”) [2], [3]. Su ch observations
are amplified by the (exaggerated) popular perception of cry ptocurrency
being again completely anonymous [27] with some authors cla iming that
the payments and transfer completed with bitcoin are untrac eable [42,4].
The claim becomes more serious when security researchers in dicate the
existence of techniques such as crypto currency “tumblers” or “mixers”,
with specific examples of existing services which combine tr ansfers that
are happening at roughly the same time, and/or then re-route them to
the final destinations [6]. In general, tumbling techniques obfuscate the
provenance, possession, and movement of crypto currencies [12]. This
seems extremely strong but is not, due to the fact that the com muni-
cations are in general monitored, and their meta-data are re tained and
stored, which is enshrined in legislation in most jurisdict ions worldwide.
Until recently the law enforcement community has a very simp listic view
of these issues reflected by a very limited vocabulary and sim plistic an-
swers (such as: “forbid crypto currency”). Actual criminal or terrorists
operations are very few, and operate with cash primarily cf. [65]. Crypto
currencies are used primarily to solicit donations. Accord ing to Chapter
8 in [65] the role of crypto assets in terrorism financing has i ncreased in
2020 and the score was changed from low to medium.
It is not productive to just claim pretended anonymity or lac k thereof
dependingon whois talking. Inevitablyaspecialist viewis emergingwith
a more detailed vocabulary. In recent years, criminal inves tigations have
just shifted to specialized high-tech entities, which have developed the
necessary expertise andtools [18]. From anapplied cryptog raphypoint of
view, nothing is completely new and there is a long game where payment
cryptography and security develop in many steps [21]. Even M onero was
discovered to be traceable in open academic research, which methods
are now implemented by specialist firms [18]. The main point i s that
privacy is temporary, and can be disappear if additional fut ure events
take place, or if additional countermeasures against anony mity are in
place, orifadditionaldataarecollected andappropriatea ctionsaretaken
bycertainentities.Whensome academics aretryingveryhar dtoenhance
anonymity [4], others are wiser showing that privacy is impo ssible to
achieve in general [35].
We need to get the basic facts right. The primary reason to use cryp-
tography is to prevent theft and enable more business to make remote
payments easier. Cryptography makes owning and controllin g assets eas-
ier. It also creates new types of thefts which have never exis ted before, cf.
[52], [54] however this is now within limits of cryptographi c engineering
which is fragile and knows many points of failure. In particu lar prob-
lems at operation due to human factors cf. [52], [54] and unex pected
additional events which are deliberately engineered [35]. A mix of cryp-
tography and secure hardware devices with anti-cloning pro tections is
nevertheless an undeniable helper at the heart of our “Chip a nd Pin”
bank cards and today’s bitcoin wallets. Users can physicall y control pay-
ments and authorize transactions themselves and this crypt o + secure
hardware revolution was inevitable and was started by the ba nks them-
selves as early as in the 1980s [21]. A secure hardware device [21] with a
private cryptographic key has three primary functions. One is to prove
authenticity and to authorize payments or transfers remote ly, over large
distances while minimizing disclosures. Another is to prev ent theft, for
example through tamper-resistance and user authenticatio n with a PIN.
The third is an anti-clone functionality, which is very stro ng with digital
signatures, because the private key can be used but not compr omised,
and it will never even leave the secure element inside the sma rt card. No
one can obtain or extract it, and the bank card is already a tru stless pay-
ment system because users need only to trust themselves, so t hat they
no longer need to trust the bank which does not know the secret keys.
Inevitably, payment industry embraced this technology, as on all the
three accounts (remote acceptance, anti-cloning, anti-th eft), it is vastly
superior to any payment technology, which was developed ear lier, for ex-
ample with paper money and bank notes. New forms of electroni c money
and new payment technologies are driving old solutions out o f business
(potentially) where cryptography is a crucial ingredient. There is no end
in making financial systems more robust and less prone to subt le insider
threats and attacks, and such systems naturally distrust ev ery single
transaction participant, including the banks and their com puter systems
themselves. Interestingly, anonymity was yet totally abse nt. It arrives to-
day, three decades later, and it comes in many different flavor s, such as
receiver anonymity, sender anonymity or un-linkability, t race-ability for
transfers, etc. Now it crucial to see that anonymity is not so mething that
cryptographers produce, or something they are good at (!). C ryptogra-
phycan traditionally encryptdata, which is not really done in blockchain
where ledgers are highly transparent, designed for broader verifiability.
Instead, cryptography is meant to authenticate payments an d transac-
tions through hashing and digital signatures. This part of c ryptography
is very solid, and the primary reason to use cryptography is t hat it solves
the theft and remote authorization problems extremely well , as no one
can steal bitcoins by forging a digital signature. This stro ng ability is the
primary reason why bitcoin does not need a bank or a notary any more,
and anyone can be their own bank if they wish to. However, it is a mis-
conception or it is rather na¨ ıve to believe that cryptograp hy is here or
even that it can at all solve personal and financial privacy pr oblems. We
have a broad spectrum of “privacy enhancing technologies” a nd a lot of
innovation such as Zero-Knowledge proofs, all of which howe ver are in
fact imperfect. Yet privacy is potentially impossible to ac hieve in general
[35]. All we need is to apply current law enforcement and AML r ules [41].
Privacy does not really exist, when the law enforcement has t he ability
to monitor all our communications metadata, which is a stron g legally
enforced capability in most jurisdictions. There is only a h andful of well-
identified geographical hotspots for illicit activity [18] . Cybersecurity is
in general an endless game between attackers and defenders, and even
if cryptography makes unbreakable locks, so that bitcoins c annot be al-
tered or stolen even by very powerful entities, modern crypt ography is
yet weak and poorly suited to solve or address our privacy pre occupa-
tions. In fact potentially no technology can solve these pro blems in a
satisfactory way [35]. The law enforcement and security ser vices simply
need to adapt and pay more attention to technology (and crypt ography),
which works on both sides, helping attackers and defenders a like.
9 Challenges for Law Enforcement
A more balanced assessment comes from the United Kingdom Nat ional
RiskAssessmentofMoneyLaunderingandTerrorist Financin g[64]which
– in 2015 – rated digital currencies as a low risk, saying that (at the time)
the criminal use of crypto currency is focused predominantl y on the on-
line market places for the sale and purchase of certain illic it goods and
services. The same report indicated that such risk could ris e in the future
and the current use of digital currency as a method by which te rrorists
raise or movemoneyout oftheUKprovides aviableand working method
of doing so [8].
Law enforcement serious concerns also include the relative ease in which
the perpetrators, such as the terrorist organizations or or ganized crimi-
nal group operatives, can increase and enhancetheir operat ional security.
They may, for example, use anonymous email services for the p urpose of
setting-up the crypto currency wallets and – for further aut hentication
– use the anonymous prepaid phones [2]. Although some countr ies intro-
duced laws which theoretically require the identification a nd registration
of the mobile phone and SIM-card buyers, it is technically ve ry easy to
obtain any numberof SIM-cards usingtheservices of theso-c alled “straw
buyers” or “smurfs”, that is persons who offer their identific ation docu-
ments in exchange for money [37], the alternative being the p urchase of
virtual, pre-registered SIM-cards.
From the point of view of the law enforcement, the additional enhance-
ment of operational security for the criminal offenders and t errorists
wishing to exploit blockchain technologies in the form of cr ypto currency
is the availability of bitcoin (or other virtual currency) a t Automated
Teller Machines (ATMs) and the freestanding electronic pay ment con-
soles enabling purchase of and payments in bitcoin. For exam ple, such
un-monitored (lacking any kind of surveillance) consoles a re available in
Romania – and they may be used with the application of the simp le op-
erational security techniques mentioned above (single-us e, anonymous e-
mail accounts, single-use “burner” mobile phone and prepai d SIM-card)
[8].
The examples of the threats that raise the concerns of the law enforce-
ment and counter-terrorism agencies in regards to crypto cu rrencies in-
clude the online postings by the supporters of the so-called ISIS (or ISIL:
the Islamic State or Iraq and the Levant) that include YouTub e videos,
discussion forum links and links to research and anonymity p rovided by
bitcoins, where direct references are made to using digital currencies to
transfer funds into countries where conventional or tradit ional methods
of financial transactions are difficult because of lack of netw ork capacity
or government surveillance and regulation [3]. The analyst s suggest that
Bitcoins were used in a number of successful attacks, such as the bomb-
ing attacks conducted by the ISIS-inspired lone wolf on the s hopping
mall in Jakarta, Indonesia in 2015, and in the November 2015 c oordi-
nated terror attacks in Paris. The same authors indicate, th at the Inter-
net sites associated with terrorist organizations have sta rted to collect
donations in bitcoins [3]. Other researchers state explici tly that, given
the lack of adequate controls over transactions with bitcoi n, the risk of
the anonymous transactions between entities financing terr orism is ex-
tremely high [55]. Some scholars consider crypto currency t o be the next
step in the evolution of the more traditional ways of financin g terrorism
internationally. They refer to the ancient system called Ha wala, used
in the Middle East and Asia to transfer funds across borders i n a safe
and convenient manner: funds are deposited with the hawala b roker who
arranges for the funds to be available from another hawala br oker in a
different country (both hawala brokers then settle accounts through the
normal process of trade). Hawala can be used for terrorism fin ancing and
money laundering because funds do not actually cross border s, removing
the international money trail [3]. The process may be consid ered slow
and inefficient nowadays, but with the development of new tech nologies,
such as crypto currency, it can be significantly accelerated , providing
that the contemporary “brokers” (or intermediaries) are tr usted, thus
minimizing the risk of the authorities learning about the il legal activity.
Some security experts indicate, that – apart from the terror ism financ-
ing - crypto currencies are an ideal vehicle for money launde ring, bribery
and financing of illegal activities. The rationale behind it is to disguise
the origins of illicit proceeds throughout a series of trans actions prior to
integrating the crime proceeds into the legitimate financia l system. In re-
gards to use of crypto currency, the process (divided into th ree stages of:
placement, layering and integration) looks as follows [14] : In the place-
ment stage the crime proceeds are introduced into the financi al system
by acquiring crypto currencies. After the crime proceeds ha ve entered
the financial system, the perpetrator engages in a series of t ransactions
to distance the funds from their source. It is the layering st age, where the
funds may be channeled through the purchase of crypto curren cies or by
transferring money electronically through a series of cryp to currency ac-
counts. It can be further disguised as payment for goods and s ervices or
through the use of intermediaries who purchase crypto curre ncies under
the reporting threshold in countries where such currencies are regulated
(to avoid triggering identification or reporting requireme nts). In the final
stage of integration, the “cleaned” money is introduced to t he legal mar-
ket, where it appears to be legally earned [14]. Obviously, s uch stages of
money transfer and money laundering are present also in the m ore “tra-
ditional” forms of introducing the proceeds of crime to the r egular legal
market, but the aforementioned methods of operational secu rity and the
accessibility plus the sheer speed of transactions facilit ated by the use
of crypto currencies significantly improves the pace and sec urity of such
dealings, providing that the persons involved maintain the operational
security algorithms at all stages of their behavior.
According to the most recent report by RAND Corporation [10] , there
is little evidence of the adoption of crypto currencies by te rrorist orga-
nizations, and little motivation to do so. This is changing a nd lone-actor
extremists and loosely associated groups are now likely to u se crypto
currencies, simply because these systems are nowadays very widely used.
10 Crypto Currency Related Crimes: Overview
Classification and Stats
Our research indicates that criminality involving crypto c urrency allows
for profits that are much higher than with conventional felon ies. In ad-
dition, stealing virtual money appears to be less risky than robbing a
bank or selling drugs. In this section we will attempt to clas sify crypto
currency crimes in several categories and sub-categories. We are not the
first to propose this type of classification, see [13] which do cument is no
longer publicly available, and has a broader focus on polici ng the whole
so called dark web activity. Our approach is more basic and ce ntered
around payment technology: we “follow the money”. Our goal i s also to
achieve some degree or proportionality: we spend more time o n crimes
which were prominent in the recent years and had a large econo mic or
public policy impact, cf. [17]. Our initial short classifica tion has 9 major
categories, which are not entirely disjoint, and which are a s follows:
1.Crypto Hacks . Crypto currency itself is hacked. This doesn’t hap-
pen very often but the impact can be enormous. Major issues ca n
be as follows.
(a) The peer network malfunctions or attackers achieve impo rtant
network advantage (e.g. censoring or undoing transactions ).
(b) The integrity and authenticity of digital signatures is broken.
(c) The integrity of blockchain itself is compromised due to a hash
function weakness.
(d) Smart contracts or cross-chain swaps or oracle/referen ce servers
fail to work correctly e.g. in DAO heist with 50 M $losses [63].
(e) The system is secure but implementation is insecure (e.g . with
side channel attacks the private key of multiple users can be
compromised and all their coins stolen).
(f) There is failure at operation, for example with bad rando ms or
weak passwords [52,54].
2.Thefts. A crypto currency exchange or a business entity is hacked
or dishonest, and money is stolen.
(a) Indiscriminate loss: the whole exchange is a victim of a t heft.
Such events are typically widely publicized, e.g. [15] and p ages
22-32 in [18], one notable exception was Crex24 hack [18].
(b) Fraudulent withdrawal: a hacker manages to impersonate a user
and withdraw his funds.
(c) Fraud at payment deposit stage: Server that accepts cryp to cur-
rency (e.g. online market place) is hacked, money is stolen.
(d) Client using crypto currency is exposed in interaction o r hacked,
his money is stolen.
3.Investment Fraud@Exchanges.
(a) Pure ponzi schemes. For example with OneCoin, PlusToken and
Wotoken and similar, several billions of dollars in deposit s were
collected from naive investors. cf. [18] and [11].
(b) A crypto currency operator deliberately creates barrie rs or lim-
itations: disabling withdrawals or deposits, banning cert ain cat-
egories of users, creating fake technical incidents, etc.
(c) A crypto exchange will list or delist coins on false prete xts in
order to manipulate the market.
(d) Promoting alternative coins which have little or no valu e. Pro-
duction of fake news about crypto coins. For example the Wall
Street Journal have once written that GAW miners was the
world’sfastestgrowingbitcoinminingoperation.Laterth efounder
was charged with defrauding hundreds of individuals around the
world for 9 M $, cf. [38].
(e) Dishonest use of information provided by users or trader s, e.g.
making financial bets against customers.
4.Exit Scams. They can occur not only at exchanges.
(a) Users at a crypto exchange cannot withdraw funds due to ar bi-
trary decisions or on false technical pretexts.
(b) Adopters of a specific crypto currency or ICO investors se e the
sellers of this token disappear.
(c) Failure to deliver coins: customer pays for crypto curre ncy but
he doesn’t receive any.
(d) With miners: see 5.c.
5.Software and Hardware Scams provide malicious or fake tools
or services:
(a) Insecure wallets which expose users to thefts.
(b) Criminals recompile and modify some free open-source so ftware.
(c) Failure to deliver miner equipment paid for by customers .
(d) Miners with backdoors [16].
6.Web Scams : a fraudulent web site imitating a legit company which
is attracting investor activity e.g. fake exchanges.
7.Malware can steal crypto currency at one of.
(a) At server/exchange side.
(b) At client wallet side.
(c) Or mining crypto currency at user expense.
8.Ransomware/Extortion . Crypto currency can be used for ransom
payment with blackmail or kidnapping etc.
9.Law Enforcement Failures .
(a) Police cannot seize crypto currency because the owner do es not
collaborate, for example he does not reveal his password.
(b) Crimesarespecificallycraftedtooperateacrossjurisd iction bound-
aries or with unreliable offshore business entities.
(c) Concealingmonetaryflowsfromtheauthorities, e.g.mon eylaun-
dering with crypto currency.
10.1 Scams, Ransoms and Other Blockchain Fraud
According to a recent crime report by CipherTrace from Febru ary 2021,
cf. [17], ever since 2019, fraud and misappropriation are th e dominant
form of crypto currency crime. Then come the thefts, and final ly we
have ransomware which comes 3rd. According to [11] scams acc ount for
54 %, while ransomware accounts for 7 % of criminal activity. We cover
ransomware in more detail in Section 10.2 below. In 2019 scam s have
received an enormous 10 billion dollars from naive investor s, cf. [18,11].
Another author have identified 132 scams defrauding investo rs for a total
of 16 billions of dollars since 2012, see [40]. Interestingl y after 2019, in
2020 the scam activity has apparently substantially declin ed cf. Fig. 1
in [17]. In 2020-21 we have seen many scams related to Covid. S cams
remain a very major problem at all times. They concern an extr emely
large population of more than 200 million amateur crypto inv estors [24]
and beyond.
10.2 Ransoms Are Back
A very major area of concern for the law enforcement agencies and se-
curity services is the potential behind the use of crypto cur rencies in
ransomware attacks. In the famous older case of the CryptoLo cker mal-
ware of 2013, the trojan in question was using the 256-bit AES cryp-
tography to encrypt user files so that they became irrecovera ble. Nearly
250,000 individuals and businesses around the world suffere d because of
the CryptoLocker attack, which earned an estimated 30 milli on USD for
its developer [27] in a period of just 100 days between mid-20 13 and May
2014 [42]. Following [11] the total amount of ransoms paid in 2019 was
about 100 M $in one year in 2019, and this amount has more than tripled
in 2020 to reach an estimated 350 M $. The ransomware attacks pose the
extremely serious threat where they victimize not only the i ndividuals
and small business, where the potential ransom is relativel y small, but
when they target banks, telecoms, health service and critic al national in-
frastructure. Since CryptoLocker, ransomware offenders tr ied to attack
among others: the telephone provider TalkTalk in November 2 015, many
banks etc. Bitcoin remains the primary method of paying the r ansom [8].
Privacy-enhanced coins such as Dash account for less than 2 % , cf. [44].
A major ransomware attack was certainly the coordinated, wo rldwide
WannaCry assault of May 2017. It infected 230,000 computers in 150
countries and the total amount of losses was estimated at 4 bi llion USD
[44]. Among the targeted institutions were the Deutsche Bah n, FedEx,
National Health Service (NHS) in the UK, governmental and Po lice in-
stitutions in several countries, airlines, universities, automobile manufac-
turers, etc. The attack was stopped within a few days of its di scovery due
to emergency patches released by Microsoft, and the discove ry of the kill
switch that prevented infected computers from spreading Wa nnaCry fur-
ther. A preliminary evaluation by security experts stated t hat the attack
originated from North Korea [59]. Then in 2018 and 2019 ranso mware
has declined, to explode with more force in 2020, cf. page 31 i n [11].
In 2020 the most prominent victims of ransomware were among o ther
Barnes&Noble, LG or University Hospital of New Jersey [11]. A signif-
icant observation in recent and major attacks, is that crimi nals publish
some of the data online in order to increase the pressure on th e victims
[11]. For example, in February 2021, the source code of the bl ockbuster
video game maker CD Project was stolen. The company declined to pay
the ransom. Then reportedly the code was then auctioned on da rk mar-
kets for 7M $, see [32].
This resurgence of ransoms in 2020 is not at all surprising an d seems
to be a logical consequence of two trends. The first is that los ses are
reported in dollars and clearly the bitcoin price has increa sed up to 8
times in 2020 making some older ransom strains programmed to request
certain standardized amounts in bitcoins substantially mo re profitable.
A second observation is that, with the global Covid pandemic , businesses
are more desperate to maintain business continuity at all co sts, and thus
more inclined to pay ransoms, for example Travelex reported ly paid 2.3
million in ransom, see [18]. In 2020 we have seen malicious Co vid-related
Android applications which spied on users, encrypted files a nd asked for
ransoms, cf. page 23 in [18]. Peer to peer financial systems ar e a substan-
tial challenge for the law enforcement and security agencie s and have a
set of characteristics making them very attractive to the cr iminal and
terrorist enterprises [2]: a certain level of anonymity (if the necessary
operational security precautions are implemented), globa l reach (being
geo-political border agnostic [29] and allowing to carry on the transac-
tions through third countries), the speed (facilitating th e quick transac-
tions thus limiting the chance of them being intercepted or b locked), low
cost to use (dependent of the fees of the intermediaries in ca se of the
crypto currency transfer, tumbling and exchange), relativ e ease of use
(being increasingly user friendly – as it is true with most of the emerging
technologies, TOR being the prime example), difficulty to tra ck by the
authorities, and the lack of legal regulations and control o ver the decen-
tralized crypto currency ledgers servers and distributed o r collaborative
electronic systems.
10.3 On Kindnapping Front
There are several high-profile kidnappingcases where ranso ms in millions
of dollars were paid. For example, in 2017, a criminal group k idnapped
an employee of a United Kingdom-registered crypto currency exchange
in Kiev, Ukraine. The kidnappers released the victim on 29 De cember,
reportedly following a ransom payment equivalent to USD 1 mi llion in
Bitcoin. Several similar events were reported and we refer [ 20] for a com-
prehensive review of this topic. It appears that crypto curr ency ransoms
are not the norm or not yet for kidnappings in general. Instea d, kid-
nappers are targeting wealthy individuals in general. It ap pears that in
several cases of kidnapping where crypto currency payments were re-
quested, the kidnappers were able to competently exploit op en or public
information to identify individuals, whose crypto currenc y wealth have
been publicly known.
11 New Criminal Battlefield Revisited
In 2011, when the notion of the “Internet as a new criminal bat tle-
field” was first introduced [28], the blockchain concept and t he crypto
currencies were virtually unknown outside of the specialis ts and crypto
enthusiasts’ circles. However, for these and many other tec hnological de-
velopments, the use of any technology for the purposes that a re (directly
or potentially) illegal in nature, is somewhat normal, as th ere is no rea-
son why criminals would not adopt a new tool especially if it i s readily
available, easy to use and inexpensive. Additionally, the y ounger gener-
ation of criminals we are dealing with are more likely to embr ace the
Internet and technology and will consider it to be their natu ral environ-
ment. Therefore, we can assume that the frequency of on-line based and
cyber-enabled crime increases in parallel to the generatio nal changes.
It needs to be stressed that the Internet and new technology a re used
at all stages of criminal behavior. From the planning and pre paration,
through the completion of an act, destroying or altering the evidence,
up to the stage of preparing an alibi or manipulating witness es [28]. We
could compare these technologies to a multi-purpose tool, w hich was not
originally designed to cause harm, but which was later skill fully and cre-
atively adapted, by those who found other and previously unf oreseen
applications for it. Now we should not ignore the fact that ce rtain very
specific types of crimes, and this is primarily ransomware , are specif-
ically enabled by this new technology. In the “old world”, it was harder
for criminals to receive payments without being caught or mo nitored.
Blockchain technology also offers the possibility to defer t he moment at
which the criminal takes the money for potentially unlimite d time, and
this is a completely new scenario that has never existed befo re. There-
fore we must also recognize that beyond being just a tool, blo ckchain
can genuinely be a disruptive technology and modify the land scape of
criminal activity in which criminals engage.
A useful comparison here is to the credit card payment histor y: there are
several countries including the UK, France and Malaysia whe re the in-
troduction of Chip and Pin technology [21] was motivated par tly by the
quick increase of crime and fraud rates. In the same way, the v ery possi-
bility of using credit cards to purchase goods over the Inter nethas played
a major role in crime, and created a pre-bitcoin situation wh ere stealing
data has become a potential moneymaker. Bitcoin has just amp lified the
necessity to innovate, in the areas of payment and the digita l economy,
and is the next logical step after the credit cards. First we h ad credit
cards since the beginning of the 20th century, cf. [21], then in 1990s the
credit cards have become trustless, in the sense that they po ssess private
keys which authenticate transactions and they are the only e ntity able
to authenticate payments so that our money is safe against fr aud even
when we do not trust the bank. It is easy to see that even the ban k
cannot forge or alter transactions made with Chip and Pin cre dit cards.
Finally we have various forms of disintermediation. First o f all payment
companies such as VISA, Mastercard or PayPal have deprived t he banks
from substantial amount of income from payments, and the inc ome goes
to the multinational firms, rather than traditional high str eet banks.
Then we have a new wave of disintermediation, which seems “an archic”
like bitcoin. Strangely, money lands at the end in some large crypto
exchanges which are based in the United States primarily cf. [17]. Busi-
nesses of that kind have enjoyed phenomenal growth rates. Be ing a fan or
a user of bitcoin currency, in spite of undeniable extremely large impact
and popularity [24], is possibly somewhat a smoke screen which hides a
broader business reality. A large number of new unregulated para-bank
companies are competing for “geek” customers which are fans of bit-
coin and other innovations, and any method to capture their a ttention
and their deposits is good. Disintermediation and business competition
across borders is the main pattern. We simply observe banks l osing busi-
ness and customers to certain offshore businesses. Crypto ex changes can
now also compete wtih banks on getting funding from investor s. In 2021,
a major crypto currency exchange, coinbase, has successful ly become a
regular traded company on Nasdaq stock exchange with a marke t cap of
70 billion approximately at the time of writing, which is com parable to
some of the largest banks in the world.
Bitcoin is just a part of an inevitable trend in which payment technol-
ogy and financial markets must change to serve the needs of the global
digital economy better, and to respond and adapt to what the n ew tech-
nology can enable and offer. We have a continuous wave of busin ess
disruption, where jobs and business are moving to different j urisdictions.
This is hardly avoidable. The crime is evolving likewise, ta king new and
unprecedented forms
An interesting point, is that in parallel, blockchain creat es a new liberal
“geek” offshore jurisdiction where maths and code are law. In terestingly
it is also a self-policing jurisdiction. It is meant to regul ate itself or to
work in very different ways than the incumbent banking sector . We need
to recognise that our old financial system was very costly to r un, for
example the police forces of the whole planet collaborate on a very large
scale in order to police fake currency crime. This problem di sappears
with bitcoins, as un-forge-ability is a standard feature in modern cryp-
tography. It is not true that the security is systematically degraded or
that the lack of regulation is systematically a problem. On o ne side,
bitcoin facilitates certain specific forms of crime such as r ansoms, cf.
Section 6.2 or certain types of transgressions are allowed o r tolerated
in the crypto economy. Then on the other side, security is rei nforced at
many places. Accordingly, many crimes do not happen anymore . Many
attacks or thefts occur only in very specific circumstances a nd typically
one just cannot take bitcoins which are not theirs, because o f strong
cryptography, see Section 6.1 and Section 6.2.
11.1 Law Enforcement Shortcomings
A key problem is that the law enforcement is radically unprep ared to
deal with the new types of payment business and supra-nation al business
disruption, due to – among others – the administrative and le gal con-
straints, lack of funding (translating in majority of the co untries to the
inability to compete financially with the private sector whe n it comes to
hiring the properly trained and skilled IT experts), lack or expertise and
training, ineffective or non-existent exchange of informat ion, inability or
difficulty in cross-border cooperation, the general lack of k nowledge and
experience in regards tocyber-and cyber-enabledthreats [ 36]. According
to our recent interviews with the Intelligence and security practitioners,
the problem of the abuse of modern technologies by criminals and ter-
rorist requires the multi-disciplinary approach involvin g the co-operation
of the law enforcement, criminal justice, computer scienti sts and the in-
dustry [36]. No matter how clich´ e it might sound, the most si gnificant
improvement in the current situation can probably be achiev ed just by
raising awareness and education of all parties involved. Th is specifically
relates to the law enforcement community. Qualitative stud ies performed
during the EC FP7 PRIME Project [36], [37] research show that the level
of knowledge about modern technologies among the Police offic ers world-
wide is extremely limited. One of the objectives of this pape r is to make
the computer science community and IT industry aware of this fact.
Law enforcement professionals have a very vague understand ing of the
emerging technologies and their potential for abuse by the c riminal and
terrorist enterprises. Moreover, we have unrealistic expe ctations about
the potential and skills of the Police among the general publ ic and com-
puter science industry representatives. The so-called “CS I Effect” orig-
inally related to the impossibility to achieve (the alleged ) “standards”
in forensic sciences is currently starting to impact the pop ular expecta-
tions towards “cyber investigations”, resulting in na¨ ıve and improbable
anticipations about the skills and capabilities of the Poli ce forces. It is
thus necessary to re-consider and re-work the existing stra tegies and
methodologies of Police education. No matter how idealisti c it might
sound, in order to do it, it is necessary to involve the comput er science
industry in such a framework, as the rapid changes and develo pments in
technology render the traditional law enforcement trainin g based on the
well-established and long-term curriculum largely useles s. A highly plau-
sible approach [36] is to engage in public-private partners hips, with law
enforcement practitioners, lawyers, computer science exp erts and cyber-
forensics specialists. We recommend using the approach dev eloped by
the field of Crime Science, namely the crime-scripting, unde rstood as a
way of deconstructing an offence into its basic component act ions, and
also criminal horizon scanning [31], [5]. Recent developme nts in the area
are the new EU-funded project Titanium developing blockcha in analytic
tools named GraphSense and DarkNet Monitor. Interpol has a w orking
group and they have published at intepol.int web site a draft taxonomy
under development of crimes involving darknets and crypto c urrencies,
see [33].
11.2 Difficulties and Perspectives
Finally, it is important to re-consider the future of the leg al framework in
which the well-established, emerging and forthcoming tech nologies will
operate. As mentioned earlier [29], cyber-enabled crimina l or terrorist
acts are geopolitically border agnostic, as their perpetra tors can operate
simultaneously in several jurisdictions, and the flexibili ty and potentially
high anonymity available to them hamper the potential for pr evention,
detection, and prosecution. From the legal standpoint, the international
harmonization and unification of the “cyber law” is a slow pro cess. In
theory, it is more likely in the case of operational and proce dural do-
main (including problem prevention, identification, and re cognition; risk
detection and assessment; evidence investigation, gather ing, and presen-
tation; and crisis management regulations), although – as o ur findings
imply [36] – numerous substantial discrepancies still exis t.
11.3 Restoring Audit with Focused AML
Money laundering cannot be just policed by banning some cryp tographic
inventionssuch as ZeroCash, butrather by“focused AML”, te rm used by
a US law firm in [41]. In a focused AML approach we disclose addi tional
private transaction information to our bank or exchange, bu t not to
a larger crowd. For example, many systems such as Stealth Adr ess in
Monero or ZeroCash have socalled “view keys”for this purpos e. The fact
is that these keys enable to establish high standards of proo f. They are
cryptographically unforgeable, andpotentially theyhave more valuethan
current AML reporting standards at crypto exchanges, where documents
submitted could for example be fake, or authentic which were carefully
crafted to show an inaccurate picture.
12 The New Economy is Here and it Deserves
a Legal Protection
In the discussion of the problem of the Internet as a new battl efield, we
see that law enforcement and the criminal offenders compete f or domi-
nation [29]. At the same time technology solutions and financ ial services
compete for the sake of business, with well-known moral haza rds, namely
compromising public safety and security in order to acquire a larger mar-
ket share. It was noted that although it might seem natural th at such
issues should be a domain of politicians and legislators, th e design of
universal strategies and intervention measures is actuall y in the hands of
scientists, industry, and end-users (security services). A central role is in
fact going to be played by new emerging disciplines and new em erging
professions, which are all in sense also new chapters of mode rn Computer
Science. In particular we have the topics of Big Data, Applie d Cryptog-
raphy, Artificial Intelligence and Deep Learning. We see tha t anonymity
and innovation is disruptive for law enforcement sector as m uch as they
are for thebankingandpaymentindustry.Wecan anticipate t hatthe law
enforcement sector needs to change their culture radically in the sense
that they need to become knowledge-based, employ radically more tech-
nicians, research engineers, data scientists, and special ists with PhDs
in related fields. Following [4] criminal activities involv ing crypto cur-
rencies require to develop novel analytic methodology and t ools. Apart
from the cultural shift, it is very difficult to expect the inte rnational
legal community to come up with a comprehensive vision or str ategy,
when dealing with challenges related to emerging technolog ies and busi-
ness and software ecosystems which lead to radical changes i n criminal
landscape. This is a process, and it requires collaboration on the inter-
national scale [13]. As of today, we do not yet have an all-inc lusive and
up-to-date, universally agreed set of solutions [13]. One o f the reasons
for this is that different nations have very different levels a t which they
have embraced new technology and cyber-security. Another p roblem is
that different nations have different views on free speech, ci vil liberties
such as usage of private communication or privacy enhancing technology,
and personal or consumer rights and freedoms. Some countrie s will be
tempted to address the emerging cybercrime problems from th e point of
viewof nations, which stay onthe topof newtechnology imple mentation.
Other nations will try to avoid complication and maybe say: “ forbid or
regulate the payment technology we do not understand or mast er well
enough”. Instead of having the philosophical or typical pro blem of main-
taining the delicate balance between two fundamental expec tations: the
right to privacy which is recognized as a human right, and the right to
safety which is more about how industrial nations protect th eir citizens
and businesses. We observe that different nations approach t he problem
differently due to their culture and history.
12.1 On Risks of Banning or Blacklisting
Money launderingcannotbe just policed bybanningsome powe rful cryp-
tographic inventions such as ZeroCash. We need to embrace ne w tech-
nology rather than see each new technology development as an anomaly,
which we cannot handle. Banning is in fact hurting business, innova-
tion and the 99.9 % of users. In [1] authors study payments fro m the
point of view of network topology and connectivity and define fitness
as the ability of the node to attract new connections. Then th ey show
that the accounts that have high fitness are of two sorts. They are either
short-lived and indulge in malicious or criminal activitie s, or they are
long-lived and they represent large organizations. Here is the dilemma:
policing crime without negatively impacting businesses.
13 Perspectives of Regulating and Policing
Financial Innovation Provider Businesses
We need a pro-innovation and pro-business agenda: whether t he new
technologies such as blockchain are just a “hype” or not, nat ions are
trying to attract the innovators to create jobs in their coun tries. In some
sense new technology is always an uncertain bet from the poin t of view
of investing in it, and assessing how successful or how influe ntial they
might become. If so, it is perfectly logical for the countrie s to embrace
disruptive innovations, maintaining their original parad igm of indepen-
dence and freedom, and hoping that some unicorn companies ar e going
to become rich and pay taxes, in a friendly jurisdiction whic h figures out
the right mix of freedom to innovate and regulation. Such phi losophy
was attempted in New York with Bit License and in France with A MF
licencing. Then we deal with the associated criminal activi ty as it comes,
with the idea of proportionality. In contrast, over-regula ting and plan-
ning to be ten steps ahead of criminals, is an expensive strat egy for law
enforcement agencies and security services.
13.1 Can Safeguards which Prevail In Financial
Regulation Survive in the Immaterial World?
We need to look at the spirit of laws and regulations which hav e under-
pinned the payment and see that many ideas are obsolete with n ew tech-
nology, and also with how modern capitalism works at large. T he whole
idea is that a company can issue a “security” and this process must be
very heavily policed by lawmakers and law enforcement agenc ies such as
SEC in order to protect the investors is potentially obsolet e. Since 2000
numerous financial scandals and investment bubbles have sho wn that
investors do and will fall into traps and buy the trendy share or coin,
which is not always even trying to do a good job as an investmen t. Even
properly regulated securities on the stock markets have fai led to protect
millions of investors in practice.
The crucial question here is that issuing a security is heavi ly regulated,
and is expected to be a reliable link between what investors e xpect to
own or invest in, and some material business operations or as sets. We
want investors to know what they are buying and investors are entitled
to be treated fairly and get the same share of corporate profit s in pro-
portion, as for example the founders of the company. However the whole
idea of business assets being material and subject to polici ng by accoun-
tants, have become totally obsolete in the recent years. Now the 5 top
tech companies on S+P500 stock index are worth more than 20 % o f the
whole index, and the combined value of US tech companies in ge neral is
more than the whole stock market in Europe. The key observati on is that
a great part of what we see on the stock market are immaterial a ssets
and intangible property, or assets which are non-exclusive . An Internet
platform or system can benefit potentially anyone, and it is n ot possible
to monopolize the virtual wealth stemming from the network e ffects, the
economies of scale and collaboration easily. In this light, it is wrong to
believe that bitcoin benefited only some scammers or specula tors. On
Internet forums we observe that there is a broad population o f people,
who are not unhappy with the profitability of their crypto cur rency in-
vestments. In some sense investors were right to invest in th e Internet
bubble in 2000 and in Tesla stocks or Bitcoin and Ethereum tod ay. They
understood that tremendous amounts of wealth, power and inn ovation
are at stake, and are going to be created somewhere here, for e xample
with new electric cars.
In fact it remains difficult to capture, own or monopolize all t his wealth.
Stock markets and government regulation of how securities a re issued
have frequently and again, failed to protect against invest ors investing
in wrong assets. Some crypto currency enthusiasts contend t hat crypto
currency companies will soon be allowed to issue securities and they will
be fully compliant with US Laws and regulations. This is no ha ppen-
ing yet and so far such companies have been facing severe regu latory
backlash. In July 2021 many companies (e.g. Binance, UniSwa p) have
suddenly halted trading crypto tokens which get too close in to imitat-
ing stock market shares. Arguably however, one day, maybe in 10 or
20 yeas, securities could be eventually issued on blockchai ns. This is be-
cause blockchains canprovideimportant andextensivenewf unctionality,
which does not exist in current financial markets.
In our view, both technology and law enforcement should work in their
own way, on improving technical standards and safeguards. U ltra conser-
vative blacklisting, banning or fear mongering attitudes, are rarely com-
patible with disruptive new technology which creates new jo bs, new ways
of doingthings, or newways tosecure propertyor engage inbu siness con-
tracts, and this is how our economy evolves of improves. Cryp to market
regulations should protect the small to average investors, primarily not
against taking active part, and effectively co-funding the d evelopment
of new disruptive technology, but against rogue traders, ro gue develop-
ers, faulty cryptography standards, market manipulators, and predatory
market mass surveillance leading to targeted fraud and crim e activities.
14 Conclusion
Financial crime or tax avoidance are serious problems, whic h however
should not conceal the fact that it is hardly possible to forb id users from
using alternative unregulated financial services. Large so ftware ecosys-
tems such as Bitcoin or Ethereum blockchains are nowadays co nsidered
as good investment opportunities, and have year after year g ained an im-
mense popularity. Bitcoin did not go to zero, instead it has c rossed the
60,000 $mark. Many cryptocurrencies havehigh prices duetoartifici ally
created scarcity and poor investment opportunities elsewh ere. However
there is no scarcity of developers bringing a plethora of inn ovative DeFi
and crypto protocols to market. The blockchain ledgers grow every day
and occupy hundreds of gigabytes. There is no scarcity of tra nsactions
either. As of July 2021, blockchain has 200 million of users [ 24]. Every
single day 1 million bitcoin wallets are in use.
14.1 Actionable Recommendations
We need to uphold the free market economy while protecting in vestors
from predatory forces and thieves through better privacy, b etter cryp-
tography and better cybersecurity. If we look at the figures w e see that
the biggest threats to individual customers are not thefts o r ransoms, but
primarily scams whichsimply imitate theinvestmentopport unities inthe
new economy [11,40]. Then, comes ransomware, a major threat on the
rise, where victims are primarily businesses. Crypto curre ncy coins offer
some unique features such decentralized ownership and prog rammable
decentralized trading which brings to us, a completely new w orld of busi-
ness functionalities.
A controversial feature is privacy for all parties involved in financial
transactions. Arguably future blockchain and cybersecuri ty standards
should operate on the principle of proportionality and tack le the actual
cybercrimeratherthanbanandforbidinadvance.Proportio nalitywould
suggest to enable more privacy in the financial sector: it ben efits the
99.9% of legitimate activity. This even though it also helps the 0.1 % of
illicit activity which remains small [17]. According to a re cent paper by
a specialist US lawyer firm [41] the societal or economic bene fits from
privacy coins are substantial and “substantially outweigh their risks”.
According to this law firm, the situation is broadly under con trol, and
existing AML regulations already “properly and sufficiently cover” those
risks w.r.t. money laundering.
Acknowledgements
Although the preparation of this paper was not directly fund ed by a
designated research grant, our research and expertise have been sup-
ported indirectly from different grants and by our universit ies. Nicolas
Courtois has also worked as a consultant for several crypto c urrency
companies in UK, France and the US. His background and inspir ation
in his security engineering and payment security research a nd teaching
were eight years spent working as a crypto R&D engineer for th e French
smart card industry (Bull CP8, now part of Gemalto), and seve n years
of interactions and engagement with the crypto currency com munity. He
was also funded by the EU with project RIBS on Resilient Infra struc-
ture and Building Security. Kacper Gradon acknowledges the support of
UCL Jill Dando Institute of Security and Crime Science. Some of the
ideas expressed here arouse during the preparation of the de liverables
of the EU-funded PRIME Project and throughout the duration o f the
U.S. State Department Fulbright Scholarship at Institute o f Behavioral
Science University of Colorado at Boulder.
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|
{
"id": "2109.01047"
}
|
2011.13837
|
A theory of transaction parallelism in blockchains
|
Decentralized blockchain platforms have enabled the secure exchange of
crypto-assets without the intermediation of trusted authorities. To this
purpose, these platforms rely on a peer-to-peer network of byzantine nodes,
which collaboratively maintain an append-only ledger of transactions, called
blockchain. Transactions represent the actions required by users, e.g. the
transfer of some units of crypto-currency to another user, or the execution of
a smart contract which distributes crypto-assets according to its internal
logic. Part of the nodes of the peer-to-peer network compete to append
transactions to the blockchain. To do so, they group the transactions sent by
users into blocks, and update their view of the blockchain state by executing
these transactions in the chosen order. Once a block of transactions is
appended to the blockchain, the other nodes validate it, re-executing the
transactions in the same order. The serial execution of transactions does not
take advantage of the multi-core architecture of modern processors, so
contributing to limit the throughput. In this paper we develop a theory of
transaction parallelism for blockchains, which is based on static analysis of
transactions and smart contracts. We illustrate how blockchain nodes can use
our theory to parallelize the execution of transactions. Initial experiments on
Ethereum show that our technique can improve the performance of nodes.
|
http://arxiv.org/pdf/2011.13837v4
|
Massimo Bartoletti, Letterio Galletta, Maurizio Murgia
|
cs.CR
|
cs.CR
|
Logical Methods in Computer Science
Volume 17, Issue 4, 2021, pp. 10:1–10:44
https://lmcs.episciences.org/Submitted Nov. 30, 2020
Published Nov. 18, 2021
A theory of transaction parallelism in blockchains
MASSIMO BARTOLETTI, LETTERIO GALLETTA, AND MAURIZIO MURGIA
aUniversity of Cagliari, Italy
e-mail address : bart@unica.it
bIMT School for Advanced Studies, Lucca, Italy
e-mail address : letterio.galletta@imtlucca.it
cUniversity of Trento, Italy
e-mail address : maurizio.murgia@unitn.it
Abstract. Decentralized blockchain platforms have enabled the secure exchange of crypto-
assets without the intermediation of trusted authorities. To this purpose, these platforms
rely on a peer-to-peer network of byzantine nodes, which collaboratively maintain an
append-only ledger of transactions, called blockchain . Transactions represent the actions
required by users, e.g. the transfer of some units of crypto-currency to another user, or
the execution of a smart contract which distributes crypto-assets according to its internal
logic. Part of the nodes of the peer-to-peer network compete to append transactions to the
blockchain. To do so, they group the transactions sent by users into blocks , and update their
view of the blockchain state by executing these transactions in the chosen order. Once a
block of transactions is appended to the blockchain, the other nodes validate it, re-executing
the transactions in the same order. The serial execution of transactions does not take
advantage of the multi-core architecture of modern processors, so contributing to limit the
throughput. In this paper we develop a theory of transaction parallelism for blockchains,
which is based on static analysis of transactions and smart contracts. We illustrate how
blockchain nodes can use our theory to parallelize the execution of transactions. Initial
experiments on Ethereum show that our technique can improve the performance of nodes.
1.Introduction
Decentralized blockchain platforms like Bitcoin and Ethereum allow mutually untrusted
users to create and exchange crypto-assets, without resorting to trusted intermediaries.
These exchanges can be either simple transfers of an asset from one user to another one, or
they can be the result of executing complex protocols, called smart contracts . All the actions
performed by users are recorded on a public data structure, called blockchain , from which
everyone can infer the amount of crypto-assets owned by each user. The disintermediation
stems from the fact that maintaining the blockchain does not depend on trusted authorities:
rather, this task is collaboratively performed by a peer-to-peer network, following a complex
consensus protocol which guarantees the consistency of the blockchain also in the presence
of (a minority of) adversaries in the network.
LOGICAL METHODSl IN COMPUTER SCIENCE DOI:10.46298/LMCS-17(4:10)2021© M. Bartoletti, L. Galletta, and M. Murgia
CC
Creative Commons
10:2 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Users interact with the blockchain by sending transactions , which may request direct
transfers of crypto-assets, or invoke smart contracts which in turn trigger transfers according
to the programmed logic. The sequence of transactions on the blockchain determines, besides
the balance of each user, the state of each smart contract. The nodes of the peer-to-peer
network process the transactions sent by users, playing either the role of miner or that of
validator . Miners group transactions into blocks, execute them serially to determine the
new blockchain state, and append blocks to the blockchain. Validators read blocks, and
re-execute their transactions to update their local view of the blockchain state. To do this,
validators process transactions exactly in the same order in which they occur in the block,
since choosing a di erent order could potentially result in inconsistencies between the nodes.
Executing transactions in a purely sequential fashion is quite e ective to ensure the
consistency of the blockchain state, but in the age of multi-core processors it fails to properly
exploit the computational capabilities of nodes. By enabling miners and validators to concur-
rently execute transactions, it would be possible to improve the eciency and the throughput
of the blockchain. Although there exist a few works that address this problem (we discuss
them in Section 1.3 below), their approach is eminently empirical, and they are focussed
only on Ethereum. A comprehensive study of the theoretical foundations of transaction
parallelism in blockchains would improve the understanding of these optimizations, and it
would allow to extend them to other blockchains beyond Ethereum.
1.1.Contributions. This paper exploits techniques from concurrency theory to provide a
formal backbone for parallel execution of transactions in blockchains. More speci cally, our
main contributions can be summarised as follows:
We introduce a general model of blockchain platforms, parameterized over the observables
and the semantics of transactions (Section 2). Building upon it, we de ne the semantics
of a blockchain by iterating the semantics of its transactions: this re
ects the standard
implementation of nodes, where transactions are evaluated in sequence, without any
concurrency. We show that the two most widespread blockchain platforms, i.e. Bitcoin
and Ethereum, can be expressed as an instance of this general model.
We introduce two notions of swappability of transactions (Section 3). The rst one is
extensional: two adjacent transactions can be swapped if this preserves the blockchain state.
The second notion | strong swappability | is intensional: two adjacent transactions
can be swapped is the static approximations of their read/written observables satisfy a
simple condition, inspired by Bernstein's conditions for the parallel execution of processes.
Basically, these conditions require that the observables written by a transaction are
not read or written by the other transaction. Theorem 3.12 shows that the strong
swappability relation is included in the extensional relation. Theorem 3.17 shows that, if
we repeatedly exchange adjacent strongly swappable transactions, the resulting blockchain
is observationally equivalent to the original one.
For Bitcoin, we show that the static approximations checked by the strong swappability
condition can be easily inferred by transactions: the least approximations of the written
observables are the transaction inputs and outputs, while those of the read observables are
the transaction inputs (Lemma 3.18). For Ethereum obtaining precise approximations is
more complex, because of its Turing-complete contract language. We discuss in Section 3.2
a few tricky cases, and we report in Section 5 our experience with a novel tool to statically
Vol. 17:4 A theory of transaction parallelism in blockchains 10:3
detect swappable Ethereum transactions. We further show that, for both Bitcoin and
Ethereum, strong swappability is stricter then swappability (Examples 3.21 and 3.25).
Building upon strong swappability, we devise a true concurrent model of transaction
execution (Section 4). To this purpose, we transform a block of transactions into an
occurrence net , describing exactly the partial order induced by the swappability relation.
We model the concurrent executions of a blockchain in terms of the step ring sequences
(i.e. nite sequences of setsof transitions) of the associated occurrence net. In Theorem 4.6
we establish that the concurrent executions are semantically equivalent to the serial one.
Finally, we describe how miners and validators can use our results to parallelize transactions,
exploiting their multi-core architecture (Section 5). An initial experimental validation of
our technique on Ethereum, which exploits a novel static analyser of Ethereum bytecode,
shows that there are margins to make it applicable in practice.
1.2.Overview of the approach: ERC-721 tokens. We illustrate the main elements
of our theory by considering an archetypal Ethereum smart contract, which implements a
\non-fungible token". A non-fungible token represents a digital version of real-world assets,
e.g. access keys, pieces of arts, and serves as veri able proof of authenticity and ownership
within a blockchain network. This kind of contracts are quite relevant: currently, token
transfers involve 50% of the transactions on the Ethereum blockchain [ tok], with larger
peaks due to popular contracts like Cryptokitties [You17].
We sketch the implementation of the Token contract (the full code is in the Appendix),
using Solidity, the main high-level smart contract language in Ethereum. This contract
follows the standard ERC-721 interface [ ESES ,FFB19 ] and de nes functions to transfer
tokens between users, and to delegate their trade to other users.
In Ethereum, a smart contract is similar to an object in an object-oriented language:
it has an internal state, and a set of functions to manipulate it. Users and contracts are
identi ed by their addresses .
The state of the contract Token is de ned by the following mappings:
mapping ( uint256 => address ) owner ;
mapping ( uint256 => bool ) exists ;
mapping ( address => uint256 ) balance ;
mapping ( address => mapping ( address => bool )) operatorApprovals ;
Tokens are uniquely identi ed by an integer value (of type uint256 ), while users are iden-
ti ed by an address (the address 0 denotes a dummy owner). The mapping owner associates
tokens to their owners' addresses, exists tells whether a token has been created or not, and
balance gives the number of tokens owned by each user. The mapping operatorApprovals
allows users to delegate the transfer of their tokens to third parties.
The following function transferFrom transfers a token from the owner to another user:
1function transferFrom ( address from , address to , uint256 id) external {
2 require ( exists [id] && from == owner [id] && from != to && to != address (0));
3 if( from == msg.sender || operatorApprovals [ from ][ msg.sender ]) {
4 owner [id] = to;
5 balance [ from ] -= 1;
6 balance [to] += 1;
7 }
8}
10:4 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
The require assertion at line 2rules out some undesirable cases, e.g., if the token does
not exist, or it is not owned by the from user, or the user attempts to transfer the token
to himself. Once all these checks are passed, the transfer succeeds if the sender of the
transaction owns the token, or if he has been delegated by the owner (line 3). The mappings
owner and balance are updated as expected (lines 4-6).
The function setApprovalForAll delegates the transfers of all the tokens of the sender
to the operator when the boolean isApproved is true, otherwise it revokes the delegation:
function setApprovalForAll ( address operator , bool isApproved ) external {
operatorApprovals [ msg.sender ][ operator ] = isApproved ;
}
Users interact with contracts by sending transactions to the blockchain. Transactions
involve the execution of smart contract functions that may trigger contracts updates and
transfer of crypto-currency from the caller to the callee. For example, consider a user (with
address) A, which owns two tokens identi ed by the integers 1 and 2, and consider the
following transactions:
T1=A !Token :transferFrom (A;P;1)
T2=A !Token :setApprovalForAll (B;true )
T3=B !Token :transferFrom (A;Q;2)
T4=P !Token :transferFrom (P;B;1)
Intuitively, transaction T1means that A(the sender) calls the function transferFrom of
theToken contract to transfer the ownership of token 1 to user P. Transaction T2delegates
userPto manage A's tokens. Transaction T3says that Btransfers token 2 from AtoQ;
T4means that user Ptransfers token 1 to B.
Since each transaction modi es the internal state of the contract Token , the order in
which a miner executes them is relevant. For example, executing the sequence of transactions
B=T1T2T3T4results in a state where Bowns token 1, and Qowns token 2. It is easy to
see that T3can only succeed if executed after T2, because it depends on the fact that B
is delegated by A, i.e. operatorApprovals [A][B] istrue . Therefore, to run in parallel the
transactions of B, a miner would need to nd an execution schedule that does not a ect the
resulting state. Our notion of swappability formalizes this intuition: two transactions Tand
T0are swappable if they result in the same state, independently of their order (De nition 3.4).
For example, consider the transactions T1andT2above: regardless of whether T1is executed
before or after T2, after their execution we obtain a state where token 1 is owned by P, and
Bcan act as delegate of A.
Clearly, the notion of swappability outlined above is undecidable whenever the contract
language is Turing-equivalent, like in the case of Ethereum. Therefore, swappability cannot
be directly used by a miner to determine a parallel execution schedule. We overcome this
issue by resorting to a static analysis of the smart contract. The underlying idea is to
derive a syntactic approximation of swappability, called strong swappability (De nition 3.11).
This captures the fact that two transactions TandT0depend and a ect di erent portions
of a contract state. Thus, such transactions can be run in any order. For example, the
transactions T1andT2above depend on and modify di erent parts of the state of the Token
contract: therefore, they are strongly swappable.
To detect if two transactions TandT0are strongly swappable (in symbols T#T0), one
needs to statically over-approximate the state variables that may be read and written during
Vol. 17:4 A theory of transaction parallelism in blockchains 10:5
the execution of the called functions. They are strongly swappable if the set of variables
written by Tis disjoint from those written and read by T0and vice versa. This ensures that
their executions are not interfering with each other.
From the code of transferFrom , we see that T1updates the owner of token 1 and the
balance of addresses AandP(lines 4-6). The variables read by T1areexists [1],owner [1]
(line 2),operatorApprovals [A][A](line 3),balance [A](line 5), and balance [P](line 6).
Transaction T2updates operatorApprovals [A][B]. Using the same reasoning for T3and
T4, we obtain the following over-approximations of the state variables written/read by T1{T4
(we denote with WiandRithe variables written and read by Ti, respectively):
R1=fexists [1];owner [1];balance [A];balance [P];operatorApprovals [A][A]g
W1=fowner [1];balance [A];balance [P]g
R2=;
W2=foperatorApprovals [A][B]g
R3=fexists [2];owner [2];balance [A];balance [Q];operatorApprovals [A][B]g
W3=fowner [2];balance [A];balance [Q]g
R4=fexists [1];owner [1];balance [P];balance [B];operatorApprovals [P][P]g
W4=fowner [1];balance [P];balance [B]g
By the approximations above, we have that T1#T2, because (R1[W1)\W2=;=
(R2[W2)\W1. Similarly, it is straightforward to see that T2#T4andT3#T4, while the
other combinations are notstrongly swappable.
The strong swappability relation induces a partial order between transactions: this
can be exploited by a blockchain node to choose a parallel execution schedule. To do that,
from a given sequence of transactions, we build an occurrence net [BD87 ], a special kind of
Petri net with no cycles and where places can hold at most 1 mark. This net encodes the
partial order induced by the swappability relation and formalizes the concurrent semantics
of transactions. One of our main results is that any concurrent execution in the occurrence
net is equivalent to the serial one (see item (c) of Theorem 4.6).
Consider again the the sequence of transactions B=T1T2T3T4above. The associated
occurrence net is displayed in Figure 1. Intuitively, each transaction in Bcorresponds, in
the net, to a transition (rendered as a box), linked to two places (rendered as circles). A
transition can re when all its incoming places contain the mark. If two transactions are not
strongly swappable, the corresponding transitions are linked through a place. In Figure 1,
since T1and T3are not strongly swappable, the place between t1and t3ensures that t3
can be executed only after t1, so rendering the dependency implicitly de ned in B. The
same holds for T2andT3, and for T1andT4. Instead, transitions corresponding to strongly
swappable transactions can be red concurrently. In our example, this is the case for t1and
t2(since T1#T2), as well as for t3andt4(since T3#T4).
Although in our example we have considered the tricky case where the sender and
the receiver of tokens overlap, in practice this is a marginal case: in Ethereum, the large
majority of transactions in a block either involve distinct users, or invoke distinct ERC-721
interfaces.1Therefore, we expect that in practice the degree of concurrency of transferFrom
transactions is higher than shown above.
1Although we are not aware of any work to support this claim, some empirical evidence can be obtained
by inspecting the token-related transactions in https://etherscan.io/tokentxns , which shows that this
overlapping is a rare event in practice.
10:6 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
t1t2 t3
t4
Figure 1. Occurrence net for B=T1T2T3T4of the ERC-721 token.
1.3.Related work. A few works study how to optimize the execution of transactions
on Ethereum, using dynamic techniques adopted from software transactional memory.
In [DGHK17 ,DGHK18 ], miners execute a set of transactions speculatively in parallel,
using abstract locks and inverse logs to dynamically discover con
icts and to recover from
inconsistent states. The obtained execution is guaranteed to be equivalent to a serial
execution of the same set of transactions. The work [ AKP+19] proposes a conceptually
similar technique, but based on optimistic software transactional memory. The work [ SH20 ]
studies the e ectiveness of speculatively executing smart contracts in Ethereum. After
sampling past blocks of transactions (from July 2016 to December 2017), the authors replay
them by using a speculative execution engine, and measure the speedup obtained by parallel
execution. The results show that simple speculative strategies are enough to obtain non-
negligible speed-ups. Another observation of [ SH20 ] is that many of the data con
icts (i.e.
concurrent read/write accesses to the same state location) arise in periods of high trac,
and they are caused by a small number of popular contracts, like e.g. ERC-20 and ERC-721
tokens. The experiments in [ DGHK17 ] suggest that parallelizing transaction execution
may lead to a signi cant improvement of the performance of nodes: the benchmarks on a
selection of representative contracts show an overall speedup of 1.33x for miners and 1.69x
for validators, using only three cores.
A main di erence between these works and ours is that they study empirical aspects
of transaction parallelism (e.g., the speedup obtained on a given benchmark), while ours
is more focussed on the theoretical counterpart. Still, our theory is not intended to serve
as a justi cation of the correctness of the above-mentioned approaches. Actually, we
follow a di erent path to transaction parallelism, based on static analysis of transactions,
rather than on speculative execution. The reason for this divergence lies in the fact that
optimizations based on speculative execution of transactions are not fully compatible with
current blockchain platforms. Indeed, since speculative execution is non-deterministic,
miners need to communicate the chosen schedule of transactions to validators, which
otherwise cannot correctly validate the block. This schedule must be embedded in the mined
block: since current blockchains do not support this kind of block metadata, implementing in
practice these approaches would require a \soft-fork" of the blockchain. Instead of performing
dynamic checks, our approach relies on a static analysis to detect potential con
icts. Miners
can use any static analysis to execute transactions in parallel; once they have appended a
block, validators just need to execute its transactions, possibly exploiting another static
analysis to parallelize execution while preserving the semantics of the block. In this way,
our approach is compatible with any blockchain platform, without requiring a soft-fork.
Vol. 17:4 A theory of transaction parallelism in blockchains 10:7
Our approach is based on static analyses of the variables read and written by transactions.
Although the literature describes various static analyses of smart contracts, most of them
are focussed on nding security vulnerabilities [ MCJ18 ], and they do not produce the
approximations needed for our purposes. A few papers propose static analyses of read/written
variables, but they are not speci cally targeted to Ethereum bytecode contracts. The
recent work [ PKS21 ] implements a static analysis that approximates the portion of a
contract state a ected by the execution of a transaction. This analysis is then exploited to
evaluate the parallel execution of transactions over multiple shards [LNZ+16]. Although the
commutativity relation inferred by the analysis of [ PKS21 ] is similar of our swappability
relation, it is not directly usable on arbitrary Ethereum contracts, since the analysis
of [PKS21 ] is targeted to contracts written in the functional contract language Scilla [ SNJ+19].
Actually, the vast majority of transactions in Ethereum are sent to contracts written in
Solidity (see footnote 7 in Section 5), hence this assumption could undermine the applicability
of the analysis of [ PKS21 ] in the wild. The work [ DLP11 ] describes an analysis based on
separation logic, and applies it to resolve con
icts in the setting of snapshot isolation for
transactional memory in Java. When a con
ict is detected, the read/write sets are used to
determine how the code can be modi ed to resolve it. The work [ CCG08 ] presents a static
analysis of read and written locations in a C-like language with atomic sections, and uses it
to translate atomic sections into standard lock operations. Designing precise static analyses
for Solidity could perhaps take inspiration from these works.
In the permissioned setting, Hyperledger Fabric [ ABB+18] natively supports transaction
parallelism. It follows the \execute rst and then order" paradigm: transactions are executed
speculatively, and then their ordering is checked for correctness [ Fab]. In this paradigm,
appending a transaction requires a few steps. First, a client proposes a transaction to a set
of \endorsing" peers, which simulate the transaction without updating the blockchain. The
output of the simulation includes the state updates of the transaction execution, and the sets
of read/written keys. These sets are then signed by the endorsing peers, and returned to the
client, which submits them to the \ordering" peers. Ordering peers group transactions into
blocks, and send them to the \committing" peers, which validate them. A block T1Tnis
valid when the keys read by transaction Tiare not written by a transaction Tjwithj <i .
Finally, validated blocks are appended to the blockchain.
A preliminary version of this work was presented at COORDINATION 2020 [ BGM20 ].
The current version substantially extends it, generalising the theory to arbitrary blockchain
platforms (while [ BGM20 ] is focussed only on Ethereum). Besides making it possible to
extend our results to blockchains beyond Ethereum, this generalization has allowed us
to re ne some of our results to UTXO-based blockchains like Bitcoin. The current work
also contains the complete technical machinery, including proofs, of our theory, and the
experimental validation of our optimization technique on Ethereum.
2.An abstract model of blockchains
In this section we introduce a general model of blockchain platforms, abstracting from the
actual form of transactions, from the language to write smart contracts, and from the fact
that transactions are grouped into blocks. We then show how to instantiate this model to
the two most widespread blockchain platforms, i.e. Bitcoin and Ethereum.
De nition 2.1 (Blockchain platform) .Ablockchain platform is a tuple (T;O;V;;0;JK),
where:
10:8 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Tis a set of transactions (ranged over by T;T0;:::);
Ois a set of observables (ranged over by p;q;::: );
Vis a set of values (ranged over by v;v0;:::);
O*Vis a set of valid blockchain states (ranged over by ;0;:::);
02is the initial state ;
JK2T!is the state transition function (we write JTKfor J(;T)K).
The set of all nite sequences of transactions is denoted by T, and the empty sequence
is denoted by ". The set Twith the concatenation operator and the neutral element "
is a monoid, referred to as the free monoid over T. A blockchain Bis an element of T.
The semantics of a blockchain Bstarting from a state , denoted as JBK, is obtained by
iterating the semantics of its transactions:
J"K= JTBK=JBK0where0=JTK
We write JBKfor JBK0, where0is the initial state. We say that a blockchain state is
reachable if=JBKfor some B.
Astate update :O*Vis a function which de nes how values associated with
observables are modi ed. We denote with fv=pgthe state update which maps the observable
pto the value v. Given a blockchain state and a state update , applyingtoresults
in a blockchain state such that, for all observables p:
()p=(
p ifp2dom
p otherwise
We useP;Q;::: to range over sets of observables.
2.1.Bitcoin. Bitcoin [ Nak08 ] is the rst crypto-currency based on a decentralized ledger.
Its mechanism to transfer currency (the bitcoin, B) is based on the Unspent Transaction
Output (UTXO ) model. This means that each transaction spends the outputs generated by
one or more previous transactions, and it creates new outputs, that can be spent by later
transactions according to programmable redeem conditions. This model contrasts with the
so-called account-based model, implemented, e.g., by Ethereum, where transactions update
a global state, recording the amount of crypto-currency in each account, and updating
the state of smart contracts. By contrast, in Bitcoin the state is given by the unspent
transactions outputs, which represent either Bdeposits redeemable by users or the state of
smart contracts [ ABC+18]. Although the language for specifying redeem conditions is quite
basic, complex smart contracts can be crafted by suitably chaining transactions [BZ18].
We now formalise the basic functionality of Bitcoin within our general blockchain model,
simplifying or omitting the parts that are irrelevant for our subsequent technical development.
Transactions. Bitcoin transactions are records with the following elds:
outis the list of outputs . Each output is a record of the form fscr:e;val:vg, where e
is ascript , andv0 is the amount of bitcoins stored in the output. Intuitively, a later
transaction can spend the bitcoins stored in a transaction output by providing a witness
which satis es its script.
inis the list of inputs . Each input is a pair ( T;i), meaning that the transaction wants to
spend thei-th output of the transaction T;
Vol. 17:4 A theory of transaction parallelism in blockchains 10:9
witis the list of witnesses , of the same length as in. Intuitively, if the j-th input is ( T;i),
then thej-th witness must make the i-th script of Tevaluate to true.2
We let frange over transaction elds, and we denote with T:fthe content of eld fof
transaction T. We write T:f(i) for thei-th element of the sequence T:f, when in range. We
interchangeably use the notation ( T;i) and T:out(i) for transaction outputs . We use A;B;:::
to range over users, and, we just write the name Aof a user in place of her public/private
keys, e.g. we write versig (A;e) for versig (pkA;e), and sigA(T) for sigskA(T).
Bitcoin scripts are small programs written in a non-Turing equivalent language. Follow-
ing [ABLZ18], we model them as terms with the following syntax:
e::=v constant (integer or bitstring)
jee operators (2f +; ;=;<g)
jifethen eelsee conditional
je:n n -th element of sequence e(n2N)
jrtx:wit witnesses of the redeeming tx
jjej size (number of bytes)
jH(e) hash
jversig (e;e0) signature veri cation
Besides constants v, basic arithmetic/logical operators, and conditionals, scripts can
access the elements of a sequence ( e:n), and the sequence of witnesses of the redeeming
transaction ( rtx:wit); further, they can compute the size jejof a bitstring and its hash H(e).
The script versig (e;e0) evaluates to 1 if the signature resulting from the evaluation of e0is
veri ed against the public key resulting from the evaluation of e, and 0 otherwise. For all
signatures, the signed message is the redeeming transaction (except its witnesses).
The evaluation of scripts is de ned as a function JKT;i, which takes two additional
parameters (used for signature veri cation): the redeeming transaction T, and the index i
of the redeeming input/witness. The result of the semantics can be an integer or a bitstring.
The rules of the semantics are standard: we refer to [ABLZ18] for a formalization.
Example 2.2. Consider a transaction of the form:
T0
in(1):"
wit(1):"
out(1):fscr:versig (A;rtx:wit);val: 80Bg
out(2):fscr:versig (B;rtx:wit);val: 20Bg
The inand wit elds are empty, making T0acoinbase transaction (i.e., the rst
transaction in the blockchain). This transaction has two outputs: ( T0;1) allows Ato redeem
80B, while ( T0;2) allows Bto redeem 20 B. Assume that ( T0;1) is unspent, and that A
2Bitcoin transactions can also impose time constraints on when they can be appended to the blockchain,
or when they can be spent. Since our theory is applied to parallelize transactions within the same block,
hence satisfying exactly the same time constraints, we omit them.
10:10 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
wants to transfer 10 BtoB, and keep the remaining 70 B. To do this, Acan append to the
blockchain a new transaction, e.g.:
T1
in(1): ( T0;1)
wit(1): sigA(T1)
out(1):fscr:versig (A;rtx:wit);val: 70Bg
out(2):fscr:versig (B;rtx:wit);val: 10Bg
The in eld points to the rst output of T0, and the wit eld contains A's signature on
T1(but for the wit eld itself). This witness makes the script ( T0;1):screvaluate to true,
hence the redemption succeeds, and the output ( T0;1) is spent .
Assume now that the outputs ( T0;2) and ( T1;2) are unspent. Participant Bcan spend
both of them by appending a new transaction T2to the blockchain:
T2
in(1): ( T0;2) in(2): ( T1;2)
wit(1): sigB(T2) wit(2): sigB(T2)
out(1):fscr:H(rtx:wit) = 51;val: 30Bg
In this case, the recipient of the 30 Bis not explicitly speci ed by the script ( T2;1):scr:
actually, any transaction which provides as witness a preimage of 51 can spend that output.
Blockchain states. We de ne observables as transaction outputs ( T;i), and the set of
values as V=f0;1g. In this way, blockchain states are partial functions 2O*f0;1g,
modelling the set of unspent transaction outputs (UTXO ). We denote with Uthe set whose
characteristic function is , i.e.U= 1f1g. Hereafter, when not ambiguous we treat T:in
as a set, rather than as a sequence, and we write T:outfor the set of pairs f(T;1);:::; (T;n)g,
wheren=jT:outj. The initial blockchain state is the UTXO fT0:outg, where T0is a coinbase
transaction (i.e., T0:in=").
State transitions. We start by de ning when a transaction is valid in a blockchain state.
De nition 2.3 (Valid Bitcoin transactions) .We say that a transaction Tisvalid in a
blockchain state (in symbols, BT) when the following conditions hold:
(1) ( T0;j)2U, for each ( T0;j) inT:in
(2) J(T0;j):scrKT;i=v6= 0, for each ( T0;j) inT:in
(3)P
p2T:inp:valP
q2T:outq:val
We say that Tisconsistent when there exists some such thatBT.
Condition (1)requires that all the inputs of Tare unspent in ; condition (2)asks that
all the scripts referred to by T:inevaluate to true, using the witnesses in T:wit; condition (3)
asks that the value of the inputs of Tis greater or equal to the value of its outputs.
We now de ne the state transition function of Bitcoin as JTK=0, where:
U0=(
(UnT:in)[T:out ifBT
U otherwise
Vol. 17:4 A theory of transaction parallelism in blockchains 10:11
We extend validity to blockchains by passing through their semantics, i.e. we write BBT
when JBKBT. Further, we write BT1Tni JT1Ti 1KBTi, for allin.3
Example 2.4. Recall the transactions T0,T1,T2from Example 2.2, and let U0=fT0:outg.
We have that 0BT1T2, and JT1T2K0is the UTXOf(T1;1);(T2;1)g.
2.2.Ethereum. Ethereum [ But13 ] is one of the most used platforms for smart contracts:
it actually implements a decentralized virtual machine that runs contracts written in a
Turing-complete bytecode language, called EVM [ Eth21 ]. Abstractly, an Ethereum contract
is similar to an object in an object-oriented language: it has an internal state, and a set of
functions to manipulate it. Each contract controls an amount of crypto-currency (the ether ),
that it can exchange with other users and contracts. Transactions trigger contracts updates,
which may possibly involve a transfer of crypto-currency from the caller to the callee.
Users and contracts are identi ed by their addresses . We use C;D;:::to range over
contract addresses, and f;g;:::for contract functions. We denote with Addr the set of all
addresses X;Y;:::, including both user and contract addresses.
Transactions. Ethereum transactions are terms of the form:
An !X:f(v)
where Ais the address of the caller, Xis the address of the called contract or user, fis the
called function, nis the amount of ether transferred from AtoX, andvis the sequence of
actual parameters. A contract has a nite set of functions, i.e. terms of the form f(x)fSg,
where fis a function name, xis the sequence of formal parameters (omitted when empty),
and Sis the function body. The functions in a contract have distinct names. We denote
with ( X) the contract at address X. We abstract from the actual syntax of S, and we just
assume that the semantics of function bodies is de ned (see e.g. [ BGM19 ,CPZ19 ,JKL+20]
for concrete instances of syntax and semantics of function bodies). For uniformity, we assume
that user addresses are associated with a contract having exactly one function, which just
skips. In this way, the statement A:transfer (n), which transfers ncurrency units to user
A, can be rendered as a call to this function.
Blockchain states. Each Ethereum contract has a key-value store, rendered as a partial
functionV*Vfrom values to values. The elements in the domain of this function are also
called keys. The set of values Vincludes basic types, e.g. integers and strings. An observable
is a term of the form X:k, i.e. a key in the key-value store at a given address. The possible
blockchain states are the partial functions 2O*Vsuch that:
for all addresses X,X:balance is de ned;
for all user addresses A,A:kis de ned i k=balance .
The second constraint allows for a uniform treatment of users and contracts. The initial
state0maps each address Xto a balance n0
X0, while all the other keys are unbound.
3The Bitcoin consensus protocol ensures that each transaction Tiin the blockchain is valid with respect
to the sequence of past transactions T0Ti 1. Since our model requires the state transition function to be
total, we make the operation of appending invalid transactions idempotent.
10:12 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
State transitions. LetConst be a set of constant names x;y;::: . We denote with JSKX
;
the semantics of the statement S. This semantics is either a blockchain state 0, or it is
unde ned (denoted by ?). The semantics is parameterised over a state , an address X
(the contract wherein Sis evaluated), and an environment :Const*V, used to evaluate
the formal parameters and the special names sender and value . These names represent,
respectively, the caller of the function, and the amount of ether transferred along with the
call. We postulate that sender andvalue are not used as formal parameters.
We de ne the auxiliary operators + and on blockchain states as follows:
(X:n) =f(X:balance )n=X:balanceg (2f +; g)
i.e.,+X:nupdatesby increasing the balance ofXofncurrency units.
De nition 2.5 (Valid Ethereum transactions) .A transaction T=An !X:f(v) isvalid
in a blockchain state (in symbols, BT) when the following conditions hold:
(1)A:balancen
(2) if f(x)fSg2 (X), then JSKX
A:n+X:n;fA=sender;n=value;v=xg6=?
We say that Tisconsistent when there exists such thatBT.
Condition (1)requires that A's balance is sucient to transfer nether toX; condition (2)
asks that the function call terminates in a non-error state.
We de ne the semantics of a transaction in a blockchain state as follows:
JAn !X:f(v)K=(
JSKX
A:n+X:n;fA=sender;n=value;v=xgifBTandf(x)fSg2 (X)
otherwise
If the transaction is valid, the updated state is the one resulting from the execution of
the function call. Note that nunits of currency are transferred to Xbefore starting to
execute f, and that the names sender andvalue are bound, respectively, to Aandn. If the
transaction is not valid, i.e. A's balance is not enough or the execution of ffails, then the
transaction does not alter the blockchain state. Invalid transactions can actually occur in
the Ethereum blockchain, but they have no e ect on the state of contracts: so, our semantics
makes them identities w.r.t. the append operation.
Example 2.6. Consider a contract at address Cwhich includes the following functions:
f0()fx:=1g f1()fifx= 0thenB:transfer (1)g f2(y)fy:transfer (value )g
The rst function only sets the value of the key xto 1; the second one transfers a unit of
ether to address Bwhenxis 0; the last one always sends a unit of ether toy. Consider a
blockchain B=T0T1T2where:
T0=A0 !C:f0() T1=A1 !C:f1() T2=A1 !C:f2(B)
Let0be a state such that 0A:balance2. The semantics of Bin0is:
JBK0=0f1=C:xg A: 2 +B: 1 +C: 1
where the semantics of the single transactions is:
JT0K0=Jx:=1 KC
0;fA=sender;0=valueg=0f1=C:xg=1
JT1K1=Jifx= 0thenB:transfer (1)KC
1 A:1+C:1;fA=sender;1=valueg=1 A: 1 +C: 1 =2
JT2K2=Jy:transfer (1)KC
2 A:1+C:1;fB=y;A=sender;1=valueg=2 A: 1 +B: 1
Vol. 17:4 A theory of transaction parallelism in blockchains 10:13
3.Swapping transactions
We de ne two blockchain states to be observationally equivalent when they agree on the
values associated to all observables. The actual de nition of equivalence is a bit more general,
allowing us to restrict the set Pof observables over which we require the agreement.
De nition 3.1 (Observational equivalence) .For allPO, we de ne P0i 8p2P:
p=0p. We say that and0areobservationally equivalent , in symbols 0, when
P0holds for all P.
The following lemma ensures that our notion of observational equivalence is an equiva-
lence relation, and that it is preserved when we restrict the set of observables:
Lemma 3.2. For allP;QO: (i)Pis an equivalence relation; (ii) if P0and
QP, thenQ0; (iii)=O.
We extend the relations above to blockchains, by passing through their semantics. For
allP, we de ne BPB0i JBKPJB0Kholds for all reachable (note that all the
de nitions and results in this paper apply to reachable states, since the unreachable ones do
not represent actual blockchain executions). We write BB0when BPB0holds for all P.
A relation RTTis a is a congruence (with respect to concatenation) if:
BRB0=) 8B0;B1:B0BB1RB0B0B1
The following lemma states that is a congruence: therefore, if BandB0are observationally
equivalent, then we can replace Bwith B0in a larger blockchain, preserving its semantics.
Lemma 3.3.is a congruence relation.
We say that two transactions are swappable when exchanging their order preserves
observational equivalence.
De nition 3.4 (Swappability) .We say that two transactions T6=T0areswappable , in
symbols T T0, when TT0T0T.
The theory of trace languages originated from Mazurkiewicz's works [ Maz88 ] allows us
to study observational equivalence under various swappability relations. In general, given an
alphabet and a symmetric and irre
exive relation I (which models independence
between two elements in ), the Mazurkiewicz's trace equivalence 'Iis a congruence between
words on . Intuitively, all the words in the same equivalence class of 'Irepresent equivalent
concurrent executions. In our setting, is the set of transactions, and Iwill be instantiated
with various swappability relations. The fact that 'Iis a congruence will allow us to replace
a sequence of transactions with an equivalent one within a blockchain.
De nition 3.5 (Mazurkiewicz equivalence) .LetIbe a symmetric and irre
exive relation
onT. The Mazurkiewicz equivalence 'Iis the least congruence in the free monoid Tsuch
that:8T;T02T:TIT0=)TT0'IT0T.
To exemplify De nition 3.5, let I=f(T1;T2);(T2;T1)g. The equivalence class of
the word T0T1T1T2T0under the relation 'IisfT0T1T1T2T0;T0T1T2T1T0;T0T2T1T1T0g.
Note that, starting from the word T0T1T1T2T0, the other words in its equivalence class can
be obtained by swapping adjacent occurrences of T1andT2. This re
ects the fact that T1
andT2are assumed to be concurrent, as they are related by I.
10:14 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Intuitively, all the words in the same equivalence class (with respect to 'I) represent
equivalent executions. This is made formal by Theorem 3.6 below, which ensures that the
Mazurkiewicz equivalence constructed on the swappability relation is an observational
equivalence. Hence, we can transform a blockchain into an observationally equivalent one by
a nite number of swaps of adjacent swappable transactions.
Theorem 3.6.' .
Note that the converse of Theorem 3.6 does not hold: indeed, B' B0requires that
BandB0have the same length, while BB0may also hold for blockchains of di erent
lengths (e.g., B0=BTwhere Tis a transaction which does not alter the state).
Safe approximations of read/written observables. The relation is undecidable
whenever the contract language is Turing-equivalent, e.g., in the case of Ethereum. When
is undecidable, to detect swappable transactions we can follow a static approach. First,
we over-approximate the set of observables read and written by transactions (De nition 3.7).
We then check a simple condition on these approximations (De nition 3.14) to detect if two
transactions can be swapped. Of course, the quality of the approximation is crucial to the
e ectiveness of the approach. In general, the coarser the approximation, the stricter the
induced swappability relation: therefore, an overly coarse approximation would undermine
the parallelization of transactions.
In De nition 3.7 we state that Psafely approximates the observables written byT(in
symbols,Pj=wT) when executing Tdoes not alter the state of the observables not in P.
De ning the set of read observables is a bit trickier: we require that executing Tin two
states that agree on the values of the observables in the read set results in two states that
di er at most on the observables where they did not agree before the execution of T.
De nition 3.7 (Safe approximation of read/written observables) .Given a set of observables
Pand a transaction T, we de ne:
Pj=wT i 8Q:Q\P=;=)TQ"
Pj=rT i 8B;B0;Q:BPB0^BQB0=)BTQB0T
Example 3.8. Recall from Example 2.6 the Ethereum transaction:
T2=A1 !C:f2(B) where f2(y)fy:transfer (1)g
The execution of T2a ects the balance ofA,BandC; however, C:balance is rst incre-
mented and then decremented, so its value is unchanged. Then, fA:balance;B:balancegis
a safe approximation of the observables written byT2, i.e.fA:balance;B:balancegj=wT2.
A safe approximation of the observables read byT2isP=fA:balanceg. To prove
this, consider two blockchains BandB0, and a set of observables Qsuch that BPB0and
BQB0. We have two cases:
IfJBKA:balance<1, then T2is not valid in B, and so JBT2K=JBK. Since BPB0,
then JB0KA:balance<1, so T2is not valid also in B0, from which we have JB0T2K=JB0K.
IfJBKA:balance =n1, then T2is valid in B, and T2a ects exactly A:balance and
B:balance , as it transfers 1 ether fromAtoB. Since BQB0, the states of BT2and
Vol. 17:4 A theory of transaction parallelism in blockchains 10:15
B0T2may only di er on A:balance orB:balance . However:
JB0T2KA:balance =n 1 = JBT2KA:balance
JB0T2KB:balance =JB0KB:balance + 1 = JBKB:balance + 1 = JBT2KB:balance
Therefore, in both cases BT2QB0T2, and so we have proved that fA:balancegj=rT2.
Widening a safe approximation (either of read or written observables) preserves its
safety; further, the intersection of two write approximations is still safe. From this, it follows
that there exists a least safe approximation of the observables written by a transaction.
Lemma 3.9. Let2fr;wg. Then:
(a)ifPj=TandPP0, thenP0j=T;
(b)ifPj=wTandQj=wT, thenP\Qj=wT.
The following example shows that, in general, part (b) of Lemma 3.9 does not hold for
read approximations.
Example 3.10. LetCbe an Ethereum contract with functions:
f(x)fk:=x;k0:=xg g()fifk6=AthenB:transfer (balance )else skipg
and let T=A0 !C:g(). Note that, in any reachable state , it must be C:k=C:k0.
LetQbe such that BQB0, and let=JBK,0=JB0K, letn=C:balance , and let
n0=0C:balance . Appending TtoBandB0will result in:
JTK=(
C:n+B:nifC:k6=A
otherwiseJTK0=(
0 C:n0+B:n0if0C:k6=A
0otherwise
IfBfC:kgB0, then the conditions C:k6=Aand0C:k6=Aare equivalent. Therefore,
JBTK=JTKQJTK0=JB0TK, and so we have proved that fC:kgj=rT. Similarly, we
obtain thatfC:k0gj=rT, sincekandk0are always bound to the same value. Note however
thatfC:kg\fC:k0g=;isnota safe approximation of the observables read by T. For
instance, if C:k=A6=0C:k0andC:balance =0C:balance , then appending TtoB
or to B0results in states which di er in the balance of C.
Strong swappability. We use safe approximations of read/written observables to detect
when two transactions are swappable, recasting in our setting Bernstein's conditions [ Ber66 ]
for the parallel execution of processes. More speci cally, we require that the set of observables
written by Tis disjoint from those written or read by T0, and vice versa. When this happens,
we say that the two transactions are strongly swappable .
De nition 3.11 (Strong swappability) .We say that two transactions T6=T0arestrongly
swappable , in symbols T#T0, when there exist W;W0;R;R0Osuch thatWj=wT,
W0j=wT0,Rj=rT,R0j=rT0, and:
R[W
\W0=;=
R0[W0
\W
The following theorem ensures the soundness of our approximation: if two transactions
are strongly swappable, then they are also swappable. Since its proof depends on notions that
have yet to be de ned, we postpone it at the end of the section. The converse implication
does not hold neither in Bitcoin nor in Ethereum, as shown by Examples 3.21 and 3.25.
10:16 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Theorem 3.12. T#T0=)T T0.
Theorem 3.13 states that the Mazurkiewicz equivalence '#is stricter than' . Together
with Theorem 3.6, if Bis transformed into B0by exchanging adjacent strongly swappable
transactions, then BandB0are observationally equivalent.
Theorem 3.13. '#' .
Parameterised strong swappability. Note that if the contract language is Turing-
equivalent, then nding approximations which satisfy the disjointness condition in De ni-
tion 3.11 is not computable, and so the relation # is undecidable. This is because strong
swappability abstracts from the actual static analysis used to compute the safe approxima-
tions: it just assumes that these approximations exist. De nition 3.14 below parameterises
strong swappability over a static analysis, which we render as a function from transactions to
sets of observables. Formally, Wis astatic analysis of written observables when W(T)j=wT,
for all T; similarly, Ris astatic analysis of read observables when R(T)j=rT, for all T.
De nition 3.14 (Parameterised strong swappability) .LetWand Rbe static analyses of
written/read observables. We say that two transactions T6=T0arestrongly swappable w.r.t.
WandR, in symbols T#W
RT0, if:
R(T)[W(T)
\W(T0) =;=
R(T0)[W(T0)
\W(T)
By the de nition, it directly follows that T#W
RT0implies that T#T0. Further, if WandR
are computable, then #W
Ris decidable. Later on, we will show that the relations #W
Rand #
are equivalent in Bitcoin (Theorem 3.22).
Lemma 3.15. T#W
RT0=)TT0W(T)T0T
Proof. By De nition 3.14, W(T)\W(T0) =;and R(T)\W(T0) =;. Since W(T0)j=wT0, by
De nition 3.7 we have T0W(T)"andT0R(T)". SinceW(T)is a congruence, TT0W(T)T.
Since R(T)j=rT,T0R(T)"and T0W(T)", by De nition 3.7 we have T0TW(T)T. By
simmetry and transitivity of (Lemma 3.2), we conclude TT0W(T)T0T.
The following lemma states that the relation #W
Ris a sound approximation of swappability.
Lemma 3.16. T#W
RT0=)T T0
Proof. By applying Lemma 3.15 twice, we obtain TT0W(T)T0Tand T0TW(T0)TT0. Let
P=On(W(T)[W(T0)). SinceP\W(T) =;=P\W(T0), by applying De nition 3.7 twice we
obtain"PTand"PT0. SincePis a congruence, TT0PT0T. Summing up:
TT0P[(W(T)[W(T0))T0T
from which we obtain the thesis, since P[(W(T)[W(T0)) =OandO=.
Note that if T#T0, then there exist WandRsuch that T#W
RT0. Then, from Lemma 3.16
it follows that TandT0are swappable. This proves Theorem 3.12, from which in turns we
obtain Theorem 3.13. Putting it all together, we have proved the inclusions:
Theorem 3.17. '#W
R ' # ' .
Vol. 17:4 A theory of transaction parallelism in blockchains 10:17
3.1.Swapping transactions in Bitcoin. By instantiating our general blockchain model
to Bitcoin, we can re ne some of the swappability results presented before. In particular,
in Bitcoin we can easily construct safe approximations of the observables read/written by
a transaction, by just considering their inputs and outputs (Lemma 3.18). Further, while
strong swappability is stricter than swappability (Example 3.21), strong and parameterized
strong swappability coincide in Bitcoin (Theorem 3.22).
The following lemma provides the least safe approximations of the observables read
and written by consistent transactions. For inconsistent transactions, these approximations
are just the empty set (Lemma 3.19). Intuitively, the observables written by Tcan be
approximated as T:in[T:out, because Tspends all the transaction outputs in T:in, and
creates the transaction outputs in T:out. Instead, the read observables can be approximated
asT:in, since by De nition 2.3, executing Tfrom two states which agree on T:inleads to
two states which only di er on the observables for which they di ered before.
Lemma 3.18. LetTbe a consistent Bitcoin transaction, and let:
W=T:in[T:out R=T:in
Then,W(resp.R) is the least safe approximation of written (resp. read) observables.
Proof. We rst show that WandRare safe approximations of written / read observables,
and then that they are the least ones.
Wis a safe approximation of written observables. LetQbe such that Q\(T:in[T:out) =;.
For all blockchain states , we have that J"K=, and:
JTK=0whereU0=(
(UnT:in)[T:out ifBT
U otherwise
SinceQand T:in[T:outare disjoint, we have that Q0. Therefore, TQ". By
De nition 3.7, it follows that T:in[T:outj=wT.
Ris a safe approximation of read observables. Assume that B0T:inB1. Then, Tis valid
inB0i it is valid in B1. LetQbe such that B0QB1, let0=JB0Kand1=JB1K.
Fori2f0;1g, we have that:
JTKi=0
i whereU0
i=(
(UinT:in)[T:out ifiBT
Ui otherwise
Since0Q1, it follows that 0
0Q0
1, and therefore B0TQB1T. By De nition 3.7,
it follows that T:inj=rT.
Wis the least safe approximation of written observables. By contradiction, let W0(W
be such that W0j=wT, and letR0(Rbe such that R0j=rT. Letp2WnW0. Since
fpg\W0=;andW0j=wT, then Tfpg". Since Tis consistent, there exists such
thatBT. Then, JTK=0, whereU0= (UnT:in)[T:out. Sincep2T:in[T:out,
it follows that 0p6=p, and so T6fpg"| contradiction. Therefore, Wis the least
approximation of the observables written by T.
Ris the least safe approximation of read observables. LetQ=T:out. Since Tis consistent,
there exists a reachable 0such that0BT. Since0is reachable, there exists B0such that
JB0K=0. Let B=B0T0, where T0spendsRnR0. Then, BR0B0andBQB0. Since
R0j=rT, it must be BTQB0T. Since in Bsome of the inputs needed by Thave been
spent, we have that Tis not valid in B, and so JBTK=JBK. On the other hand, since Tis
valid in B0, then JB0TK=00, whereU00= (U0nT:in)[T:out. SinceQ=T:outbelongs
10:18 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
toJB0TKbut not to JBTK, it follows that BT6QB0T| contradiction. Therefore, Ris
the least approximation of the observables read by T.
Lemma 3.19. Tis inconsistent if and only if ;j=wTand;j=rT.
Proof. For the \only if" part, assume that Tis inconsistent. For j=w, for allwe have that
JTK==J"K. Therefore, for all Qe have TQ", from which it follow that ;j=wT.
For allj=r, for all B,B0andQwe have that if BQB0then BTQB0T. Therefore,
;j=rT. For the \if" part, assume that ;j=wT. Then, for all Qit must be TQ", i.e.
T". By de nition of this implies that, for all ,JTK=J"K=. Therefore, Tis not
valid in any blockchain state , and so Tis inconsistent.
By exploiting the results above, we can provide an alternative sucient condition for
(strong) swappability. If Tis valid in some state where it is also possible to append another
transaction T0before T(i.e., T0Tis valid in that state), then TandT0are strongly swappable.
This is a peculiar property of UTXO-based blockchains like Bitcoin: in Example 3.26 we
show that this is not the case for Ethereum.
Lemma 3.20. In Bitcoin, if there exists such thatBTandBT0T, then T#T0.
Proof. Letbe such that BTandBT0T. Then, by condition (1) of De nition 2.3:
T0:in\T:out =T:in\T0:out =T:in\T0:in=T:out\T0:out =; (3.1)
By Lemma 3.18, T:in[T:outandT:inare safe approximations of written/read observables.
By (3.1), these approximations satisfy the condition of De nition 3.11, and so T#T0.
The following example shows that the converse of Theorem 3.12 does not hold in Bitcoin,
i.e. there exist transactions which are swappable but not strongly swappable.
Example 3.21 (Swappable transactions, but not strongly) .Consider the transactions
in Figure 2, where the scripts and currency values are immaterial (we just assume that
condition (2)of De nition 2.3 is satis ed for each matching input/output pair). We show
that T1and T3are swappable. Let be a blockchain state. If T1is not valid in , then
JT1T3K=JT1T3Kholds trivially, since also T3is not valid. Otherwise, if BT1:
JT1T3K=JT3K0 whereU0= (UnT1:in)[f(T1;1);(T1;2)g
=0since06BT3, as ( T2;1)62U0
JT3T1K=JT1K since6BT3, as ( T1;1)62U
=0whereU0= (UnT1:in)[f(T1;1);(T1;2)g
Therefore, T1andT3are swappable. We now show that they are not strongly swappable.
Assume that T1is consistent. Then, by Lemma 3.18 it follows that W1=T1:in[T1:out
is the least safe approximation of the observables written by T1. Letbe such that T1
is valid in . Then, T3is valid in JT1T2K, and so by Lemma 3.18 is also follows that
W3=T3:in[T3:outis the least safe approximation of the observables written by T3. Since
(T1;1)2W1\W3, then T1andT3are not strongly swappable.
Finally, we prove that strong and parameterized strong swappability coincide in Bitcoin.
Theorem 3.22. LetT,T0be consistent Bitcoin transactions. If T#T0, then T#W
RT0, using
the static analyses W(T) =T:in[T:outandR(T) =T:in.
Vol. 17:4 A theory of transaction parallelism in blockchains 10:19
T1
in(1) :
out(1) : out(2) :T2
in(1): ( T1;2)
out(1):
T3
in(1): ( T1;1) in(2): ( T2;1)
out(1):
Figure 2. Transactions T1andT3are swappable but not strongly swappable.
Proof. Since T#T0, then there exist safe approximations Wj=wT,W0j=wT0,Rj=rT, and
R0j=rT0such that
R[W
\W0=;=
R0[W0
\W. Since Tand T0are consistent,
then by Lemma 3.18 the static analyses W(T) =T:in[T:outandR(T) =T:ingive their least
safe approximation of written/read observables. Then, W(T)W,W(T0)W0,R(T)R,
and R(T0)R0. Then, the disjointess condition required by De nition 3.14 holds for the
static analyses, and so T#W
RT0.
3.2.Swapping transactions in Ethereum. We now illustrate our notions of swappability
in Ethereum through a series of examples. We postpone to Section 5 a discussion on how
to approximate the observables read/written by Ethereum contracts, so to compute the
parameterized strong swappability relation.
Example 3.23 (Swappability) .Recall the contract Cand the blockchain B=T0T1T2from
Example 2.6. By De nition 3.4, we have that:
T0 T2(see Figure 3, top left). Indeed, regardless of whether T0is appended to the
blockchain before or after T2, after their execution we obtain the same state: 0f1=C:xg,
whenA:balance1, andf1=C:xgotherwise.
T16 T2(see Figure 3, top right). Let be such that C:x6= 0 andA:balance = 1. If
we append T1before T2we obtain the state 0= A: 1 +C: 1 and in this state T2is
idempotent. Instead, the result of executing T2before T1is the state 00= A: 1+B: 1
and in this state T1is idempotent. Therefore, T16 T2.
T06 T1(see Figure 3, bottom). Depending on how we append T0andT1we obtain two
di erent states. Let be such that C:x= 0 andA:balance1. If we append T1
before T0we obtain the state 0f1=C:xg. Instead, if we append T0and then T1we obtain
the statef1=C:xg A: 1 +C: 1.
Example 3.24 (Strong swappability) .LetCbe the contract of Example 2.6, and let
f3()fskipgbe a function of a contract D. Then, consider the following transactions:
T3=A1 !D:f3() T4=B1 !C:f2(F)
where A,B, andFare account addresses. Intuitively, T3#T4because they are operating
on observables of di erent addresses. Formally, consider the following safe approximations
of the written/read observables of T3andT4:
W3=fA:balance;D:balancegj=wT3R3=fA:balancegj=rT3
W4=fB:balance;F:balancegj=wT4R4=fB:balancegj=rT4
10:20 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
0= A: 1 +B: 1
f1=C:xg 0f1=C:xg
f1=C:xgT
0
T0
Abalance1T2
T2
Abalance
<1Abalance1T2
Abalance
<1T2
T0
(a)Proof of T0 T2.0= A: 1 +C: 1
Cx6= 0
Abalance = 1
00= A: 1 +B: 1T2
T1
T2
T1
(b)Proof of T16 T2.
0= A: 1 +B: 1 0f1=C:xg
Cx= 0
Abalance1
f1=C:xg f1=C:xg A: 1 +C: 1T0=T1
T0
T1
(c)Proof of T06 T1.
Figure 3. Proofs for T0 T2andT06 T1. A transition Tfromcan be
taken only if the guard below the arrow is satis ed in .
Since (W3[R3)\W4=;= (W4[R4)\W3, the two transactions are strongly swappable.
Now, consider the following transaction that calls the function f2with the address A:
T5=B1 !C:f2(A)
This transaction transfers 1 currency unit from BtoA. Intuitively, since T5touches
A:balance , then it should not be swappable with T3and T4. Formally, consider the
following safe approximations W5andR5:
W5=fB:balance;A:balancegj=wT5R5=fB:balancegj=rT5
SinceW3\W56=;6=W4\W5, then:(T3#T5) and:(T4#T5).
The following example shows that the converse of Theorem 3.12 does not hold, i.e. there
may exist transactions that are swappable but not strongly swappable. This is because of
static analyses could produce false negatives.
Example 3.25 (Swappable transactions, but not strongly) .Consider the following functions
of a contract C1, and the following transactions sent by users AandB:
h1()fif sender =A&&k1= 0thenk1:=1else throwg T1=A1 !C1:h1()
h2()fif sender =B&&k2= 0thenk2:=1else throwg T2=B1 !C1:h2()
We have that T1andT2are swappable. To see why, consider the following two cases:
(1)a statewhereA:balance>1,B:balance>1,C1:balance =n,C1:k1= 0 and
C1:k2= 0. Init holds that:
JT1T2K=f1=C1:k1;1=C1:k2;n+2=C1:balanceg=JT2T1K
Vol. 17:4 A theory of transaction parallelism in blockchains 10:21
(2)a statesuch thatA:balance<1, orB:balance<1, orC1:k16= 0, orC1:k26= 0.
Since it is not possible that the guards of h1andh2are both true, one of T1orT2raises
an exception, leaving the state una ected. Then, also in this case we have that
JT1T2K=JT2T1K
However, T1andT2arenotstrongly swappable. Intuitively, this is because there exist
reachable states ;0such thatC1k1= 0 =0C1k2. Formally, consider the following sets
W1=fA:balance;C1:balance;C1:k1gW2=fB:balance;C1:balance;C1:k2g
which are the least safe over-approximations of the written observables by T1and by T2,
respectively. This means that every safe approximation of T1must include the observables
ofW1, and similarly for the set W2. SinceW1\W26=;, then T1#T2does not hold.
The following example shows that Lemma 3.20, which is speci c to Bitcoin and UTXO-
based blockchains, does not hold on Ethereum.
Example 3.26. Recall the functions f0()fx:=1gand f1()fifx= 0thenB:transfer (1)g
from Example 2.6, and consider the following transactions:
T1=A1 !C:f1() T5=A1 !C:f0()
Letbe a state such that C:x= 0 andA:balance2. We have that BT1, and so
JT1K= A: 1 +B: 1. Further, BT5T1andBT1T5. Then:
5;1=JT5T1K=f1=C:xg A: 2 +C: 11;5=JT1T5K=f1=C:xg A: 2 +B: 1
Hence, T1andT5are not swappable, because 1;5and5;1di er in the balances of CandB.
4.True concurrency for blockchains
Given a swappability relation R, we transform a sequence of transactions Binto an occurrence
netNR(B), which describes the partial order induced by R. Our main result is that any
concurrent execution of the transactions in Bwhich respects this partial order is equivalent
to the serial execution of B(Theorem 4.6).
Occurrence nets. We start by recapping the notion of Petri net [ Rei85 ]. A Petri net is a
tuple N= (P;Tr;F;m0), where Pis a set of places ,Tris a set of transitions (with P\Tr=;),
andF: (PTr)[(TrP)!Nis aweight function . The state of a net is a marking , i.e. a
multiset m:P!Nde ning how many tokens are contained in each place; we denote with
m0the initial marking. The behaviour of a Petri net is speci ed as a transition relation
between markings: intuitively, a transition tis enabled at mwhen each place phas at least
F(p;t) tokens in m. When an enabled transition tis red, it consumes F(p;t) tokens from
each p, and produces F(t;p0) tokens in each p0. Formally, given x2P[Tr, we de ne the
presetxand the postsetxas multisets:x(y) =F(y;x), andx(y) =F(x;y). A transition
tisenabled atmwhentm. The transition relation between markings is de ned as
mt !m t+t, where tis enabled. We say that t1tnis a ring sequence from mtom0
when mt1 !tn !m0, and in this case we say that m0isreachable from m. We say that m0
isreachable when it is reachable from m0.
Anoccurrence net [BD87 ] is a Petri net such that: (i) jpj1 for all p; (ii)jpj= 1 if
p62m0, andjpj= 0 if p2m0; (iii) Fis a relation, i.e. F(x;y)1 for allx;y; (iv) Fis a
10:22 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Tr=f(Ti;i)j1ing P=f(;t)jt2Trg[f (t;)jt2Trg[
(t;t0) t<t0
where ( T;i)<(T0;j),(i<j )^:(TRT0)
F(x;y) =8
><
>:1 ify=tand
x= (;t) orx= (t0;t)
1 ifx=tand
y= (t;) ory= (t;t0)
0 otherwisem0(p) =(
1 if p= (;t)
0 otherwise
Figure 4. Construction of a Petri net from a blockchain B=T1Tn.
acyclic, i.e.8x;y2P[Tr: (x;y)2F^(y;x)2F=)x=y(where Fis the re
exive
and transitive closure of F).
From blockchains to occurrence nets. We describe in Figure 4 how to transform a
blockchain B=T1Tninto a Petri net NR(B), where Ris an arbitrary relation between
transactions. Although any relation Rensures that NR(B) is an occurrence net (Lemma 4.1),
our main results hold when Ris a strong swappability relation. The transformation works as
follows: the i-th transaction in Bis rendered as a transition ( Ti;i) inNR(B), and transactions
related by Rare transformed into concurrent transitions. Technically, this concurrency is
speci ed as a relation <between transitions, such that ( Ti;i)<(Tj;j) whenever i<j , but
TiandTjare not related by R. The places, the weight function, and the initial marking of
NR(B) are chosen to ensure that the ring ot transitions respects the relation <.
Lemma 4.1. NR(B)is an occurrence net, for all RandB.
Step ring sequences. Theorem 4.6 establishes a correspondence between concurrent
and serial execution of transactions. Since the semantics of serial executions is given in terms
of blockchain states , to formalise this correspondence we use the same semantics domain
also for concurrent executions. This is obtained in two steps. First, we de ne concurrent
executions of Bas the step ring sequences (i.e. nite sequences of setsof transitions) of
N#(B). Then, we give a semantics to step ring sequences, in terms of blockchain states.
We denote nite sets of transitions, called steps , asU;U0;:::. Their preset and postset
are de ned asU=P
p2Upand U=P
p2Up, respectively. We say that Uisenabled
atmwhenUm, and in this case ring Uresults in the move mU !m U+U. Let
U=U1Unbe a nite sequence of steps. We say that Uis astep ring sequence from m
tom0ifmU1 !Un !m0, and in this case we write mU !m0.
Concurrent execution of transactions. To execute transactions in parallel, the idea is
to execute them in isolation , and then merge their changes, whenever they are disjoint. The
state updates resulting from the execution of a transaction are formalised as in Section 2.
Anupdate collector is a function that, given a state and a transaction T, gives a
state update = (;T) which maps (at least) the updated observables to their new values.
In practice, update collectors can be obtained by instrumenting the run-time environment
of blockchains, to record the state updates resulting from the execution of transactions. We
Vol. 17:4 A theory of transaction parallelism in blockchains 10:23
formalise update collectors in De nition 4.2 by abstracting from the implementation details
of such an instrumentation:
De nition 4.2 (Update collector) .We say that a function is an update collector when
JTK=((;T)), for allandT.
There exists a natural ordering of update collectors, which extends the ordering between
state updates (i.e., set inclusion, when interpreting them as sets of substitutions): namely,
v0holds when8;T: (;T)0(;T). The following lemma characterizes the least
update collector w.r.t. this ordering.
Lemma 4.3 (Least update collector) .Let?(;T) = JTK , where we de ne 0 asS
0p6=pf0p=pg. Then, ?is the least update collector.
The merge of two state updates is the union of the corresponding substitutions; to avoid
collisions, we make the merge unde ned when the domains of the two updates overlap.
De nition 4.4 (Merge of state updates) .Let0,1be state updates. When dom0\
dom1=;, we de ne 01as follows:
(01)p=8
><
>:0pifp2dom0
1pifp2dom1
? otherwise
The merge operator enjoys the commutative monoidal laws, and can therefore be
extended to ( nite) sets of state updates.
We now associate step ring sequences with state updates. The semantics of a step
U=f(T1;1);:::; (Tn;n)ginis obtained by applying to the merge of the updates
(;Ti), for alli21::n| whenever the merge is de ned. The semantics of a step ring
sequence is then obtained by folding the semantics of its steps.
De nition 4.5 (Semantics of step ring sequences) .We de ne the semantics of step ring
sequences, given and , as:
J"K
= JUUK
=JUK
0where0=JUK
=M
(T;i)2U(;T)
Concurrent execution of blockchains. Theorem 4.6 below relates serial executions of
transactions to concurrent ones (which are rendered as step ring sequences). Item (a)
establishes a con
uence property: if two step ring sequences lead to the same marking,
then they also lead to the same blockchain state. Item (b) ensures that the blockchain,
interpreted as a sequence of transitions, is a step ring sequence, and it is maximal (i.e.,
there is a bijection between the transactions in the blockchain and the transitions of the
corresponding net). Finally, item (c) ensures that executing maximal step ring sequences is
equivalent to executing serially the entire blockchain.
Theorem 4.6. LetB=T1Tn. Then, in N#W
R(B):
(a)ifm0U !mandm0U0
!m, then JUK?
=JU0K?
, for all reachable ;
(b)f(T1;1)gf (Tn;n)gis a maximal step ring sequence;
(c)for all maximal step ring sequences U, for all reachable ,JUK?
=JBK.
10:24 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
(;tf) (;th)(;tg) (tf;)
(th;) (tg;) (tf;tg)
tf th tg
Figure 5. Occurrence net for Example 4.7.
Example 4.7 (Occurrence Net construction in Ethereum) .Consider the following Ethereum
transactions and functions of a contract C:
Tf=A0 !C:f() f()fifx= 0theny:=1else throwg
Tg=A0 !C:g() g()fify= 0thenx:=1else throwg
Th=A0 !C:h() h()fz:=1g
Let the following sets be safe approximations of the corresponding transactions
Pw
f=Pr
g=fC:yg;Pr
f=Pw
g=fC:xg;Pw
h=fC:zg;Pr
h=;
where the subscript denotes the transaction and the superscript denotes if the set approxi-
mates the read or written keys, e.g., Pw
fsafely approximates the keys written by Tf, whereas
Pr
gsafely approximates the keys read by Tg.
By De nition 3.11 we have that Tf#ThandTg#Th, but:(Tf#Tg). By instantiating
the construction of Figure 4 using the relation #, we obtain the occurrence net N#(TfThTg)
of Figure 5, where tf= (Tf;1),th= (Th;2), and tg= (Tg;3). From this occurrence net is
easy to see that transition tgcan only be red after tf, while thcan be red independently
from tfandtg. This is coherent with the fact that This swappable with both TfandTg,
while TfandTgare not swappable.
Recall that ?is the least update collector, i.e. a function that given a state returns
the minimal update mapping quali ed keys to their new values. To run in parallel the
transactions, we execute them in isolation and then we merge their e ect, by merging their
state updates. For example, given a state such thatC:x=C:y= 0 the minimal updates
fortf;tg, and thare:
?(;Tf) =f1=C:yg ?(;Tg) =f1=C:xg ?(;Th) =f1=C:zg
By De nition 4.5 the parallel execution of tf;tg, and thinresults in the following states
Jftf;thgK?
=(f1=C:ygf 1=C:zg) =f1=C:y;1=C:zg
Jftg;thgK?
=(f1=C:xgf 1=C:zg) =f1=C:x;1=C:zg
Jftf;tggK?
= (f1=C:ygf 1=C:xg) =f1=C:y;1=C:xg
Note that, for all the serial execution of Tfand Th(in both orders) is equal to their
concurrent execution (similarly for TgandTh):
JTfThK=JThTfK=f1=C:y;1=C:zg=Jftf;thgK?
JTgThK=JThTgK=f1=C:x;1=C:zg=Jftg;thgK?
Instead, for all such thatCx=Cy= 0 the concurrent executions of Tfand Tgmay
di er from serial ones:
JTfTgK=f1=C:yg JTgTfK=f1=C:xg Jftf;tggK?
=f1=C:y;1=C:xg
Vol. 17:4 A theory of transaction parallelism in blockchains 10:25
This is due the fact that tfandtgarenotconcurrent in the occurrence net of Figure 5.
Now let U=ftf;thgftggbe a maximal step ring sequence of N#(B). Since tfandth
are concurrent by item (c) of Theorem 4.6 we can conclude that the semantics of Uin the
stateis equivalent to the serial one of B=TfThTg:
JBK=f1=C:ygf1=C:zg=JUK?
It is worth noticing that any other maximal step ring sequence of N#(B) results in the
same state. For example, consider U0=ftfgftg;thg, where the places ( tf;), (tg;) and
(th;) contain one token each, while the other places have no tokens. Since UandU0lead
to the same marking, by item (a) of Theorem 4.6 we conclude that
JUK?
=JU0K?
Now, consider U00=fthgftf;tgg. Although U00is maximal, it is not a step ring sequence,
since the second step is not enabled, therefore, no items of Theorem 4.6 apply to U00. This
is coherent with the fact that U00does not represent any sequential execution of B.
5.Experimental validation
In this section we discuss how to exploit our theoretical results in practice to improve the
performance of blockchain nodes. We start by sketching the algorithm used by miners and
validators to construct blocks. Miners should perform the following steps:
(1)gather from the network a set of transactions, and put them in an arbitrary linear order
B, which is the mined block;
(2) compute the relation #W
RonB, using a static analysis of read/written observables;
(3) construct the occurrence net N#W
R(B);
(4)execute the transactions in Bconcurrently according to the occurrence net, exploiting
the available parallelism.
The protocol followed by validators is almost identical to that of miners: the main
di erence is that step 1 is skipped, and at step 2, the relation #W
Ris computed starting from
the block Bto be validated. Note that the static analysis used by a validator could be
di erent from the analysis used by the node which mined B, and therefore the occurrence
net could be di erent from that used by the miner. However, this is not a problem: from
item (c) of Theorem 4.6 it follows that executing Bon any occurrence nets built on any
static analysis of read/written variables leads to the same state. In this way, blocks do not
need to carry the occurrence net as metadata: this makes our approach is compatible with
any blockchain platform, without requiring a soft-fork.
For the case of Bitcoin, we argue that implementing this algorithm is straightforward:
indeed, Lemma 3.18 allows to compute the strong swappability relation directly from
the transactions inputs and outputs. For Ethereum the problem is more complex, since
the algorithm relies on a static analysis of the observables read/written by transactions.
Therefore, in the rest of this section we evaluate the feasibility of our approach on Ethereum.
To this purpose, we implement a prototype analyser of Ethereum bytecode, and we evaluate
its precision on a relevant contract. We then compare the time of sequential executions
of blocks against their parallel executions (which includes the time for the static analysis).
Despite the limitations of the static analysis tool (that we discuss at the end of the section),
we nd that our technique improves the execution time in our experiment.
10:26 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Sequential execution Net construction Parallel execution Total time
41:38 ms 4 :4 ms 25 :02 ms 29 :42 ms
Table 1. Average times for executing Lottery sequentially and in parallel.
The total time is the sum of times for analyzing the contract bytecode,
computing the occurrence net, and of running the transactions in parallel.
Analysing Ethereum bytecode. In general, precise static analyses at the level of the
Ethereum bytecode are dicult to achieve, since the language has features like dynamic
dispatching and pointer aliasing which are notoriously a source of imprecision for static
analysis. As far as we know, none of the analysis tools for Ethereum contracts exports an
over-approximation of read/written keys which is usable to the purpose of this paper. The
only tool we know of that outputs such an over-approximation is ES-ETH [ Mar19 ], but it has
several limitations which make its output too coarse to be usable in practice. So, to perform
an empirical validation of our approach we develop a new prototypical tool [ Tos20a ]. Our
tool takes as input the EVM bytecode of a contract and a sequence of transactions, and gives
as output the occurrence net, using the construction in Section 4. The tool implements as a
standalone library [ Tos20b ] a static analysis that over-approximates the read and written
keys for each function of a given smart contract.
Before presenting the design underlying our static analyzer, we brie
y recall the EVM
memory model and how the bytecode generated by Solidity compiler is organized (see [ Eth21 ,
Woo14 ] for further details). The execution of a smart contract involves three kinds of
memory: (i) the world state , i.e. a mapping from addresses to account information (e.g.,
balances, functions, etc.); (ii) the contract storage , mapping keys to values; (iii) the working
memory , i.e. a stack which stores function parameters, local variables and temporary values
created during the function execution. The EVM machine features instructions to load and
store values from these memories, e.g., SSTORE and SLOAD operate on the world state.
The Solidity compiler splits the generated bytecode in two sections: the constructor
code and the runtime code . The constructor code is executed upon contract creation, and
typically returns the runtime code to be deployed on the blockchain. The runtime code is
executed upon a function call. This code rst initializes the contract storage and the stack,
and then transfers the control to the body of the function called in the transaction.
Our static analysis symbolically executes both the constructor and runtime code. Since
EVM bytecode has no explicit notion of function declaration, we analyze the constructor code
and the rst part of the runtime code to detect which functions are declared in the contract
and where their code is located. Once we identify the functions, we analyze their code
separately. For each function we compute three sets: the sets of keys that are read/written
by the function, and the set of calls made to external contracts. To construct these sets we
exploit a symbolic semantics of EVM instructions, that operates on abstract versions of the
stack and memory storage. Intuitively, the analysis of each instruction results in an abstract
value, specifying the operation performed and the a ected keys.
Experiments. We experimentally validate our approach by estimating the potential speed
up achieved by running transactions in parallel. To this purpose we consider a contract
Vol. 17:4 A theory of transaction parallelism in blockchains 10:27
(;tn)
tn
(tn;)tj0(tn;tj0)
(;tj0) (tj0;) tj1(tn;tj1)
(;tj1) (tj1;)
tc0(tj0;tc0)
(;tc0) (tc0;) tc1(tj1;tc1)
(;tc1) (tc1;)
tr0(tc0;tr0)
(;tr0) (tr0;) tr1(tc1;tr1)
(;tr1) (tr1;)
(tr1;tw) (tr0;tw)(;tw)
tw
(tw;)(tc0;tr1) (tc1;tr0)
Figure 6. Occurrence net for the Lottery contract.
which implements a two-players lottery (see Listing 2 in the Appendix for its Solidity code).
Intuitively, a user who wants to participate in the lottery performs the following steps:
(1)join the game by sending a certain amount of cryptocurrency, representing the bid;
(2)commit to a secret string by sending its hash, which is stored on the contract state;
(3)once both players have completed the commit phase, they can reveal their secrets,
independently from each other;
(4)once both players have revealed, anyone can call the winfunction to transfer the bets to
the winner, who is determined according to the parity of the length of players' secrets.
Once the contract has been initialized (with transaction tn), a complete execution of
the lottery then requires 7 transactions: tj0,tc0,tr0, representing the join,commit and
reveal of the rst player, tj1,tc1,tr1for the second player, and twfor invoking the win
function. Figure 6 displays the occurrence net computed by our tool from a single complete
execution of the lottery. The occurrence net shows that players can join,commit and reveal
independently from each other. However, the commit transactions can be red only after
both join have been red, while the reveal transactions can be red only after both commit .
Further, the wintransaction can be red only after both players have revealed their secrets.
To estimate the possible speed up obtained by running the transactions in parallel, we
play the whole lottery 10 times, generating a total amount of 70 transactions (besides the
contract creation). We rst run these transactions sequentially, and measure the execution
10:28 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
time of each transaction. Then, we use our tool to nd a parallel schedule, and compute the
time spent if the transactions were run in parallel.
We carry out our experiments on a laptop machine with Intel Core i5-3320M CPU @
2.60GHz and 4Gb of RAM.4We use geth5to setup a development chain, and True6to
deploy a local instance of the Lottery contract on this chain.
We rst compute the sequential execution time by summing up the time spent for
running each transaction, as reported by the logs of geth. The rst column of Table 1
displays the time of sequential execution, averaged over 10 measurements.
Then, we analyze the sequence of transactions using our tool, obtaining the occurrence
net. The second column of Table 1 displays the average time spent by the tool to analyze
the transactions and to build the occurrence net (again, the measurements are repeated
for 10 times). From the occurrence net, we estimate the average time required by the most
expensive parallel schedule. This schedule is computed as the longest and most expensive
path (in terms of time) of the occurrence net. The time required to execute this schedule
is in the third column of Table 1. Note that estimating the cost of the parallel execution
in this way implies that we are assuming to have a sucient number of threads to execute
the transactions (in the Lottery experiment, two threads are enough), and that once a
transaction is assigned to a thread it is executed with no latency or queuing time. Finally,
the fourth column of Table 1 displays the total time required to analyze the transactions
and to run them in parallel.
Although the experiment is carried with simplifying assumptions and on a single contract,
the results of Table 1 are a rst empirical evidence of the practical applicability of our
approach, and that parallelizing the execution of transaction may lead to performance
improvements in Ethereum nodes. We discuss below some current limitations and possible
improvements of our experimental validation.
Limitations and possible improvements. The current version of our static analysis
tool of Ethereum bytecode has been developed under some simplifying assumptions. First,
the tool can only analyse contracts whose bytecode respects the following conditions, which
are always satis ed for bytecode obtained by the Solidity compiler: (i) the constructor code
always returns the runtime code; (ii) the runtime code does not access the world state in
response to a call with an invalid function signature; (iii) when a transaction calls a valid
function, the runtime code always transfers the control to the body of the function. While
the tool could be adapted to updates of the Solidy compiler, pieces of bytecode not generated
by the compiler may easily violate these conditions, and it seems implausible to obtain a
precise analysis without making any assumption on the structure of bytecode. However, this
should not be an issue in practice, since the vast majority of transaction currently occurring
in Ethereum blocks looks like to call contracts with a veri ed Solidity source7.
A second simpli cation used in our tool is that the over-approximation of the keys
read/written by a transaction does not exploit the transaction elds (besides the called
4Our scripts and data are available online at https://github.com/lillo/lmcs-analysis-validation
5https://geth.ethereum.org/
6https://www.trufflesuite.com/
7Although we are not aware of any research explicitly quantifying the fraction of Ethereum transactions
directed to Solidity contracts, some empirical evidence of this conjecture can be obtained by inspecting
blocks and their transactions in https://etherscan.io/ , which displays the Solidity code of target contracts.
According to [ OHJ20 ],72% of all transactions sent to contracts target contracts with veri ed source code.
Vol. 17:4 A theory of transaction parallelism in blockchains 10:29
contract and function). Thus, di erent calls to the same function but with di erent
actual parameters result in the same over-approximation. Although this simpli es the
implementation, it may decrease the precision of the analysis, because the values of the
function parameters are left abstract. Consequently, the occurrence net constructed by the
tool contains more dependencies than strictly needed. For instance, the tool would not
detect the swappable transactions in the ERC-721 example described in Section 1.2, since
there the transaction elds are essential to obtain a precise over-approximation. A possible
improvement could be to re ne the analysis tool using all the transaction elds.
Finally, the measurements we performed in our experiment are too coarse-grained to
allow a precise estimation of the speed up achieved by running the transactions in parallel.
For example, we did not consider the overhead required to maintain the threads and to
dispatch the transactions when executing the schedule given by the occurrence net. To
precisely measure this overhead, one would need to integrate our approach with an Ethereum
node, and use it to compute the achieved speed up. Although preliminary, the results of our
experiment shown in Table 1 are positive enough to make us believe that a speed up will be
con rmed also when taking into account these overheads.
6.Conclusions
We have proposed a theory of transaction parallelism for blockchains, aimed at improving
the performance of blockchain nodes. We have started by introducing a general model of
blockchain platforms, and we have shown how to instantiate it to Bitcoin and Ethereum, the
two most widespread blockchains. We have de ned two transactions to be swappable when
inverting their order does not a ect the blockchain state. Since swappability is undecidable
in general, we have introduced a static approximation, called strong swappability, based on a
static analysis of the observables read/written by transactions. We have rendered concurrent
executions of a sequence of transactions as step ring sequences in the associated occurrence
net. Our main technical result, Theorem 4.6, shows that these concurrent executions are
semantically equivalent to the sequential one. An initial experimental assessment of our
approach in Ethereum shows that there are margins to make it applicable in practice.
We remark that our work does not address the problem of selecting and ordering
transactions to maximize the gain of the miner, i.e. it does not proposes strategies to construct
blocks of transactions (step 1 in the miner algorithm described in Section 5). Rather, our
theory studies how to exploit the available parallelism to execute a block of transactions,
assuming that the block is given (which is always the case for validators). Miners can follow
di erent strategies to construct blocks, driven by the economic incentives provided by the
blockchain platform. In Bitcoin, miner incentives are given by block rewards and by the fees
paid by users for each transaction included in a block. In Ethereum, besides these incentives,
miners can extract value directly from smart contracts by suitably ordering users' transactions
and inserting their own. This form of miner extractable value has become prominent with
the emergence of DeFi contracts like decentralized exchanges [ DGK+20,QZG21 ,ZQC+21].
Once a miner has formed a block of transaction according to its strategy, our theory tells
how to speed up its execution by parallelizing transactions.
In Ethereum, malevolent users could attempt a denial-of-service attack by bloating
the blockchain with transactions directed to contracts which are hard to statically analyse.
This would make a na ve miner spend a lot of time executing the static analysis on these
adversarial transactions. This kind of attacks can be mitigated by miner strategies which
10:30 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
put a strict upper bound to the execution time of the analysis. Note that, since most
transactions in Ethereum are directed to a small number of well-known contracts, like e.g.
ERC tokens, DeFi contracts, etc. [ OHJ20 ], to achieve an e ective speed up it would be
enough to parallelize the transactions sent to these contracts, and execute the transactions
sent to unknown contracts without any concurrency.
Aiming at minimality, our model does not include the gas mechanism , which is used
in Ethereum to pay miners for executing contracts. The sender of a transaction deposits
into it some crypto-currency, to be paid to the miner which appends the transaction to
the blockchain. Each instruction executed by the miner consumes part of this deposit;
when the deposit reaches zero, the miner stops executing the transaction. At this point,
all the e ects of the transaction (except the payment to the miner) are rolled back. Our
transaction model could be easily extended with a gas mechanism, by associating a cost to
statements and recording the gas consumption in the environment. Remarkably, adding gas
does not invalidate approximations of read/written keys which are correct while neglecting
gas. However, a gas-aware analysis may be more precise of a gas-oblivious one: for instance,
in the statement ifkthen flong();x:=1elsey:=1 (where flongis a function which exceeds
the available gas) a gas-aware analysis would be able to detect that xis not written.
Acknowledgements. Massimo Bartoletti is partially supported by Aut. Reg. Sardinia
project \Sardcoin" . Letterio Galletta is partially supported by MIUR project PRIN
2017FTXR7S \Methods and Tools for Trustworthy Smart Systems" . Maurizio Murgia
is partially supported by MIUR PON \Distributed Ledgers for Secure Open Communities" .
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Appendix A.Proofs for Section 3
Proof of Lemma 3.2. Items (i) and (ii) are trivial. The inclusion Ois trivial, and
Ofollows from item (ii).
Proof of Lemma 3.3. Direct from the fact that semantics of transactions is a function,
and it only depends on the blockchain states after the execution of BandB0, which are
equal starting from any blockchain state , since BB0.
Theorem 3.6.' .
Proof. By de nition,' is the least equivalence relation closed under the rules:
"' "['0]T' T['1]T T0
TT0' T0T['2]B0' B0
0B1' B0
1
B0B1' B0
0B0
1['3]
LetB' B0. We have to show BB0. We proceed by induction on the rules above.
For rules ['0]and ['1], the thesis follows by re
exivity, since is an equivalence relation
(Lemma 3.2). For rule ['2], the thesis follows immediately by De nition 3.4. For rule ['3],
rst note that B=B0B1andB0=B0
0B0
1. By the induction hypothesis it follows that:
B0B0
0and B1B0
1
Therefore, by two applications of Lemma 3.3:
B=B0B1B0B0
1B0
0B0
1=B0
Proof of Lemma 3.9. Item (a). For the case =w, letPj=wTandPP0. LetQbe
such thatQ\P0=;. We have to show that TQ". SincePP0, it must be Q\P=;.
Then, since Pj=wT, it must be TQ", as required. For the case =r, letPj=rT
andPP0. We have to show that, for all B1;B2, ifB1P0B2andB1QB2, then
B1TQB2T. But this follows immediately by the fact that PP0andPj=rT.
Item (b). Let Rbe such that R\(P\Q) =;. SincePj=wTand (RnP)\P=;, then:
TRnP"
Similarly, since Qj=wTand (RnQ)\Q=;, we have that:
TRnQ"
By assumption R\(P\Q) =;, then (RnP)[(RnQ) =R. By De nition 3.1, we conclude:
TRT(RnP)[(RnQ)"
Lemma A.1. B'#B0=)BB0
Proof. Direct by Theorems 3.6 and 3.13.
10:34 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Appendix B.Proofs for Section 4
Lemma B.1.is commutative and associative, with p:?as neutral element.
Proof. Trivial.
Lemma B.2. If12=, then=12.
Proof. Since12is de ned, it must be dom1\dom2=;. Letpbe an observable.
We have two cases:
p2dom. Since dom = dom1[dom2, we have two subcases:
{p2dom1. Then,p=1p. By disjointness, p62dom2, and hence 12p=1p.
{p2dom2. Then,p=2p=12p.
p62dom. Then,p62dom1,p62dom2, and sop=?=12p.
Lemma B.3. IfB1CT1andB2CT2, then B1B2C(T1[T2).
Proof. By induction onjB2j. For the base case, it must be B2="and hence T2=;. Then,
B1B2=B1andT1[T2=T1. Therefore, the thesis coincides with the rst hypothesis.
For the induction case, it must be B2=B0
2T, withjB0
2j=n. Furthermore, it must be
T2=fTg[T02, for some T02such that B0
2CT02. By the induction hypothesis:
B1B0
2C(T1[T02)
Then:
B1B0
2T=B1B2C(fTg[T1[T02) =T1[T2
Lemma B.4. LetBand Tbe such that B=B1T0B2=)T#W
RT0. Then, for all B0,
JB0KR(T)JBKJB0Kand JB0KW(T)JBKJB0K.
Proof. A simple induction on jBj, using De nition 3.7 for the induction case.
We now formalize when a blockchain Bis a serialization of a multiset of transactions T.
De nition B.5 (Serialization of multisets of transactions) .We de ne the relation Cbetween
blockchains and multisets of transactions as follows:
"C[]BCT
BTC([T] +T)
Lemma B.6. IfT#W
RT0for all T02TandBCTthen, B=B1T0B2=)T#W
RT0.
Proof. By a simple induction on jTjwe can conclude that, whenever Bis of the form B1T0B2
for some B1;B2andT0, we have that T02T. The thesis then follows immediately.
Lemma B.7. Ifpj=rT,B1pB2andB1qB2, then JB1TKqJB1K(JB2K;T).
Proof. Let1= ( JB1K;T) and2= ( JB2K;T). By De nition 4.2, JB1TK=JB1K1. Let
p2Q. We have two cases:
p2dom2.
JB1K2p=2p=JB2K2p=JB2TKp=JB1TKp
p62dom2.
JB1K2p=JB1Kp=JB2Kp=JB2K2p=JB1TKp
De nition B.8. Let be a state updater, and let Wbe such that8T:W(T)j=wT. We say
that and Warecompatible when8;T: dom (;T)W(T).
Vol. 17:4 A theory of transaction parallelism in blockchains 10:35
We extend the semantics of transactions to nite multisets of transactions. Hereafter,
we denote with [] the empty multiset, with [ T1;:::; Tn] the multiset containing T1;:::; Tn,
and withA+Bthe sum between multisets, i.e. ( A+B)(x) =A(x) +B(x) for allx.
De nition B.9 (Semantics of multisets of transactions) .We denote the semantics of a
multiset of transactions T, in a stateand an update collector , as JTK
, where the partial
function JK
is de ned as: JTK
=L
T2T(;T).
Hereafter, we say that a multiset Tis strongly swappable w.r.t. a relation R# when:
8T2T;8T02T [T] :TRT0
Lemma B.10. LetTbe strongly swappable w.r.t. #W
R, letBCT, and let be compatible
with W. Then, for all B0:JTK
JB0K=JBKJB0K.
Proof. By induction on jBj. For the base case, it must be B="andT=;, and hence
J;K
JB0K=JB0K=J"KJB0K. For the induction case, it must be B=B0T, withjB0j=n.
Clearly,T= [T] +T0for someT0such that B0CT0. Let ( JB0K;T) =T. By the induction
hypothesis:
JT0K
JB0K=JB0KJB0K(B.1)
Notice that:
JT0K
JB0K=JB0K0(B.2)
where0L
T02T0(JB0K;T0)). Let ( JB0K;T) =T. SinceTis strongly swappable w.r.t. #W
R
and is compatible with W, it must be dom0\domT=;, and hence ( 0T) is de ned.
Then, it must be:
JTK
JB0K=JB0K(0T)
=JB0K0T By Lemma B.2
=JT0K
JB0KT By Equation (B.2)
=JB0KJB0KT By Equation (B.1) (B.3)
We have that:
JBKJB0K=JB0TKJB0K=JB0KJB0K0
T
where0
T= ( JB0
0KJB0K;T). Since domTW(T) and domTR(T), it follows
immediately that JB0KJB0KTpJBKJB0Kfor allp62W(T). It remains to show that
JB0KJB0KTW(T)JBKJB0K. First notice that, by Lemmas B.6 and B.4:
JB0KR(T)JB0KJB0KJB0KW(T)JB0KJB0K
Then, by Lemma B.7:
JB0KJB0KTW(T)JBKJB0K
And hence:
JB0KJB0KT=JBKJB0K(B.4)
The thesis JTK
JB0K=JBKJB0Kthen follows by Equations (B.3) and (B.4).
10:36 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
The following theorem ensures that the parallel execution of strongly swappable trans-
actions is equivalent to any sequential execution of them.
Theorem B.11. LetTbe strongly swappable w.r.t. #W
R, and let BCT. Then, for all :
JTK?
=JBK
Proof. Direct by Lemma B.10 and by the fact that every Wis compatible with ?.
Aparellelized blockchain Bis a nite sequence of multisets of transactions; we denote with
"the empty sequence. We extend the semantics of multisets (Theorem B.9) to parallelized
blockchains as follows.
De nition B.12 (Semantics of parallelized blockchains) .The semantics of parallelized
blockchains is de ned as follows:
J"K
= JTBK
=JBK
JTK
We write JBKfor JBK
0, where0is the initial state.
We also extend the serialization relation C(De nition B.5) to parallelized blockchains.
De nition B.13 (Serialization of parallelized blockchains) .We de ne the relation Cbetween
blockchains and parallelized blockchains as follows:
"C"B1CTB2CB
B1B2CTB
The following theorem states that our technique to parallelize the transactions in a
blockchain preserves its semantics.
Theorem B.14. Let each multiset in Bbe strongly swappable w.r.t. #W
R, and let BCB.
Then, for all :
JBK?
=JBK
Proof. By induction on the rule used for deriving BCB.
Rule:"C".
The thesis follows trivially, since J"K==J"K?
.
Rule:B1CTB2CB
B1B2CTB.
By Theorem B.11, for some reachable 0it must be JB1K=0=JTK?
. By the induction
hypothesis, JB2K0=JBK?
0. The thesis then follows by:
JB2K0=JB1B2K JBK?
0=JTBK?
Lemma B.15. LetNR(T1Tn) = ( P;Tr;F;m0). Then (Tr;<)is a partial order.
Proof. Transitivity and re
exivity hold by de nition. For antisymmetricity, assume that
(Ti;i)<(Tj;j) and ( Tj;j)<(Ti;i). Then, it is easy to verify that ijandji, and so
i=j. Since TiandTjare uniquely determined by iandj, we have that Ti=Tj. Therefore,
(Ti;i) = ( Tj;j), as required.
Lemma B.16. NR(B)is an occurrence net, for all RandB.
Vol. 17:4 A theory of transaction parallelism in blockchains 10:37
Proof. By De nition 4, the rst three conditions of the de nition of occurrence net are easy
to verify. To prove that Fis acyclic, we proceed by contradiction. Assume that there is
a sequence x=x0;x1;:::xmsuch that ( xi;xi+1)2Ffor all 0i<m , andx0=xmwith
m> 0. Notice that the above sequence alternates between transitions and places, and so,
sincem> 0, at least one place and one transition occur in x. Further, a place between two
transitions t6=t0can exist only if t<t0. Therefore, if t;t0occur in x, it must be t<t0and
t0<t. So, if xcontains at least two transitions, by Lemma B.15, we have a contradiction.
If only one transition t= (T;i) occurs in x, then there is a place of the form ( t;t) occuring
inx. Therefore, t<t, which implies i<i | contradiction.
Lemma B.17. LetN= (P;Tr;F;m0)be an occurrence net. For all t;t02Tr, ift6=t0then
t\t0=;.
Proof. By contradiction, assume that p2t\t0with t6=t0. Then,ft;t0gp, and hence
jpj2 | contradiction with constraint (i) of the de nition of occurrence nets.
Lemma B.18. Letmbe a reachable marking of an occurrence net N. Then:
(1)Ifmt !m0andmt !m00, then m0=m00(determinism).
(2)Ifmt !m0,mt0
!m00andt6=t0, then there exists m000such that m0t0
!m000andm00t !m000
(diamond property).
(3)Ifmt ! !t0
!then t6=t0(linearity).
(4)Ifmt !mthenjtj= 0(acyclicity).
Proof. For item 1, by de nition of the ring of transitions of Petri Nets it must be m0=
m t+t=m00.
For item 2, since mt !m0andmt0
!m00, it must be:
tm m0=m t+t
t0m m00=m t0+t0
By Lemma B.17, t0is enabled at m0, and tis enabled at m00. Then, by de nition of ring:
m0t0
!m0 t0+t0and m00t !m00 t+t
Then:
m0 t0+t0= (m t+t) t0+t0
= (m t0+t0) t+t(ast0m)
=m00 t+t
Hence, the thesis follows by choosing m000=m0 t0+t0.
Item 3 follows directly by induction on the length of the reduction !, exploiting the fact
that Fis a partial order.
Item 4 follows by the fact that Fis a partial order.
Lemma B.19. LetN= (P;Tr;F;m0)be an occurrence net, and let mbe a reachable marking,
such that, for some t,m0,m00:
10:38 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
mn+1m0
m00t=t
Then, m00 !nm0.
Proof. By induction on n. For the base case, it must be m !1m0, and hence mt0
!m0for
some t0. Since mt !m00, by the contrapositive of item 2 of Lemma B.18 (diamond property)
it follows t=t0. So, by item 1 of Lemma B.18 (determinism) we have that m0=m00. Clearly:
m00 !0m0
For the induction case, let n=m+ 1, for some m. Then, for some t0;m000:
mt0
!m000 !m+1m0
Ift=t0, then by item 1 of Lemma B.18 (determinism) it follows that m000=m00, and so
we have the thesis m00 !nm0. Otherwise, if t6=t0, by item 2 of Lemma B.18 (diamond
property), there must exists m1such that:
m00t0
!m1and m000t !m1
We are in the following situation:
m000m+1m0
m1t=t
Sincem+ 1 =n, by the induction hypothesis:
m1 !mm0
Therefore, we have the thesis:
m00t0
!m1 !mm0
Lemma B.20. Let(T;i);(T0;j)be transitions of NR(B). Ifmis a reachable marking, then:
(T;i)<(T0;j)and m(T;i) ! =) m6(T0;j) !
Proof. By the construction in Figure 4, since ( T;i)<(T0;j), then p= ((T;i);(T0;j)) is a
place of the occurrence net, and F((T;i);p) = 1 and F(p;(T0;j)) = 1.
De nition B.21 (Independency) .LetNbe an occurrence net. We say that two transitions
tandt0areindependent , in symbols tIt0, ift6=t0and there exists a reachable marking m
such that:
mt !and mt0
!
We de ne'as the least congruence in the free monoid Trsuch that, for all t;t02Tr:
tIt0=)tt0't0t.
Lemma B.22. LetNbe an occurrence net, with a reachable marking m. IfmU !then tIt0,
for all t6=t02U.
Vol. 17:4 A theory of transaction parallelism in blockchains 10:39
Proof. Since mU !, then mt !for all t2U.
Lemma B.23. LetNbe an occurrence net, and let mbe a reachable marking. If mt1 !m0
andmt2 !m0, then t1't2.
Proof. We proceed by induction on the length of the longest reduction among mt1 !m0
and mt2 !m0. For the base case, the thesis is trivial as both t1andt2are empty. For
the induction case, assume that t1is longer or equal to t2(the other case is symmetric).
Lett1=t1t0
1. We rst show that t2is not empty. By contradiction, if t2is empty, then
m=m0. But then, by item 4 of Lemma B.18 (acyclicity) it follows that t1is empty as well:
contradiction. Therefore, t2=t2t0
2for some t2andt0
2. Clearly, t1is longer than t0
1andt0
2.
Letmt1 !m1andmt2 !m2. We have two subcases.
Ift1=t2, by determinism (Lemma B.18) it follows that m1=m2. Let m00=m1. By the
hypothesis of the theorem, we have m00t0
1 !m0and m00t0
2 !m0. Then, by the induction
hypothesis we have t0
1't0
2, and so the thesis t1t0
1't2t0
2follows since'is a congruence.
Ift16=t2, then by De nition B.21 it must be t1It2. By the diamond property (Lemma
B.18), there exists m00such that m1t2 !m00andm2t1 !m00. By linearity (item 3 of Lemma
B.18), m06t1 !and m06t2 !. By Lemma B.19, applied on m2, there exists tsuch that
m00t !m0andjtj+ 1 =jt0
2j. So, we are in the following situation:
m0
m1
m m00
m2
m0=t2
t01
t2t1
t2
tt1
t0
2
=t1
Therefore, we have that:
m1t2 !t !m0and m1t0
1 !m0
m2t1 !t !m0and m2t0
2 !m0
Notice thatjt2tj=jt1tj=jtj+ 1 =jt0
2jjt0
1j<jt1j. Hence, by applying the induction
hypothesis twice:
t2t't0
1and t1t't0
2
Then, since'is a congruence:
t1t2t't1t0
1and t2t1t't2t0
2
Since t1It2, then t1t2t't2t1t. By transitivity of ':
t1=t1t0
1't1t2t't2t1t't2t0
2=t2
10:40 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
De nition B.24. For all sequences of transitions t, we de ne the set tr(t) of the transitions
occurring in tas:
tr(t) =ftj9t1;t2:t=t1tt2g
and we extend trto step ring sequences Uas follows:
tr(U) =[
fUj9U1;U2:U=U1UU2g
Lemma B.25. Ift't0then tr (t) =tr(t0).
Proof. Trivial by De nition B.21.
Lemma B.26. LetNbe an occurrence net, and let mbe a reachable marking. If mU1 !m1,
mU2 !m2and tr (U1) =tr(U2), then m1=m2.
Proof. Since mU1 !m1andmU2 !m2, there exist sequentialisations t1ofU1andt2ofU2
such that mt1 !m1andmt2 !m2. Since by hypothesis tr(U1) =tr(U2), then tr(t1) =tr(t2).
We proceed by induction on the length of t1. The base case is trivial, as m=m1=m2. For
the inductive case, suppose t1=t1t0
1, withjt0
1j=n. By determinism, there exists a unique
marking m0
1such that mt1 !m0
1(a single step). Since tr(U1) =tr(U2), it must be t2=t2t0
2,
withjt0
2j=n. Let m0
2be the unique marking such that mt2 !m0
2(a single step).
There are two subcases.
Ift1=t2, then m0
1=m0
2, and so the thesis follows directly by the induction hypothesis.
Ift16=t2, by the diamond property (item 1 of Lemma B.18), there exists m0such that
m0
1t2 !m0andm0
2t1 !m0. Since t22tr(t0
1), by linearity (item 3 of Lemma B.18) it follows
that m16t2 !, and hence, by applying Lemma B.19 on m0
1we obtain m0t00
1 !m1for some
t00
1. Summing up, we have that:
m0
1t0
1 !m1 and m0
1t2t00
1 ! m1
Then, by Lemma B.23, t0
1't2t00
1, and hence:
t1=t1t0
1't1t2t00
1
By Lemma B.25:
tr(t1) =tr(t1t2t00
1)
Similarly, we can conclude that m0t00
2 !m2for some t00
2and that:
tr(t2) =tr(t2t1t00
2)
Since tr(t1) =tr(t2), we can conclude:
tr(t00
1) =tr(t00
2)
Sincejt00
1j=n 1<n+ 1 =jt1j, the thesis follows by the induction hypothesis.
Lemma B.27. Let(T;i)and(T0;j)be transitions of NR(B). Then:
(T;i) I (T0;j) =) TRT0
Proof. By De nition B.21, ( T;i) I (T0;j) implies that ( T;i)6= (T0;j) and there exists some
reachable marking msuch that m(T;i) ! and m(T0;j) ! . By contradiction, assume that
:(TRT0). Then, since i<j orj >i , by De nition 4 we would have that ( T;i)<(T0;j) or
(T0;j)<(T;i). Then, by Lemma B.20 we obtain a contradiction.
Vol. 17:4 A theory of transaction parallelism in blockchains 10:41
De nition B.28. LetNR(B) = ( P;Tr;F;m0). We de ne :Tr!Tas (T;i) =T. We
then extend to a function from steps to multisets of transactions as follows:
(;) = [] (U[ftg) = [ (t)] + (U)
Finally, we extend to nite sequences of steps as follows:
(") =" (UU) = (U) (U)
Lemma B.29. LetNR(B) = ( P;Tr;F;m0), and let Ube a step ring sequence. Then, for
alland:
JUK
=J (U)K
Proof. Straightforward by De nitions 4.5 and B.12.
Lemma B.30. Ift't0holds in NR(B), then (t)'R (t0).
Proof. De ne:
'0=
(t;t0) (t)'R (t0)
It suces to show that ''0. Notice that'0is a congruence satisfying:
TRT0=) (T;i)(T0;j)'0(T0;j)(T;i)
But then, by Lemma B.27, it follows that '0also satis es:
(T;i) I (T0;j) =) (T;i)(T0;j)'0(T0;j)(T;i)
Since'is the smallest congruence satisfying this implication, we have ''0.
Theorem 4.6. LetB=T1Tn. Then, in N#W
R(B):
(a)ifm0U !mandm0U0
!m, then JUK?
=JU0K?
, for all reachable ;
(b)f(T1;1)gf (Tn;n)gis a maximal step ring sequence;
(c)for all maximal step ring sequences U, for all reachable ,JUK?
=JBK.
Proof. For item (a), assume that m0U !mandm0U0
!m. A standard result from Petri nets
theory ensures that there exists sequentializations tofUandt0ofU0such that:
m0t !mand m0t0
!m
By Lemma B.23, it must be t't0. Then, by Lemma B.30:
(t)'#W
R (t0)
By Lemma A.1:
J (t)K=J (t0)K
By Lemmas B.22 and B.27, it follows that all multisets of transactions in (U), as well as
those in (U0), are strongly swappable w.r.t. #W
R. Therefore, by Theorem B.14:
J (U)K?
=J (U0)K?
Then, by Lemma B.29:
JUK?
=JU0K?
For item (b), note that a transition ( Ti;i) is enabled if all transitions ( Tj;j) withj <i have
been red. Sof(T1;1)gf (Tn;n)gis a step ring sequence. Moreover, f(T1;1)gf (Tn;n)g
contains all the transactions of N#W
R(B), and so, by linearity (Lemma 3) it is maximal.
10:42 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
For item (c), let U0=f(T1;1)gf (Tn;n)g. By item (b), we have that U0is a maximal
step ring sequence. It is easy to see that BC (U0). By Theorem B.14:
J (U0)K?
=JBK
Since UandU0are both maximal, by Lemma B.26 and by item (a), it follows that:
JU0K?
=JUK?
Since JU0K?
=J (U0)K?
(by Lemma B.29) we have that:
JUK?
=JBK
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license, visit https://creativecommons.org/licenses/by/4.0/ or send a letter to Creative
Commons, 171 Second St, Suite 300, San Francisco, CA 94105, USA, or Eisenacher Strasse
2, 10777 Berlin, Germany
Vol. 17:4 A theory of transaction parallelism in blockchains 10:43
Listing 1. A simple ERC-721 token implementation
pragma solidity >= 0.4.2;
contract Token {
mapping ( uint256 => address ) owner ;
mapping ( uint256 => address ) approved ;
mapping ( uint256 => bool ) exists ;
mapping ( address => uint256 ) balance ;
mapping ( address => mapping ( address => bool )) opApprovals ;
function ownerOf ( uint256 tkId ) external view returns ( address ) {
require ( exists [ tkId ]);
require ( owner [ tkId ] != address (0));
return owner [ tkId ];
}
function balanceOf ( address addr ) external view returns ( uint256 ) {
require ( addr != address (0) );
return balance [ addr ];
}
function approve ( address addr , uint256 tkId ) external {
require ( exists [ tkId ]);
require ( owner [ tkId ] == msg.sender && addr != msg.sender );
approved [ tkId ] = addr ;
}
function setApprovalForAll ( address op , bool isApproved ) external {
operatorApprovals [ msg.sender ][ op] = isApproved ;
}
function getApproved ( uint256 tkId ) external view returns ( address ) {
require ( exists [ tkId ]);
return approved [ tkId ];
}
function isApprovedForAll ( address addr , address op) external view returns
( bool ) {
return operatorApprovals [ addr ][ op ];
}
function transferFrom ( address from , address to , uint256 tkId ) external {
require ( exists [ tkId ]);
require ( from == owner [ tkId ] && from != to);
require (to != address (0) );
if( from == msg.sender || operatorApprovals [ from ][ msg.sender ] ||
approved [ tkId ] == msg.sender ) {
owner [ tkId ] = to;
approved [ tkId ] = address (0);
balance [ from ] -= 1;
balance [to] += 1;
}
}
function mint ( address to , uint256 tkId ) external {
require (! exists [ tkId ]);
require (to != address (0) );
exists [ tkId ] = true ;
owner [ tkId ] = to;
balance [to] += 1;
}
}
10:44 M. Bartoletti, L. Galletta, and M. Murgia Vol. 17:4
Listing 2. A two-players lottery contract
pragma solidity >=0.4.22 <0.6.0;
contract Lottery {
address public owner ;
address payable player0 ; address payable player1 ;
address payable winner ;
bytes32 hash0 ; bytes32 hash1 ;
string secret0 ; string secret1 ;
constructor () public {
owner = msg.sender ;
}
function join0 () payable public {
require ( player0 == address (0) && msg.value > .01 ether );
player0 = msg.sender ;
}
function join1 () payable public {
require ( player1 == address (0) && msg.value > .01 ether );
player1 = msg.sender ;
}
function commit0 ( bytes32 h) public {
require ( msg.sender == player0 && hash0 ==0) ;
hash0 = h;
}
function commit1 ( bytes32 h) public {
require ( msg.sender == player1 && hash1 ==0) ;
hash1 = h;
}
function reveal0 ( string memory s) public {
require ( msg.sender == player0 );
require ( hash0 !=0 && hash1 !=0 && hash0 != hash1 );
require ( keccak256 (abi . encodePacked (s))== hash0 );
secret0 = s;
}
function reveal1 ( string memory s) public {
require ( msg.sender == player1 );
require ( hash0 !=0 && hash1 !=0 && hash0 != hash1 );
require ( keccak256 (abi . encodePacked (s))== hash1 );
secret1 = s;
}
function win () public {
uint256 l0 = bytes ( secret0 ). length ;
uint256 l1 = bytes ( secret1 ). length ;
require (l0 !=0 && l1 !=0) ;
if(( l0+l1) % 2 == 0) {
winner = player0 ;
}
else {
winner = player1 ;
}
winner . transfer ( address ( this ). balance );
// reset state for next round
player0 = player1 = address (0) ;
hash0 = hash1 = 0;
secret0 = secret1 = "";
}
}
|
{
"id": "2011.13837"
}
|
1805.02818
|
Blockchain for the IoT: Opportunities and Challenges
|
Blockchain technology has been transforming the financial industry and has
created a new crypto-economy in the last decade. The foundational concepts such
as decentralized trust and distributed ledger are promising for distributed,
and large-scale Internet of Things (IoT) applications. However, the
applications of Blockchain beyond cryptocurrencies in this domain are few and
far between because of the lack of understanding and inherent architectural
challenges. In this paper, we describe the opportunities for applications of
blockchain for the IoT and examine the challenges involved in architecting
Blockchain-based IoT applications.
|
http://arxiv.org/pdf/1805.02818v1
|
Gowri Sankar Ramachandran, Bhaskar Krishnamachari
|
cs.DC, cs.CR, cs.CY
|
cs.DC
|
Blockchain for the IoT: Opportunities and Challenges
Gowri Sankar Ramachandran
University of Southern California
Email: gsramach@usc.eduBhaskar Krishnamachari
University of Southern California
Email: bkrishna@usc.edu
Abstract —Blockchain technology has been transforming the
financial industry and has created a new crypto-economy in the
last decade. The foundational concepts such as decentralized
trust and distributed ledger are promising for distributed, and
large-scale Internet of Things (IoT) applications. However,
the applications of Blockchain beyond cryptocurrencies in
this domain are few and far between because of the lack
of understanding and inherent architectural challenges. In
this paper, we describe the opportunities for applications of
blockchain for the IoT and examine the challenges involved in
architecting Blockchain-based IoT applications.
1. Introduction
Satoshi Nakamoto laid the foundation for the Blockchain
technology in 2008 by presenting a solution for decentral-
ized trust among unknown entities [1]. BitCoin, the first de-
centralized digital currency, impacted financial institutions,
and a wide-number of cryptocurrencies entered the market
in the following years. The majority of blockchain applica-
tions currently involve digital cryptocurrencies, where the
users exchange monetary value with each other through the
decentralized framework.
Enabling decentralized trust through a consensus proto-
col and distributed storage through a tamper-proof ledger
are the critical features of the Blockchain technology. Any
application that involves multiple stakeholders can benefit
from these features because it enables transparent interac-
tions without requiring a trusted third party. IoT applications
in the context of smart cities and supply chain management
consist of numerous stakeholders, where the Blockchain
technology can be used to strengthen the confidence among
the involved entities and organizations.
Although the technology has been around for almost a
decade, its technical underpinnings are made clearer only
in the last two years. On the one hand, architects designing
IoT applications are fully aware of the limitations and ca-
pabilities of contemporary IoT platforms and technologies.
On the other hand, Blockchain developers and enthusiasts
understand the practical details of the Blockchain frame-
works and their viability on different classes of computation
and storage platforms. We notice a gap between the two
communities, and it is essential to bridge this gap to fully
exploit the capabilities of blockchain technology beyond
cryptocurrencies and FinTech applications.This paper presents the promises of Blockchain for
IoT and describes the challenges and limitations of the
blockchain by correlating the architectural elements of IoT
with the Blockchain. Furthermore, the paper also discusses
the fundamental design questions for the application devel-
opers who are designing and implementing applications at
the intersection of Blockchain and IoT.
Section 2 provides an overview and the architecture
of IoT. Building blocks and architectural elements of the
blockchain are presented in Section 3. Section 4 discusses
the opportunities for applying blockchain for the IoT. Sec-
tion 5 describes the challenges and open questions. Finally,
Section 7 concludes the paper.
2. Overview of the IoT
Application areas of IoT include air quality monitoring,
smart cities, supply chain management, and production line
monitoring. Internet-of-Things comprises of computation,
communication, sensing, and actuation functionalities, and
such functionalities are distributed throughout the network.
IoT architecture can be broadly classified into three layers
as shown in Figure 1.
End-device Layer: The end-device layer comprises of
sensors, low-power embedded platforms, wireless commu-
nication technologies, and power sources. Low-power em-
bedded IoT platforms act as a hub for sensors and one or
more wireless communication technologies. IoT platforms
are typically deployed in challenging and hard-to-reach en-
vironments. Thus, it is essential to keep the devices running
longer on battery power or harvested energy. IETF defines
the devices in this layer as very constrained sensor motes
with limited processing and storage capabilities, and they
are referred to as class 0 devices. The end-device layer
is the most resource-constrained layer in IoT application
architecture.
Edge-device Layer: The edge-device layer is responsi-
ble for collecting sensor data from end-devices. This layer
consists of a network gateway for handling inbound and
outbound communications with the end-device layer. Also,
the data from multiple end-devices are processed in this
layer to meet the real-time demands of the application.
Devices at this layer are more capable than the end-device
layer with respect to computation and storage capabilities.arXiv:1805.02818v1 [cs.DC] 8 May 2018
Figure 1. Layered architecture of the Internet-of-Things applications.
Server or backend layer: The server or the cloud
backend layer is responsible for storage and visualization
functionalities. End-users of the IoT application interact
with this layer for monitoring and control of their infras-
tructure. Web servers, data analytics engines, and databases
operate at this layer to cater the demands of the end-users.
Devices in this layer have the maximum processing and
storage capacities among all the layers in the stack.
Table 1 summarizes the resource capacities at different
layers of the IoT stack. The end-device layer is a constrained
layer with insufficient resources for computation, commu-
nication, and storage, while the server or the backend layer
consists of maximum resources.
Application of any new technology and protocol to the
IoT must consider the resource capacities of different layers
before their deployment.
IoT applications following the above architecture have
been widely used in various deployments, but the integration
of blockchain into such an architecture remains challenging
as discussed in Section 5. The overview of blockchain and
its fundamental building blocks are presented in the next
section.
3. Overview of the Blockchain technology
The Blockchain technology uses the combination of
cryptography, a consensus algorithm, and a distributed
ledger to create a decentralized and trustworthy platform.
In this section, we will discuss the three key aspects of
Blockchain technologies.Cryptographic Digital Signature : The public-key cryp-
tography is used in blockchain to generate a signature for
Blockchain transactions. Users carry out transactions by cre-
ating a digital signature using their private keys. Recipients
in the blockchain network verify the transaction using the
public key of the sender to ensure that the transaction is
indeed signed by the sender. Source or end-devices sign the
transactions when they create a transaction.
Distributed Ledger : Blockchain use a distributed stor-
age to record the transactions. In essence, all the platforms in
the network store either the entire transactions or a subset
of transactions. All the nodes in the network come to a
consensus (using a consensus algorithm) before entering the
transactions into the ledger. This feature makes blockchain
effectively immutable.
Consensus algorithm : Blockchain does not rely on a
centralized server for verification and validation of transac-
tions. Instead, Blockchain uses a peer-to-peer model, and all
the decisions within the network are made by the partici-
pating members through a consensus protocol.
Figure 2 shows the components of Blockchain. The
core functionalities of Blockchain are distributed across the
hardware wallet, light nodes, and full nodes in a blockchain
network. Table 1 presents the resource requirements of
blockchain at different layers.
4. Opportunities
The core building blocks of blockchain such as public-
key cryptography, distributed ledger, and consensus algo-
Figure 2. Components of Blockchain.
TABLE 1. R ESOURCE REQUIREMENTS OF IOTAND BLOCKCHAIN .
Layers CPU Memory Power Budget Bandwidth
Internet-of-Things (IoT)
End-device Low Low Low Low
Edge-device Medium Medium High High
Server and Backend High High High High
Blockchain
Hardware Wallet Low Low Low Low
Light Node Medium Medium High High
Full Node High High High High
rithms are promising for the IoT. We will describe the
opportunities for applying blockchain technologies to the
IoT in this section.
Privacy/anonymity: Transactions in Blockchain use
the digital identity generated using public-key cryp-
tography and a hashing algorithm. IoT applications
with sensitive information can leverage these mech-
anism to hide real identity in the network.
Monetary exchange of data and compute: Mone-
tary exchange of data and compute: IoT applications
in the area of smart cities use sensors in combination
with crowdsourcing to deliver digital services to the
city population. Monetary rewards may be essen-
tial to involve the community members in smart
city applications and to leverage the edge resources
such as computation power, storage, and bandwidth.
Blockchain technology can also be used to set up a
monetary system to issue tokens to the community
members for their participation.
Record transactions for account and audit: The
data from IoT applications are transported through
infrastructure owned by multiple organizations. Sup-
ply chain monitoring focuses on tracking and moni-
toring assets throughout the supply chain process.Traditional supply chain monitoring systems rely
on a centralized architecture, wherein all the data
from assets are stored in a central database. Using
blockchain for recording the data in a decentralized
ledger increases the trust while moving assets (real
or digital) through infrastructure owned by multiple
and diverse stakeholders.
Smart Contracts Nick Szabo introduced the con-
cept of Smart Contracts [2] as an alternative to
the traditional paper-based contracts. A smart con-
tract is a digital contract embedded in the system,
which gets executed when the conditions declared
in the agreement are met. Smart contracts arbitrate
transactions autonomously while exchanging assets
between parties or dealing with non-trusted members
in a blockchain network. IoT applications, for exam-
ple, can employ smart contracts when transporting
sensor data through infrastructures owned by mul-
tiple stakeholders and selling data produced by the
sensors
5. Challenges
We now discuss the challenges that arise in applying
blockchain for the IoT.
Resource constraints: IoT platforms have limited
resources for computation, communication, and stor-
age, while Blockchain technologies demand exces-
sive resources. Class A low-power IoT platforms
have less than 10 KB of data memory and less
than 100 KB of program memory [3], while a
Blockchain node requires memory in the order of
GBs [4]. In addition, the computation requirements
of consensus algorithms such as Proof-of-Work are
well-beyond the capabilities of low-power, resource-
constrained IoT devices. Contemporary Blockchain
technologies are therefore ill-suited for such low-
power IoT devices because their resource demands.
From Figure 1, the end-devices and edge-devices
does not have the capacity to execute the Blockchain
processes, and the server layer is ideally suited for
contemporary Blockchain technologies. Such an ap-
proach might connect a centralized IoT deployment
to a decentralized blockchain network, where the
server layer of the IoT deployment acts as the entry
point to the blockchain network.
Bandwidth requirements: Platforms in the
Blockchain network have to interact with other
platforms in the network to participate in the
consensus process. Due to the decentralized nature
of the consensus process, platforms in the network
exchange information about the blockchain to
validate transaction and to create new blocks.
IoT devices operating at end-device layer have
severe bandwidth constraints, which also means the
contemporary blockchain solutions are not suited
for end-devices. Edge-devices and servers may have
sufficient bandwidth, but it is important to note
that the bandwidth requirement of blockchain may
exceed the bandwidth requirement of the application
itself, at least with most contemporary blockchain
protocols.
Security: Blockchain technology follows a decen-
tralized architecture, wherein all the devices in the
network coordinate and cooperate with each other
through pre-defined protocols. Thus, the devices stay
connected to the blockchain network for participat-
ing in the consensus process. This always-connected
feature makes IoT devices potentially more suscep-
tible to security attacks.
Latency demands: IoT applications typically con-
sist of a collection of data producers and data con-
sumers, and in some cases, the data consumers react
to an event and perform an actuation. The intro-
duction of Blockchain technology in this context
may reduce the responsiveness if the data consumer
may be required to wait for the conclusion of the
consensus process before reacting to an event. Con-
temporary Blockchain technologies are not suitable
for time-sensitive IoT applications that need fully
confirmed transactions.
Transaction fees: Most open Blockchain technolo-
gies charge a fee for transactions, and use it torewarding the nodes involved in consensus process.
IoT devices cannot store all the data to such a
blockchain since storing the data to a blockchain
incurs a transaction fee. If one wishes to put data
from IoT devices on such a blockchain, it may need
to be aggregated to reduce the transaction fees, but
in this case it would be important to make sure that
the aggregation process does not filter out essential
information. Alternatively, an architecture where the
data itself is transported off the chain and only
hashed values or key transaction records are stored
on the blockchain for verification and provenance
purposes may be preferred.
Permissioned vs public: Contemporary blockchain
technologies can be broadly classified into two cat-
egories as public and permissioned blockchains.
Public blockchains such as Bitcoin and Ethereum
allow anyone to become a part of the network
without any authorization. Anyone wishing to par-
ticipate in the public blockchain can simply down-
load and install the necessary frameworks, and this
type of blockchain technologies require substan-
tial resources for consensus process. Permissioned
blockchains, on the other hand, consists of au-
thorized members in the network. This type of
blockchain may be suitable for IoT applications
involving multiple known organizations as the net-
work consists of authorized members, which open up
opportunities for fast, higher-throughput consensus
protocols.
Partition tolerance for intermittently connected
devices: IoT applications in the space of supply
chain monitoring consist of mobile devices with
intermittent connectivity. Also, the end-devices run-
ning on batteries use duty cycling to prolong the
lifetime. Furthermore, the devices operating on the
wireless bands regulated by ETSI and FTC has to
adhere to the bandwidth limitations enforced federal
authorities. In such scenarios, the devices connect to
a server or edge-device intermittently to exchange
data. Assuming an architecture in which the server
is acting as a lightweight node for recording the IoT
data to a blockchain, the server has to download
and store the headers of the blockchain to keep
itself synchronized. For intermittently connected IoT
devices, the cost of running a lightweight node for
recording IoT data in a blockchain network may
outweigh the benefits because of the bandwidth,
computation, and storage costs. New blockchain pro-
tocols and frameworks are essential for reducing
the infrastructure cost when using blockchain for
recording IoT transactions and DAG-based protocols
such as IOTA provide partition tolerance by making
it easy to merge transactions from partitioned parts
of the network.
Transaction Volumes - these are quite severely lim-
ited on most current open, permissionless blockchain
technologies, also preventing high volume sensor
data applications from being carried directly on the
blockchain.
Physical interface weakness - As cyber-physical
systems, individual sensors and actuators could be
hacked or misused to report false or erroneous data
that gets logged on to the blockchain in an im-
mutable fashion.
6. Related Work
Literature combining blockchain and IoT contributes
security and privacy solutions. Kshetri [5] validates the
applications of blockchain for securing the IoT. Tomer et
al.contribute CIoTA [6] to detect anomalies in IoT appli-
cations. CIoTA applies the concepts of blockchain in com-
bination with extensible Markov Model (EMM) to identify
malicious activities. Dorri et al. [7] presents the gaps in
contemporary security and privacy methods, and contribute
LSB, a lightweight and scalable blockchain for IoT security
and privacy. LSB’s lightweight protocols reduce the band-
width and computation costs. Pietro et al. [8] investigate
the communication overhead of blockchain synchroniza-
tion protocols for the IoT and highlight the uplink and
downlink bandwidth demands. PlaTIBART [9] is a testing
framework to manage and deploy blockchain networks for
transactive IoT applications. Hossein et al. [10] introduce
a distributed data storage framework for IoT applications
using the blockchain. [10] ensures that the IoT data owner-
ship stays with the stakeholders. These papers address some
of the challenges in described in Section 5, however, the
architectural details and performance implications are not
clearly addressed, especially for resource-constrained IoT
platforms.
The opportunities and challenges of applying blockchain
for the IoT are presented in the literature. Huckle et al. [11]
discuss the applications of blockchain for monetizing IoT
applications, but their work does not describe the chal-
lenges. Seyoung et al. [12] demonstrates how blockchain
can be used for storing sensor data using smart con-
tracts. Canoscenti et al. [13] reviews the use cases of the
blockchain and highlights the open problems in integrity,
anonymity, and adaptability when storing IoT data in a
decentralized network. The authors of [8] analyze the com-
munication overhead of blockchain synchronization for the
IoT. Unlike [12] and [13], our work focuses on architec-
tural challenges and performance implications when using
blockchain for IoT for data storage, monetization, security,
and privacy.
7. Conclusion
Blockchain technology has already made a significant
impact in the cryptocurrency applications. The fundamental
building blocks - distributed ledger, consensus mechanisms,
and public-key cryptography - of blockchain technology is
promising for IoT and supply chain monitoring applications.
We have discussed the architecture of IoT applications andmapped the functional blocks of the blockchain technology
to reveal the architectural challenges involved in applying
blockchain for the IoT. Next, we have presented oppor-
tunities for applying blockchain for the IoT. Finally, we
concluded with the challenges which need to be addressed
to fully exploit the benefits of blockchain technologies in the
IoT domain. Despite the challenges, blockchain technologies
are highly promising for resolving security, privacy, and trust
issues in multi-stakeholder application environments.
Acknowledgments
This work is supported by the USC Viterbi Center for
Cyber-Physical Systems and the Internet of Things (CCI).
References
[1] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008.
[2] N. Szabo, “Smart contracts,” Unpublished manuscript , 1994.
[3] C. Bormann, M. Ersue, and A. Keranen, “Terminology for
constrained-node networks,” Internet Requests for Comments, RFC
Editor, RFC 7228, May 2014, http://www.rfc-editor.org/rfc/rfc7228.
txt. [Online]. Available: http://www.rfc-editor.org/rfc/rfc7228.txt
[4] (2018) Running a full node. https://bitcoin.org/en/full-node#
minimum-requirements.
[5] N. Kshetri, “Can blockchain strengthen the internet of things?” IT
Professional , vol. 19, no. 4, pp. 68–72, 2017.
[6] T. Golomb, Y . Mirsky, and Y . Elovici, “CIoTA: Collaborative IoT
Anomaly Detection via Blockchain,” ArXiv e-prints , Mar. 2018.
[7] A. Dorri, S. S. Kanhere, R. Jurdak, and P. Gauravaram, “LSB:
A Lightweight Scalable BlockChain for IoT Security and Privacy,”
ArXiv e-prints , Dec. 2017.
[8] P. Danzi, A. Ellersgaard Kalør, ˇC. Stefanovi ´c, and P. Popovski, “Anal-
ysis of the Communication Traffic for Blockchain Synchronization of
IoT Devices,” ArXiv e-prints , Nov. 2017.
[9] M. A. Walker, A. Dubey, A. Laszka, and D. C. Schmidt, “PlaTI-
BART: a Platform for Transactive IoT Blockchain Applications with
Repeatable Testing,” ArXiv e-prints , Sep. 2017.
[10] H. Shafagh, L. Burkhalter, A. Hithnawi, and S. Duquennoy, “Towards
Blockchain-based Auditable Storage and Sharing of IoT Data,” ArXiv
e-prints , May 2017.
[11] S. Huckle, R. Bhattacharya, M. White, and N. Beloff, “Internet
of things, blockchain and shared economy applications,” Procedia
Computer Science , vol. 98, pp. 461 – 466, 2016, the 7th
International Conference on Emerging Ubiquitous Systems and
Pervasive Networks (EUSPN 2016)/The 6th International Conference
on Current and Future Trends of Information and Communication
Technologies in Healthcare (ICTH-2016)/Affiliated Workshops.
[Online]. Available: http://www.sciencedirect.com/science/article/pii/
S1877050916322190
[12] S. Huh, S. Cho, and S. Kim, “Managing iot devices using blockchain
platform,” in 2017 19th International Conference on Advanced Com-
munication Technology (ICACT) , Feb 2017, pp. 464–467.
[13] M. Conoscenti, A. Vetr, and J. C. D. Martin, “Blockchain for the
internet of things: A systematic literature review,” in 2016 IEEE/ACS
13th International Conference of Computer Systems and Applications
(AICCSA) , Nov 2016, pp. 1–6.
|
{
"id": "1805.02818"
}
|
2107.11592
|
Blockchain Transaction Processing
|
A blockchain is an append-only linked-list of blocks, which is maintained at
each participating node. Each block records a set of transactions and their
associated metadata. Blockchain transactions act on the identical ledger data
stored at each node. Blockchain was first perceived by Satoshi Nakamoto as a
peer-to-peer digital-commodity (also known as crypto-currency) exchange system.
Blockchains received traction due to their inherent property of
immutability-once a block is accepted, it cannot be reverted.
|
http://arxiv.org/pdf/2107.11592v2
|
Suyash Gupta, Mohammad Sadoghi
|
cs.DB, cs.CR, cs.DC
|
cs.DB
|
Blockchain Transaction Processing
Suyash Gupta, Mohammad Sadoghi
Synonyms
• Blockchain Data Management
• Blockchain Consensus
• Cryptocurrency
Definitions
A blockchain is an append-only
linked-list of blocks, which is main-
tained at each participating node.
Each block records a set of transac-
tions and their associated metadata.
Blockchain transactions act on the
identical ledger data stored at each
node. Blockchain was first perceived
by Satoshi Nakamoto (Satoshi 2008)
as a peer-to-peer digital-commodity
(also known as crypto-currency) ex-
change system. Blockchains received
traction due to their inherent property of
immutability—once a block is accepted,
it cannot be reverted.Overview
In 2008, Satoshi Nakamoto (Satoshi
2008) introduced the design of an unan-
ticipated technology that revolutionized
the research across the distributed
systems community. Nakamoto pre-
sented the design of a peer-to-peer
digital-commodity exchange system,
which although employed by several
participants, prevents the use of a cen-
tralized design. Nakamoto envisioned a
system where the participants exchange
commodities among themselves in a
democratic, decentralized and trans-
parent manner while upholding their
right to privacy. Nakamoto visualized
this digital-commodity as a monetary
token that could be used by partici-
pants to provide or receive services.
This led to the birth of Bitcoin —a
cryptocurrency—and introduction of a
new design paradigm Blockchain .
A blockchain in its simplest form is
anappend-only linked-list of blocks.
1arXiv:2107.11592v2 [cs.DB] 4 Aug 2021
2 Suyash Gupta, Mohammad Sadoghi
Fig. 1: Basic Blockchain Representations
Each block in this chain is linked to
the previous block in the chain Gupta
et al (2019a, 2020a). Blockchains are
often termed as immutable as modifying
an existing block requires modifying
all the previous blocks in the chain.
Each block includes a set of transactions
and the associated meta-data. Figure 1
presents a schematic representation of a
blockchain. Blockchain systems guar-
antee decentralization as the full-copy
of the chain is maintained by several
participants1. Moreover, a block is only
accepted into the chain after all the
participants have reached consensus on
theorder andcontents of the block. In
specific, admittance of a block to the
chain implies that the transactions iin the
block have been executed and verified.
Hence, blockchain helps in achieving
key properties such as democracy and
transparency.
A blockchain system can be de-
scribed as a collection of layers. At
theapplication layer , there are clients,
which send their transactions to a set of
severs to process. The communication
among the clients and servers take
place at the networking layer. Servers
participate at the ordering layer to
assign a unique order to each incoming
client transaction in a Byzantine Fault-
Tolerant (henceforth referred to as BFT)
manner. Following a successful order-
ing, the client transaction is processed
1In sharded blockchain systems, no shard may
have complete copy but data is still securely
replicated.at the execution layer and persisted in
the immutable ledger at the storage
layer . Clients and servers also employ
necessary cryptographic constructs to
securely exchange messages among
each other.
The preceding discussion allows us
to summarize that a blockchain system
aims at providing a safe and resilient
storage for transactions. In the suc-
ceeding sections, we will discuss these
concepts in detail and will illustrate the
mechanisms pertaining to blockchain
transaction processing. We will also
study key principles required to order
and validate these client transactions
and provide analysis of some existing
blockchain applications.
Key Research Findings
Each blockchain system can be visu-
alized as a secure representation of a
traditional database system (Nawab
2018). Similar to a database system,
each blockchain application also re-
ceives transactions from multiple
clients. In its vanilla form, a blockchain
transaction is a collection of read or
write operations. Clients issue these
transactions to the servers for processing
and exchange of digital-commodities.
In general, each server in a
blockchain application stores a full-
copy of the chain. Hence, without any
loss of generality, we can claim that
servers of a blockchain system are
replicas of each other. In specific, a
blockchain application lays down a
replicated design where each replica
participates in ordering and executing
the incoming client transaction.
Blockchain Transaction Processing 3
Prior works have shown that it is
possible to make a replicated system
handle failures (Lamport 1998; Steen
and Tanenbaum 2017). In any replicated
system, replicas participate in a fault-
tolerant consensus protocol to decide
the order to execute a client transaction.
Blockchain applications also adhere
to this philosophy. They employ a
BFT consensus protocol to achieve
consensus under byzantine failures. But,
why do BFT protocols need to handle
byzantine failures? As a blockchain
system promotes democracy, it permits
display of adversarial behavior by
malicious replicas during consensus. To
tackle such malicious activities, each
blockchain application relies on the
design and properties dictated by a BFT
consensus protocol.
Blockchain Topologies
A key parameter that renders the
design of a blockchain system is its
underlying application. On the basis of
permissions available to a participating
node, a blockchain application can be
categorized blockchain as permissioned,
permissionless or hybrid (Pilkington
2015; Cachin and Vukolic 2017).
Although the blockchain community
agrees on the characteristics of a
permissionless or public blockchain
infrastructure, there is a lack of concise
definitions to explain other models.
On the basis of topology, we cate-
gorize a blockchain systems under four
heads: public, private, permissioned
and hybrid. Figure 2 presents a pic-
torial representation of the different
categories. In these figures, nodes that
lack any connections are disallowedfrom participating in the management
of the blockchain. Further, we use
circles to demarcate different zones of
operation; certain nodes are allowed to
lead the consensus (or create the next
block) while some nodes are allowed to
participate in the consensus protocol.
Public Blockchain systems, such
as Bitcoin (Satoshi 2008) and
Ethereum (Wood 2015), allow any
node to participate in the consensus
process and propose the next valid block
for the chain. Hence, a public or per-
missionless blockchain system upholds
its democratic nature by providing each
node with equal probability2of creating
the next block to be added to the chain.
Private Blockchain systems run at
the other extreme end of the spectrum.
These blockchain systems permit only
a specific set of nodes to be part of
the consensus protocol and restrict the
creation of next block to an even smaller
subset of nodes. Private blockchain de-
signs are attractive to large multi-sector
companies and banks, which may chose
to allow some of their customers to
participate in the consensus protocol,
while restricting creation of next block
to its employees.
Hybrid Blockchain systems attain
a middle ground between the two
extremes. Although these systems allow
any node to be part of the consensus
protocol, they restrict the task of propos-
ing and creating the next block to a
designated subset of replicas. For in-
stance, Ripple (Schwartz et al 2014)—a
cryptocurrency—supports a variant of
the hybrid model. In Ripple, only some
public institutions have the permissions
2The equal probability of creating a block is
only guaranteed when all the nodes have ex-
actly same amount of resources, and each node
is working independently.
4 Suyash Gupta, Mohammad Sadoghi
(a) Public Blockchain.
(b) Hybrid Blockchain.
(c) Permissioned Blockchain.
(d) Private Blockchain.
Fig. 2: Topologies for Blockchain Systems.
to select the transactions that will be
part of the next block.
Amidst all these topologies, per-
missioned blockchain systems have
successfully created a niche space for
their design (Androulaki et al 2018;
Gupta et al 2020c). Permissioned
blockchain applications allow any node
participate in the consensus protocol but
require the identities of all participants
to be known a priori. Although partici-
pants loose their privacy, permissioned
blockchain applications provide each
participant equal opportunity to propose
the next block. Notice that permissioned
blockchain applications place no other
special restrictions on the behavior of
a participant. Hyperledger Fabric (An-
droulaki et al 2018), Libra coin (Libra
2019) and R ESILIENT DB (Gupta et al2020c) are some of the state-of-the-art
permissioned blockchain applications
and fabrics.
Blockchain Transactional Flow
The initial block of any blockchain is
termed as the genesis block (Decker and
Wattenhofer 2013). Genesis block is
a special block that is numbered zero,
and is hard-coded in every blockchain
application. Each other block links to
some previously existing block. Hence,
a blockchain grows by appending new
blocks to the existing chain.
A transaction in a blockchain system
is identical to any distributed or OLTP
transaction (TPP Council 2010) that acts
Blockchain Transaction Processing 5
on some data. Traditional blockchain
applications (such as Bitcoin) consist
of transactions that represent an ex-
change of money between two entities
(or users). Each valid transaction is
recorded in a block, which can can con-
tain multiple transactions, for efficiency.
Immutability is achieved by leveraging
strong cryptographic properties such as
hashing (Katz and Lindell 2007).
Figure 3 illustrates the three main
phases required by any blockchain
application to create a new block. The
client transmits a transactional request
to one of the participants. This partici-
pating node multicasts the client request
to all other nodes. We term this phase
asTransaction Dissemination . Once, all
the nodes have a copy of client request,
they initiate a consensus protocol.
The choice of underlying consensus
protocol affects the time complexity and
resource consumption. The winner of
the consensus phase proposes the next
block and transmits it to all other nodes.
This transmission process is equivalent
to adding an entry (block) to the global
distributed ledger.
Blockchain Consensus
At the core of any blockchain applica-
tion is a BFT consensus protocol which
states that given a client transaction,
the aim of this consensus protocol is
to ensure all the non-faulty replicas
assign the same order to this trans-
action. Depending on the underlying
topology, we can broadly categorize
consensus protocols into two categories:
permissionless consensus protocols and
permissioned consensus protocols.Achieving fault-tolerant distributed
consensus is an age-old problem.
Commit protocols such as Two-Phase
Commit (Gray 1978), Three-Phase
Commit (Skeen 1982) and Easy-
Commit (Gupta and Sadoghi 2018,
2020) help in reaching agreement
among the participants in a parti-
tioned distributed databases (Qadah
and Sadoghi 2018; Qadah et al 2020;
Sadoghi and Blanas 2019). However,
commit protocols can only handle node
failures and are unsafe under message
delay or loss.
Paxos (Lamport 1998) and View-
stamped Replication (Oki and Liskov
1988) allow a distributed system of
replicas to achieve consensus in the
presence of crash-faults . In a system
ofnreplicas, a system employing
Paxos for consensus can handle up to
nfailures where n2f+1. Notice
that these ffailures need not be simple
replica crashes but can also take form
of message losses and delays. However,
crash-fault tolerant protocols such as
Paxos and Viewstamped Replication
cannot handle any malicious behavior.
A byzantine-fault tolerant protocol
aims at reaching consensus in a system
ofnreplicas where at most freplicas
can act as byzantine and n3f+1.
Traditional BFT protocols promote
aprimary-backup model where one
replica is designated as the primary and
other replicas act as backups. It is the
task of the primary to initiate consensus
among all the backups. Notice that all
the above discussed protocols, such as
Two-Phase Commit, Paxos and so on,
follow the primary-backup model. The
key reason primary-backup model is
preferred is because of its simplicity and
its ability to blame the primary for an
unsuccessful consensus.
6 Suyash Gupta, Mohammad Sadoghi
Fig. 3: Blockchain Flow: Three main phases in any blockchain application are represented. (a)
Client sends a transaction to one of the server, which it disseminates to all the other servers. (b)
Servers run the underlying consensus protocol, to determine the block creator. (c) New block is
created, and transmitted to each node, which also implies adding to global ledger.
Recent blockchain applications
present several new protocols for achiev-
ing consensus: Proof-of-Work (Jakob-
sson and Juels 1999; Satoshi 2008),
Proof-of-Stake (King and Nadal 2012)
and Proof-of-Authority (Parity Tech-
nologies 2018). Prior works have shown
that these consensus protocols provide
similar guarantees as traditional BFT
protocols (Garay et al 2015). Hence,
in the rest of this section, we illustrate
some of the state-of-the-art blockchain
protocols for both permissioned and
permissionless systems.
Permissioned Consensus
A decade prior to the inception of the
first blockchain application, the problem
of achieving fault-tolerant distributed
consensus problem had already excited
practitioners and researchers (Lamport
1998; Oki and Liskov 1988; Castro
and Liskov 1999). Distributed systems
research community agreed that a
byzantine-fault tolerant system can only
be considered correct if it is both safeandlive. A replicated system is called
assafe if all its replicas are consistent,
that is, have the same state. A replicated
system is termed as liveif its replicas are
able to make progress, that is, process
incoming client requests.
A majority of existing BFT protocols
guarantee safety under asynchronous
environment, that is, messages can
get loss, delayed or duplicated, and
up to freplicas may act byzantine.
Further, any BFT protocol employs
cryptographic constructs to prevent
malicious replicas from impersonating
non-faulty replicas. As clients send
their transactions to other replicas, so
each client uses digital signatures to
sign its message (Menezes et al 1996;
Katz and Lindell 2007). For all other
messages, depending on the algorithmic
steps, the system can employ either
asymmetric-key digital signatures or
less-expensive symmetric-key message
authentication codes (Katz and Lindell
2007). Hence, we assume authenticated
communication : malicious replicas can
impersonate each other, but no replica
can impersonate a non-faulty replica.
Further, replicas will accept only those
Blockchain Transaction Processing 7
messages which are well-formed, that is,
have valid message authentication codes
or digital signatures (as applicable).
PBFT. Practical Byzantine Fault Tol-
erance (Castro and Liskov 1999) if often
considered as the first protocol to present
a practical design for achieving byzan-
tine fault-tolerance in a distributed sys-
tem. P BFT follows the primary-backup
model where the primary replica initi-
ates the consensus among all the repli-
cas. It is the responsibility of the primary
to ensure all the backup replicas success-
fully order every incoming client trans-
action otherwise it risks replacement. If
the primary is non-malicious and the net-
work is reliable, P BFT guarantees con-
sensus in three phases.
PBFTprotocol starts when a client C
wants a transaction to be executed and
sends a request mto the primary replica
P. The primary Pchecks if the client
signature is valid and if this is the case,
it creates a P RE-PREPARE message and
sends that message to all the backups.
This P RE-PREPARE message includes a
sequence number (an integer) and a hash
of the client request. The sequence num-
berkstates the order to execute the trans-
action while the hash acts as a digest,
which can be used in future communica-
tions as an alias for the client request3.
When a replica Rreceives a
PRE-PREPARE message from the pri-
maryP, it performs the following
checks: (i) verifies the client signature
onm, (ii) checks if Pis the primary,
and (iii) ensures the sequence number
khas not already been used. If the
PRE-PREPARE message passes all the
checks, Ragrees to support primary’s
order for this request and sends a
3Client requests are often of the order of several
kilobytes and sending an hash instead optimizes
the communication.PREPARE message to all the replicas.
When a replica Rreceives P REPARE
messages from 2 freplicas in support
of the request msent by P, thenR
marks the request as prepared . This
information gives Ran assurance that
a majority of non-faulty replicas are
also agreeing to order this request at
sequence k. Next,Racknowledges the
prepared request by sending a C OMMIT
message to all the replicas. When a
replicaRreceives C OMMIT messages
from 2 f+1 replicas, then Rachieves a
unique guarantee on the order of m, that
is, a majority of non-faulty replicas have
also prepared m. This allows replica R
to go ahead and execute the request m
as the k-th request. Finally, Rsends the
result of executing mas a response to
the client C.
The client Cneeds f+1 matching
responses from distinct replicas, to mark
its request mas complete. It is possible
that the client may not receive sufficient
number of matching responses. To
handle such cases, the client initiates
a timer prior to sending its request. In
specific, each client waits on a timer
for receiving f+1 identical responses.
If the client timeouts while waiting
forf+1 responses, then it forwards
its request mto all the replicas. When
abackup replica Rreceives a client
request m, it forwards that request to the
primary Pand starts its timer. If P
fails to send a P RE-PREPARE message
corresponding to m, thenRconcludes
thatPis byzantine and initiates pri-
mary replacement. Existing literature
terms this primary replacement process
asview-change because each primary
represents a view of the system. The
view-change protocol only starts when
at least f+1 replicas are ready to
replace the primary. This condition is
8 Suyash Gupta, Mohammad Sadoghi
necessary as up to freplicas can be
byzantine and may even request replace-
ment of a non-faulty primary. Hence,
when at least f+1 replicas request
replacement, remaining replicas assume
that there is at least one non-faulty
replica which has been affected.
For a successful view-change to take
place, a new primary has to be selected.
PBFT follows a simple principle: if the
replica with index iis the current pri-
mary, then replica with index jwill be
the next primary, where j= (i+1)mod
n. But, how does a replica concludes
that it is time for it to act as the new
primary. When any replica Rreceives
VIEW-CHANGE messages from 2 f+1
distinct replicas that want to elect it as
the primary, then it initiates the process
of switching to next view. Notice that
the process of switching to next view re-
quires ensuring all the replicas have the
common state. Thus, the new primary
also needs to provide this information as
part of the N EW-VIEW message.
Zyzzyva. It is evident from P BFT’s
design that it requires three phases of
communication of which two necessitate
quadratic communication complexity.
Hence, there is a need for optimized
protocols, which can achieve the same
goals with much lesser communication
overheads. Z YZZYVA (Kotla et al
2007) presents a twin-path protocol that
achieves consensus in a single linear
phase if there are no failures. All the
replicas in the Z YZZYVA start in the
fast-path and switch to the slow-path
under failures. Note that a recent work
has illustrated that Z YZZYVA is unsafe
under failures (Abraham et al 2017).
In Z YZZYVA , when a non-primary
replica Rreceives a P RE-PREPARE
message from the primary P, it as-
sumes that the primary is non-faulty andagrees to execute this request. Such an
execution is termed as speculative as
the replica Ris unaware of the state at
other replicas. In specific, a byzantine
primary could have equivocated and sent
different replicas distinct client requests.
Once the replica Rexecutes the request,
it sends the reply to client C. The client
Cmarks the request complete if it
receives matching identical responses
from at least 3 f+1 replicas.
A keen reader can easily notice that
the onus is on the client to ensure system
is safe. Further, when n=3f+1, then
the client has to wait for responses from
all the replicas. Due to these restrictions,
ZYZZYVA ’s fast-path works only if
there are no failures. In Z YZZYVA ,
the client waits on a timer while ex-
pecting 3 f+1 responses. If the client
timeouts prior to receiving responses,
then it initiates the slow-path . In the
slow-path, client has to summarize the
state it received from different replicas
and needs to decide whether primary
replacement needs to be initiated or a
simple recovery protocol is sufficient
to ensure system remains live. Clearly,
the slow-path is no longer linear and
requires multiple phases. Moreover, if
the client is malicious, then the replicas
could be momentarily unsafe until
there is a good client. Another key
challenge with twin-path protocols is
finding the optimal timeout value. Prior
works have shown that finding a timeout
value can be hard and Z YZZYVA faces
severe reduction is throughput under
failures (Clement et al 2009a,b; Gupta
et al 2021a).
SBFT.The key aim behind the design
of S BFT (Golan Gueta et al 2019) is
to make a consensus protocol that can
guarantee safe consensus with linear
message complexity in periods of no
Blockchain Transaction Processing 9
failures. In fact, like Z YZZYVA , SBFTis
also a twin-path protocol. S BFTemploys
threshold signatures to achieve linear
communication complexity.
Threshold signatures are based on
asymmetric cryptography. In specific,
each replica holds a distinct private key ,
which it can use to create a signature
share . Next, one can produce a valid
threshold signature given at least tsuch
signature shares from distinct replicas
(the exact value of tis dependent on the
underlying consensus protocol).
At a closer look, it seems like S BFT
requires more phases than P BFT. This
occurs because S BFT linearizes each
phase of P BFT through use of threshold
signatures. In S BFT, when a replica R
receives a P RE-PREPARE message, it
agrees to support from the primary’s
sequence by generating a threshold
share. The replica Rsends this share
to a specific replica designated as the
collector . When a collector receives
message from at least 3 f+2c+1 repli-
cas it generates a threshold signatures
and sends this signature to all the repli-
cas. When a replica receives a threshold
signature from the collector, it executes
the request to generate a response, cre-
ates a threshold share on this response
and sends these to a specific replica
designated as the executor . The executor
waits for f+1 identical responses and
combines them into threshold signature.
Next, the executor sends this signature
to all the replicas and clients.
For S BFT’s fast path to work as
stated, either there should be no failures
or at least 3 f+2c+1 replicas should
participate in consensus where up to
c>0 replicas can crash-fail (no byzan-
tine failures). Moreover, the primary can
act as both collector and executor but
SBFT suggests using distinct replicas infast path. If the collector timeouts wait-
ing for threshold shares from 3 f+c+1
replicas, it switches to the slow path,
which requires two additional linear
phases to complete consensus.
HotStuff. In any primary-backup
BFT protocol, if the primary acts
malicious, then the protocols employ the
accompanying view-change algorithm
to detect and replace the malicious
primary. This view-change algorithm
leads to a momentary disruption in
system throughput until the resumption
of service.
HOTSTUFF (Yin et al 2019) proposes
eliminating the dependence of a BFT
consensus protocol from one primary by
replacing primary at the end of every
consensus. Although this rotating leader
design escapes the cost of a view-change
protocol, it enforces an implicit sequen-
tialparadigm. Each primary needs to
wait for its turn before it can propose a
new request.
In H OTSTUFF , in round i, the replica
with identifier imod nacts as the pri-
mary and proposes a request to all the
replicas. Each replica on receiving this
request, creates a threshold share and
sends to the replica Rwith identifier
(i+1)mod n. IfRreceives threshold
shares from 2 f+1 replicas, then it com-
bines them into a threshold signature and
initiates the consensus for round i+1
by broadcasting its proposal along with
the computed threshold signature. No-
tice that replicas have not executed the
request and replied to the client. H OT-
STUFF ’s aim is to linearize the consen-
sus proposed by P BFT protocol, which
it does by splitting each phase of P BFT
into two using threshold signatures. To
reduce the communication, it chains the
phases. Hence, a replica executes the re-
quest for the i-th round once it receives
10 Suyash Gupta, Mohammad Sadoghi
a threshold signature from the primary
of(i+3)-th round. Evidently, chaining
helps H OTSTUFF to some extent but it
does not eliminate its sequential nature.
This sequential nature forces H OTSTUFF
to loose out on an opportunity to process
messages out-of-order.
PoE. Proof-of-Execution (henceforth
referred to as P OE) consensus protocol
aims at achieving consensus in three
linear phases without relying on any
twin-path model (Gupta et al 2021a).
Further, P OE recognizes that no one
size fits all systems (Singh et al 2008).
Hence, its design is independent of
the choice of underlying cryptographic
signature scheme. This implies that the
POE protocol can employ both sym-
metric and asymmetric-cryptographic
signature schemes depending on the
application environment.
The design of P OE is built on three
key insights. First, P OE prevents use of
any twin-path paradigm as switching
from fast to slow-path requires depen-
dence on timeouts, which degrades
system performance. Second, P OE
allows replicas to speculatively execute
the requests but facilitates rollbacks
in case of inconsistencies. Final, P OE
allows out-of-order processing, which
eliminates any bottlenecks associated
with sequential consensus protocols.
For the sake of brevity, we will de-
scribe P OE built on top of threshold sig-
natures. In P OE, the client Cinitiates
execution by sending its request mto the
primary P. To initiate replication and
execution of mas the k-th transaction,
the primary proposes mto all replicas by
broadcasting a P ROPOSE message.
After a replica Rreceives a P ROPOSE
message from P, it checks whether at
least 2 fother replicas also received the
same proposal from P. To perform thischeck, each replica agrees to support the
first k-th proposal it receives from the
primary by sending a S UPPORT message
that includes its unique threshold share
to the primary. The primary Pwaits for
2f+1 threshold shares, and on receiv-
ing such shares, it combines them into
athreshold signature and broadcasts as
a C ERTIFY message. When a replica
Rreceives the C ERTIFY message, it
view-commits tomas the k-th transaction
in view v. AfterRview-commits to m,
Rschedules speculative execution of
m. Consequently, mwill be executed by
Rafter all preceding transactions are
executed. After execution, Rinforms
the client of the order of execution
and of any execution result. A client
considers its transaction successfully
executed after it receives identical
response messages from 2 f+1 distinct
replicas.
Aardvark. The design philosophy
behind Aardvark is distinct in compari-
son to existing BFT protocols (Clement
et al 2009b). It aims at building a
robust BFT protocol that can continue
performing under failures. Hence, in
the failure-free cases, Aardvark attains
lower throughput than a majority of the
existing BFT protocols.
In Aardvark, prior to sending its
request to the primary, the client signs
the request using both digital signatures
and message authentication codes.
This prevents malicious clients from
performing a denial-of-service attack
as it is expensive for client to sign each
message twice. Aardvark also employs
apoint-to-point network rather than
the multicast network for exchange of
messages among clients and replicas.
The key intuition behind such a choice
is to disallow a faulty client or replica
from blocking the complete network.
Blockchain Transaction Processing 11
Aardvark also periodically changes the
primary replica. Each replica tracks the
throughput of the current primary and
suggests replacing the primary when
there is a decrease in its throughput.
To perform such tracking, each replica
sets a timer and measures the rate of
primary’s responses.
RBFT. The key intuition behind the
design of R BFT is to facilitate detection
ofclever malicious primaries (Aublin
et al 2013). R BFTextends Aardvark and
aims to detect those malicious primaries,
which cannot be detected by simple
timers suggested by Aardvark.
In Aardvark, a clever primary can
avoid detection by delaying messages
just slightly below the timeout threshold.
Such a primary can throttle the system
throughput without risking eviction.
To tackle this challenge, R BFT insists
running f+1 independent instances of
the Aardvark protocol on each replica.
One of these instances is designated as
themaster while other instances act as
backups . Irrespective of the designation
of an instance, all the instances order all
the requests. However, only the master
instance executes the requests.
The key task of the backup instances
is to monitor the performance of the
master instance. If any backup instance
observes a degradation of the system
throughput at the master, it broadcasts a
message to elect a new primary. Further,
to guarantee at least one of the f+1
instances is led by a non-faulty replica,
RBFT requires each instance to be led
by a distinct replica. In comparison to
both P BFTand Aardvark, R BFTrequires
an additional phase, which is used to
propagate the client requests across all
the replicas.
RCC. Although R BFT successfully
utilizes redundancy to detect clevermalicious primaries, it also wastes
excessive bandwidth by requiring
all the instances to order the same
set of requests. Resilient Concurrent
Consensus (henceforth referred to as
RCC) paradigm resolves this issue by
parallelizing the consensus (Gupta et al
2019b, 2021b). In specific, R CCruns
at each replica multiple instances of a
primary-backup protocol.
The key challenge with the design
of primary-backup protocols is their re-
liance on the primary. This dependence
can severely affect the throughput and
scalability of these protocols. The pri-
mary replica not only receives all client
requests but is also responsible for en-
suring consensus is reached on the order
for these requests among all other repli-
cas. If the primary fails to ensure consen-
sus, then all remaining replicas need to
replace this primary. This replacement
process is necessary as, without it, non-
faulty replicas may never converge. Un-
fortunately, primary replacement is not
cheap, as it requires pausing consensus
on all outstanding requests until the pri-
mary is replaced.
RCCaims at making a BFT consen-
sus primary agnostic. To achieve such a
property, R CCadvocates running zpar-
allel instances at each replica. Further,
RCCensures that each instance is man-
aged by a distinct replica. Using par-
allelization, R CCensures that the non-
faulty replicas are always accepting and
ordering client requests , this indepen-
dent of any malicious behavior or attack.
We now present the design of R CC
paradigm that parallelizes the seminal
PBFT consensus protocol. For the sake
of explanation, we assume R CCworks
inrounds . Each round of R CCincludes
three stages :parallel consensus ,unifi-
cation , and execution . The notion of a
12 Suyash Gupta, Mohammad Sadoghi
round helps in generating a common or-
der and recovering from instance failures
but it does not prevent individual pri-
maries from working independently.
Prior to any round, R CCrequires
each replica to prepare to run zinstances
of P BFT protocol in parallel. A round
rbegins when the primary of each in-
stance proposes a client request. Firstly,
in the parallel consensus stage, each
instance runs P BFTon its client request.
Secondly, in the unification stage, the
replica waits for all its zinstances to
complete replication (reach consensus
on their respective requests). If every in-
stance successfully replicates a request,
then a common order for execution of
these requests is determined. If one or
more instances are unable to replicate
requests, then the primaries for those
instances must be faulty and recovery
is initiated. Finally, in the execution
stage, each replica executes all the client
requests in the common order.
GeoBFT. Existing BFT protocols do
not distinguish between the local and
global communication, which is a nec-
essary requirement to enable geo-scale
deployment of a blockchain system. To
resolve this challenge, Geo-Scale Byzan-
tine Fault-Tolerant consensus protocol
(henceforth referred to as G EOBFT) that
uses topological information to group all
replicas in a single region into a single
cluster (Gupta et al 2020b). Likewise,
GEOBFTassigns each client to a single
cluster. This clustering helps in attaining
high throughput and scalability in geo-
scale deployments. G EOBFToperates in
rounds, and in each round, every clus-
ter will be able to propose a single client
request for execution. Each round con-
sists of the three steps: local replication ,
global sharing , and ordering and execu-
tion, which we further detail next.At the start of each round, each
cluster chooses a single transaction of a
local client. Next, each cluster locally
replicates its chosen transaction in a
Byzantine fault-tolerant manner using
PBFT. At the end of successful local
replication, P BFT guarantees that each
non-faulty replica can prove successful
local replication via a commit certificate .
Next, each cluster shares the locally-
replicated transaction along with
its commit certificate with all other
clusters. To minimize inter-cluster
communication, we use a novel op-
timistic global sharing protocol . Our
optimistic global sharing protocol has a
global phase in which clusters exchange
locally-replicated transactions, followed
by a local phase in which clusters
distribute any received transactions
locally among all local replicas. Finally,
after receiving all transactions that are
locally-replicated in other clusters, each
replica in each cluster can determin-
istically order all these transactions
and proceed with their execution . After
execution, the replicas in each cluster
inform only local clients of the outcome
of the execution of their transactions
(e.g., confirm execution or return any
execution results).
Permissionless Consensus
Permissionless applications inspired by
Nakamoto’s Bitcoin (Satoshi 2008) ad-
vocate a public blockchain system where
any replica can participate in the consen-
sus. Hence, the identity of a participat-
ing replica can be protected. This design
property requires the underlying consen-
sus protocol used to order the transac-
tions to expend the resources of a partic-
Blockchain Transaction Processing 13
ipant. In specific, each participant needs
to spend some of its resources if it wants
to propose the next block. If such a re-
source consumption is not enforced, then
a malicious participant can create multi-
ple pseudonymous identities and subvert
the system, also known as the Sybil at-
tack (Douceur 2002).
Proof-of-Work. Bitcoin relies on the
Proof-of-Work (henceforth referred to
as P OW) protocol to achieve consensus
among a set of replica. P OW protocol
builds on top of a simple intuition “What
is mathematically hard to compute but
easy to verify?” Hence, P OW protocol
requires the computation to be expen-
sive, that is, it should deplete some
resources of the prover .
In P OW protocol, the participating
nodes compete among themselves to
propose the next block by solving a
complex puzzle. In nature, several
computationally hard problems exist,
such as Diophantine Equation, RSA
Factorization, One-way Hash Functions,
and so on. Among these hard problems,
following the Nakamoto’s vision, P OW
protocol is associated with the computa-
tion of one-way hash functions such as
computation of a 256-bit SHA3 value.
When a node Nsuccessfully computes
this hash value, it disseminates this so-
lution to all other nodes for verification.
Any node can verify this solution to
check N’s claim.
The main critic behind P OW’s design
is that leads to excessive wastage of
energy. Permissionless applications that
employ P OW consensus have to set
large targets to prevent Sybil attacks.
Further, P OW’s design facilitates unfair
practices—higher the computational
capabilities a node has higher are its
chances of solving the complex puzzle.
Such a design promotes pooling ofresources where several nodes work
together to compute the hash. Moreover,
a node has to be given incentives to
participate in the P OW consensus. If the
incentives are not sufficient, then nodes
may decline creating the next block,
which in turn can either stall the system
or compromise its security.
Another issue with the P OW consen-
sus protocol is that it can lead to tricky
situations where it is hard to determine
the next block in the chain. For instance,
two nodes N1andN2may solve the com-
plex puzzle at the same time. In such a
case, it is possible that one half of the
remaining participants may receive a so-
lution from N1before N2while the other
half receives solution from N2before N1.
To handle this scenario, some form of
resolution mechanism is needed, which
would lead to wastage of resources of
either N1orN2as both of their blocks
cannot be appended to the chain. Notice
that any new block added to the chain in-
cludes the hash of the previous block.
Proof-of-Stake. In P OW protocol,
miners have to deplete their computa-
tional resources in order to earn the right
to create the next block. Each miner
who controls a fraction sof the total
computational power, has a probability
nearly equal to sto create the next block.
Proof-of-Stake (henceforth referred
to as P OS) presents a principle that
contrasts the resource usage philoso-
phy of P OW. In a blockchain system
employing P OS protocol, a replica
possessing a higher stake than the other
replicas gets a chance to create a new
block (Bentov et al 2016). In specific,
the probability a replica possessing
a fraction sof the total stakes in the
system creates the next block is s. The
key security rationale behind P OS is that
replicas who have some stake involved
14 Suyash Gupta, Mohammad Sadoghi
in the system are also well-suited to
ensure its security.
PPCoin or PeerCoin (King and
Nadal 2012) is often regarded as the
first implementation of P OS. The key
motivation behind PPCoin’s design was
to implement a crypto-currency that
does not require participating replicas to
spend its resources in performing large
computations. Initial P OS-based design
were based on the notion of coinage . In
specific, a replica’s ability to create the
next block is determined on its value
of coinage. Coinage is calculated on
the basis of number of days a replica
has held some coins or stake. To pre-
vent Sybil attacks, P OS-based systems
require replicas to algorithm requires a
node to spend its coinage if it wants to
propose the next block.
Initial implementations of the P OS
protocol lacked the fairness criterion.
This is evident as the replica with the
highest stake gets the chance to propose
the next block. Although a high stake
replica looses its coinage once it creates
the next block, it may create the subse-
quent block if its stake is much larger in
value than that of the other replicas.
To resolve this issue, a chain-based
variant of P OS algorithm has been pro-
posed. The chain-based P OS protocol
employs a psuedo-random algorithm to
select a validator, which then creates a
new block and adds it to the existing
chain of blocks. The frequency of
selecting the validator is set to some
pre-defined time interval. Another vari-
ant of P OS algorithm follows BFT-style
consensus. In this design, the replicas
participate in a BFT protocol to select
the next valid block. Here, validators are
given right to propose the next block,
at random. The key difference between
these algorithms is the synchrony re-quirement; chain-based POS algorithms
are inherently synchronous, while
BFT-style POS is partially synchronous.
Another keychallenge for P OS-based
designs is an attack by rational stake-
holders. A rational replica would always
aim at maximizing its profit, an expected
behavior in a democracy in correspon-
dence with the Nash equilibrium Bentov
et al (2016). Rational replicas can affect
the security of P OS, as in at attempt to
maximize their gains, they may partici-
pate in multiple chains.
A rational miner could get blocks
from distinct forks of the blockchain.
To maximize its returns, a miner would
attempt to propose the next block for
each such fork. As miners don’t lose
any actual resources (like computational
energy in P OW), so they are free to
propose blocks on different chains.
This could lead to an ever-expanding
divergent network.
Proof-of-Authority. A variation of
POS algorithm to be employed in hybrid
blockchain topologies is termed as
Proof-of-Authority (henceforth referred
as P OA) (Parity Technologies 2018).
The key idea is to designate a set of
nodes as the authorities or leaders. These
authorities are entrusted with the task
of creating new blocks and validating
the transactions. P OA marks a block as
part of the blockchain if it is signed by
majority of the authorized nodes. The
incentive model in P OA highlights that
it is in the interest of an authority node
to maintain its reputation. In case an
authority acts malicious, it can loose its
status and periodic incentives. Hence,
POA does not select nodes based on
their claimed stakes.
Proof-of-Space. A consensus al-
gorithm orthogonal to the design
proposed by P OW is proof-of-space
Blockchain Transaction Processing 15
orproof-of-capacity (henceforth re-
ferred as P OC) (Ateniese et al 2014;
Dziembowski et al 2015).
POC expects nodes to provide a proof
that they have sufficient “storage” to
solve a computational problem. P OC al-
gorithm targets computational problems
such as hard-to-pebble graphs (Dziem-
bowski et al 2015) that need large
amount of memory storage to solve
the problem. In the P OC algorithm,
the verifier first expects a prover to
commit to a labeling of the graph, and
then it queries the prover for random
locations in the committed graph. The
key intuition behind this approach is
that unless the prover has sufficient
storage, it would not pass the verifica-
tion. SpaceMint (Park et al 2015)—a
POC-based cryptocurrency—claims
that P OC based approaches are more
resource efficient in comparison to P OW
as storage consumes less energy.
Blockchain Systems
We now briefly look at the design of
some of the state-of-the-art blockchain
applications and fabrics. The key aim
of this section is to illustrate the differ-
ent design practices adopted by existing
blockchain systems.
Bitcoin (Satoshi 2008) is regarded
as the first ever blockchain application.
It is a cryptographically secure digital
currency designed with the aim of dis-
rupting the traditional institutionalized
monetary exchange. Bitcoin acts as the
token of transfer between two parties
undergoing a monetary transaction. The
underlying blockchain system is a net-
work of nodes (also known as miners )
that take a set of client transactionsand validate the same by demonstrating
aproof-of-work , that is generating a
block. The process of generating the
next block is non-trivial and requires
large computational resources. Hence,
the miners are given incentives (such as
Bitcoins) for dedicating their resources
and generating the block. Each miner
maintains locally an updated copy of the
complete blockchain and the associated
ledgers for every Bitcoin user.
To ensure Bitcoin system remains
fair towards all the machines, the dif-
ficulty of proof-of-work challenge is
periodically increased. Prior works have
illustrated that Bitcoin is vulnerable to
51% attack, which can lead to double
spending (Rosenfeld 2014). The in-
tensity of such attacks increases when
multiple forks of the longest chain are
created. To avoid these attacks, Bitcoin
developers suggest the clients to wait
for their block to be confirmed before
they mark the Bitcoins as transferred.
This wait ensures that the specific block
is a little deep (nearly six blocks) in the
longest chain (Rosenfeld 2014). Bitcoin
critics also argue that its proof-of-work
consumes huge energy4and may not be
a viable solution for future.
Ethereum (Wood 2015) is
blockchain framework that permits
users to create their own applica-
tions ( smart-contracts ) on top of the
Ethereum Virtual Machine (EVM).
Ethereum utilizes the notion of smart
contracts to facilitate development
of new operations. It also supports a
digital cryptocurrency, ether , which is
used to incentivize the developers to
create correct applications. One of the
key advantage of Ethereum is that it
4As per some claims one Bitcoin transaction
consumes power equivalent to that required by
1:5 American homes per day.
16 Suyash Gupta, Mohammad Sadoghi
supports a Turing complete language
to generate new applications on top
of EVM. At the time of writing this
article, Ethereum employs a variant of
POW protocol to achieve consensus
among its miners. Ethereum makes
its miners solve challenges that were
not only computational intensive, but
also memory intensive. This design
prevented existence of miners who
utilized specially designed hardware for
compute intensive applications.
In future, Ethereum Foundation aims
to switch to a variant of P OS protocol
to reach consensus among its replicas.
The modified protocol is referred to
asCasper (Buterin and Griffith 2017).
Casper introduces the notion of final-
ity, that is, it ensures that one chain
becomes permanent in time. It also
introduces the notion of accountability ,
which penalizes any validator that at-
tempts the nothing-at-stake attack. The
penalty leveraged on such a validator is
equivalent to negating all his stakes.
Parity (Parity Technologies 2018)
is an application designed on top of
Ethereum. It provides an interface for
its users to interact with the Ethereum
blockchain. Parity allows its blockchain
community to use either Proof-of-Work
andProof-of-Authority to reach consen-
sus in their applications. Hence, if some
users select P OA consensus, then Parity
provides mechanisms for setting up the
authority nodes.
Ripple (Schwartz et al 2014) is con-
sidered as third largest cryptocurrency
after Bitcoin and Ethereum in terms
of market cap. It employs a consensus
algorithm which is a simple variant of
existing traditional BFT algorithms.
Ripple requires number of failures fto
be bounded as follows: (n 1)=5+1,
where nrepresents the total number ofnodes. Ripple’s consensus algorithm
introduces the notion of a Unified Node
List (UNL), which is a subset of the
network. Each server communicates
with the nodes in its UNL for reaching a
consensus. The servers exchange the set
of transactions they received from the
clients and propose those transactions
to their respective UNL for vote. If a
transaction receives 80% of the votes,
it is marked permanent. Notice that if
the generated UNL groups are a clique
then forks of the longest chain could
co-exist. Hence, UNLs are created in
a manner that they share some set of
nodes. Another noteworthy observation
about Ripple protocol is that each client
needs to select a set of validators or
unique nodes that they trust. These
validators utilize the ripple consensus
algorithm to verify the transactions.
Hyperledger (Cachin 2016) is a
suite of resources aimed at modeling
industry standard blockchain applica-
tions. It provides a series of Application
Programming Interfaces (APIs) for de-
velopers to create their own non-public
blockchain applications. Hyperledger
provides implementations of blockchain
systems that uses RBFT and other vari-
ants of the PBFT consensus algorithm.
It also facilitates use and development
of smart contracts. It is important to
understand that the design philosophy of
Hyperledger leans towards blockchain
applications that require existence of
non-public networks, and so, they do
not need a compute intensive consensus.
ResilientDB (Gupta et al 2020c;
Rahnama et al 2020) is a state-of-the-art
permissioned blockchain fabric, which
is designed with the aim of foster-
ing academic and industry research.
RESILIENT DB also acts as a reliable
test-bed to implement and evaluate
Blockchain Transaction Processing 17
HASHINGTOOLKIT
SIGNINGTOOLKITSECURE LAYER
STORAGE LAYER
BLOCKCHAIN
METADATA
THREADSBFT CONSENSUS
EXECUTION LAYER
NETWORKQUEUES
Fig. 4: Architecture of R ESILIENT DB.
enterprise-grade blockchain applica-
tions5. R ESILIENT DB evolved from
the ExpoDB platform (Sadoghi 2017;
Gupta and Sadoghi 2018) which is
an experimental research platform to
design and test emerging database tech-
nologies, agreement and concurrency
control protocols.
In Figure 4, we illustrate the overall
architecture of R ESILIENT DB, which
lays down an efficient client-server
architecture. At the application layer ,
we allow multiple clients to co-exist,
each of which creates its own requests.
For this purpose, they can either em-
ploy an existing benchmark suite or
design a Smart Contract suiting to
the active application. Next, clients
and replicas use the transport layer
to exchange messages across the net-
work. R ESILIENT DB also provides a
storage layer where all the metadata
corresponding to a request and the
blockchain is stored. At each replica,
there is an execution layer where the
underlying consensus protocol is run
on the client request, and the request is
ordered and executed. During ordering,
5RESILIENT DB is open-sourced at
https://resilientdb.com and code is available at
https://github.com/resilientdb.
4 8 16 32
Number of Replicas406080100120140160Throughput (KTxns/s)
ResilientDB
ZyzzyvaFig. 5: Two permissioned applications
employing distinct BFT protocols (80K
clients per experiment).
thesecure layer provides support for
cryptographic constructs.
RESILIENT DB is written entirely in
C++ and provides a graphical user inter-
faceto ease user interaction with the sys-
tem. Further, it also provide a Docker-
ized deployment that allows any user to
experience and test the R ESILIENT DB
fabric (comprising of multiple replicas
and clients) on its local machine.
The key motivation behind R E-
SILIENT DB’s design was to show that a
system-centric permissioned blockchain
fabric can outperform a protocol-centric
blockchain fabric even if the former
is made to employ a slow consensus
18 Suyash Gupta, Mohammad Sadoghi
protocol. To prove this claim, we refer to
Figure 5, which compares the through-
put achieved by two permissioned
fabrics. In this figure, R ESILIENT DB
employs the slow P BFT protocol while
the other fabric adopts the practices sug-
gested in the paper BFTSmart (Bessani
et al 2014) and employs the single-phase
linear Z YZZYVA protocol. Despite this
disadvantageous choice of consensus
protocol, R ESILIENT DB achieves a
throughput of 175K transactions per
second, scales up to 32 replicas, and
attains up to 79% more throughput.
Future Directions for Research
Although blockchain technology is just
a decade old, it gained majority of its
momentum in the last five years. This
allows us to render different elements
of the blockchain systems and achieve
higher performance and throughput.
Some of the plausible directions to
develop efficient blockchain systems
are: (i) reducing the communication
messages, (ii) defining efficient block
structure, (iii) improving the consensus
algorithm, and (iv) designing secure
light-weight cryptographic functions
Statistical and machine learning
approaches have presented interesting
solutions to automate key processes
such as Face Recognition (Zhao et al
2003), Image classification (Krizhevsky
et al 2012), Speech Recognition (Graves
et al 2013) and so on. The tools can be
leveraged to facilitate easy and efficient
consensus. The intuition behind this
approach is to allow learning algorithms
to select nodes, which are fit to act as
a block creator and prune the rest from
the list of possible creators. The keyobservation behind such a design is that
the nodes selected by the algorithm are
predicted to be non-malicious. Machine
learning techniques can play an impor-
tant role in eliminating the human bias
and inexperience. To learn which nodes
can act as block creators, a feature set,
representative of the nodes, needs to be
defined. Some interesting features can
be: geographical distance, cost of com-
munication, available computational
resources, available memory storage
and so on. These features would help in
generating the dataset that would help
to train and test the underlying machine
learning model. This model would be
ran against new nodes that wish to join
the associated blockchain application.
The programming languages and
software engineering communities have
developed several works that provide
semantic guarantees to a language or an
application (Wilcox et al 2015; Leroy
2009; Kumar et al 2014). These works
have tried to formally verify (Keller
1976; Leroy 2009) the system using the
principles of programming languages
and techniques such as finite state
automata, temporal logic and model
checking (Grumberg and Long 1994;
Baier and Katoen 2008). We believe
similar analysis can be performed in the
context of blockchain applications. The-
orem provers (such as Z3 (De Moura
and Bjørner 2008)) and proof assis-
tants (such as COQ (Bertot 2006))
could prove useful to define a certified
blockchain application. A certified
blockchain application can help in stat-
ing theoretical bounds on the resources
required to generate a block. Similarly,
some of the blockchain consensus has
been shown to suffer from Denial of Ser-
vice attacks (Bonneau et al 2015), and a
formally verified blockchain application
Blockchain Transaction Processing 19
can help realize such guarantees, if the
underlying application provides such a
claim.
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|
{
"id": "2107.11592"
}
|
1806.06738
|
The Evolution of Embedding Metadata in Blockchain Transactions
|
The use of blockchains is growing every day, and their utility has greatly
expanded from sending and receiving crypto-coins to smart-contracts and
decentralized autonomous organizations. Modern blockchains underpin a variety
of applications: from designing a global identity to improving satellite
connectivity. In our research we look at the ability of blockchains to store
metadata in an increasing volume of transactions and with evolving focus of
utilization. We further show that basic approaches to improving blockchain
privacy also rely on embedding metadata. This paper identifies and classifies
real-life blockchain transactions embedding metadata of a number of major
protocols running essentially over the bitcoin blockchain. The empirical
analysis here presents the evolution of metadata utilization in the recent
years, and the discussion suggests steps towards preventing criminal use.
Metadata are relevant to any blockchain, and our analysis considers primarily
bitcoin as a case study. The paper concludes that simultaneously with both
expanding legitimate utilization of embedded metadata and expanding blockchain
functionality, the applied research on improving anonymity and security must
also attempt to protect against blockchain abuse.
|
http://arxiv.org/pdf/1806.06738v1
|
Tooba Faisal, Nicolas Courtois, Antoaneta Serguieva
|
cs.CR
|
cs.CR
|
The Evolution of Embedding Metadata
in Blockchain Transactions
Tooba Faisal
University College London
London, UK
tooba.hashmi@gmail.comNicolas Courtois
University College London
London, UK
n.courtois@ucl.ac.ukAntoaneta Serguieva
nChain, and LSE Systemic Risk
London, UK
antoaneta@ncrypt.com
Abstract —The use of blockchains is growing every day, and
their utility has greatly expanded from sending and receiving
crypto-coins to smart-contracts and decentralized autonomous
organizations. Modern blockchains underpin a variety of appli-
cations: from designing a global identity to improving satellite
connectivity. In our research we look at the ability of blockchains
to store metadata in an increasing volume of transactions and
with evolving focus of utilization. We further show that basic ap-
proaches to improving blockchain privacy also rely on embedding
metadata. This paper identifies and classifies real-life blockchain
transactions embedding metadata of a number of major protocols
running essentially over the bitcoin blockchain. The empirical
analysis here presents the evolution of metadata utilization in the
recent years, and the discussion suggests steps towards preventing
criminal use. Metadata are relevant to any blockchain, and our
analysis considers primarily bitcoin as a case study. The paper
concludes that simultaneously with both expanding legitimate
utilization of embedded metadata and expanding blockchain
functionality, the applied research on improving anonymity and
security must also attempt to protect against blockchain abuse.
Index Terms —bitcoin, bitcoin cash, blockchain, cryptographic
key management, embedded metadata, anonymity, privacy, ran-
somware, multisig.
I. I NTRODUCTION
The use of blockchains is expanding from transferring
crypto-coins to implementing smart-contracts that service a
variety of domains. IBM and Sovrin are designing and imple-
menting a global digital identity layer enabled by blockchain:
decentralized, point-to-point exchange of information about
people, organizations, or things. [1] EtherSat is developing
a protocol for satellite connectivity utilizing blockchain: a
decentralized global area network that maximizes efficiency of
existing ground-station infrastructure. [2] nChain is creating a
blockchain tokenization layer to enable interactivity and inter-
operability among smart contracts underlying various services
[3] [4]. These are only few examples of how the technology is
expanding. Throughout the initial and the expansion stages, the
ability of a blockchain to store metadata has been exploited in
an increasing volume of transactions and with evolving focus
of utilization.
A. Embedding Metadata
Reviewing historically, and based on the bitcoin blockchain
primarily, this ability at first involved creating an Unspent
Transaction Output (UTXO) that could never be spent. That
was used with a focus on permanently and securely storinginformation (such as notary data) not directly related to the
current transaction. The destination bitcoin address in the lock-
ing script of such unspendable UTXO in a Pay-To-Public-Key
(P2PK) and Pay-To-Public-Key-Hash (P2PKH) transactions
was used as a freeform 20-byte field to store metadata, and
the the transaction was recorded on the blockchain. [5] Then,
concerns were raised that the unspendable outputs could never
be removed from the UTXO database, causing the database
to increase forever. In response, Bitcoin Core version 0.9
introduced the RETURN operator, explicitly creating such
outputs as provably unspendable and excluded from the UTXO
set. [6] Simultaneously, the allowance for metadata increased
from 20 to 80 bytes. Thus, legitimate non-payment data
could be stored on the blockchain without increasing the
UTXO database. However, concerns were raised that non-
payment data stored in OP RETURN outputs could allow
meta-protocols to run permission-free with criminal intent.
That led to a large proportion of miners not processing
OPRETURN transactions, and a corresponding proportion
of metadata not being recorded on the blockchain. The more
recent trend is storing information in the redeem script of pay-
to-script-hash (P2SH) transactions. P2SH were standardized
with Bitcoin Improvement Proposal (BIP): 16, as a powerful
new type of transactions that simplifies the use of complex
script. [7] The hash of the redeem script is restricted to 20
bytes, but the script itself and the size of metadata are not
restricted. These transactions are spendable, and the full script
must be revealed when spending a P2SH UTXO. At this stage,
the utilization of embedded metadata is focused on variety of
applications such as tokenization, blockchain-enforced smart
contracts, and related access to secure databases. Some con-
cerns about ransomware remain.
Among the innovative uses of P2SH-embedded metadata are
the tokenization of assets (tangible, intangible, divisible, and
non-divisible), the blockchain-enforcement of smart contracts,
the blockchain-recorded progress through the complex condi-
tionality structure of a smart contract, the efficient blockchain-
registered exchange of various tokenized entities underlying
smart contracts, and the blockchain-recorded access links and
access privileges to off-chain databases. [8] Such databases
can be Distributed Hash Table (DHT) databases that store
smart-contract templates, or conditions for the exchange of
and the characteristics of entities underlying smart contracts,arXiv:1806.06738v1 [cs.CR] 18 Jun 2018
or software programs implementing intelligent agents capable
of controlling various types of smart contracts. The described
utilization of embedded-metadata serves as middleware that
supports the development and execution of any specific smart
contract.
In this paper, we identify millions of recorded blockchain
transactions embedding metadata and classify them according
to meta-protocols they support. That allowed us to observe,
from one of its many perspectives, the broader and impor-
tant question about technology adoption. We further analyze
empirically the evolution of embedding metadata, and suggest
security steps towards preventing criminal use. Metadata are
relevant to any blockchain, and our analysis is based on
bitcoin primarily. Bitcoin has long historical data and the
largest market-share, but is considered inert to innovation in
more recent years.1Our conclusion is that simultaneously with
both expanding legitimate utilization of embedded metadata
and expanding blockchain functionality, the applied research
on improving anonymity and security must continue with
protecting against blockchain abuse.
B. Ecosystems
Bitcoin is the first and has long been the most popular cryp-
tocurrency and blockchain. All B-transactions are recorded in
the immutable, append only, blockchain data-structure, where
the key features and elements include: blocks, transactions
stored in the blocks, and inputs, outputs, lock-time included
in each transaction according to the transactions’ format.
The unspent outputs UTXO are monitored by the miners in
validating transactions, and each input and output contain
scripts – locking, unlocking, redeem scripts. [9] Depending
on the type of transaction – P2PK, P2PKH, multisig, P2SH
– the scripts may contain public keys, hashed public keys,
multiple public keys, signatures based on private keys, multiple
signatures, metadata, hashed metadata, and OP codes. The
entire transaction is hashed using SHA-256 and this hash
typically serves as a globally unique Transaction ID (TXID).
[10]
The bitcoin script language Script is a Forth-like stack-based
execution language. Each transaction is processed by every
bitcoin validating node and the node’s validation software
executes, independently for each of the transaction’s inputs,
the unlocking script in the input alongside a corresponding
UTXO’s locking script. A transaction is valid if the cryp-
tographic puzzles in all UTXO referenced by its inputs are
solved by the inputs’ scripts, i.e. all spending conditions are
satisfied. Then these UTXO are removed from the UTXO
database but remain permanently recorded on the blockchain.
[5]Script is a stateless and predictable language, and Bitcoin
Core currently includes 174 active Script opcodes (and 15
disabled) including 14 reserved opcodes, of the following
types: push-value, flow-control, stack-ops, splice-ops, bit-
logic, arithmetic, crypto, locktime, template-matching, and
reserved-words. [11] [12]
1Bitcoin has however broken grounds for crypto-currencies, and new
currencies, such as bitcoin cash BCH, are actively pursuing innovation.The Script in the more actively innovating bitcoin-cash
BCH-blockchain is introducing further opcodes, by re-
designing and re-testing functionalities previously intended
(to an extent) by now disabled bitcoin-script opcodes, as
well as by introducing new functionalities. [13] The bitcoin-
cash network is undergoing a protocol upgrade in May 2018,
supporting on-chain scalability, new transaction signatures,
and a new difficulty adjustment algorithm. [14] The blocksize
limit is adaptable, with an increased default of 8MB, and quite
larger sizes are being tested on the bitcoin-cash testnet. [15]
A new SigHash reusable signing mechanism ensures replay
protection under a chain split, an improved hardware-wallet
security, and elimination of the quadratic hashing problem. It
provides for users creating transactions with a fork-specific ID,
which are invalid on forks lacking support for the mechanism.
[16] A new difficulty adjustment algorithm allows miners to
migrate from the bitcoin chain as desired and provides protec-
tion against hashrate fluctuations. [17] Multiple independent
teams develop bitcoin-cash software, assisted by peer-review
workgroups, in contrast to the single-group development of
Bitcoin Core. The development of bitcoin cash is decentralized
and the ecosystem is dynamic. The focus is on protocol
developments and on building Software Development Kits
(SDK) that provide for the implementation and support of
smart contracts and applications. [18]
II. I MPROVING PRIVACY AND SECURITY
Extending blockchain functionality and legitimate utiliza-
tion of embedded metadata demands effective protection
against blockchain abuse. The effective protection is supported
by active applied research on anonymity and security.
A. Privacy
Privacy is a key desirable feature of all public and some
private blockchains. Adoption and usage of bitcoin demon-
strates early developments in distributed P2P payment systems
anonymity engineering, and the privacy levels offered by
current bitcoin psuedo-anonymous ledger is not very strong
[19] [20]. Improving this is a major and difficult problem.
It is not obvious how to reconcile ledger transparency and
the desire for better privacy, and there is no easy quick-fix
solution. Some early solutions involve using one public key
only once. Using many different keys per user immediately
raises the question of key management. In order to avoid the
necessity for regular backups of fresh private keys generated
at random, deterministic key derivation functions have been
introduced. Hierarchical Deterministic (HD) wallets [21] use
Elliptic Curve mathematics in order to calculate the public
keys without revealing the private keys. HD wallets also allow
users to derive various keys in a deterministic way from a
single human readable seed. Using several keys there at the
same time, however, like joining payments made to several
keys belonging to the same user, compromises privacy. [22]
In addition to HD wallets, there exist several other methods
improving bitcoin anonymity.
Mixes : Mixing services or tumblers can be used to
improve the anonymity of users by taking their coins
and exchanging them with coins of other users, while
hiding their identities. These services charge commissions
between 1-3 %, and also need to be trusted not to steal
users’ coins. [19].
CoinSwap : This is a similar concept to Mixing. If Alice
wants to pay Charlie, she can send her coins to Bob
instead of Charlie, and then Charlie can send a fresh
unrelated coin to Bob. In order to resolve the theft
problem, a central authority can manage these swaps. If
any of the three misbehaves, the swap may be resolved
by using hash-locked transactions that are linkable in the
public-ledger. [20]
Fair Exchange : This method allows the users to hide their
identities by exchanging coins. Ideally, the fair exchange
requires that either both parties involved in the transition
receive each others items or none do. [23]
CoinJoin : In CoinJoin users collaborate and create trans-
actions where inputs of several users are mixed together.
The transaction is not valid and will not be accepted by
the network until all the signatures are provided [24].
Further methods that require attention include stealth ad-
dresses and dark wallets:
a) Stealth Address: One way to break the linkability in
blockchain is to ask a recipient for two destination addresses,
and then make two transactions and broadcast them into the
network few seconds apart [22]. This concept has been further
improved leading towards Stealth Address (SA) techniques,
which are forms of non-interactive key exchange protecting
privacy of users receiving payments. The origins of these tech-
niques could be tracked to [25] [26]. Instead of a public key,
the payee advertises a long unique static identifier, which is
used by the payer to generate one time address to send money.
A stealth address do not appear in the B-blockchain; instead,
random ephemeral public keys are generated and used. SA
addresses use the Diffie-Hellman key-exchange mechanism,
which allows the sender and receiver to exchange information
and jointly generate some ephemeral public keys. Only lower-
level derived keys will appear on the blockchain.
Fig. 1: DH key exchange in Stealth Address technique
For example, Bob advertises his (multiple-use) public key
on his web page. There exist several variants of SA addressees.
A basic method that uses permanent public/private identities of
two participants, Alice and Bob, is adapted from [27] next. Weconsider a basic Diffie-Hellman key exchange, as presented in
Fig. 1. Let G be the generator point on Elliptic curve, and
let ’a’ and ’b’ be the private keys of the sender (Alice) and
the receiver (Bob), correspondingly. Alice and Bob use here
their permanent identities that will typically appear on the
blockchain (this will change in more advanced SA methods).
Alice and Bob publish a:G andb:G, correspondingly, and
keep their private keys confidential. Alice computes a:(b:G),
and Bob equivalently computes b:(a:G). Their shared secret
isS=a:(b:G) =b:(a:G) =a:b:G , which no one else can
compute. Next, Alice sends money to the transfer address E,
and Bob detects the transaction and spends its UTXO. For
example:
E=H(S):G
looks like a random B-addres. However, Bob knows the
corresponding private key as he knows the common secret
S, and can scan the bitcoin blockchain for Eto appear.
This solution is still not quite secure, as Alice also knows
the private key and may spend the UTXO before Bob. An
improved asymmetric stealth address uses a stronger spending
keye. [8] For example,
E=H(S):G+b:G and e=H(S) +b
Then, the sender Alice can no longer spend the transaction
output, and can only compute the public key E. This method
is still not ideal, as it is static and deterministic. In order to
mitigate this, the sender can replace her permanent identity a
by a random number r, and publish r:G by typically using
OPRETURN in the very next output. In this case, one-
time destination key and address are generated. This is not
yet the best SA technique, and can be improved further by
using 2 public keys, b:Gandv:G, where v:G is a view key2.
Knowledge of the private part of the view key allows to build
read-only wallets that can see transactions (undo anonymity)
but cannot spend. Even a more robust stealth address has been
proposed that protects against private key compromise, due to
thefts, bad random attacks or Spectre/Meltdown type vulner-
abilities [27]. Potential further development can use metadata
based on processing various combinations of different partial
biometric features, when generating signatures and keys. [28]
Improved approaches to generating hierarchical asymmetric
ephemeral keys and addresses have also been proposed and
implemented in Nakasendo, an SDK supporting bitcoin cash
applications, as well as applications for any blockchain based
on elliptic-curve cryptography [18].
b) Dark Wallet: In order to enhance anonymity further,
light-weight wallets that use both stealth-address and CoinJoin
techniques have been created and termed Dark Wallets (DW).
[29] Stealth addresses are discussed in detail in the previous
section. A brief reminder on CoinJoin tells that a transaction
of one user is combined with that of a random other user,
who is making a payment at around the same time. Dark
wallet is currently in its alpha testing state [30], as a Chrome
2View keys are also used in Monero and other CryptoNote-based currencies,
and and were first described in [25].
extension enabled in developer mode. During this study, it has
been working on and off for brief periods of time.
B. Security
Innovative technologies are subject to abuse, as a series of
incidents have demonstrated, including the recent Facebook
data abuse by Cambridge Analitica affecting 87 million users.
[31] Blockchain has also been abused, as the ransomware
attack on UK NHS showed last year, affecting many people.
[32] The adoption of bitcoin in ransomware crime is a major
event of recent years [33]. Very recently, the first incident has
been reported, as well, of a ransomware accepting bitcoin-
cash payments. [34] In order to protect blockchain expansion
into services benefiting the society, it is necessary to address
the issue of its abuse. Innovation in terms of security and
prevention from ransomware must be an integral part in the
development of smart contracts and blockchain-based services.
Next, we briefly introduce the key ransomware types and
existing defenses. In later sections of this paper, we review
them in relation to bitcoin and bitcoin cash, and suggest some
solutions.
a) Overall Rise of Ransomware: Ransomware is a class
of malware aiming to force users to pay a ransom in order
to regain full access to their system [35]. This terminology
covers a wide range of malicious software programs, including
CryptoLocker, Locky, Cryptowall, KeyRanger, SamSam, Tel-
saCrypt, TorrentLocker and others [36] [19] The history of ran-
somware goes back to 2004, and the early software included
screen lockers that were easy to remove or circumvent. Their
level of sophistication, however, has been improving since
then. Since 2013, a more harmful type of software has been
developed. Though the programs are still called ”lockers”, they
are not just lockers but quietly search for specific files and
encrypt them, and then ask for ransom in order to decrypt.
Only in the last few years, since bitcoin raised in popularity,
ransomware has been combined with B-payments. [33] [32]
CryptoLocker : This is a well-known ransomware, since
Sep. 2013. CryptoLocker v3 uses Advanced Encryption
Standard (AES)-128 in Cipher Block Chaining (CBC)
mode [36] and RivestShamirAdleman (RSA)-2048 for
encryption of a header [37]. AES-128 is a symmetric
key algorithm with 128-bit keys, and RSA-2048 is an
asymmetric encryption algorithm using 2048-bit keys.
This combination makes it most likely impossible to
decrypt, without paying the ransom.
TorrentLocker Etc. These are different strains of ran-
somware that have used AES differently: particularly in
Counter (CTR) and CBC modes. [38]
TeslaCrypt : This type of malware has been active since
2015, and provides customer support for the victims. It
uses Elliptic Curve cryptography (ECC), an advanced
key-derivation scheme, and has an ECC master private
key that is later made public.
Locky : Locky is a more recent and more sophisticated
ransomware, since 2016. It uses Domain Generation Al-
gorithm (DGA) to prevent blacklisting of domain names,as well as custom encrypted communications. Locky
also uses strong (RSA-2048 + AES-128) file encryption,
and targets and encrypts over 160 different file types,
including virtual disks, source codes and databases [39].
Locky has spread in two countries in particular, the
United States and France, and uses The-Onion-Router
(TOR) hidden servers. [36]
Further advanced ransomware techniques are discussed in
Section IV-B. A recent study by IBM reports 6,000% of overall
increase in ransomware in 2016 compared to 2015, and finds
that 70% of business victim paid the hackers. [40]
b) Ransomware Defenses: There exist a number of OS-
level countermeasures to avoid infection. Such measures in-
clude white-listing executables in user data directories [41],
avoiding mapping backup drives [38], and disrupting the
malware when using the Microsoft Crypto API [36].
Data Backups – False Good Solution : It may seem that
all ransomware is harmless if the data is backed-up on
a regular basis and the back-up drives are encrypted.
However, the problems go far beyond, and just restoring
files or partitions from backups is not the best strategy.
This destroys forensic evidence about how the malware
propagates and how it operates, and makes the fight again
malicious software more difficult. This also leaves our
systems wide open and in the same state as before in-
fection: they can be later re-infected by malware through
the same channels. For example, a main infection channel
for Crypto-Locker was the Gameover Zeus botnet, which
had existed earlier. [41]
Propagation of Ransomware : Computer security experts
must be able to monitor and analyze the infections. It
is very useful to know how the ransom gets here in the
first place. The problem is that there exist extensive and
offensive expertise and experience, which have emerged
over years of contrived action against the anti-virus
industry. Different types of malware infection propagation
and social engineering techniques are exploited to help
criminals diffuse their unsolicited encryption payloads.
III. U SE AND ABUSE OF BLOCKCHAIN TRANSACTIONS :
EMPIRICAL ANALYSIS
A. Methodology and Results
The length of OP RETURN script is currently 80 bytes,
where the first two bytes always are hex 6a, followed by
two bytes indicating the length in hex of the metadata-record
that starts from byte number 5. With its protocol upgrade
from version 1.0 to 1.1, on May 15, 2018, the OP RETURN
relay size will increase to 223 bytes, only on the bitcoin-cash
blockchain. [14] Information about the two blockchains has
been updated, corrected and analyzed in this study, by the
contributing authors, based primarily on the OpReturnTool
from [42].
The APIs of blockchain.info and coinsecrets.org are queried
by this software. Blockchain.info is used only to get the
latest block number. Then coinsecrets.org, a dedicated API
for OP RETURN transactions, is queried to extract their Time
stamp, Transaction ID, hash and ASCII code. The incoming
data are recorded into a text file and exported to Excel
[43]. Several methods were used, in order to identify the
evolution of stealth-address techniques. We have performed
experimental transactions, and further analysis on the observed
patterns, to identify potential DW transactions and patterns.
Among those that could not be related to a known protocol,
there are transactions potentially related to criminal activities.
To identify the transactions from another wallet implement-
ing stealth address, such as SX [44], its documentation has
been consulted and observed patterns inside transactions are
matched with our dataset. Overall, 22 protocols are identified
and the rest of the OP RETURN transactions are marked as
unattributed. The prefix and ASCII for all the protocols are
analyzed, and based on the analysis further three protocols
have been identified: YEJ, BITCC, and Counterparty. How-
ever, their share inside the dataset is quite slim ( 0%), and
only Counterparty has been included for further analysis.
Fig. 2: Time evolution of OP RETURN transactions
The first OP RETURN transaction, identified in our study,
appears on Mar. 29, 2013. This corrects [20], where the
first such transaction is identified as appearing a year later.
Thus, our dataset consists of data since Mar. 29, 2013 (block
#228596) to Jul. 6, 2017 (block #474451). In 2013, only 430
OPRETURN transactions are found, and all of them are in
the unattributed category. From 2014 to 2017, we observe
a significant increase in the volume of such transactions,
reaching over 2 million per year in 2017. (see Fig. 2) A
detailed examination shows that 51% of these transactions
correspond to the 22 known protocols, explained briefly in
Table I. About 49% of the transactions remain unattributed.
The experimental dark-wallet transactions are identified in
the unattributed section of the dataset, and have the prefix 6a-
26-06, where 6a is the opcode for OP RETURN, 26 is hex of
the length of metadata that follows, and 06 distinguishes dark-
wallet transactions from other protocols. The whole dataset is
scanned and 2762 transactions are found with this pre-fix.
The time evolution of transactions is illustrated with Fig. 3.
B. Analysis and Discussion
Linkability of transactions affects their anonymity, and sev-
eral techniques have been adapted in the B-systems to address
that issue. Approaches such as CoinJoin, Fair Exchange andTABLE I: Protocols Using op return Opcode
Protocol Contribution(%) Usage
Unattributed 49% Not identified
Blockstore 8.5% Key value store
Factom 4.14% Notary/Doc
Omni Layer 10.3% Assets
Blocksign 0.06% Notary/Doc
Colu 10.11% Assets
Stampery 2.60% Notary/Doc
Eternity wall 0.16% Any Messages
Bitproof 0.03% Notary/Doc
Open Assets 8.09% Assets
Ascribe 2% Digital Arts
Monegraph 2.7% Digital Arts
Coinspark 1.1% Assets
Proof of Existence 0.22% Notary/Doc
Original My <0.01% Notary/Doc
Open Provenance <0.01% Proof of ownership
Remembr <0.01% Notary/Doc
Crypto copyright <0.01% Notary/Doc
LaPreuve <0.01% Notary/Doc
ProveBit <0.01% Notary/Doc
Blockchain Notary <0.01% Notary/Doc
Counterparty <0.01% Assets
Stampd <0.01% Notary/Doc
Fig. 3: Time evolution of potential DW transactions
CoinSwap, typically need a third-party involvement to achieve
anonymity, and the honesty of the third party is not guaranteed.
The latest stealth-address techniques seem currently effective.
There, the receiver advertises its static, unique identifier and
the sender generates a one-time key. There is no apparent way
that a blockchain observer can relate transactions to the same
payee. Stealth-address approaches are introduced to bitcoin
and bitcoin cash, and have been used in monero and vertcoin.
Stealth-address, dark-wallet and SX transactions appear as
unable-to-decode by block explorers.
We have identified some DW and SX transactions
among transactions unattributed to known protocols, in
the OP RETURN database we have extracted from the
blockchain. It is noted that a smaller number of SX-related
transactions are identified, as that protocol is less user-friendly
than DW. A large number of transactions remain unattributed,
and part of the issue is that meta-protocols are not required
to coordinate and register unique identifiers. Therefore, many
legitimate protocols don’t use distinctive pre-fixes [45] that
help decode/classify OP RETURN transactions they produce,
Fig. 4: The share of unattributed protocols is increasing.
and those transactions remain unattributed. This reason is even
more valid now and in foreseeable future, as the anticipated
proliferation of blockchain technology is through smart con-
tracts that benefit users rather than ransomware them, and
brings positive rather than destructive effect on society. Smart
contracts are implemented and executed through embedding
metadata in OP RETURN and P2SH transactions, and there-
fore the number of transaction with embedded metadata will
continue to rise. It is also valid to anticipate that criminals will
exploit the new functionalities. We address both these issues
in Section IV next.
IV. R ANSOMWARE , KEYMANAGEMENT AND METADATA
A. Ransom Payments
Ransomware has always existed, but it has been associated
with substantial risks to receive ransom payments without
detection. With the increased popularity of Bitcoin in the
last few years, criminals have started abusing the technology,
in order to avoid detection. [46]. Receiving one or multiple
ransom payments in Ballows very good initial anonymity.
[33]. A new unique B-address is created to receive payment
from each victim. As long as the coins are not yet spent, there
is no way to track who has received the ransom, and it can be
spent in the future. [35] Once spending starts, the linkability
of transactions is weakening anonymity and some transactions
could be traced. [47] However, criminals can use various
proxies, and carefully move and mix money in arbitrary ways
for a long time, in order to diminish their chances of being
traced.
Many companies and individuals will and do pay ran-
somware to get their data back, though advised the contrary by
the authorities. Some individuals bought Bfor the first time
when became victims of ransomware, and some companies
buy Bin advance to be able to pay in case of an attack.[46] This affects the image of the technology, as illustrated
by Google Trends during the attack on NHS last May. Fig. 5
presents the online interest in bitcoin and ransomware for the
first two quarters of last year, and identifies they both spiked
in May. [48] The image affects the development and adoption
of innovative and legitimate blockchain-based services. The
Fig. 5: Google search interests: Ransomware vs Bitcoin
January–December 2017
technology is still moving forward, however, as Fig. 5, because
the positive potential has been recognized. The target now is
decoupling and disassociation from ransomware.
B. Public Key Generation and Diversification
A variant of the Curve-Tor-Bitcoin (CTB)-Locker, targeting
web servers, generates a unique B-address for every infection.
Once the ransom is received, hackers produce a transaction
using OP RETURN and embedding a decryption key inside.
[49] Other ransomware, such as the Locky payment system,
rely on the anonymity features of TOR. It uses TOR hid-
den servers that remain operational years after the infection.
The server software, presumably operating without human
intervention, has automatically adjusted to B-price over the
years: initially asking for B2, and recently for much smaller
amounts. A Locky decryptor tool, made available to the
victims on the same TOR website when they connect again
after paying, is received on the blockchain [39]. One of the
onion servers used with Locky is twbers4hmi6dx65f.onion,
and we have observed that after payment they contain mal-
ware such as Variant.Zusy.185950. The Locky payments also
seem to be automatically aggregated into larger pots of
B50 or 100. An address associated with Locky ransom is
1Cjqt4C17sXYrWkrRyPr73RjrjZu1fuHMV, though it is not
clear if the address belongs to an exchange, a mixing service,
or is still criminally controlled. If we look from this address
backwards, the blockchain allows identifying victims of Locky
who have paid ransom in standardized amounts of B1 or 0.25.
C. Future Risks and Mitigation
The combination of all topics we study above present
a major risk: using stealth addresses, CoinJoin, TOR, and
possibly smart contracts, in order to further automate and
streamline the process of ransom threat and payment. We
are not there yet but in future, criminals can hide their
identity behind the increasing number of active blockchain
participants, while shuffling money around. Dark wallet has
Fig. 6: Share of unattributed protocols.
been a prominent B-wallet [30] focusing on anonymity. It
implements stealth-address with CoinJoin techniques. Yet DW
deployment has raised concerns about its potential abuse. [29]
This study has analyzed DW transactions and found that they
cannot be associated with known protocols. The number of
DW transactions is still low: the wallet is in its alpha state
and has not recently been operating properly. [30] It can be
expected, however, that a more dangerous DW-successor will
be inevitably created. [26] We propose that companies using
blockchain to implement complex protocols, should provide
means of audit capable of distinguishing legitimate traffic they
produce from potentially criminal activity.
Though bitcoin and bitcoin cash are studied here, all
blockchains are imminently subject to abuse, and fall within
the wider range of FinTech and SocTech technology targeted
with criminal intent. Bitcoin has existed longer and had the
largest market-share among blockchains. On the one hand,
it has traversed through challenges to bring visibility to the
technology and recognition of its potential, and opportunities
to and drive for emerging competition. On the other hand, this
position has made bitcoin most targeted by criminal activity:
the wider and more active is a blockchain network, the better it
can be both used and abused. Empirical history has also shown
that the more a cryptocurrency is used, the more valuable it is.
Further blockchains are getting momentum and raising their
market-share, and are imminently attracting criminal intent.
The other case-study in this paper, bitcoin cash, is representa-
tive of recently set up but high-momentum blockchains, and
has risen to 4th market-share. Only one ransomware has been
reported using bitcoin cash, seven months after its launch.
This contributes to the observation that relative maturity is
a precondition for targeted abuse of a blockchain.
With this paper, we raise the requirement that security
and robustness against ransomware and other abuse must be
of equal priority in innovation as are functionalities directly
impacting market-share. Another factor is the rate of inno-
vation itself. For example, blockchain cash is supported by
several rather than one development team, and releasing a
new protocol this May that introduces new op-codes, increasesblocksize, and provides a new algorithm stepping towards
blockchains’ interoperability. The need for innovation has been
a main reason for the bitcoin cash fork. The development
teams have started releasing SDKs to support the blockchain
community in implementing higher-level apps, as well. Mul-
tiple teams and continuous innovation contribute to resistance
to abuse. We have to note that a large part of identified
transactions without attributed protocols are associated with
legitimate meta-protocols, but there is no registry of meta-
protocols to serve as a reference for identification. The use of
P2SH transactions and the development of smart contracts are
trends for a foreseeable future, and will expand significantly
the range of meta-protocols. Therefore, we suggest that an
off-chain DHT registry of meta-protocols is set up, and a
corresponding unique indicator/identifier for each type of pro-
tocol is embedded in transactions that use/implement it. The
DHT repository will have secure selective access, so that the
meta-protocols are not compromised but allow corresponding
level of audit. We anticipate that with the proliferation of
smart contracts, the legitimate types of meta-protocols will
greatly outnumber malicious ones. However, the system will
still be vulnerable to large-scale outlier cyber-attacks, if deci-
sive, expert, priority solutions are not developed, maintained
and updated. If anticipation is that criminals will use smart
contracts, then monitoring smart contracts can be developed
to identify, protect against, and prevent their activity.
V. C ONCLUSION
This paper addresses blockchain adoption from the per-
spective of the evolution and utilization of metadata, and
therefore, from the perspective of the evolution of proto-
cols that are implemented and executed through embedding
additional information in blockchains. We identify, analyze
and discuss reasons for the role of metadata, including em-
pirical analysis; and suggest approaches towards protecting
the expanding blockchain functionality against criminal abuse
and ransomware. Challenges, in terms of exploiting/improving
anonymity and prioritizing/raising security, are clearly stated,
and intended and unintended consequences are addressed.
We review key characteristics of ransomware and of expert
approaches protecting against it. The paper suggests that
blockchains should provide some level of transparency of what
they are used for. We need to improve the auditability of
blockchain transactions and smart-contract protocols. This can
be facilitated for example by DHT databases of protocols,
with secure and selective authorization or audit access. An-
other major lesson learned from our research on blockchain
technology adoption and on metadata usage in blockchains,
is to see that new technology must innovate and change all
the time in order to near its full potential and that protocols
and usage of blockchains must evolve with time. We need to
continue to improve privacy of blockchains, while mitigating
and monitoring their problematic or maybe criminal usage.
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|
{
"id": "1806.06738"
}
|
2209.12809
|
Evolutionary Dynamics of Sustainable Blockchains
|
The energy sustainability of blockchains, whose consensus protocol rests on
the Proof-of-Work, nourishes a heated debate. The underlying issue lies in a
highly energy-consuming process, defined as mining, required to validate
crypto-asset transactions. Mining is the process of solving a cryptographic
puzzle, incentivised by the possibility of gaining a reward. The higher the
number of users performing mining, i.e. miners, the higher the overall
electricity consumption of a blockchain. For that reason, mining constitutes a
negative environmental externality. Here, we study whether miners' interests
can meet the collective need to curb energy consumption. To this end, we
introduce the Crypto-Asset Game, namely a model based on the framework of
Evolutionary Game Theory devised for studying the dynamics of a population
whose agents can play as crypto-asset users or as miners. The energy
consumption of mining impacts the payoff of both strategies, representing a
direct cost for miners and an environmental factor for crypto-asset users. The
proposed model, studied via numerical simulations, shows that, in some
conditions, the agent population can reach a strategy profile that optimises
global energy consumption, i.e. composed of a low density of miners. To
conclude, can a Proof-of-Work-based blockchain become energetically
sustainable? Our results suggest that blockchain protocol parameters could have
a relevant role in the global energy consumption of this technology.
|
http://arxiv.org/pdf/2209.12809v1
|
Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, Luciano Pietronero, Carlo Gola
|
physics.soc-ph, nlin.AO
|
physics.soc-ph
|
Evolutionary Dynamics of Sustainable Blockchains
Marco Alberto Javarone,1, 2,Gabriele Di Antonio,1, 3, 4Gianni
Valerio Vinci,3, 5Luciano Pietronero,1and Carlo Gola6,y
1Centro Ricerche Enrico Fermi, Rome, Italy
2University College London - Centre for Blockchain Technologies, London, UK
3Istituto Superiore di Sanit a, Rome, Italy
4Universit a degli Studi Roma Tre, Rome, Italy
5Universit a Roma Tor Vergata, Rome, Italy
6Banca d'Italia, Rome, Italy
(Dated: September 27, 2022)
The energy sustainability of blockchains, whose consensus protocol rests on the Proof-of-Work,
nourishes a heated debate. The underlying issue lies in a highly energy-consuming process, de ned
as mining, required to validate crypto-asset transactions. Mining is the process of solving a crypto-
graphic puzzle, incentivised by the possibility of gaining a reward. The higher the number of users
performing mining, i.e. miners, the higher the overall electricity consumption of a blockchain. For
that reason, mining constitutes a negative environmental externality. Here, we study whether min-
ers' interests can meet the collective need to curb energy consumption. To this end, we introduce
the Crypto-Asset Game, namely a model based on the framework of Evolutionary Game Theory
devised for studying the dynamics of a population whose agents can play as crypto-asset users or
as miners. The energy consumption of mining impacts the payo of both strategies, representing
a direct cost for miners and an environmental factor for crypto-asset users. The proposed model,
studied via numerical simulations, shows that, in some conditions, the agent population can reach
a strategy pro le that optimises global energy consumption, i.e. composed of a low density of min-
ers. To conclude, can a Proof-of-Work-based blockchain become energetically sustainable? Our
results suggest that blockchain protocol parameters could have a relevant role in the global energy
consumption of this technology.
I. INTRODUCTION
The Blockchain [1, 2] is a distributed ledger able to
manage transactions of digital tokens, constituting a
form of digital money, without relying on nancial in-
stitutions serving as trusted third parties. The underly-
ing mechanisms of this technology exploit cryptographic
protocols, so we refer to digital tokens as crypto-assets.
Although all implemented protocols have a role in the
functioning of a blockchain, the fundamental one is the
Nakamoto consensus protocol. The latter uses the Proof-
of-Work (PoW), i.e. a cryptographic proof that relies on
solving a crypto-puzzle for validating transactions and
o ering solid protection from attacks, such as double-
spending [3]. More in detail, particular users de ned as
miners collect crypto-asset transactions not yet recorded
in the Blockchain in data structures called blocks. The
miners' goal is to attach their blocks to the head of the
Blockchain. To this end, miners have to show a PoW that
can be obtained by performing a process called mining.
The rst miner to complete the PoW receives a reward, in
the form of crypto-asset, which constitutes an economic
incentive. Mining is a competition based on nding a
random number (called nonce) whose value depends on
the block at the head of the Blockchain and transactions
contained in the newly generated block. Also, mining
marcojavarone@gmail.com
yThe usual disclaimer appliesresembles a lottery, requiring miners to use substantial
computational resources to succeed. Then, getting high
success probabilities equal high energy consumption in
a mining competition, as winning depends on the num-
ber of used resources. The whole mechanism entails that
each new block attached to the chain forms a 'compu-
tational shield' preserving transactions recorded in the
previously mined blocks. In summary, miners are funda-
mental for blockchains based on the PoW. On the other
hand, the energy consumption of PoW blockchains con-
stitutes a non-trivial issue [4]. The latter directly im-
pacts the miners' electricity bill and, indirectly, the whole
society since a fraction of energy gets wasted for vali-
dating transactions. Even if there are other consensus
mechanisms, for instance, the Proof-of-Stake [5, 6] and
the Proof-of-Authority [7, 8], the PoW is currently the
most used [9]. Therefore, energy consumption is at the
core of a heated debate around the societal impact of
blockchains |see for instance [10{12]. The relevance of
the debate motivates this investigation. On one side, we
observe miners that compete to get high pro ts and allow
Blockchain to work. On the other, we see a waste of elec-
tric energy. Can the need to curb energy consumption
meet the individual interest of miners? Stimulated by
this question, we study the above-described scenario as a
social dilemma. To this end, using the framework of Evo-
lutionary Game Theory [13{23] we propose a stochastic
evolutionary game named Crypto-Asset Game (CAG).
The proposed game has two strategies, i.e. mining and
using crypto-assets. On varying the game conditions,arXiv:2209.12809v1 [physics.soc-ph] 26 Sep 2022
2
e.g. the miners' reward, we analyse the evolution of the
strategy distribution occurring in an agent population
playing CAG. In particular, the evolutionary dynamics
of CAG entail agents can change their strategy depend-
ing on the accumulated payo via a stochastic process,
usually de ned strategy revision phase. Hence, a crypto-
asset user (or just user hereinafter) can become a miner
and vice versa at no cost. Rational agents take decisions
(i.e. select a strategy) to maximise a pro t. We aim to
study whether a population of rational agents can reach
a strategy pro le that minimises global energy consump-
tion. Finally, let us report that game-based approaches,
devised for studying various aspects of blockchains, have
been previously proposed, as in [24{29]. The remainder
of the paper is organised as follows: Section II introduces
the Crypto-Asset Game. Section III presents results of
numerical simulations. Then, Section IV highlights the
main nding and, eventually, Section V discusses future
developments.
II. THE CRYPTO-ASSET GAME
The Crypto-Asset Game represents the essential dy-
namics of a simpli ed economic system composed of two
kinds of agents, i.e. miners and crypto-asset owners
that produce and consume crypto-assets, respectively.
More in detail, miners produce crypto-assets through
their mining activity, while crypto-asset owners consume
crypto-assets by executing transactions [30]. For the sake
of brevity, from now on, we refer to crypto-asset owners
as users. Accordingly, CAG is a two-strategy game whose
agents can choose between mining ( m) and using crypto-
assets (u). Both strategies are associated with a payo .
The miner payo includes a reward and an energy cost,
while the user payo includes a gain and a contribution
they have to pay. The users' gain represents bene ts
related to using a blockchain, while their contribution
represents various costs associated with this technology,
as later described. In addition, the overall energy con-
sumption of the blockchain a ects the users' payo as
their gain gets multiplied by the miners' density. Follow-
ing the above prescriptions, the payo structure of CAG
reads
(
u=1
mNu
5 cu
m=hRi C(1)
whose parameters have the following meaning. Namely,
mis the density of miners, Nuis the number of users
locally involved in a game iteration (later detailed), cu
is the contribution paid by users for participating in the
Blockchain system, Ris the miners' reward and, even-
tually,Cis the electricity cost of mining. The miners'
reward is taken as hRi=R
Nm, withNmtotal number of
miners in the population. This choice is motivated con-
sidering that an equal amount of resources in a lottery-
like competition (i.e. the mining competition) entailsthat, on average, a miner gets a reward Rdivided by the
total amount of miners. Even if the number of miners is
related to the amount of energy wasted in a lottery-like
competition, PoW-based blockchains cannot work with-
out them. Hence, the presence of miners is mandatory
for our economic system, which gets investigated con-
sidering a list of assumptions. Firstly, we assume min-
ers cannot take any pro t if there are no crypto-asset
transactions (i.e. if there are no users), although real
blockchains allow mining also empty blocks. That as-
sumption, combined with the need to have at least one
miner in the population, leads to constraining agents'
payo to zero when m= 0 andu= 0 (i.e. density
of users). Then, we assume miners have the same com-
putational resources, and users can switch to the mining
strategy at no cost. Lastly, Candcuare considered
constants albeit, in real scenarios, costs and contribu-
tions could vary depending on various internal and ex-
ternal factors. Let us now brie
y clarify the meaning of
the contribution cu. To this end, we may refer to the
Public Goods Game [31] (PGG), whose payo structure
is similar to that of CAG. In the PGG, there are two
strategies, namely cooperation and defection. Coopera-
tors make a contribution typically of unitary value, while
defectors contribute nothing. CAG does not include co-
operators or defectors, yet the contribution curesembles
the coin paid by cooperators in the PGG, although it
might have a fractional value (see also [32]). In this con-
text,curepresents the adoption costs of new technology
and the consequences of using it, e.g. waiting for the
validation of transactions. Now, observing that the ratio
between costs and bene ts of new technology a ects the
related di usion in a population [33], we highlight the rel-
evance ofcufor studying blockchain dynamics. Finally,
for the sake of simplicity, we assume the electrical cost C
is denominated using the crypto asset of the game, whose
market value is constant. Once clari ed the meaning of
parameters and values constituting the game payo , we
describe how the agent population evolves. The evolu-
tion process is the same as that usually considered in
other evolutionary games, such as the PGG. In partic-
ular, a population arranged over a networked structure
at the beginning is equally divided between users and
miners. A networked structure entails that agents have
a limited number of neighbours and, among them, the
number of users corresponds to Nu. After each game it-
eration, agents undergo a strategy revision phase de ned
by an updating rule. The latter, in this work, is realised
via a Fermi-like distribution [31] and de ned as
P(sx sy) =1
1 e 1
K(y x)(2)
so that the probability the y-th agent imposes its strat-
egy, i.e.sy, on thex-th agent hinges on the di erence
in their payo , namely yandx, and a parameter K
representing the temperature (or noise) of the system.
In real scenarios, some human factors can be represented
by the parameter temperature (or noise). For instance,
3
the temperature can represent the degree of rationality in
individuals, or the uncertainty in economic agents, when
they take decisions. More in detail, the higher the tem-
perature, the higher the probability a strategy revision
occurs randomly. Likewise, the lower the temperature,
the higher the strategy revision results from a rational
choice. At high temperatures, i.e. with a lot of noise, the
strategy revision phase becomes a coin
ip. The strategy
revision phase occurs at each game iteration once agents
have played CAG in all groups of belonging. For clarity,
considering a population arranged over a bi-dimensional
square lattice with continuous boundary conditions, each
agent belongs to 5 di erent groups identi ed as follows.
Then, considering the x-th agent, its rst group is iden-
ti ed considering that agent with its four direct neigh-
bours. The other four groups are de ned considering the
x-th agent being part of a group whose central agent is
one of its neighbours, so the x-th agent occupies a side
position. In doing so, agents play the game in ve dif-
ferent groups collecting the related payo , whose total
amount is compared with that received by miners via
equation 2. Finally, there are three possible outcomes,
two representing full-order (i.e. full of users and full of
miners) and one representing co-existence of strategies.
Assuming a payo equal to zero when m= 0 oru= 0
leads to oscillatory dynamics, e.g. in the case of no min-
ers (i.e.m= 0), the strategy revision phase becomes a
coin
ip and, on average, half of the population turns the
strategy to mining at the next iteration. Hence, the two
full-order con gurations cannot constitute equilibria of
the strategy distribution. Therefore, our analysis focuses
on the emergence of oscillatory behaviours and assessing
the phases of strategy co-existence resulting from game
dynamics under di erent conditions.
III. RESULTS
In this section, we study the Crypto-Asset Game con-
sidering populations of di erent size N, from 102to 104
agents, distributed over a regular square lattice with con-
tinuous boundary conditions. Results are averaged over
100Nattempts, and performed considering a user con-
tribution equal to cu= 0:1 andK= 0:5 if not stated oth-
erwise. The rst analysis relates the value of the reward
Rwith the density of miners. Figure 1 presents outcomes
for a population of size 100 obtained at di erent reward
valuesR. Interestingly, we nd that rewards smaller than
R= 3100 in a population with 104agents, lead to steady-
states characterised by an oscillatory behaviour. More in
detail, as shown in Figure 1, a low reward entails that al-
most half of the population changes continuously strat-
egy over time, albeit the average behaviour suggests a
con guration more stable with only few miners. Then,
increasing the reward Rup to 3150 for the same pop-
ulation, after an initial phase characterised by several
oscillations like in the previous case, the agents reach a
steady state with a very low density of miners. Highervalues ofRlead to higher densities of miners till reach-
ing a unitary miners density for R= 48000. As soon as
R> 50000 an oscillatory behaviour, conceptually similar
to the one observed at very low Rvalues, emerges. In the
last case, almost the whole population oscillates between
a miners density equal to 1 and about 0 :6. Such analysis
is then repeated on varying the size of the population
and allows us to identify threshold values in range of the
rewardRfor each speci c case. In gure 2 we show the
threshold values of reward, we name critical rewards, on
varying the population size.
According to the model, CAG has several parameters
that can be investigated to assess their e ect on the agent
population, with particular interest for the density of
miners. Starting with the temperature K, we consider
a population with 2500 agents, to analyse the outcomes
at di erent degrees of rationality. Such analysis is per-
formed with special attention to two ranges, the rst one
forK2[0;1] and the second range for K2[1;50]. Pre-
vious investigations on dilemma games, as the Prisoners'
dilemma and the Public Goods Game, reported that for
the range of low temperatures the density of cooperators
increases as the temperature increases from 0 to 0 :5 and
then the bene cial e ect of the temperature on support-
ing cooperation reduces [34]. Then, as reported in [31],
increasing the temperature the dynamics of an evolution-
ary game resembles that of the voter model [35]. Namely,
too high temperatures entail agents change strategies by
a process equivalent to a coin
ip. In this case, we cannot
map cooperation to the use of a token and defection to
mining, because as described before, proof-of-work-based
blockchains need miners, while populations are perfectly
healthy without defectors. The result of this analysis is
shown in gure 3. We nd that for temperatures between
0 and 1 the behaviour is similar to the one we observe
in social dilemma games. Notably, increasing the tem-
perature in that range, the density of miners reaches a
small peak with around T= 0:75. Then, as the tem-
perature goes towards high values, the density of miners
increases till 0 :5, i.e. as expected, as soon as agents re-
duce their rationality, the game resembles a voter model,
and both strategies survive in the population almost with
the same density (i.e. 0 :5). The agents' payo is a rele-
vant component of a game. Then, as shown in Figure 4,
we study the average payo of both strategies by varying
the reward in a population with N= 104agents. Results
indicate that miners can reach the highest payo , on av-
erage, as the reward is slightly higher than the critical
value for that population size |see the second plot from
the left-hand side in Figure 4. Then, increasing the re-
ward, the emerging con gurations lead both strategies to
become poorly convenient. That justi es the behaviour
we observed analysing the density of miners on varying
the reward R. When only very few miners survive, min-
ers and users receive the highest possible payo they can
reach, and the population bene ts from a virtuous (in
terms of energy consumption) steady state. We conclude
the section studying how the user contribution cua ects
4
FIG. 1. Density of miners over time on varying the miners reward Ras indicated on the top of each plot. Each simulation
trial lastsN100 time steps. Red line indicates the average density value, while a few single trials are reported by black lines.
From left to right, results obtained with the following rewards: R= 1600,R= 3150,R= 48000,R= 60000
FIG. 2. Scaling of the critical reward ^Ron varying the pop-
ulation size. The Lindicates the size of a (lattice) square
side. Thus, for instance, L= 50 refers to a population with
L2= 2500 agents.
the miners density |see Figure 5. As reported, a too
low user contribution entails the density of miners is not
optimal as too many miners survive in the population.
That result is neither trivial nor expected, and it resem-
bles the behaviour we observe for too small rewards R,
i.e. smaller than a critical value ^R(for each population
size). Finally, as the cugets close to 1, the density of
miners slightly increases. In summary, a user contribu-
tion ranging from 0 :1 to 0:75 supports optimised strategy
distributions.
IV. DISCUSSION
Energy sustainability is a critical issue of Proof-of-
Work-based blockchains and feeds a heated debate. This
class of blockchains requires computational resources for
FIG. 3. Density of miners at the steady-state (i.e. after
100N) on varying the temperature Tin a population com-
posed ofN= 2500 agents, playing with a reward R= 900.
The standard error of the mean is very small on all samples,
remaining hidden inside the circles on the curve.
verifying transactions, which might lead to high energy
consumption. In an economic system composed of ratio-
nal agents, the energy sustainability of a PoW blockchain
can be treated as a social dilemma. In the proposed
model, miners and crypto-asset users interact by playing
a game and getting a payo , depending on their strat-
egy. The miners' payo includes a reward whose market
value [36{38] can make mining highly pro table. Thus,
the resulting dilemma on one end of the scale has the
individual interest of miners and, on the other, the col-
lective interest in curbing electricity consumption. The
global energy consumption of a blockchain is linked to the
nature of the mining competition. Namely, the higher
the number of resources used for mining, the higher the
average reward miners get over time. Increasing the com-
putational power entails paying higher electricity costs.
5
FIG. 4. Average payo hiover time, obtained by users (blue) and miners (purple line) on varying the reward Rin a population
withN= 104agents.
FIG. 5. Miners density on varying the user contribution cu
in a population with N= 2500 agents. Error bars report the
standard error of the mean for each sample.
Yet, as in lotteries, whose chances of success increase
by buying more tickets, chances of success in the min-
ing competition increase by using more computational
resources. Sadly, albeit miners pay their electricity bills,
such a competition entails society wastes a fraction of
energy. The overall scenario, characterised by the above-
described dilemma, can be studied by the framework of
Evolutionary Game Theory. Therefore, motivated by
all these observations, we propose a two-strategy game
called the Crypto-Asset Game. The complexity of an
economic system leads us to make several assumptions
in the proposed model. For instance, miners and users
can change strategy at no cost, all miners have the same
computational resources, the price of electricity is con-
stant, and the value of the crypto-asset in the market
does not change. In summary, CAG simpli es real sce-
narios. Notwithstanding, we deem CAG captures the
essential dilemmatic aspects a ecting rational individu-
als using blockchains. The results of our model, obtainedvia numerical simulations, show a rich spectrum of pos-
sible steady states that the agent population can reach.
More speci cally, depending on the game parameters, the
agent population can converge toward steady-states char-
acterised by a small number of miners or toward oscil-
latory behaviours characterised by a fraction of agents
continuously switching between the two strategies. Such
an oscillatory behaviour agrees with previous studies re-
lated to energy aspects of PoW blockchains [39]. We
recall that the parameters of CAG represent the proto-
col parameters of PoW blockchains. Finding steady-state
con gurations corresponding to optimal strategy distri-
butions, i.e. with a limited number of miners in the pop-
ulation, suggests that the protocol parameters may have
a relevant role in blockchain sustainability. Among such
parameters, the miners' reward is probably the most im-
portant. More speci cally, results indicate that rewards
too low and too high are detrimental to blockchain en-
ergy sustainability, as the density of miners is relatively
high in both cases. Remarkably, we identify a range of
values that rewards can take to ensure the evolution of
a population towards a virtuous strategy distribution.
Such a range of values depends on the population size.
The smaller the population size, the smaller the value a
reward can take for being bene cial (for the society it-
self). In real blockchains, the miners' reward reduces over
time [2], so the mining activity gets preserved by other
mechanisms, such as the transaction fees that users can
add (voluntarily) to their transactions. To conclude, we
observe that the de nition of a range of bene cial re-
wards appears, in terms of sustainability, in agreement
with real-world blockchain dynamics. Notably, as more
users adopt and use a crypto-asset, the miners' reward
gets adapted according to various factors, such as those
mentioned before. That does not entail rewards change
to comply with environmental needs however, the min-
ers' reward and the community size in a real blockchain
do not seem uncorrelated parameters.
6
V. CONCLUSIONS
In summary, we deem that the proposed model cap-
tures fundamental aspects of PoW blockchains. Notwith-
standing, several model features constitute relevant ele-
ments for future research. Just to cite a few, elements
deserving further attention relate to the introduction of
switching costs for representing the e orts required for
mining [40], the volatility of the crypto-asset value in the
market [41], and other mechanisms, such as the halving of
the miners' reward. In addition, in light of the structure
of transaction networks [30, 42{44] we observe in vari-ous cryptocurrencies, the interaction topology of the pro-
posed model deserves additional investigations. For in-
stance, to study whether network structures [45], such as
scale-free networks and small-world networks, can a ect
the strategy pro le of a population playing the Crypto-
Asset Game, as observed in social dilemmas [46{49]. Let
us conclude by remarking that the optimal steady states
reached by agents in some game conditions suggest that
protocol designers may have a relevant role in the ener-
getic impact of PoW-based blockchains. In particular,
the miners' rewards could get designed to promote virtu-
ous user behaviours, making, in the long run, this tech-
nology sustainable and environmentally-friendly.
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|
{
"id": "2209.12809"
}
|
2108.08656
|
Max-min Fairness Based Faucet Design for Blockchains
|
In order to have transactions executed and recorded on blockchains such as
the Ethereum Mainnet, fees expressed in crypto-currency units of the blockchain
must be paid. One can buy crypto-currency called Ether of the Ethereum
blockchain from exchanges and pay for the transaction fees. In the case of test
networks (such as Rinkeby) or scientific research blockchains (such as
Bloxberg), free crypto-currency, Ether, is distributed to users via faucets.
Since transaction slots on the blocks, storage and smart contract executions
are consuming blockchain resources, Ethers are distributed by fixed small
amounts to users. Users may have different amount of Ether requirements; some
small amounts and some large amounts during different times. As a result,
rather than allowing the user to get a fixed small amount of Ether, a more
general distribution mechanism that allows a user to demand and claim arbitrary
amounts of Ether, while satisfying fairness among users, is needed. For this
end, Max-min Fairness based schemes have been used in centralized settings. Our
work contributes a Max-min Fairness based algorithm and its Solidity smart
contract implementation that requires low transaction costs independent of the
number of users. This is important on the Ethereum blockchain, since a smart
contract execution with transaction costs depending on the number of users
would mean block gas limit exhaustion problem will eventually be met, making
the smart contract ineffective. We report tests which confirm that the low
transaction cost aims have been achieved by our algorithm.
|
http://arxiv.org/pdf/2108.08656v1
|
Serdar Metin, Can Özturan
|
cs.DC, cs.CR
|
cs.DC
|
Max-min Fairness Based Faucet Design for Blockchains
Serdar Metin1,, Can ¨Ozturan1
Bo˘gazic ¸i University, Bebek, ˙Istanbul, Turkey
Abstract
In order to have transactions executed and recorded on blockchains such as the
Ethereum Mainnet, fees expressed in crypto-currency units of the blockchain must
be paid. One can buy crypto-currency called Ether of the Ethereum blockchain
from exchanges and pay for the transaction fees. In the case of test networks
(such as Rinkeby) or scientific research blockchains (such as Bloxberg), free
crypto-currency, Ether, is distributed to users via faucets. Since transaction slots
on the blocks, storage and smart contract executions are consuming blockchain
resources, Ethers are distributed by fixed small amounts to users. Users may
have different amount of Ether requirements; some small amounts and some large
amounts during different times. As a result, rather than allowing the user to get
a fixed small amount of Ether, a more general distribution mechanism that allows
a user to demand and claim arbitrary amounts of Ether, while satisfying fairness
among users, is needed. For this end, Max-min Fairness based schemes have been
used in centralized settings. Our work contributes a Max-min Fairness based algo-
rithm and its Solidity smart contract implementation that requires low transaction
costs independent of the number of users. This is important on the Ethereum
blockchain, since a smart contract execution with transaction costs depending on
the number of users would mean block gas limit exhaustion problem will eventu-
ally be met, making the smart contract ineffective. We report tests which confirm
that the low transaction cost aims have been achieved by our algorithm.
Keywords: Blockhain, Faucet, Max-min Fairness, Resource Allocation
Corresponding author
Email addresses: serdar.metin@boun.edu.tr (Serdar Metin),
ozturaca@boun.edu.tr (Can ¨Ozturan)
Preprint submitted to arXiv.org August 20, 2021arXiv:2108.08656v1 [cs.DC] 19 Aug 2021
1. Introduction
Since its conception in 2008 with Bitcoin [1], blockchain technologies have
been the focus of much attention. Although successful at achieving its initially
proposed purpose of providing a peer-to-peer electronic cash system, Bitcoin was
conceived as an autonomous global currency with guaranteed scarcity, and as
such, offered limited programmability and functionality. The designers of the
following generation of blockchain systems mainly problematised this point and
endeavoured on expanding blockchain capabilities. With the introduction of smart
contracts by the Ethereum [2], the blockchain technology met Turing-complete
programming functionalities.
Our work aims to address a recurrent question in computer science, within the
blockchain context: the fair allocation of shared resources. We focus on the fair
allocation of intrinsic resources of blockchains. Since a blockchain is a distributed
ledger, operated in a distributed manner, the ability to operate on the blockchain
(e.g. executing a transaction or a smart contract function, or deploying a smart
contract) is a shared, limited resource. We look at the fair allocation of this re-
source.
In the Ethereum blockchain ecosystem that offers smart contract functionality,
the resource usage mentioned above are quoted in terms of gas, which refers to the
cost necessary to perform a transaction on the blockchain. The gas is priced using
the blockchain’s intrinsic crypto-currency, called Ether in the case of Ethereum.
Hence, just like a number of litres of petrol (priced as USD per litre) is needed in
order to have a car travel a number of kilometers, a number of gas units (priced as
Ether per gas unit) is needed to execute a number of instructions in a blockchain
transaction. Thus, the problem collapses down to the distribution of the system’s
intrinsic crypto-currency. In commercial public networks like Ethereum Mainnet,
the distribution process relies on the competition to create new Ether units of the
blockchain currency, and the trading of the already generated Ethers. However, on
test networks such as Rinkeby or scientific research blockchains such as Bloxberg
[3] alternative Ether distribution mechanisms are used.
Afaucet is one such mechanism, which offers free currency to users accord-
ing to some predefined policy. In general, faucets offer a fixed amount of currency
for a given time period or block span. For example, Bloxberg blockchain provides
0.2 Ethers via its web based faucet [4]. However, this mechanism can be exploited
simply by making recurrent requests and accumulating the obtained currency. For
this reason, it cannot be accounted for as a fair distribution scheme. Max-min Fair-
ness [5, 6] is a distribution scheme that is widely employed in different contexts
2
where fairness is a system requirement (e.g. cpu scheduling, bandwidth allocation
etc.), and it can also be considered for the fair distribution of currency in faucet
systems.
On the Ethereum blockchain, the size of each block is bound by a maximum
amount of gas that can be spent per block. This upper bound on the gas amount
is known as the block gas limit . A contract function will not be able to execute
if its gas cost exceeds the block gas limit. We refer to this problem as the block
gas limit exhaustion problem. Hence, smart contract functions should be designed
and implemented in such a way that their execution does not consume too much
gas which may lead to block gas limit exhaustion problem. If gas limit is reached,
it will simply mean the contract function cannot be executed which in turn may
mean the smart contract can no longer operate properly.
In this work, we first implement Max-min Fairness algorithm in the blockchain
context as a smart contract, as it is originally implemented in centralized systems.
After demonstrating the shortcomings of this implementation in the blockchain
context (i.e. block gas limit exhaustion problem), we contribute an algorithm
that actuates the Max-min Fairness scheme in the blockchain context without run-
ning into the original implementation’s shortcomings. We name our algorithm
Autonomous Max-min Fairness (AMF), since it is operated autonomously by the
users in the system, as opposed to the original algorithm, in which the distribution
operation is done centrally by an authority. Figure 1 illustrates the operations of
centralised and authority driven faucet smart contract implementation in (a) and
autonomous and decentralised implementation driven by crowds of users in (b).
In the former scenario, the distribution is done with a single call to the distribute
function by the authority node in the beginning of the epoch, whereas in the latter
the users make multiple calls to the claim function throughout the epoch in order
to obtain their own share.
We further extend the study to a weighted version of Max-min Fairness scheme,
in which case the users are assigned weights for their respective shares, according
to some prioritisation policy. In the tests we run, the weight of each user’s share
is defined to be the reciprocal of the total demand volume of the user, up to and
including the then present demand. By discouraging unnecessary demands, this
policy leads to higher sharing incentives among users. Moreover, it secures fair-
ness of distribution in the long run, since latter allocations are mediated with the
former demands of a given user. We name this algorithm Weighted Autonomous
Max-min Fairness (WAMF).
The remainder of the article is organised as follows: In the next section, we re-
view the literature on related work. Having laid out the background on blockchain
3
Figure 1: The call sequences of the (a) Authority driven faucet, and (b) Autonomous faucet (driven
by crowds of users), in a distribution cycle. Blue arrows indicate low-cost transactions (demand
and claim), and red arrow indicates a high-cost transaction (distribute).
studies, in Section 3 we state our problem in the light of the observations from
Section 2. We continue with a section where we justify our design decisions
concerning the experimentation environment. Following that, in Section 5 the
Max-min Fairness scheme and its adaptation to the present context is explained.
In Section 6, the implementation details are laid out. Once the implementation is
explained, the results of the experiments are given in Section 7. We discuss the
results in Section 8, and conclude the article in Section 9 with the prospects of
possible follow-up studies.
2. Related Work
Blockchain technologies have been proposed for a number of user applications
(e.g. [7, 8, 9]), and also for background services (e.g. [10, 11]).
By the introduction of tokenised economies, blockchain systems are rendered
4
capable of governing allocation and trade of resources [12]. A token is a data
structure with certain attributes and operations defined on them, serving for rep-
resenting items or value. The first two standards that are developed for token
economies are ERC 20and ERC 721, which define divisible and non-divisible, or
as they are so called, fungible andnon-fungible tokens, respectively. Although
compatible with our setting, for reasons of simplicity we did not use token stan-
dards in our implementation.
In many areas in computer science where the problem of distributing shared re-
sources is encountered, Max-min Fairness [5, 6] has been considered a fair method
[13, 14]. It is also the main method employed in the present study.
The question of fair sharing first arose in the context of operating systems,
where scheduling the resources of a single computer (e.g. processor time) among
processes was the main problem [15]; followed by the problem of distributing the
same resources among users [16], typically at the computer centres of universi-
ties. Similar problems are addressed in the computer networks literature over the
allocation of link bandwidth [17, 13]. Fair scheduling algorithms have also been
the focus of attention in grids [18].
With the advancements in distributed systems, and new paradigms in cluster
and high-performance computing, the problem of fairness evolved yet to larger
scales, and new questions arose. In this context, typically, service providers charge
users for the common resource that is demanded by, and allocated to them. The
same question is now expressed in terms of charging fairness: how much should
each demand cost, for it to be fair among clients [19] ? Should each type of
resource cost the same, and if not how are they traded [20] ?
3. Problem Statement
As indicated in Section 2, the criteria for fair allocation is intimately related
with the context the problem is situated in. A number of observations that stand
out to be relevant are as follows:
3.1. Abstraction for Demands: The level at which distribution is done
As observed in Section 2, the allocation may be done at process-level [15],
user-level [16], or group-level [21]. The faucet system that we contribute is de-
signed so as to provide fair distribution of internal currency among users, with
the assumption that the users are identified and registered for making demands.
Therefore, the abstraction for the demands are at the user level .
5
3.2. Abstraction for Resources: The number of resource types and their relation-
ship with each other
If the resources are abstracted to be homogeneous, the main problem is pricing
a unit resource [19]. On the other hand, if more than one type of resources ab-
stracted, the problem should be extended as to address how they are traded among
each other [20].
A key factor for determining the price of a cryptocurrency is its by then present
and ultimate total supply [22]. In the system we developed, the growth of the total
supply is predefined by an immutable policy, securing the ground for a calculable
and predictable pricing mechanism. Since the domain we restricted the present
study into is non-commercial blockchain systems, pricing here does not refer to
monetary pricing, but rather the cost of operations in terms of intrinsic cryptocur-
rency.
For simplicity, abstraction for the resource is kept at single resource type,
which is the intrinsic currency of the blockchain ecosystem, which in our case
is the Ether cryptocurrency. With Ether, a user can pay for storage as well as exe-
cution cost of smart contract functions on the Ethereum blockchain, providing the
basis for a unified resource type.
3.3. Temporal Granularity: The frequency at which the allocation procedure
takes place
For addressing this issue, the blockchain is divided into epochs, which we
define as a constant number of successive blocks. According to the original Max-
min Fairness algorithm, the distribution should be done at the beginning of each
epoch, which we implemented as such. In the algorithms we develop, we also
expand the allocation procedure over the whole span of the epoch, to be decen-
trally carried out by users, in the so called claim rounds . The temporal setting will
further be described in Sections 6 and 7.
4. Design Decisions on the Experimentation Environment
Although there are a number of design decisions for setting up a blockchain
system to carry out experiments on, one key factor is the proof scheme employed
in the consensus protocol. In the present study, the experiments are carried out on
the Parity implementation of a permissioned Ethereum blockchain [23]. The main
concern for this choice is to decouple two independent, yet intertwined questions
specific to the blockchain environments. Blockchains are a means both for decen-
tralisation, and for securing digital trust. By decoupling these two questions and
6
allowing to concentrate on decentralisation premise, permissioned blockchains
are ideal experimentation environments for blockchain operation analysis. Let us
elaborate on that.
Various proof schemes, employed in blockchains’ consensus protocols, neces-
sitate different trust assumptions, and equivalently, offer different levels of digital
trust to the ecosystems they are embedded in. If one imagines proof schemes on a
scale for their provision of trust, Proof-of-Work (PoW) blockchains reside on one
extreme, since they assume no preliminary digital trust, and provide all the digital
trust needed via their operation. For this reason they are referred to as trustless
computation environments, in the sense that no prior trust is needed among the
users to be involved in the operation of the ecosystem. Initial blockchains such as
Bitcoin [1] and Ethereum [2] employ PoW based consensus protocols.
Although they can operate stand-alone trust-wise, PoW blockchains expend
enormous physical resources (e.g. electricity, processing power) and their oper-
ation is costly. Other proof schemes were proposed to replace PoW in order to
eliminate these costs. These schemes provided different levels of trust, compen-
sated by extra-digital measures to different extents, inversely proportional with the
former. Among these are: Proof-of-Space [24], Proof-of-Stake [25, 26], Proof-of-
Prestige [27], Proof-of-Activity [28], Proof-of-Useful-Work [29], with different
utilities and limitations they bear.
Proof-of-Authority (PoA) blockchains, residing on the other extreme, provide
no trust via their operation, and rely solely on extra-digital measures (e.g. reserve
the right to operate on the blockchain only to trusted parties) to secure trust.
The trust structure described above for PoA is equivalent to the trust structure
of the conventional computation environments, which is referred to as Pretty Good
Privacy (PGP) trust chain [30]. The PGP scheme secures trust with the assumption
of the presence of a trust anchor , a party that can be unconditionally trusted, and
from that point, other parties are trusted either by the direct reference of, or by a
chain of references rooted at the trust anchor. In the PoA setting, authority nodes
act as trust anchors.
We implemented our algorithms in Solidity programming language and run
on an Ethereum Virtual Machine (EVM) environment [31], and more specifically,
its Parity implementation, as mentioned above. The main reason for selecting
this framework is its wide use among blockchain ecosystems. Many blockchain
ecosystems and blockchain based systems utilise either EVM or virtual machines
similar to EVM, and support Solidity programming language for smart contracts
(e.g. [32, 33, 34, 35] etc.), and for this reason there are also studies available
on the performance [36], security [37], and inspection [38] of the programming
7
language. Not only is it a widespread programming language, Solidity is also
Turing complete [2], which makes it well suited for general purpose computations.
It is a high-level, easy to read, object oriented script language.
A number of smaller design decisions are taken concerning the parameters of
Parity Ethereum Virtual Machine and the procedure of the experiments, which is
left to be discussed in Section 7, since their explanation relies occasionally on the
implementation of the algorithms we present.
5. Max-min Fairness Model
The main objective of the Max-min Fairness scheme is to maximise the mini-
mum share given to a user, and its mechanism is based on a trivial fairness scheme,
where resources are uniformly distributed among the demanders, each one of the
ndemanders obtaining1
nof the resource. Max-min Fairness improves the trivial
scheme on the premise that not every demander would demand as much as the
share that is reserved for them. Accordingly, the Max-min Fairness allocation al-
gorithm takes recursive iterations over the list of demanders, reallocating unused
shares of the underdemanders among the overdemanders.
In the first iteration, starting with the smallest demand and proceeding in the
ascending order, the algorithm allocates the demanders the minimum of1
nof the
capacity ( c) and their demands (i.e. minfc
n; dug). At the end of the first iteration,
some demanders are fully supplied, and some capacity is left over. The algorithm,
in turn, proceeds with updated n0andc0, until either all demands are fully supplied,
or the capacity is depleted.
The operation of the scheme can be seen in Figure 2, and its pseudo-code
in Algorithm 1. In the pseudo-code the demand heaps are denoted by D0and
D1, and individual demands in these heaps are represented by lower case letters,
subscripted with u, for user id number (i.e. du).
The balances of users are kept in a vector, and the balance of user uis repre-
sented with bu. At each iteration, the maximum available amount to be allocated
to each user is recalculated by dividing the remaining capacity by the number of
remaining demands, and denoted by s, representing the unit share .
To illustrate the operation of the algorithm we may consider the following ex-
ample: Suppose that a resource of 30units will be shared among three users, with
the demands expressed as <4;11;15>. The algorithm distributes the resource in 3
iterations. The rounds and the shares assigned in each round can be seen in Table
1.
8
Demand
Heap 0Demand
Heap 1
SuppliedLeftover
Demand
Leftover
DemandUser
Demands
Leftover
Capacity
Figure 2: The operation of Max-min Fairness Algorithm
User 1User 2User 3Share Capacity
Demands 4 11 15 30
Iteration 1 4 10 10 10 6
Iteration 2 0 1 3 3 2
Iteration 3 0 0 2 2 0
Total 4 11 15
Table 1: An exemplary distribution according to Max-min Fairness scheme
9
How the unsatisfied demand, or the leftover capacity will be treated after a
distribution period is a decision of policy. In our current work, we implement a
policy that discards all the unsatisfied demands, in the case of capacity depletion,
and transfers the leftover capacity to the next distribution period, in the case of
satisfying all the demands.
The amount that is reserved for each epoch is denoted by C. We call this
amount the epoch capacity , and in the present study, we took it to be constant.
The actual amount that is distributed in an epoch is denoted by c, and it is at least
as much as C, since it is added to cat the beginning of each epoch (i.e. Algorithm
1 line 2).
In Algorithm 1, the lines 4 20constitute the main, or outer loop of the
algorithm, which is responsible for repeating the inner loop (lines 10 18) until
either the demands or the capacity is depleted. It starts with calculating the share
(lines 5 9), and then starts the inner loop. Once the proceeding of the inner loop
is completed, the demand heaps exchange their functions (line 19) and the outer
loop takes another iteration.
The inner loop accounts for iterating on and processing the demands in the
active heap. In line 11the demand volume and the user id at the root of the heap
is read into a variable and deleted from the heap. After that the minimum of user
demand and unit share (i.e. minfc
n; dug) is assigned to the user in lines 12 14.
The control structure in lines 15 17checks whether the demand is fully satisfied.
If not, the leftover demand is inserted to the heap with the user’s id (line 16) to be
processed in further iterations.
Another version of Max-min Fairness is weighted Max-min Fairness , in which
case the users are weighted over some predefined policy, and the shares are calcu-
lated with the weights assigned to each user, individually. In this version, instead
of the number of demands, the total capacity is divided by the total weight in
order to calculate the unit share ( s). In turn, the user share (sufor user u) is calcu-
lated for each user by multiplying the unit share with the user’s weight. The users
are allocated the minimum of their demands, and their individually assigned user
shares.
10
Symbol Meaning
C Amount of resource that is added
to the existing capacity at every
epoch, C2Z+
Di Set of demand heaps, i2f0;1g
U Set of users u2fu1; : : : ; u ng
c The existing capacity, initialised
at0, incremented by Cat every
epoch
s Unit share
u Useru,u2U
dui Demand of user ustored on heap
Di
bu Resource balance of user u,bu2
Z0; u2U
Table 2: Symbols used in CMF (Algorithm 1) and their meanings
Algorithm 1: Max-min Fairness (CMF)
1FU N C T I O N : DI S T R I B U T E (D; U; c )
2c c+C;
3i 0;
4while Di:size()>0andc >0do
5 ifc < D i:size then
6 s 1;
7 else
8 s j
c
Di:sizek
;
9 end
10 while Di:size > 0andc >0do
11 (dui; u) Di:delMin ();
12 a min (s; dui);
13 bu bu+a;
14 c c a;
15 ifdui> sthen
16 D1 i:insert (dui s; u);
17 end
18 end
19 i 1 i;
20end
21return ;
Accordingly, the formula for calculating the unit share sis:
11
s=cPn
j=1wj
and the user share suis given by:
su=wus=wucPn
j=1wj
We develop autonomous algorithms called AMF (unweighted version) and
WAMF (weighted version) for actuating the Max-min Fairness scheme. In WAMF,
the weights are defined to be the reciprocals of the total amount of demands users
have made up to the distribution time. This aims at incentivizing users to make
minimal demands suitable to their needs, in order not to be disadvantageous in the
long run. The implementation details of WAMF algorithm, as well as its pseudo-
code is presented in Section 6.
6. Implementation
The conventional setting to utilise Max-min Fairness typically includes a cen-
tral unit (either an individual process running on a central processor or a dedicated
administrative host in a computer network) calculating the shares and carrying out
the iterative assignments. This is applicable to the blockchain context, but not
without potential drawbacks. The main bottleneck in such an adaptation is the
block gas limit, which imposes an absolute upper bound for the number of opera-
tions that may take place within the processing of a single block. For this reason,
we implemented two algorithms and compared them. The implementations are
available at [39]
The first algorithm is the Conventional Max-min Fairness (CMF) . This algo-
rithm is implemented as if it operates in the conventional computational setting.
The demands are collected for a given time period or block span, which is re-
ferred to as an epoch in this study. At the beginning of the following epoch these
demands are supplied resources in the Max-min Fairness order by a single node
(typically an authority node) in one step with the distribute function.
In the second algorithm, the demands are collected in a given epoch, and the
demanders claim their reserved share by calling a claim function in the claim
rounds of the following epoch. We call this approach Autonomous Max-min Fair-
ness (AMF) , since there is no need for a central node to carry out the execution,
and the system is operated autonomously by its users. The operation of AMF
emulates the original algorithm identically, except for the last iteration where the
12
distribution is in the first come first served order among overdemanders. Origi-
nally, the last iteration is in the ascending order of demand volumes, as are all the
preceding iterations.
We implemented both unweighted as well as the weighted versions of Max-
min Fairness for the AMF. The reason for not implementing a weighted version of
CMF is due to its gas cost structure (elaborated on in Section 7.1). In the following
subsections we give the implementation details of the algorithms.
6.1. Conventional Max-min Fairness
As it is in the conventional setting, CMF utilizes two min-heaps, exchanging
the demands among each other in each iteration. The operation scheme and the
pseudo-code is the same as it is described in Section 5 (i.e. Figure 2 and Algorithm
1).
Since Solidity does not offer a built-in data structure for min-heaps, we im-
plemented it during the development of CMF. We kept the implementation of the
min-heap minimal in order to keep the gas cost at minimal. Only the amount of
demand, and the id (i.e. unique user number given to each user) of the demanding
user is stored and operated on. The remainder of the user attributes are fetched
from other data structures when needed (e.g. while writing to user balance), by
using the user id as the key.
We used an array implementation of heap, a complete binary tree, where the
values are kept in a node array and the insert anddelete minimum functions are
implemented so that they index and move the nodes according to the min-heap or-
ganisation. This is also immune to degeneration attacks, in which case an attacker
feeds the tree with selective input to make one branch grow disproportionately,
forcing heap functions run in O(n)instead ofO(log(n))time.
We present the performance of CMF, as well as the min-heap, in Section 7.1.
6.2. Autonomous Max-min Fairness
In AMF, the epochs are divided into claim rounds. At the end of each round,
the remaining number of demands, the remaining capacity, and the resulting share
is recalculated. The rounds proceed in this manner until either the capacity is de-
pleted, or all demands are supplied. The rounds are used to emulate the iterations
of the outer loop (lines 4 20of Algorithm 1) of the distribute function.
In order to avoid repetition, we give the pseudo-code only for the weighted
version (WAMF), since it is more general as compared to the unweighted version
(AMF), the latter being the same algorithm with fewer steps. The pseudo-code
of WAMF is presented in Algorithm 2. The symbols for the additional variables,
13
and their meanings are given in Table 4. The calculation of weights is obscured
from the pseudo-code for the ease of review, and the weights are simply shown
as constant variables. The calculation of weights is described in detail in the next
subsection.
In AMF, instead of a single-handedly operating distribute function, there is a
claim function, which after necessary checks, allows the user assign her allocated
share to herself. Each user is expected to execute the function individually, to have
carried out the iterations of the inner loop of the distribute function (lines 10 18
of Algorithm 1), in a decentralized manner.
Any share unclaimed in its due round/epoch is lost. It is included in the fol-
lowing round/epoch as part of the leftover capacity. In a given epoch, users may
make new demands for the next epoch, while claiming their share for the previ-
ous. The time frame can be traced in Table 3 over the demands and corresponding
claims, and can be seen more explicitly in Figure 3.
User 1 User 2 User 3 Share Capacity
Demand 1 4 11 15
Round 1
Epoch 1 Claim 0 Round 2
Round 3
Demand 2 11 3 8 30
Round 1 4 10 10 10 6
Epoch 2 Claim 1 Round 2 1 3 3 2
Round 3 2 2 0
Demand 3 7 8 12 10 30
Round 1 10 3 8 10 9
Epoch 3 Claim 2 Round 2 1 9 8
Round 3
Demand 4 17 13 5 38
Round 1 7 8 12 12 11
Epoch 4 Claim 3 Round 2
Round 3
Demand 5 .. .. .. .. 41
Round 1 13 13 5 13 10
Epoch 5 Claim 4 Round 2 4 10 6
Round 3
Table 3: An exemplary distribution carried out with AMF
In AMF the demands are kept in a map, rather than a min-heap, since it is nec-
essary for each user to be able to access their own demand entry, while claiming it.
In the present implementation, the demands are kept for one epoch, and claimed
in the following. For this reason, a circular buffer of size two is kept for each
14
Epoch-3-
Demand-1
Demand-2Claim-1Epoch-1 Epoch-2
...Claim-3
Demand-4Epoch-4
...Claim-4Epoch-5
Claim-2
Demand-3Time
Figure 3: Time frame for the matching demand andclaim function calls
user, in order to prevent an incoming demand in a given epoch to overwrite the
previous epoch’s demand, before it is claimed. This leads to a two dimensional
(2xn) demand vector, where the demands for even and odd epochs are kept sep-
arately. Additionally, the variable for keeping the epoch in which the demand was
made (for preventing an obsolete demand to interfere with later demands) is im-
plemented; likewise as a circular buffer of size two, in order to separate between
the even and the odd epochs.
In addition to the restructured demand , and the newly introduced claim func-
tions, AMF includes a state update function, which is called at the beginning of
both. The state update function checks the block number, and calculates the epoch
and the round in which the called function will be executed (lines 3and10, re-
spectively). The number of blocks for the duration of an epoch and a round, is
also a parameter of the system, which we experimented on in the present study,
and commented on in the results section.
The pseudo-code in Algorithm 2 is organised in three functions, namely, up-
date state (lines 1 15),demand (lines 16 24), and claim (lines 25 45). At
the beginning of each function (in lines 2,18, and 27) a local selector variable
(i) for the circular buffers is declared and defined. When called in a given epoch,
the state update and the claim functions assume the same selector values, and de-
mand function assumes its binary complement. That is to say ivalues proceed as
<0;1;0;1; ::: > for the state update andclaim functions, and as <1;0;1;0; ::: >
for the demand function.
In line 3, the epoch number ( E) is checked for. If the value of Eis found to
be obsolete, it is updated. Once the epoch number is updated, the round number,
15
Symbol Meaning
C Amount of resource that is added to the existing capacity at every epoch, C2Z+
B Current block number
O The block number at which the contract was deployed, offset
E Epoch number
R Round number
RE Reset epoch, the epoch at which the total weight was last reset
ES Number of blocks in an epoch, epoch span
RS Number of blocks in a round, round span
U Set of users u2fu1; : : : ; u ng
Wi Total weight for even and odd epochs, i2f0;1g
a Demand volume, amount
u Useru,u2U
dui Demand of user uin list i,i2f0;1g
deui The last epoch user umade a demand, i2f0;1g
ceu The last epoch user umade a claim
cru The last round user umade a claim
bu Resource balance of user u,bu2Z+
wu Weight of user u
c The existing capacity, initialised at 0, incremented by Cat every epoch
Table 4: Symbols used in Algorithm 2 and their meanings
the capacity, and the unit share are also updated (lines 5 7), and the function
returns. If epoch number is found to be up-to-date, a similar check is done for the
round number in line 10. This check, when it returns positive, leads to the update
of the round number and the unit share (lines 11 12), and the function returns.
If no update is required, the function returns without making any changes in the
state.
After updating the state and setting the selector variable, in line 19the demand
function checks whether the user has made a demand in the then present epoch.
If the user has made a demand, the function returns without registering the newly
arrived demand. If not, the demand amount ( a) is written to the corresponding
slot in the circular demand buffer of the user, and the demand epoch of the user
is updated to be the then current epoch (lines 20 21). In the following line the
function checks whether any demands have been made by other users in the then
current epoch. If not, the total weight is set to the user’s weight (line 23), which
resets the total weight variable for the next epoch. The variable for keeping the
last epoch in which the total weight is reset ( RE) is updated in line 24. If demands
have been made by other users prior to the then current call (i.e. RE=E) the
16
weight of the user is added to the total weight, to be accounted for in the next
epoch (line 26).
The claim function, similar to the demand function, starts with updating the
state and initiating the selector variable. It continues with a number of checks (line
33). Unless the demand has been done in the previous epoch and is greater than
0, or if the capacity is depleted, the function returns without taking any further
action. Following that in line 36the function checks whether the user has made
any claims in the then current epoch. If so, the last round the user made a claim
is checked (line 37). If that also turns positive, which means the user has claimed
her fair share for the round, the function returns without making any assignments.
If the check in line 36turns out negative, meaning this is the user’s first claim
in the then present epoch, the variable for the last epoch the user made a claim
(ceu) is updated (line 41). After that, a similar variable for the round ( cru) is up-
dated in line 41. Next, the assignment operations similar to the ones in Algorithm
1 is done in lines 44 46.
Note that this algorithm differs from the CMF algorithm in that the leftover
demands are not inserted into another heap; they remain in the map. Instead, the
fully satisfied demands are removed from the cumulative weight variable in lines
42 44, having the same effect as deleting the minimum in CMF algorithm. This
way, as long as there is an unsatisfied demand, the user’s weight is included in the
total weight, and the unit share is calculated accordingly. At the end of the epoch,
all demands are obsoleted.
17
Algorithm 2: Weighted Autonomous Max-min Fairness (WAMF)
1FU N C T I O N : UP D A T E ST A T E (O; B; E; ES; RS )
2i E mod 2;
3ifE <B O
ES
then
4 E B O
ES
;
5 R j
(B O)%ES
RSk
;
6 c c+C;
7 s bc=W ic;
8 return
9end
10ifR <j
(B O)%ES
RSk
then
11 R j
(B O)%ES
RSk
;
12 s c=W i;
13 return ;
14end
15return ;
16FU N C T I O N : DE M A N D (u;a)
17UP D A T E ST A T E (O; B; E; ES; RS );
18i (E+ 1) mod 2;
19ifdeui6=Ethen
20 dui a;
21 deui E;
22 ifRE < E then
23 Wi wu;
24 RE E;
25 else
26 Wi Wi+wu;
27 end
28end
29return ;
30FU N C T I O N : CL A I M (u)
31UP D A T E ST A T E (O; B; E; ES; RS );
32i E mod 2;
33ifdeui6=E 1orc= 0 ordui= 0then
34 return ;
35end
36ifceu=Ethen
37 ifcru=Rthen
38 return ;
39 end
40else
41 ceu E;
42end
43cru R;
44bu bu+ min ( dui; swu);
45dui dui min (dui; swu);
46c c min (dui; swu);
47ifdui= 0then
48 Wi Wi wu;
49end
50return ;
18
6.3. Weighted Autonomous Max-min Fairness
As the operation of the algorithm is described in Section 6.2, the only part that
is left to be explained in this subsection is the calculation of weights.
We defined weights to be the multiplicative inverses of the total demand vol-
ume, up to and including the then present demand. This poses a problem in the
smart contract context, since Solidity does not offer floating point data types. In
other words, since the demand volumes are defined to be positive integers, it is
not possible to keep weights as they are, since the value needs floating point data
type to be stored. Instead, we keep the total demand volume for each user ( dtufor
useru), introduce an intermediary variable p(standing for precision ) and take the
weight equal to:
wu=p
dtu
We get rid of this intermediary variable while calculating the unit share. There-
fore, instead of
s=cPn
u=1wu
we use:
s=cpPn
u=1wu
since
s=$
cpPn
u=1p
dtu%
=$
cPn
u=11
dtu%
Similarly, while calculating the user share we use the intermediary variable p:
su=6664sj
p
dtuk
p7775
As long as the value of pis larger than the total demand volume of the user, we
obtain non-zero weights fromj
p
dtuk
. For p= 10k; k2 Z+is the number of
decimal places stored for weights.
19
7. Results
In this section, we present the results over the gas costs used as the main
performance metric. The tests are run on Parity Ethereum 2:7:2, and the contracts
are implemented using Solidity 0:5:13, thus the gas costs are according to the
definitions given thereby.
In our tests, we run Parity in development mode and used its instant seal con-
sensus algorithm, in which each transaction is placed in an individual block and
inserted instantly to the blockchain. A convenient metric for measuring time is
the block number. In the deployment of the system, this metric can be used with
the block latency to come up with rough temporal estimations.
Since block latency is a policy parameter for each blockchain ecosystem, tak-
ing block number as the main temporal performance metric is convenient also in
terms of generalizability of the results. As it is presented here, our results are
independent of consensus algorithm, and block latency parameters.
The results for each algorithm are presented in the subsections below. The
data are available at [39]
7.1. CMF Results
As indicated in Section 6.1, in the CMF, the demand vector is implemented
as an array of two min-heaps, exchanging the demands among each other at each
iteration. The demands arriving from the users are collected in D0for the span of
an epoch. At the end of the epoch, the distribute function is called by the authority
node, and the distribution is done. The first iteration is done over D0, taking all
demands from the smallest to the largest, granting the available share to the user,
and finally either deleting the minimum demand, if it is completely supplied, or
deleting it from D0and inserting it to D1, otherwise, to be supplied in the next
iterations if possible. The heaps exchange functions, and the process is repeated
until either all the demands are supplied, or the capacity for the epoch is exhausted
(see Algorithm 1)
Gas usage averages for n= 100 entry sets are shown in Table 5. For com-
parison, the gas performance of a general case heap implementation [40], called
Eth-heap, is provided next to our results:
Considering the 8:000:000block gas limit, the heap operations impose an up-
per bound of 60entries to be processed per block, on average, as seen with the cost
of operations in Table 5. This number is to be further lowered with the additional
cost of assignment operations, needed to record the fair share of each user to her
balance.
20
Function Present Study Eth-heap
Insert 95.459 101.261
Delete Minimum 133.272 170.448
Table 5: Average gas costs for Insert andDelete Minimum functions
The finding immediately implies that an algorithm implemented as a smart
contract and relying on a central node to carry out the distribute function, cannot
support more than10users, assuming that 3iterations are necessary on average
for a distribution process to complete. The exact number is a function of how dis-
perse the demands are, since the number of delete/insert operations is dependent
on the number of iterations necessary to answer all the demands, which in turn is
dependent on how disperse the demands are.
This is also the reason why a weighted version of CMF has not been imple-
mented in the present study. The extra cost of calculating and storing weights will
make the weighted version perform even worse than the unweighted version.
7.2. AMF and WAMF Results
The first advantage to be pointed out for AMF is that it virtually has no limit
for the number of users that the system can support. The average gas costs of
demand andclaim functions for a system with 10;50;100and500users can be
seen in Table 6. The tests have been carried over in a setting where users have
made demands, and claimed their demands in the succeeding epoch. The results
indicate that several demand andclaim function calls can be included within a
block, without running into the block gas limit exhaustion problem.
Function No. of Users AMF WAMF
Demand10 70:245 79 :732
50 67:351 77 :135
100 66:989 76 :835
500 66:700 71 :365
10 46:800=140:401 46 ;643=145:931
Claim 50 42:240=126:720 44 :852=134:558
(Avg./Total) 100 42:114=126:344 44 :763=134:289
500 42:047=126:143 45 :319=135:959
Table 6: Average and total gas costs of AMF/WAMF demand andclaim functions for various
numbers of users.
21
The results also indicate that the cost of demand andclaim functions do not
grow with the growing number of users. On the contrary, there is a slight decrease
in the average costs, with the growing number of users. The reason for this is
the fact that in each epoch the first call to both functions are costlier, since state
variables are updated in these calls. With large sample sizes, this difference tends
to even out better as compared to the relatively smaller sample sizes.
It should also be noted that the epochs and rounds should last enough for each
user to be able to make claims and demands. Since the instant seal engine de-
ployed in the tests place each transaction in an individual block, the epoch and
round spans are so chosen as to allow each user be able to make claims and de-
mands within an epoch. The parameters of the system that the tests have been
carried on have been shown in Table 7.
According to this, in a setting with nusers, in the first epoch, nblocks are
used for user registration function calls and 2nblocks are filled with empty trans-
actions in order to synchronise the process. The following demand function calls
occupied nmore blocks, concluding the first epoch. From the second epoch on,
the sequence is 3rounds of claim in 3nblocks, followed by nblocks of demand
for the next epoch. Three sets are run (adding up to 4epochs), and the averages
are collected.
Parameter Value Definition
Number of Users nThe number of users
in the system
Epoch Capacity 20nThe amount to be
distributed for each
epoch
Epoch Span 4nThe duration of an
epoch in number of
blocks
Round Span nThe duration of a
round in number of
blocks
Demand Interval [10;30) The interval from
which the demands
are drawn
Table 7: The values used in the tests for AMF and WAMF.
One thing that should be accounted for is that the average cost of demand
22
function declines throughout the rounds. The reason for this is, some demands
have been fully supplied in the previous epoch, thus, fewer calls to claim function
lead to the full execution of the function (i.e. calls from users whose demands
have already been satisfied return without making any assignments). The average
claim costs of rounds for Max-min and Weighted Max-min Fairness schemes can
be seen in Table 8.
Round AMF WAMF
1 64 :677 67 :211
2 32 :717 36 :158
3 28 :749 32 :589
Average 42:047 45 :319
Total 126:143 135 :959
Table 8: The costs of the claim functions over rounds, in a setting with n= 500 users.
The number of rounds, as indicated in Section 7.1 is a function of the initial
distribution of the demands. In our tests, we drew random demands from an ap-
proximately uniform distribution offered by Javascript Math.random() function,
in the range [10;30), and the epoch capacity is set to 20n, so that on average the
overdemands and underdemands could balance each other out.
In all the simulations with a Python script, the distribution is completed in 3
iterations. Therefore, in the tests presented here, we run the system for 3rounds
of claims. The results are cross-checked with the Python simulations and proved
identical. We suspect that with the parameters used in this study, 3iterations might
be an upper bound, but we do not have a proof. Further investigation needs to be
carried out to in order to come up with a theoretical bound.
Another variable that can be parameterized according to the policy and that
would effect gas costs is the size of the variables used to represent amounts. The
size of the variables can be chosen smaller to save from the extra cost of unused
space. The necessary sizes for the variables is dependent on the total amount that
is planned to be distributed in the long run, maximum available allocation in an
epoch and the maximum number of epochs to distribute all the resource etc. In the
present study, all the variables are implemented as their 256bit defaults, in order
not to lose generality.
23
8. Discussion
The main bottleneck, and the main performance metric of the present study
is the gas consumption, and this is arguably a natural approach for studies on
blockchain systems. However, the results presented in this study are not to be
taken for their absolute values. Low level improvements may be introduced in
coding or compilation, leading to lower transaction costs. The aim of this ap-
proach is to demonstrate the availability, and the cost structure of the Max-min
Fairness algorithm, and its different implementations.
Accordingly, the present study demonstrates, over the failure of CMF to sup-
port more than 10 users, that it is not feasible for Max-min Fairness scheme
to be implemented in the blockchain context as it is implemented in the con-
ventional computational settings. In principle, because of the block gas limit,
blockchain systems are not well suited for algorithms, which cannot be efficiently
distributed to be processed by multiple computing parties, with partial data, and
asynchronously. A single transaction to carry out a function with heavy computa-
tional burden is not a working strategy while developing software for blockchain
systems.
This is in accordance with the distributed nature and the philosophy of the
blockchain systems. In contrast with the centralized systems, blockchains aim to
distribute both the work and the control among its users. For this reason, they are
incentive driven , as opposed to centralized systems, which are authority driven .
That is to say, centralized systems rely on an authorized component (e.g. operating
system kernels, load balancers, web servers etc.) to carry out the computation;
whereas blockchain systems rely on incentivising its users to operate the system
in a way that the outcome will turn out to be the desired computation. Both AMF
and WAMF are designed taking those points into consideration. Consequently,
they offer scalable solutions for blockchain systems.
Another possibility to consider is changing the capacity replenishment policy.
In the present study, the capacity is replenished by a constant quantity Cat the
beginning of each epoch. Instead, the tests can be run with varying quantities of
replenishment over time, possibly according to some function of epoch number
(i.e.C=f(E)). This may serve as a distribution mechanism for systems that run
on donations, like election rallies or other types of fund raising projects, where
public transparency, responsibility, incentivisation, and participation are matters
of consideration. This kind of a distribution mechanism lends these projects the
opportunity to be publicly transparent, and make commitments (e.g. declaring the
weights for the expenditure items) prior to raising funds, since the system assures
24
the enforcement of declared commitments, by the virtue of its immutability.
9. Conclusion
In the present study we addressed the problem of fair distribution of shared
resources within the blockchain systems context. We worked on the intrinsic re-
sources of blockchains, and developed faucets as smart contracts, running dif-
ferent implementations of Max-min Fairness Algorithm, which is traditionally
accepted realizing fairness in the literature.
It has been demonstrated that the Max-min Fairness algorithm, as it is imple-
mented in the conventional programming contexts, cannot support a public system
because of the scaling of its gas cost structure. Two autonomous implementations
of the algorithm are offered as a solution, and the tests have shown that these
implementations can support wide public use of the system without running into
block gas limit exhaustion problem.
Although, in the present, the faucets are mainly utilised as tools for distributing
the native currency of the test networks, the operation of faucet systems need
not be limited to this use case. These systems have the ability to represent any
resource type, and accordingly, to fairly allocate them, as briefly discussed in the
preceding section.
The faucet algorithms presented in this study are designed for single resource
planning. For the prospective studies we might propose focusing on multi-resource
planning problems. One way would be keeping each resource type separate and
distribute them in parallel, with the algorithms developed in the present study.
This will assume an independence of the supply and the value of the resources. If
the resources are dependent on each other, alternative solutions must be proposed
and evaluated.
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29
|
{
"id": "2108.08656"
}
|
1906.04421
|
The merits of using Ethereum MainNet as a Coordination Blockchain for
Ethereum Private Sidechains
|
A Coordination Blockchain is a blockchain with the task of coordinating
activities of multiple private blockchains. This paper discusses the pros and
cons of using Ethereum MainNet, the public Ethereum blockchain, as a
Coordination Blockchain. The requirements Ethereum MainNet needs to fulfil to
perform this role are discussed within the context of Ethereum Private
Sidechains, a private blockchain technology which allows many blockchains to be
operated in parallel, and allows atomic crosschain transactions to execute
across blockchains. Ethereum MainNet is a permissionless network which aims to
offer strong authenticity, integrity, and non-repudiation properties, that
incentivises good behaviour using crypto economics. This paper demonstrates
that Ethereum MainNet does deliver these properties. It then provides a
comprehensive review of the features of Ethereum Private Sidechains, with a
focus on the potential usage of Coordination Blockchains for these features.
Finally, the merits of using Ethereum MainNet as a Coordination Blockchain are
assessed. For Ethereum Private Sidechains, we found that Ethereum MainNet is
best suited to storing long term static data that needs to be widely available,
such as the Ethereum Registration Authority information. However, due to
Ethereum MainNet's probabilistic finality, it is not well suited to information
that needs to be available and acted upon immediately, such as the Sidechain
Public Keys and Atomic Crosschain Transaction state information that need to be
accessible prior to the first atomic crosschain transaction being issued on a
sidechain. Although this paper examined the use of Ethereum MainNet as a
Coordination Blockchain within reference to Ethereum Private Sidechains, the
discussions and observations of the typical tasks a Coordination blockchain may
be expected to perform are applicable more widely to any multi-blockchain
system.
|
http://arxiv.org/pdf/1906.04421v2
|
Peter Robinson
|
cs.CR
|
cs.CR
|
The merits of using Ethereum MainNet as a
Coordination Blockchain for Ethereum Private
Sidechains
Peter Robinson
Protocol Engineering Group and Systems (PegaSys), ConsenSys
peter.robinson@consensys.net
School of Information Technology and Electrical Engineering, University of Queensland, Australia
peter.robinson@uqconnect.edu.au
Abstract —A Coordination Blockchain is a blockchain with the
task of coordinating activities of multiple private blockchains.
This paper discusses the pros and cons of using Ethereum
MainNet, the public Ethereum blockchain, as a Coordination
Blockchain. The requirements Ethereum MainNet needs to fulfil
to perform this role are discussed within the context of Ethereum
Private Sidechains, a private blockchain technology which allows
many blockchains to be operated in parallel, and allows atomic
crosschain transactions to execute across blockchains. Ethereum
MainNet is a permissionless network which aims to offer
strong authenticity, integrity, and non-repudiation properties,
that incentivises good behaviour using crypto economics. This
paper demonstrates that Ethereum MainNet does deliver these
properties. It then provides a comprehensive review of the
features of Ethereum Private Sidechains, with a focus on the
potential usage of Coordination Blockchains for these features.
Finally, the merits of using Ethereum MainNet as a Coordination
Blockchain are assessed. For Ethereum Private Sidechains, we
found that Ethereum MainNet is best suited to storing long term
static data that needs to be widely available, such as the Ethereum
Registration Authority information. However, due to Ethereum
MainNet’s probabilistic finality, it is not well suited to information
that needs to be available and acted upon immediately, such as the
Sidechain Public Keys and Atomic Crosschain Transaction state
information that need to be accessible prior to the first atomic
crosschain transaction being issued on a sidechain. Although this
paper examined the use of Ethereum MainNet as a Coordination
Blockchain within reference to Ethereum Private Sidechains, the
discussions and observations of the typical tasks a Coordination
blockchain may be expected to perform are applicable more
widely to any multi-blockchain system.
Index Terms —blockchain, mainnet, private, ethereum,
sidechain, coordination
I. I NTRODUCTION
This paper analyses the advantages and disadvantages of
using Ethereum MainNet as a Coordination Blockchain , by
demonstrating the ways in which a Coordination Blockchain
may be leveraged in a blockchain network that runs several
parallel blockchains. We conduct an in-depth review of the
features of Ethereum Private Sidechains, which is an exam-
ple of such a blockchain system, to explore their potential
usage of Coordination Blockchains as an exposition of using
Coordination Blockchains more generally. The analysis builds
on the Symposium on Distributed Ledger Technology paperFuture of Blockchain [1], and other work on Ethereum Pri-
vate Sidechains including: Requirements for Ethereum Private
Sidechains [2],Ethereum Registration Authorities [3],Anony-
mous Pinning [4], and Atomic Crosschain Transactions [5].
Ethereum MainNet is the largest public deployment of the
Ethereum platform. It is a permissionless network, allowing
any node to join the network. It is said to offer good authen-
ticity, integrity, and non-repudiation properties, along with an
economic system to discourage transaction spamming [6], [7].
To date there has been no work that has analysed all of these
assertions. This paper remedies this deficiency by carefully
analysing whether these properties are successfully delivered.
Sidechains are blockchains that rely on a separate
blockchain, a Coordination Blockchain, for their overall utility.
This could be to enhance security by pinning the state of the
sidechain to the Coordination Blockchain [4], for addressing
information [3], or for storing data that is used across all
sidechains. We analyse the appropriateness of using Ethereum
MainNet as a Coordination Blockchain for the various fea-
tures of sidechains, using as a reference Ethereum Private
Sidechains.
This paper is organised as follows: the Background sec-
tion briefly introduces Ethereum MainNet, the platform that
forms the basis for this paper. We describe the concept of
private blockchains and the enterprise version of Ethereum,
and introduce the concept of block ‘finality’. Next crypt-
analysis of message digest and asymmetric algorithms is
reviewed given classical and quantum cryptanalytical tech-
niques. The Ethereum MainNet Features section analyses
whether Ethereum MainNet delivers authenticity, integrity,
non-repudiation, and crypto-economic anti-spam properties.
The Ethereum Private Sidechains section describes the fea-
tures of Ethereum Private Sidechains and their usage of Coor-
dination Blockchains. The Pros and Cons of using Ethereum
MainNet as a Coordination Blockchain section analyses the
advantages and disadvantages of using Ethereum MainNet as
the Coordination Blockchain for each of the Ethereum Private
Sidechain features.
1arXiv:1906.04421v2 [cs.CR] 13 Jun 2019
II. B ACKGROUND
A. Ethereum
1) Ethereum MainNet: Ethereum [8] is a blockchain plat-
form that allows users to upload and execute computer pro-
grams known as Smart Contracts. Ethereum Smart Contracts
can be written in a variety of Turing Complete languages,
the most popular being Solidity [9]. Source code is compiled
into a bytecode representation. The bytecode can then be
deployed using a contract creation transaction. Contracts have
a special constructor function that only runs when the contract
creation transaction is being processed. This function is used
to initialize memory and call other contract code. Miners
execute the bytecode inside the Ethereum Virtual Machine
(EVM). At present, each miner must execute all transactions
for all contracts and hold the current value of all the memory
associated with all of the contracts. The Ethereum community
is actively working on methodologies to scale the Ethereum
network by sharding the blockchain [10].
Ethereum transactions update the state of the distributed
ledger but do not return values. They fall into three categories:
Ether transfer, contract creation, and calling a function on a
contract. Ether transfer transactions move Ether from the user’s
account to another account. Contract creation transactions put
code into the distributed ledger and call the constructor of the
contract code, setting the contract data’s initial state. Function
call transactions call a function on a contract and result in
updated state. Contract creation and function call transactions
also allow Ether to be transferred. All types of transactions
must be signed by a private key corresponding to an account
and include a nonce value that prevents replay attacks. In
addition to Ethereum transactions, “View” function calls can
be executed on the Smart Contract code. These View function
calls return a value and do not update the state of the Smart
Contract.
Executing code and accessing resources, such as memory,
costs certain amounts of “Gas”. The “Gas Cost” of executing
code is closely tied to the real world cost of executing each
type of instruction. Miners preferentially mine transactions that
are prepared to pay a higher “Gas Price”. Accounts instigating
transactions specify the “Gas Price” they are prepared to pay
for their transaction and specify the maximum amount of gas
a transaction can use known as “Start Gas”. This commits
an account holder to paying up to a certain amount of Ether
for the transaction. Any unused gas is returned to the account
holder at the end of the transaction. Transactions that run out
of gas prior to completion are aborted, with all of the gas
being expended.
In the Ethereum public network, “MainNet”, all contract
code and data are readable by any user of any node that
connects to the network. Smart Contracts on Ethereum Main-
Net can only perform permissioning in contract code, limiting
which accounts can update the state of a contract. However,
there is no mechanism to limit which users can read contract
code and data.2) Private Blockchains and Enterprise Ethereum: Private
blockchains are blockchain networks that are established be-
tween nodes operated by enterprises [2]. Only permissioned
nodes belonging to participating enterprises are allowed to join
the private blockchain’s peer-to-peer network and only per-
missioned accounts belonging to participating enterprises are
allowed to submit transactions to the nodes. These blockchains
provide the privacy and permissioning required by enterprises
[11].
The need for security and permissioning features over and
above what is available in standard Ethereum [11] has led to
a range of platforms being developed. J.P. Morgan developed
Quorum [12], a fork of the Golang Ethereum implementation
called Geth [13]. ConsenSys’s Protocol Engineering Group,
PegaSys created Pantheon [14], an Ethereum MainNet com-
patible client that aims to meet the permissioning and privacy
requirements of the Enterprise Ethereum Client Specification
[11]. Hyperledger Fabric [15] is a distributed ledger platform
originally created by IBM and now hosted by The Linux
Foundation. Similar to Quorum and Pantheon, the platform
offers privacy and permissioning features. Whereas Quorum
offers Ethereum based private transactions, Pantheon offers
private smart contracts that are private to a set of participants.
Hyperledger Fabric offers the ability to host one or more
smart contracts on a private blockchain called a “channel”.
Hyperledger Fabric allows multiple channels to be operated on
the one network, thus allowing for multiple sets of private con-
tracts between different sets of participants to operate on the
one network. An analysis of the merits of Hyperledger Fabric
and Quorum has been analysed elsewhere (see Requirements
for Ethereum Private Sidechains [2]).
3) Finality: A block is deemed final when it can no longer
be changed. All transactions contained within a finalised block
are also deemed final.
Ethereum transactions are included in blocks. An Ethereum
MainNet miner that solves the Proof of Work cryptographic
puzzle can add a block to the end of the blockchain. If two
or more miners solve the puzzle simultaneously, then two or
more chains are created with common ancestors, and this is
known as a fork [16]. In Bitcoin the longest chain of blocks
is deemed to be the valid blockchain [17], [18]. In Ethereum,
the fork choice is solved by means of a modified Greediest
Heaviest Observed Subtree (GHOST) protocol [16] that takes
into account the mining power in creating blocks that have
links to the main chain, but have become stale [19]. These
blocks are commonly referred to as uncle blocks. The weight
of a block relates to the number of previous blocks in the chain
and uncle blocks. The heaviest chain of blocks is deemed to be
the valid blockchain. If an Ethereum MainNet miner becomes
aware of a heavier chain than it knew about, it should then
only attempt to add blocks to the new chain. Blocks on the old
heaviest chain that are not in common with the new longest
chain are deemed reordered . If none of the transactions in
a reordered block have been included in the blocks of the
new longest chain, then the block can be included as an uncle
block. Otherwise, the transactions that are not included in the
2
Fig. 1. Pinning
reordered chain need to be included in a new block. There is
no certainty that these transactions will be included in a new
block, or that transactions in a proposed uncle block will be
included in the blockchain.
As more blocks are added to the end of Ethereum Main-
Net’s blockchain, the probability of a miner finding a longer
blockchain and reordering the blockchain is reduced [16]. This
is because a miner would need to repeatedly solve the Proof
of Work cryptographic puzzle for each block faster than all
other miners. As the probability of a block being reordered
is reduced, the probability of the transactions included in a
block being final increases. Hence, Ethereum MainNet is said
to have, probabilistic finality [18].
Consensus algorithms such as Istanbul Fault Byzantine
Tolerant (IBFT) [20] and Istanbul Fault Byzantine Tolerant
version 2 (IBFT2) [21] used in consortium blockchains give
instant finality, where once a transaction has been included in
a block minted by a validator, it can no longer be changed.
4) Pinning: The state of a blockchain or sidechain can be
represented by the Block Hash of a block. The Block Hash of a
final block could be submitted to a contract on a Coordination
Blockchain at regular intervals [4], as shown in Figure 1.
This process is know as pinning . Regularly pinning sidechain
state helps to protect minority sidechain participants from
state reversion due to collusion by the majority of sidechain
participants [4].
B. Cryptanalysis
This section provides background material on cryptanalysis
that is needed to understand the analysis of the security
properties of Ethereum MainNet.
1) Message Digest Algorithm Cryptanalysis: Message di-
gest algorithms have three main security properties: Preim-
age Resistance, Second Preimage Resistance, and Collision
Resistance. Message digest algorithms are commonly called
Cryptographic Hash algorithms, or simply Hash algorithms.
Given a Hash algorithm h, the three security properties can be
stated as:
Preimage Resistance : Given y, it is difficult to determine
xsuch that y=h(x).Second Preimage Resistance : Given yandx1, it is
difficult to determine x2such that y=h(x1) =h(x2)
andx16=x2.
Collision Resistance : It is difficult to determine x1and
x2such that h(x1) =h(x2)andx16=x2.
2) Classical Computing Cryptanalysis: Gordon Moore, co-
founder of Intel, stated in his ”Moore’s Law” that the number
of transistors on an integrated circuit doubles approximately
every two years [22]. With the increased number of transistors
has come a decrease in transistor size, which has resulted in
decreased power consumption per transistor. This has resulted
in an increase in computation power, while keeping the power
consumption relative static over a fifty year period. This rate
of increase of computation power and decrease of transistor
size though slowing, is still continuing [23]. Additionally, new
alternative approaches are being developed to deliver increased
computational power [24].
Classical computational power can be used to break algo-
rithms such as message digest algorithms by trying all possible
combinations using a “Brute Force” attack. Complexity theory
predicts how many attempts are likely to be needed to break
an algorithm. For message digest algorithms, using classical
computing power, the complexity of breaking an algorithm’s
Preimage Resistance or Second Preimage Resistance property
isO(N), where Nis the number of combinations of the digest
output, whereas the complexity of breaking an algorithms
Collision Resistance is O(p
N).
The USA’s National Institute of Standards and Technology
(NIST) defines Security Strength [25] as, “A number associated
with the amount of work (that is, the number of operations)
that is required to break a cryptographic algorithm or system.”
Security Strength and complexity are related. The Security
Strength of a message digest algorithm’s Preimage and Second
Preimage Resistance properties is log2Nand the Collision
Resistance Security Strength is log2p
N. Recall that log2N
corresponds to the message digest output length in bits. As
such, the algorithm SHA-256 ’s Preimage and Second Preim-
age Security Strength is 256-bits and its Collision Resistance
Security Strength is 128 bits, assuming classical computers
[25].
In some instances, a message digest output is truncated.
For example in Ethereum, Keccak-256 is used to generate
account numbers with the output truncated from 256-bits to
160-bits. In this usage, the analysis of Security Strength re-
mains unchanged: the complexity and hence Security Strength
relates to the number of possible values of the digest output. If
a message digest output is truncated then the Security Strength
of the overall algorithm is proportionally reduced.
NIST defines algorithms with Security Strengths of 80, 112,
128, 192, and 256 bits [26]. NIST have mandated the phasing
out of 80-bit Security Strength algorithms in 2010 and, based
on Moore’s Law, had indicated the phasing out of 112-bit
Security Strength algorithms by 2030.
3) Quantum Computing Cryptanalysis: Quantum comput-
ers are expected to allow all currently used popular asymmetric
cryptographic algorithms to be defeated and are expected to
3
reduce the Security Strength of message digest and symmetric
cipher cryptographic algorithms [27]. Aggarwal et al. [28]
estimate that ECC 256-bit schemes will be able to be compro-
mised with a Quantum computer using the Shor algorithm [29]
in less than ten minutes sometime between 2027 and 2040.
Grover’s algorithm [30] provides a speedup for database
search style algorithms, such as searching for a message digest
preimage or second preimage. Using Grover’s algorithm the
complexity of message digest algorithm’s Preimage or Second
Preimage Resistance properties are reduced from O(N)to
O(p
N). This means that the Security Strength assuming a
sufficiently powerful quantum computer is half that when
compared to the Security Strength due to classical computing
power.
Brassard and Tapp [31] claimed to have developed an
algorithm for use with quantum computers that reduces the
complexity of finding message digest collisions to O(3p
N).
Bernstein [32] has refuted this claim, stating that there is
no real advantage provided by Brassard and Tapp’s algorithm
given the cost - performance analysis over classical computing
power. However, Aaronson and Shi [33] have determined a
tight lower bound for the complexity of the collision problem
asO(3p
N). As such, despite Bernstein’s refutation of Brassard
and Tapp’s algorithm, it can be conjectured that another
algorithm may be found that meets the theoretical bound, that
has a better cost - performance metric.
Despite the reduced Security Strength offered by message
digest algorithms, assuming a quantum computer, they are
unlikely to be a point of weakness in the near term. Developing
a complex quantum computer that can defeat message digest
algorithms is expected to be significantly more complex than
developing one to defeat ECC 256-bit [34]. As such, it is likely
that a quantum computer that can be used to attack message
digest algorithms will not be available until at least the 2030s.
4) Algorithmic Weaknesses: Researchers search for weak-
nesses in algorithms. These weaknesses when found can
reduce the effective Security Strength offered by the algorithm.
For example various weakness have been found in the MD-5
message digest algorithm [35] [36]. It is impossible to predict
if a weakness in an algorithm such as Keccak-256 will be
found, and the degree to which the algorithm would be weak-
ened with such a compromise. Algorithmic weaknesses will
not be considered in the analysis of Ethereum MainNet given
the uncertainty as to whether such weakness will be found,
when they will be found, and the impact such weaknesses
might have.
III. E THEREUM MAINNETFEATURES
This section discusses in detail the features of Ethereum
MainNet that are important to its usage as a Coordination
Blockchain.
A. Authentication
The International Telecommunications Union (ITU) define
authentication in X.805 [37] as:...serves to confirm the identities of communicat-
ing entities. Authentication ensures the validity of
the claimed identities of the entities participating
in communication (e.g., person, device, service or
application) and provides assurance that an entity is
not attempting a masquerade or unauthorized replay
of a previous communication.
In the context of Ethereum, this means ensuring Ethereum
transactions are directly attributable to participants who oper-
ate Ethereum Accounts.
Ethereum transactions are signed using the private key
belonging to a participant [8]. The public key associated with
the private key can be derived from the transaction signature of
any transaction signed by the private key. The account number
is the twenty-byte truncated Keccak-256 message digest of
the public key.
In Ethereum, each transaction includes a nonce [8]. The
initial nonce value for each account is zero. The nonce is
incremented for each successfully mined transaction. Miners
reject transactions with out of order or repeated nonces. Doing
this protects Ethereum from transaction replay attacks.
The nonce value is represented as a 64-bit signed number
in Geth [13] and Pantheon [14]. Adding one to the maximum
representable number would result in the largest negative
number. If this situation was not guarded against in the code,
it would lead to unexpected results, and possibly an authen-
tication failure. However, 63-bits is large enough such that
even if a single account issued every transaction on Ethereum
MainNet, and could craft sufficiently small transactions and
could have the gas limit increased such that they could execute
1000 transactions per second, the nonce value would not wrap
around for 584 million years.
Ethereum private keys are 256-bits long. The signature
algorithm ECDSA / Keccak-256 using the secp256k1
curve is used for signing transactions. The secp256k1 curve
has been analysed and found to not have any weaknesses
[38]. This signature algorithm provides 128-bits of Security
Strength [26] assuming Classical Cryptanalysis. The conver-
sion of the public key to an account number using a twenty-
byte truncated Keccak-256 message digest offers 160-bits
of Security Strength assuming Classical Cryptanalysis, as an
attacker would need to exploit the Second Preimage Resistance
property of the message digest function to determine another
public key which could hash to the same value as the authentic
public key. As such, overall the Ethereum signing mechanism
provides 128-bits of Security Strength assuming Classical
Cryptanalysis.
NIST has issued guidance that usage of algorithms offer-
ing 112-bit Security Strength assuming Classical Cryptanal-
ysis should be phased out by 2030 [25]. This means that
Ethereum’s transaction signing technique should be secure
well beyond 2030, assuming Classical Cryptanalysis, given
its 128-bit Security Strength.
If an attacker had access to a sufficiently powerful Quantum
Computer, they could determine private keys associated with
the public keys. The attacker could observe transactions that
4
have been submitted and determine the public keys associ-
ated with each transaction using the standard ecrecover
technique [8]. Once an attacker had access to a private key,
they could issue arbitrary transactions using that private key.
Aggarwal’s [28] analysis indicates that the authenticity of
transactions may be able to be compromised in this way some
time after 2027.
The Ethereum community have recognised the threat that
Quantum Cryptanalysis poses to Ethereum transaction signing.
There are plans to roll-out “Account Security Abstraction”
changes that will authenticate transactions programmatically
using user supplied code [39] [40] [41]. This would allow
for users to choose to use Quantum Cryptanalysis resistant
algorithms.
In summary, the existing transaction authentication tech-
niques are likely to be secure until at least 2027. Prior to
2027, Ethereum is likely to be upgraded to mitigate the
threat of quantum computers, thus ensuring the authenticity
of transactions into the future.
B. Integrity
ITU defines data integrity [37] as:
... ensures the correctness or accuracy of data. The
data is protected against unauthorized modification,
deletion, creation, and replication and provides an
indication of these unauthorized activities.
In the context of Ethereum, this means ensuring that authenti-
cated transactions and data in the distributed ledger are stored
such that they can not be modified.
Ethereum transactions are combined into blocks using
Merkle Patricia trees [8]. Similarly, data in the distributed
ledger is protected using Merkle Patricia trees. Compromising
values in the Merkle Particia trees would require breaking the
Second Preimage Resistance property of Keccak-256 . This
is unlikely to occur in foreseeable future using either Quan-
tum or Classical Cryptanalysis techniques. However, there is
always the possibility that a weakness in Keccak-256 will
be found.
C. Non-Repudiation
ITU defines non-repudiation [37] as:
...provides means for preventing an individual or
entity from denying having performed a particular
action related to data by making available proof
of various network-related actions (such as proof
of obligation, intent, or commitment; proof of data
origin, proof of ownership, proof of resource use).
It ensures the availability of evidence that can be
presented to a third party and used to prove that
some kind of event or action has taken place.
In the context of Ethereum, this means ensuring that authenti-
cated transactions are stored such that they can not be revoked.
Ethereum blocks are linked together using Keccak-256
message digests. Compromising this linkage would require
breaking the Preimage Resistance property of Keccak-256 ,
which is unlikely to occur in foreseeable future.As discussed in Section II-A3, Finality, Ethereum Main-
Net has probabilistic finality . When blocks are added to
the blockchain after a block containing a transaction, the
probability of a miner proposing a heavier chain that does
not include the block decreases. The number of blocks added
after a block is known as the number of block confirmations .
Nakamoto [17] showed the probability of a Bitcoin block
being removed after six blocks, assuming an attacker has 10%
of the mining power was 0.00024 . A greater number of block
confirmations should be observed if an attacker were assumed
to have a greater percentage of the total mining power available
to them, or if the user wished to have greater certainty that
the block was not going to be removed.
In 2016, Gervais [42] determined that 37 Ethereum MainNet
block confirmations were needed to offer the same level of se-
curity as six Bitcoin block confirmations, assuming Ethereum
was being attacked with 30% of mining power. Since 2016, the
mining power devoted to Ethereum has increased considerably
such that a 30% attack now seems inconceivable. Major miners
are unlikely to attack their own network as this would risk
devaluing the cryptocurrency they are mining [43], [44]. The
maximum hash power which can be rented in a straightforward
way is 5% [45]. Purchasing hardware to generate 30% hash
power (174TH/s [46]) would cost in excess of US$400 million
[47].
Scaling the results of Gervais’s work [42] based on the
changed mining rewards of Bitcoin and Ethereum, the changed
valuations, and allowing for a 10% mining power attack,
indicates that eight Ethereum block confirmations corresponds
to six Bitcoin confirmations. Using a different methodology,
Buterin [48] determined that six to twelve confirmations where
required to deem a transaction final, depending on the level of
risk a user was prepared to assume.
Based on a fourteen second target block time and assuming
twelve confirmations, a block on Ethereum MainNet could
be deemed final in approximately three minutes. The finality
time is not a precise number as the block time is randomly
distributed with an average of fourteen seconds.
When Ethereum MainNet client vendors and miners agree
to changes in the Ethereum protocol, the system is updated
via changes known as Hard Forks . A Hard Fork requires all
Ethereum MainNet client vendors to release updated software
which will activate new functionality at a certain Ethereum
MainNet block number. For the Spurious Dragon Hard Fork
in November 2016 [49] the changes were implemented slightly
differently. This resulted in the Ethereum MainNet blockchain
forking for some hours [50]. The fork is resolved once the
vendors software has been corrected. However, it is possible
that a transaction which was part of a block accepted into the
fork which was discarded was reverted and not resubmitted to
the blockchain. This type of forking and state reversion due
to mismatched feature implementation is much less likely to
occur now and in the future than it did in 2016 as Ethereum
MainNet clients undergo significantly more review and testing
than they did in 2016 [51], [52].
If an attacker could dedicate 51% of the total mining power
5
to attacking the network, they would be able to mount a
51% Attack [53]. This would allow the attacker to rewrite the
history of the blockchain. The three largest Ethereum MainNet
miners could collude to mount such as attack. However, these
miners are disincentivized to do such an attack as this would
adversely affect confidence in Ethereum MainNet. This would
lead to a dramatic drop in the value of Ether [43], [44],
substantially decreasing the value of their Ether and their
Ethereum infrastructure investments.
Though the Ethereum MainNet system typically can not be
modified, after a re-entrancy bug was exploited in the DAO
attack [54], the system was modified to reverse the results
of the attack. Doing this caused some to question trust in
blockchain systems and Ethereum MainNet in particular [55].
However, this type of irregular state change [56] to reverse
the results of such an attack appear unlikely to occur again
in Ethereum MainNet. Despite a bug in the Parity Wallet
contract that resulted in hundreds of millions of dollars of
funds becoming inaccessible, proposals to alter history to
restore the funds have been refused [57] [58].
D. Crypto Economic Anti-Spam
As described in Section II-A1, each transaction on Ethereum
MainNet costs Gas to execute, which participants pay for
with Ether. Ethereum MainNet currently aims to produce new
blocks each 14 seconds with eight million Gas available for
each block [46]. Each transaction has as a minimum cost,
the transaction fee, that is currently 21,000 Gas. Simple
balance transfers between accounts just cost the transaction
fee, whereas complex function calls can cost more than a
million gas. As the block gas limit is eight million, it means
that no transaction can use more than eight million gas.
This translates to Ethereum MainNet supporting between four
transactions per minute and twenty-seven transactions per
second. A typical simple transaction, adding a Pin to a pinning
contract , costs 64972 Gas [4]. Given the eight million Gas
limit, 8.8 of these transactions could execute per second.
Participants are disincentivized from flooding the network
with transactions as each transaction has an economic cost.
The cost of Gas depends on the block utilisation [59]. His-
torically, the Gas price has spiked high when block utilisation
has been high [60]. If many entities attempted to issue adding
a Pin to a pinning contract transactions regularly, such that
the block utilisation was high, then the cost of issuing the
adding a Pin to a pinning contract transactions would increase.
This would incentivise the entities to find alternatives, such as
reducing the frequency of submitting the transactions.
E. Summary
Based on the analysis in this section, it can be said that
Ethereum MainNet contains transactions for which the authen-
ticity and integrity is certain. Once twelve blocks have been
appended to the block containing a transaction, the probability
of the blockchain being reorganised such that the transaction
is reverted is small. As such, Ethereum MainNet offers strongnon-repudiation properties. Ethereum’s Gas mechanism oper-
ates as an effective anti-spam tool.
IV. E THEREUM PRIVATE SIDECHAINS
Ethereum Private Sidechains are Ephemeral, On-demand,
Permissioned, Private, Confidential, blockchains that allow for
Atomic Crosschain Transactions. They are Ephemeral in that
they are created, they operate, and then they can be archived
when they are no longer needed. Their On-demand nature
allows them to be created when needed between parties that
have no prior relationship. Permissioning ensures that only
authorised nodes are able to join a sidechain. Their design is
such that to the greatest extent possible, their membership and
their transactions are kept Private. Confidentiality is ensured
by encrypting the sidechain data when being communicated
between nodes and stored on nodes. Atomic Crosschain Trans-
actions enable transactions that update state across sidechains
atomically.
Ethereum Private Sidechains have been described in terms
of their requirements [2], and aspects of their technology [3],
[4], [5]. This paper is the first to present this technology holis-
tically. Additionally, this paper introduces the idea of pinning
the final state of a sidechain prior to archiving, thus allowing
the sidechain to be reinstated if needed, and introduces the
idea of using Ethereum MainNet gas pricing as a mechanism
for rate control of Atomic Crosschain Transactions.
A. Ephemeral
Ethereum Private Sidechains are Ephemeral : they are cre-
ated, they are used for a period, and then archived when they
are no longer required. This limited lifespan matches many
real world requirements, such as Letters of Credit and other
business deals, which have a limited lifespan. The ability
to archive the blockchain data in a sidechain is in contrast
to existing blockchain technologies that are designed to be
operational indefinitely.
The life span of a sidechain could vary widely. For us-
ages in which sidechains are used to deploy a contract and
automatically negotiate a deal, it might only be needed for
some minutes, hours or days. Other usages, such as an Oracle ,
require a long or indefinite lifespan. Indefinite lifespans can
be accommodated by never archiving the sidechain.
While a sidechain is operational, the sidechain could be
pinned to a Coordination Blockchain at regular intervals [4].
Regularly pinning sidechain state helps to protect minority
sidechain participants from state reversion due to collusion by
the majority of sidechain participants [4].
A key aspect of Ephemeral sidechains is the requirement
to be able to restart the sidechain after archiving. This can
be achieved by pinning the last block of the sidechain to a
Coordination Blockchain. Now that the Block Hash of the last
block has been securely stored in the Coordination Blockchain,
the state of the sidechain can then be stored offline. To restart
the sidechain, the stored data is compared against the final
Block Hash to confirm the correct state is being used to restart
the sidechain.
6
B. On-demand Between Parties with No Prior Relationship
Ethereum Private Sidechains need to be able to be deployed
between parties that have no prior relationship. That is, the
parties need to be able to establish a sidechain without
knowing each others’ node IP addresses, cryptographic keys,
or other information required to set-up a secure connection.
Establishing sidechains in this dynamic way is in contrast
to existing permissioned blockchains that are largely static
systems that require complex set-up. For example, set-up of a
Quorum [12] network requires enode addresses (IP addresses
and Ethereum account numbers) for each node to be shared out
of band with all other nodes. Adding new nodes to the network
requires this sharing and manual intervention on each node.
The on-demand sidechain establishment is analogous to
a user of a web browser establishing a secure connection
with a web server by simply entering in a URL such as
https://example.com/. The user does not know the IP address of
the computer corresponding to example.com or the public key
that can be used to verify the communications emanating from
example.com . However, using the domain name, some initial
trust, and the Domain Name Service (DNS) and Transport
Layer Security (TLS) protocols, they are able to establish
a secure connection. Similarly, Ethereum Private Sidechains
need to be able to establish a secure sidechain using just
domain names.
The Ethereum Registration Authorities system is a set of
smart contracts that can be used to provide discoverable
information to enable establishment of sidechains between
organisations with no prior relationship [3]. A Coordination
Blockchain could be used to locate the information using
domain names that can be grouped according to differ-
ent trust levels and different trust relationships. Moreover,
a Coordination Blockchain that provides organisations with
a secure, decentralized, censorship-resistant mechanism for
storing information that can be located using domain names
and grouped according to different trust levels and different
trust relationships would overcome the limitations of previous
technologies that did not provide the security and censorship
resistance properties that users of blockchain technologies
expect.
C. Permissioned
Ethereum Private Sidechains need to be operated by au-
thorised nodes using authorised Ethereum accounts. These
requirements match those of the Enterprise Ethereum Client
Specification [11]. The implementation of these requirements
do not use a Coordination Blockchain.
D. Private
Ethereum Private Sidechains should, to the greatest extent
possible, keep their membership private from other sidechains
they interact with and from any Coordination Blockchains they
use to facilitate their actions.E. Confidential
Ethereum Private Sidechains should encrypt their
blockchain and state data such that the transaction information
is kept confidential, both when it is communicated between
nodes on a sidechain and when it is stored in a node’s local
data store. The implementation of this feature does not use a
Coordination Blockchain.
F . Atomic Crosschain Transactions
Ethereum Private Sidechains technology needs to enable
transactions that update state across sidechains atomically
[5]. That is, if an Atomic Crosschain Transaction is across
sidechains A, B, and C, then the state updates related to
the transaction are either applied on all sidechains or ignored
on all sidechains. A Coordination Blockchain holds a Cross-
chain Coordination Contract. This contract is used to indicate
that an Atomic Crosschain Transaction has commenced, has
been committed, or should be ignored. The contract acts as
a common time-out reference for all sidechains and helps
prevent denial of service attacks. The data in the Crosschain
Coordination Contract needs to be available until the last
sidechain using it has been archived.
The Atomic Crosschain Transaction system uses threshold
signatures to prove values across sidechains. The public key
that corresponds to the private key shares held by each of the
sidechain validators is known as a Sidechain Public Key. This
key needs to be available to all sidechains that need to verify
values coming from a sidechain. As such, this value should
be stored on a Coordination Blockchain. The Sidechain Public
Key needs to be re-generated and uploaded to the Coordination
Blockchain each time a validator is added or removed from
the sidechain. Assuming that sidechain membership is largely
static, this regeneration and upload is likely to be a rare event.
V. P ROS AND CONS OF USING ETHEREUM MAINNET AS A
COORDINATION BLOCKCHAIN
The subsections below analyse the advantages and dis-
advantages of using Ethereum MainNet as the Coordina-
tion Blockchain for the operations of an Ethereum Private
Sidechain. The findings of the subsections are summarised in
Table I.
A. Private Node Discovery - Ethereum Registration Authori-
ties
The Ethereum Registration Authorities system [3] uses
smart contracts on a Coordination Blockchain to enable
discovery of sidechain node address and cryptographic key
information, as described in Section IV-B. As the information
is used to bootstrap a sidechain, it is fundamental to the entire
Ethereum Private Sidechain system that this information is
authentic.
The data in the Ethereum Registration Authority smart
contracts is largely static. That is, the IP address and crypto-
graphic key information, once set, changes rarely. Given this
largely static data, the economic cost of storing information on
Ethereum MainNet would only be incurred rarely. It is likely to
7
Ethereum Private Sidechain Ethereum MainNet as Coordination Blockchain
Operation Advantages Disadvantages
Discover using Ethereum Good authenticity, integrity, and
Registration Authorities non-repudiation properties.
Permissionless, public network,
enables discovery.
State Pinning & Good authenticity, integrity, and Economic cost.
Final State Pinning non-repudiation properties. Increased congestion on
Ethereum MainNet.
Pinning and disputes are
public.
State Pinning & Leverage Ethereum MainNet Pins take more time to become
Final State Pinning security properties while final on Ethereum MainNet
via an intermediate minimising cost and congestion. than if pinned directly.
private blockchain Pinning and disputes are not Sidechain participants must
public. observe all levels of pinning.
Sidechain Public Key Public keys widely available. Significantly delays when first
Atomic Crosschain
Transactions can be issued.
Atomic Crosschain Leverages Ethereum MainNet Significantly delays when first
Transaction State anti-spam capabilities. Atomic Crosschain
Transactions can be issued.
Economic gas cost.
Increased congestion on
Ethereum MainNet.
TABLE I
ADVANTAGES AND DISADVANTAGES OF USING ETHEREUM MAINNET AT COORDINATION BLOCKCHAIN FOR ETHEREUM PRIVATE SIDECHAINS
cost less that US$1.00 to set-up an enterprise in the Ethereum
Registration Authority system on Ethereum MainNet, based
on current prices [3].
Sidechain users who wish to establish a sidechain need to
be able to access the bootstrap information stored in Ethereum
Registration Authority smart contracts for the system to be
useful. The information needs to be stored on a permissionless
network or a permissioned network that has a black list of
banned nodes. Doing this allows users who have no prior
relationship with the operators of the Coordination Blockchain
to access the information.
B. State Pinning
A private blockchain state pinning approach should be
used to prevent state reversion as described in Section IV-B.
Posting Pins to Ethereum MainNet leverages the authenticity,
integrity and non-repudiation properties of Ethereum MainNet.
However, submitting transactions costs money. Pinning once
per hour for a year would cost US$508 [4]. Additionally, if
many sidechains pinned to Ethereum MainNet simultaneously,
it would cause transaction congestion. Another issue with
pinning directly to Ethereum MainNet is that any disputes
that occur would need to occur on Ethereum MainNet, thus
making the participant list of the sidechain public.
Pins could be posted directly to a smart contract on
Ethereum MainNet, or could be posted via a smart contract
on an intermediate blockchain using a hierarchical pinning
approach [4]. Using a hierarchical pinning approach, many
private blockchains could treat another private blockchain
as a Coordination Blockchain posting Pins to it. This pri-
vate blockchain could in turn post Pins to another private
blockchain or to Ethereum MainNet. This is shown diagram-
matically in Figure 2. Pinning to a hierarchy of Coordination
Fig. 2. Hierarchical Pinning
Blockchains in this way means that only a small number of
Pins on Ethereum MainNet could be used to secure a large
number of private blockchains. The cost of submitting Pins to
the private blockchain could be either free or significantly less
than Ethereum MainNet.
A benefit of pinning directly to Ethereum MainNet, rather
than via an intermediate blockchain, is that the pinned state
becomes final faster. That is, if a Pin is posted to a pri-
vate blockchain, whose state is in turn pinned to Ethereum
MainNet, then the sidechain Pin could be deemed to become
final only once the private blockchain in pinned to Ethereum
MainNet.
Posting Pins via a private blockchain significantly reduces
the cost of pinning, as only one blockchain needs to sub-
mit transactions to pin its state to Ethereum MainNet, and
sidechains can pin to that private blockchain. Doing this
reduces the number of transactions on Ethereum MainNet, thus
reducing congestion, and means that the cost of submitting
transactions is only incurred once for the private blockchain,
8
rather than once for each sidechain.
A disadvantage of posting Pins via a private blockchain
is that participants of the sidechain need to observe and
be ready to challenge Pins being posted at each level of
the hierarchy. If sidechain state Pins are posted directly to
Ethereum MainNet, then the sidechain participants only need
to observe the pinning contract on Ethereum MainNet.
An additional benefit of pinning to a private blockchain is
that the chain’s permissioning could be set such that only
certain nodes could view the blockchain and only certain
accounts could submit transactions to the blockchain. Pinning
directly to Ethereum MainNet means that the organisation
pinning to the contract is public. If there is a dispute, then
masked participants will need to unmask themselves, and thus
link themselves to the sidechain and the other organisations
on the sidechain. If an intermediate blockchain was used, then
the pinning and any disputes could happen in a more private
setting.
C. Final State Pinning for Archiving
Final State Pinning is the same as State Pinning, with the
exception that rather than the pinning being on an ongoing
basis, it is just to pin the final state of a sidechain prior
to archiving, as described in Section IV-B. As such, the
advantages and disadvantages are similar to those described in
the previous section. As only one pin is posted, the concerns
over having to observe pins on a private blockchain in addition
to Ethereum MainNet are not significant as the observation is
for a single event. Similarly, concerns over cost of posting
pins to Ethereum MainNet and congestion are reduced. As
such, the advantages are reduced to the pin becoming final
sooner and the disadvantages are reduced to any dispute over
the value of the pin being public.
D. Sidechain Public Keys
As described in Section IV-F, the Atomic Crosschain Trans-
actions feature needs Sidechain Public Keys to be stored on a
Coordination Blockchain. The Sidechain Public Keys need to
be stored in a contract [5] that allows voting on new public
keys, and allows masked and unmasked participants. Given the
participants are the same as those for the pinning scheme, it
makes sense for these to be stored in the same contract as the
pinning information. Keeping the logic in the same contract
for pinning and holding the Sidechain Public Keys is useful
as it means that membership changes need to only occur in
one contract. However, the Sidechain Public Keys need to be
visible by all sidechains that wish to verify information coming
from the sidechain, whereas the pinning information need only
be visible by sidechain participants and government regulators
who would be appealed to in case of dispute.
Given the Sidechain Public Key is likely to be set once
only, the economic cost of storing the key is likely to only
be incurred once. No analysis of the gas cost of setting a
Sidechain Public Key has been undertaken yet. However, given
the small size of the public keys, 48bytes, the incremental
gas cost of storing the public key is likely to be in the order of60,000 Gas, assuming the voting infrastructure has already
been set-up. However, if the voting infrastructure did need to
be set-up, the gas cost could be much larger.
If a sidechain was short lived, then incurring the cost of
setting up the voting infrastructure and posting the Sidechain
Public Key to Ethereum MainNet could be deemed consid-
erable. However, if the sidechain was long lived, then this
relative cost might not be deemed as significant.
A disadvantage of using Ethereum MainNet to hold
Sidechain Public Keys is transactions take at least 12blocks
before they should be deemed final (see Section III-C). This
means that, given a target block time of fourteen seconds,
users could not use the Sidechain Public Keys for Atomic
Crosschain Transactions for three minutes after the transaction
that posts the Sidechain Public Key is included in a block on
Etheurum MainNet.
E. Atomic Crosschain Transaction State
The Atomic Crosschain Transactions capability described
in Section IV-F uses a Crosschain Coordination Contract
to control when a crosschain transaction has started, been
committed, or should be ignored. This information need to
be available to all validators on all sidechains involved in the
crosschain transaction. The information in the contract needs
to be available until the last sidechain using the contract is
archived.
Storing the Atomic Crosschain State on Ethereum MainNet
means that each Atomic Crosschain Transaction costs money
to execute. This economic cost could be seen as an advantage,
as it provides an anti-spam control external to the sidechain
system. However, forcing enterprises to incur a cost for each
crosschain transaction is likely to be viewed as an unnecessary
cost.
Additional issues with storing the Atomic Crosschain State
on Ethereum MainNet is that this would leak the participants
of a sidechain, as a transaction would need to be submitted
linking the sidechain and the participant. Furthermore, this
would leak the rate that the participant was issuing crosschain
transactions.
In a similar way that storing Sidechain Public Keys on
Ethereum MainNet delays when the first Atomic Crosschain
Transaction can be issued, as discussed in Section V-D, storing
Atomic Crosschain Transaction State could delay the effective
start of each transaction. This is because sidechain participants
might want to wait for blocks that contain transactions that
indicate the Atomic Crosschain Transaction start to be final
prior to acting on the start indication.
VI. C ONCLUSION
Coordination Blockchains perform various coordination
tasks in private blockchain systems. We used Ethereum Private
Sidechains as an exposition of such a system, highlighting the
features of Ethereum Private Sidechains and discussing each
feature’s need to leverage a Coordination Blockchain. Based
on the unique requirements of each feature and coordination
9
activity, we examine whether public Ethereum MainNet would
be a suitable platform for each of those tasks.
We found that Ethereum Registration Authority smart con-
tracts of Ethereum Private Sidechains need to store long term
data that have to be available in a permissionless blockchain.
Ethereum MainNet would therefore be well suited to this task,
as it is a permissionless blockchain that incentivises good
behaviour using crypto economics, and provides good au-
thenticity, integrity, and non-repudiation properties. Ethereum
MainNet’s strong security properties are also useful for State
Pinning and in particular Final State Pinning, where the data
needs to be stored securely for long periods of time. However,
pinning directly to Ethereum MainNet could lead to congestion
on Ethereum MainNet, would incur high costs, and would
lead to the membership of a sidechain becoming public in
the case of a dispute over the value of a Pin. These issues are
significantly reduced by pinning via an intermediate private
blockchain. However, doing this introduces other issues, such
as participants having to observe pinned values at multiple
levels in the pinning hierarchy and the pinned values taking
longer to become final. Ethereum MainNet is not an appro-
priate location for Coordination Blockchain information that
needs to be final quickly, such as Sidechain Public Keys and
Atomic Crosschain Transaction State.
ACKNOWLEDGMENTS
This research has been undertaken whilst I have been
employed full-time at ConsenSys and have been completing
my PhD part-time at University of Queensland. I acknowledge
the support of my PhD supervisor Dr Marius Portmann. I
thank Dr Catherine Jones, Horacio Mijail Anton Quiles, David
Hyland-Wood, and Sandra Johnson for reviewing this paper
and providing astute feedback.
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11
|
{
"id": "1906.04421"
}
|
1902.10865
|
A Survey on Applications of Game Theory in Blockchain
|
In the past decades, the blockchain technology has attracted tremendous
attention from both academia and industry. The popularity of blockchain
networks was originated from a crypto-currency to serve as a decentralized and
tamperproof transaction data ledger. Nowadays, blockchain as the key framework
in the decentralized public data-ledger, has been applied to a wide range of
scenarios far beyond crypto-currencies, such as Internet of Things (IoT),
healthcare, and insurance. This survey aims to fill the gap between the large
number of studies on blockchain network, where game theory emerges as an
analytical tool, and the lack of a comprehensive survey on the game theoretical
approaches applied in blockchain related issues. In this paper, we review game
models proposed to address common issues in the blockchain network. The issues
include security issues, e.g., selfish mining, majority attack and Denial of
Service (DoS) attack, issues regard mining management, e.g., computational
power allocation, reward allocation, and pool selection, as well as issues
regarding blockchain economic and energy trading. Additionally, we discuss
advantages and disadvantages of these selected game models and solutions.
Finally, we highlight important challenges and future research directions of
applying game theoretical approaches to incentive mechanism design, and the
combination of blockchain with other technologies.
|
http://arxiv.org/pdf/1902.10865v2
|
Ziyao Liu, Nguyen Cong Luong, Wenbo Wang, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim
|
cs.GT
|
cs.GT
|
1
A Survey on Applications of Game Theory in
Blockchain
Ziyao Liu, Nguyen Cong Luong, Wenbo Wang, Member, IEEE, Dusit Niyato, Fellow, IEEE, Ping Wang, Senior
Member, IEEE, Ying-Chang Liang, Fellow, IEEE, and Dong In Kim, Fellow, IEEE
Abstract —In the past decades, the blockchain technology has
attracted tremendous attention from both academia and industry.
The popularity of blockchain networks was originated from a
crypto-currency to serve as a decentralized and tamperproof
transaction data ledger. Nowadays, blockchain as the key frame-
work in the decentralized public data-ledger, has been applied to
a wide range of scenarios far beyond crypto-currencies, such as
Internet of Things (IoT), healthcare, and insurance. This survey
aims to fill the gap between the large number of studies on
blockchain network, where game theory emerges as an analytical
tool, and the lack of a comprehensive survey on the game theoreti-
cal approaches applied in blockchain related issues. In this paper,
we review game models proposed to address common issues in the
blockchain network. The issues include security issues, e.g., selfish
mining, majority attack and Denial of Service (DoS) attack, issues
regard mining management, e.g., computational power allocation,
reward allocation, and pool selection, as well as issues regarding
blockchain economic and energy trading. Additionally, we discuss
advantages and disadvantages of these selected game models and
solutions. Finally, we highlight important challenges and future
research directions of applying game theoretical approaches to
incentive mechanism design, and the combination of blockchain
with other technologies.
Index Terms —Blockchain, game theory, security, mining man-
agement.
I. I NTRODUCTION
In the past decade, with the popularity of digital crypto-
currencies, e.g., Bitcoin [1], blockchain technology has at-
tracted tremendous attention from both academia and industry
[2]. The blockchain was first proposed in [1] to serve as a
crypto-currency transaction ledger, and is currently widely
adopted for a large number of crypto-currencies, such as
Ethereum [3], Ripple [4], and EOS [5]. The blockchain
technology guarantees the tamperproof ledger, transparent
transactions, and trustless but secure tradings in a decentralized
network. Thus, the blockchain network is recently applied in
a wide range of scenarios far beyond crypto-currencies, such
as Internet of Things (IoT) [6], healthcare [7], and insurance
[8]. In general, blockchain is a distributed public data-ledger
maintained by achieving the consensus among a number of
Ziyao Liu, Nguyen Cong Luong, Wenbo Wang, and Dusit Niyato
are with the School of Computer Science and Engineering, Nanyang
Technological University, Singapore 639798 (email: ziyao002@ntu.edu.sg,
clnguyen@ntu.edu.sg, wbwang@ntu.edu.sg, dniyato@ntu.edu.sg).
Ping Wang is with the Department of Electrical Engineering & Computer
Science, Lassonde School of Engineering, York University, 4700 Keele St.,
LAS 2016 Toronto, ON M3J 1P3, Canada (email: pingw@yorku.ca).
Y .-C. Liang is with Center for Intelligent Networking and Communications
(CINC), University of Electronic Science and Technology of China, Chengdu,
China. (email: liangyc@ieee.org).
D. I. Kim is with Department of Electrical and Computer Engineering,
Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea
(email: dikim@skku.ac.kr).nodes in a Peer-to-Peer (P2P) network. More specifically, the
verified transaction data is stored in a chain of blocks, i.e.,
a basic data structure of blockchain, and the chain grows
in an append-only manner with all new verified blocks to
it. This process involves several operations such as verifying
transactions, disseminating blocks, and attaching blocks to the
blockchain.
As such, the blockchain requires a number of consensus
nodes to participate in the network. The rational nodes per-
form actions or strategies that aim to maximize their own
utility. Moreover, the malicious nodes may launch attacks
that damage the blockchain networks. To address these secu-
rity challenges, consensus protocols such as Byzantine Fault
Tolerance (BFT) protocol [9] can be adopted. However, the
consensus protocols require a centralized permission controller
and only achieve the consensus among a very small group
of nodes. Such a consensus protocol is thus not applicable
to the blockchain network that is a decentralized and large-
scale system. Different optimization approaches and solutions,
e.g., a Markov Decision Process (MDP) [10], are used to
analyze and optimize strategies of the blockchain nodes to
prevent their misbehaviors. However, the optimization ap-
proaches do not take into account the interactions among the
nodes. Recently, game theory [11] has been applied as an
alternative solution in the blockchain network. Game theory
is a study of mathematical models of strategic interaction
between rational decision-makers [12]. Thus, game theory can
be used to analyze the strategies of the consensus nodes as well
as the interactions among them. Through the game theoretical
analysis, the nodes can learn and predict mining behaviors1of
each other, then having optimal reaction strategies based on
equilibrium analysis. Moreover, game theory can be utilized to
develop incentive mechanisms that discourage the nodes from
executing misbehaviors or launching attacks. As such, game
theory is natural in the decision making of all the consensus
nodes in the blockchain networks.
Currently, there are some surveys related to the blockchain.
However, the existing surveys do not discuss the applications
of the game theory in the blockchain. In particular, the survey
in [13] provides a comprehensive introduction of bitcoin net-
work, the surveys in [14], [15], [16] present security and pri-
vacy issues in the bitcoin network, the survey in [17] presents
the blockchain applications on Internet of Things (IoT), the
survey in [18] discusses the integrations of blockchain and
edge computing. To the best of our knowledge, there is no
survey specifically discussing the use of game theory, as an
1In blockchain systems which incentive nodes to participate in the con-
sensus process of data record with digital tokens, the consensus nodes are
frequently referred as block miners and their operations are referred as mining.
Copyright c
2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future
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2
efficient analysis tool, in blockchain networks. This motivates
us to deliver the survey with the comprehensive literature
review on the game models in the blockchain network. For
convenience, the related works in this survey are classified
based on issues in the blockchain network. The major issues
consist of (i) security issues such as selfish mining attacks
and Denial-of-Service (DoS) attacks, (ii) mining management
issues such as computational power allocation, fork chain
selection, pool selection, and reward allocation, and (iii)
applications atop the blockchain such as energy trading.
The rest of this paper is organized as follows. Section
II briefly describes the general architecture of blockchain.
Section III presents the fundamentals of game theory and game
models that are commonly used in blockchain. Section IV
discusses applications of game theory for security issues in
blockchain. Section V presents applications of game theory for
the mining management in blockchain. Section VI discusses
applications of game theory atop blockchain platforms. Section
VII outlines challenges and future research directions. Section
VIII summarizes and concludes the paper.
II. O VERVIEW AND FUNDAMENTALS OF BLOCKCHAIN
In this section, we give an overview of blockchain on
its concepts, data organization, working mechamism, and
incentive compatibility.
A. Overview of Blockchain
The blockchain was first proposed as a decentralized tam-
perproof ledger which records a set of transactions. These
transactions are verified through a decentralized consensus
process among the trustless agents before attaching to the
chain. Here, we summarize the key advantages that blockchain
networks can offer as follows.
Decentralized network: Due to the distributed network
which allows every computing unit to utilize its compu-
tational power to take part in the blockchain, and that each
transaction in the blockchain must achieve the agreement
among all the nodes through the consensus protocol, the
monopoly in centralized network can be removed in the
blockchain.
Tamperproof ledger: The cryptographic techniques used
in blockchain ensure that any change on the transaction
data in blockchain can be observed by all the nodes in
the network. This means that the transaction recorded in
the blockchain cannot be altered and tampered, unless the
majority of nodes are compromised.
Transparent transaction: All the transactions in the
blockchain can be traced back for verification, and
these transactions are transparent to all the nodes in the
blockchain network.
Trustless but secure trading: By using the digital asym-
metric key signature, the blockchain network guarantees
that only the sender and receiver which possess the pair
of asymmetric key can execute the transaction, without
intervention of any trust third-party.B. Data Organization and Workflow of Blockchain
Cryptographic data organization plays an extremely impor-
tant role in the blockchain structure. We first introduce some
basic components supporting the data organization within
blockchain networks.
Transaction: Transaction is the most basic component of
blockchain. A transaction is proposed by the blockchain
user and is composed of the transaction data which
specifies the value in concern, e.g., the digital tokens in
a crypto-currency, the addresses of the sender and the
receiver, as well as the corresponding transaction fee [1].
Block: A block is composed of a block header and a
certain amount of transactions. The block header specifies
the hash pointer and merkle tree data structure.
Hash pointer [13]: The hash pointer of the current block
contains the hash value associated with the previous
block, which also contains the hash pointer to the block
before that one. Thereby, the hash pointers can be used
to build a link of records, i.e., blockchain.
Merkle Tree [19]: A merkle tree or hash tree is a tree
in which each leaf node is marked by the hash value of
the transaction data of a block, and those non-leaf nodes
are marked by the hash value of the concatenation of its
child nodes. This structure makes it impossible to tamper
the data in blockchain privately.
Block 1
Header
Prev Hash
NounceBlock 0
(Genesis)
Header
Nounce
Transaction Data...
Hash 01
Hash 0 Hash 1
Tx0Merkle Root
Hash 23
Hash 2 Hash 3Transaction DataBlock 2
Header
Prev Hash
Nounce
Transaction DataBlock 3
Header
Prev Hash
Nounce
Transaction Data
Tx1 Tx2 Tx3
Fig. 1: An illustrative example of blockchain data structure
where the transactions are included in the block and the block
is represented by a merkle root.
As shown in Fig. 1, a typical blockchain is an appending-
only, ever-growing list of blocks, which are linked sequentially
using the hash pointers as a linear linked list. More specifically,
the block header includes a hash pointer which is associated
with the previous block, and transaction data is represented as
merkle trees.
Atop the basic cryptographic data organization, maintaining
the blockchain network needs blockchain nodes to dissem-
inate the transaction, store the data into blocks, verify the
transaction, and eventually reach a consensus. The blockchain
working mechanism works as follows (see Fig. 2).
An initiated transaction is broadcast to the distributed
network through a node.
The nodes in the blockchain verify the transaction as well
as the node which broadcasts the transaction.
More than one node may bundle different subset of
newly verified transactions into their candidate blocks and
broadcast them the to the entire network.
3
Consensus Nodes Blockchain Network Blockchain Users
Hash of k
Transaction Data
BlockHash of k-1
...Block 0
Block k-1...
Block k
Local ReplicaInitiating
TransactionsBroadcasting and
Verfying TransactionsMining for Block
ValidationUpdating
BlockchainBuilding Blocks
Fig. 2: An overview of the blockchain workflow.
All or part of the nodes in the blockchain network
participate in the block validation by executing some
certain functions defined by the consensus protocol.
The verified block is attached to the blockchain, and every
node updates its local replica, i.e., the local views of
whole ledger-data, of the blockchain.
In general, not all the nodes can be authenticated to join
the blockchain network to execute the consensus protocol.
According to the access control scheme [20] that determines
which node can join the network, the blockchain platforms are
classified into permissionless schemes, i.e., public blockchains,
and permissioned schemes including private and consortium
blockchains. When choosing the permissioned access control
scheme, e.g., Hyperledger fabric [21], the consensus needs to
be reached among only a small group of authenticated nodes,
and thus the permissioned blockchain network usually adopts
BFT protocols, e.g., Byzantine Paxos [22]. On the contrary, in
permissionless blockchain, e.g., Ethereum [3], any node can
participate in the network, and some other consensus protocols
are applied, such as Proof of Work (PoW) and Proof of Stake
(PoS). Here we list some widely-used blockchain platforms
and their consensus protocols in Table I.
TABLE I: Some Widely-used Blockchain Platforms
Platform Name Ledger type Consensus Protocol
Bitcoin [1] Public Proof of Work (PoW)
Ethereum [3] Public PoW & Proof of Stake
Hyperledger Fabric [21] Consortium Pluggable algorithm
EOS [5] Private Delegated Proof of Stake
Stellar [23] Public & Private Stellar consensus protocol
Quorum [24] Private Majority voting
Ripple [4] Private Probabilistic voting
C. Incentive Compatibility within Blockchain
In blockchain network, the consensus protocol guarantees
achieving the agreement among the nodes. A reliable consen-
sus protocol needs to satisfy properties [25]. (i) Correctness:
each node adopts the content and the order of transactions
in the confirmed blockchain structure. (ii) Consistency: each
node updates its local blockchain structure if a new block
header is confirmed. (iii) Traceability: all transactions can
be traced back for confirmation. However, in some case,
disagreements may exist among the nodes. For example, the
local blockchain replica of all the nodes are unable to be
synchronized simultaneously due to the distributed network.
Under this case, the nodes might maintain different blockchain
ledgers, and thereby the fork chains appear. This means that
the nodes may deviate from the protocol of maintaining thelongest chain2. Therefore, the blockchain consensus protocol is
expected to be incentive compatible [25]. This means that any
node will suffer from financial loss, e.g., waste of investment
in mining machine, whenever the node deviates from the
protocol.
Currently, the most popular blockchain consensus protocol
is the PoW-based Nakamoto consensus protocol [1]. The
Nakamoto protocol achieves the consensus by solving a math-
ematical puzzle, i.e., by finding a hash value which satisfies
a certain condition. The first node that solves the puzzle can
broadcast the verified block to the blockchain network, and
obtains the reward and the transaction fee. This process of
solving puzzle and obtaining the reward is called mining. The
design of the mining mechanism relies on both cryptography
[26] and game theory [12].
Although the PoW protocol is widely used among the
blockchain platforms, the incentive compatibility of the proto-
col has been openly questioned from game theoretical per-
spectives [27]. The reason is that achieving the Nakamoto
consensus involves nodes joining the network, executing the
protocol, and maintaining the ledger. The nodes may deviate
from the protocol to increase their own utilities. For example,
the node may not broadcast its newly discovered blocks but
choose to withhold the block to increase its utility [25]. The
node trades off between the cost of withholding the block
which is associated with the other nodes’ strategies, and the
mining reward and then chooses its strategy. To analyze the
interactions among these consensus nodes, the game theoret-
ical models (see Section III) are developed and applied [28].
In addition to the security issues, nodes’ mining management
in blockchain, e.g., computational power allocation [29] and
reward allocation [30], adopt game models for the analysis
as well. Apart from the Nakamoto protocol, game models are
also widely used for analyzing the incentive compatibility with
other consensus protocols, e.g., Proof of Stake (PoS) protocol
[31]. Therefore, to easily understand the applications of game
theory in blockchain, the next section presents an overview
and fundamentals of game models used in this survey.
III. O VERVIEW AND FUNDAMENTALS OF GAME THEORY
Game theory provides a set of mathematical tools for
analyzing the interaction among rational decision-makers. In
a game, each decision-maker as a player chooses its strategy
to maximize its utility, given the other players’ strategies. The
following briefly presents the game theoretic approaches which
have been widely applied to analyze the interactions within the
blockchain network. To interpret the definition of the game,
some important terminologies are given below.
Player: A player is a decision-maker in the game. In the
blockchain, players can be miners, mining pools, or the
blockchain users.
Utility: A utility, i.e., a payoff, an interest, or a revenue
reflects the player’s expected outcome.
2Due to the different strategies that nodes make to maximize their own
utilities, the nodes may attach new verified blocks to the different blocks in
blockchain, and thereby fork chains appear. The consensus protocols regulate
the nodes to apply their work on the longest chain.
4
Strategy: A player’s strategy is a set of actions, choices
or decisions that the player can perform to achieve
its expected outcome. In general, the player’s utility is
determined based on not only the player’s own strategy,
but also the other players’ strategies.
Rationality: A player is rational, i.e., self-interested, if the
player always maximizes its own payoff.
A. Non-cooperative Game
In a non-cooperative game, the players do not cooperate
by forming coalitions or by reaching agreements. In general,
the term non-cooperative does not imply that the players
do not cooperate with each other, but it means that any
cooperation which might arise must be with no communication
of strategies among the players. In other words, the strategy
that the player takes must be spontaneous, and each player is
rational.
Consider a blockchain network in which miners as the
players invest strategically in computational power to com-
pete for a reward from mining successfully. The miners are
rational and the non-cooperative game can be used to model
the interaction among the miners. Assume that there are N
miners, i.e., players, and Piis a set of strategies of miner
i, whereP=P1PNis the Cartesian product
of the sets of individual strategies. Let pi2Pibe the
strategy of miner i. A vector of strategies of Nminer is
p= (p1;:::;p N), and a vector of corresponding payoffs is
= (1(p);:::; N(p))2RN, wherei(p)is the utility of
playeri, e.g., mining rewards or the transaction fees, given
the miner’s chosen strategy and strategies of the others. Each
miner chooses its best strategy p
ito maximize its utility. A set
of strategies p= (p
1;:::;p
N)2Pis the Nash equilibrium
if no miner can gain higher utility by changing its own strategy
when the strategies of the other miners remain unchanged, i.e.,
8i;pi2Pi:i(p
i;p
i)i(pi;p
i); (1)
where pi= (p1;:::;p i 1;pi+1;:::;p N)is a vector of strat-
egy of all miners except miner i.
The inequality in (1) demonstrates the equilibrium state
of the game. At the Nash equilibrium, the players have no
incentive to deviate from their current strategies. However,
there is no Nash equilibrium in some cases, or multiple
equilibria exist. Thus, it is important to check the existence
and uniqueness of the Nash equilibrium to analyze a non-
cooperative game. The existence and uniqueness of equilib-
rium theory [32] demonstrates that the strictly concave game
can achieve the unique equilibrium asymptotically. Here, the
concave game means that the utility functions of players are
concave, and this can be proved by computing the second-
order derivative of the utility function [12].
The non-cooperative theory can be applied to a broad range
of blockchain based scenarios. For example, it can be used for
computational power allocation [29] or fork chain selection
[33]. Also, it can be used for pool selection regarding the
mining rewards allocation [30]. Atop the blockchain based
platform, the non-cooperative game theory is applied to ana-
lyze the interaction between blockchain users and miners, e.g.,cheating among the buyers and sellers in blockchain network
[34]. Moreover, it is widely adopted in analysis of security
issues within the blockchain, e.g., pool block withholding
attacks [35].
B. Extensive-form Game
The aforementioned non-cooperative game can be used to
analyze both the static game, i.e., the game that has no notion
of time and no player has any knowledge of other players’
actions in advance, and the dynamic game, i.e., the game
in which the players’ strategies are made following a certain
predefined order. The dynamic game can be represented in an
extensive form to illustrate the sequencing of players’ possible
moves, their choices at every decision point, information that
each player has about the other players’ moves, and their
payoffs for all possible game outcomes. In game theory,
the extensive-form game describes the interaction among the
players using a game tree illustrating decisions made at
different points with their payoffs represented at the end of
each branch. Consider the scenario of fork chain selection, the
miner chooses a certain chain to mine on at the beginning of
every round of mining competition, given the actions taken by
the other players in previous mining rounds. At some points,
the blockchain forks and leads to the structure similar to a
branching tree. Thus, the tree-like extensive-form game can
be efficiently applied for the analysis as shown in Fig. 3 in
which the players can choose between two chains to mine.
2
21(0,0)
(2,1)
(1,2)
(3,1)C1C1
C1
C2C2C2
Miner 1
Miner 2
Fig. 3: The game has two players, i.e., miner 1 and miner 2.
The initial node belongs to miner 1 meaning that the miner 1
makes its strategy first. The miner 1 chooses between Chain 1,
i.e., C1, and Chain 2, i.e., C2. The miner 2 chooses between
C1 and C2 after its observation of the action of miner 1. There
are four payoffs represented by the four terminal nodes of the
game tree: (C1,C1), (C1,C2), (C2,C1) and (C2,C2).
Assume that an extensive-form game is composed of many
smaller games, i.e., subgames. Each subgame can be ex-
pressed as a static non-cooperative game. A set of strategies
p= (p
1;:::;p
N)2Pis a subgame perfect equilibrium if it
represents a Nash equilibrium of every subgame. A common
method for obtaining the subgame perfect equilibrium in an
extensive-form game is backward induction. The backward
induction first considers the decision that might be made in the
last move and then reasons back from the end of the problem
to the previous one until the induction reaches the first move
of the game. In the game as presented in Fig. 3, if miner 1
5
chooses C2, miner 2 will choose C1 to maximize its utility and
miner 1 receives 1. If miner 1 chooses C1, miner 2 will choose
C2 and miner 1 receives 2. Therefore, miner 1 prefers choosing
C1 and miner 2 choosing C2. The strategies of miners are
the Nash equilibrium of each subgame and thus achieve the
subgame perfect equilibrium.
In blockchain based platform, the extensive-form game is
applied for selection of entering the blockchain market or not
[36], selection of transactions to be included in the block [37],
and optimization of pool’s mining rewards allocation [38]. The
extensive-form game has been also adopted for analyzing the
security issues within the blockchain. It was used to analyze
the selection of fork chain [39], determination of forming the
collusion [40], and cheating among the blockchain users [41].
C. Stackelberg Game
Similar to the extensive-form game, another game that
involves in a certain predefined ordered strategies taken by
players is Stackelberg game [12]. In the Stakelberg game, the
players include leaders and followers . The followers decide
their strategies after observing the strategies of the leaders.
Both the leaders and the followers are typically rational that
aim to maximize their own utilities.
To understand how the Stackelberg game works, we con-
sider a blockchain based edge computing network which
involves two players, i.e., the service provider and the miner
[42]. The service provider possesses the computational power
which can be offered to the miner as service, and the provider
can set the service price to charge the fee for profit. The
miner optimizes its demand of computational power to the
provider to maximize its utility, taking its cost into account.
As such, the service provider sets the price first, and then the
miner decides its demand. Thus, the Stackelberg game can
be used to model the interaction between the service provider
and the miner. Assume P1andP2are the sets of strategies of
the service provider and the miner, respectively. The service
provider chooses its strategy p1from setP1to maximize its
utility1(p1;p2), and the miner chooses its strategy p2from
setP2to maximize its utility 2(p1;p2). The optimization
problems of the leader and the follower together form the
Stackelberg game. The objective of such a game is to find
a Stackelberg equilibrium.
Definition 1. LetBR 2(p1)define the best response mapping
of the follower. Then, the point (p
1;p
2)is called the Stackel-
berg equilibrium of the game if the following conditions hold:
p
22BR2(p
1), and
p
12arg max
p1max
p22BR2(p1)(p1;p2).
To find the Stackelberg equilibrium, the backward induction
method is typically used. Since the leader first takes its strategy
and then the follower chooses its strategy, the Stackelberg
strategy guarantees the service provider to achieve its payoff
at least as much as the corresponding Nash equilibrium. The
reason is that when choosing the Stackelberg strategy, the
service provider actually optimizes its decision which will
maximize its utility. This feature makes the Stackelberg game
suitable for many scenarios in blockchain based applications.
For example, the Stackelberg game is adopted for settingtransaction fees and selection of miners for verification [43],
determination of cyber-insurance price [44], and analyzing
the supply-demand relationship in the blockchain based edge
computing platform [45].
D. Stochastic Game
A stochastic game can be seen as several static non-
cooperative games that are repeated over time. Each static non-
cooperative game is called state of the game. The stochastic
game executes stochastic transitions among the states of the
game. In the stochastic game, the players can change their
strategies based on the past actions and transitions behaviors
of the other players [46].
The stochastic game can be applied efficiently to analyze the
miners’ selection of chains to mine (see Section II) regarding
the transitions of blockchain structure. The stochastic game
typically is composed of (i) a finite set Iof players , e.g., the
miners, (ii) a space Mof states, e.g., blockchain structures,
(iii) a strategy set S, and (iv) a transition probability Pfrom
MS. Each miner has a payoff function gnwhich is often
taken to be the discounted sum of the stage payoffs. The game
starts at an initial state m1, and at stage t, each miner observes
the blockchain structure mtand then chooses its strategy si
t,
i.e., selects a chain to mine. Every miner receives an immediate
payoffgi
nassociated with the current state and the miners’
strategies. Then, the game moves to a new state mt+1. The
game process is repeated until it reaches a common solution
called Markov Perfect Equilibrium (MPE) [47] that is the
refinement of the subgame perfect equilibrium (see Section III-
B). The Markov perfect equilibrium is a set of strategies that
achieve the Nash equilibrium of every state of the stochastic
game [12]. In the case of fork chain selection, following the
Nakamoto protocol, i.e., mining on the longest chain, is the
Markov equilibrium.
Apart from the chain selection, the stochastic game can
be used for mining management. For example, the selection
between investing in computational power or leaving the
mining [48], and the selection of chain to mine [49]. Moreover,
stochastic game has also been widely applied to security
issues. It was used to analyze the selection between honest
mining and selfish mining [50], the decision of the proper time
to release the mined block [28], and the selection of adding a
block to the chain [51].
IV. A PPLICATIONS OF GAME THEORY FOR SECURITY
A. Selfish Mining Attack
Selfish mining is a type of subversive strategies in PoW
based blockchain systems [52] that attackers, i.e., malicious
miners or mining pools, may not broadcast the newly mined
blocks but choose to (i) withhold the block or (ii) hold and
then release the block at a proper time. Under this case, honest
miners waste their computational power in finding the block
discovered already, and malicious miners can therby increase
their probability of finding the next block. The pool block
withholding (PBWH) attack is one common selfish mining
attack [53]. In the PBWH attack, the attacking pool infiltrates
the attacked pool, and the infiltrating miners perform the block
6
withholding (BWH) attack, i.e., withhold all the blocks newly
discovered in the attacked pool. To prevent such an attack, it
is crucial to analyze strategies of the miners and pools as well
as the interaction among them. A Markov Decision Process
(MDP) [54] can be used to analyze the strategy and utility
of the individual player, i.e., the miner or the pool. However,
the MDP does not take into account the interaction among
multiple players. Alternatively, game theory can be effectively
applied.
The authors in [35] adopt a non-cooperative game to analyze
the interaction among the pools. This scenario is illustrated
in Fig. 4 with two selfish pools as players. The strategy of
each player is to determine its infiltration rate, i.e., the fraction
of its computational power for performing the infiltration. In
the case of attack, the attacking pool obtains its utility not
only from its honest miners, but also from the infiltrating
miners that perform the BWH attack within the attacked pool.
The objective of the player is to optimize its infiltration rate
thereby maximizing its utility. In particular, the player’s utility
is a function of the computational power and the infiltration
rate. By using the second-order derivative with respect to the
infiltration rate, the utility function is proved to be concave.
Thus, there exists a unique Nash equilibrium in which neither
players can improve its own utility by changing its strategy,
i.e., the infiltrate rate. At the equilibrium, the infiltrate rate
is always greater than zero. This means that launching the
PBWH attack is always the best response of each player.
Simulation results illustrate that the pool can improve its utility
by launching the PBWH attack only when the pool controls a
strict majority of the total computational power. However, in
the case that two pools attack with each other, the utility of
each pool is less than that if neither pool attacks.
MinersPool 1Bitcoin Network
InfiltrationPool 2
Miners Miners
Fig. 4: Two pools case that both pools launch the PBWH
attack, i.e., infiltrates each other with its miners that perform
the BWH attack [35].
The case in [35] is similar to the famous Prisoners’ Dilemma
in game theory [12] that results in the utility loss of the miners.
To avoid the miners’ dilemma, the miners can choose one of
the solutions as follows. The first solution is that the miners
would intend to join private pools that will not involve the
PWH attack. As a result, big mining pools may be divided
into many small pools spontaneously, and eventually this may
lead to a better environment for the Bitcoin system as a whole.
The second solution is that the miners perform so-called Zero
Determinant (ZD) strategies [68]. This solution is presented
in [50] that the authors model a two-miner mining case as a
stochastic iterative game.
Different from a typical strategy that aims to improve
players’ own profits, the ZD strategy is used to control an
outcome of the opponents in a certain range so as to avoid alow social welfare, i.e., the whole pool’s profit [69]. In this
game, the two players are an altruistic miner, i.e., a miner
which attempts to maximize the social welfare, and a selfish
miner, i.e., a miner which only aims to improve its own profit.
Their strategies include cooperation, i.e., mining honestly,
and launching the BWH attack to the other miner. Note that
the altruistic miner and selfish miner choose their strategies
probabilistically based on each other’s strategy selected in the
last iteration. The analysis shows that so long as the altruistic
miner applies strategies according to the determinant function,
i.e., a linear function which is associated with players’ profit
factor, the profit of the selfish miner is in a range from mutual
cooperation to mutual attack regardless of strategies adopted
by the selfish miner. Thus, the altruistic miner can indeed
motivate the selfish miner to mine cooperatively by performing
ZD strategies so as to restrict the selfish miner’s profit to
achieve the highest social welfare. The simulation results show
that the proposed game can achieve a higher social welfare
than that of the pool game proposed in [35]. However, the
proposed game does not consider the profit of the altruistic
miner. This means that the altruistic miner may not have an
incentive to perform the ZD strategy.
The two-pool-attacker scenario in [50] can also be found in
[56]. However, in addition to the PBWH attack, the authors
in [56] consider the miners’ migration among the pools. In
particular, the miners of a pool can be migrated to another pool
and launch the PBWH attack to increase the profit. To analyze
the average payoff of the miner and the miners’ stochastic mi-
gration process, the Concurrent Mean-payoff Game (CMPG)
is adopted as presented in [56]. CMPG (see Section III) is
a two-player game with a finite state space where at each
state, both players choose their strategies simultaneously [46].
Here, the players are pool 1and pool 2, and the state of the
game includes the number of migrated miners of pool 1and
that of pool 2. The strategy of a pool is to determine (i) the
number of its miners to be migrated to the other pool and (ii)
the miners which perform the PBWH attack. The number of
migrated miners is determined depending on the attractiveness
levels of the other pool, i.e., the ratio of the pool’s total mining
reward to the total computational power of its miners. If a pool
is infiltrated by miners of the other pool, the attractiveness
level of the pool decreases. This decrease can be observed
by the whole blockchain network, and thus the other pool
can adjust its migration strategy based on the observations. In
general, the pool’s profit depends not only on the state, i.e.,
the allocation of miners for migration, but also on its chosen
strategy. The experimental results show that if the miners in
pool 1 stochastically migrate to pool 2 according to the pool
2’s attractiveness level, then the mean-payoff objective, i.e.,
the average profit, of pool 2can be guaranteed against any
strategy of pool 1. However, the mean-payoff objective may
not be guaranteed in multi-player scenarios. Such a scenario
can be investigated in the future work.
The aforementioned approaches, i.e., [35], [50] and [56],
are constrained to the interaction among only two pools.
Considering a multi-pool scenario, the authors in [55] adopt
the Computational Power Splitting (CPS) game [70] to model
the PBWH attack. To improve their expected payoffs, the
7
TABLE II: A Summary of Game Theoretical Applications for Security.
REF.GAME MODEL PLAYER ACTION STRATEGY PAYOFF SOLUTIONSelfish Mining Attack[35]Non-cooperative
gameMining poolsInfiltrate other pools
to launch BWH
attackDetermination of the
infiltration rateMining rewards
minus costNash equilibrium
[55]Splitting gameOne miner and
poolsDistribute mining
power for selfish
miningDetermination of the power
distributionMining rewards
minus costMixed strategy
Nash equilibrium
[56]Mean-payoff
gameMining poolsMigrate to other
pools to launch
PBWH attackDetermination of the
migration rateMean-payoffMean-payoff
objective
[50]Stochastic game MinersBlock withholding
(BWH) attackSelection between honest
mining and selfish miningSocial welfareZero-
Determinant
strategy
[57]Non-cooperative
gameMinersSelfish propagation
attackSelection of identity
duplication and transactions
relayingMining rewards Nash equilibrium
[33]Non-cooperative
gameMiners Fork chain Selection of fork to mine Transaction fees Nash equilibrium
[58]Non-cooperative
gameMinersDelay submitting
sharesDecision of the proper time to
submit sharesMining rewards Nash equilibrium
[28]Non-cooperative
gameMinersSelect or create a
chain to mineSelection of the chain to mine Mining rewards Nash equilibriummajority Attack[28]Stochastic game Miners BWH attackDecision of the proper time to
release the blockMining rewards Nash equilibrium
[59]Non-cooperative
gameMinersPost smart contract
transaction of
mining on private
chainSelection between working on
smart contract transaction and
honestly miningTransaction fees
and mining
rewardsNash equilibrium
[51]Stochastic game MinersCompete to fork
chainSelection of adding the block
to the chainMining rewards
minus costNash equilibrium
[60]Non-cooperative
gameAttacking and
defending minersIssue whale
transaction to attract
miners mine on the
private chainDetermination of the
threshold of attack cost and
block selectionMining reward
minus costNash equilibrium
[61]Sequential gameAttacking and
defending minersBuy stake to launch
majority attackDetermine the cost of attack
and selling selectionFunction of profit
and interestNash equilibrium
[28]Non-cooperative
gameAttacking and
defending minersGoldfinger attackDecision of forming cartel
and determination of the tax
paid to the attackerProfits minus cost Nash equilibrium
[43]Stackelberg
gameBlockchain users
and minersForm cartel to
launch majority
attackSetting transaction fee and
selection of recruiting minersProfits minus costStackelberg
equilibriumDoS Attack[62]Non-cooperative
gameMining pools DDoS attackSelection of launching attack
or notProfits minus cost Nash equilibrium
[63]Sequential game Mining pools DDoS attack Chosen of the attack level Profits minus cost Nash equilibrium
[64]Repeated game Mining poolsDDoS attack under
a reputation-based
schemeSelection of launching attack
or notProfits associate
with the loss of
reputationNash equilibrium
[65]Non-cooperative
gameOne server and
devicesDDoS attack in
edge networkSelection between executing
or sending request and
launching attackProfits minus cost Nash equilibriumOther security issues[66]Non-cooperative
gameGroups of
information
sharing networkForm group and
infiltrate other
groups to withhold
dataDetermination of infiltration
rateProfits minus cost Nash equilibrium
[40]Extensive-form
gameClouds of cloud
computing
networkCollude to output
the same wrong dataSelection of collusion or notFunction of
payment and
depositSequential
equilibrium
[41]Extensive-form
gameBuyer and seller
of the blockchain
trading systemCheats of buyer or
sellerSelection of cheating or notProfits associated
with depositsSubgame perfect
Nash equilibrium
[34]Non-cooperative
gameBuyer and seller
of the blockchain
trading systemCheats of buyer or
sellerSelection of cheating or notProfits associated
with depositsNash equilibrium
[67]Coordination
gameV oter and
verifiersManipulate data of
data verification
systemStatement of the correctness
of dataProfits associated
with depositsNash equilibrium
[44]Stackelberg
gameBlockchain
users, one
provider, and
one insurerPurchase insurance
to compensate for
the attackDetermination of the service
price, service demand, and
insurance priceProfits minus costStackelberg
equilibrium
8
players, i.e., the miners or the pools which own positive
computational power, can choose to (i) attack other pools,
i.e., distribute their computational power to other pools and
launch the BWH attack, and (ii) honestly follow or arbitrarily
deviate from the pool’s protocol. In the case that the player
chooses to attack, the strategy of the player is to determine
(i) the distribution of its computational power, and (ii) the
portion of its mining power holding attack as presented in
Fig. 5. The objective is to maximize the player’s profit, which
is defined as the sum of mining rewards received from all
the pools. For any given strategies of the other miners, there
always exists a computational power allocation for a miner
to increase its profit and cause the other pool a loss. In
other words, honestly mining is not the best response of the
players and the game thus has no pure Nash Equilibrium
strategy. Nonetheless, the game has a unique mixed strategy
equilibrium at which each player has an incentive to launch
the PBWH attack probabilistically rather than mining honestly.
Simulation result shows that the best strategy of the players is
to comply with the following rules. First, the players launch
the PBWH attack which improves their profits. Second, the
attackers spend the computational power less than a specific
fraction on the PBWH attack to gain more profit than mining
honestly. Finally, the attackers should attack big pools rather
than small pools. Both work in [35] and [55] arrive at some
consistent findings from different perspective.
Bitcoin Network
Pool 1
Player
α
𝛽1 Pool 2Pool
n-1Pool n ...
𝛽2
𝛽𝑖
𝛽𝑛−1
𝛽𝑛
Fig. 5: The player distributes its computational power to
several pools and launchs the BWH attack, where is the
computational power owned by the player, and irepresents
the fraction of mining power that the player allocates to the
pooli[55].
The approaches discussed above, i.e., [35] and [55], con-
sider only the mining reward. In practice, the Bitcoin systems
also provide the transaction fee [1]. When the block creation
reward dominates the mining reward, the miners may not
broadcast transactions to the others immediately so as to
increase their expected profits [71]. This is called selfish propa-
gation attack . To address the attack, the authors in [57] propose
an incentive mechanism for the miners to propagate the trans-
actions. The proposed mechanism is designed such that each
miner receives a propagation reward from the blockchain sys-
tem according to its behaviors in the propagation process (see
Section II-B). To maximize the gained propagation reward,
each miner strategically chooses to duplicate itself, i.e., add
fake identities before relaying the transaction, or to relay the
transaction immediately, given the strategy profile of the other
miners. The interaction among the miners can be modeled as
a non-cooperative game as presented in [57]. In the game,the players are miners which are aware of the transaction.
Each player not only strategically relays the transaction but
also works on PoW. The authorizing player, i.e., the player
which solves the PoW, and the players which are in the same
relay chain with the authorizing player gain a certain reward.
Other players gain nothing. This scenario is illustrated in
Fig. 6. By using the iterative removal of dominated strategies
[12], the game is proved to admit a unique Nash equilibrium.
At the Nash equilibrium, only transaction propagating and
no-duplication strategies, i.e., the Nash equilibrium strategy,
survive after dominated strategy removal. However, if there
are not sufficient players which are connected with each other,
the selfish propagation attack cannot be guaranteed to be
prevented.
𝑇1
𝑇2
𝑇3
𝑇3′
𝑇4
𝑇5
𝑇3′′ Transaction
relay chain
Propagation
reward
α
α
α
α
α
α
3α total
Duplications of 𝑇3
β
𝑇𝑖
𝑇𝑗
…
…
Fig. 6: An example of the transaction relay process that the
transaction flows from T1toT5.T1toT4relay the transaction
thus gain reward .T5solves the PoW thus gains reward .
T3adds two fake identities, i.e., T0
3andT00
3, before relaying
the transaction thereby gains 3 in total [57].
Other works on understanding the vulnerability of propa-
gation mechanism without mining rewards can also be found
in [28], [72], [73]. The authors in [33] demonstrate that with
only block creation rewards, it is attractive enough for miners
to extend the blocks that have the most available transaction
fees rather than to follow the longest chain. Each miner intends
to fork the head of the chain actively and leaves transactions
unclaimed selectively to maximize its profit. Such an attack
is called undercutting attack , and the miner that performs
the undercutting attack is called undercutter . The scenario is
illustrated as in Fig. 7 where “Option Two” corresponds to
the undercutting attack. If the miner performs the undercutting
strategy, it may gain nothing if its block is not in the longest
chain eventually. The undercutter strategically performs un-
dercutting strategy so as to attract the other miners to mine
on the forked chain. Meanwhile, the other miners consider
whether to mine on the forked chain or not to maximize
their profits. Thus, the interaction among the miners can be
modeled as a repeated game that in every stage of mining, each
miner chooses to perform honest mining or undercutting. The
game theoretical analysis shows that if a miner’s undercutting
strategy follows a certain function to maximize the size of
the block, then the strategy is also the best response for all
miners. This is under the constraint that if the miners fork,
they must perform undercutting. Thus, the Nash equilibrium
exists as all miners adopt the same undercutting strategy. The
simulation results show that when each miner applies a no-
regret learning algorithm, even with 66% of miners mining
9
honestly, undercutting is profitable than mining honestly. As a
result, there could be many unclaimed transactions left which
is detrimental to the whole blockchain network. The same
conclusion is reached in [74] through a non-game theoretical
method. However, if the simulation takes network latency into
account, the undercutters may have sufficient time to include
all the transactions into the block, and thus the undercutters
have no incentive to leave any transaction to the next miner.
Possible Scenario: Head of the
longest chain contains 500 units of
transaction fees. 20 units remain.
500 20Option One: Extend the longest
chain. Claim 20 units for self,
leave 0 for next miner.
500
20
Option Two: Fork the longest
chain. Claim 270 units for self,
leave 250 for next miner.
500
250
0
270
Fig. 7: An example of undercutting attack. Option one corre-
sponds to honest mining that the miner mines on the longest
chain. The miner in Option Two performs undercutting attack
that forks the longest chain and claims more reward compared
with that of option one [33].
Different from attacks among the pools, another variation of
selfish mining attack inside the mining pool which performs
on the pay per last N shares (PPLNS) [75] is introduced in
[58]. PPLNS is a popular pool mining reward mechanism.
Instead of distributing a block reward among miners in the
pool in the current round, PPLNS distributes the reward
among miners that have submitted shares3already in the latest
PPLNS window. The PPLNS window includes the number of
shares submitted continuously that the latest share is the full
solution of PoW. Specifically, shares in the PPLNS window
are regarded as the effective shares. The miner that submits
effective shares obtains the reward according to its proportion
of all effective shares. Under this mechanism, the miner may
launch the delay attack. In the delay attack, the miner first
delays submitting the shares, i.e., by holding the discovered
shares, if the miner finds the solution of PoW, the miner
releases all delayed shares and then submits the solution
immediately. Thus, more reward can be obtained because of
the higher fraction of shares in the latest PPLNS window. This
scenario is illustrated in Fig 8.
For each miner in the same pool, there are two phases during
mining. In the first phase, the miner only collects shares for
delay. In the second phase, the miner submits every share
immediately, i.e., through honest mining. To maximize the
expected profit of launching the delay attack, each miner needs
to choose proper time to transit its phase according to the
strategies of the other miner. Otherwise, the miner may lose
the reward of all its delayed shares. Thus, the authors in [58]
model the interaction between miners in the same pool as a
non-cooperative game. It is proved that the Nash equilibrium
exists if the computational power of the most powerful miner
3A share is a hash value which is easier to be found, compared with the
valid hash of the block. This means that shares can be used to measure the
computational power that miner possesses.
212112$ 1212$112222$ ... ...N
NN
1
Miner 1Share2
Miner 2Share$-- Solution of Pow, N=7Miner 2 launches
a delay attackPool with PPLNSFig. 8: An example of delay attack in a pool with PPLNS.
The pool includes two miners, i.e., miner 1 and miner 2. The
size of PPLNS window in this case is 7. Miner 2launches a
delay attack [58].
meets a certain condition. This condition is associated with the
PPLNS window size, and complexity of finding the solution
of PoW. At the Nash equilibrium, each miner of the pool
is at the turning point between two phases. This means that
the miner has no incentive to deviate from honest mining,
and thus the miner would not delay its shares. Such a pool
is called the incentive compatible pool. Simulation results
show that if the pool is not incentive compatible, although
the fraction of delaying miners decreases with a parameter
related to the window size and the complexity of solving PoW,
regardless of power distribution, the game cannot reach the
Nash equilibrium.
B. Majority Attack
The security of blockchain is achieved through the dis-
tributed consensus of miners. This consensus is only reliable
with the assumption that no single miner can hold more than
50% of the network’s computational power [1]. Theoretically,
to gain its profit, the miner invests more in the computational
power, and it may possess more than 50% of the network’s
computational power [30]. In this case, the miner would
be able to halt payments, reverse transactions, prevent new
transactions from confirmation, and double-spend coins [2],
[27], [76]–[81]. The attack is called 51% attack. As such,
the assumption of the distributed consensus may not be valid
any longer, and the security of blockchain is not guaranteed.
More specifically, theoretical analyses [54], [59], [82] show
that the miner which possesses only a relatively large part
computational power can also achieve the similar goal. In
general, we label this type of attack associated with a large
group of miners as the majority attack.
When the majority attack is performed, mining on the fork
chain may happen. The condition under which a miner has an
incentive to mine on the fork is investigated in [28]. Although
the miners follow the longest chain rule under the Nakamoto
protocol, at some points, the chain can fork that leads to a
structure similar to a branching tree [83]. To maximize the
profit, i.e., the reward of creation of a new block, each miner
aims to extend selectively any of the existing branches or to
create a new branch, given the strategy of the other miners. A
non-cooperative game can thus be applied. Since if more than
50% of the network’s total computational power are extending
the longest chain, deviating from honest mining only leads
10
to the waste of the miner’s computational power of mining.
The reason is that the mined block would not achieve the
Nakamoto consensus with the majority of miners and thereby
be orphaned. This lowers the miner’s profit, and thus following
the longest chain would be the best response of the rest of other
miners. Therefore, the game has a Nash equilibrium in which
all miners extend the longest chain. If a cartel of miners which
possesses more than 50% of the network’s computational
power forks a chain, following the rule of longest chain would
not be the best response for the other non-cartel miners, and
thus the Nash equilibrium will be shifted to another one that
every miner mines on the fork. Similar conclusion is reached
in [82]. If the fraction of computational power deviating from
extending the longest chain is more than a value around 1=4,
each miner has an incentive to mine on the fork.
Compared with [28], a more general majority attack where
the miner can not only choose which branches to mine but
also determine whether or not to release the mined block
is investigated in [84]. The miner can probabilistically hide
newly mined blocks and mine on the fork. Since this is similar
to that the miners play a game with incomplete information
of blockchain state among each other [85], a stochastic game
can be applied as presented in [84]. The miner’s expected
utility is a function of the miner’s action, i.e., the allocation
of the miner’s computational power, and the current state of
the game, i.e., the structure of the block tree at present. In the
case that the miner’s computational power is equal to a profit
threshold, the expected utility of mining on a fork is equal to
that of mining on the longest chain regardless of the current
state. Thereby, when the miner’s computational power is less
than the profit threshold, the miner has no incentive to deviate
from mining on the longest chain which is the best response of
the miner and the Nash equilibrium can be obtained. As shown
in the simulation results, when the obtained profit threshold
is approximately 0.42, the miner with at most 36% of the
total computational power cannot gain more than 36% of the
total rewards. Meanwhile, the miner with computational power
more than 46% always has an incentive to deviate from the
longest chain rule. These results are more accurate than that
obtained by MDP-based scheme [54].
Furthermore, by using the smart contract [86], the authors in
[59] illustrate that the miner or the pool which controls only
38.2% of the network’s total computational power can gain
more reward by deviating from the protocol. The attacking
miner uses its full computational power to mine on its private
chain while posting a smart contract transaction. This contract
transaction includes a hashing puzzle, i.e., the solution of PoW,
of its private chain. Any miner that solves the puzzle can
receive the reward from the puzzle’s giver, i.e., the attacker,
in exchange for the solution. Thereby, the attacker may gain
more profit when its private chain is longer than the public one.
For each time that the attacker posts a hashing puzzle through
the smart contract, the other miners have two strategies: (i)
work on the puzzle in the contract, and (ii) mine on the
public chain. Each miner tries to maximize its expected utility,
given the set of strategies of the other miners. The interaction
among the miners except the attacker can thus be modeled as
a non-cooperative game. When the attacker controls more than38.2% of the network’s total computational power, the miner’s
utility of working on the puzzle with probability is greater
than that of the mining the longest chain, and the attack is thus
launched successfully. This means that each miner will work
on the puzzle with probability and mine on the public chain
with probability 1 . Thus, the game is proved to admit a
mixed strategy Nash equilibrium. The game in [59] is under
the assumption that miners always mine on the longest chain.
However, if some miners perform the selfish mining strategy,
the reward of solving the hashing puzzle on a private chain
provided by the attacker may not be attractive enough to the
other miners. Thus, the attack may fail.
In addition to posting the smart contract as presented in
[59], majority attack can also be launched by the attackers
offering monetary bribes [87]. To prolong the fork chain
thereby increasing its successful attack probability, the attacker
can attract other rational miners to mine on the fork by issuing
awhale transaction , i.e., a transaction with a high transaction
fee. Since issuing the whale transaction is similar to bribing
the other miners, such an attack is also called bribery attack
[88]. The attacker’s problem is to determine the cost of the
attack, i.e., the transaction fee, to maximize its profit. Also, the
other miners’ problem is to trade off the profit of mining on the
fork against the reward of mining on the public chain. A non-
cooperative game can be thus used to model the interaction
between the attacker and the other miners as presented in [60].
Both theoretical analysis and simulation results show that if the
attacker’s mining power is greater than a profit threshold, the
cost of the attack decreases, i.e., the attacker’s profit increases,
as the attacker’s mining power increases. Here, the profit
threshold is a function of the computational power used to
mine on the fork, and the number of blocks by which the fork
chain is ahead of the public chain. Meanwhile, any miner
that possesses as much mining power as the attacker’s has
an incentive to mine on the fork chain. However, the Nash
equilibrium of the game is not discussed.
To avoid such majority attack, the existing miners can act
as a defender actively adding honest nodes to the blockchain
network. This case is investigated in [51]. The system model
consists of one attacker, i.e., the miner which intends to fork a
private chain, and one defender, i.e., the miner which honestly
mines on the public chain. To obtain the mining rewards, the
attacker and the defender compete to build the blocks for the
private and public chains in a sequence of stages, respectively.
The historical strategies and the probabilistic stage transitions
can be observed by both the attacker and the defender. Thus,
the interaction between the attacker and the defender can be
modeled as a stochastic game. In the game, the strategies of
the defender are (i) defending , i.e., actively adding the honest
nodes to avoid the majority attack, and (ii) doing nothing , i.e.,
letting the blockchain network run as usual. If the winning
probability of the attacker to fork successfully is greater than
a certain value, the defender’s utility of defending is greater
than that of doing nothing. This means that the defending
strategy is the best response of the defender and the game
reaches the Nash equilibrium. Here, the value is a determined
based on the cost of adding honest nodes, the number of nodes
added actively to the blockchain network, and the total mining
11
power that the attacker has. Otherwise, the defender has no
incentive to avoid the attack. However, the simulation results
should use actual data gathered from real blockchain networks,
e.g., Blockr.io [89] and Blockchain.info [90], to verify the
practicability of the game model.
The aforementioned approaches, i.e., [51], [59], [60], [84],
consider the motivation of the attack within the system.
However, the attacker’s motivation can be also based on the
incentive outside the system and this type of attack is called
Goldfinger attack [91]. The attacker, i.e., miners, can receive
some utility from devaluing the cryptocurrency (a.k.a. currency
measured in digital tokens in the blockchain network), by
forming a cartel to impair the consensus among miners, i.e.,
launch the majority attack. The defenders, i.e., the other
miners, intend to preserve the value of the currency. To prevent
the currency from being devalued, the defender makes a bid,
i.e., similar to a tax to keep the currency alive, to the attacker.
Meanwhile, the defender trades off the cost of making the
bid and the profit of preserving the currency. Therefore, a
non-cooperative game can be used to model the interaction
between the attacker and the defender as presented in [28]. The
utility of the miner is a function of the value of the currency,
the bid, and the possibility of the currency being attacked.
The analysis shows that the defender can maximize its utility
by using the first-order optimality condition in which the bid
satisfies a certain constraint associated with the computational
power distribution. If such a bid exists, the game is at the Nash
equilibrium point where the attacker has no incentive to attack.
Otherwise, the currency will have a zero value. However, in a
real case, the defender does not know the attacker’s expected
utility. If the attacker makes a strong claim about the imminent
attack, the defender has no incentive to preserve the currency
because of the possible high cost and thus no equilibrium
exists.
Apart from the PoW system, the majority attack happens
in Proof-of-Stake (PoS) systems [31]. In a PoS system, each
agent, i.e., a stake-holder, can earn interest by holding crypto-
currency (CC) units (see Section III). To improve the interest,
the agent can make a price offer to buy CC units from other
agents [92]. As an agent possesses more than 50% of CC units
of the system, this agent can halt and reverse any payments or
transactions. Thus, the consensus of the system is broken and
CC loses its value. Only the agent that intends to devaluate
the CC obtains the profit, e.g., law enforcement, outside the
system. The attack is typically launched in multiple stages
[61], and thus each agent, i.e., the attacker or one of other
agents, can observe the historical strategies of each other and
then optimize its own strategy. Therefore, a sequential game
can be used to model the interaction between the attacker and
other agents as proposed in [61]. In the game, the players
include one attacker and other agents. The attacker trades off
the profit of devaluating the CC against the cost of making
offers and the loss of interest. In the case that the profit of
devaluating the CC is greater than the interest of holding the
CC, by using the backward induction method, the game is
proved to admit a unique Nash equilibrium. At the equilibrium,
the attacker has an incentive to buy more than 50% of CC
units, and other agents are willing to sell the CC to the attackersince they know that the CC has no value. However, the
attacker can succeed in its attack at no cost by announcing to
other agents about launching the majority attack before making
the price offer. The reason is that if the agents believe that the
attack succeeds, they will sell the CC to the attacker regardless
of the price that attacker offers. The Nash equilibrium may not
exist in this case.
Blockchain User
Miner iUnverified Transaction...Transaction feeMining pool
Unverified block
Verifiers
Unverified block
Verifier 1
Unverified blockUnverified blockMiner 1...
Verifier ni
Fig. 9: An example that demonstrates the relationship
among blockchain user, miners and verifiers in consortium
blockchain. The miners recruit some other miners, i.e., ver-
ifiers, to verify the transaction [43].
The majority attack also exists in the PoS based consortium
blockchain [93]. In the system, the blockchain user produces
transactions for verification and pays the transaction fee. Due
to the limited number of miners, some miners can launch the
majority attack, i.e., halt or reverse transactions by forming
a cartel. Thus, in addition to competing to solve the crypto-
puzzle, the pre-selected miners recruit some other miners, i.e.,
verifiers, to verify the transaction. This results in recruitment
cost and propagation delay that reduce the utility of the pre-
selected miners [94]. In this case, the blockchain user acting as
the leader sets the transaction fee for relative secure verifica-
tion. The pre-selected miners acting as the followers, given the
other miners’ strategies, trade off the transaction propagation
delay and recruitment cost against the transaction fee offered
by the blockchain user. This scenario is illustrated in Fig 9.
Therefore, the interaction between the blockchain user and the
pre-selected miners can be modeled as a Stackelberg game as
presented in [43]. By using the second-order derivation, the
blockchain user and pre-selected miners’ utility functions are
proved to be concave. Thereby, they can jointly maximize their
utility through backward induction. The simulation results
show the bigger variation range of propagation delay brings
lower utility of the blockchain user. However, the game model
is under the assumption of complete information of the all
miners’ strategy. The Bayesian game model [95] can be used
to analyze the incomplete information case.
C. Denial of Service (DoS) Attack
Due to the distributed structure of peer-to-peer (P2P) net-
work in blockchain with the Nakamoto consensus protocol,
each miner can observe the PoW done by their peer miners
[1]. However, if the P2P network is interfered or disrupted
by some attackers, the attacked miners’ resources available
for transaction propagation and verification may be exhausted.
Thus, the attacked miners would not complete the mining
12
process to gain the mining rewards and their expected profit.
Such an attack is called Denial-of-Service (DoS) [96].
The mining pools can perform the DoS attack as presented
in [62]. More specifically, to maximize the mining reward,
the mining pools can choose (i) to trigger the distributed DoS
(DDoS) attack that lowers the other mining pools’ expected
payoff, or (ii) to invest in additional computational power, e.g.,
by buying more mining machines, to increase its possibility
of solving the next PoW. Each mining pool trades off the
cost of the investment and attack associated with the other
pools’ strategies, as well as the uncertainty of launching the
attack successfully. Therefore, a non-cooperative game can
be adopted to analyze the interaction among the pools with
different sizes. In the game, there are two players, i.e., a
big pool and a small pool. The other pools own the rest
of computational power. The payoff of different strategies of
the two players can be expressed in a matrix in terms of
the computational power distribution, the increasing rate of
network’s computational power over time, and the probability
of launching the DDoS attack successfully. This matrix is
presented in Table III where PsandPbare the payoffs of
the small pool and the big pool, respectively.
TABLE III: Payoff matrix with launching DDoS attack
Investment(I) Attack(A)
Investment(I) Ps(I;I ),Pb(I;I )Ps(I;A ),Pb(I;A )
Attack(A) Ps(A;I ),Pb(A;I )Ps(A;A ),Pb(A;A )
Since investing in computational power is the only best
response of both big and small pools when computational
power distribution satisfies an inequality and vice versa for
launching the DDoS attack, the unique Nash equilibrium can
be obtained under different computational power distribution.
Simulation results show that mining pools have different in-
centive to perform DDoS attack under different computational
power distribution. Due to the higher expected payoff, each
pool has a greater incentive to attack larger pools than smaller
ones and the larger mining pools have a greater incentive to
perform the DDoS attack than smaller ones. These results are
consistent with the empirical evidence on the prevalence of
DDoS attacks in the Bitcoin system as presented in [96]. The
authors in [62] also consider the incentive of mining pools as a
whole. However, in a real case, the individual miners have an
incentive to hop among the pools and then the computational
power distribution changes. Thus, the Nash equilibrium may
be shifted.
Apart from only focusing on the short-term impact of DDoS
attacks on mining pools as presented in [62], the authors in
[63] study the long-term impact. An ongoing DDoS attack
causes some long-term impacts that individual miners may
migrate, i.e., leave the attacked pool and participate in other
pools. The model consists of two pools. At every stage of
mining competition, each pool chooses an attack level, i.e., the
fraction of its computational power to launch the attack to the
other pool. Choosing the attack level affects both the short-
term utility consequences (as studied in [62]) and the long-
term consequences. In particular, the long-term consequences
affect the computational power distribution of mining poolsin the next stage. Therefore, the interaction between the two
pools can be modeled as a sequential game. By using the
second-order derivative, the utility function of the mining pool,
i.e., the player, is proved to be concave under the condition
that the attack cost is greater than a certain value. This value
is associated with the level of attracting miners to participate
in the pool, and the migration rate of miners that are not
affected by the attack. Thus, the game can reach a unique
Nash equilibrium at which both the mining pools have no
incentive to launch the DDoS attack. However, if the condition
is not satisfied, the game reaches another Nash equilibrium
at which one of the players attacks while the other remains
not attacking. For the future work, a general case of multiple
mining pools can be investigated.
To avoid such DDoS attack, the authors in [64] propose
a reputation-based scheme in which each miner is assigned
a reputation value that evaluates the miner’s performance of
mining honestly against launching DDoS attack. The pool
managers send invitation probabilistically only to a subset
of miners according to the miners’ reputation values. Only
miners that receive invitations from pool managers can mine
for the pool. Otherwise, the miner has to mine for itself, and
this is not preferable for the miner with small computational
power. To maximize the profit, each miner chooses to attack
or mine honestly while optimizing the profit of launching
attack and minimizing the probability to be excluded from pool
managers’ invitation because of the decrease of its reputation
value from the attack. Since the reputation value is updated
periodically, and each miner determines its strategy based on
the future utility associated with the other miners’ reputation
value and strategy, a repeated game is used to model the
interaction among miners. By removing the strictly dominated
strategies of the game according to the miner’s utility function,
the unique Nash equilibrium can be obtained such that the best
response of each miner is not to launch the attack. Similar
to the analysis presented in [63], the reason is that even the
miner can gain some utilities in the current stage of mining
competition by launching the attack, the miner will lose many
future mining opportunities due to its lower probability of
being invited to mine for the pool. However, implementing
this reputation-based scheme through the simulation-based
approach is not discussed in the paper.
Similar to [64], a punishment scheme based on the action
record in blockchain to suppress the attack motivation is
proposed in [65]. Nevertheless, the scheme is applied to an
edge network instead of the blockchain system. The network
model consists of mobile devices and one server located in the
edge network. The mobile devices can (i) send service requests
to the server, or (ii) launch the DoS attacks to gain their
illegal profits. The server can choose (i) to execute the service
requests, or (ii) to launch the attack on the devices. Each device
or the server can decide its strategy according to the other’s
historical strategy recorded in the blockchain. Therefore, the
interaction between the mobile device and the server can be
modeled as a non-cooperative game. The utility of both the
players, i.e., the mobile device and the server, is a function
of the cost and profit of launching the attack and executing
the request, and a punishment factor related to their historical
13
strategies. Since the players can maximize their utility by
not attacking under a certain constraint associated with the
punishment factor, not attacking is the best response of the
players, and thus the game can reach the Nash equilibrium.
Simulation results also show that both mobile device and edge
server tend to not attack if the punishment factor is large and
the attack rate of the server decreases compared with that of
the non-punishment scheme. However, the existence of the
Nash equilibrium may not be guaranteed in a multi-player
scenario.
D. Other Security Issues
The underlying blockchain technology of bitcoin is now
being applied to many new scenarios such as edge networks,
cloud computing, e-business and information sharing [13],
[97], [98]. In particular, a series of security problems regarding
false data sharing [40], [66], [67], distrustful goods trading
[34], [41] and cyber-insurance [44], can be resolved by using
a blockchain-based scheme.
1) False Data sharing: Blockchain-based scheme is applied
to the false data sharing scenario. In most of the traditional
data sharing application scenarios, the users transfer data either
to other users or to a centralized authority for verification.
However, the users are reluctant to share the cyber-security
information due to the concern about the distrust, the possible
false information, the privacy vulnerabilities, and the lack
of incentive [99]. To address these problems, the authors in
[66] propose a blockchain-based information sharing (iShare)
framework. In the iShare framework, organizations, i.e., users
participating in sharing cyber-attack information, receive a
reward after the information transaction is proved authentic in
the blockchain. The organizations can form a group to share
information and gain the reward together similarly to forming
the mining pool in bitcoin systems [75]. However, some group
members can form a sub-group and infiltrating in another
group to gain more profit by not releasing the information in
the infiltrated group. This is similar to launching the PBWH
attack (see Section IV-A) as presented in [35]. In the two-
group case, each group determines the number of organizations
to infiltrate to the other group to maximize its profit. Thus, the
non-cooperative game can be used to analyze the interaction
between the two groups. Each group’s utility is determined
based on the size and number of infiltrating organizations
of the two groups. Since the utility function of the group is
concave, each group can maximize its profit when the number
of infiltrating organizations satisfies the first-order optimality
condition. The unique Nash equilibrium can be obtained at
this point in which not launching the attack can be the best
response for each group. The Nash equilibrium shifts when
the number of infiltrating organizations satisfies the different
constraint and thus launching attack can also be the best
response for the group. However, a general case of multi-
groups can be investigated for the future work.
False information risk among the users and the lack of
incentive can also be found in the traditional cloud computing
scenario. The cloud users may not entirely trust the computing
results returned from the cloud provider. Thus, the verifiabilitybecomes a critical requirement by the cloud users. The existing
techniques, e.g., [100], for verifying correctness of the result
cannot be done at a reasonable cost. A blockchain-based
scheme with smart contract can be used to address the issue
as proposed in [40]. In the scheme, the cloud user pays two
clouds, using smart contract, for computing the same task
and then collects and crosschecks the results from the two
clouds to verify the correctness. However, the two clouds can
collude with each other, i.e., output the same wrong result,
to gain an extra profit. To maximize the utility, each cloud
chooses to compute honestly or to collude to trade off the
profit obtained from the cloud user’s payment and the loss
of the deposit, i.e., a sum of money that guarantees the
security for the delivery of the correct result. The cloud’s
expected utility function is determined based on not only its
present strategy but also the imperfect information of the other
clouds’ historical strategies over time. Thus, the extensive-
form game can be used to analyze the interaction between the
two clouds. By using the backward induction, each cloud is
proved to obtain the strictly dominant strategy that maximizes
its utility function at every information set in every sub-
game, and thus the game can reach the unique sequential
equilibrium. At the sequential equilibrium, both clouds have
no incentive to deviate from computing honestly, i.e., not to
collude. Simulation results show that the proposed scheme can
achieve a low cost compared with the techniques from [100].
The reason is that the cloud users only need to pay the cost
of employing two clouds for computing the same task.
Nevertheless, although the smart contract has the advantages
as presented in [40], a major limitation exists that only data in
the blockchain is processed, and trusted entities are required
to verify the correctness of the external data that will be
brought into the blockchain. The trusted entities can launch an
attack by manipulating the data to gain an extra profit [101].
The authors in [67] propose a decentralized entity scheme
to prevent the attack. The model consists of the voters and
the verifiers. The voter can vote, i.e., state the data as either
true or false, for random data once the voter submits a small
deposit to the system. The verifier can vote for the chosen
data after submitting a large deposit. Each participant, i.e., the
voter or the verifier, can receive a reward if its correctness
statement is the same as that of the other participants. Thus,
a coordination game can be used to analyze the interaction
between the voter and the verifier. According to the definition
of the coordination game [12], it can be easily proved that
the game has two Nash equilibria in which the participants
state the same correctness. At the Nash equilibrium, rational
participants have no incentive to deviate from voting honestly
if the majority participants give the honest statement. The
simulation results show that the proposed game can achieve a
zero probability of data manipulation.
2) Distrustful Goods Trading: The distrust of goods trading
can also be mitigated by applying blockchain based smart
contract as presented in [41]. The proposed smart contract
involves two participants, i.e., one seller and one buyer. The
participants are required to place a sufficiently large deposit
for the reliable transaction which will be returned only after
the transaction is completed. The participants can choose to
14
cooperate, i.e., execute the transaction honestly, or to attack,
i.e., cheat another participant, e.g., by double spending. To
maximize the utility, each participant has to take into account
the tradoff between the cost, i.e., the loss of the deposit, and
the profit of launching the attack given the other participant’s
strategy. The seller takes its strategy before the buyer does,
and thus an extensive-form game can be formulated. The
utility of the player, i.e., the seller or the buyer, is determined
based on the deposit, the value of the goods and the price
set in the smart contract agreement. By using the backward
induction, the game is proved to admit a unique subgame
perfect Nash equilibrium at which both players perform the
transaction honestly. However, how to implement the proposed
smart contract is not discussed.
Using a deposit for buying and selling goods can also be
found in [34]. The transaction is insured by the deposit of both
participants, i.e., the buyer and the seller. The buyer’s strategy
profile includes (i) PC: pay and confirm the transaction, (ii)
PD: pay and leave the system with denying the transaction,
and (iii)Lb: leave the system without paying. The seller’s
strategy profile includes (i) SC: ship the goods and confirm
the transaction, (ii) SD: ship the goods and leave the system
with denying the transaction, and (iii) Ls: leave the system
without shipping. Each participant’s payoff is determined
based on the value of the goods and its deposit given the
other participant’s strategy. The interaction between the two
participants can be modeled as a normal-form game. By using
the iterative removal of dominated strategies [12], the game
is proved to have a unique Nash equilibrium if both the
participants’ deposits are greater than the goods’ value. At
the Nash equilibrium, the PC and SC strategies are the best
response of the buyer and the seller, respectively. Simulation
results show that if the deposits of both participants are greater
than the value of the goods, the sum of buyer’s money and
the value of the seller’s goods remain unchanged for the whole
system. This means that the buyer’s money is exchanged into
the goods successfully, and the seller’s goods is exchanged into
the money with no loss. However, in practice, the participant
may not be perfectly knowledgeable of the other participant’s
strategy. More sophisticated game models and tools can be
considered.
3) Cyber-Insurance: Different from suppressing the attack
motivation as presented in [34], [40], [41], [66], [67], the
authors in [44] propose a cyber-insurance scheme [102] to
compensate for the losses of the attacked blockchain partic-
ipants. The model includes multiple blockchain users, one
blockchain provider, and one cyber insurer. Each user needs
to choose a service offered by the provider and maximize its
utility given the other users’ service demands. Given the users’
demand, the provider’s problem is to invest in the computing
resource to increase its profit. To alleviate losses of being
attacked, the blockchain provider also purchases insurance
from the cyber-insurer. The cyber-insurer sets the price of the
insurance based on the perceived risk level of the provider.
Typically, the provider and the insurer offer the service first,
and the user then chooses the service. Thus, the interaction
among the users, the provider, and the insurer can be modeled
as a Stackelberg game. By exploiting the characteristics ofthe Jacobian matrix [32] to analyze the utility functions of
the players, the game is proved to admit a unique Stackelberg
equilibrium. The simulation results show that the provider can
maximize its utility at a unique point which is in accordance
with the uniqueness analysis. However, in practice, the insurer
cannot completely know the risk level of the provider, and the
Bayesian game can be adopted.
V. A PPLICATIONS OF GAME THEORY FOR MINING
MANAGEMENT
Under the Nakamoto protocol, anyone within the blockchain
network is allowed to play the role of the mining competition,
transaction dissemination and verification in order to obtain
the profit [1]. Each miner or mining pool involved manages
what strategy it will perform to maximize its payoff given
the others’ strategies, and game theory can thus be effectively
applied. In this section, we will survey the applications of
game theory in the mining management including computa-
tional power allocation, fork chain selection, block size setting,
pool selection and reward allocation.
A. Individual Mining
1) Computational Power Allocation: Bitcoin mining is a
competition that miners contend with each other by investing
in computational power to win mining rewards. To maximize
the utility, each miner determines the allocation of its computa-
tional power, i.e., whether or not to invest in the computational
power, given the other miners’ strategies. Therefore, a non-
cooperative game is applied to analyze the interaction among
the miners in [29]. The miner’s utility is a function of its
computational power, the mining rewards and the marginal
cost, i.e., the average cost for the miner to invest in a unit
of computational power. By using the second-order derivative,
the miner’s utility function is proved to be concave. Thus, a
unique Nash equilibrium exists at which investing is the best
response of each miner as long as the miner’s computational
power satisfies a condition. Here, the condition is determined
based on the computational power and the marginal cost of the
miner and the entire bitcoin network. At the equilibrium, it is
found that the decision on the investment is not affected by
the value of the mining rewards. Moreover, every miner can
have a positive utility for any level of other miners’ strategies
which consequently can prevent a monopoly.
Different from [29] in which the miners choose whether
or not to participate and then keep their chosen strategies,
the authors in [48] consider a case in which the miners can
choose “arrival”, i.e., investing in the computational power,
and “departure”, i.e., leaving the mining, at any time. In
general, the strategy of each miner depends on the state of the
blockchain network, i.e., the number of miners participating
in the mining, given other miners’ strategies. A stochastic
game can be applied to analyze the miners’ strategies as
presented in [48]. The miner’s utility is a function of the
number of the miners in the system, the arrival and departure
rates of the miners, the rate of PoW getting solved, the cost
and the reward of the mining. By transforming the utility
function to the Bellman equation [121] and then calculating
15
TABLE IV: A Summary of Game Theoretical Applications for Mining Management.
REF.GAME MODEL PLAYER ACTION STRATEGY PAYOFF SOLUTIONIndividual mining[29]Non-cooperative
gameMinersComputational
power allocationSelection of investment in
computational power or notMining rewards
minus costNash equilibrium
[48]Stochastic game MinersComputational
power allocationSelection between investing
and leavingMining rewards
minus costSubgame perfect
equilibrium
[103]Cournot game MinersComputational
power allocationDetermination of the amount
of investment in
computational powerMining rewards
minus costNash equilibrium
[104]Non-cooperative
gameMinersComputational
power allocationSelection of proper time to
start using the mining
machinesMining rewards
minus costNash equilibrium
[45]Stackelberg
gameService provider
and minersComputational
power allocationDetermination of service price
and service demandProfit minus costStackelberg
equilibrium
[105]AuctionService provider
and minersComputational
power allocationDetermination of the bid for
serviceProfit minus cost Individual utility
[106]AuctionService provider
and minersComputational
power allocationDetermination of the bid for
serviceProfit minus cost Social welfare
[107]Sequential game Miners Fork chain selectionSelection of reporting mined
block and mining on the
longest chain or notMining rewardsSequential
equilibrium
[49]Stochastic game Miners Fork chain selection Selection of branch to mine Mining rewardsSubgame perfect
equilibrium
[39]Extensive-form
gameMiners Fork chain selectionSelection of mining on the
fork or notMining rewards
minus costNash equilibrium
[108]Extensive-form
gameMiners Fork chain selectionSelection between
strategically or stubbornly
deviating the protocol and
following the protocolMining rewards
minus cost and
punishment-robust
equilibrium
[109]Coordination
gameMiners Fork chain selectionDetermination of updating
blockchain version or notMining rewards Nash equilibrium
[110]Coordination
gameBlockchain users
and minersFork chain selectionChosen between two fork
chainsMining rewards Nash equilibrium
[111]Repeated game Miners Fork chain selectionSelection of forming the
coalition or notMining rewards
minus costSocial welfare
[112]Non-cooperative
gameMiners Fork chain selectionSelection of forming the
coalition or notProfit minus cost-coalition-safe
3Nash
equilibrium
[31]Non-cooperative
gameMiners Fork chain selectionSelection of forming the
coalition or notMining rewards Nash equilibrium
[113]Non-cooperative
gameMiners Block size settingDetermination of the block
sizeTransaction fees
and mining
rewardsNash equilibrium
[72]Non-cooperative
gameMiners Block size settingChosen transaction to be
included in the blockTransaction fees
and mining
rewardsNash equilibrium
[114]Non-cooperative
gameMiners Block size settingChosen of upper bound of
block sizeTransaction fees Nash equilibrium
[37]Extensive-form
gameMiners Block size settingChosen transaction to be
included in the blockTransaction feesSequential
equilibrium
[115]Non-cooperative
gameBlockchain users Block size settingSelection of paying the
transaction fee or notProfit minus
transaction feeNash equilibrium
[116]Non-cooperative
gameMiners Block size settingSelection to be included in
the committeeMining rewards Nash equilibriumPool mining[117]Coalitional
gameMiners and pools Pool selection Chosen of the pool to join Mining rewardsCooperative
equilibrium
[118]Evolutionary
gameMiners and pools Pool selection Chosen of the pool to switchMining rewards
minus costNash equilibrium
[119]Coalitional
gameMiners and pools Pool selectionSelection between forming
the pool and joining the poolMining rewardsNon-myopic
Nash equilibrium
[30]Non-cooperative
gameMiners and pool
managerReward allocationSelection of reporting shares
and allocating rewardsMining rewards Nash equilibrium
[120]Repeated gameMiners and pool
managerReward allocationSelection of reporting shares
and allocating rewardsMining rewards Nash equilibrium
[38]Extensive-form
gameMiners and pool
managerReward allocationDetermination of the
computational power
allocation and optimizing the
reward allocationMining rewards
minus cost and
charged feeSubgame perfect
equilibrium
16
the first-order derivative, the utility function is proved to be
monotonic increasing if the cost of mining is greater than a
threshold. Thus, investing the maximum power is the dominant
strategy of each miner regardless of the state of the blockchain
network, and the game has a subgame perfect equilibrium. The
simulation results show that the utilities of the miner under
different arrival rates gradually converge to the same curve,
i.e., the game reaches the equilibrium.
In addition to the case in [29] and [48] that the miner
can only choose to invest in the computational power or not,
the authors in [103] investigate the amount of computational
power that the miner determines to invest to win the mining
rewards, given the other miners strategies. The probability that
the miner solves the PoW in a given time can be assumed to
follow an exponential distribution [1]. As such, the Nakamoto
protocol essentially formalizes an exponential race. A Cournot
game [122] can be thus used to analyze the interaction among
the miners as presented in [103]. The miner’s utility is a
function of the mining rewards, the computational power, and
the marginal cost of the investment. The game is then proved
to admit a symmetric Nash equilibrium by simply showing that
the marginal revenue, i.e., the average revenue for the miner
to invest in a unit of computational power, is equal to the
marginal cost. At the equilibrium, each miner can optimize its
investment and has no incentive to deviate from honest mining.
The aforementioned approaches, i.e., [29], [48] and [103],
consider the case that the mining reward dominates the trans-
action fee. Nevertheless, when the transaction fee dominates
the mining reward4, the miner will adjust its allocation of
computational power by choosing strategically the proper time
to start using its mining machines, i.e., the machines used for
mining process which require electricity for their operation,
to mine given the other miners’ strategies. The reason is that
miners have no incentive to mine unless the accumulated
transaction fees sufficiently exceed a certain threshold. Thus,
the non-cooperative game can be used to analyze the inter-
action among the miners as presented in [104]. Each miner’s
utility is a function of the starting time, the operation time,
the proportion of the miner’s machines, and the probability
distribution function of the block finding time. The numerical
analysis is thus used to find the Nash equilibrium of the
game. The simulation results show that the miners that own
the same number of mining machines eventually converge to
the same starting time, meaning that the game reaches the
Nash equilibrium. However, how to prove the uniqueness of
the Nash equilibrium is not discussed.
Although blockchain has been widely deployed in many
scenarios as presented in [29], [48], [103], [104], deploy-
ing blockchain applications in mobile environments is still
challenging because the mining process consumes high com-
putational power from mobile devices. An edge comput-
ing paradigm has been recently introduced in the mobile
blockchain networks for offloading the mining tasks of mobile
devices, i.e., the miners [42]. The system model is illustrated
4Take the bitcoin as example, the bitcoin code includes a statement which
declares that the mining reward will drop by half after about four years
(210,000 blocks). Thereby, the mining reward will eventually be dominated
by the transaction fee.in Fig 10. However, an important issue is how to allocate
efficiently the limited edge computing resources of service
providers to the miners. The authors in [45] model the in-
teraction among the service provider and the miner as a two-
stage Stackelberg game. The service provider acts as the leader
setting the price of the service, and then the miner acts as
the follower choosing its computational service demand, given
the service price and the other miners’ strategies. The utility
of service provider is a function of the profit obtained from
charging the miners, the miners’ service demand, the time that
the miner takes to mine a block, and the cost of electricity.
The utility of the miner is a function of the computational
service demand, the service price, the cost and the rewards
of the mining. By using the backward induction, the game
is proved to admit a unique Stackelberg equilibrium which is
supported by the simulation results. However, in practice, the
players cannot know the perfect information of each other, and
the Bayesian game can be adopted.
Traditional sealed-bid auctions, e.g., the Vickrey auction
[123], can also be used to guarantee that the edge computing
resources are allocated to the miners which value the resources
most. However, designing the optimal auction is challenging.
The authors in [105] propose to apply deep learning techniques
to achieve the optimal auction for the computing resource
allocation in the blockchain network. The model consists of
one service provider, i.e., the seller or auctioneer, and multiple
mobile users as miners, i.e., bidders. The miners compete a
computing resource unit of the service provider by submitting
bids, i.e., the prices that the miners are willing to pay.
Upon receiving the bids, the service provider determines the
allocation rule, i.e., winning probabilities of the miners, and
the conditional payment rule to the miners. The allocation and
payment rules are implemented by using neural networks. The
neural networks are constructed based on an analytical solution
of the optimal auction, i.e., the Myerson theory [124]. As
such, the auction mechanism learned by the neural networks
is optimal in terms of maximizing the revenue of the service
provider while ensuring the economic properties, i.e., incentive
compatibility and individual rationality. The simulation results
show that the proposed scheme outperforms the traditional
sealed-bid auction [123] in terms of revenue. However, the
proposed scheme is constrained to a single computing resource
unit that may not meet the needs of the miners.
Different from the auction in [105] that the service provider,
i.e., the auctioneer, maximizes its individual utility, the authors
in [106] investigate the case of maximizing the social welfare
of the entire blockchain network. Under the same model as that
in [105], the utility of the mobile user and service provider is a
function of the mining rewards, the computational power, the
service price, the demand of the miner, and the robustness of
the network associated with the distribution of the computa-
tional power. By transforming the social welfare maximization
auction problem to a problem of non-monotone submodular
maximization with knapsack constraints [125], the algorithm
of achieving the social optimum can be developed. The
simulation results show that the algorithm not only achieves
the good performance in maximizing the social welfare, but
also guarantees the truthfulness, individual rationality and
17
Mobile device
(bidder)Service Provider (Auctioneer)
Mobile device
(bidder)Data synchronization
Mobile device
(bidder)Bid
BidBid
Fig. 10: An example of the system model of edge computing
in mobile blockchain network. The mobile devices compete
for the computational power by submitting the bid, and the
service provider determines the allocation rule of its service.
computational efficiency. However, the algorithm is designed
for the offline auction which is not applicable for real-time
trading scenarios.
2) Fork Chain Selection: Under the Nakamoto protocol,
there are sequential PoW puzzles that the next puzzle depends
on the solution of the previous one. Since each miner needs
to choose to (i) report its found puzzle to mine on the longest
chain, or (ii) not to report the found puzzle and to the next
puzzle secretly, given the publicity of previous puzzles, fork
chain may appear. To maximize the utility, the miner trades
off reporting the puzzle to gain the mining rewards and not
reporting the puzzle to mine on fork. Meanwhile, the miner is
uncertain whether it is the first one to find the solution of the
puzzle. Thus, a sequential game with imperfect information
can be applied to model the interaction among the miners
as presented in [107]. The miner’s utility is a function of
the distribution of the computational power, the probability
of winning to solve the PoW, and the other miners’ belief of
the upcoming publicity of the puzzles. By using the backward
induction, the game is proved to admit a multiplicity of
sequential equilibrium. This means that both reporting and not
reporting can be the best response of each miner depending
on the computational power that the miner uses to solve
the puzzle. However, the authors only consider a three-miner
case, and a general case with any number of miners can be
investigated.
After finding the solution of the PoW as discussed in [107],
the miner probabilistically chooses which branch to mine, i.e.,
to choose a certain chain to attach its block to, among the tree-
like branches of the blockchain network structure. If the miner
chooses the branch which will not be the longest chain, the
miner’s effort to solve PoW is wasted. A stochastic game can
be used to analyze the strategies of the miners as presented in
[49]. The miner’s utility is determined based on the miner’s
computational power, the number of blocks solved by the
miner, the mining rewards, and the difficulty of solving the
PoW. By using the backward induction, the game is proved
that mining the longest chain is a subgame perfect equilibrium.
However, the current longest chain may not be the longest
one after several rounds of mining competition. Portions of
the historical transactions may be abandoned.
Similar to [49], the authors in [39] demonstrate that fol-lowing the protocol, i.e., mining on the longest chain, is the
Nash equilibrium. However, the model in [39] is based on the
PoS system in which the fork chain randomly selects the coin
from the set of coins owned by miners at each time step (see
Section III). Thus, an extensive-form game can be applied.
The miner chooses whether or not to mine on the fork, given
the other miners’ strategies. The miner’s utility is a function
of the stake, the mining rewards, the coins of miners selected
by the fork, and a discounted factor. Since the cost of mining
on the fork increases with the miner’s stake, for a sufficiently
large stake of the miner, the cost overweighs the profit gained
from the mining rewards. By restricting access to the miners
with the large stake, the rest of miners have no incentive to
deviate from mining on the longest chain, and the game thus
reaches the Nash equilibrium. Empirical data obtained from
Blockchain.info [90] supports the theoretical analysis.
Extended from [39], the authors in [108] investigate the
case of miners choosing the fork chain in an upgraded PoS
system. In the upgraded system, the latest block is called the
parent block, and concurrent blocks attached to the parent
block are called the leaf blocks. Instead of following the
longest chain protocol, miners can choose the leaf blocks
to be attached to the parent block. To model the interaction
among the miners in the tree-like structure of the system, an
extensive-form game can be applied. The miners’ strategies
include deviating from the protocol stubbornly, following the
protocol, and strategically choosing whether or not to deviate
from the protocol to maximize their utility. Since there is only
one leaf block that can reach the consensus to win the reward,
the utility of the miner is a function of the reward, the cost
of losing the block, and the punishment of deviating from the
protocol, given the other miners’ strategies. The punishment
is implemented by taking away the deposit of the miner that is
deposited in advance. When the fraction of the stubborn miners
is less than 1=3, each miner cannot increase its utility more
thanor decrease its utility more than 1=by deviating from
the protocol. Thus, the game has a unique -robust equilibrium
[116]. The simulation results show that only when the fraction
of the deviated miners is greater than a quarter, the utilities
of the miners that follow the protocol decrease, as the number
of the deviated miners increases. This is consistent with the
theoretical analysis.
Furthermore, when the fork chain appears, the miners need
to decide whether to update the blockchain version, i.e.,
to acknowledge the fork as a hard fork or not. The hard
fork is a permanent divergence from the previous version
of the blockchain which requires the miners to upgrade the
blockchain software. Since having more miners participating
in a particular chain version increases the value of the version,
the miner’s strategy depends on not only its individual profit,
but also the other miners’ profits. Thus, a coordination game
can be used as presented in [109]. In the game, the miner
gains a zero-profit if the miner’s strategy is not consistent
with those of the majority of miners, and thus the game is
admitted a unique Nash equilibrium. At the equilibrium, every
miner chooses to stay on the current version or to upgrade
the version. However, organizing the voting of upgrading the
blockchain version remains a topic for further research.
18
Similar to [109], the authors in [110] propose to use the
coordination game approach for choosing the fork chain.
However, the players in the game in [110] include blockchain
users and miners. To maximize the utility, both types of players
need to choose between two fork chains to participate. Here,
the utility of a blockchain user is a function of the users’
distribution of choosing certain chain, the transaction fees, and
the strategies of the miners. The miner’s utility is a function
of the distribution of the users between two fork chains,
the computational power, the mining rewards, and the other
miners’ choice of the chain. If the number of the blockchain
users choosing a certain chain is greater than a threshold, the
utility of the players can be proved to be monotonous. Thus,
the game has a unique Nash equilibrium that all of the players
choose the same chain. Otherwise, a mixed strategy Nash
equilibrium exists such that players choose the chain randomly.
The simulation results show that the user will choose to remain
on a certain chain when the number of the users on this chain
is greater than a certain value which is in accordance with the
theoretical analysis. However, the case which involves multiple
fork chains can also be investigated.
The aforementioned approaches, i.e., [109] and [110], show
that the miners can coordinate, i.e., through forming a coali-
tion, to increase their utilities by deviating from the honest
mining. To address this issue, the authors in [111] propose an
upgrade scheme for the blockchain protocol. In the upgrade
scheme, the mining reward is delayed to allocate to the
miner that finds the solution of the PoW. Also, the miner
can receive variable discounted rewards during several rounds
of mining after the miner finds the solution. Extended from
the coordination game model in [110] to its infinite form, a
repeated game is then adopted in [111] where each miner
chooses whether to form the coalition or not in every round
of mining. The utility of each miner is a function of its
computational power, the mining rewards, the difficulty of
solving the PoW, the cost of mining, the number of rounds for
allocating the discounted rewards, and the discounted factor
of the rewards. It is approved that if the discounted factor
meets an inequality, the miner’s utility of honest mining is
greater than that of forming the coalition. This means that
the game has a unique subgame perfect equilibrium at which
the inequality is satisfied, and all the miners perform honest
mining.
Similar to [111], the authors in [112] propose a scheme
to prevent the miners from forming the coalition. In the
scheme, the transactions are first included in a buffer block,
and the miner mines on the buffer block by solving the PoW.
Only after the buffer block is broadcast and verified, this
buffer block becomes the real block and will be attached
to the blockchain. The miner can choose whether or not to
form the coalition, i.e., deviating from the honest mining,
given the other miners’ strategies. Thus, the interaction among
the miners can be modeled as a non-cooperative game. The
miner’s utility is a function of the computational power, the
number of the blocks in a round of mining, the difficulty of
solving the PoW, the distance between the buffer block and
the blockchain, the cost and the rewards of mining, and the
transaction fees. By calculating the ratio of the upper boundto the lower bound of the coalition’s profit, the multiplicative
increase in utility is proved to be less than (1 + 3). Here,
the coalition controls less than a < 1=2fraction of the
computational power, and the constant satisfies <0:3. This
means that no coalition that controls less than a fraction of
the computational power can gain more than a factor (1 + 3)
of the mining rewards and transaction fees by deviating from
the protocol. Therefore, the game has a -coalition-safe 3
Nash equilibrium.
Different from the PoW based coalition as discussed in
[109]–[111], the coalition in the PoS based system is inves-
tigated in [31]. In PoS, the miner’s stake, i.e., a parameter
associated with the amount of the miner’s cryptocurrency and
the time that miner has been holding the cryptocurrency, is
updated at the end of each round of mining and the stake
will be reset to zero after the miner discovers the block
(see Section III). The higher stake means less difficulty in
mining the block. Thereby, the miner chooses whether or not
to form the coalition for holding more stakes to lower the
mining difficulty, given the other miners’ strategy. Thus, a
non-cooperative game can be applied. The miner’s utility is
a function of the stake, the mining rewards, the number of
times that the miner discovers the block and transactions to be
included in the block. Since the miners of the coalition, even
deviating from the protocol, cannot obtain the utility which
exceeds that of the non-coalition, the game is thus proved to
have a unique Nash equilibrium at which every miner follows
the protocol. However, forming the coalition is not the only
way to increase the miner’s stake. To increase the holding time
and thereby increase the stake, the miner has an incentive to
hold its cryptocurrency without mining. As a result, there is
no mining miner, and the entire blockchain network crashes.
3) Block Size Setting: When mining the bitcoin, the miner
can earn more transaction fees by including more transactions
in its block. However, it also decreases the miner’s probability
of gaining the mining reward [73] for a number of reasons,
e.g., resulting in a longer propagation time for reaching a
consensus. Each miner needs to determine strategically the
block size, i.e., the number of transactions to be included
in a block, to maximize its utility, given the other miners’
strategies. Thus, the authors in [113] model a two-miner case
as a non-cooperative game. The miner’s utility is a function of
its computational power, block size, and the time to reach the
consensus. Since the first-order derivative of the miner’s utility
function with respect to the block size is always less than zero
when the unit transaction fee and the mining reward meet a
certain condition, the strategy that all of the miners include
no transaction in their block is a unique Nash equilibrium.
However, if the transaction fee or the mining reward change,
the Nash equilibrium shifts to the strategy that all of the miners
include transactions in their block.
To avoid the case that all miners include no transaction
in their block as presented in [113], the authors in [72]
demonstrate the necessity of setting the maximum block size.
Same as the game approach as presented in [113], the miner
chooses the transactions to be included in a block at every
round of the mining competition. The miner’s utility is a
function of its computational power and the transaction fees
19
associated with the block size and the bitcoin mining reward.
The transactions that one miner does not include in its block
will be included by another miner before the next round of the
mining competition. Thus, when the block size is unlimited,
the strategy of including all transactions by all the miners
regardless of the fee is the unique Nash equilibrium. It is also
found that unbounded transaction fee leads to the same Nash
equilibrium. However, inflations of the computational power
distribution may have an impact on the existence of the Nash
equilibrium of the game.
An analysis of setting a proper block size can also be found
in [114]. The authors propose a Bitcoin-unlimited scheme to
increase the throughput of the bitcoin system. In the scheme,
each miner chooses its own upper bound of the block size,
and invalidates and discards the excessive block, i.e., the block
with the size larger than its upper bound. To maximize the util-
ity, the miner trades off the transaction fees and the probability
of its block being orphaned based on its mining power, given
the other miners’ strategies. Thus, a non-cooperative game can
be used to model the interaction among the miners. Since any
miner that chooses different upper bound gains zero utility, the
game is proved to admit a unique Nash equilibrium at which
all miners choose the same upper bound. Since only the blocks
with appropriate sizes would be added to the blockchain, the
block size under the proposed scheme gradually increases to
the maximum limit associated with the network capacity. This
means that the divergence on the block size is always bounded
and the throughput of the system increases. The simulation
results show that if all miners have different bounds, the
miners that possess large computational power intend to form
a coalition to gain extra profit. However, this is harmful to
maintaining the bitcoin’s decentralized structure.
However, the unlimited block size [114] may not lead to a
higher throughput of the bitcoin system. The reason is that any
two blocks may have collisions, i.e., the miners simultaneously
choose the same subset of transactions to be included in the
blocks. This situation wastes the computational power for ver-
ification and lowers the throughput of the system. To address
this issue, the authors in [37] propose an alternative bitcoin
protocol. In the protocol, the system selectively incorporates
transactions of off-chain blocks into the main chain and awards
creators, i.e., miners, of the accepted transactions even if the
creators’ blocks are not part of the main chain. Each miner
chooses the transactions to be included in its block and trades
off the transaction fees and probability of the collision. The
miners are partially aware of other miners’ strategies and take
their strategies sequentially. Thus, an extensive-form game
can be used to model the interaction among the miners. The
utility of the miner is a function of the position of its block
in the main chain, the discount factor, and the fees of the
chosen transactions. By using the backward induction, the
game is proved to admit a sequential equilibrium at which the
miners probabilistically choose the transaction to minimize the
collision. As a result, the proposed protocol achieves a higher
throughput which is consistent with the simulation analysis.
However, the game has several other Nash equilibria at which
the miners’ utilities are much less than that of the sequential
equilibrium.Moreover, even with the unlimited block size as presented in
[114], there is still a limitation on transactions to be included
in the block. The limitation is imposed by the waiting time,
i.e., the time that a transaction of the blockchain user waits in
a queue to be included in the block. The blockchain user can
choose (i) to pay a transaction fee to the miner to reduce the
waiting time, or (ii) not to pay any fee and may experience
a longer waiting time. The miner can decide to stay on or
to leave the mining according to the expected profit of the
transaction fees and the cost. Thus, the interaction between
the miners and the users can be modeled as a non-cooperative
game as presented in [115]. The miner’s utility is a function
of the number of miners in the network, the rate of solving
the PoW, the exchange rate between the bitcoin value and
the dollar, the transaction fees, the rewards and the cost of
the mining. The user’s utility is a function of the exchange
rate, the transaction fee, the waiting time, the profit of the
included transaction, and the fraction of users that pay the
fee. The constraint between the number of miners and the
rate of solving the PoW can be obtained, when the miner’s
and the user’s utility are both greater than zero. This means
that if the constraint is satisfied, the game has a unique Nash
equilibrium. At the equilibrium, the miner chooses to stay on
the mining and the user chooses to pay the transaction fee.
Empirical evidence from blockchain.info [90] is implemented
to validate the theoretical analysis. However, multiple Nash
equilibria can exist if the constraint is not satisfied.
As presented in [115] that the waiting time limits the
throughput of the blockchain network, the authors in [116]
propose a novel protocol that greatly reduces the waiting time
of the transaction to reach the Nakamoto consensus. In the
protocol, there is a committee including a certain number
of members, i.e., miners. The block found by any miner
is verified only when the majority of members reach the
consensus. This miner is then selected as a member in the
committee and ranked based on its computational power. The
utility of the member is a function of the computational power,
the mining rewards and the other members’ strategy. Thus, a
non-cooperative game can be applied. Since the member gains
the positive profit only when the member follows the protocol,
i.e., chooses the block with higher rank, the game is proved to
admit a unique Nash equilibrium. At the equilibrium, the chain
is never forked and the confirmation time for preventing from
the double spending is unnecessary. As a result, the throughput
of entire the blockchain network increases.
B. Pool Mining
1) Pool Selection: To reduce the volatility of the mining
rewards and to maximize the utility, miners can form a coali-
tion, i.e., mining pool [75], and cooperate with the members,
i.e., miners in the pool, by following the reward allocation of
the pool. Thus, a coalitional game [11] can be used to analyze
the interaction among the miners and the pools as presented in
[117]. Since the communication delay of the bitcoin network
leads to the non-linearity of the pool’s mining rewards, the
rewards cannot be distributed stably among the members. This
means that there are always some miners having an incentive
20
to leave their pools and join other pools to increase their
utility. As a result, no cooperative equilibrium exists in the
game. Additionally, as more transactions are processed in the
bitcoin system, the non-linearity effect on the mining rewards
increases, and thus miners are more likely to switch pools,
i.e., select and join the pool which benefits them most.
During pool selection, each miner first randomly selects a
mining pool to start mining with and then switches to another
pool after a time period according to its expected utility.
The distribution of the miners in mining pools of the whole
blockchain network evolves over time based on the miners’
strategies. Thus, the framework of evolutionary game [126]
can be used to analyze the dynamic process of the miners’
pool selection as proposed in [118]. If the replicator dynamics
[127], i.e., the growth rate of the size of the pools, is equal
to zero, the distribution of the miners reaches evolutionary
stability [128]. Here, the utility of the miner is a function of
its computational power, propagation delay, the mining reward
and the mining cost. By exploiting the characteristics of the
Jacobian matrix of the replicator dynamics in a two-mining-
pool network, the game is proved to admit conditionally a
unique Nash equilibrium, i.e., the evolutionary stability.
The miners in a PoS system can also form coalitions, i.e.,
pools, to increase their utilities. The miners need to trade off
the cost and the expected profit of forming the pool. For
this, each miner chooses (i) to form a pool as a leader, or
(ii) to allocate its stake to pools that are already created by
the other miners given the reward scheme of the system. In
particular, the miner first determines the amount of stake to be
allocated to be the leader and then calculates the best possible
allocation of mining rewards. Thus, a coalitional game can
be applied to analyze the respective aspect of interactions
among the miners and the pools as presented in [119]. The
results of backward induction illustrate that both the games
have a unique non-myopic Nash equilibrium [129]. At the
equilibrium, the certain number of pools are formed with
the same size. The rewards are distributed evenly among all
miners, except for pool leaders that get an additional gain. The
simulation results show that starting from no pool, the game
quickly converges to multiple pools of an equal size which is
consistent with the theoretical analysis.
2) Reward Allocation: Admittedly, the mining pool’s re-
ward allocation, i.e., the algorithm used to share mining
rewards among miners, has a significant impact on the utilities
of the miners [75]. The miner can choose to report shares, i.e.,
preimage solutions for a block that meets the requirement set
by the pool manager [25], immediately or to delay the report-
ing given the reward allocation of the pool. The pool manager
needs to select the reward allocation algorithm according to
the miners’ expected utility. Thus, a non-cooperative game
can be used to analyze the interactions between the miners
and the pool manager as presented in [30]. If a certain
condition is satisfied, the strategy that each miner reports
the shares immediately is the Nash equilibrium. Here, the
condition is associated with the miner’s computational power,
the probability of finding the full solution of the PoW, the
number of reported shares, and the number of the completed
rounds of the mining competition.However, the approach proposed in [30] considers only
the single share. Namely, each miner reports the share only
one time during mining. In practice, the miners can report
the shares repeatedly, and the pool manager can optimize its
reward allocation to maximize its utility. Thus, a repeated
game can be applied as presented in [120]. The game is
proved that the pool manager can use the geometric-pay, i.e.,
a certain reward function, to achieve the social optimum.
The simulation results show that the expected utility of the
geometric-pay pool, i.e., the pool that allocates its mining
rewards following geometric distribution, is greater than those
of both the proportional pay pool, i.e., the pool that shares
mining rewards evenly among the shares, and the PPLNS pool
which is in accordance with the theoretical analysis.
As the miners participate in mining pools to reduce the
volatility of the mining rewards, a large pool may become
even larger. It may lead to a centralization against to the funda-
mental decentralized structure of the blockchain. However, the
authors in [38] demonstrate that this situation will not happen.
During each round of mining, the miner chooses to allocate its
computational power to a certain pool according to the state
of the blockchain, i.e., the distribution of computational power
among the pools. The pool manager adjusts the fees charged
to the participated miners to maximize its profit, given the
state of the blockchain. Thus, an extensive-form game can
be applied to analyze the interaction between the miners and
the pool managers as presented in [38]. The miner’s utility
is a function of the computational power, fee charged by the
pool, the distribution of miners among the pools, the cost and
the rewards of the mining. If the fee charged by the pool
manager satisfies a condition, the game reaches a subgame
perfect equilibrium. Here, the condition is associated with the
number of the remaining miners in the same pool. At the
equilibrium, the large pools charge a higher fee than the small
pools. The miners thus choose the small pools to participate
to maximize their utility. As a result, the centralization will
not happen. Empirical evidence from Bitcoinity and Bitcoin
Wiki supports the theoretical analysis.
VI. A PPLICATIONS OF GAME THEORY ATOP BLOCKCHAIN
PLATFORM
A. Crypto-currency Economic
1) Transaction Transparency: Under the Nakamoto proto-
col, the entire history records linked to the transaction are
transparent to all the blockchain miners and users. This may
cause a series of problems. For example, blockchain miners
intend to include the transaction in high quality, i.e., most of
the history of the transaction is legalized and reliable, into the
block rather than the transaction in low quality. The reason
is that the transactions that can be traced back to the darknet
markets or ransomware payment may be added to the blacklist
of the government. The large transaction in not high quality
may thus be orphaned by the miners regarding the possible
huge loss. To mitigate the risk of transaction in not high quality
being orphaned, the user mixes strategically its payment, i.e.,
splits its payment of transaction into several small ones in
different qualities. This scenario is illustrated as in Fig. 11.
21
Since the miner’s possible loss decreases due to the smaller
size of the transaction, the transaction that is not in high quality
may be included into the block. The user checks the quality of
the other user’s transaction sequentially and a sequential game
can thus be used to analyze the interaction among the users as
presented in [130]. The user’s utility is a function of the quality
of the transaction, the value of the post-transaction and the
cost of mixing the payment. By using the backward induction,
the game is proved to admit multiple subgame perfect Nash
equilibria. At the equilibrium, each user mixes their payment
in a single transaction instead of sending multiple individual
transactions. However, the transaction size, the cost and the
rewards of mining can be taken into account in a more general
case.
Input1
Input2Output1
Output2Transaction A
Input1
Input2Output1
Output2Transaction C
Input1
Input2Output1
Output2Transaction B5BTC
2BTC
3BTC
1BTC3BTC
3BTC4
13BTC
2BTC
Fig. 11: An example of the mixing payment: the transaction
A is discovered to be a ransom payment and all of its outputs
are added to the blacklist. The transaction B is in high quality.
To avoid transaction C to be orphaned by the miners, the user
mixes the payment of the transaction C by the payment of
transaction A and transaction B.
Under the scenario in [130], the user that mixes the payment
of transaction makes money flows more difficult to trace. This
is harmful for the entire blockchain system. To address this
issue, the authors in [131] investigate the optimal level of
transaction transparency and propose a reliable trading system.
Since each blockchain user has a unique public key, the
user can use the crypto-currency to trade goods with another
user directly. To avoid the transaction information, e.g., the
ownership of a certain sum of money, being exploited for
crime, the proposed system restricts the user’s ability to view
the complete transaction information attached to the public
keys. Thereby, before delivering the goods for trading to other
users, the user trades off the expected profit and possible loss
in terms of the incomplete transaction information to choose
whether or not to perform the trading. The trading can thus
be organized as an infinitely repeated game in discrete time
as presented in [131]. The user’s utility is a function of the
trading quantity, trading price, the probability of the trade
being performed, the allocation of the goods for trading, and
the cost of trading. By defining the inequality that the user’s
utility of offering a positive trading price is greater than that
of the offering a zero trading price, i.e., the transaction failure,
the constraint between the trading price and the allocation can
be obtained. This means that if the constraint is satisfied, the
game has multiple Nash equilibria. In any equilibrium of the
game, the user has an incentive to split large transaction into
small ones and trades with several other users.Although the transparency of transaction information causes
a series of problems, e.g., malicious uses of information,
as presented in [130] and [131], it enables the entrant, i.e.,
the new blockchain user, to possess an endogenous high
reputation, i.e., the ability of performing the reliable trading.
Thereby, potential users have more incentive to enter the trad-
ing system compared with the traditional real-world trading
system where only the user that has high reputation can attract
customers to trade with. Although the blockchain-based trad-
ing system facilitates the entry for potential entrant, the trading
competition in the system increases and collusion among the
users arises due to the information transparency. Thus, each
potential entrant chooses whether or not to enter the system
regarding the trade-off between the expected utility after the
entry and the enhanced competition as well as collusion, given
the other entrants’ strategies. Since the potential entrant makes
its choice repeatedly in discrete time period, a repeated game
can be applied as presented in [132]. The entrant’s utility
is a function of the probability that one customer joins the
trading in a time period, the probabilistic distribution of the
reputation, the profit that can be obtained by the trading and
the cost of entry. Since both the utility of the entrant and
the social welfare of the system are higher than those of the
traditional trading system, each potential entrant entering the
system is proved to be the Nash equilibrium. However, the
balance between transparency and privacy of the blockchain
trading system still remains a topic for further research.
2) Crypto-currency Value: In the last decade, hundreds
of crypto-currencies are adopted in the worldwide financial
market. Each crypto-currency has its value which depends
on its transaction rate, transaction fees, mining rewards and
its fiat exchange rate. The miners need to choose a certain
currency to mine according to the value of the crypto-currency
and the competition from the other miners. Given the other
miners’ strategies, the miner can choose to keep mining on
the same crypto-currency or change its strategy to mine on
another one. Since the incentive of all miners, i.e., players, to
change their strategy can be expressed using a single global
function, i.e., the potential function [135], the potential game
can be applied as presented in [133]. The potential function is
of the distribution of the miners on mining different crypto-
currencies, the computational power, the value and the reward
allocation of the crypto-currencies. By using the induction
of the better-response learning algorithm [135], the game is
proved to admit more than one Nash equilibrium. However,
how to achieve a desired equilibrium is not discussed.
The authors in [36] further investigate the relationship
between the value of the crypto-currencies and the population
size of the users. Given a certain blockchain-based crypto-
currency, the user can choose whether or not to participate
in the blockchain platform with a cost and to hold a cer-
tain amount of the crypto-currency, given the other users’
strategies. Since the user makes its strategy based on the
productivity of the blockchain platform, i.e., the state which
represents the quality or the usefulness of the blockchain
platform, an extensive-form game can be adopted to analyze
the interaction among the users as presented in [36]. The user’s
utility is a function of the transaction supply and demand, the
22
TABLE V: A Summary of Game Theoretical Applications for Crypto-currency Economic.
REF.GAME MODEL PLAYER ACTION STRATEGY PAYOFF SOLUTIONCrypto-currency Economic[130]Sequencial
gameBlockchain usersSetting transaction
transparencySelection of mixing payments Profits minus costSubgame perfect
Nash equilibrium
[131]Repeated game Blockchain usersSetting transaction
transparencySelection of performing
trading or notFunction of
expected profits
and possible lossNash equilibrium
[132]Repeated game Blockchain usersSetting transaction
transparencyChosen of entering the system
or notProfits minus cost Nash equilibrium
[133]Potential game MinersDetermination of the
crypto-currency
valueSelection between keeping
mining and switching to mine
on another coinCrypto-currency
value and mining
rewardsNash equilibrium
[36]Extensive-form
gameBlockchain usersDetermination of the
crypto-currency
valueChosen of entering the system
or notProfits minus costMarkov
equilibrium
[134]Non-cooperative
gameBlockchain usersDetermination of the
crypto-currency
valueDetermination of the
allocation of real money and
investment in computational
powerFunction of
computational
power and
population size of
usersNash equilibrium
size of the blockchain users, the participation cost and the
profit of holding the crypto-currency. By exploiting the char-
acteristics of the Hamilton-Jacobi-Bellman (HJB) equation
[136] transformed from the user’s utility, the game is proved
to admit a unique Markov equilibrium. At the equilibrium,
the high crypto-currency value attracts more potential users
to participate which reflects the future growth of the user
population size, and the expectation of future growth leads
reciprocally to a higher crypto-currency value.
Similar to [36], the authors in [134] demonstrate that the
value of the crypto-currency is derived by the computational
power of the blockchain network and the population size of
the users. The user determines the amount of real money
to be allocated in the transaction in the blockchain, and the
miner determines the investment in the computational power
in exchange for the mining profit according to the strategies
of both the other users and miners. A non-cooperative game
can thus be applied as presented in [134]. The larger number
of users attract more investment in computational power,
and more computational power means the stronger consen-
sus within the blockchain network and the higher crypto-
currency value, and thereby leads to more users to participate
in the blockchain network. Thus, the reciprocal interaction
between the computational power and the user population size
captures the equilibrium value of the crypto-currency. This
equilibrium value of crypto-currency depends on the users’
preferences, e.g., the risk aversion and the censorship aversion,
and the usefulness of the network. The empirical data from
Blockchain.info [90] supports the theoretical analysis.
B. Energy Trading
Increasing distributed renewable energy users, e.g., solar
rooftops and energy storage units, gradually changes the
centralized structure of conventional power system. The rea-
son is that the distributed energy users produce the energy
and thereby users can trade their energy with each other
directly. Therefore, by utilizing the decentralized structure of
the blockchain network for trading information exchange, the
blockchain-based energy trading systems are proposed. Each
energy user in the system can decide the amount of energy to(i) buy from the conventional power system, (ii) buy renewable
energy from other users, (iii) store its harvest energy, and (iv)
sell its energy to the other users.
When the energy exchange price is set by the users, the
interactions among the users can be modeled as games. For
example, in [137], a potential game [135] is applied to achieve
the social optimum. Considering the energy demand variation,
a non-cooperative game is adopted in [138]. The authors in
[94] propose a credit-based energy trading system and model
the interaction between the users and the credit bank as a
Stackelberg game. Otherwise, when the energy exchange price
is set by the system where the users bid for the exchange
price, the auction models can be applied to achieve the social
optimum as presented in [139] and [140].
VII. C HALLENGES AND FUTURE DIRECTIONS
In Sections IV , V , and VI, we provide an in-depth survey on
applications of game theory to address a wide range of issues
in the blockchain networks and related systems. However, with
the fast evolution of the blockchain technologies and their
applications, a plethora of emerging problems remain open
for further studies, many of which can be solved using game
theory. In this section, we expand our discussion to some
challenges as well as research directions with blockchain,
where the mathematical tools of game theory may exert further
potential for system analysis and mechanism design.
A. Challenges from Game Theory Perspective
1) Existence of Nash Equilibria: Most references reviewed
in this survey discuss the existence of the unique Nash equi-
librium. At the Nash equilibrium, the players, e.g., the miners
or the pools, have no incentive to deviate from their current
strategies. However, in practice, multiple Nash equilibra can
exist, and thus it is challenging for the players to choose
the optimal strategy or solution. For example, for the mining
management [115], with the existence of Nash equilibra, the
miners can choose between staying and leaving, and the
blockchain users choose between paying or not paying the
transaction fee. In this case, finding the solution among the
23
Nash equilibria to achieve a social optimum for the whole
network is a challenge. Similarly, for the crypto-currency
economic [133], how to achieve a social optimal equilibrium
in the crypto-currency market is very challenging.
2) Implementation of Game Models: The applied game
models proposed in aforementioned reviews have its limi-
tation. For example, due to the first-mover advantage, the
Stackelberg game is widely used to solve many issues in
blockchain network. However, the blockchain network is a
type of decentralized system with a number of distributed
nodes, i.e., players. Therefore, how leader nodes observe
the strategy of each follower node, make optimal decisions,
and find the equilibrium is one big challenge. To address
the challenge, the meanfield games [141] can be applied for
analyzing the performance of the whole blockchain network
with large number of miners where individual miners have
relatively negligible impact upon the network. In addition,
evolutionary games can be adopted for analyzing mining
pools’ formation and evolution. Stochastic games can be
used for analyzing more complex scenarios, such as miners’
probabilistic selection of transactions to be included, blocks to
be verified and broadcast, and chains to be attached and mine.
B. Open Issues and Research Directions for Applications of
Game Theory in Blockchain
1) Throughput Improvement: Blockchain technologies have
been adopted in a number of scenarios. However, the through-
put, i.e., capacity of processing requested transactions, of
blockchain networks limits the scope of blockchain applica-
tions. The major reasons for this issue are the long block
creation time and limited block size [113]. However, block
creation time and the block size cannot be easily changed
for improving the throughput. The analyses in [114] show
that miners intend to form a coalition if the block size is
unlimited. This is harmful to maintaining the decentralized
structure of the blockchain network. Also, the authors in [115]
demonstrate that even with the unlimited block size, there is
still a limitation on throughput imposed by the waiting time
for transactions to be included in blocks. Thus, to improve
the throughput, blockchain protocols in terms of the efficient
block creation and the proper block size need to be further
developed, and game theory can be a useful tool.
2) Alternative Consensus Mechanisms: In blockchain net-
works, e.g., PoW networks, every node performs several
certain tasks to maintain the consensus across the blockchain.
However, reaching the consensus needs nodes to repeat tasks
consuming a large amount of electricity [29]. Thus, an al-
ternative consensus mechanism to PoW such as Proof of
Useful Work or Resources (PoUWR) [142] may be used.
For example, computing hash value in PoW network can be
replaced with performing stochastic gradient descent for neural
network training [142]. Due to the difference between the
tasks in terms of data volume, expected accuracy and variable
dimension, the strategies of nodes to obtain a puzzle solution
are different from those in the PoW network. Therefore, it is
necessary to apply game approaches to analyze the interaction
among nodes in the process of PoUWR competition, e.g., thecomputational power allocation between PoUWR and PoW,
the tradeoff between the payoff and the cost, and security
issues regarding the deviation from the PoUWR protocol.
3) Permissioned Ledger Types: Public blockchain has been
adopted in many applications. Public blockchain allows any-
one to participate to be a node, and it has no control by regu-
latory agencies, industries, or governments. In addition to the
public blockchain, permissioned blockchain ledger types such
as consortium blockchain, become another interesting appli-
cation of Nakamoto’s blockchain implementation. Consortium
blockchains can be considered to be semi-decentralization. The
reason is that not everyone can participate in the network,
and the consortium blockchain is maintained by a group of
pre-selected nodes, allowing for a greater degree of con-
trol over the network by regulators. As such, the consor-
tium blockchains involve multiple entities and stake-holders,
i.e., the pre-selected nodes, the verification nodes, and the
blockchain users. To model and analyze complex interactions
among the entities and stake-holders, game theory can be
adopted as a useful tool. For example, the non-cooperative
games can be used to analyze the pre-selected node selection,
Stakeleberg games can be applied to analyze the interaction
between the pre-selected nodes, i.e., the leaders, and the
verification nodes, i.e., the followers. Also, evolutionary games
can be used to analyze the formation of mining pools in
permissioned blockchain networks.
4) Incorporating Blockchain Technologies into Other Sce-
narios: As blockchain is a versatile technology, it is also
possible to incorporate blockchain into other emerging net-
work and application scenarios. For example, the authors in
[45] introduce a blockchain-based edge computing paradigm
in which mobile users offload their computing tasks to com-
puting service providers and pay the corresponding fees. This
paradigm addresses the implementation issue of blockchain
applications on resource-limited mobile services. However, the
blockchain-based edge computing paradigm raises resource
management issues. For example, how to motivate the service
providers to contribute their computing resources. Game the-
ory can be efficiently used to design incentive mechanisms.
For example, auction schemes can be adopted to improve
the utility or revenue of the service providers. Also, the
Stackelberg game can be applied to improve both the utility
of the computing service providers and the mobile users.
Predictably, by taking advantage of game theory to analyze
and design incentive mechanisms, blockchain technologies can
be widely incorporated into multi-agent scenarios beyond the
crypto-currencies, e.g., mobile blockchain networks, informa-
tion sharing scenarios, and energy trading markets.
VIII. C ONCLUSIONS
This paper has presented a comprehensive survey of the
applications of game theory in blockchain. Firstly, we have
given an overview of blockchain with its structure, workflow,
and incentive compatibility. Then, we have introduced the
basic knowledge of game theory and several game models
with the objective to understand the motivations of using
game theory to analyze interactions among different compo-
nents in blockchain. Afterwards, we have provided reviews
24
and analyses using game theory in detail to deal with a
variety of problems regarding security, mining management
and blockchain applications. Finally, we have outlined existing
challenges as well as future research directions.
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|
{
"id": "1902.10865"
}
|
2104.05849
|
Reward Mechanism for Blockchains Using Evolutionary Game Theory
|
Blockchains have witnessed widespread adoption in the past decade in various
fields. The growing demand makes their scalability and sustainability
challenges more evident than ever. As a result, more and more blockchains have
begun to adopt proof-of-stake (PoS) consensus protocols to address those
challenges. One of the fundamental characteristics of any blockchain technology
is its crypto-economics and incentives. Lately, each PoS blockchain has
designed a unique reward mechanism, yet, many of them are prone to free-rider
and nothing-at-stake problems. To better understand the ad-hoc design of reward
mechanisms, in this paper, we develop a reward mechanism framework that could
apply to many PoS blockchains. We formulate the block validation game wherein
the rewards are distributed for validating the blocks correctly. Using
evolutionary game theory, we analyze how the participants' behaviour could
potentially evolve with the reward mechanism. Also, penalties are found to play
a central role in maintaining the integrity of blockchains.
|
http://arxiv.org/pdf/2104.05849v2
|
Shashank Motepalli, Hans-Arno Jacobsen
|
cs.GT, cs.CE, cs.MA
|
cs.GT
|
Reward Mechanism for Blockchains Using
Evolutionary Game Theory
Shashank Motepalli, Hans-Arno Jacobsen
Department of Electrical and Computer Engineering, University of Toronto
shashank.motepalli@mail.utoronto.ca, jacobsen@eecg.toronto.edu
Abstract —Blockchains have witnessed widespread adoption in
the past decade in various fields. The growing demand makes
their scalability and sustainability challenges more evident than
ever. As a result, more and more blockchains have begun to adopt
proof-of-stake (PoS) consensus protocols to address those chal-
lenges. One of the fundamental characteristics of any blockchain
technology is its crypto-economics and incentives. Lately, each
PoS blockchain has designed a unique reward mechanism, yet,
many of them are prone to free-rider and nothing-at-stake
problems. To better understand the ad-hoc design of reward
mechanisms, in this paper, we develop a reward mechanism
framework that could apply to many PoS blockchains. We
formulate the block validation game wherein the rewards are
distributed for validating the blocks correctly. Using evolutionary
game theory, we analyze how the participants’ behaviour could
potentially evolve with the reward mechanism. Also, penalties
are found to play a central role in maintaining the integrity of
blockchains.
Index Terms —Evolutionary game theory, blockchain, PoS,
BFT, incentive mechanism
I. I NTRODUCTION
Bitcoin began the blockchain revolution by enabling a peer-
to-peer version of electronic cash [1]. Over the past decade,
Bitcoin grew as a digital reserve worth more than a trillion
dollars [2]. Ethereum further extended these concepts to create
programmable money using smart contracts [3]. This idea led
to numerous applications [4], [5]. However, the increasing
popularity has exposed the drawbacks of such systems, that
is proof-of-work-based (PoW) systems do not scale [6], [7].
Furthermore, PoW raises concerns about its high carbon
footprint [8]. Lately, more and more blockchains are migrating
to proof-of-stake (PoS) protocols for state-replication while
addressing scalability and sustainability challenges [9]–[11].
PoS systems employ validators for processing the transactions,
similar to the miners in PoW systems. In the PoS system,
the participants often lock their tokens to become validators.
These validators participate in the consensus process to decide
on the next state of the ledger. With each new block added,
i.e., with every state transition, tokens are minted to reward
the validators for processing the transactions. A validator can
also increase their stake by buying the tokens traded in the
secondary markets. Since the entire security in PoS systems
relies on stake, the economics of tokens are more critical than
ever.Each PoS blockchain tends to approach its reward mech-
anism design in a unique way (see Table I). We designedly
establish it as reward mechanism and not an incentive mech-
anism because we define a reward as both, the sum of
incentive andpenalty . Some PoS blockchains such as Polkadot
and Cardano reward every registered validator irrespective
of whether they contributed to the consensus process [15],
[19]. Algorand goes even further to reward everyone who
owns its tokens [12], [13]. Whereas, Avalanche, Cosmos,
and Ethereum 2.0 only reward validators who participate in
the consensus process [14], [16], [17]. In case everyone is
rewarded equally, a validator can become a free rider enjoying
the rewards without doing the work. Furthermore, blockchains
differ a lot in terms of the reward they give. Most blockchains
provide rewards proportional to the stake validators’ pledge.
On the other hand, Ethereum 2.0 and Polkadot reward equally
irrespective of the validators’ stake [17], [19]. As there is
nothing at stake to lose in some designs, validators can act
in ways that might affect the integrity of the ledger. Few
blockchains introduced penalties to address the nothing at
stake problem. However, there are stark differences among
PoS systems regarding penalties. Ethereum 2.0 and Cosmos
penalize (i.e., slash) even if the validator is not participating
in the consensus protocol [16], [17]. Polkadot penalizes if
and only if the validator signs conflicting transactions [19].
Whereas, Algorand, Avalance, and Cardano foresee no notions
of penalty in their design.
The problem this paper addresses is understanding the
reward mechanism in PoS blockchains by proposing a unified
framework. This problem is interesting because there are
a plethora of contradicting approaches adopted by current
blockchains. Since the reward mechanism is crucial to the
integrity of PoS systems, it is critical to understand the impli-
cations of reward mechanisms. The central idea we analyze
is how to design rewards (i.e., payoffs) such that rational
behaviour would promote honesty. To abstract, there are three
questions to consider towards reward mechanism design in a
PoS system. Firstly, should all validators be rewarded equally,
even if they don’t contribute to consensus? Secondly, should
penalities be imposed for integrity in blockchains? Thirdly,
would honesty emerge as a stable state?
In this paper, we use game theory to formulate block valida-
tion as a game. Block validation is a necessary action for pro-
gressing the blockchain. The validators reach a consensus by
validating and voting on each proposed block and get rewarded This research has in part been funded by NSERC.
978-1-6654-3924-4/21/$31.00 ©2021 IEEEarXiv:2104.05849v2 [cs.GT] 8 Jul 2021
TABLE I: Reward mechanism in prominent PoS blockchains
Reward every validator Proportional to stake Penalize not voting Penalize conflicting transactions References
Algorand 3 3 7 7 [11]–[13]
Avalance 7 7 7 7 [14]
Cardano 3 7 7 7 [9], [15]
Cosmos 7 7 3 3 [10], [16]
Ethereum 2.0 7 3 3 3 [17], [18]
Polkadot 3 3 7 3 [19]
for their goodwill. It is a multiplayer game. The rewards for
validators are not just dependent on individual strategy but also
on what the majority votes. Assuming validators are rational,
i.e., selfish, and want to maximize their payoff, our goal is to
design rewards for the game such that voting on the honest
strategy always remains rational. We begin with a simple
reward matrix, adjust it to address free-riding and nothing-
at-stake problems. Finally, we use evolutionary game theory
to analyze long term impact of whether the honest strategy is
a stable state. We ran simulations to confirm the same.
The contributions of this paper are three-fold. (1) To the
best of our knowledge, we are among the first to formulate
the block validation game that can be applied on any PoS
blockchain. (2) We are also among the first to analyze how
validators change strategies in blockchains using evolutionary
game theory. (3) We establish the need for penalties for the
integrity of PoS blockchains.
This paper is organized as follows. In Section II, we dis-
cuss necessary background about PoS and evolutionary game
theory. We review related work in Section III. In Section IV,
we formalize the block validation game addressing the free-
rider and nothing-at-stake problems. We derive the evolution-
arily stable strategies (ESS) in Section V and experimentally
confirm ESS in Section VI. Finally, we conclude with future
directions in Section VII.
II. B ACKGROUND
A. Proof of Stake
Proof of Stake is a state-replication mechanism designed
for public blockchains wherein parties that might not trust
the other need to reach consensus. The core of PoS relies
on the simple idea that one would not devalue the assets
one owns. Hence, the stakeholders accept the responsibility
of maintaining the security of the PoS blockchain. In a PoS
system, the stakeholders can stake their tokens to become
validators on the network. The validators in PoS assume the
role of miners in PoW systems by verifying the transactions
and creating new blocks.
PoS systems are still nascent and evolving. However, there
are two broad categories of PoS concerning participation from
validators:
Probabilistic PoS: This is similar to Bitcoin’s Nakamoto
consensus. Instead of deciding the block proposer propor-
tional to computational power in Nakamoto consensus, a
block proposer is selected based on its stake in the PoS
system. The higher its stake, the higher is its probability
of being selected in the random block comit process.
After being selected, the block proposer proposes thenext block to be added on top of the previous block.
Cardano [15] adopts this approach.
BFT-based PoS: This is a weighted Byzantine fault-
tolerant (BFT) consensus protocol wherein weights are
proportional to the stake validators own. Each block
requires a byzantine agreement for it to be appended
on the blockchain. When the majority of validators vote,
we reach finality on the given block. In BFT-based PoS
systems, more validators need to participate in the con-
sensus process. Examples of this implementation include
Algorand [11], Cosmos [10].
There are many variants of PoS systems, and the above is
not an exhaustive list. For instance, Ethereum 2.0 is adopting
a hybrid of both probabilistic PoS and BFT-based PoS [18].
In this paper, we concentrate on BFT-based PoS protocols.
Let us walk through a lifecycle of a generic transaction on
a BFT-based PoS blockchain. A client generates a transaction
and sends it to a validator who gossips the transaction to other
validators. The validators verify and store valid transactions in
their memory pool. When a validator is selected to become
block proposer, it goes through its memory pool of valid
transactions and accumulates a few of them to generate a
block to broadcast to all other validators. The validators
simulate the transactions in a block, verify their correctness,
and sign their approval or disapproval for that block [20]. The
block proposer collects signatures from all the validators. If a
quorum confirms the validity of a block, we reach finality on
the block. Since most BFT protocols tolerate up to one-third
of its nodes to fail, the quorum size is set to two-thirds [10],
[21].
B. Evolutionary Game Theory
The origin of evolutionary game theory dates back to 1973
when John Maynard Smith and George R. Price formalized
it to study the evolving populations for lifeforms in biology
in their work, ”The logic of animal conflict” [22]. Though
it started as a concept in evolutionary biology to explain
the Darwinian evolution of species proving the survival of
the fittest [23], the theories of evolutionary game theory
were adopted by economists [24]–[26], psychologists [27]–
[29], among others. In classical game theory, the success
of a strategy depends on the strategy itself. In contrast,
in evolutionary game theory, the game is played multiple
times among numerous players. The success of an individual
strategy depends not only on the strategy itself but also on
the frequency distribution of alternative strategies and their
successes.
The fitness of a strategy in a population is the payoff
received for choosing a strategy, given the population state,
i.e., the frequency of each strategy [30]. In terms of the usual
conventions of a game, the higher the payoff, the higher the
fitness. Analogous to the Nash equilibrium in classical game
theory, there is the evolutionarily stable state (ESS) in evolu-
tionary game theory. A strategy is in ESS if it is unaffected
by a small fraction of invading mutants that choose a different
strategy [31], [32]. In other words, if for a sufficiently small
mutant population, the fitness of the incumbent strategy is still
higher than that of any of the strategies by mutants, the players
stick to the incumbent strategy, making it an ESS. We use ESS
to prove that our reward design for the blockchain network is
stable.
III. R ELATED WORK
This work is closely related to two categories of related
work. Firstly, the economics of PoS blockchains. Secondly,
the applications of game theory and evolutionary game theory
in blockchains. We reviewed the economics of existing PoS
blockchains in Table I.
A comprehensive survey summarizing numerous applica-
tions of game theory to blockchains is provided by Liu et
al. [33]. There is large interest in the game-theoretic anal-
ysis of attacks such as selfish mining attacks [34], block
withholding attacks [35], among others. Few recent works
formalize evolutionary games for selection of mining pools
and shards in PoW-based blockchains [36]–[38]. However,
these works focus on PoW consensus protocols and have
limited applicability to PoS systems.
Our work also relates to reward design using game theory,
studied for example in [39]–[41]. However, their focus re-
mained on PoW single-shot games and not evolutionary ones.
A data-sharing incentive model for smart contracts based on
evolutionary game theory is proposed in [42]. Nevertheless,
the reward design at the protocol layer is not analyzed. We
are among the first to study reward mechanism in blockchains
using evolutionary game theory.
IV. T HEBLOCK VALIDATION GAME
We employ game theory to model the strategic interactions
among the validators in a blockchain network. A normal form
gameG(N,Mi,Ui) has three key components: i) the players
N, ii) the strategies, denoted by M, iii) the payoffs for all the
potential strategies, denoted by U. We use payoff and reward
interchangeably in this work. In our game, the validators v, in
the validator set, verifying the blocks are the players, stated
below asN.
N=fvijvi2ValidatorSetg (1)
These validators contribute to the consensus protocol by
approving or disapproving the blocks proposed to be appended
to the ledger. Each block Bicontains a set of transactions txk
as follows.
Bi=ftx0;tx1;tx2;:::tx ng (2)
Every validator owns a local copy of the blockchain state
generated from listening to blocks on the network. The valida-
tors simulate the transactions on their local blockchain stateto verify their correctness. The correctness of a transaction
depends on several factors including, but not limited to,
the authentication of the sender’s signature, the balance on
the sender’s account, verifying the sequence number of the
accounts of the sender, and the logic of the smart contract.
We define a valid block as follows.
Definition 1. Valid Block. The block Biis valid if and only
if every transaction in the block is correct.
Ifcorrect (tx)provides the correctness of a transaction, a
valid block is represented as follows.
valid (Bi) =correct (tx0):::^correct (txn);8txk9Bi(3)
The validators have two potential strategies. They can either
act honestly or maliciously. An honest validator approves valid
blocks and disapproves invalid blocks. Any actions that deviate
from honest behaviour are considered malicious, including
disapproving a valid block or approving a block that is not
valid. If a validator does not participate in the consensus
process, it is considered malicious behaviour. Malicious be-
haviour accounts for all types of failures in the system, such
as crash failures, network failures, and Byzantine failures. The
validators’ strategies, where M, the set of strategies, is defined
as follows. Here, handmrepresent honest and malicious
strategies, respectively.
M=fh;mg (4)
By signing with their private key, validators respond to the
proposer, stating whether they approve or reject the proposed
block. A validator can at the most influence but cannot control
the decisions of others. The validators could only pursue
pure strategies, i.e., a validator can either act honestly or
maliciously on a single block but not a combination of both.
An individual validator has little or no influence on the final
decision, as the final decision depends on the population state.
The population state Xfor blockBiis the distribution of the
strategies of the validator set in that block; it is given below.
XBi=xh
xm
(5)
wherexh0,xm0 andxh+xm= 1. In the following,
we look into the reward matrix for this game.
A. Universal Reward: The Basic Reward Matrix
The rewards play a crucial role in directing validators’
strategies. We assume all the validators are rational, i.e., they
are selfish and play to maximize their rewards. Our goal is
to design rewards such that the best response for a rational
validator would be acting honestly. A validator’s reward is
not just dependent on its strategy. The rewards also depends
on how other validators act, i.e., the population state. Each
validator decides individually, knowing very little about the
others’ strategies. BFT consensus protocols require a quorum
Q, at least two-thirds of the validator set N, to reach a
consensus. The quorum is a subset of the validator set as
follows.
QBmN;jQBmj2
3jNj (6)
We assume that every round terminates with a decision on a
block, either by reaching an honest quorum or a malicious
quorum. In other words, we assume to reach a two-thirds
quorum for each block. If x0:67, thenXBkandX0
Bkare the
population states, for honest and malicious quora, respectively.
XBk=x
1 x
;X0
Bk=1 x
x
x0:67 (7)
In case we do not achieve a consensus, the system proposes
a nil block after a timeout. A nil block is an empty block,
leading to no approved transactions and no rewards to anyone
in that consensus round. Though BFT protocols assume an
honest majority, we do not want to rule out the possibility of a
malicious majority, given the high stakes involved. Malicious
behaviour could be subtle to be labelled by the system.For
instance, if a few validators form a cartel and choose to censor
transactions by certain entities; this behaviour is not malicious
according to BFT consensus. Even in the cases of hard forks to
adopt new rules is subjective and does not constitute malicious
behaviour. In this paper, we limit our discussion about what
constitutes a Byzantine transaction in the specified network
and assume an altruistic quorum as a basis for designing
rewards in the system. In other words, we reward anyone who
adheres with a two-thirds majority, and we consider anyone
who digresses from it as Byzantine. Every block provides
rewards to all the validators for contributing to the consensus.
The security and integrity of the blockchain network de-
pends on the liveness of validators and the availability of
data. The validators incur an expense ein terms of net-
work, computation, and storage resources for verifying blocks.
The blockchain system provides incentives to reward honest
behaviour and meet desired security. The incentive iis an
aggregate sum of transaction fees on each transaction txof
that block and the block rewards R. The users pay transaction
fees for processing their transactions. The blockchain system
provides the block rewards for generating new blocks. The
effective reward rfor reaching a consensus on the next block
Bmfor a validator viis the difference between incentive iand
costc. Here,Nis the size of the validator set. Since PoS and
BFT protocols are not computationally expensive operations,
we assume the costs eto be lower, when compared to PoW
mining. The incentive iis given as follows.
iBm=NX
0fees(txi) +R
Bm(8)
The reward is the difference between incentive and expense
for running the node, it is stated below.
rBm(vi) =i
N
Bm e (9)
Theorem 1. The reward has to be positive for the network to
be secured.TABLE II: Universal Reward Case: The Reward Matrix
Quorum
Validatorhonest malicious
honest r r
malicious r+e’ r+b
Proof. If the expense incurred is negative, it means that
running the validator incurs an expense without any incentive.
Since validators are rational, they would choose to maximize
their reward by not participating in the consensus process. If
the majority of honest validators act rational, the security of
the system is compromised. For the system to be secured, the
incentive should be greater than the expense. In other words,
rewards should be positive, rBm0.
Some validators might exhibit malicious behaviour such
as double-spending to gain benefits, often much higher than
the rewards. Let the benefit gained by the malicious cartel
of validators be B. It is fair to assume that some malicious
validators might be more beneficial than others. Let the
reward for Byzantine behaviour be represented by b, where
0bB.
Each validator selects their strategy for every block. The
reward for the validator is dependent on the strategy chosen
by the quorum (see Table II). The reward matrix Uis stated in
Equation 10. If the validator acts honest in an honest quorum,
it earns an effective reward of r. If the validator acts malicious
in an honest quorum, it reaps a reward of r+e’, where 0
e’ecould be the cost saved by not running the validator.
In the case of a malicious quorum, an honest and malicious
validator would be making randr+b, respectively. In case
of malicious behaviour in the malicious quorum, the reward
could ber+b+e’. Because we assume the Byzantine reward
to be generally higher than the cost, be’, we considered it
asr+bin the reward matrix.
U=r r
r+e’r+b
(10)
Analysis. A validator receives a maximum reward when it
leads a malicious cartel. Acting maliciously always has better
incentives than acting honestly, so a rational validator would
choose the no-regret strategy to act maliciously. This could
lead to a malicious quorum, hence, a security concern. A
rational validator chooses the sure-win case of maximizing
their reward by eby not participating in consensus, as they do
not incur computational expenses.
B. Reward For Work: Addressing Free-Riding
As seen in the previous section, malicious behaviour is
an optimal strategy for a rational validator. This behaviour
results in cartels or worse, never leads to reaching a quorum.
The validators would be rewarded more ( r+e) even without
validating as long as they are in the validator set. This
behaviour is a classical case of the free-rider problem wherein
the validators earn ”something for nothing” [43]. If more
honest validators act rationally, the security of the blockchain
network is compromised. To overcome this challenge, only the
TABLE III: Reward For Work Case: The Reward Matrix
addressing Free Rider Problem
Quorum
Validatorhonest malicious
honest r -e
malicious -e‘ r+b
validators who participate in consensus should earn incentives.
In other words, the validators who do not participate in the
consensus process should not earn incentives. The reward
function is updated to incentivize only those who are in the
quorum as follows.
rBm(vi) =(
(i
N`)Bm e ifvi2Q
0 ifvi=2Q(11)
Here,N`is the size of the quorum. It ranges from two-
thirds to cardinality of the validator set. Let e`be the cost
for malicious behaviour, which is equal to eif it participates
and 0, otherwise.
The reward matrix is updated to reward only those who
worked (see Table III). With an honest quorum, a validator
choosing an honest strategy would earn an effective reward r,
whereas a validator choosing a malicious strategy would result
in a maximum of zero payoffs if it saves on computational
resources. However, if you act honestly in the malicious
quorum, you incur an expense ewithout any reward. A
malicious validator that forms the malicious quorum earns the
reward ofr+b. The updated reward matrix is given below.
U=r e
e`r+b
(12)
Analysis. The updated reward matrix ensures that a ra-
tional validator would participate in the consensus process.
They continue to act honestly, assuming an honest quorum.
However, if the malicious majority takes over the quorum,
an honest validator would not earn incentives while incurring
operational expenses. The best response for a rational validator
would be signing all the blocks irrespective of whether they
are valid or not.
C. Penalty Case: Addressing Nothing at Stake
Rational validators being selfish agents to maximize their
reward, approve all the blocks irrespective of whether the
blocks are valid or not. This behaviour guarantees to reward
them for every block that gets appended onto the blockchain.
Though this is not a serious threat if only a few validators do
it. However, if malicious players build a quorum, this would
be a security threat to the system. It affects the social welfare
of the system. It could easily lead to a situation wherein we
might have conflicting forks of the ledger, leading to multiple
ledger states.
Consider a scenario where a malicious validator double
spends to create multiple versions of the truth. If the malicious
validator is selected as a block proposer, as shown in Fig. 1, it
could propose two different blocks from the same parent block.
The malicious validator could double-spend by broadcasting
blocks that have conflicting transactions. As discussed, therational validators would approve both the blocks to be in
the quorum to increase their reward. If we reach a quorum
of rational validators, we would have both blocks reaching
quorum. Both these chains could grow indefinitely in parallel.
The fork resolution strategies such as the longest chain rule [1]
or GHOST protocol [44] cannot resolve which fork is the valid
fork, leading to multiple sources of truth. Since the equilibrium
for a validator is to act rationally, this is a security threat. We
need to re-design the reward mechanism to avoid this practice.
Fig. 1: Forked chains, where attacker double spends after
Block 1
Our goal is to ensure that rational behaviour is acting
honestly. We need to punish the validators that deviate from
the honest strategy. One option could be not paying the future
incentives for validators that diverge from acting honestly for
the nextnblocks, where nis a fixed number. It could also be
jailing the validator from the validator set. Another option is
imposing a penalty for diverging from the honest quorum.
We borrow from Behavioural Economics to improve the
integrity of the system. Kahneman and Tversky concluded
that ”losses loom larger than gains” while explaining the
concepts of Loss aversion in Prospect Theory [45], [46]. They
conducted numerous experiments to prove that people when
presented with alternatives, go for choices that either lead to
sure wins or avoid losses. Theory of Loss Aversion concluded
that the pain of losing has a much stronger psychological
impact than the pleasure of gaining the same amount. Few
studies have further proved that participants are also willing
to behave dishonestly to avoid a loss than to make a gain [47].
The loss aversion experiments explain why penalties are more
effective than incentives in motivating people to behave in
a certain way [48]. We use the same principles to motivate
validators to participate honestly instead of being malicious
validators in the network.
To address nothing at stake, we penalize validators that
deviate from the honest quorum. Let pbe the penalty for
deviating from the quorum, where p >0. The stake lost in
penalty is designed to be much greater than the incentive
received for being in the quorum. The loss to the stake also
reduces the chances of becoming a validator in the future. The
TABLE IV: Penalty Case: The Reward Matrix addressing
Nothing at Stake
Quorum
Validatorhonest malicious
honest r -p
malicious - p r+b
updated reward function for a validator viis given below.
rBm(vi) =(
(i
N`)Bm e ifvi2Q
p if vi=2Q(13)
The updated matrix is given below (see Table IV). Acting
honestly in an honest quorum gives reward rwhile acting
maliciously in a honest quorum leads to a penalty of e` +p,
sincep >> e, we ignore e. In the rare event of malicious
majority, acting honestly would result in penalty p. If a
validator is acting maliciously in a malicious majority, it earns
a rewardr+bdepending on its role in the malicious cartel.
U=
r p
pr+b
(14)
Analysis. Given the reward matrix, the validators are better
off by acting honestly in an honest quorum than acting
maliciously in a malicious quorum. However, under the core
assumption of any blockchain network of the majortity of the
network being honest, the best response for a rational validator
is to act honestly.
V. E VOLUTIONARILY STABLE STRATEGY
In the previous section, we designed the payoffs using
classical game theory that studies one-shot games where the
players make rational choices evaluating probable outcomes.
Their outcomes were not just dependent on their strategy
but also on the population state of the network. Under the
assumption of an honest quorum in the penalty case, we
proved that a rational validator’s best response would be to
act honestly under the no-regret strategy. Unlike classical one-
shot games , the same set of validators play the game multiple
times, once for each block consensus round, heading towards
a potential shift in their strategy in the following rounds.
Malicious validators could form cartels to persuade the rational
validators, who are acting honestly, to alter their strategy over
the next few blocks. To study how the population state is
evolving as the blockchain progresses and confirm if acting
honestly is a stable state, we apply evolutionary game theory
to our setting. We study evolutionarily stable strategies (ESS),
a strategy that cannot be invaded by other strategies.
We can determine ESS by a simple experiment. Assume all
the validators choose a particular strategy. In other words, the
whole population of validators is either honest or malicious.
If a small number of mutants deviate from the incumbent
strategy, we analyze whether these minority mutants have a
better or worse payoff than that of the incumbent strategy. If
they do have a better payoff, the incumbent validators would
eventually shift to a mutant strategy. If the mutant strategy
performs worse, no mutants would invade the incumbent
strategy making the incumbent strategy is an ESS [49].1) Everyone is honest: Consider the case where all valida-
tors are honest ( h), then the incumbent strategy is given as
follows:
XBh=1
0
(15)
Let us now examine whether validators being honest is an
ESS. When all the validators are honest, if a small percentage
of a mutant population invade the network, the incumbent
validators continue remaining honest, we consider the honest
strategy as ESS. We can tolerate up to one-third of the total
validators as being mutants, acting maliciously. The population
state with mutants is X0
Bh.
X0
Bh=1
0:33 (16)
The fitnessFis the payoff for choosing a particular strategy,
given the population state X0
Bh. The fitness of the incumbent
strategy, honest , isF(h), and the fitness of the mutant strategy,
malicious , isF(m). We analyze the fitness for the three cases:
Universal reward case: F(h) =randF(m) =r,F(h) =
F(m)andF(m)>0.
Reward for work case: F(h) =randF(m) = e,
F(h)>F(m)andF(m) = 0
Penalty case :F(h) =randF(m) = p,F(h)>F(m)
andF(m)<0
If the fitness of the incumbent’s strategy is greater than
that of the mutant’s strategy, the incumbents won’t alter
their strategy. This makes the incumbent’s strategy an ESS.
In the universal reward case, we have a mixed ESS since
both the strategies have equal fitness; this situation could
lead to a potential shift over time. In the other cases, being
honest is ESS. Though the incumbent’s honest strategy is
ESS in both cases. Here, the penalty case has a strong ESS,
because according to the theory of loss aversion, penalizing
is more powerful in influencing the decision than not gaining
incentives.
2) Everyone is malicious: We consider the case where all
the validators choose being malicious as their incumbent strat-
egy. Similar to the previous situation of an honest population,
we can prove that everyone being malicious would remain
malicious and the malicious strategy is an ESS.
In the universal reward case, we have no pure ESS. Both
the strategies of being honest and malicious can be invaded
by others. For the rest of the cases, we have two ESS, either
honest or malicious populations, subject to the relative values
of the incentives, benefits and penalties.
Theorem 2. The security of a PoS blockchain system depends
on the population state during the genesis.
Proof. Let us consider the penalty case. With decent penalties,
both honest and malicious strategies are ESSs in this game
because neither can be invaded by the other. The strategy that
dominates over time is the one that starts in the majority.
If honest validators start the network, the honest strategy
would be incumbent and the system will remain in ESS.
(a) Universal reward case, the malicious
minority takes over
(b) Without penalties, honest strategy can
tolerate 10% malicious
(c) Without penalties, the malicious minor-
ity takes over, starting with one-third
(d) If penalty is 50% of their total stake
(e) If penalty is 100% of their total stake
(f) 51% honest and 49% malicious would
lead to malicous ESS
Fig. 2: Experiments to study evolutionarily stable states
Similarly, if malicious validators start the network, the network
is malicious. The malicious strategy would be the incumbent
strategy and will remain in ESS. Hence, the population state
of the genesis of the network plays a crucial role for PoS
blockchains.
VI. E XPERIMENTAL EVALUATION
We performed experimental evaluation to confirm whether
penalties are important and what percentage of malicious
population can PoS system tolerate. Our methodology was to
learn how the population state proportions evolve over the gen-
erations, the block rounds, using the GameBug software [50].
The defaults for the relation between variables and the baseline
value of the validator, used in our experimental simulations,
are given in Table V. If the expense is x, the reward is taken
as10x. The default Byzantine benefit and penalty are choosen
as100xfor a validator with a baseline balance of 1000x.
TABLE V: Simulation variables and relative values
Variable Symbol Value
expense e x
reward r 10x
Bynzantine benefit b 100x
penalty p 100x
We ran experiments for the reward matrix of the universal
reward case (Table II) and the reward for work case (Table III)
with the assumption of more than 90% of the validators are
honest. We observe that the malicious strategy takes over when
everyone on the network is rewarded (see Fig. 2a). Blue and
red signify the proportions of validators with honest and mali-
cious strategies, respectively. The X-axis tracks the population
proportions, and the Y-axis tracks the generations, i.e., block
rounds. In the no-penalty case, the honest strategy is an ESS,
unaffected by invading mutants (see Fig. 2b). However, we do
not have honest ESS with malicious population above 10%.
We also ran simulations by relaxing the initial proportions
at the quorum size of consensus. In other words, the initial
proportions of honest and malicious validators are two-thirds
and one-third, respectively. For the reward matrix in the reward
Fig. 3: With penalties, honest strategy is ESS, can tolerate
one-third malicious
for work case (Table III), the proportion of honest drops
significantly over the generations, within 75 block rounds (see
Fig. 2c). Though the honest strategy is ESS, it takes longer to
reach ESS with a high initial proportion of malicious valida-
tors. Over generations, the validators would favor a malicious
strategy because of high Byzantine benefit if the network
starts with a one-third proportion of malicious validators. For
the reward matrix in the penalty case (Table IV), the honest
strategy is an ESS since validators do not shift from the
incumbent strategy (see Fig. 3). High penalties play a crucial
role in this behaviour, we run a few experiments to verify the
same. We observe that the higher the penalties, the sooner the
entire network becomes honest. We increased the penalties to
be 50% and 100% of the the baseline to plot Fig. 2d and
Fig. 2e, respectively. We observe that with higher penalties,
we reach the state of only honest population quickly. For the
penalty case, we tried a 51% honest majority with default
values, we do not observe an honest ESS. The network cannot
tolerate 51% malicious behaviour (see Fig. 2f).
VII. C ONCLUSIONS
We formulated the block validation game and designed
rewards to address free riders and nothing at stake challenges.
Using evolutionary game theory, we proved the importance
of penalties in maintaining the integrity of the ledger. In
the future, we intend to extend this work to asynchronous
environments and also analyze how the game adapts when the
delegation of stake is allowed.
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|
{
"id": "2104.05849"
}
|
2106.11446
|
Bitcoin's Crypto Flow Network
|
How crypto flows among Bitcoin users is an important question for
understanding the structure and dynamics of the cryptoasset at a global scale.
We compiled all the blockchain data of Bitcoin from its genesis to the year
2020, identified users from anonymous addresses of wallets, and constructed
monthly snapshots of networks by focusing on regular users as big players. We
apply the methods of bow-tie structure and Hodge decomposition in order to
locate the users in the upstream, downstream, and core of the entire crypto
flow. Additionally, we reveal principal components hidden in the flow by using
non-negative matrix factorization, which we interpret as a probabilistic model.
We show that the model is equivalent to a probabilistic latent semantic
analysis in natural language processing, enabling us to estimate the number of
such hidden components. Moreover, we find that the bow-tie structure and the
principal components are quite stable among those big players. This study can
be a solid basis on which one can further investigate the temporal change of
crypto flow, entry and exit of big players, and so forth.
|
http://arxiv.org/pdf/2106.11446v2
|
Yoshi Fujiwara, Rubaiyat Islam
|
q-fin.GN, cs.CR
|
q-fin.GN
|
arXiv:2106.11446v2 [q-fin.GN] 6 Jul 2021Bitcoin’s Crypto Flow Network
Yoshi Fujiwara1and Rubaiyat Islam1
1Graduate School of Information Science, University of Hyog o, Kobe 650-0047, Japan
E-mail: yoshi.fujiwara@gmail.com
(Received May 31, 2021)
How crypto flows among Bitcoin users is an important question for understanding the structure and
dynamics of the cryptoasset at a global scale. We compiled al l the blockchain data of Bitcoin from
its genesis to the year 2020, identified users from anonymous addresses of wallets, and constructed
monthly snapshots of networks by focusing on regular users a s big players. We apply the methods of
bow-tie structure and Hodge decomposition in order to locat e the users in the upstream, downstream,
and core of the entire crypto flow. Additionally, we reveal pr incipal components hidden in the flow
by using non-negative matrix factorization, which we inter prete as a probabilistic model. We show
that the model is equivalent to a probabilistic latent seman tic analysis in natural language processing,
enabling us to estimate the number of such hidden components . Moreover, we find that the bow-tie
structure and the principal components are quite stable amo ng those big players. This study can be a
solid basis on which one can further investigate the tempora l change of crypto flow, entry and exit of
big players, and so forth.
Presented at the conference “Blockchain in Kyoto 2021”, and forthcoming in the JPS Conference
Proceedings.
KEYWORDS: Bitcoin, cryptoasset, bow-tie structure, Hodge decomposi tion, non-negative
matrix factorization, latent Dirichet allocation, comple x network
1. Introduction
Cryptoasset or cryptocurrency is essentially a digital led ger to record transactions between cred-
itors and debtors, just like money. The digital system is bas ed on a collection of non-centralized
ledgers, called blockchain, which contains all the histori cal record of transactions among anonymous
users. Today there are many cryptoassets being exchanged in markets with fiat currencies and also
with each other. The market capitalization is so huge in tota l ranging from one to a few trillion USD,
and highly volatile potentially having a big impact even on a sset markets and prices of non-crypto at
a global scale.
In this paper, we study Bitcoin, the largest one dominating n early half of the market capitalization
at the time of writing. We attempt to understand the flow of cry pto as a complex network comprising
of the users as nodes and the crypto flow as links. There are a nu mber of studies from such a viewpoint
of complex network on cryptoassets. See [1–16] for example, and references therein.
Specifically, in this paper, we focus on “big players” who are defined as persistently appearing
users, likely to be involved in transactions of high frequen cies and large amounts, and address the
following questions. First, it is important to identify the users in the upstream, downstream, or core in
the entire crypto flow. We shall examine the so-called “bow-t ie” structure of the network of those big
players to classify the location of the crypto flow based on th e binary relationship of links. Second, we
measure the location in a more quantitative way by using the i nformation of flow along the links in the
combinatorial method of Hodge decomposition. Third questi on is to extract “principal components”
hidden in the entire crypto flow so as to uncover a certain numb er of latent factors or components.
1
Fourth, because the network is changing in time, what can one say about the stability of the crypto
flow?
In Section 2, we describe our dataset of Bitcoin to identify t he anonymous addresses representing
wallets into users. Then we define regular users as big player s and construct networks. In Section 3.1,
we perform the bow-tie analysis to locate users in the stream of crypto flow. In Section 3.2, we use the
method of Hodge decomposition to quantify the location of us ers. In Section 3.3, we introduce non-
negative matrix factorization as a method of matrix decompo sition to reveal principal components
hidden in the flow. We shall show in Section 3.4 that the method can be interpreted as a probabilistic
model, from which one can estimate the number of components. In Section 3.5, we find about a dozen
of principal components among several hundred big players, and show that the temporal change of
the network is quite stable. In Section 4, we discuss about se veral aspects, being worth further investi-
gation, and conclude in Section 5. We add Appendix A for the id entities (actual names, business, and
so forth) of selected users. Appendix B illustrates the netw orks in adjacency matrices. Appendix C
briefly summarizes the above mentioned probabilistic model in relation to latent Dirichlet allocation,
from which the estimation on the number of components is done in Appendix D.
2. Data
We employ the dataset of all transactions recorded in the Bit coin blockchain from the genesis
block (first block issued on January 9, 2009) until the block o f height 63,299 (inclusive; issued on
June 4, 2020). Each transaction is a transfer of a certain amo unt of BTC (monetary unit of Bitcoin)
from one or more addresses to others as we will see shortly. We call such a transfer of BTC as crypto
flow. An address is something like a wallet possessed by a user who can be an individual or, more
frequently today, an agent in the business of exchanges, ser vices, gambling, and so forth.
In the dataset, total number of transactions was 1.38 billio n, while the number of di fferent ad-
dresses was about 657 million. To study crypto flow of Bitcoin , one needs to know users, rather than
the addresses. However, it is not straightforward to identi fy users from addresses because of the very
nature of anonymity inherent in the core technology of block chain. See [17] for technical details.
Let us employ a simple but useful method to identify users fro m addresses to construct a giant
graph comprising of nodes as users and edges as crypto flow. We shall see that more than 60% of the
addresses can be identified with users. Additionally we will see that a number of users can be revealed
with their actual names, types of business, and sometimes ge ographical location at a global scale.
Then we define regular users as big players in order to focus on a subgraph comprising of frequently
appearing users who are involved in crypto flow with huge amou nts of BTC. The subgraph will be
studied in the subsequent sections.
2.1 Identification of Users from Addresses
Consider an example of such a transaction (TX) that one day Al ice transferred 1 BTC to Bob:
TX1:{a1,a2}→{ a123,a1}, (1)
where the addresses a1anda2belong to Alice, while a123belongs to Bob. Alice needs more than
one address as input of TX 1, because a single one was not su fficient to fulfill the amount of 1 BTC.
Output of TX 1includes a1representing the change. Another day, Alice did another tra nsaction:
TX2:{a1,a3}→{ a45,a3}, (2)
where the address a3also belongs to Alice. Obviously, multiple addresses, if an d only if they appear
in an input of a transaction, belong to the same user, namely h er wallets. As a consequence from both
of (1) and (2), it follows that a1,a2,a3can be identified to belong to the same user. Note that a2and
2
Fig. 1. Rank-size plot for the number of addresses identified with us ers. Size (horizontal) is the number of
addresses identified with users of type A (a group of addresse s appearing in multiple inputs of transactions, so
identified as user; see main text). Rank (vertical) is the des cending order of the size. Maximum size is nearly
20 million, while minimum is 2 and average is 6.7.
a3did not appear in any record of transactions, TX 1and TX 2. By looking at all the transaction in the
history, one can identify many addresses with users.
This simple but useful method to identify users from address es was proposed by [1] and has been
extensively used in the literature (see [4, 5] and the data of [18, 19], for example). We implemented
an efficient algorithm of this method to process the above mentione d 657 million addresses in the
1.375 billion transactions. We found that 402 million addre sses (more than 60%) can be identified
to obtain 60 million di fferent users (denoted as type A ). The rest of 255 million addresses, which
never appeared as multiple inputs in any transactions, are r egarded as different users ( type B ). This
procedure results in 315 million users all together.
Fig. 1 shows a rank-size plot, in which the size is the number o f addresses identified with users
of type A, and the rank is its descending order. The minimum si ze is 2 by construction, while the
average is 6.7. It is interesting to observe that the rank-si ze plot is highly skewed with the maxi-
mum size being nearly 20 million! We labeled all the users of t ype A in a sequential user ID cor-
responding to the rank (e.g. 0000012345 ), while users of type B is labeled with its address (e.g.
3CjqmbuRA1LEWmLHiWoSWHcWuTEVPfU24P ).
A bunch of transactions is packed into a block in the blockcha in. Each block has a timestamp
of its birth, which is UTC (Coordinated Universal Time) when the block was mined or born as an
empty ledger to be filled with transactions. We associate the time with the transactions contained in
the block. Each block is mined in a mean time of 20 minutes or lo nger, as is well known, so it should
be understood that by time we mean a rough estimate on when the transaction was made within an
accuracy of an hour or so. One can use this information of the t imestamp to obtain the intra-day
activities of users. Fig. 2 is a schematic diagram for typica l three users, presumably located in Asia,
Europe including Africa, and USA.
Moreover, with the help of laborious investigation by [19], it is possible to unravel the identity
of users of type A with actual names, types of business, and so metimes geographical location for
many users. Appendix A is the summary of the unraveled identi ty. Table A·1 gives the classification
of business into exchanges, gambling, pools, services and o thers. Nearly one third in the list are
exchanges. Table A·2 is the list of countries of those exchan ges, where China, UK, and USA are
3
Fig. 2. Schematic diagram for the intra-day activities of users. To p is a world map with UTC (Coordinated
Universal Time). The bottom three plots depict the number of transactions involving three users. Each plot
shows the numbers of transactions of out-flow (green), in-flo w (blue), self-loop (magenta), and their total (bold
red), where each number is accumulated during the year 2019. Out-flow, in-flow, and self-loop are respectively
the transaction in which the user is its source, destination , and both of them simultaneously. A peak of the red
line shows the most active time, corresponding to the daytim e of USA, Europe, and Asia (left to right). Arrows
indicate noon of specific locations.
dominant, followed by Canada, Australia, Brazil, Singapor e, and Russia. In fact, as found in the
top of Table A·3, the user ID 0000000000 corresponding to the maximum size in Fig. 1 is actually
Bit-x.com andXapo.com , the former of which is an agent of exchange in South Africa.
Exchanges are a typical category of “big players” in the sens e that they actually hold a huge
number of individuals and agents as customers resulting in a large number of transfers. As a matter
of fact, the daily number of transfers has an interesting wee kly pattern. There is significantly less
activity in weekends than in weekdays as found in recent data of Bitcoin1(see our previous study
[21,22]). Such a weekly pattern implies that those institut ional agents are dominant in the entire flow
of crypto.
2.2 Crypto Flow Network
Now all the transactions among addresses are converted into transfers from users to users, each
of which has the following information:
•user of source s,
•user of destination d,
•amount of Bitcoin transferred from stod, i.e. s→d,
•UTC time of transfer (from the block containing the transact ion).
During a certain period of time T, for a pair of users, there can be more than one transfer as
depicted in Fig. 3 (see the left-hand side). In this example, there are three transfer of crypto for i→j,
one for j→i, and also two for i→i. The last case of self-loop is possible, because one can rece ive a
change in a transaction, and also because di fferent addresses are possibly identified with a user, like
1Cryptoasset of XRP has a similar weekly pattern according to [20]. In the early history of Bitcoin, the weekly pattern
was quite the opposite; more active in weekends, presumably because individuals were big players at that time.
4
i j0.1
0.2
0.4
0.11
2i jfij= 0.7
gij= 3
fji= 0.1
gji= 1 fii= 3
gii= 2Aggregated
Fig. 3. Aggregation of transactions to construct crypto flow networ k.Left: During a given period of time,
for a pair of users iand j, three transfers for i→jwith 0.1, 0.2, 0.4 in BTC, one of j→iwith 0.1, and two
fori→iwith 1 and 2. Right : After aggregation, one has i→jwith frequency fi j=3 and amount of flow
gi j=0.7. Similarly, j→iwith fji=1 and amount of flow gji=0.1, and i→iwith fii=2 and gii=3.
an exchange. Given a time-scale T, it would be reasonable to aggregate these transactions as s hown
in the right-hand side of Fig. 3.
After the aggregation, one has a network comprising of nodes as users and edges with direction
given by the transfer of crypto, frequency and amount of tran sfer occurred during the period of time.
Let us denote the following variables which represent the st rength or weight of each edge by
frequency fi j≡frequency of transfers for i→j,
amount of flow gi j≡amount of total transfers for i→j.
Regarding the time-scale Tfor aggregating the transactions and the epoch to select in t he histor-
ical data, we choose one month and the calendar year of 2019. B y examining the time-series for the
daily number and amount of transactions, we assumed that the period of one month is adequate to
study the stability and temporal change of the crypto flow. Sh orter period may lead to a trivial result
for the stability and could be insu fficient to detect the temporal change, if any change is present . Also
longer period would be misleading due to non-equilibrium na ture of the system. The year 2019 was
chosen as the epoch, in which one does not see violent bubble o r crush in the price.
The number of all the users is huge, 315 million users in total . Fortunately, however, it is not
necessary to include all of them, because most of them do not a ppear frequently. In the next section,
we shall extract only a tiny part of the network by focusing on the “regular users” who appeared
everyday during the specified period.
2.3 Regular Users as Big Players
For our purpose in this paper, it is su fficient to focus on the crypto flow with high frequency and
big amount of Bitcoin, because infrequent and /or small amount of flows is obviously unimportant
for the understanding the entire flow. In other words, it su ffices to focus on “big players” who are
playing some dominant role in the game of crypto flow. It would be possible to define such a big
player in different ways. In this study, we define it by looking at how persis tently the user appears in
transactions during our specified period of time.
Fig. 4 depicts examples of users who appear in di fferent numbers of transactions on daily basis.
The user of the top case is persistent in committing transact ions with other users, which can be labeled
as a “regular user”. The middle case changes the persistency from being inactive with no transaction
with anyone else to being active in an abrupt way. The bottom c ase has little activity, just a few
transactions on particular days having a strong intermitte ncy.
We define regular users as those appearing everyday during the period of one year in 2 019, and
use them as big players. The number of regular users was 479. T hen we construct a subgraph in each
month, which is comprised of the regular users as nodes and th e crypto flow as links, the latter of
which are aggregated as described in Fig. 3. Thus we have 12 sn apshots of such subgraphs, each
5
Fig. 4. Illustrative examples for the activities of users. Each plo t depicts the daily number of transactions,
in each of which the user is either source or destination of th e transaction for the period of year 2019. Self-
loops (case of the same source and destination) are excluded . Top is a “regular” user appearing every day. The
middle user became active from being inactive, while the bot tom one has intermittency in its activity. We focus
on regular users in this paper.
Fig. 5. Activities of regular users, defined by each user’s peak of tr ansactions, in UTC. Shaded region corre-
sponds to the daytime of Asia, Europe, and USA (left to right) .
corresponding to each month in the year, from January to Dece mber. Summarizing the processing of
the whole dataset, we constructed the snapshots of networks denoted by Gt=(Vt,Et), where tis the
month, Vtis the set of regular users, Etis the set of links among them, each having the frequency and
amount as depicted in Fig. 3.
We add Fig. 5 showing the histogram for the UTC time of highest activities of all the regular
6
users in the year of 2019. This information gives geographic al locations of those regular users.
3. Analysis of Crypto Flow Network
3.1 Basic Properties of Network
For each month t, we constructed a network denoted by Gt=(Vt,Et) as described in the preceding
section. Table I is the summary of basic properties of the net works in the year 2019. Vtcorresponds
to the regular users in each month, the number of which is show n by the column|Vt|. The column
|Et|is the number of edges, namely the number of di fferent crypto flow from one user to another or
to itself (self-loops as shown in parentheses). Most of the u sers have self-loops. Temporal change of
the network causes the changes of VtandEt. The column of|Vt∩Vt+1|is the number of users that are
common to successive months in their appearance. One can see that most of the users are appearing
successively. The same is true for the edges as shown in the co lumn of|Et∩Et+1|. In other words,
the network is not changing drastically in terms of the entry and exit of nodes and edges during the
time-scale of months.
Table I. Summary of basic properties of the networks in the year 2019, January to December.
t|Vt| | Et| | Vt∩Vt+1| |Et∩Et+1|GWCC GSCC/IN/OUT/TE
01 470 17,215(408) 468 13,313 468(3) 327 /24/113/4
02 470 16,658(407) 468 13,357 468(3) 322 /20/120/6
03 473 17,618(408) 471 13,942 470(4) 327 /25/113/5
04 473 17,691(415) 470 13,960 469(5) 336 /16/115/2
05 472 17,903(416) 471 14,012 468(5) 330 /23/112/3
06 471 17,787(412) 469 13,705 467(5) 318 /23/124/2
07 472 17,292(410) 468 13,108 470(3) 333 /21/110/6
08 468 16,534(402) 467 12,628 466(3) 321 /18/123/4
09 470 16,288(407) 468 12,652 466(5) 325 /23/113/5
10 469 16,317(402) 468 12,476 467(3) 322 /21/119/5
11 470 15,859(409) 466 12,080 468(3) 324 /23/116/5
12 466 15,584(390) — — 466(1) 323 /18/120/5
Each column represents the following.
t=month of the year 2019
|Vt|=number of nodes
|Et|=number of edges (number of self-loops in parentheses)
|Vt∩Vt+1|=number of nodes common to successive months
|Et∩Et+1|=number of edges common to successive months
GWCC=number of nodes in giant weakly connected component (number of components in parentheses)
GSCC/IN/OUT/TE=number of nodes in giant strongly connected component /IN/OUT/tendrills
We found that most of the users have self-loops, as shown in th e parentheses of column |Et|,
with the frequencies fiiand the amounts giibeing highly correlated with the number of addresses
identified in the preceding Section 2.1 as naturally expecte d. Because our main interest in this paper
is the crypto flow from one user to another, we remove all the se lf-loops in what follows.
Adjacent matrices with the strength of links given by the fre quencies fi jofGtfor all t’s are
illustrated in Appendix B. One can see that the overall pictu re does not change in time, but the
illustration does not help to uncover the nature of connecti vity and flow.
To see the connectivity of network, namely how those regular users are linked among them and
also how they are located in the stream of cypto flow, let us exa mine the property of connected com-
7
Fig. 6. Temporal change of bow-tie structure in an alluvial diagram . Monthly data of networks in the year
2019, January to December (from left to right). Vertical bla ck segments in each month show the nodes of
corresponding network grouped into GSCC (giant strongly co nnected component), IN, OUT, and TE (tendrills)
in the bow-tie structure. Horizontal bands represent trans itions among such groups from one month to its
successive one.
ponents. First, decompose Gtinto weakly connected components (WCC), i.e. connected com ponents
when regarded as an undirected graph. We found that there exi sts a giant WCC (GWCC) containing
most of the users. See the column GWCC of Table I. There was onl y a small number of disconnected
components as shown in the same column.
Then, in order to identify the location of users contained in the GWCC, we employed the well-
known analysis of “bow-tie” structure [23]. In general, GWC C can be decomposed into the following
parts:
GSCC Giant strongly connected component: the largest connected component when viewed as a
directed graph. One or more directed paths exist for an arbit rary pair of firms in the component.
INThe nodes from which the GSCC is reached via at least one direc ted path.
OUT The nodes that are reachable from the GSCC via at least one dir ected path.
TE“Tendrils”; the rest of the GWCC.
It follows that
GWCC=GSCC+IN+OUT+TE (3)
GSCC is the core of the crypto flow’s circulation. The IN and OU T parts are upstream and down-
stream of the flow respectively. The users in the part of IN are playing a role of suppliers of crypto,
while the OUT users are considered to be consumers of crypto.
Table I shows the bow-tie structure in the column of GSCC /IN/OUT/TE. For example, in Septem-
ber, 470 users are located into GSCC (325 users), IN (23), OUT (113), and TE (5). One can observe
that a large fraction of the users in the GWCC is located in the GSCC, as one can easily interpret this
fact in the way that those regular users are circulating cryp to globally. There are a less fraction of the
users in IN and OUT with asymmetry in the numbers.
It would be interesting to see how the individual users are lo cated in the temporal change of the
network. Fig. 6 depicts such a diagram of temporal change fro m one month to its successive one in the
whole year. One can see that the groups of GSCC, IN, and OUT are very stable in each membership
of users. This fact means that those users appearing in succe ssive months are playing stable roles in
the crypto flow’s circulation and the location of upstream an d downstream. We remark that analysis
8
of bow-tie structure is based on the binary links, namely eit her presence or absence among the nodes,
but not on the strength of links such as frequency and amount o f crypto flow. In the next section, we
shall see how to quantify the location of users by using the so -called Hodge decomposition.
3.2 Hodge Decomposition
Helmholtz-Hodge-Kodaira decomposition, or simply Hodge d ecomposition, is a combinatorial
method to decompose flow on a network into circulation and gra dient flow. Original idea dates back
to the Helmholtz theorem in vector analysis, which states th at under appropriate conditions any vector
field can be uniquely represented by the sum of an irrotationa l or rotation-free (curl-free) vector field
and a divergence-free (solenoidal) vector field. The theore m can be generalized from Euclidean space
to graph and other entity as shown by Hodge, Kodaira and other s. See [24–26] for readable exposition.
The method has a wide range of applications in the studies suc h as neural network [27], economic
networks [28, 29], and also our previous work on Bitcoin and [ 30].
We recapitulate the method briefly for the present manuscrip t to be self-contained. Let Ai jdenote
the adjacency matrix:
Ai j=1 if there is a link of transfer from user itoj,
0 otherwise.(4)
We excluded all the self-loops, implying that Aii=0. Each link has a flow, denoted by ˜Fi j, either of
the frequency, fi j, or the amount, gi j, of the transfer from itoj(see Fig. 3). Define
˜Fi j=fi jorgi jifAi j=1,
0 otherwise .(5)
Note that there can be a pair of users such that Ai j=Aji=1 and ˜Fi j,˜Fji>0.
Let us define a “net flow” Fi jby
Fi j=˜Fi j−˜Fji (6)
and a “net weight” wi jby
wi j=Ai j+Aji. (7)
Note that wi jis symmetric, i.e., wi j=wji, and non-negative, i.e., wi j≥0 for any pair of iandj2.
Hodge decomposition is given by
Fi j=F(c)
i j+F(g)
i j, (8)
where the circular flow F(c)
i jsatisfies/summationdisplay
jF(c)
i j=0, (9)
which implies that the circular flow is divergence-free. The gradient flow F(g)
i jcan be expressed as
F(g)
i j=wi j(φi−φj). (10)
Thus the weight wi jserves to make the gradient flow possible only where a link exi sts. We refer to
the quantityφias the Hodge potential . Large value ofφiimplies that the user iis in the upstream of
the entire network, while small values implies iis in the downstream.
2It is remarked that (7) is simply a convention to consider the effect of mutual links between iandj. One could multiply
(7) by 0.5 or an arbitrary positive number, which does not cha nge the result significantly for a large network.
9
0 5 10 15 20 25
-2 -1 0 1 2Frequency
Hodge potentialGSCC
IN
OUT
Fig. 7. Distributions for Hodge potentials of the users in GSCC (bla ck), IN (blue), and OUT (red). The
average of all the potential values is set to be zero (vertica l dotted line). Data: 2019-09.
Combine (8), (9), and (10), one can derive the following equa tion to determineφi.
/summationdisplay
jLi jφj=/summationdisplay
jFi j, (11)
fori=1,..., N. Here, Li jis the so-called graph Laplacian and defined by
Li j=δi j/summationdisplay
kwik−wi j, (12)
whereδi jis the Kronecker delta.
It is easy to show that the matrix L=(Li j) has only one zero mode (eigenvector with zero
eigenvalue). The presence of this zero mode simply correspo nds to the arbitrariness in the origin of φ.
All the other eigenvalues are positive (see, e.g., [30]). Th erefore, (11) can be solved for the potentials
by fixing the potentials’ origin. We assume that the average v alue ofφis zero.
Fig. 7 depicts the distributions for Hodge potentials of the users in GSCC, IN, and OUT. One can
see that the entire set of distributions is bimodal having tw o peaks at positive and negative values,
while there are a number of values around zero. Obviously, th ey correspond to IN, OUT, and GSCC,
each being located in the upstream, downstream, and core of t he entire crypto flow. Moreover, there
exists a correlation between the value of the Hodge potentia l and the net amount of demand or supply
of crypto by each user. See [30] for details, where we studied a daily snapshot of the network including
all the users, not only big players. We claim that the same pro perty holds also for the monthly data
restricted to big players of regular users.
3.3 Non-negative Matrix Factorization
It would be a natural question whether there are distinctive ingredients of flows in the crypto flow
or not. The analysis of bow-tie structure is based merely on t he binary relationship of links, so does
not give such information, because the crypto flows from upst ream to downstream with circulation in
the giant strongly connected component that occupies a larg e fraction of the entire network. In other
10
words, are there any “principal components” that constitut e the entire flow in a decomposition? In
order to find such principal components or latent factors in t he transfer of crypto among big players,
we shall apply non-negative matrix factorization (NMF) to the strength of links, namely the matrix of
the frequencies and amounts of transfer. We recapitulate th e method here. See [31–33] and references
therein for introduction.
LetXbe an N×Mnon-negative matrix, in general, to start with; that is, its elements are all
non-negative, denoted as X≥0. NMF gives an approximation of Xby a product of two matrices:
X≈S D, (13)
where S,DareN×KandK×Mnon-negative matrices, S,D≥0, respectively3. In practice, one
expects that Kis much smaller than NandMso that the factorization gives a compact representation
ofX. We shall assume that N=Mfor our application of crypto flow among Nusers in what follows.
Explicitly in components, (13) reads
Xsd≈K/summationdisplay
k=1SskDkd, (14)
where the indices sanddrepresent source and destination ( s,d=1,..., N) respectively, and Xsdis
the strength of crypto flow, quantified by frequency fsd, amount gsd, or similar variables, from stod
in a certain period of time. We choose
Xsd=fsd, (15)
in this paper. See Fig. B·1 in Appendix B for the illustration ofXsd. We would expect that K≪N,
because of the sparsity of X. How to determine Kis discussed later.
The approximation in (13) is actually given by the following optimization:
min
S,D≥0F(X,S D), (16)
where the function F(·,·) is the so-called Kullback-Leibler (KL) divergence defined by
F(A,B)=KL(A/bardblB)≡/summationdisplay
i,j/parenleftigg
Ai jlogAi j
Bi j−Ai j+Bi j/parenrightigg
. (17)
Note that F(A,B)=0 if and only if A=B. The reason why we choose the particular function of
(17) will be clarified later4. Technically, one can solve (16) iteratively with the initi alization of S,D
using non-negative double singular value decomposition (s ee the review [32] and references therein).
Although the iterative algorithm yields local minima, our n umerical solutions under di fferent random
seeds gave essentially the same decomposition.
To understand the meaning of the decomposition, let us consi der how a source distributes flow to
different destinations. For an arbitrary source s, (14) can be written as
Xs≈K/summationdisplay
k=1SskDk, (18)
whereXsis the vector of s-th row of X, andDkis the vector of k-th row of D. Equation (18) means
that the flow from the source scan be expanded in terms of “basis” vectors, Dk(k=1,..., K). The
3The decomposition is not unique due to trivial degrees of fre edom. One is permutation, S D=Sππ−1D, whereπis
a permutation matrix simply exchanging indices. Another is scale transformation, S D=Sσσ−1D, whereσis a diagonal
matrix with all elements positive. We shall see that these de grees are fixed after appropriate normalization and orderin g.
4See Section 3.4 for a probabilistic interpretation of choos ing the KL divergence. Another functional form, often used,
is the so-called Frobenius norm: F(A,B)=(1/2)/summationtext
i,j(Ai j−Bi j)2, which leads to a di fferent probabilistic model.
11
components (Dk)d=Dkdrepresent how destinations are distributed among users in the k-th NMF
component. It is convenient to normalize Dkby L1-norm, that is, by defining
/tildewideDkd≡Dkd
Dkwhere Dk≡/summationdisplay
dDkd, (19)
so that one has /summationdisplay
d/tildewideDkd=1, (20)
for all k. With respect to this normalized basis vectors, the expansi on in (18) is rewritten as
Xs≈K/summationdisplay
k=1(SskDk)/tildewideDkwhere ( /tildewideDk)d=/tildewideDkd, (21)
Thus the outgoing flow from the source sis approximately expressed by a linear combination of K
normalized basis vectors /tildewideDkwith coefficients given by SskDk.
Similarly, consider how a destination dcollects flow from di fferent sources. For an arbitrary
destination d, (14) reads
Xd≈K/summationdisplay
k=1DkdSk, (22)
whereXdis the vector of d-th column of X, andSkis the vector of k-th column of S. The components
of (Sk)s=Sskrepresent how sources are distributed among users in this k-th NMF component. Define
/tildewideSsk≡Ssk
Skwhere Sk≡/summationdisplay
sSsk, (23)
and one has /summationdisplay
s/tildewideSsk=1, (24)
for all k. Then (22) is rewritten as
Xd≈K/summationdisplay
k=1(DkdSk)/tildewideSkwhere ( /tildewideSk)s=/tildewideSsk. (25)
Thus the incoming flow to the destination dis approximately expressed by a linear combination of K
normalized basis vectors /tildewideSkwith coefficients given by DkdSk.
How can one determine K? Obviously, the larger Kis, the better the approximation (13) is, but
with less parsimonious representation of the data. In the ne xt section, let us make a detour to examine
this issue from a di fferent perspective.
3.4 NMF as a probabilistic model
We can interpret the NMF as a probabilistic model. Denote the right-hand of (14) by
ξsd≡/summationdisplay
kSskDkd, (26)
which are regarded as parameters to be estimated from the dat aXsuch that Xsdis assumed to be a
random number chosen from a Poisson distribution with the pa rameterξsdas
P(x|ξ)=e−ξξx
x!. (27)
12
It is easy to see that the log likelihood function L(ξsd)≡logP(Xsd|ξsd) takes the maximum value at
ξsd=Xsd. Then one can introduce a quantity to measure how much the est imation of the parameters
is good, that is/summationdisplay
s,d(L(Xsd)−L(ξsd))=/summationdisplay
s,d/parenleftigg
XsdlogXsd
ξsd−Xsd+ξsd/parenrightigg
, (28)
to be minimized . One can see that this quantity is equivalent to the KL diverg ence in (17)5.
To express the entire framework in probabilistic terms more explicitly, let us normalize the data
Xin (15) by
/tildewideXsd=Xsd/summationtext
s′,d′Xs′d′. (29)
Then let us rewrite (14) as
/tildewideXsd≈/summationdisplay
krk/tildewideSsk/tildewideDkd, (30)
where/tildewideDkdand/tildewideSskwere given by (19) and (23) respectively, and
rk≡SkDk/summationtext
k′Sk′Dk′, (31)
which satisfies that/summationtext
krk=1. Let us denote the right-hand side of (30) by
psd≡/summationdisplay
krk/tildewideSsk/tildewideDkd, (32)
which satisfies that/summationtext
s,dpsd=1. We remark that the normalized weight rkdefined by (31) gives
the information of relative importance of the k-th NMF component in the expansion with normalized
basis vectors in (32). One can determine the ordering of NMF c omponents uniquely according to the
magnitudes of rk.
Suppose that there are Nftransfers in total during a period of time. For each pair of so urce and
destination, sandd, generate a transfer s→dwith the probability given by psd, being independently
of other pairs. Under the assumption of a small probability o fpsdand a large number of Nf,Xsd
follows a Poisson distribution with the parameter, ξsd=Nfpsd.
It turns out that the decomposition in (26), or equivalently (32), has an interesting connection
with machine learning. In natural language processing, it i s often necessary to extract topics among
documents comprising of words or terms. In a situation of uns upervised learning, the task is to infer
topics as hidden or latent variables, which can explain a col lection of documents, each being an
unordered set of terms. Probabilistic latent semantic anal ysis (PLSA) is a probabilistic model for
doing such a task [36]. Suppose that there are Ndocuments and Mterms. Then the occurrence of
terms can be expressed by a document-term matrix Xwith size N×M, each element of which is the
frequency of occurrence of a term in a document. Topics are la tent variables to explain the data X. A
topic is actually a probability distribution for the occurr ence of terms with di fferent probabilities. A
document can have a mixture of topics. An example is a documen t on “influence of hosting Olympics
to economy” with a mixture of topics on sports and economy.
One of the widely used model of PLSA is latent Dirichlet alloc ation (LDA). See Appendix C and
references therein. For our purpose, it su ffices to understand how terms are generated at locations of
documents in a probabilistic way. The probability that a ter m is chosen at a location in a document is
given by the sum of Kfactors, each of which is the product of two probabilities; t he probability that
5We became aware that this argument is known in the literature . If one assumes Gaussian instead of Poisson, one would
have Frobenius norm for KL divergence in (17). See [34]. One o f the present authors (YF) learned a hint on the argument
from Itsuki Noda in his application of NMF to transportation data [35].
13
0 0.2 0.4 0.6 0.8 1
0 10 20 30 40 50Measure (to be minimized)
#ComponentsCaoJuan (2009)
Arun (2010)
Deveaud (2014)
(a) Comparison of three methods [38–40] 0 5 10 15 20 25 30 35 40 45
0 10 20 30 40 50LDA score (in arbitray units)
#Components99% interval
avgerage (20 simulations)
(b) Monte Carlo simulations by the method [39]
Fig. 8. Determining K, the number of NMF components, by using the concept of cohere nce in LDA. (a) Dif-
ferent methods [38–40] are compared. Each measure of cohere nce is drawn in the vertical axis so that it is to
be minimized to find the optimal number of components. Maximu m and minimum values in the region of K
are scaled to 1 and 0 respectively to make the comparison easi er. One can see that K=11∼13 are optimal.
(b) Monte Carlo simulations by the method [39] with 20 runs fo r each K. Averages (points) and 99% level
(gray band, narrow) calculated from standard errors are dra wn. We conclude that K=13 is optimal from this
result (b).
a topic is selected in the document and the one that the term is chosen under the selected topic. See
the equation of (C·11) in Appendix C. One can immediately see that (C·11) is essentially the same
as (26), or equivalently (32).
Thus the matrix decomposition of NMF can be put in the framewo rk of probabilistic model of
PLSA and LDA. As a bonus, one can adopt the method of estimatin g the number of topics to our
problem of determining the number of NMF components, denote d by Kin both cases. Interested
readers are guided to look at the literature [37–40] and othe rs given at the end of Appendix C. Let us
take a look at our results in the next section finishing the det our of this section.
3.5 Result of NMF for Crypto Flow
We first show a few results for a snapshot of September in the ye ar 2019 (denoted as 2019-
09), in order to verify if the idea in the preceding section wo rks to determine the number of NMF
components. Fig. 8 shows the measures of coherence by employ ing three different methods of LDA
[38–40]. The methods give mostly the same optimal values of K, namely K=11∼13, consistently
as shown in Fig. 8 (a). We found that the measure given in [39] i s relatively stable and potentially
useful to determine a specific value of K. So we performed Monte Carlo simulations in Fig. 8 (b), and
were able to determine the optimal value as K=13. For this data, Xhas the dimension of N=470,
so we conclude that one can have a small number of NMF componen ts that can explain the entire
flow among those regular users.
In Appendix D, we summarize the same result for the data in all the other months of the year
2019. We found that the optimal number Kis quite small in the range more than 10 and less than 20,
much smaller than the number of users, N∼500 (see Table I). Additionally, Kis relatively stable
irrespectively of the temporal change. See Table D·1 and Fig . D·1.
Let us examine each NMF components obtained with the optimal value of K. Fig. 9 and Fig. 10
show the NMF components in terms of the basis vectors, /tildewideDkand/tildewideSk, respectively for k=1,..., K.
In Fig. 9, each plot shows the vector components of ( /tildewideDk)d=Dkdmeaning how destinations are
distributed among users din the k-th NMF component. Similarly in Fig. 10, each plot shows the
14
vector components of ( /tildewideSk)s=Sskmeaning how sources are distributed among users sin the k-
th NMF component. See (19) and (23), and also note the normali zation therein. Note that in each of
Fig. 9 and Fig. 10, the plots are ordered (from top to bottom) i n the descending order of the probability
rkgiven in (31).
One can immediately notice from the figures that the componen ts of these basis vectors are con-
centrated on a limited number of users, but are not distribut ed among many users. To quantify the
effective number of the concentration, let us use the inverse He rfindahl-Hirschman index, abbrevi-
ated as IHH, which is defined as follows. Consider “shares” xi≥0 among i=1,..., Nthings with
the sum equal to 1, i.e./summationtext
ixi=1. The IHH is defined by
IHH≡N/summationdisplay
i=1x2
i−1
. (33)
When the shares are equal, xi=1/Nfor all i, then IHH=N. On the other hand, when there is the
strongest concentration, namely, xi=1 for a particular iandxi=0 otherwise, then IHH =1. So
IHH can give an estimate of the e ffective number of large shares6. The idea can be applied to the
basis vectors, because the vectors are normalized in the sam e way as shares. In Fig. 9 and Fig. 10, we
displayed all the calculated IHH’s. One can see that the IHH’ s are quite small ranging from a few to
a dozen or so, compared with the total number of users N=470 for the data 2019-09 (see Table I).
How can we use the NMF components to understand the crypto flow ? Choose a particular user s
as a source s. The flow from swas approximately expressed by a linear combination of Knormalized
basis vectors /tildewideDk, each depicted in Fig. 9, with the coe fficients given in (21), i.e. SskDk. The coeffi-
cients represent the strength of the decomposed flow from the source s. A similar argument holds by
choosing a particular user das a destination. The flow to dwas expressed by a linear combination in
(25) with the coefficients, DkdSk.
For example, consider the user with ID 0000000000 that is located in the GSCC, as a source s.
Fig. 11 shows the coe fficients corresponding to Kcomponents; see (a). One can see that the coe ffi-
cients are non-zero at only four components. Such a sparsene ss tells that the flow from this user can
be expressed with a few components. And the corresponding co mponents have non-zero values at a
small number (recall the IHH’s) of the vector components, Dkd, as shown in Fig. 9, implying that the
users corresponding to these non-zero components constitu te a cluster for the outgoing flow from the
source s.
The same user0000000000 can be regarded as a destination din the GSCC. Fig. 11 (b) shows
the coefficients, again non-zeros at only one or two components. Toget her with Fig. 10, one can
find another cluster composed of a small number of users for th e incoming flow to the d. Similar
arguments hold for the users 0000006178 and0000000012 , respectively located in the IN and OUT.
See Fig. 11 (c) and (d). In this way, one can find clusters for ei ther of or both of the outgoing and
incoming flows of each user.
Each NMF component can be represented by a matrix, because th e non-negative matrix of Xsd
or its probabilistic counter part psdcan be expressed by (18) or (32). It is possible to depict each
component kby the matrix of /tildewideSsk/tildewideDkdin the normalized way. Fig. 12 and Fig. 13 illustrate such
matrices for the data of 2019-09. This should be compared wit hXsdin Appendix B. One can see that
the NMF components provide sparse matrices.
Finally, we find that the NMF components are relatively stabl e in the temporal change of network.
Fig. 14 (a) shows the result for cosine similarities of the NM F basis vectors Dkfor the two successive
months of 2019-09 and 2019-10. Fig. 14 (b) shows the result fo rSkfor the same data. In either of
these results, one can see that the NMF components are quite s imilar except only a few permutation
of indices.
6It is remarked that the paper [20] in this volume also applied but modifies Herfindahl-Hirschman index in an interesting
way to characterize the frequencies of transactions in XRP.
15
0 0.25 0 100 200 300 400Destination (user index)
k= 1: Prob=27.3%, IHH= 5
0k= 2: Prob=13.8%, IHH=35
0 0.25 k= 3: Prob= 9.3%, IHH=10
0 0.25k= 4: Prob= 8.9%, IHH= 9
0 0.25k= 5: Prob= 7.6%, IHH= 8
0k= 6: Prob= 6.7%, IHH=16
0k= 7: Prob= 6.2%, IHH=15
0 0.25 0.5 0.75 k= 8: Prob= 5.2%, IHH= 2
0 0.25k= 9: Prob= 4.4%, IHH= 3
0 0.25k=10: Prob= 3.6%, IHH= 5
0k=11: Prob= 3.1%, IHH=12
0 0.25 k=12: Prob= 2.8%, IHH= 6
0 0.25 0.5 0.75 1
0 100 200 300 400k=13: Prob= 1.2%, IHH= 2
Fig. 9. NMF components /tildewideDk(k=1,..., 13) from top to bottom. Each plot ( /tildewideDk)d=Dkdshows how desti-
nations are distributed among users din the k-th NMF component. Note that /tildewideDkis normalized, i.e./summationtext
dDkd=1
for all k. See (19) and (20) in the main text. Also shown in each plot are the probability of the component,
denoted by “Prob”, and the inverse Herfindahl-Hirschman ind ex “IHH” representing the e ffective number of
dominant users in the component. Data: 2019-09.
16
0 0.25 0.5 0 100 200 300 400Source (user index)
k= 1: Prob=27.3%, IHH= 4
0 0.25 k= 2: Prob=13.8%, IHH= 9
0 0.25 0.5k= 3: Prob= 9.3%, IHH= 3
0 0.25k= 4: Prob= 8.9%, IHH= 8
0 0.25 0.5k= 5: Prob= 7.6%, IHH= 3
0 0.25k= 6: Prob= 6.7%, IHH= 5
0 0.25k= 7: Prob= 6.2%, IHH= 5
0 0.25 0.5 k= 8: Prob= 5.2%, IHH= 4
0 0.25 0.5 0.75 1 k= 9: Prob= 4.4%, IHH= 2
0 0.25 0.5k=10: Prob= 3.6%, IHH= 4
0 0.25k=11: Prob= 3.1%, IHH= 6
0 0.25k=12: Prob= 2.8%, IHH= 5
0 0.25 0.5 0.75 1
0 100 200 300 400k=13: Prob= 1.2%, IHH= 2
Fig. 10. NMF components /tildewideSk(k=1,..., 13) from top to bottom. Each plot ( /tildewideSk)s=Sskshows how sources
are distributed among users sin the k-th NMF component. Note that /tildewideSkis normalized, i.e./summationtext
sSsk=1 for all k.
See (23) and (24) in the main text. See the caption of Fig. 9 for “Prob” and “IHH” in each plot. Data: 2019-09.
17
0.51Coe/c01.(a) For a souce in GSCC
0.51Coe/c01.(b) For a destination in GSCC
0.51Coe/c01.(c) For a souce in IN
0.51
1 2 3 4 5 6 7 8 9 10 11 12 13Coe/c01.
Component k(d) For a destination in OUT
Fig. 11. Coefficients, with which crypto flow from or to a selected user is exp anded with respect to the NMF
components. The selected users are 0000000000 (a,b) included in the GSCC of bow-tie structure, 0000006178
(c) in the IN, and0000000012 (d) in the OUT. The user 0000000000 can be source (a) and destination (b).
The user of (c) is a source, and the user of (d) is a destination . The expansion is given by (21) for the selected
source s, and by (25) for the selected destination d. Data: 2019-09.
18
Fig. 12. NMF components k=1,..., 8 as matrices defined by /tildewideSsk/tildewideDkdin (32). Data: 2019-09.
19
Fig. 13. (Continued) NMF components k=9,..., 13 as matrices defined by /tildewideSsk/tildewideDkdin (32). Data: 2019-09.
20
(a) Cosine similarity of NMF basis vectors Dk
(b) Cosine similarity of NMF basis vectors Sk
Fig. 14. Temporal change of the NMF components from one month tto its successive month t+1. (a) Cosine
similarities calculated for all the pairs of Dk(k=1,..., 13) between t(vertical) and t+1. (b) The same for
all the pairs of Sk. For the vertical and horizontal axes in (a) and (b), the orde r of indices along vertical and
horizontal axes corresponds to the descending order of the p robability rkin (31). Data: 2019-09 and 2019-10.
21
4. Discussions
Let us briefly discuss about several aspects that would be wor th further investigation.
First, while we succeeded to extract the NMF components and f ound that the components have
non-zero values only at a relatively small number of users, w e still did not identify those users by
exploiting the fact. It is quite likely the case that the extr acted users must play important roles in each
of the NMF components, either of key destinations or key sour ces. We attempted to identify a tiny
fraction of such users by matching the list of such users with the identity given in Appendix A, but the
identification was not su fficient in order to interpret the meaning of corresponding NMF components.
Instead, such intra-day activities as shown in Fig. 2 of Sect ion 2.1 could give us the geographical
locations of those key users, possibly uncovering the crypt o flow in each NMF component at a global
scale. This issue remains to be investigated.
Second, even if the temporal change of the network in terms of the NMF components has such a
stable structure as found in Fig. 14, we noticed that there ex ists interesting change of a few compo-
nents in the same figure. A keen reader may have noticed that th e components k=3,4,5,6 at time
tare changed into among themselves at time t+1, while the cosine similarities are close to 1. This
means that the probabilities rkfor those components were changed from one month to the next. Also
one can notice that the optimal number of NMF components show ed a slow variation during the pe-
riod (recall Table D·1 of Appendix D). These facts might give us a hint for how to treat the temporal
change of network by paying attention to those slowing varyi ng aspects. Additionally, while we fo-
cused only on regular users appearing everyday during the pe riod under study, it would be necessary
to include the process of entry and exit of big players.
Third, technically, we regarded the method of NMF as a probab ilistic model that shares the same
stochastic process as in the probabilistic latent semantic analysis (PLSA). As a bonus, we were able
to employ the latent Dirichlet allocation (LDA) and its know n methods to estimate the number of
topics in the context of topic model, or the number of NMF comp onents in out context. In principle,
one could start with the full-fledged Bayesian framework in t he LDA and its extension and variations.
It would be worth pursuing in this direction, which is also re lated to the second point above, because
there are several studies on how to treat temporally changin g topics of documents in a long time-span.
Fourth, our methods in this paper can be easily applied to di fferent cryptoassets including Ethereum
and XRP. We are aware of the paper [20] in this volume, which is in a similar line of study. It would
be interesting to apply our methods to the data of XRP.
Finally, it would be an extremely interesting problem how th e crypto flow among big players is
related to the prices in the exchange markets with fiat currencies and also with oth er cryptoassets. It
is quite likely that the bubble /crash and their precursors might force the big players to rea ct during
such turmoils in a di fferent way from tranquil periods. For example, exchanges nee d to reallocate
cryptoassets in the necessity of making a reservoir or doing a release of cryptoassets under the risk.
5. Summary
Our purpose in this study on the cryptoasset of Bitcoin is to u nderstand the structure and tem-
poral change of crypto flow among big players. We compiled all the transactions contained in the
blockchain of Bitcoin cryptoasset from its genesis to the ye ar 2020, identified users from anonymous
addresses, and constructed snapshots of networks comprisi ng of users as nodes and links as crypto
flow among the users. While the whole network is huge, we extra cted sub-networks by focusing on
regular users who appeared persistently during a certain pe riod. Specifically, we extracted monthly
snapshots during the year of 2019, and selected roughly 500 r egular users.
We first analyzed the bow-tie structure from the binary relat ionship of flow, and then performed
the Hodge decomposition based on the strength of flow defined b y frequencies and amounts, in order
22
to locate users in the upstream, downstream, and core of the e ntire crypto flow. We found that the
bow-tie structure is stable during the period, implying tha t those regular users have di fferent roles in
the crypto flow.
Then, to reveal important ingredients hidden in the flow, we e mploy the method of non-negative
matrix factorization (NMF) to extract a set of principal com ponents. We discussed that the NMF
method can be regarded as a probabilistic model, which is equ ivalent to a probabilistic latent semantic
analysis and its typical model of latent Dirichlet allocati on. This observation brought us a method to
estimate an optimal number of NMF components, which turned o ut to be a dozen or so. We found
that the NMF components have non-zero values corresponding to a limited number of users, telling
us their roles of destinations or sources of the crypto flow. A dditionally, we found that the NMF
components are quite stable in the temporal change for the ti me-scale of months.
There remain several points including the further investig ation on the users contained in those
NMF components, a treatment of temporally changing network , and technically interesting issues to
be pursued in the future.
Acknowledgment
We would like to thank Hideaki Aoyama, Yuichi Ikeda, and Hiwo n Yoon for discussions, Hiroshi
Iyetomi for solving a technical issue of Hodge decompositio n, Itsuki Noda for clarifying his applica-
tion of NMF on transportation data, Takeaki Uno for an e fficient algorithm to identify users, Shinya
Kawata and Wajun Kawai for technical assistance. This work i s supported by JSPS KAKENHI Grant
Numbers, 19K22032 and 20H02391, by the Nomura Foundation (G rants for Social Science), and
also by Kyoto University and Ripple’s collaboration scheme .
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American Statistical Association ,101(476):1–30, 2006.
25
Appendix A: Identity of Users of Type A
WalletExplorer.com [19] is a web site providing information about identity of ad dresses in
Bitcoin blockchain. The site merges addresses together, if they are part of the same wallet, and also
identifies wallets with actual names. According to the site, the method to merge addresses is:
Just a basic algorithm is used to determine wallet addresses . Addresses are merged to-
gether, if they are co-spent in one transaction. So if addres ses A and B are co-spent in
transaction T1, and addresses B and C are co-spent in transac tion T2, all addresses A, B
and C will be part of one wallet.
Sometimes, an address belongs to some service but it was neve r co-spent with others. Then
that address stays unnamed. It is typically more often at add resses with higher amount (as
there is no need to co-spending).
This method is precisely the same as [1], which is the one we em ployed in Section 2.1. In addition,
the identification of actual names is done by WalletExplorer.com as follows:
In most of the cases, I registered to service, made transacti on(s) and saw which wallet
bitcoins were merged with, or from which wallet it was withdr awn.
There is probably no easier way how to discover names other th an this.
Please note that the name database is not updated, so it does n ot contain newer exchanges
(or newer wallets of existing exchanges).
We matched our data with the one in [19] to obtain the identity and additional attributes of users
of type A (see Section 2.1 for the type). Table A·1 is the class ification into exchanges, services,
gambling, historic, and mining pools. Table A·2 shows the li st of countries that exchanges belong to.
Table A·3 is the complete list of this matching.
Table A·1. Classification of identified users (compiled from [19])
Classification #Users Examples
Exchanges 84 Bittrex.com ,Huobi.com ,Bit-x.com ,HitBtc.com
Old/Historic 83 AgoraMarket ,EvolutionMarket ,SilkRoadMarketplace
Services/Others 45 Xapo.com ,ePay.info ,Cubits.com
Gambling 41 999Dice.com ,CoinGaming.io ,SatoshiMines.com
Pools 11 BTCCPool ,SlushPool.com ,BitMinter.com
Total 264 —
Table A·2. Countries of identified exchanges (compiled from [19])
Country #Users
China 14
UK 13
USA 13
Canada 4
Australia 3
Brazil 3
Singapore 3
Russia 3
Denmark 2
Finland 2Country #Users
Mexico 2
Netherlands 2
Poland 2
Portugal 2
South Africa 2
Austria 1
Belize 1
Croatia 1
Czech 1
Germany 1Country #Users
Iran 1
Korea 1
Luxembourg 1
Malta 1
Panama 1
Taiwan 1
Thailand 1
Vanuatu 1
Vietnam 1
Total 84
26
Table A·3. : Identity of Users (compiled from [19])
Definitions
No: sequential number
User ID: an arbitrarily but uniquely assigned IDs to each use r in our data
#Addr. (1): number of addresses identified to each User ID in o ur data (rows are sorted by this column)
#Addr. (2): the same as (1) but provided by WalletExplorer.com (at the timing of writing)
No. User ID #Addr. (1) #Addr. (2) Name Category Country
10000000000 18,913,420 1 Bit-x.com Exchanges South Africa
20000000000 18,913,420 1 Xapo.com Services/Others —
30000000001 13,110,033 12,469,250 ePay.info Services/Others —
40000000002 5,302,867 1 Cryptopay.me Services/Others —
50000000002 5,302,867 1 Cubits.com Services/Others —
60000000002 5,302,867 1 Luno.com Exchanges South Africa
70000000002 5,302,867 1 VirWoX.com Exchanges Austria
80000000002 5,302,867 1 Xapo.com Services/Others —
90000000002 5,302,867 487,776 CoinPayments.net Services/Others —
100000000006 2,180,236 1,488,034 Xapo.com Services/Others —
110000000010 1,630,483 1 Huobi.com Exchanges China
120000000010 1,630,483 957,652 Cubits.com Services/Others —
130000000011 1,538,476 1,377,461 Bittrex.com Exchanges USA
140000000012 1,413,904 1 Bitstamp.net Exchanges Luxembourg
150000000014 1,043,379 279,697 Huobi.com Exchanges China
160000000017 992,726 1 VirWoX.com Exchanges Austria
170000000017 992,726 1 Xapo.com Services/Others —
180000000018 988,300 940,605 Poloniex.com Exchanges USA
190000000022 912,950 133,020 AnxPro.com Exchanges China
200000000022 912,950 770,486 CoinTrader.net Exchanges Canada
210000000025 845,559 1 Cubits.com Services/Others —
220000000028 811,809 2 Cubits.com Services/Others —
230000000030 778,990 651,547 999Dice.com Gambling —
240000000035 682,176 1 MoonBit.co.in Services/Others —
250000000037 659,820 656,699 CoinGaming.io Gambling —
260000000038 631,985 1 Luno.com Exchanges South Africa
270000000041 581,525 1 Cubits.com Services/Others —
280000000042 546,515 291,443 Luno.com Exchanges South Africa
290000000043 523,330 522,056 LocalBitcoins.com Exchanges Finland
300000000045 498,001 498,001 AgoraMarket Old/Historic —
310000000049 478,476 343,039 Bitstamp.net Exchanges Luxembourg
320000000054 420,632 420,632 EvolutionMarket Old/Historic —
330000000055 412,338 249,883 Cryptonator.com Services/Others —
340000000057 398,349 1 Xapo.com Services/Others —
350000000060 377,140 305,518 Cryptopay.me Services/Others —
360000000061 372,753 372,753 SilkRoadMarketplace Old/Historic —
370000000063 350,036 350,036 SilkRoad2Market Old/Historic —
380000000069 341,160 61,103 MercadoBitcoin.com.br Exchanges Brazil
390000000071 325,365 1 Xapo.com Services/Others —
400000000075 307,489 307,451 BTC-e.com Exchanges Russia
410000000077 294,238 1 Xapo.com Services/Others —
420000000079 278,973 2 Cubits.com Services/Others —
430000000082 266,695 254,601 SatoshiMines.com Gambling —
440000000083 263,074 262,940 YoBit.net Exchanges Russia
450000000086 250,920 191,689 Bitcoin.de Exchanges Germany
460000000090 241,250 223,781 BitcoinFog Services/Others —
470000000093 238,480 238,476 Cex.io Exchanges UK
480000000101 206,542 156,203 CoinJar.com Services/Others —
490000000107 197,164 197,155 NitrogenSports.eu Gambling —
500000000114 189,776 189,776 AlphaBayMarket Services/Others —
510000000116 187,189 187,086 HitBtc.com Exchanges UK
520000000120 186,000 186,000 BitPay.com Services/Others —
530000000135 168,885 1 Luno.com Exchanges South Africa
540000000158 146,381 146,381 NucleusMarket Services/Others —
550000000159 145,978 1 Cryptonator.com Services/Others —
560000000159 145,978 1 Cubits.com Services/Others —
570000000169 140,594 1 Bitcoin.de Exchanges Germany
Continue to next page
27
Table A·3 continued from previous page
No. User ID #Addr. (1) #Addr. (2) Name Category Country
580000000169 140,594 1 Cubits.com Services/Others —
590000000169 140,594 1 Poloniex.com Exchanges USA
600000000176 134,559 134,559 Cryptsy.com Exchanges USA
610000000182 131,979 1 Poloniex.com Exchanges USA
620000000190 125,004 125,004 PocketDice.io Gambling —
630000000195 122,249 1 Bitstamp.net Exchanges Luxembourg
640000000199 120,548 120,491 FortuneJack.com Gambling —
650000000201 119,119 119,065 AbraxasMarket Old/Historic —
660000000208 115,775 115,775 CoinKite.com Services/Others —
670000000210 114,458 114,458 Kraken.com Exchanges USA
680000000216 109,798 1 Luno.com Exchanges South Africa
690000000217 109,151 109,151 Instawallet.org Old/Historic —
700000000219 107,479 85,122 Bleutrade.com Exchanges Brazil
710000000234 96,890 96,890 SecondsTrade.com Gambling —
720000000239 92,226 72,581 HolyTransaction.com Services/Others —
730000000244 90,473 44,416 CoinSpot.com.au Exchanges Australia
740000000252 85,637 85,626 MintPal.com Old/Historic —
750000000253 85,566 84,679 Hashnest.com Exchanges China
760000000259 83,723 83,517 BtcTrade.com Exchanges China
770000000263 82,695 27,343 BTCJam.com Services/Others —
780000000267 80,987 73,965 OKCoin.com Exchanges China
790000000269 80,086 1 Poloniex.com Exchanges USA
800000000270 79,712 79,712 Bter.com Exchanges China
810000000274 78,849 78,849 BitZino.com Gambling —
820000000278 78,119 78,048 OKCoin.com Exchanges China
830000000282 76,997 1 Cubits.com Services/Others —
840000000282 76,997 52,925 BitoEX.com Services/Others —
850000000286 74,602 74,602 Rollin.io Gambling —
860000000300 68,956 67,795 CloudBet.com Gambling —
870000000302 68,658 55,098 VirWoX.com Exchanges Austria
880000000307 67,114 1 Cubits.com Services/Others —
890000000312 66,748 1 Luno.com Exchanges South Africa
900000000318 65,367 1 VirWoX.com Exchanges Austria
910000000325 64,803 64,803 BTCC.com Exchanges China
920000000347 57,770 57,753 MaiCoin.com Exchanges Taiwan
930000000350 56,952 56,952 BTCCPool Pools —
940000000358 55,757 55,757 PandoraOpenMarket Old/Historic —
950000000359 55,703 1 MercadoBitcoin.com.br Exchanges Brazil
960000000362 55,167 55,167 Paxful.com Exchanges USA
970000000366 54,640 54,640 PrimeDice.com Gambling —
980000000370 53,639 53,639 SheepMarketplace Old/Historic —
990000000374 53,102 53,102 Cavirtex.com Exchanges Canada
1000000000387 50,878 50,878 BlackBankMarket Old/Historic —
1010000000402 48,602 48,518 BX.in.th Exchanges Thailand
1020000000403 48,525 19,434 MoonBit.co.in Services/Others —
1030000000407 48,178 33,372 HaoBTC.com Services/Others —
1040000000412 47,295 47,281 Matbea.com Exchanges UK
1050000000444 42,750 41,150 SatoshiDice.com Gambling —
1060000000447 42,327 41,866 BitcoinWallet.com Services/Others —
1070000000468 40,270 1 Cubits.com Services/Others —
1080000000480 39,013 1 VirWoX.com Exchanges Austria
1090000000482 38,880 1 Huobi.com Exchanges China
1100000000501 36,999 36,999 Justcoin.com Old/Historic —
1110000000508 35,537 1 Bitcoin.de Exchanges Germany
1120000000508 35,537 28,567 SafeDice.com Gambling —
1130000000511 35,453 35,453 McxNOW.com Old/Historic —
1140000000512 35,433 35,433 C-Cex.com Exchanges UK
1150000000527 34,149 34,149 MiddleEarthMarketplace Old/Historic —
1160000000533 33,436 33,389 Vircurex.com Exchanges China
1170000000537 32,823 32,823 Purse.io Services/Others —
1180000000539 32,701 32,701 SatoshiBet.com Gambling —
1190000000542 32,017 27,693 SwCPoker.eu Gambling —
1200000000545 31,940 30,755 BitBargain.co.uk Exchanges UK
1210000000556 30,965 30,965 SealsWithClubs.eu Old/Historic —
Continue to next page
28
Table A·3 continued from previous page
No. User ID #Addr. (1) #Addr. (2) Name Category Country
1220000000559 30,624 22,599 BlockTrades.us Exchanges USA
1230000000562 30,251 16,052 CoinMotion.com Exchanges Finland
1240000000563 30,187 30,183 OkLink.com Services/Others —
1250000000571 29,256 29,256 Huobi.com Exchanges China
1260000000591 27,653 23,515 Bit-x.com Exchanges South Africa
1270000000605 26,622 26,622 BtcDice.com Old/Historic —
1280000000614 26,013 24,204 BitBay.net Exchanges Poland
1290000000615 25,960 25,960 Betcoin.ag Gambling —
1300000000626 25,257 1 Cubits.com Services/Others —
1310000000649 23,841 20,866 Paymium.com Services/Others —
1320000000672 22,597 22,597 Loanbase.com Services/Others —
1330000000676 22,304 22,304 Coinroll.com Gambling —
1340000000687 21,693 21,693 FaucetBOX.com Services/Others —
1350000000690 21,640 10,439 CoinHako.com Exchanges Singapore
1360000000717 20,484 18,001 FYBSG.com Exchanges Singapore
1370000000747 19,810 19,605 TheRockTrading.com Exchanges Malta
1380000000762 18,997 18,997 BlueSkyMarketplace Old/Historic —
1390000000774 18,489 18,489 Crypto-Games.net Gambling —
1400000000794 17,705 17,705 Coin-Swap.net Old/Historic —
1410000000804 17,531 1 Luno.com Exchanges South Africa
1420000000869 15,965 15,965 AnoniBet.com Gambling —
1430000000874 15,905 15,905 ChangeTip.com Services/Others —
1440000000881 15,757 15,757 Bitmit.net Old/Historic —
1450000000891 15,495 15,495 CoinApult.com Services/Others —
1460000000902 15,260 15,260 BtcMarkets.net Exchanges Australia
1470000000926 14,566 14,566 Inputs.io Old/Historic —
1480000000934 14,394 1 Huobi.com Exchanges China
1490000000959 13,713 11,210 Vaultoro.com Exchanges UK
1500000001006 12,486 12,486 CryptoStocks.com Services/Others —
1510000001007 12,456 12,456 BitAces.me Old/Historic —
1520000001071 11,221 11,221 Coins-e.com Exchanges Canada
1530000001072 11,220 11,220 Igot.com Exchanges Belize
1540000001093 10,901 10,900 SatoshiRoulette.com Gambling —
1550000001177 9,667 1 Bittrex.com Exchanges USA
1560000001199 9,512 9,512 Crypto-Trade.com Old/Historic —
1570000001235 9,165 9,165 Cryptorush.in Old/Historic —
1580000001240 9,122 9,122 BTCOracle.com Gambling —
1590000001255 8,967 8,967 Genesis-Mining.com Services/Others —
1600000001313 8,430 8,430 Exmo.com Exchanges UK
1610000001325 8,371 4,343 SlushPool.com Pools —
1620000001355 8,120 8,120 VaultOfSatoshi.com Old/Historic —
1630000001368 8,032 8,032 BitcoinVideoCasino.com Gambling —
1640000001393 7,865 7,865 BTCGuild.com Old/Historic —
1650000001395 7,857 7,766 Peerbet.org Gambling —
1660000001397 7,848 7,848 796.com Exchanges Vanuatu
1670000001421 7,585 7,585 Btc38.com Exchanges UK
1680000001438 7,479 7,479 Betcoins.net Old/Historic —
1690000001497 7,109 7,109 LiteBit.eu Exchanges Netherlands
1700000001568 6,788 6,207 Bitbond.com Services/Others —
1710000001588 6,669 5,369 HappyCoins.com Exchanges Netherlands
1720000001631 6,477 6,477 Bitcoin-Roulette.com Old/Historic —
1730000001666 6,309 6,309 AllCoin.com Old/Historic —
1740000001685 6,242 6,242 Coin.mx Old/Historic —
1750000001739 6,009 4,726 LakeBTC.com Exchanges China
1760000001750 5,966 5,966 777Coin.com Gambling —
1770000001760 5,934 5,934 GHash.io Pools —
1780000001796 5,762 5,762 DoctorDMarket Services/Others —
1790000001862 5,481 5,481 Coinomat.com Exchanges UK
1800000001916 5,297 5,295 Coinmate.io Exchanges UK
1810000002012 5,024 5,024 BitVC.com Exchanges China
1820000002024 4,996 4,875 SatoshiCircle.com Gambling —
1830000002039 4,953 1 Luno.com Exchanges South Africa
1840000002055 4,896 4,896 MyBitcoin.com Old/Historic —
1850000002098 4,775 4,775 AllCrypt.com Old/Historic —
Continue to next page
29
Table A·3 continued from previous page
No. User ID #Addr. (1) #Addr. (2) Name Category Country
1860000002155 4,629 4,629 GermanPlazaMarket Services/Others —
1870000002169 4,605 4,605 MasterXchange.com Old/Historic —
1880000002183 4,551 4,272 CoinCafe.com Exchanges USA
1890000002192 4,530 4,242 BitKonan.com Exchanges Croatia
1900000002197 4,516 4,516 QuadrigaCX.com Exchanges Canada
1910000002216 4,451 4,451 BitElfin.com Old/Historic —
1920000002243 4,377 4,377 OrderBook.net Exchanges USA
1930000002251 4,363 3,800 SpectroCoin.com Exchanges UK
1940000002259 4,354 4,354 Bitcurex.com Exchanges Poland
1950000002265 4,338 4,338 Coinichiwa.com Gambling —
1960000002281 4,292 4,277 Betcoin.tm Gambling —
1970000002305 4,232 4,227 MeXBT.com Exchanges Mexico
1980000002403 3,999 3,999 Bitfinex.com Exchanges China
1990000002424 3,947 1,571 CoinVault Old/Historic —
2000000002464 3,861 3,861 BetsOfBitco.in Old/Historic —
2010000002471 3,840 3,840 JetWin.com Gambling —
2020000002587 3,607 3,607 BitZillions.com Gambling —
2030000002617 3,545 3,543 Korbit.co.kr Exchanges Korea
2040000002661 3,485 3,485 BTCPop.co Services/Others —
2050000002849 3,216 2,181 YABTCL.com Gambling —
2060000002922 3,121 3,121 BIToomBa.com Old/Historic —
2070000002952 3,086 3,086 BitYes.com Old/Historic —
2080000002965 3,075 2,640 BetMoose.com Gambling —
2090000003031 2,979 2,978 CoinURL.com Services/Others —
2100000003139 2,829 2,829 CannabisRoadMarket Old/Historic —
2110000003195 2,760 2,760 Ice-Dice.com Old/Historic —
2120000003211 2,744 2,744 ChBtc.com Exchanges China
2130000003249 2,713 2,713 CoinArch.com Exchanges Singapore
2140000003310 2,645 2,645 Comkort.com Old/Historic —
2150000003340 2,618 2,618 BitNZ.com Services/Others —
2160000003348 2,614 2,614 CleverCoin.com Exchanges USA
2170000003388 2,575 2,575 CoinMkt.com Old/Historic —
2180000003637 2,355 2,355 DiceBitco.in Old/Historic —
2190000003756 2,276 2,276 BitcoinVietnam.com.vn Exchanges Vietnam
2200000003840 2,221 2,221 Indacoin.com Exchanges UK
2210000004134 2,023 2,023 BitClix.com Services/Others —
2220000004187 1,992 1,992 Coin-Sweeper.com Old/Historic —
2230000004271 1,948 1,948 GoCelery.com Services/Others —
2240000004570 1,812 1,812 Playt.in Old/Historic —
2250000004580 1,804 1,796 Bitcash.cz Old/Historic —
2260000004586 1,802 1,802 CampBX.com Exchanges USA
2270000004817 1,713 1,713 BTCLend.org Services/Others —
2280000004840 1,704 1,704 CoinChimp.com Exchanges Russia
2290000004863 1,699 1,699 BtcExchange.ro Old/Historic —
2300000004882 1,690 1,690 AdmiralCoin.com Old/Historic —
2310000005002 1,643 1,643 Bitcoinica.com Old/Historic —
2320000005121 1,594 1,594 Gatecoin.com Exchanges China
2330000005399 1,508 1,508 BetChain.com-old Gambling —
2340000005547 1,471 1,471 BabylonMarket Old/Historic —
2350000005637 1,443 1,443 HelixMixer Services/Others —
2360000006104 1,314 1,314 Bylls.com Services/Others —
2370000006381 1,246 1,246 Btcst.com-pirateat40 Old/Historic —
2380000006688 1,189 1,189 PocketRocketsCasino.eu Old/Historic —
2390000006697 1,188 1,188 Bitso.com Exchanges Mexico
2400000006747 1,178 1,178 BTCt.com Old/Historic —
2410000006753 1,176 1,176 DaDice.com Old/Historic —
2420000007201 1,095 1,095 Cryptonit.net Exchanges UK
2430000007312 1,076 1,076 BitStarz.com Gambling —
2440000007547 1,040 1,040 Ccedk.com Exchanges Denmark
2450000007673 1,020 1,020 Satoshi-Karoshi.com Gambling —
2460000007783 1,005 1 VirWoX.com Exchanges Austria
2470000008133 978 978 Just-Dice.com Old/Historic —
2480000008213 968 968 CryptoLocker Old/Historic —
2490000008232 965 965 GreenRoadMarket Services/Others —
Continue to next page
30
Table A·3 continued from previous page
No. User ID #Addr. (1) #Addr. (2) Name Category Country
2500000008325 953 953 CoinRoyale.com Gambling —
2510000008352 950 950 CryptoBounty.com Old/Historic —
2520000009025 876 876 1Coin.com Exchanges China
2530000009100 869 679 Coingi.com Exchanges Panama
2540000010744 746 746 BitcoinWeBank.com Old/Historic —
2550000010833 741 741 EmpoEX.com Exchanges USA
2560000011035 729 729 FairProof.com Gambling —
2570000011546 700 700 UseCryptos.com Exchanges Portugal
2580000011749 686 1 Cubits.com Services/Others —
2590000011857 681 1 VirWoX.com Exchanges Austria
2600000012513 670 669 AntPool.com Pools —
2610000012569 668 668 Coinbroker.io Exchanges USA
2620000012639 665 665 UpDown.BT Old/Historic —
2630000013022 648 648 DiceNow.com Gambling —
2640000013139 643 643 Dagensia.eu Old/Historic —
2650000013870 614 614 WatchMyBit.com Services/Others —
2660000014175 604 604 MPEx.co Old/Historic —
2670000014703 593 593 Banx.io Exchanges USA
2680000015353 572 572 CloudHashing.com Old/Historic —
2690000015549 565 565 Eligius.st Pools —
2700000016775 527 527 Europex.eu Old/Historic —
2710000017189 515 515 EveryDice.com Old/Historic —
2720000018179 499 499 Brawker.com Old/Historic —
2730000019489 473 471 10xBitco.in Old/Historic —
2740000021056 440 1 Cubits.com Services/Others —
2750000021084 440 439 BitMinter.com Pools —
2760000021286 436 436 ExchangeMyCoins.com Exchanges Denmark
2770000023185 402 402 BW.com Pools —
2780000023304 400 400 Chainroll.com Old/Historic —
2790000024708 390 390 DiceCoin.io Gambling —
2800000025249 382 382 FoxBit.com.br Exchanges Brazil
2810000025389 380 380 PonziCoin.co Old/Historic —
2820000025554 377 377 Birwo.com-old Old/Historic —
2830000027509 351 338 Zyado.com Exchanges Portugal
2840000028306 341 341 SuzukiDice.com Old/Historic —
2850000028897 334 1 Cubits.com Services/Others —
2860000032828 297 297 KnCMiner.com Pools —
2870000033285 293 293 BitcoinPokerTables.com Gambling —
2880000034753 280 280 Polmine.pl Old/Historic —
2890000034912 279 279 MineField.BitcoinLab.org Gambling —
2900000045961 234 234 SmenarnaBitcoin.cz Old/Historic —
2910000062902 195 195 Dgex.com Old/Historic —
2920000064830 190 190 BitLaunder.com Services/Others —
2930000072390 170 170 BitMillions.com Old/Historic —
2940000074148 166 1 Cubits.com Services/Others —
2950000078381 157 1 Cubits.com Services/Others —
2960000100904 125 125 Vic-Socks.to Services/Others —
2970000108438 117 117 Gatecoin.com Exchanges China
2980000109665 116 1 VirWoX.com Exchanges Austria
2990000114095 112 112 Bitcoin-24.com Old/Historic —
3000000157019 96 1 Cubits.com Services/Others —
3010000180141 85 1 Poloniex.com Exchanges USA
3020000200218 77 77 BetcoinDice.tm Old/Historic —
3030000206094 75 75 Bitfury.org Pools —
3040000265341 59 1 Cubits.com Services/Others —
3050000296609 53 53 50BTC.com Old/Historic —
3060000302266 52 1 Cubits.com Services/Others —
3070000302626 52 1 Cubits.com Services/Others —
3080000332619 49 49 BtcEur.eu Old/Historic —
3090000417570 40 1 VirWoX.com Exchanges Austria
3100000434094 38 1 Luno.com Exchanges South Africa
3110000482184 35 35 SecureVPN.to Services/Others —
3120000489034 34 1 Xapo.com Services/Others —
3130000539744 32 32 MinersCenter.com Old/Historic —
Continue to next page
31
Table A·3 continued from previous page
No. User ID #Addr. (1) #Addr. (2) Name Category Country
3140000661573 28 28 DiceOnCrack.com Old/Historic —
3150000727720 26 1 Cubits.com Services/Others —
3160000881383 23 1 Bittrex.com Exchanges USA
3170001047894 20 20 ActionCrypto.com Old/Historic —
3180001068642 20 1 Cubits.com Services/Others —
3190001453639 15 1 CoinMotion.com Exchanges Finland
3200001482058 15 15 ASICMiner Old/Historic —
3210001615111 14 14 Telco214 Pools —
3220001849904 12 12 BTradeAustralia.com Exchanges Australia
3230001949672 12 12 PinballCoin.com Old/Historic —
3240002236961 11 1 MoonBit.co.in Services/Others —
3250002437346 10 1 Cubits.com Services/Others —
3260002652760 9 1 Xapo.com Services/Others —
3270002867200 9 1 Cubits.com Services/Others —
3280003042379 8 1 Xapo.com Services/Others —
3290003656659 7 1 Cubits.com Services/Others —
3300003733569 7 1 Luno.com Exchanges South Africa
3310004053833 7 7 SimpleCoin.cz Exchanges Czech
3320004615543 6 6 LuckyB.it Gambling —
3330004719731 6 1 Cubits.com Services/Others —
3340005083698 6 1 Cubits.com Services/Others —
3350005134566 6 1 Cubits.com Services/Others —
3360005316858 6 1 LakeBTC.com Exchanges China
3370006291300 5 1 Cryptopay.me Services/Others —
3380006553479 5 1 Xapo.com Services/Others —
3390006787887 5 1 Cubits.com Services/Others —
3400006884235 5 5 Exchanging.ir Exchanges Iran
3410007872688 4 1 Cubits.com Services/Others —
3420008064964 4 4 FoxBit.com.br Exchanges Brazil
3430008251183 4 1 Cubits.com Services/Others —
3440009938959 4 1 Cubits.com Services/Others —
3450010541278 4 1 Bitcoin.de Exchanges Germany
3460010933272 4 4 ePay.info Services/Others —
3470011084834 4 1 Cubits.com Services/Others —
3480011993584 3 1 Cubits.com Services/Others —
3490013456456 3 3 EclipseMC.com Pools —
3500014115232 3 1 Xapo.com Services/Others —
3510014366385 3 1 Cubits.com Services/Others —
3520014572029 3 1 StrongCoin.com-fee Services/Others —
3530015020521 3 1 Poloniex.com Exchanges USA
3540015254424 3 1 Cubits.com Services/Others —
3550017326362 3 1 Cubits.com Services/Others —
3560018289896 3 1 Huobi.com Exchanges China
3570018738066 3 1 Cubits.com Services/Others —
3580020356858 3 1 Poloniex.com Exchanges USA
3590021062591 3 1 Cubits.com Services/Others —
3600021163228 3 3 ePay.info Services/Others —
3610021447975 2 1 Cubits.com Services/Others —
3620022923752 2 1 Cubits.com Services/Others —
3630023037394 2 1 Cubits.com Services/Others —
3640024428379 2 1 Cubits.com Services/Others —
3650026028525 2 1 Cubits.com Services/Others —
3660033718608 2 1 Xapo.com Services/Others —
3670034424835 2 1 VirWoX.com Exchanges Austria
3680034774492 2 1 Cubits.com Services/Others —
3690035023024 2 1 MercadoBitcoin.com.br Exchanges Brazil
3700035093871 2 1 Huobi.com Exchanges China
3710036244970 2 1 Cubits.com Services/Others —
3720036353675 2 1 Cubits.com Services/Others —
3730036389109 2 2 Dispenser.tf Old/Historic —
3740039716556 2 1 Poloniex.com Exchanges USA
3750040013060 2 1 Cubits.com Services/Others —
3760045999467 2 2 DeepBit.net Old/Historic —
3770046840841 2 1 Cubits.com Services/Others —
Continue to next page
32
Table A·3 continued from previous page
No. User ID #Addr. (1) #Addr. (2) Name Category Country
3780047562289 2 1 Cubits.com Services/Others —
3790049075064 2 1 Cubits.com Services/Others —
3800050709449 2 1 Poloniex.com Exchanges USA
3810051958689 2 2 CoinWorker.com Services/Others —
3820052140982 2 1 Cubits.com Services/Others —
3830053961615 2 1 Cubits.com Services/Others —
3840055586746 2 1 Cubits.com Services/Others —
3850056053704 2 1 Luno.com Exchanges South Africa
3860058665155 2 1 Xapo.com Services/Others —
3870058696998 2 1 Cubits.com Services/Others —
3880058767317 2 1 Cubits.com Services/Others —
3890059011161 2 1 Bittrex.com Exchanges USA
End of Table A·3
33
Appendix B: Illustrating Adjacent Matrices of GtorXsd
Fig. B·1 illustrates the adjacency matrices for all the netw orks Gtin Table I, or equivalently the
non-negative matrix Xsdgiven by (15).
Fig. B·1. Adjacent matrices for all the networks Gtin Table I, or equivalently Xsdgiven by (15). Colors show
the strength of links, expressed by the frequencies.
34
Appendix C: Topic Model of Latent Dirichlet Allocation (LDA )
Topic models in natural language processing and machine lea rning are probablistic models for
how terms appear in documents in a given corpus. Assumed is th e term frequency, irrespective of the
sequential order of term occurences; such an assumption is o ften called “bag-of-words”. A topic is
a latent variable in the model for explaining term frequency occurrences. Latent Dirichelt allocation
(LDA) [41] is such a model widely used in a variety of applicat ions. See [37, 42, 43] and references
therein for introduction and applications. In this appendi x, we briefly summarize the topic model only
for the purpose of explicitly relating it with non-negative matrix factorization (NMF).
Ddocuments in a corpus (a collection of documents) are given w ith a vocaburary of Vterms
(different words). Document d(d=1,..., D) comprises Ndwords, possibly with duplication, each
denoted by wdn(n=1,..., Nd).Ktopics are assumed to be present for the given corpus. A topic is
a latent or hidden variable giving a probability distributi on for term occurrences. If a document is an
article on sports, terms like “swimming” and “gymnastics” a re likely to occur, while “market” and
“employee” will be unlikely.
Consider the example of an article on “influence of hosting Ol ylmics to economy” in a corpus of
newspaper. It would be natural to consider that the article b elongs to two topics of sports and economy.
In topic models, documents are not assumed to belong to a sing le topic, but to simultaneously belong
to multiple topics, and the topics vary among documents. Top ic models aim at modeling such mixed
membership.
Document dhas a topic distribution , which is a multinomial distribution with the parameters:
θd=(θd1,...,θ dK), (C·1)
whereθdk=p(k|θd) is the probability that a topic kis assigned to the document d, satisfying
θdk≥0 andK/summationdisplay
k=1θdk=1. (C·2)
Then a topic zdn∈{1,..., K}is assigned to each word wdnforn=1,..., Nd. Each topic kis aterm
distribution , which is also a multinomial distribution with the paramete rs:
φk=(φk1,...,φ kV), (C·3)
whereφkv=p(v|φk) is the probability that a term voccurs, satisfying
φkv≥0 andV/summationdisplay
v=1φkv=1. (C·4)
The topic model of LDA can be most readily understood by looki ng at how it generates words in
documents as follows.
1. For each topic k=1,..., K, generate the parameters φkfor the term distribution by
φk∼Dir(β), (C·5)
where∼reads that a variable is realized as a sample from the distrib ution on the right-hand side,
and Dir(·) is the Dirichlet distribution7.β=(β1,...,β V) is a set of hyper-parameters with βi>0.
7Dirichlet distribution is defined by
Dir(x|ξ)=Γ/parenleftig/summationtextI
i′=1ξi′/parenrightig
/producttextI
i′′=1Γ(ξi′′)×I/productdisplay
i=1xξi−1
i,
where Iis the number of variables and it is assumed that xi≥0 for all iand/summationtextI
i=1xi=1.Γ(·) is the gamma function defined
byΓ(u)=/integraltext∞
0tu−1e−tdtforu>0. Dirichlet distribution is a generalization of Beta distr ibution (the case I=2).
35
2. Now for each document d=1,..., D,
(a) generate the parameters θdfor the topic distribution by
θd∼Dir(α), (C·6)
whereα=(α1,...,α K) is a set of hyper-parameters with αk>0.
(b) For each word wdn(n=1,..., Nd)
(i) genrate a topic by
zdn∼Cat(θd), (C·7)
where Cat(·) is the categorical distribution8, and then
(ii) genrate a word from the vocaburary by
wdn∼Cat(φk=zdn). (C·8)
Note that a topic is assigned to each occurence of word wdnat the location nof the document d,
depending on how the document dhas a mixture of topics, but is not associated with each term vin
the vocaburary of Vterms. In the previous example, the term “swimming” occurs w ith a relatively
high probability if the topic of sports is assigned at the loc ation of occurence, but the term will not be
likely to occur if the topic of economy is assigned at the same location.
Given the number of topics, K, and the hyper-parameters, βandα, the modelMcan be specified
by the parameters {θd}d=1,...,Dand{φk}k=1,...,K, while the dataDcomprises Ddocuments, each of
which is a set of words {wdn}n=1,...,Ndford=1,..., D. In the Bayesian framework of
p(M|D )=p(D|M )·p(M)
p(D). (C·9)
the prior distribution p(M) is given by the Dirichlet distributions of (C·5) and (C·6). It is easy to show
that the likelihood p(D|M ) can be written by
logp(D|M )=D/summationdisplay
d=1Nd/summationdisplay
n=1logK/summationdisplay
k=1θdk·φkwdn, (C·10)
which follows from the assumption of bag-of-words and the in dependence of documents. Estima-
tion of parameters by maximum likelihood or Bayesian framew orks has a difficulty due to the log of
sum in (C·10), so there are a variety of technical methods suc h as EM (expectation and maximiza-
tion), variational Bayes, and MCMC (Markov chain Monte Carl o) algorithms (see [37, 42, 43] and
references therein).
For our purpose, let us turn out attention to how a word is chos en from the Vterms in the vocabu-
rary in the Ddocuments, given the model parameters. Looking at (C·7) and (C·8), one can see that
the probability that a term vis chosen as a word in the document d, denoted byλdv, is given by
λdv=K/summationdisplay
k=1θdk·φkv, (C·11)
where it is assumed that
λdv≥0 andV/summationdisplay
v=1λdv=1. (C·12)
8Categorical distribution is a multinomial distribution fo r a single trial (a roll of a dice), i.e.
Cat(v|φ)=φv,
where v∈{1,..., V}andφ=(φ1,...,φ V) withφv≥0 for all vand/summationtext
vφv=1.
36
(Note that (C·11) is the factor in the likelihood (C·10)). In terms of matrices, the D×Vmatrix,
denoted by Λ, with elementsλdvis represented by a product of two matrices; the D×Kmatrix
Θwith elementsθdktimes the K×VmatrixΦwith elementsφkv. Note that it is usually the case
thatK≪VandK≪D. One can immediately see that (C·11) is actually a non-negat ive matrix
factorization (NMF) of Λby two generally much smaller matrices:
Λ=Θ·Φ (C·13)
under the conditions of (C·2) and (C·4), from which (C·12) fo llows.
A document-term matrix is the D×Vmatrix, the rows and columns of which correspond to
the documents and terms respectively, and each element is th e frequency (or any other measure that
represents some intensity) for the term occurences. One can regard the document-term matrix as a
realization in the sense that each element at column vand row drepresents how the term vwas
realized in the document daccording to the categorical (multinomial) distribution w ith the parameter
λdv. See the main text in Section 3.4.
How to determine the number of topics, K, is important. There have been a number of works on
this issue. A class of studies [38–40] is based on some measur e to compute similarities of extracted
topics, called coherence. The idea is that an optimal value o fKis the point where the overall dissim-
ilarity among topics achieves a maximum, because extracted topics should be di fferent to a certain
degree. Other approaches include the idea of perplexity [37 ], hierarchical Dirichlet process [44], and
so forth. It is beyond the present paper to review these appro ches. Interested readers are guided to look
at them and reviews [42, 43] with references therein. In the m ain text, we use the idea of coherence
[38–40], which we find to work well for our purpose.
37
Appendix D: Optimal Number of NMF Components
In Section 3.4, we showed that the matrix decomposition of NM F can be regarded as a probablis-
tic model, which is equivalent to a model of probabilistic la tent semantic analysis (PLSA) in natural
language processing, in particular, latent Dirichlet allo cation (LDA). The number of NMF compo-
nents is such an analogous parameter as the number of topics i n the latter. Then, as a bonus, one can
employ the method of estimating the number of topics to our pr oblem of detemining the number of
NMF components, K. We found that three di fferent methods in the literature [38–40] give a consis-
tent result for the optimal value of K, and also that one of them [39] is relatively stable in giving the
optimal (see Fig. 8).
In Fig. D·1, we summarize the same result for the data in all th e other months in the same year.
We found that the optimal number Kis not volatile in its variation among di fferent months, and also
small in its value ranging around 11–18. Table D·1 shows the o ptimal values for all the months of the
year 2019.
Table D·1. Optimal values for the number of NMF components, K, estimated from Fig. D·1
Month Optimal K
01 14
02 16
03 18
04 16
05 16
06 18
07 15
08 12
09 13
10 11
11 13
12 13
38
Fig. D·1. Determining K, the number of NMF components, by minimizing the measure of c oherence [39].
We performed Monte Carlo simulations with 20 runs for each nu mber of components. Averages (points) and
99% level (gray band, narrow) calculated from standard erro rs are drawn. Data: all months in the year 2019.
39
|
{
"id": "2106.11446"
}
|
2403.00018
|
Crypto Technology -- Impact on Global Economy
|
The last decade has been marked by the evolution of cryptocurrencies, which
have captured the interest of the public through the offered opportunities and
the feeling of freedom, resulting from decentralization and lack of authority
to oversee how cryptocurrency transactions are conducted. The innovation in
crypto space is often compared to the impact internet had on human life. There
is a new term called Web 3.0 for denoting all new computing innovations arising
due to the blockchain technologies. Blockchain has emerged as one of the most
important inventions of the last decade with crypto currencies or financial use
case as one of the domains which progressed most in the last 10 years. It is
very important to research about Web 3 technologies, how it is connected to
crypto economy and what to expect in this field for the next several decades.
|
http://arxiv.org/pdf/2403.00018v1
|
Arunkumar Velayudhan Pillai
|
cs.CR, cs.CY
|
cs.CR
|
Volume 3 | Issue 1 | 1Crypto Technology - Impact on Global Economy Short Article
*Corresponding Author
Arunkumar Pillai, Central Washington University, United States.
Submitted: 2024, Jan 16; Accepted: 2024, Feb 09; Published: 2024, Feb 14Arunkumar Pillai*
Citation: Pillai, A. (2024). Crypto Technology - Impact on Global Economy. J Curr Trends Comp Sci Res, 3(1), 01-05.Central Washington University, United States
J Curr Trends Comp Sci Res, 20241. Introduction and Background
The last decade has been marked by the evolution of
cryptocurrencies, which have captured the interest of the public
through the offered opportunities and the feeling of freedom,
resulting from decentralization and lack of authority to oversee
how cryptocurrency transactions are conducted. The innovation
in crypto space is often compared to the impact internet had on
human life. There is a new term called Web 3.0 for denoting all new
computing innovations arising due to the blockchain technologies.
Blockchain has emerged as one of the most important inventions
of the last decade with crypto currencies or financial use case as
one of the domains which progressed most in the last 10 years. It
is very important to research about Web 3 technologies, how it is
connected to crypto economy and what to expect in this field for
the next several decades.
Economics research so far has provided little insight into the
economic relevance of cryptocurrencies. Most existing models of
cryptocurrencies are built by computer scientists who mainly focus
on the feasibility and security of these systems. Crucial issues such
as the incentives of participants to cheat and the endogenous nature
of some key variables such as the real value of a cryptocurrency in
exchange have been largely ignored. Such considerations, however,
are pivotal for understanding the optimal design and, hence, the
economic value of cryptocurrency as a means of payment. Crypto
currency uses an underlying Blockchain technology which solves
an important double spend problem for financial industry. The
original Bitcoin blockchain solved the double spend problem
using a costly distributed computing transaction validation process
also known as mining. Blockchain technologies have evolved
to handle many complex problems and one of the most popular
blockchain Ethereum can be looked at as a powerful computer that
can solve several day-to-day problems. This paper analyses crypto
industry’s overall contribution to economy thus far and what areas
it will continue to contribute and make significant economical
contributions. The analysis will include the potential newer
innovations that are still in early stages and role of regulators to
help accelerate the industry. What is the Impact of Cryptocurrencies on Global Economy?
The growth of the digital assets industry, particularly in the last 12
years with the rise of Bitcoin, has been unprecedented. It has not
only become larger than many countries in a short period of time,
but it has also created numerous job and economic opportunities.
The majority of these jobs were created in the aftermath of the
industry's boom in 2017. From 2015 to 2019, the percentage of
crypto-related jobs in the mainstream economy increased by nearly
1,500%. There are currently over 17,000 different cryptocurrencies
being traded on 459 exchanges, with a total value of about $1.7
trillion.
Consider that the applications of blockchain technology have
greatly expanded in recent years. Non-fungible tokens (NFTs)
for digital art sales, decentralized autonomous organizations
(DAOs) for fundraising, and play-to-earn games with blockchain
features are all recent innovations. These new applications further
complicate the sector and increase the demand for new talent [1].
Technology jobs in crypto industry grew 395% in the U.S. from
2020 to 2021, outpacing the wider tech industry, which saw a
98% increase [2]. There is also a surge in the influx of funding in
the industry. Investors worldwide poured $30 billion into crypto
and blockchain startups in 2021, according to Pitchbook data. At
the same time, public interest in crypto exploded as high profile
evangelists like Elon Musk praised the technology—and crypto
companies entered the mainstream, as evidenced by the newly-
christened Crypto.com Arena in Los Angeles.
Institutional economics understands the economy as made of rules.
Rules (like laws, languages, property rights, regulations, social
norms, and ideologies) allow dispersed and opportunistic people
to coordinate their activity together. Rules facilitate exchange
— economic exchange but also social and political exchange as
well. What has come to be called crypto economics focuses on the
economic principles and theory underpinning the blockchain and
alternative blockchain implementations. It looks at game theory
and incentive design as they relate to blockchain mechanism Journal of Current Trends in Computer Science Research
ISSN: 2836-8495
Volume 3 | Issue 1 | 2
J Curr Trends Comp Sci Res, 2024design. Institutional crypto economics is interested in the rules that
govern ledgers, the social, political, and economic institutions that
have developed to service those ledgers, and how the invention of
the blockchain changes the patterns of ledgers throughout society.
Institutional crypto economics gives us the tools to understand
what is happening in the blockchain revolution — and what we
can’t predict.
Blockchains are an experimental technology. Where the blockchain
can be used is an entrepreneurial question. Some ledgers will move
onto the blockchain. Some entrepreneurs will try to move ledgers
onto the blockchain and fail. Not everything is a blockchain use
case. We probably haven’t yet seen the blockchain killer app yet.
Nor can we predict what the combination of ledgers, cryptography,
peer to peer networking will throw up in the future. This process
is going to be extremely disruptive. The global economy faces
(what we expect will be) a lengthy period of uncertainty about
how the facts that underpin it will be restructured, dismantled, and
reorganized.
The best uses of the blockchain must be ‘discovered’. Then they
have to be implemented in a real world political and economic
system that has deep, established institutions that already service
ledgers. That second part will not be cost free. Best Analogy
would be that of how TikTok a social media application using short
videos revolutionized the entire social media land scape using their
inventive approach. Blockchain technology brings a significant
paradigm shift to internet. It now elevates the internet community
to not only interact, share, comment on original content, but
provides a fool proof model for original content ownership. This
model can be a huge once an application developer solves an
interesting real-life use case using this technology.
The blockchain and associated technological changes will
massively disrupt current economic conditions. The industrial
revolution ushered in a world where business models were
predicated on hierarchy and financial capitalism. The blockchain
revolution will see an economy dominated by human capitalism
and greater individual autonomy. How that unfolds is unclear at
present. Entrepreneurs and innovators will resolve uncertainty,
as always, through a process of trial and error. No doubt great fortunes will be made and lost before we know exactly how this
disruption will unfold.
What will be the Major Innovations Using Blockchain
Technology?
From financial services to retail and e-commerce, media and
entertainment, healthcare, IT, government, and energy: Almost
every sector is expected to adopt Web3.0 blockchain. Because
Web3.0 relies heavily on blockchain, many wrongly believe that
its fate is inevitably linked to the volatile cryptocurrency market.
However, cryptos are just one part of the new sector [3]. Gartner
explains that while cryptocurrencies crashed in the first half of
2022, decisionmakers should not assume that the value of Web3.0
technology is affected. According to the research and consulting
firm, Web3.0 tech will soon reach its adoption tipping point and
industries from aircraft maintenance to food safety will tokenize
their applications. Web3 blockchain will completely transform the
existing conventional processes of the different sectors. In 2023,
the Web3.0 blockchain technology sector will be worth more
than six trillion dollars, and Web3.0 will continue to grow at a
CAGR of 44.6% from 2023 to 2030 [4]. The concept of Web3.0
implies data ownership and decentralized control. The first version
of the internet, Web1.0, was built solely on content produced by
governments, organizations, and businesses. This web was mainly
oriented to information and slowly but gradually shifted to a
consumer-driven space [5].
DAOs are a type of organizational structure that have been built
through the power of blockchain. DAOs are a group of like-
minded individuals who work together for the long-term success
of the project or creator that they are backing. DAOs offer “a say”
with voting rights usually offered if you are a token holder of the
project that you’re a member of [6].
2. Growth of Crypto Economy
Research using existing data available in another research. We use
the market capitalization of crypto currencies and coins operating
in the Web 3 technologies. The following diagram will help to
understand the market cap growth of Crypto economy in last
decade. Market cap is a good indicator for measuring the product
value of an industry.
Volume 3 | Issue 1 | 3
Figure 1: Crypto currency market cap comparison
The digital assets industry has experienced rapid growth in the
past several years, reaching a market capitalization of $3 trillion in
November 2021. However, the past year has been difficult for the
industry, with a 70% drop in market cap to around $790 billion in
December 2022. While this decline may appear concerning, it is
important to consider the industry's performance within the larger
context of the global economy. It is possible that the market is
behaving similarly to the high-tech industry and may be preparing
for the next phase of growth. Overall, the data suggests that there
is still significant potential for growth in the digital assets industry.
Using the secondary data analysis, one can investigate a successful
business use case solved by block chain technologies by Ripple
Labs, US based block chain company. Native use case of cross
border payments using Web 3 technology. Ripple which owns a
coin in the symbol of XRP is a very successful crypto protocol
which has established a unique business use case of cross border
payments using blockchain. Their open source, permission less and
decentralized technology is carbon neutral and can settle a cross
border transaction in 3-5 seconds compared to the conventional
Swift transaction protocol which can take up to 3-4 business days.
XRP transaction fees cost $0.0002 per transaction on average.
The global cross-border payments will reach US $156 trillion in
2022. Thus, confirming it as a trillion-dollar market. According
to Juniper research, solely the B2B cross-border payments will be
a $35 trillion economy in 2022 [7]. Key advantages of block-
chain operated cross border payments are (a) Faster settlement – A
block chain enabled cross border payment takes minutes or sec-onds compared to up to 5 business days for the conventional cross
border payments, (b) Cost effective – transaction fees is limited to
the blockchain operator, (c) Enhanced security – Blockchain cross
border payments leverage the same crypto technology that is used
for crypto currencies. A private key or digital signature becomes
invalid in case of any hacking event, (d) Improved transparency
– every transaction and holdings are easy to view on an explorer.
The participants can view the transactions and entries in the sys-
tem is validated.
Another secondary data to look for signals about the Web 3 indus-
try growth would be about the private equity investments in this
sector. In 2020 in average $4.8 billion were invested across web 3
and crypto related startups. In 2021 this average raised up to $31.7
billion (7 times bigger), a quick reminder that 2020-2021 were the
peak of the corona crisis. In the first 2 quarters of 2022 despite the
global economic crisis and war in the middle of Europe, the crypto
sector Fundraised additional $15 billion which is even more than
in the same quarter last year.
Though all the factors that should have negatively influenced the
Web 3 sector, and the mood of investors, the industry keeps show-
ing an exceptional growth through the last 3 years. At this time a
lot of people might feel themselves unready to commit anything
and develop their product with such market conditions which
shows the general strength and future potential of this sector. Web3
future application land scape can be understood from the follow-
ing diagram [8].
J Curr Trends Comp Sci Res, 2024
Volume 3 | Issue 1 | 4
Figure 2: Web 3 Future
Web3 native companies are companies that utilize decentralized
technologies such as blockchain and operate on the Web3 platform.
These companies have the potential to disrupt a wide range of
industries and create new opportunities for both themselves
and incumbent companies. There are three levels at which this
disruption is likely to occur: assets, infrastructure, and services.
In the asset realm, new and unexplored assets such as stablecoins,
non-fungible tokens (NFTs), and tokenized real estate may continue
to be created and gain popularity. This presents opportunities
for incumbent companies to facilitate access to these assets or
tokenize their own existing assets. In terms of infrastructure, there
is a need for the development of core infrastructure to support the
new Web3 assets. Incumbent companies may have the opportunity
to partner with Web3 native companies to innovate their offerings
and support the growth of the necessary infrastructure. Finally, new
Web3 native services such as marketplaces, payment networks, and
deposit and loan platforms may emerge and potentially replicate
the functionality of traditional services. Incumbent companies
may choose to partner with these Web3 disruptors to tap into new
services and bring enhanced value propositions to their user bases
while retaining traditional protections and economics.
3. Conclusion
This paper looked at research topic of crypto technologies and
how it will impact the overall global economy. It considered three
potential research method to conduct the analysis and determined
the secondary research method as the most effective mechanisms for conducting this research. The research looked at crypto
economy market cap, the job generation in last decade in this
industry and innovation landscape within Web 3 to determine that
Web3 and crypto technologies are powerful disruptors in the digital
industry. It has proved its value in very real business problems
such as cross border payments. While it needs to be expanded to
a lot more use cases, we can already see that industry is making
huge progress towards the next wave of growth in decentralized
finance and decentralized organizations. There is still a big gap
in the regulatory frameworks that exist for this industry to strive.
This paper analyzed potential measures such as registering as
a state trust or national trust charter, undergoing annual audits,
establishing reasonable controls and board governance, meeting
basic cybersecurity standards, implementing KYC and AML
policies, establishing a licensing and registration regime, requiring
strong consumer protection rules, and prohibiting market
manipulation [9-12].
This paper analyzed broader crypto technology industry and its
impact on global economy using the past 10 years of business
data. It concluded that crypto technologies have opened a new
paradigm shift in digital technology using block chain powered
decentralization framework. This is truly an innovation that can be
compared to some of the original foundational technologies that
power internet or even as basic innovation as electricity. There
is a big role in this industry for improving regulatory framework
which can help to make it free from fraud and bad actors trying
J Curr Trends Comp Sci Res, 2024
Volume 3 | Issue 1 | 5to spoil the trust of the industry and in turn slowing the down the
speed of innovation.
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Copyright: ©2024 Arunkumar Pillai. This is an open-access article
distributed under the terms of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are credited.
https://opastpublishers.com/ J Curr Trends Comp Sci Res, 2024
|
{
"id": "2403.00018"
}
|
2104.01764
|
A Big Data Analysis of the Ethereum Network: from Blockchain to Google
Trends
|
First, a big data analysis of the transactions and smart contracts made on
the Ethereum blockchain is performed, revealing interesting trends in motion.
Next, these trends are compared with the public's interest in Ether and
Bitcoin, measured by the volume of online searches. An analysis of the crypto
prices and search trends suggests the existence of big players (and not the
regular users), manipulating the market after a drop in prices. Lastly, a
cross-correlation study of crypto prices and search trends reveals the pairs
providing more accurate and timely predictions of Ether prices.
|
http://arxiv.org/pdf/2104.01764v1
|
Dorsa Mohammadi Arezooji
|
q-fin.TR
|
q-fin.TR
|
A Big Data Analysis of the Ethereum Network:
from Blockchain to Google Trends
Dorsa Mohammadi Arezooji
Center for Complex Networks and Social DataScience
(CCNSD)
Department of Physics, Shahid Beheshti University
Tehran, Iran
d.mohammadiarezooji@se19.qmul.ac.uk
Abstract
:
First,
a
big
data
analysis
of
the
transactions
and
smart
contracts
made
on
the
Ethereum
blockchain
is
performed,
revealing
interesting
trends
in
motion.
Next,
these
trends
are
compared
with
the
public's
interest
in
Ether
and
Bitcoin,
measured
by
the
volume
of
online
searches.
An
analysis
of
the
crypto
prices
and
search
trends
suggests
the
existence
of
big
players
(and
not
the
regular
users),
manipulating
the
market
after
a
drop
in
prices.
Lastly ,
a
cross-correlation
study
of
crypto
prices
and
search
trends
reveals
the
pairs
providing
more
accurate
and
timely predictions of Ether prices.
Keywords:
Big
Data,
Ethereum,
Bitcoin,
Hadoop,
Spark,
Cross-correlation, Complexity
1. Introduction
Blockchains
technology
and
cryptocurrencies
such
as
Ethereum
and
Bitcoin
have
attracted
the
rapidly
increasing
attention
of
many
industries
and
researchers
from
various
fields.
Part
of
the
Blockchain’s
appeal
to
physicists
and
data
scientists
stems
from
its
complexity
and
underlying
interdependencies
that
affect
and
take
influence
from
other
complex systems namely financial and social networks.
The
intuition
behind
bringing
social
networks
into
consideration
while
analyzing
blockchains,
lies
within
their
decentralized
structure.
At
its
core,
decentralization
was
introduced
to
remove
heterogeneity
from
systems.
In
the
case
of
cryptocurrencies,
this
would
cause
governments
and
central
banks
to
lose
their
monopoly
in
financial
markets.
This
would
also
mean
that
the
network
itself
(active
users)
now
controls
the
changes
in
the
blockchain.
Needless
to
say,
the
power
of
decentralization
depends
on
how
decentralized
the
network
really
is.
In
other
words,
in
the
case
of
disproportionately
weighted
“central”
nodes,
the
network
would
be
prone
to
severe
heterogeneity
threats
[1].
Several
studies
have
explored
the
degree
of
centrality
in
decentralized
financial
networks,
revealing
that
they
may
not
be as decentralized as one would expect [2].
2. Background
The
initial
idea
that
led
to
the
birth
of
blockchain
was
first
proposed
in
1990
by
S.
Haber
and
W.
S.
Stornetta.
They
introduced
a
novel,
computationally
feasible
set
of
procedures
to
timestamp
digital
data,
which
would
make
it
impractical
to
alter
the
timestamp
after
its
creation
[3].
One
of
the
most
important
implications
of
this
approach
is
that
there
would
be
no
need
for
a
third-party
service
to
keep
record
of
the
timestamps.
Since
then,
blockchain
technology
has
revolutionized
many
fields
including
healthcare
[4,5],
transportation
[6,7],
digital
forensics
[8],
and
cybersecurity
[9,10]
due
to
its
reliability,
immutability,
and
transparency.
These
characteristics
are
the
direct
results
of
the
blockchain
structure:
data
is
divided
into
a
collection
of
blocks
that
are
all
linked
together
by
means
of
cryptography.
This
structure
prevents
tampering
with
any
arbitrary
block
without
changing
all
others;
hence,
achieving
immutability.
Furthermore,
the
data
stored
in
any
node
across
the
network
is
visible
to
all
users,
thus,
transparency
is
maintained.
The
decentralized
data
handling
also
prevents
the
two
parties
in
a
transaction
to
retroactively
manipulate
data
stored
in
the
network.
In
a
nutshell,
the
blockchain
establishes
a
general
agreement
that
verifies
the
details
of
a
transaction
without
the need to trust the parties involved [11].
2.1. The Ethereum Blockchain
The
Ethereum
blockchain
is
a
type
of
distributed
ledger
technology
(DLT),
a
general
term
used
for
databases
that
store
and
share
information
in
a
decentralized
network
of
independent
nodes.
Even
though
most
terms
used
in
the
context
of
Ethereum
are
not
exclusive
to
this
particular
blockchain,
there
are
a
few
terms
that
are.
This
section
provides
a
brief
introduction
to
the
technical
terms
related
to
the
Ethereum
blockchain
that
are
used
in
this
paper.
A
comprehensive
explanation
of
terms
and
concepts
can
be
found in the Ethereum whitepaper (ethereum.org).
2.1.1. Gas
On
the
Ethereum
blockchain,
the
cost
of
performing
transactions
or
processing
smart
contracts
is
measured
by
gas.
The
price
of
gas
itself
is
not
constant,
but
is
reported
by
miners
based
on
the
complexity
and
computational
resources
required
for
the
execution
of
each
block.
Gas
fees
are
calculated
in
ether
(ETH),
which
is
Ethereum’s
native
currency.
The
smallest
denomination
of
ether
is
named
wei
(1e-18
ETH).
Gas
price
is
usually
reported
in
Gwei
(1e-9
ETH).
2.1.2. Smart Contract
The
concept
of
smart
contracts
was
introduced
by
N.
Szabo
in
1994
in
an
unpublished
manuscript
and
then
formally
in
1997
[12].
Smart
contracts
are
essentially
blockchain-based
applications,
concisely,
self-executing
programs
which
contain
the
terms
and
agreements
between
the
parties
involved.
The
feature
that
sets
smart
contracts
apart
is
that
they
automatically
verify
whether
the
terms
of
the
agreement
have
been
fulfilled
or
not.
Additionally,
to
ensure
reliability,
fault
tolerance,
and
transparency,
the
codes
(smart
contracts)
are
replicated
on
many
nodes
in
the
blockchain.
Interestingly,
the
Ethereum
blockchain
has
hosted
roughly
1.5
million
smart contracts in the last few years [13].
2.2. Big Data Processing
Big
data
is
a
term
used
to
describe
data
that
is
too
large
to
be
processed
with
conventional
methods,
and
keeps
growing
exponentially
with
time.
Big
data
processing
tools
and
frameworks
such
as
Apache
Hadoop
enable
distributed
processing
of
large-scale
data,
a
task
made
possible
by
a
network
of
heterogeneous
computation
units.
In
essence,
a
Hadoop
cluster
can
be
a
cluster
of
commodity
PCs
(or
virtual
machines)
that
are
connected
and
communicate
with
a
master
node.
The
philosophy
of
Hadoop
is
“moving
the
computation
to
the
data”.
This
entails
each
node
serving
as
both
a
storage
unit
and
a
processing
unit.
These
two
components,
HDFS
(Hadoop
Distributed
File
System
-
storage)
and
YARN
(Yet
Another
Resource
Negotiator
-
computation),
have
been
designed
to
work
together
in
a
single
cluster
[14,15].
By
distributing
the
data
and
performing
the
computation
in
parallel,
Hadoop
is
able
to
achieve
scalability
from
a
single
node
to
thousands
of
nodes.
Hadoop
uses
MapReduce
to
distribute
the
data
among
worker
nodes.
Similarly
to
the
blockchain,
the
data
is
split
into
a
number
of
blocks,
each
assigned to a node across the cluster.
Apache
Spark
is
another
big
data
framework
which
is
built
on
top
of
HDFS.
By
reducing
disk
read
and
write
operations,
Spark
provides
fast
in-memory
processing
[16].
It
is
also
suitable
for
both
batch
and
streaming
data,
making
it
a
versatile
tool
for
a
vast
range
of
big
data
processing
tasks.
These
tasks
include
machine
learning,
graph
processing,
and
interactive
queries
on
big
data.
Finally,
Spark
is
written
in
Scala but has APIs in Java, R, and Python.
2.3. The Social Factor
As
it
was
previously
mentioned,
the
interaction
between
complex
networks
such
as
the
Ethereum
blockchain
and
social
networks
can
be
quite
fascinating.
Analyzing
social
factors
may
offer
an
opportunity
to
gain
insight
into
the
events
that
induce
changes
in
the
market.
With
this
in
mind,
Google
Trends
is
chosen
as
a
data
source
for
assessing
public
interest
in
cryptocurrencies
such
as
Ether
and
Bitcoin.
The
data
shows
the
public
interest
measured
by
the
aggregated
sum
of
online
searches.
Google
Trends
data
is
publicly
available with a few restrictions on data frequency.
3. Data and Tools
The
Ethereum
dataset
used
for
this
analysis
was
originally
collected
from
the
dumps
uploaded
to
a
repository
on
Google’s
BigQuery,
now
available
as
a
public
dataset.
The
dataset
was
uploaded
to
a
Hadoop
cluster
and
stored
in
HDFS.
Due
to
its
high
speed
and
performance,
Spark
(in
this
study,
specifically
its
python
API,
PySpark)
was
used
to
process
the
dataset
(in
order
of
TBs).
The
monthly
total
number
of
transactions,
average
gas,
and
gas
price
were
aggregated
and
extracted from the dataset.
4. Analyses and Results
An
initial
analysis
of
the
data
is
visualized
in
fig
1,
revealing
a
surge
in
the
number
of
transactions,
including
smart
contracts,
in
early
2018.
Interestingly,
this
event
coincides
with
the
sudden
surge
in
Ether
and
Bitcoin
prices.
The
possible
links
between
Ether
and
Bitcoin
prices
will
be
discussed
in
more
depth.
Another
observation
points
to
a
rather
steady
decrease
in
gas
price,
with
a
slight
increase
in
the
beginning
or
at
the
end
of
each
year.
In
addition,
the
average
gas
used
for
transactions
seem
to
have
reached
a
steady
state
as
of
late
2017.
Finally,
since
with
more
complex
contracts,
more
gas
would
be
needed,
the
strong
correlation
(pearson
correlation
coefficient
of
0.94)
between
smart
contract’s difficulty and required gas can be explained.
Fig. 1.
Ethereum contracts and transactions over time
Moving
forward,
Google
Trends’
data
is
incorporated
into
the
analysis
as
an
exogenous
feature.
The
first
logical
notion
could
be
that
with
more
people
searching
Ethereum
online,
more
of
them
might
invest
and
make
transactions
on
the
network.
Hence,
the
number
of
transactions
and
the
public
interest
in
Ethereum
are
depicted
in
fig
2.
It
should
be
noted
that
the
y
axis
is
indicative
of
only
the
number
of
transactions
and
not
the
volume
of
online
searches.
Daily
Google
Trends
data
have
been
collected
in
180-day
intervals,
then
scaled
and
concatenated
and
finally
normalized
on
the
scale
of
100.
For
a
better
visualization,
they
have
been
scaled
once
more
to
fit
the
y
axis
of
the
plot.
The
final
results
show
that
following
the
first
abrupt
increase
in
Google
Trends’
data
in
mid
2017,
the
number
of
transactions
didn’t
experience
the
same
dramatic
change,
possibly
pointing
to
a
lack
of
trust
in
the
cryptocurrency
and
the
blockchain.
In
any
case,
in
early
2018
the
number
of
transactions
on
the
platform
reached
its
highest
as
the
volume
of
online
searches
also
hit
an
all-time
high.
A
rudimentary
conclusion
could
be
that
an
increase
in
the
public’s
interest
in
a
cryptocurrency
corresponds
with
an
increase
in
the
number
of
transactions
made.
Nevertheless,
perhaps
a
more
enticing
objective
is
utilizing
this
information
to
predict
how
these
factors
affect
the price of ETH.
Fig. 2.
Number of transactions vs. online search trends
In
line
with
the
same
analogy
as
above,
the
price
of
ETH
and
Bitcoin
(BTC)
are
plotted
in
fig
3,
along
with
their
respective
search
volumes.
It
is
speculated
that
the
surge
in
ETH/USD
in
2018
was
correlated
with
the
rise
in
BTC/USD,
which
itself
was
triggered
by
Tether
(USDT).
A
study
of
the
Bitcoin
blockchain
by
Griffin
and
Shams
showed
that
Tether-based
transactions
on
the
Bitcoin
blockchain
were
timed
following
the
drop
in
BTC/USD,
setting
in
motion
the
soaring of Bitcoin prices [17].
Fig. 3.
Cryptocurrency prices vs. online search trends
At
first
glance,
a
side
by
side
analysis
of
the
crypto
pair
shows
a
similar
trend.
Both
ETH/USD
and
BTC/USD
skyrocketed
in
early
2018,
following
a
rise
in
the
public’s
interest
in
the
crypto
market.
However,
after
the
similarly
sharp
fall
in
search
volumes
during
2018,
crypto
prices
(especially
ETH)
did
not
drop
as
expected
according
to
the
public’s
interest.
This
could
be
indicative
of
the
existence
of
another
entity
other
than
the
ordinary
users
entering
the
market:
whales
(large
and
powerful
entities)
holding
on
until
after
the
drop
to
invest
heavily.
This
assumption
is
in
accordance
with
the
conclusions
made
by
Griffin
and
Shams,
stating
that
crypto
prices
are
not
solely
influenced
by
supply
and
demand
[17].
Their
analysis
suggests
that
the
distortion
in
Bitcoin
prices
was
caused
by
a
large
player
on
Bitfinex
using Tether to invest in large sums of Bitcoin.
Considering
the
mechanisms
responsible
for
the
change
in
crypto
prices,
a
statistical
analysis
of
their
returns
can
prove
to
be
beneficial
in
deciding
which
assets
to
invest
in.
To
obtain
a
stationary
time
series
representing
the
changes
in
crypto
prices,
logarithmic
returns
of
ETH/USD
and
BTC/USD are computed and plotted against time in fig
4.
Fig. 4.
Cryptocurrency log returns over time
Comparing
the
probability
density
function
(PDF)
of
Ether
and
Bitcoin’s
log
returns
(fig
5),
it
can
be
deduced
that
both
seem
to
follow
a
gaussian
distribution.
However,
Bitcoin’s
log
returns’
PDF
appears
to
be
narrower
and
more
peaked
than
Ether,
hence,
it
would
be
a
more
suitable
choice
for
risk-averse investments.
Fig. 5.
Cryptocurrency log returns histogram
A
more
descriptive
and
quantifiable
summary
of
the
log
returns
are
available
in
table
1.
Interestingly,
Ether’s
log
returns
hit
a
maximum
of
80%,
almost
3
times
higher
than
that
of
Bitcoin.
Nonetheless,
Ether’s
minimum
log
return
had
dropped to -81%, about 1.5 times lower than that of
Bitcoin.
Table 1.
Statistical summary of prices and log returns
ETH/USD
BTC/USD
%R
ETH
%R
BTC
Mean
247.30
6355.57
3.38
0.25
std
284.43
6588.89
6.90
4.02
Min.
0.43
203.18
-81.26
-31.59
1
st
Qu.
12.83
821.19
-2.27
-1.15
2
nd
Qu
182.87
6148.42
0.12
0.23
3
rd
Qu
323.72
9177.89
2.87
1.86
Max.
1845.82
47884.18
80.40
21.45
Finally,
to
understand
how
long
it
takes
for
changes
to
be
reflected
in
crypto
prices,
the
cross-correlation
between
the
4
time
series
are
calculated.
The
time
lag
which
corresponds
to
the
maximum
cross-correlation,
denoted
as
“lag
max”,
shows
the
number
of
days
until
the
second
time
series
is
maximally
correlated
with
the
first.
Figure
6
illustrates
a
heatmap
of
the
maximum
cross-correlation
between
each
pair
of
the
four
time
series.
The
highest
cross-correlation
scores
belong
to
the
(ETH/USD,
BTC/USD),
(GoogleETH,
GoogleBTC),
and
(ETH/USD,
GoogleBTC)
pairs
respectively,
suggesting
that
these pairs could serve well as predictors.
Fig 6.
Max cross-correlations heatmap
The
changes
in
cross-correlations
are
visualized
against
time
lag
in
fig
6.
It
can
be
observed
that
the
longest
time
lag
belongs
to
the
(ETH/USD,
GoogleBTC)
pair.
In
simple
terms,
this
indicates
that
an
increase
in
the
volume
of
online
searches
for
Bitcoin
correlates
with
an
increase
in
the
ETH/USD
approximately
a
month
later.
Based
on
time
lags,
the
(ETH/USD,
GoogleBTC),
(ETH/USD,
BTC/USD),
and
(ETH/USD,
GoogleETH)
pairs
are
the
strongest
contenders
for timely predictions.
It
should
be
noted
that
in
order
to
use
this
information
for
future
analyses
and
prediction
purposes,
the
strength
of
cross-correlations
should
be
taken
into
consideration
as
well.
Thus,
the
(ETH/USD,
BTC/USD)
and
(ETH/USD,
GoogleBTC)
pairs
provide
both
strong
and
timely
predictions
compared
to
other
pairs.
Finally,
it
is
crucial
to
bear
in
mind
that
while
the
above
statements
are
centered
around
cross-correlation,
this
concept
should
not
be
confused
with
causation.
Studying
the
possible
causal
links
requires
utilizing
other
methods
such
as
Bayesian
structural
learning
and a deep understanding of economics and Blockchain.
Fig. 7.
Cross-correlations vs. time lag
5. Discussion and Conclusions
The
analyses
in
this
study
suggest
that
considering
the
couplings
between
social
and
financial
complex
systems
helps
making
better
predictions
about
asset
prices.
Although
here
the
links
between
only
two
assets
were
explored,
it
would
be
beneficial
to
extend
this
analysis
to
a
wider
range
of
components.
Furthermore,
this
approach
helps
identify
market
manipulations
and
crypto
price
bubbles
caused
by
entities
other
than
the
public.
Future
analyses
may
incorporate
other
sources
of
social
data
such
as
twitter
to
gain more insight.
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|
{
"id": "2104.01764"
}
|
2312.04173
|
Contract Wallet Using Emails
|
We proposed a new construction for contract wallets, smart contract
applications that allow users to control their crypto assets. Users can
manipulate their crypto assets by simply sending emails with no need to manage
keys. These emails are verified using zero-knowledge proof (ZKP) along with
their attached digital signatures that the sender domain server (SDS) generates
according to DomainKeys Identified Mail. Unless the SDS forges the emails, the
crypto assets remain secure in the proposed system. Moreover, the existing SDSs
can be used as is by outsourcing additional work to a third party that is not
necessarily trusted. The system supports various functions to manipulate crypto
assets. We produced a tool for variable-regex mapping (VRM) that enables
developers to build a new function without ZKP skills. For example, using the
tool, we built a demo application where users can exchange crypto assets via
Uniswap only with emails. The published version of this paper is available at
https://doi.org/10.1109/ICBC56567.2023.10174932.
|
http://arxiv.org/pdf/2312.04173v1
|
Sora Suegami, Kyohei Shibano
|
cs.CR
|
cs.CR
|
Contract Wallet Using Emails
Sora Suegami
Dept. of Information and Communication Engineering
The University of Tokyo
Tokyo, Japan
suegamisora@g.ecc.u-tokyo.ac.jpKyohei Shibano
Dept. of Technology Management for Innovation
The University of Tokyo
Tokyo, Japan
shibano@tmi.t.u-tokyo.ac.jp
Abstract —We proposed a new construction for contract wal-
lets, smart contract applications that allow users to control their
crypto assets. Users can manipulate their crypto assets by simply
sending emails with no need to manage keys. These emails
are verified using zero-knowledge proof (ZKP) along with their
attached digital signatures that the sender domain server (SDS)
generates according to DomainKeys Identified Mail. Unless the
SDS forges the emails, the crypto assets remain secure in the
proposed system. Moreover, the existing SDSs can be used as
is by outsourcing additional work to a third party that is not
necessarily trusted. The system supports various functions to
manipulate crypto assets. We produced a tool for variable-regex
mapping (VRM) that enables developers to build a new function
without ZKP skills. For example, using the tool, we built a demo
application where users can exchange crypto assets via Uniswap
only with emails. The published version of this paper is available
at https://doi.org/10.1109/ICBC56567.2023.10174932.
Index Terms —contract wallet, account abstraction, zero-
knowledge proof
I. I NTRODUCTION
In Ethereum blockchain, contract wallets hold users’ crypto
assets and provide more functional management of these assets
than standard wallet services such as MetaMask. For example,
some contract wallets require multiple digital signatures to
transfer crypto assets [1]. However, most of them require
users to install new software or access new web pages. In
other words, users cannot manipulate their crypto assets with
existing familiar tools, e.g., email.
To solve this problem, we proposed Email Wallet , a con-
tract wallet that enables users to manage their crypto assets by
simply sending emails. Our system is based on a technique in
[2] and [3] that verifies the sender domain of emails with zero-
knowledge proof (ZKP). The crypto assets stored in our system
are secure unless the SDS forges the user’s email, and the
user needs to manage no private keys. Additional work for our
system can be outsourced to a third party called an aggregator,
which is neither a user nor an SDS, and not necessarily trusted.
Our new technique, variable-regex mapping (VRM), allows
a developer to build a new function to manipulate assets
without ZKP skills.
II. S YSTEM OVERVIEW
Our system is one of the contract wallets operated by users,
aggregators, and the Ethereum blockchain. Users are identifiedby their email addresses. They deposit their crypto assets in a
smart contract, called a wallet contract, by specifying their
email addresses and transferring the assets to the contract
address. The deposited assets are manipulated according to
functions defined as manipulation rules. Each manipulation
rule consists of an ID, a regular expression (regex), and another
smart contract called a manipulation contract that implements
the manipulation method. The regex (e.g., “Transfer \d{1,20 }
wei Ether.”) is decomposed into fixed parts (e.g., “Transfer”)
and variable parts (e.g., “ \d{1,20 }”), and the manipulation
contract has access to the latter values. The user specifies the
ID of the manipulation rule in the email title and writes the
email body to match the corresponding regex.
The user’s email is processed by the SDS that supports
DomainKeys Identified Mail (DKIM) and an aggregator (Fig-
ure 1). Herein, we assume that a public key of the SDS
is published in the domain name system and is previously
registered in the wallet contract. We suppose the SDS follows
pkcs1-v1 5 defined as RFC 3447 [4]: it computes a hash
value of the email with SHA256 and generates an RSA digital
signature for that value. It is also assumed that the SDS does
not change the public key or forge the user’s email and the
aggregator publishes its own email address.
1) The user sends an email to the aggregator’s email
address.
2) The SDS attaches a digital signature to the email ac-
cording to the DKIM protocol.
3) The aggregator uses the ID in the email title to search
for a manipulation rule and checks that the email content
satisfies the corresponding regex.
4) The aggregator generates a proof with the received
email. Then, it creates a transaction including the proof,
sender’s email address, RSA public key, ID, and values
of the variable parts of the regex and submits it to the
wallet contract.
The wallet contract verifies the proof with the other submitted
data and checks that the SDS’s public key matches the one reg-
istered in the contract. If the verification succeeds, the wallet
contract invokes the manipulation contract corresponding to
the ID to manipulate the crypto assets of that user.
A. Implementation
Our implementation consists of a ZKP circuit, smart con-
tracts, and the tool for the VRM. The circuit was built with 978-8-3503-1019-1/23/$31.00 ©2023 IEEEarXiv:2312.04173v1 [cs.CR] 7 Dec 2023
Fig. 1. Email Wallet architecture
the halo2 library developed by Privacy & Scaling Explorations
team [5]. The circuit takes the email header/body and the RSA
digital signature as a witness (or private input) and the sender’s
email address, RSA public key, ID, and values of the variable
parts of the regex as an instance (or public input). It computes
the hash value of the email using a SHA256 chip that supports
variable-length input and a base64 conversion chip. Then, it
verifies the signature using an RSA verification chip for the
hash value and the public key. It also uses a regex verification
chip to verify that a string that combines the fixed parts with
the values of the variable parts satisfies the regex. The last
chip is implemented according to the idea proposed in [3].
It transforms a regex into an equivalent finite automaton and
verifies that the initial state is transitioned to an accepted state
by a given string. Notably, the maximum string length is fixed
because the circuit size must be fixed.
Our smart contracts, i.e., the wallet contract and the manip-
ulation contracts, are implemented with Solidity. The wallet
contract stores each user’s email address and balance per
currency unit as user information. It also maintains an ID and
address of the manipulation contract for each manipulation
rule.
The tool for the VRM is used to automatically generate a
new regex verification chip. Specifically, for each manipulation
rule, a developer defines a regex and implements a manip-
ulation contract with Solidity. The tool outputs a bytecode
of a verifier contract that verifies a proof for the circuit
corresponding to the regex. The developer only has to call
that contract before manipulating the crypto assets, with no
prior knowledge of ZKP.
III. D EMO APPLICATION
We developed a demo application according to the descrip-
tion in Section II. There are two manipulation rules:
•Rule 1: If a user with Xsends an email with the message
“Transfer A T toY,” the balance of TforXandYwill
be decreased and increased by A, respectively.
•Rule 2: If a user with Xsends an email with the message
“Swap A T 1toT2via Uniswap,” the amount AofT1in
the balance of Xis exchanged for T2with Uniswap, and
the balance of T2is increased.
Herein, XandYdenote email addresses; AandTrepresent
a remittance amount and its currency unit, respectively. Using
Rule 1, the user can transfer crypto assets deposited in thewallet contract by specifying the balance/currency unit and the
recipient’s email address. Furthermore, Rule 2 allows the user
to exchange those assets for another currency using Uniswap.
The wallet contract address is used to send and receive assets
to and from Uniswap.
Using the tool for the VRM, we obtained the regex verifi-
cation chips and bytecodes of the verifier contracts necessary
for Rules 1 and 2.
A. Tests
We tested our implementation using the following scenario.
There are two users, Alice and Bob. Aditionally, there is a
SDS and an aggregator. First, Alice deposited 0.01 ETH in
the wallet contract. Alice then transferred 0.005 ETH to Bob’s
email address using Rule 1. Bob converted 0.005 ETH to
DAI with Uniswap using Rule 2. We confirmed that Alice’s
final balance is 0.005 ETH and Bob holds only some DAI,
which amounts to 0.005 ETH when exchanged on Uniswap.
We also tested invalid cases. In cases where the attached digital
signature was invalid or the email did not match any valid
regexs, the submitted proof did not pass the verification and
the users’ balances were not modified. These results show the
tool for the VRM exports correct regex verification chips and
the bytecodes for Rules 1 and 2.
IV. C ONCLUSION
In this study, we proposed a contract wallet that enables
users to operate their crypto assets by simply sending emails.
The user only needs to trust existing SDSs without managing
any private keys. The user’s crypto assets are manipulated
according to various rules, and the VRM technique allows
developers to build new rules without writing any ZKP cir-
cuits. We developed a demo application and proved that the
user can transfer crypto assets and exchange them for different
currencies just by sending an email to an aggregator.
ACKNOWLEDGMENT
This work has been supported by Endowed Chair for
Blockchain Innovation and the Mohammed bin Salman Center
for Future Science and Technology for Saudi-Japan Vision
2030 (MbSC2030) at The University of Tokyo. We appreciate
the generous technical advice and implementation help from
Mr. Aayush Gupta.
REFERENCES
[1] P. Praitheeshan, L. Pan, and R. Doss, “Security evaluation of smart
contract-based on-chain ethereum wallets,” in International Conference
on Network and System Security . Springer, 2020, pp. 22–41.
[2] S. Suegami, “Rsa verification circuit in halo2 and its applications
- privacy and scaling explorations,” 2022. [Online]. Avail-
able: https://mirror.xyz/privacy-scaling-explorations.eth/mmkG4uB2PR
peGucULAa7zHag-jz1Y5biZH8W6K2LYM
[3] A. Gupta, “An ecdsa nullifier scheme and a proof of identity application,”
Master’s thesis, Massachusetts Institute of Technology, September 2022.
[4] I. N. W. Group, “Rfc 3447: Public-key cryptography standards (pkcs) #1:
Rsa cryptography specifications version 2.1,” https://www.rfc-editor.org/
rfc/rfc3447, February 2003, (Accessed on 01/01/2023).
[5] P. . S. Explorations, “privacy-scaling-explorations/halo2,” https://github.
com/privacy-scaling-explorations/halo2, (Accessed on 01/01/2023).
|
{
"id": "2312.04173"
}
|
2207.07490
|
The Effect of Crypto Rewards in Fundraising: From a Quasi-Experiment to
a Dictator Game
|
Conditional thank-you gifts are one of the most widely used incentives for
charitable giving. Past studies explored non-monetary thank-you gifts (e.g.,
mugs and shirts) and monetary thank-you gifts (e.g., rebates that return some
of the donations to the giver). Following the rapid growth of blockchain
technology, a novel form of thank-you gifts emerged: the crypto rewards.
Through two studies, we analyze crypto thank-you gifts to shed light on
fundraising designs in the digital world. In Study I, we examine the Ukrainian
government's crypto fundraising plea that accepts donations in both Ethereum
and Bitcoin. We find that Ethereum is substantially more effective in enticing
giving than Bitcoin, as the hourly donation count increased 706.07% more for
Ethereum than for Bitcoin when crypto rewards are present. This is likely
because the crypto rewards are more likely to be issued on Ethereum than
Bitcoin. However, the decrease in contribution sizes is also more substantial
in Ethereum than in Bitcoin in response to the crypto rewards. In Study II, we
conducted a laboratory experiment following a dictator game design to
investigate the impact of crypto rewards in a more general scenario, with the
crypto rewards specified as non-fungible tokens (NFTs). The crypto rewards in
Study II carry no monetary value but only serve to recognize donors
symbolically. As such, the NFT thank-you gifts did not effectively induce
people to donate; a traditional 1:1 donation matching strictly outperforms both
the condition without thank-you gifts and the condition with NFT thank-you
gifts. Nevertheless, the NFT thank-you gifts effectively increased the
contribution sizes, conditional on the choice to give, when the NFT's graphic
design primes donor identity and encompasses the charity recipient.
|
http://arxiv.org/pdf/2207.07490v3
|
Jane, Tan, Yong Tan
|
cs.CY, econ.GN, q-fin.EC
|
cs.CY
|
THEEFFECT OF CRYPTO REWARDS IN FUNDRAISING : FROM A
QUASI -EXPERIMENT TO A DICTATOR GAME
Xue (Jane) Tan
Information Technology & Operations Management
Southern Methodist University
Dallas, TX 75275
janetan@smu.eduYong Tan
Foster School of Business
University of Washington
Seattle, WA 98195
ytan@uw.edu
ABSTRACT
Conditional thank-you gifts are one of the most widely used incentives for charitable giving. Past
studies explored non-monetary thank-you gifts (e.g., mugs and shirts) and monetary thank-you gifts
(e.g., rebates that return some of the donations to the giver). Following the rapid growth of blockchain
technology, a novel form of thank-you gifts emerged: the crypto rewards. Through two studies, we
analyze crypto thank-you gifts to shed light on fundraising designs in the digital world. In Study
I, we examine the Ukrainian government’s crypto fundraising plea that accepts donations in both
Ethereum and Bitcoin. We find that Ethereum is substantially more effective in enticing giving than
Bitcoin, as the hourly donation count increased 706.07% more for Ethereum than for Bitcoin when
crypto rewards are present. This is likely because the crypto rewards are more likely to be issued
on Ethereum than Bitcoin. However, the decrease in contribution sizes is also more substantial in
Ethereum than in Bitcoin in response to the crypto rewards. In Study II, we conducted a laboratory
experiment following a dictator game design to investigate the impact of crypto rewards in a more
general scenario, with the crypto rewards specified as non-fungible tokens (NFTs). The crypto
rewards in Study II carry no monetary value but only serve to recognize donors symbolically. As
such, the NFT thank-you gifts did not effectively induce people to donate; a traditional 1:1 donation
matching strictly outperforms both the condition without thank-you gifts and the condition with
NFT thank-you gifts. Nevertheless, the NFT thank-you gifts effectively increased the contribution
sizes, conditional on the choice to give, when the NFT’s graphic design primes donor identity and
encompasses the charity recipient.arXiv:2207.07490v3 [cs.CY] 10 Sep 2024
Crypto Rewards in Fundraising
Keywords Charitable Giving ·Cryptocurrency ·Monetary Incentive ·Ethereum ·Bitcoin ·Difference-in-Difference
1 Introduction
Thank-you gifts are conditional gifts, also named donor appreciation gifts in practice or donor premiums in economics,
offered by fundraisers to donors. These gifts are a prevalent feature in fundraising activities, with 1%-2% of the
fundraising revenue allocated to thank-you gifts [Recognition Art, 2023]. Previous research has predominantly
examined non-monetary thank-you gifts, such as mugs, tote bags, and shirts [Newman and Shen, 2012], as well as
monetary gifts, such as rebates [List, 2008]. This study investigates a novel form of thank-you gift: crypto rewards,
which are represented by immutable fungible and non-fungible tokens (NFTs) to crypto donors.
Crypto rewards deserve scholarly attention due to their unique position as hybrids of non-monetary and monetary thank-
you gifts. Like non-monetary thank-you gifts, they enable individuals to signal their prosociality both to themselves
and to others, leveraging the immutable nature of blockchain technology. Particularly when crypto rewards are in
the form of NFTs, they serve to publicly showcase the achievements, milestones, and social standing of their owners
within the digital realm. Meanwhile, crypto rewards possess monetary or investment value. Although they are usually
valueless when they are initially minted, their value could increase when they are exchanged if the associated causes gain
widespread support. This investment characteristic distinguishes crypto rewards from traditional monetary thank-you
gifts like rebates.
Crypto donors are becoming increasingly significant in the realm of fundraising. With 420 million cryptocurrency
users in 2023, and projections indicating a rise to 1 billion users by 2030 [The Giving Block, 2023], crypto donors
represent a burgeoning and vital demographic for charitable giving. The Giving Block, which provides solutions for
cryptocurrency donations within the nonprofit sector, reports that more than 150 nonprofits are able to raise over $100
million of annual revenue from crypto donors, as crypto fundraising allows nonprofits to access a distinct demographic –
younger, wealthier, and more dominated by male – that traditional fundraising channels often fail to reach [The Giving
Block, 2023].
The following examples demonstrate the surging prevalence of crypto rewards. The first example is Giveth
(https://giveth.io), a blockchain-based crowdfunding platform that has raised $2,239,120 for 2,875 projects from
6,646 givers at the time of writing. Giveth differs from traditional crowdfunding in that it rewards givers with GIV
tokens when they support verified projects. The GIV tokens can be used by token holders to influence the roadmap
and mission of the Giveth ecosystem through a voting mechanism. Moreover, Giveth offers NFTs as thank-you gifts
for every contribution of $100 or more to the Givth platform.1The second example is UkraineDAO, a decentralized
autonomous organization (DAO) that raises funds to help Ukraine defend itself. UkraineDAO offers the LOVE tokens
to its crypto donors as thank-you gifts. These love tokens could translate into the collective ownership of a Ukraine flag
1https://giveth.io/nft/mint
2
Crypto Rewards in Fundraising
NFT. The Ukrainian flag NFT was sold for 2,258 ETH (about $6.75 million as of then),2and donors could profit from
the sales.
This study aims to explore the potential of blockchain technology for social good by examining the impact of crypto
rewards on fundraising performance with the following questions:
• RQ1: Are crypto rewards effective in improving fundraising performance?
•RQ2: Would crypto rewards be more effective than donation matching (i.e., adding extra donations to the
giver’s contributions)?
•RQ3: When crypto rewards are in the format of NFTs, what graphic designing factors of the NFTs would
impact the fundraising performance?
The concept of providing donors with crypto rewards draws inspiration from the “airdrop” in the initial coin offering
(ICO) model, which serves as a promotional strategy to increase awareness for blockchain-based projects [Li et al.,
2021]. In ICOs, rewards are distributed to community members who engage in activities such as following a Twitter
account or joining a Telegram group. These reward tokens are typically valueless at inception but can be utilized within
blockchain-based projects to access services and goods upon their launch. In this study, we examine crypto rewards
within a broader context, where crypto donations are directed towards social causes rather than exclusively supporting
blockchain-based projects. The value of these crypto rewards is not tied to the success of a specific blockchain
project but rather to the societal reception of the associated social cause. This application of blockchain technology
warrants promising potential for social good, expanding the impact of blockchain technology beyond the confines of the
blockchain industry.
We use a mixed-method approach with two studies to examine the impact of crypto rewards as this approach is most
appropriate for studying new technologies [Velichety et al., 2019]. Study I is based on the Ukrainian government’s
crypto fundraising plea, which accepts donations from both Ethereum and Bitcoin. Donors who contribute via Ethereum
are more likely to receive the airdrop than those who contribute via Bitcoin due to Ethereum’s stronger programmability
to support smart contracts. Indeed, while donation trends of these two cryptocurrencies were similar prior to the
announcement of the airdrop, the donation trends diverged drastically after the announcement of the airdrop. We
performed both blockchain-level and transaction-level analyses to understand the impact of this airdrop, following a
difference-in-differences approach [Fricke, 2017]. At a blockchain level, We find that hourly donation counts increased
706.07% more for Ethereum than for Bitcoin in response to the airdrop. Further, the average contribution size for
Ethereum dropped 57% more than that for Bitcoin following the airdrop. At a transaction level, we performed Coarsened
Exact Matching to identify comparable wallets from Ethereum and Bitcoin based on time since the first transaction and
historical transaction volumes. We continue to find that the contribution size dropped more aggressively for Ethereum
than for Bitcoin in response to the airdrop. We further performed moderation analyses on Ethereum transactions to
2https://www.coindesk.com/tech/2022/03/02/ukrainian-flag-nft-raises-675m-for-countrys-war-efforts/
3
Crypto Rewards in Fundraising
show that the negative link between crypto rewards and contribution size is lessened when the wallet is registered with
Ethereum Name Service (ENS) and when the donation is transacted on intermediary platforms.
While Study I highlights the significant potential of using airdrops to promote social causes in crypto fundraising,
replicating this success may be challenging due to the uniquely high visibility of the event. In Study II, we assess
the impact of crypto rewards in a more general scenario by conducting a dictator game, “a celebrated workhorse of
experimental economics and social psychology” to understand charitable giving [Cartwright and Thompson, 2023]. We
recruited 268 subjects from Prolific, and the screening requirement was that the subjects must be owners or ex-owners
of NFTs. Following the protocol of dictator games [Engel, 2011], subjects first work on two copyediting tasks to earn a
flat-rate income of $2 on top of their participation fee ($1). They are then asked to allocate the $2 between themselves
and an international charity, Doctors without Borders. We use a between-subject design and assign these subjects into
five groups: a baseline group without a thank-you gift, a donation matching group that receives a 1:1 matching offer to
further support Doctors without Borders, and three NFT reward groups with different NFT designs. Unlike the airdrops
in Study I, the NFT rewards in Study II were created by the experimenters and likely offered only symbolic recognition
but not monetary value. Consequently, we found no significant effect of these NFT rewards on the decision to donate.
Consistent with previous research demonstrating the stronger impact of donation matching compared to rebates [Eckel
and Grossman, 2003, 2006], the matching group was the most effective in encouraging donations. However, the
design of NFTs significantly influenced the amount donated among those who chose to give. Specifically, donors who
perceived donor identity as relevant to themselves (as discussed in Kessler and Milkman [2018]) were likely to donate
more when the reward NFT both highlighted the donor’s identity and included the recipient’s identity. This suggests
that the alignment of the NFT design with donor identity can enhance the effectiveness of crypto rewards.
This study makes unique contributions to the literature on charitable fundraising. On the one hand, regarding whether to
donate, the differential findings of Studies I and II highlight the critical requirement for a crypto reward to be effective –
crypto rewards could successfully entice the choice to give when they are monetarily appealing to prospective donors.
This is in contrast to the detrimental effects of extrinsic motives for charitable giving suggested by past studies [Frey
and Jegen, 2001, Frey and Oberholzer-Gee, 1997]. Our findings underscore the crypto community’s strong investment
mindset, as the monetary value of crypto rewards varies with the public reception of the cause and the stake of the entity
that initiated the fundraising campaign. On the other hand, we show in Study II that the design of NFTs could serve
the role of donor identity prime to increase the gift size conditional on giving, uncovering the multi-faceted roles of
NFT rewards and contributing to the studies that examined the choices and framing of thank-you gifts [Zlatev and
Miller, 2016]. We also directly contribute to Chao [2017] and Chao and Fisher [2022], who discovered that visually
salient thank-you gifts would reduce giving by shifting people’s attention to focus on self-interest rather than their
intrinsic motives. In great contrast, the visual display of NFTs offers a novel avenue to incentivize giving. Last but not
least, we contribute to the large stream of literature on charitable giving based on dictator games. Ever since Daniel
Kahneman performed the first dictator game over three decades ago, hundreds of papers have been published based on
dictator games to understand various factors that affect individuals’ decisions regarding giving (e.g., incentives, social
4
Crypto Rewards in Fundraising
factors, distributive concerns, framing, social distance, and demographics) [Engel, 2011]. This study contributes to this
literature with the inclusion of unique features of crypto rewards used in crypto fundraising campaigns.
2 Theoretical Development
2.1 Self-interest and Charitable Giving
The nature of crypto rewards is a “thank-you” gift for making a crypto donation. Conditional thank-you gifts are
ubiquitous extrinsic incentives used in charitable fundraising, where donors get a non-monetary gift (e.g., mugs, tote
bags, and shirts) or a monetary gift (e.g., rebate) conditional on their charitable contribution [Newman and Shen,
2012, Eckel and Grossman, 2006]. The effect of extrinsic incentives on prosocial behavior has been widely studied in
the literature of economics, psychology, and information systems [Newman and Shen, 2012, Gneezy and Rustichini,
2000, Liu and Feng, 2021, Eckel and Grossman, 2006, List and Lucking-Reiley, 2002]. Prior works identify the
facilitating role of extrinsic motivations in giving because humans are primarily motivated by self-interest [Kohn, 2008].
Self-interest has become a social norm to the extent that people would be hesitant to donate to a charitable cause
even when they have strong feelings of compassion for it [Miller and Prentice, 1994, Miller, 1999]. In such cases,
extrinsic incentives could work as an “excuse” to rationalize people’s prosocial behavior by concealing their prosocial
motivations [Holmes et al., 2002]. Extrinsic motives could also manifest through reciprocity, where the thank-you gifts
activate donors’ feelings of reciprocity such that they give more [Briers et al., 2007]. As evidence, Falk [2007] finds
that the frequency of donations increased by 17% when a small gift was given to donors and by 75% when a large gift
was given.
On the contrary, people could reduce giving when extrinsic incentives are provided because extrinsic motivations
would crowd out intrinsic motivations [Frey and Oberholzer-Gee, 1997, Frey and Jegen, 2001, Gneezy and Rustichini,
2000, Bénabou and Tirole, 2006, Chao and Fisher, 2022]. Differing from extrinsic motivations, which are activated by
monetary rewards, praise, or fame, intrinsic motivations are related to activities that people undertake because they
derive satisfaction from them. As Frey and Oberholzer-Gee [1997, p.746] stated, “If a person derives intrinsic benefits
simply by behaving in an altruistic manner or by living up to her civic duty, paying her for this service reduces her
option of indulging in altruistic feelings.” Many studies discover evidence in support of the motivation crowding-out
theory. Newman and Shen [2012] find that among the donors who are willing to contribute, those who were offered a
thank-you gift donated a significantly lower amount than those who were not offered a thank-you gift. Chao [2017]
further find that the negative effect of extrinsic motivation from a thank-you gift is only present when the gift is visually
salient to occupy the prospective donor’s attention.
Despite the opposing predictions, we propose that self-interest dominates the decision of crypto donations because
investment is key to blockchain users [Kim et al., 2020].
Hypothesis 1: Crypto rewards positively impact charitable giving, especially when the crypto rewards could offer
potential returns.
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Crypto Rewards in Fundraising
2.2 Donation Matching
The next hypothesis is designed to enhance the understanding of crypto rewards by drawing a comparison with the
fundraising strategy of donation matching. Donation matching has been identified as one of the most effective means
of fundraising. It refers to the practice that a large donor (e.g., an employer or a charitable foundation) matches
individuals’ contributions to a specific cause to increase the gift. Eckel and Grossman showed in both within-subject
and between-subject lab experiments that a donation match is significantly more effective than a rebate (returning a
portion of the donation to the giver) in fundraising performance [Eckel and Grossman, 2006, 2003]. Gandullia and
Lezzi [2018] performed online experiments to show similar findings that donation matching is more effective than
rebates. Kamas and Preston [2010] showed that donation matching is effective even for self-interested donors (as
compared to social surplus maximizers and inequity averters). The studies that compared donation match with rebates
controlled for the value of the money added to the gift or returned to the donor. In our context, the value of crypto
rewards is ambiguous and key to the relative advantage of these two fundraising strategies.
As discussed, the nature of crypto thank-you gifts is multifaceted. On the one hand, crypto rewards resemble monetary
thank-you gifts, such as rebates, by stimulating giving through self-interest. On the other hand, they function similarly
to non-monetary thank-you gifts, like mugs, tote bags, and shirts, by reinforcing acts of giving through symbolic
recognition. When crypto rewards offer high monetary returns or strong symbolic recognition, they can be more
appealing than donation matching. Conversely, when the monetary return of the crypto rewards and the symbolic
recognition are low, they may not be as effective as donation matching.
Hypothesis 2: Crypto rewards are less effective than a donation match in fundraising when the crypto rewards offer
low monetary returns or symbolic recognition and are more effective otherwise.
2.3 Donor Identity Prime
As we mentioned, crypto rewards not only carry monetary value but also could reinforce one’s self-identity and public
identity as an altruist. Identity refers to a person’s sense of self that is “associated with different social categories and
how people in these categories should behave [Akerlof and Kranton, 2000, p. 715].” Kessler and Milkman [2018] find
from field experiments run by the American Red Cross that appeal that prime individuals’ identity as previous givers
results in more donations. This is because people tend to adjust their behaviors such that their behaviors will match the
norms or prescriptions associated with their identity [Akerlof and Kranton, 2000]. As discussed in Kessler and Milkman
[2018], a large stream of works has demonstrated the power of priming – even remarkably small environmental cues
could change which facet of individual identity is salient at a certain point [Steele, 1997].
Other than priming donor’s identity, it is generally believed that disclosing the recipient’s identity (through logos) could
enhance the value of the thank-you gifts. As evidence, Jung et al. [2014] considered the value of the Cal logo/UCB
affiliation in the thank-you gift of mugs, where UCB stands for University of California, Berkeley.
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Crypto Rewards in Fundraising
We propose that the graphic design of NFT gifts plays a role in the decision to give. The design that primes donor
identity and the recipient’s identity, as reflected in logos, would increase giving because the former signals prosociality,
and the latter demonstrates one’s commitment to the corresponding endeavor. Since the crypto community values
decentralization, freedom, and democracy, cryptocurrency holders likely desire to hold the crypto reward tokens that
showcase their support for causes that align with their group identity [Ramaswamy, 2022].
Hypothesis 3: The graphic design of NFT thank-you gifts that primes donor identity and the identity of the recipient
positively influences crypto donations.
3 Study I - A Quasi-experimental Study
Study I is designed to test the hypothesis of H1 based on a Ukrainian crypto fundraising campaign.
3.1 Context and Data Collection
The Ukrainian government posted pleas for cryptocurrency donations on Feb. 26 at 10:29 AM, 2022 (UTC). Since
Ukraine’s banking system was at risk of a Russian attack, crypto offered an alternative financial structure to support
Ukraine because it uses cryptography to secure transactions. This fundraising channel is different from other fundraising
efforts made by nonprofit organizations because all the funds would be directly received by the Ukrainian government,
avoiding overheads. In a tweet, the Ukrainian government announced their Ethereum and Bitcoin wallet addresses.
Ether (ETH) is the native currency traded on the Ethereum blockchain, and Bitcoin (BTC) is the currency traded on the
Bitcoin blockchain; both ETH and BTC are digital currencies based on the distributed ledger technology of blockchain.
On March 1 at 1:43 AM, Ukraine announced that an “airdrop" has not been confirmed, but formally announced on
March 2 at 1:43 AM that they would reward donors who supported Ukraine with an airdrop. The planned snapshot of
the list of donor wallet addresses would be taken the next day. While the initial announcement that “An airdrop has
not been confirmed yet" does not officially start the airdrop, people may react to this potential airdrop even before the
official announcement is posted. One day later, on March 3 at 6:37 AM, the vice prime minister of Ukraine and the
Minister of Digital Transformation of Ukraine announced the cancellation of this airdrop soon before the scheduled
snapshot. The timeline is summarized in Figure 1.3
We collected donation transactions between 1:00 AM on Feb. 26, 2022 and 6:00 PM on Mar.3, 2022, from the public
wallets of Ukraine to focus on the airdrop. We calculated the USD value of donation contributions using the historical
prices of Bitcoin and Ethereum based on the daily opening prices. There were 14,903 donation transactions on the
Bitcoin blockchain and 69,709 donation transactions on the Ethereum blockchain during this observation window. In
total, $9,874,757 was raised from the Bitcoin blockchain, and $16,043,036 was raised from the Ethereum blockchain.
We excluded extreme donations above the 99.9th percentile ($7727.13). We sum up donations from the same wallet
if they are transacted within the same minute. We further removed transactions between 2:00 AM on Mar. 1, 2022
3From the Ethereum wallet data we collected, the Ukrainian government issued NFT rewards to some Ethereum donors post
campaigns despite of this cancellation.
7
Crypto Rewards in Fundraising
Figure 1: Timeline
and 2:00 AM on Mar. 2 because this period is associated with a potential airdrop. We are left with 67,615 donation
transactions.
3.2 Identification
The introduction of an airdrop as a reward to crypto donors offers a quasi-experiment for us to understand the impacts of
the crypto rewards on donation counts and amounts. While the publicity of the fundraising event increased sharply after
the announcement of the crypto rewards due to substantial media coverage, this temporal effect equivalently applies to
both ETH and BTC because the wallet addresses for both ETH and BTC were included in the same tweet. However, the
impact of the crypto rewards is likely much stronger on ETH than BTC because only ETH supports smart contracts,
and most airdrops were issued through ETH. We perform a modified DiD analysis to exploit the difference between
the impacts of the airdrop on Bitcoin and Ethereum in order to explore the causal impacts of crypto rewards [Wing
et al., 2018]. This method has the same functional form as classic DiD but offers different interpretations based on the
differential impacts of treatment with varying intensities [Fricke, 2017]. Duflo [2001] used this method to understand
the effect of school construction on schooling and labor market outcomes by comparing regions with low and high
levels of newly constructed schools. Felfe et al. [2015] leveraged the regional variation in childcare expansion rates to
understand the effect of formal childcare on maternal employment as well as child development. This extension of DiD
requires not only the common trend assumption but also an equal effect size assumption, which posits that users in ETH
and BTC would respond similarly when high-intensity treatment (a higher likelihood of winning the crypto rewards)
was imposed on the corresponding blockchain. In Section 1 of the Appendix, we discuss the identification strategy and
assumptions in greater detail.
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Crypto Rewards in Fundraising
We perform both an aggregated analysis (Section 3.2.1) and a transactional analysis (Section 3.2.2) to estimate the
ordered treatment effect of the airdrop. The aggregated analysis provides causal inference for the hourly donation count;
the transactional analysis offers insights into contribution sizes after accounting for the potential selection process.
3.2.1 Aggregated Analysis at a Blockchain Level.
At a blockchain level, the econometric model we estimate is specified as below ( cdenotes the blockchain and tdenotes
the hours):
Outcome c,t=β0+β1Ether c×Airdrop t+β2Ether c+β3Airdrop t+β4FeeRate c,t+ηt+ϵc,t, (1)
where the dependent variable Outcome c,tcan be operationalized as two aggregated measures: the logarithm
of the number of hourly donations ( DonationCount c,t) and the logarithm of the average contribution sizes
(AvgDonationSize c,t). These outcomes are both log-transformed after adding one because they are highly skewed.
Our key independent variable, Airdrop t, is a binary variable that takes the value of one if the airdrop has been
announced but the snapshot has not been taken and zero otherwise. Ether cis a binary variable that takes the value of
one if the currency is Ether and zero if it is Bitcoin. To identify the ordered treatment effect, we also include two-way
fixed effects [Fricke, 2017]. We use ηtto represent the hourly time dummy variables that account for the time-level fixed
effects. The time effects could come from the dynamic situations in Ukraine, the increasing awareness of the crypto
fundraising event, or simply donors’ varying availability of time. Such temporal trends affect the Bitcoin and Ethereum
blockchains in the same way. The systematic difference between BTC and ETH is represented by the group-level fixed
effects, denoted as Ether c. In addition, we account for the transaction fee rate ( FeeRate c,t) for both ETH and BTC
using the transaction data on the Ethereum and Bitcoin blockchains from Google Big Query. For ETH, we calculate the
average gas price at every point in time; for BTC, we calculate the ratio between fee and output value at every point in
time. The coefficient of our interest is β1as it indicates the differential impacts of the airdrop on the blockchains of
Bitcoin and Ethereum. For the aggregated data, we have 134 discrete observation points for both Ethereum and Bitcoin.
3.2.2 Transactional Analysis at a Wallet Level.
The aggregated analysis sheds light on the difference in donation behavior between the two blockchains when crypto
rewards become available. However, it does not account for the selection process that individual donors go through
when deciding whether to give via ETH or BTC. Specifically, people with a strong investment mindset may choose
to donate via ETH to increase the likelihood of receiving crypto rewards in the first place. To mitigate the selection
issue, we perform matching at a wallet level before examining the outcome of donation sizes ( DonSize w,t), where
we use wto denote the index for a wallet and tfor time. We no longer examine AvgDonSize orDonCount , which
are aggregated measures. The matching process proceeds as follows. First, for every donation made on BTC and
ETH, we collected wallet information from BlockChair by using the paid APIs (https://blockchair.com/). Second, we
removed all wallets that have exactly one receiving transaction and one sending transaction. These wallets were only
9
Crypto Rewards in Fundraising
used for this fundraising plea, and their owners are more likely to be driven by an opportunistic motive. Blockchair
keeps the latest 100 transactions for every wallet, and we removed wallets whose latest 100 transactions did not include
their donation to Ukraine. This allows us to exclude extremely active users, who are likely organizations rather than
individuals; it also allows us to accurately construct the number of historical transactions for every wallet. Third, we
removed ETH wallets that registered to use the Ethereum Name Service (ENS) because these accounts are less likely to
be comparable with BTC wallets. ENS is a paid decentralized naming system that allows users to use human-readable
names instead of hexadecimal characters to find Ethereum addresses. Then, we performed a wallet-level matching using
Coarsened Exact Matching (CEM) based on (1) years since the first spending transaction ( TimeSinceFirstSend w,t),
(2) years since the first receiving transaction ( TimeSinceFirstRec w,t), and (3) the logarithm of past transaction
counts ( Log(PastTrans w+1)). After matching, we are left with 4,498 BTC transactions and 26,577 ETH transactions
in 157 stratums and the corresponding weights were calculated to guarantee comparability. We achieved a good balance
between treatment and control, as the standardized mean difference (SMD) for TimeSinceFirstSend w,tis 0.034, the
SMD for TimeSinceFirstRec w,tis -0.0004, and the SMD for Log(PastTrans w+ 1) is -0.003. These SMDs are
well below the threshold of 0.1. We then estimate the weighted linear regression model and incorporate the subclass to
generate average treatment effects. Similar to the aggregated analysis, we included Airdorp tto represent whether the
crypto rewards were available and Ether wto indicate whether the corresponding wallet is from Ethereum. We also
included FeeRate w,t, which is calculated at a minute granularity, hourly time dummies, and other variables we have
used for the matching process to account for the time-varying features that could affect the contribution sizes. Based on
the resulting matched samples, we estimate the following model:
DonSize w,t=β0+β1Ether w×Airdrop t
+β2Ether w+β3Airdrop t+β4FeeRate w,t+β5TimeSinceFirstSend w,t
+β6TimeSinceFirstRec w,t+β7Log(PastTrans w+ 1) + ηt+ϵw,t.(2)
3.3 Data
In Table 1, we summarize the aggregated data regarding DonCount andAvgDonSize in Panel A for Bitcoin and in
Panel C for Ethereum. We summarize the transactional data in terms of PastTransaction in Panel B for Bitcoin and
in Panel D for Ethereum.
We performed Welch’s t-test to compare the conditions with and without the airdrop because the samples in different
groups have different variances. From Table 1, we learn that the donation counts were not significantly different for
Bitcoin before and after the crypto rewards were available (t=-1.60). However, the donation counts were significantly
higher for Ethereum when the crypto rewards became available (t=8.46). The average donation size dropped significantly
for both Bitcoin and Ethereum (t=-2.60 for Bitcoin and t=-9.98 for Ethereum), and this drop is more substantial for
Ethereum. Further, the past transaction number for the donated wallets with and without the airdrop did not drop
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Crypto Rewards in Fundraising
Table 1: Summary Statistics by Groups
Airdrop t=0 Airdrop t=1 Welch’s t-test
Mean Median S.E. Mean Median S.E. t-stats
Panel A. Aggregated Contributions on Bitcoin ( Ether c=0)
DonCount c,t 106.75 71 104.92 86.83 84 37.82 -1.60
AvgDonSize c,t 180.65 161.85 72.90 152.32 141.35 49.62 -2.60**
Panel B. Transactional Contributions on Bitcoin ( Ether c=0)
PastTransaction c,i 147.52 11 2389.07 60.16 8 624.24 -1.24
Panel C. Aggregated Contributions on Ethereum ( Ether c=1)
DonCount c,t 95.55 56 93.56 1215.73 1451 845.36 8.46***
AvgDonSize c,t 267.16 261.96 109.32 132.70 129.53 46.51 -9.98***
Panel D. Transactional Contributions on Ethereum ( Ether c=0)
PastTransaction c,i 101.52 30 629.82 77.66 20 538.06 -3.26***
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Figure 2: Donation Counts
significantly for BTC but significantly for ETH (t=-3.26), indicating selection. The data trends are presented in Figures
2 and 3. These figures show parallel trends before the introduction of crypto rewards. From Figure 2, the hourly counts
of donations followed similar trends prior to the announcement of a potential airdrop; the hourly donation counts on
Ethereum increased after the announcement of a potential airdrop and peaked after the airdrop was confirmed. From
Figure 3, the average contribution size is higher for Ethereum before the introduction of the crypto rewards. After the
crypto rewards were introduced, the average contribution size became similar between Bitcoin and Ethereum.
Figure 3: Average Donation Size
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Crypto Rewards in Fundraising
Table 2: Results of Study I
Aggregate (Hourly) Transactions Matched Transactions
DV: DonCount AvgDonSize DonSize DonSize DonSize DonSize
(1) (2) (3) (4) (5) (6)
Airdrop ×Ether 2.087∗∗∗−0.451∗∗∗−0.737∗∗∗−0.510∗∗∗−0.884∗∗∗−0.946∗∗∗
(0.164) (0.119) (0.038) (0.038) (0.093) (0.091)
Airdrop 3.736∗∗∗−0.138 0 .249 −0.118 0 .194 −0.075
(0.625) (0.557) (1.082) (1.058) (1.078) (1.056)
Ether 0.361 0 .073 −0.163∗∗∗−0.405∗∗∗−0.267∗∗−0.229∗∗
(0.236) (0.172) (0.036) (0.036) (0.113) (0.111)
FeeRate −0.007∗0.006∗∗0.005∗∗∗0.006∗∗∗0.008∗∗∗0.007∗∗∗
(0.004) (0.003) (0.001) (0.0005) (0.002) (0.002)
TimeSinceFirstSend – – – −0.058∗∗∗– −0.181∗∗∗
– – – (0.015) – (0.025)
TimeSinceFirstRec – – – 0.139∗∗∗– 0.079∗∗∗
– – – (0.014) – (0.021)
Log(PastTrans + 1) – – – 0.092∗∗∗– 0.290∗∗∗
– – – (0.002) – (0.007)
Intercept 0.624 5 .082∗∗∗3.676∗∗∗3.525∗∗∗3.736∗∗∗3.407∗∗∗
(0.443) (0.458) (1.080) (1.056) (0.964) (0.944)
Time Dummy Yes Yes Yes Yes Yes Yes
Observations 268 267 67,615 67,615 31,075 31,075
R20.887 0.686 0.158 0.195 0.128 0.163
Adjusted R20.769 0.357 0.143 0.181 0.124 0.159
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
3.4 Results
We summarize the regression results for the aggregated analysis and transactional analysis in Table 2. Models 1 and 2
are the results of the aggregated analyses. Models 3 and 4 are the results of the transactional analysis without matching.
Models 5 and 6 are the results of the transactional analysis with matched wallets.
As can be seen from the coefficient of Airdrop ×Ether of Models 1 and 2 in Table 2, the hourly donation count
(DonCount ) of the Ethereum blockchain (plus one) has increased 706.07% more than the Bitcoin blockchain by the
airdrop. We also learn from Model 2 of Table 2 that the decrease in the average contribution sizes ( AvgDonSize ) is
about 57% more aggressive for Ethereum than for Bitcoin in response to the airdrop. This blockchain-level analysis does
not account for the potential selection issue – users who chose to donate via Ethereum may have a higher investment
mindset and thus differ from those who donated via Bitcoin. As such, the blockchain-level analysis is appropriate for
the outcome of DonCount but not so much for the outcome of AvgDonSize .
We account for the selection issue and shed light on the outcome of donation sizes ( DonSize ) by performing matching
at a wallet level and conducting transactional analyses. For the outcome of DonSize , we find consistent evidence from
the negative and significant coefficient of Airdrop ×Ether in Models 3, 4, 5, 6 that the crypto rewards likely led to a
decrease in contribution sizes.
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Crypto Rewards in Fundraising
3.5 Donor Heterogeneity Analyses
The negative link between crypto rewards and contribution size could vary by donor characteristics, and we perform
moderation analyses to deepen our understanding of donor heterogeneity based on novel blockchain-based moderators
on Ethereum.
3.5.1 Ethereum Name Service.
Cryptocurrency addresses represent long strings of numbers and letters, making it hard for one to send funds to another
using Ethereum’s networks. Ethereum Name Service is a distributed, open, and expandable naming system that
maps human-readable Ethereum addresses to hexadecimal characters. For example, the machine-readable address of
“0x00d936ef12a4Fde33Ab0FcF08F18d6A9BAbB6b97” would be translated into “john.eth” via ENS. ENS comes with
an expiration date, and users need to pay for the continuous service at the annual rate of $5 - $30. ENS adopters use
ENS IDs in various platforms as their identity - about 10% of ENS adopters in our dataset use their ENS IDs as their
Twitter handles.
We examined the moderating role of ENS adoption on the relationship between crypto rewards and donation amounts
by performing a transaction-level analysis within Ethereum. We identified a significantly positive moderating effect of
ENS adoption. The details of the analysis are documented in Section 2 of the Appendix and discussed in a later section.
3.5.2 Intermediary Platform Usage.
People could donate to the Ukraine fundraising plea by directly sending funds to the Ethereum address of the Ukraine
government (e.g., using MetaMask); they can also make donations using intermediary platforms such as Coinbase
and Binance, which are both online platforms for buying, selling, transferring, and storing cryptocurrency. From the
transactional data recorded in Etherscan, a direct donation’s sender corresponds to an individual Ethereum address,
while an indirect donation transaction’s sender would be the intermediary platform of “Coinbase” or “Binance.” It is
believed that direct transfers of funds would more likely make donors eligible for winning the crypto rewards, and the
indirect transfers of funds via intermediary platforms would disqualify users from receiving the crypto rewards.4. As
reported in Section 2 of the Appendix, we identify a positive moderation effect of intermediary platform usage.
3.6 Robustness Checks
To validate the results of our main analyses, we perform a battery of robustness checks. First, we removed extremely
small-sized donations (i.e., donations less than $5 or $1) for both Bitcoin and Ethereum, and our findings remain
unchanged (Appendix 3.1). Second, we altered the definition of treatment and allowed the treatment to end not on the
scheduled snapshot but when it was cancelled. Our results remain unchanged (Appendix 3.2). Third, we change the
observation window to include the time window of the potential airdrop, which we have removed in the main analysis,
4As anecdotal evidence, a Youtuber recommends not to use intermediary platforms for a higher chance of receiving the crypto
rewards
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Crypto Rewards in Fundraising
and our findings remain unchanged (Appendix 3.3). Last but not least, we keep only the first donation transaction of
every wallet, and find our results unchanged (Appendix 3.4).
4 Study II – A Dictator Game in the Laboratory Setting
Study I underscores the potential of crypto rewards to stimulate giving. However, it was based on an unprecedented
fundraising event initiated by the Ukrainian government. The Ukrainian government also did not specify whether the
crypto rewards would be fungible or non-fungible, while crypto rewards used in crypto donations are oftentimes NFTs
[Liang et al., 2024].
To assess the generalizability of the findings of Study I and to test our hypotheses H2 and H3, we perform a laboratory
experiment following a dictator game. Dictator games are considered a workhorse to understand charitable giving in
both the economics and social psychology literature [Cartwright and Thompson, 2023, Engel, 2011, Frey and Jegen,
2001]. In a dictator game, the dictator determines how to split an endowment between the self and the recipient. The
dictator’s action space is complete, ranging from giving nothing to giving everything, and the recipient has no influence
on the endowment allocation. A dictator game has been used to examine various factors that influence giving behavior,
such as rebate and donation matching, fairness considerations, social norms, and intrinsic motivations [Engel, 2011,
List, 2007]. List [2007] underscored the importance of designing treatments to shed light on relevant field applications.
We follow this direction to design a dictator game that invokes the most realistic responses for a set of highly relevant
treatments concerning crypto rewards.
4.1 Experimental Design
We designed a between-subject experiment; it is preferred over a within-subject design, where subjects may be unable to
distinguish different scenarios fully [Eckel and Grossman, 2006]. We recruited 286 subjects who have NFT experience
(self-disclosed as “I own one or more NFTs, I have created one or more NFTs") from Prolific, a platform that allows
researchers to recruit and manage participants for their online research. These subjects are randomly assigned to five
groups: Control ,Matching ,NFT1 ,NFT2 , and NFT3 (Figure 4). Past studies of dictator games reveal that dictators
are more generous when the endowment is manna from heaven rather than earned by the dictators [Engel, 2011]. To
invoke realistic responses, subjects of our study are recruited to complete two copyediting tasks (Figure 5). Specifically,
subjects need to identify spelling, punctuation, and capitalization mistakes in two paragraphs of text to receive a flat-rate
payment of $2, on top of the payment of $1 for completing the survey. The tasks are standard none-depletion tasks, and
past literature has suggested a similar impact between a flat rate payment and a performance-based payment [Achtziger
et al., 2015]. After subjects are informed about their extra income for the copyediting tasks ($2), the subjects are asked
to choose among three options (the control message ): (A) keep all the bonus ($2) to themselves, (B) donate half of
the bonus ($1) to a charity (Doctors Without Borders) and keep the reminder, or (C) donate all the bonus ($2) to the
charity (Doctors Without Borders). This message is the control message available in all conditions. Doctors Without
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Crypto Rewards in Fundraising
Figure 4: Experimental Design
Borders works in over 70 countries to provide urgently needed humanitarian aid in moments of crisis. At the time of
the experiment, multiple conflicts were taking place in the world, and Doctors Without Borders is a well-deserving
charity. The fund allocation choices are semi-continuous, allowing us to assess whether to give (whether Option A is
chosen) and how much to give (whether Option B or Option C is chosen) [Engel, 2011]. If subjects are assigned to the
Matching group, they receive an additional message that if they donate either $1 or $2 to the charity, their contribution
will be matched. If they are assigned to any of the three NFT groups, they will receive an additional message with the
visual design of the NFT reward that if they donate either $1 or $2 to the charity, they will receive the NFT reward. The
NFT rewards have been minted on the Ethereum blockchain, and OpenSea links have been provided. However, these
NFT rewards likely have no resell value as they were created by the experimenters. If subjects choose to donate, they
also enter their wallet address so the NFT can be airdropped.
We included one attention check question – participants were asked to indicate which charity was mentioned as a
donation recipient; subjects were dropped if their answers were wrong. Out of the 286 subjects, 221 of them are male,
63 of them are female, one of them has a non-binary gender, and one prefers not to say. Regarding age, 39 of the
subjects are between 18 and 24; 116 are between 25 and 34; 83 are between 35 and 44; 36 are between 45 and 54; Nine
are between 55 and 64; Two of them are between 65 and 74, and one of them is above 74. In addition, we collected
their birth country, enthusiasm about blockchain, optimism about NFT, and knowledge about cryptocurrency using a
five-point Likert scale to control for their heterogeneity.
4.2 Results
Following the literature, we use a hurdle model to analyze subjects’ responses [Engel, 2011, Breitmoser, 2013]. A
hurdle model assumes that the decision to make a positive contribution and the decision of how much to give, conditional
on the decision to give, are two separate processes. It is composed of a binary decision (whether to give) using a
15
Crypto Rewards in Fundraising
Figure 5: Study II - Tasks Before Income Allocation
Table 3: Results of Study II
(1) (2)
DV:Donate
Matching 0.615∗∗(0.240) 0.667∗∗∗(0.253)
NFT 1 0.157 (0.235) 0.112 (0.245)
NFT 2 -0.124 (0.257) -0.153 (0.276)
NFT 3 -0.305 (0.262) -0.237 (0.269)
Constant −0.663∗∗∗(0.166) -0.415 (1.011)
DV:Amount
Matching 0.024 (0.135) 0.026 (0.130)
NFT 1 0.035 (0.144) 0.133 (0.156)
NFT 2 0.190 (0.166) 0.272∗(0.165)
NFT 3 0 .383∗∗(0.176) 0.426∗∗(0.168)
Constant 1.173∗∗∗(0.105) 1.151∗∗(0.516)
ln(σ) −0.855∗∗∗(0.082) −0.962∗∗∗(0.081)
Age/Optimism/Literacy No No Yes Yes
Gender/Country/Enthusiasm No No Yes Yes
Pseudo R2 0.048 0.149
N 286 286
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
logistic model and a continuous decision (how much to give) using OLS estimation for positive contributions, after
adjusting for the truncation. It suits the dictator games well, where the majority of subjects choose to keep all the
funds. We report the results in Table 3. Model 1 did not include subject characteristics, and Model 2 included subjects’
age, perceived optimism about NFT, blockchain literacy, gender, country, and enthusiasm about blockchain. From the
first stage estimation (DV: Donate ), we find from both Models 1 and 2 that only the matching treatment significantly
improved the subjects’ willingness to give. From the second stage estimation (DV: Amount ), we find that conditional
on the decision to give, NFT2 marginally improves the contribution size in Model 2, and NFT3 significantly improves
the contribution size both in Models 1 and 2. In Model 2, the estimated βfor NFT2 is βNFT 2= 0.272, and that for
NFT3 is βNFT 3= 0.426, indicating that NFT3 has a more salient impact on the decision of how much to give. Finally,
we observe a significant estimation for ln(σ), where σis the standard deviation parameter for the truncated normal
distribution in the second stage (DV: Amount ).
From Study II, we find that the crypto rewards are no longer effective in stimulating the decision to give. This is in
great contrast to Study I, and we discuss this difference in the next section. Further, matching treatment outperforms the
NFT treatments only for the outcome of whether to donate but not for the outcome of how much to give. When subjects
choose not to donate, donor identity is not self-relevant; only when they choose to donate does the donor’s identity
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Crypto Rewards in Fundraising
Table 4: Hypothesis Testing
Hypotheses Results Studies
H1 Partially supported: effective in Study I but not in Study II Studies I and II
H2 Supported for the outcome of whether to donate Study II
H3 Supported Study II
become relevant and have a positive impact on the decision of how much to give. Last but not least, Study II reveals
that donor identity alone is not sufficient; the NFT treatment would only be significant when both donor identity and
recipient identity are included in the graphic design.
5 Conclusions
5.1 Summary of Findings
We performed two studies to examine the impact of crypto rewards in fundraising campaigns. Study I leverages a
quasi-experimental design to estimate the impact of crypto rewards issued by the Ukrainian government. Study II is a
laboratory experiment following a dictator game, where the crypto rewards were NFTs issued by the experimenters.
Combining the results from Studies I and II, we conclude that H1 is partially supported. As we present in Table 4,
crypto rewards are effective in enticing giving when they offer sufficient monetary return or strongly present symbolic
recognition, as in Study I but not in Study II. We went on to show that a traditional donation matching strategy strictly
dominates the NFT crypto rewards created by the experimenters and could effectively stimulate the decision of whether
to give, supporting H2. Finally, leveraging the different graphic designs of the NFT thank-you gifts, we show that for
subjects who decided to give and thus were relevant to donor identity, NFT rewards that highlight both donor identity
and the recipient organization would effectively increase contribution sizes, conditional on giving. This result is in
support of H3.
5.2 Reconciling the Results of Studies I and II
Crypto rewards effectively motivate people to give in Study I but not in Study II. We provide two major reasons for
these seemingly conflicting results. First, the crypto rewards in Study I likely offered higher returns. The blockchain
community has been a strong supporter of Ukraine, with numerous crypto initiatives aimed at providing aid. For
example, the Ukraine flag NFT associated with LOVE tokens from UkraineDAO was sold for 2173.6 ETH.5Similarly,
the Avatar for Ukraine campaign, endorsed by the Minister of Digital Transformation of Ukraine, raised 12,656 ETH
for medical aid for Ukrainian defenders.6Given these examples, it is reasonable to expect that the crypto rewards for
the Ukrainian government’s fundraising plea in Study I would have higher monetary value than the crypto rewards
created by the experimenters in Study II. To support this mechanism, our moderation analysis in Study I shows that
5https://www.coindesk.com/tech/2022/03/02/ukrainian-flag-nft-raises-675m-for-countrys-war-efforts/
6https://www.avatarsforukraine.com/
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Crypto Rewards in Fundraising
when donors contribute through intermediary platforms, where they are less influenced by direct monetary incentives,
the reduction in contribution sizes was mitigated.
Second, the crypto rewards in Study I offer stronger symbolic recognition than those in Study II. The fundraiser in
Study I was the Ukrainian government, whereas in Study II, it was the experimenters. Crypto rewards granted by the
Ukrainian government function as immutable badges, allowing donors to publicly display their generosity and social
standing. These rewards are likely to invoke a heightened sense of doing good, enhanced reputation, and pride [Samek
and Sheremeta, 2017]. In contrast, the crypto rewards in Study II merely served as receipts for contributions of $1 or
$2 to Doctors Without Borders, using bonuses earned from a copyediting task. Given the critical role of recognition
in fundraising, the crypto rewards from Study I are more effective in encouraging giving than those from Study II
[Recognition Art, 2023]. Moreover, our moderation analysis showed that Ethereum users who adopted ENS were less
likely to reduce their contribution sizes. This finding supports the symbolic recognition mechanism, as ENS adopters
are likely more attuned to the value of recognition. Consequently, the more pronounced symbolic power of crypto
rewards of Study I made it more effective than those in Study II.
Crypto rewards likely led to a contribution size reduction in Study I but could potentially increase the contribution size
in Study II. Past studies show that people could reduce charitable contributions due to extrinsic incentives because they
shift donors’ attention away from their compassion to help and strengthen a cost-benefit mindset. Chao [2017] find that
such extrinsic incentives need to be visually salient to take effect. In Study I, the crypto rewards received extensive
media coverage and represented an unprecedented event for the crypto community. While they are not visually salient,
they are socially salient and could occupy the prospective donors’ attention. As such, this “motivational crowding-out”
mechanism manifested in Study I but not in Study II. Further, the crypto rewards of Study I were not specified when the
airdrop was announced. In contrast, the graphic design of NFT thank-you gifts was presented to the subjects in Study II.
Donor identity prime is an effective device to stimulate giving [Kessler and Milkman, 2018], and this effect is more
salient in Study II due to the presentation of the NFT designs.
5.3 Theoretical and Practical Implications
Our study began with a unique fundraising event and concluded with a general assessment of crypto rewards through
a carefully designed dictator game, yielding several important implications. First, while the Ukrainian government’s
crypto fundraising plea was highly successful, it was largely unknown whether this success was reproducible and whether
this novel thank-you gift could replace a donation matching strategy. Our study provides a nuanced understanding
of the conditions under which crypto rewards can be a powerful tool in fundraising. The success of crypto rewards
is contingent upon their ability to offer tangible value and symbolic significance. When these elements are present,
crypto rewards could potentially surpass traditional donation-matching strategies, particularly in contexts where the
fundraising cause resonates with the values of the blockchain community. However, in situations where the expected
returns and symbolic power of crypto rewards are limited, donation matching remains a more reliable and effective
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Crypto Rewards in Fundraising
strategy. Understanding these dynamics can help optimize the use of crypto rewards in future fundraising efforts,
ensuring they are deployed in contexts where they can achieve the greatest impact.
Crypto rewards, as reflected in airdrops, have been widely used as a promotional strategy by blockchain-based projects
[Li et al., 2021]; its effectiveness in supporting social causes that are not blockchain-based is unseen and barely
understood. Our study suggests that ICO has a great potential to stimulate donations to support social causes that
are not blockchain-based. To increase the societal impact of blockchain technology and accelerate the adoption
of blockchain, the founders and designers of blockchain should consider applying crypto rewards to various social
movements and activities. Our finding also indicates the necessity for blockchains to support airdrops to effectively
improve fundraising performance when crypto rewards are present. Blockchain designers should also improve the
design of blockchain-related platforms to better support airdrops. For example, currently, some airdrops can only be
issued if a donor makes a direct transfer of funds to the recipient’s wallet. Donors who use intermediary platforms (e.g.,
Coinbase) will not receive the airdrop due to technology limitations. Blockchain designers can work with intermediary
platforms to better design and streamline the airdrop process.
Last but not least, our study sheds light on the design of NFT thank-you gifts and offers two prescriptive suggestions.
First, fundraisers should showcase the NFT thank-you gifts before the commencement of fundraising efforts. This is
because the graphic design of the NFT thank-you gifts could play a role in the giving decision. Second, the thank-you
gifts could be designed to focus on both the donor’s identity and the prospective recipient’s identity to reinforce the acts
of giving.
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Crypto Rewards in Fundraising
Appendix I – Ordered Treatment Effect Identification
In both the blockchain-level and the transaction-level analyses, we leverage a modified DiD analysis to identify
the ordered treatment effect, or the average treatment effect on the treated (Ethereum donors), represented by
ATET (EB|E) =E[OutcomeE
1−OutcomeB
1|E], where E[Outcomed
t]represents the expected outcome, with
d∈[B, E, 0]andt∈[0,1]. We use d=Bto illustrate the treatment to get the crypto reward with a low probability, as
in the Bitcoin blockchain; we use d=Eto illustrate the treatment to get crypto rewards with a high probability, as
in the Ethereum blockchain; we use d= 0to illustrate the condition when the treatment of an airdrop has not been
announced or has stopped. We use t= 1to denote the time when the airdrop was available and t= 0to denote the time
when it is not available. This is equivalent to the local treatment effects discussed in Angrist and Imbens [1995]. We
can re-write this equation such that:
ATET (EB|E) =E[OutcomeE
1−OutcomeB
1|E] =E[OutcomeE
1|E]−E[OutcomeB
1|E]. (A1)
We observe E[OutcomeE
1|E]but not E[OutcomeB
1|E], and draw inferences from the Bitcoin blockchain by leveraging
the strong parallel assumption that E[OutcomeB
1|E]−E[Outcome0
0|E] =E[OutcomeB
1|B]−E[Outcome0
0|B]. This
assumption is an equal effect size assumption that likely holds in our context if ETH holders and BTC holders are
equivalently sensitive to external rewards. Specifically, it is equivalent to saying that the spike we observe from ETH in
response to the airdrop would also occur in BTC if the airdrop is more likely to be issued in BTC rather than ETH. We
believe that this assumption holds because, from a blockchain standpoint, ETH and BTC are interchangeable. While
BTC and ETH holders may hold different beliefs (e.g., about intervention and decentralization), the value of the airdrop
to ETH holders and BTC holders should be similar both in terms of monetary incentive and symbolic recognition. Thus,
we can re-write ATET (EB|E)such that:
ATET (EB|E) =E[OutcomeE
1−OutcomeB
1|E]
=E[OutcomeE
1|E]−E[Outcome0
0|E]−E[OutcomeB
1|B] +E[Outcome0
0|B],(A2)
where every component of the right side of Equation (2) is observed. Even if we believe that this assumption does not
hold (e.g., ETH holders may react more aggressively to the airdrop), we can partially identify the ordered treatment
effects as long as E[OutcomeB
0|E]−E[Outcome0
0|E] =E[OutcomeB
0|B]−E[Outcome0
0|B]. This common trend
assumption is widely used in classic DiD designs and is highly likely to hold given the common pre-intervention trends
we illustrated in the manuscript. Fricke [2017] proves that with partial identification, we can interpret the estimates as
the lower bound in the magnitude for the treatment effect.
The detailed econometric models for the blockchain-level and transaction-level analyses are presented in Equations (1)
and (2) of the manuscript.
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Crypto Rewards in Fundraising
Appendix II – Ethereum Moderation Analyses
As reported in Section 3.5 of the manuscript, we explore the mechanism behind the reduction of contribution sizes
following the announcement of crypto rewards in Study I. This is done by performing two moderation analyses
using only donation transactions in Ethereum. The two moderators of our choice are Ethereum Name Service (ENS)
adoption ( ENS ) and intermediary platform usage ( Intermediaries ). We only use Ethereum transactions for this
analysis because these two moderators only exist on the Ethereum blockchain. Since the moderation analyses only
used Ethereum transactions, we did not include hourly effects. It is possible that users who adopted ENS and used
intermediary platforms have varying wealth levels. As such, we include a control variable, NFTV alue , to account for
the wealth effect. We report the results in Table A1.
Table A1: Moderation -ENS and Intermediaries
DV: Log( DonSize ) Log( DonSize )
Airdrop −0.530∗∗∗(0.016) −0.493∗∗∗(0.015)
ENS 0.644∗∗∗(0.025) –
Intermediaries – 1.077∗∗∗(0.036)
Airdrop ×ENS 0.088∗∗∗(0.032) –
Airdrop ×Intermediaries – 0.330∗∗∗(0.057)
NFTV alue 0.024∗∗∗(0.0005) 0 .033∗∗∗(0.0005)
Intercept 3.417∗∗∗(0.015) 4 .293∗∗∗(0.913)
Hour FE Yes Yes
Observations 55,746 55,746
R20.109 0.178
Adjusted R20.109 0.176
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Appendix III – Robustness Checks for Study I
Remove Minuscule Donations
In this robustness check, we remove minuscule donations, which are usually small in size. The 5th percentile of
contribution size is $1.01 for ETH and $4.94 for BTC. We perform two analyses, with one removing donations below
$5 and the other removing donations below $1. The results are reported in Tables A2 and A3, where we find consistent
findings.
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Crypto Rewards in Fundraising
Table A2: Robustness - Remove Minuscule Donations <= $5
Aggregate (Hourly) Matched Transactions
DV: DonCount AvgDonSize DonSize DonSize
Airdrop ×Ether 2.059*** -0.398*** -0.885*** -0.941***
(0.134) (0.087) (0.096) (0.094)
Airdrop 1.933*** 0.245 0.194 0.261
(0.512) (0.406) (0.895) (0.890)
Ether 0.225 0.305*** 0.062 0.069
(0.146) (0.095) (0.096) (0.096)
TimeSinceFirstSend – – – -0.237***
– – – (0.039)
TimeSinceFirstRec – – – 0.247***
– – – (0.037)
Log(PastTrans + 1) – – – 0.098***
– – – (0.008)
FeeRate -0.007*** 0.002 0.004*** 0.004**
(0.002) (0.001) (0.002) (0.002)
Intercept 0.682* 4.987*** 3.526*** 3.129***
(0.362) (0.333) (0.790) (0.787)
Time Dummy Yes Yes Yes Yes
Observations 268 267 22,687 22,687
R20.912 0.692 0.079 0.089
Adjusted R20.820 0.370 0.073 0.083
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Table A3: Robustness - Remove Minuscule Donations <= $1
Aggregate (Hourly) Matched Transactions
DV: DonCount AvgDonSize DonSize DonSize
Airdrop ×Ether 2.162*** -0.496*** -0.773*** -0.835***
(0.144) (0.093) (0.088) (0.086)
Airdrop 1.914*** 0.240 0.117 -0.124
(0.546) (0.434) (1.009) (0.992)
Ether 0.293* 0.236** -0.179* -0.143
(0.156) (0.101) (0.107) (0.105)
TimeSinceFirstSend – – – -0.115***
– – – (0.023)
TimeSinceFirstRec – – – 0.044**
– – – (0.020)
Log(PastTrans + 1) – – – 0.241***
– – – (0.008)
FeeRate -0.007*** 0.003* 0.007*** 0.007***
(0.002) (0.002) (0.002) (0.002)
Intercept 0.661* 5.030*** 3.668*** 3.368***
(0.386) (0.355) (0.901) (0.886)
Time Dummy Yes Yes Yes Yes
Observations 268 267 29,423 29,423
R20.907 0.698 0.129 0.159
Adjusted R20.810 0.382 0.125 0.155
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
22
Crypto Rewards in Fundraising
Alternative Treatment Definition
In our main analysis, we consider the observation window from Feb. 26, 2022 to March 4, 2022, with the treatment
lasting from the official announcement of the airdrop till the planned snapshot. In this robustness check, we changed the
ending point to the cancellation of the airdrop on March 3 at 6:00 AM, 2022. The exclusion of the post-cancellation
period allows a conservative estimation of the treatment effect. As can be seen from the results in Table A4 of this
appendix, our findings stay robust.
Table A4: Robustness - Alternative Treatment Definition
Aggregate (Hourly) Matched Transactions
DV: DonCount AvgDonSize DonSize DonSize
Airdrop ×Ether 2.777*** -0.576*** -0.885*** -0.941***
(0.111) (0.105) (0.096) (0.094)
Airdrop 3.533*** -0.237 0.023 -0.095
(0.383) (0.446) (0.972) (0.952)
Ether 0.320*** 0.182* -0.290** -0.255**
(0.107) (0.102) (0.113) (0.110)
TimeSinceFirstSend – – – -0.181***
– – – (0.025)
TimeSinceFirstRec – – – 0.079***
– – – (0.021)
Log(PastTrans + 1) – – – 0.281***
– – – (0.008)
FeeRate -0.007*** 0.003* 0.008*** 0.007***
(0.002) (0.002) (0.002) (0.002)
Intercept 0.638** 5.081*** 3.766*** 3.441***
(0.269) (0.363) (0.964) (0.945)
Time Dummy Yes Yes Yes Yes
Observations 268 267 31,075 31,075
R20.956 0.693 0.127 0.163
Adjusted R20.910 0.372 0.124 0.159
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
23
Crypto Rewards in Fundraising
Alternative Time Window
In our main analysis, we removed transactions that happened after the initial announcement of “no airdrop" and the
subsequent announcement of an airdrop from the Ukrainian government because this period is associated with a possible
airdrop. In this robustness check, we included transactions that occurred during this time window and considered this
period as no airdrop. As we show in Table A5 of this appendix, our results remained unchanged.
Table A5: Robustness - Alternative Time Window
Aggregate (Hourly) Matched Transactions
DV: DonCount AvgDonSize DonSize DonSize
Airdrop ×Ether 2.029*** -0.479*** -0.751*** -0.804***
(0.160) (0.096) (0.090) (0.089)
Airdrop 1.991*** 0.194 0.069 -0.178
(0.629) (0.463) (1.082) (1.061)
Ether 0.561*** 0.156 -0.371*** -0.337***
(0.172) (0.104) (0.108) (0.106)
TimeSinceFirstSend – – – -0.200***
– – – (0.023)
TimeSinceFirstRec – – – 0.113***
– – – (0.020)
Log(PastTrans + 1) – – – 0.281***
– – – (0.008)
FeeRate -0.009*** 0.003* 0.008*** 0.007***
(0.003) (0.002) (0.002) (0.002)
Intercept 0.551 5.102*** 3.856*** 3.480***
(0.444) (0.379) (0.967) (0.948)
Time Dummy Yes Yes Yes Yes
Observations 314 313 36,425 36,425
R20.876 0.657 0.118 0.153
Adjusted R20.748 0.301 0.114 0.149
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
24
Crypto Rewards in Fundraising
Multiple Donations of the Same Wallet
It is possible for one wallet to be associated with multiple donation transactions. In our main analysis, we sum up
donations from the same wallet if they were transacted in the same minute. In this robustness check, we only keep the
first donation transaction for each wallet, and our results remain unchanged. It is notable that the observation for the
transactional analysis has reduced due to the way we process the data. As we show in Table A6 of this appendix, our
results stay unchanged.
Table A6: Robustness - Multiple Transactions from the Same Wallet
Aggregate (Hourly) Matched Transactions
DV: DonCount AvgDonSize DonSize DonSize
Airdrop ×Ether 2.231*** -0.525*** -0.843*** -0.911***
(0.151) (0.099) (0.094) (0.092)
Airdrop 1.704*** 0.304 0.347 0.114
(0.577) (0.465) (1.086) (1.064)
Ether 0.236 0.176 -0.263** -0.226**
(0.165) (0.109) (0.114) (0.112)
TimeSinceFirstSend – – – -0.184***
– – – (0.025)
TimeSinceFirstRec – – – 0.080***
– – – (0.021)
Log(PastTrans + 1) – – – 0.285***
– – – (0.008)
FeeRate -0.008** 0.004* 0.008*** 0.007***
(0.002) (0.001) (0.002) (0.002)
Intercept 0.693* 5.068*** 3.740*** 3.415***
(0.408) (0.381) (0.961) (0.942)
Time Dummy Yes Yes Yes Yes
Observations 268 267 30,802 30,802
R20.901 0.680 0.129 0.164
Adjusted R20.799 0.345 0.125 0.160
Note:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
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28
|
{
"id": "2207.07490"
}
|
2405.02547
|
Crypto Market Analysis & Real-Estate Business Protocol Proposal |
Application of Ethereum Blockchain
|
This paper examines the dynamics of the cryptocurrency market and proposes a
novel blockchain-based protocol for real estate transactions. Our analysis
includes a detailed review of price trends, volatility, and correlations within
the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and
Tether. We provide a critical assessment of the impact of significant market
events, such as the FTX bankruptcy, highlighting the vulnerabilities and
resilience of the crypto market. The study also explores the potential of
blockchain technology to innovate real estate transactions by enabling the
secure and transparent handling of property deeds without traditional
intermediaries. We introduce a blockchain protocol that reduces transaction
costs, enhances security, and increases transparency, making real estate
transactions more accessible and efficient. Our proposal aims to leverage the
inherent benefits of blockchain to address real-world challenges in real estate
transactions, providing a scalable and secure platform for property sales in a
global market.
|
http://arxiv.org/pdf/2405.02547v1
|
Sid Bhatia, Samuel Gedal, Himaya Jeyakumar Grace Lee, Ravinder Chopra, Daniel Roman, Shrijani Chakroborty
|
econ.GN, q-fin.EC
|
econ.GN
|
Stevens Institute of Technology
Crypto Market Analysis & Real-Estate Business
Protocol Proposal
Application of Ethereum Blockchain
Sid Bhatia, Samuel Gedal, Himaya Jeyakumar
Grace Lee, Ravinder Chopra, Daniel Roman, Shrijani Chakroborty
26 April 2024arXiv:2405.02547v1 [econ.GN] 4 May 2024
Contents
1 Introduction 2
2 Part I: Crypto Market Analysis 2
2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2 Detailed Overview and Crypto Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2.1 Bitcoin (BTC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2.2 Ethereum (ETH) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2.3 XRP (Ripple) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2.4 Dogecoin (DOGE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2.5 Tether (USDT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3 Crypto Selection Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.4 Market Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4.1 Price Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4.2 Volatility Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4.3 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.5 FTX Delta Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.5.1 Event Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.5.2 FTX Impact on 11/11/22 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.5.3 Immediate Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.5.4 FTX Impact in November 2022 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.5.5 Long-Term Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.5.6 Benchmark and Market Performance Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.5.7 FTX Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.6 Crypto Market Analysis Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.6.1 Volatility and Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.6.2 Market Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.6.3 FTX Crisis Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.6.4 Long-Run Market Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.7 Part I Key Takeaways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.8 Future Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Part II: Real-Estate Business Protocol Proposal 10
3.1 Business Proposal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.1 Overview of the Blockchain Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.2 Transactional Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.3 Advantages of Blockchain in Real Estate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Target Customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.1 Market Strategy and Consumer Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.2 Integration of Market Sides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Competitive Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.1 Propy: A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Competitive Advantage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.5 Implementation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.6 Economic Viability for Ethereum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.7 Comparative Analysis of Blockchain Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.8 Synthesis of the Blockchain Real-Estate Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Conclusion 17
4.1 Synthesis of Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Prospects and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Summary and Forward Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1
1 Introduction
In the dynamic realm of financial technology, blockchain and cryptocurrencies represent two of the most significant
innovations that have reshaped how transactions are conducted and assets are managed globally ( futurecryptocurrencies2022 ).
This paper delves into a dual-focused analysis and proposal. Firstly, we conduct a thorough market analysis of
a select group of cryptocurrencies, each chosen for its unique role and impact within the broader digital currency
landscape. The cryptocurrencies under review include Bitcoin, often regarded as the progenitor of all digital
currencies; Ethereum, notable for its robust smart contract capabilities; XRP, designed primarily for rapid finan-
cial transactions; Dogecoin, which began as a meme but has since gained substantial practical application; and
Tether, a stablecoin tied to the US dollar, offering a less volatile refuge within the highly fluctuant crypto market
(evolutioncryptomarket2017 ).
This study not only examines the price trends, volatilities, and inter-cryptocurrency correlations but also assesses
the impact of significant market events, such as the FTX bankruptcy, on these digital assets ( ftxresponse2023 ).
The insights garnered from this analysis aim to provide a granular understanding of how various cryptocurrencies
react to internal and external pressures, influencing investor sentiment and market dynamics.
Following the market analysis, the second focus of this paper introduces an innovative business proposal lever-
aging blockchain technology. This proposal outlines a new protocol for real estate transactions, allowing property
deeds to be securely managed and transferred without the need for traditional intermediaries such as lawyers and
brokers. By employing blockchain technology, this protocol seeks to revolutionize the real estate market by enhanc-
ing transparency, reducing transaction costs, and simplifying the transaction process for buyers and sellers across
the globe ( blockchainrealestate2021 ).
Through comprehensive analysis and forward-thinking proposals, this paper contributes to the ongoing discus-
sions surrounding the application of blockchain technology in traditional sectors, proposing not only a new way to
understand cryptocurrencies in relation to the traditional financial markets but also offering a practical application
that addresses real-world challenges in real estate transactions.
2 Part I: Crypto Market Analysis
2.1 Overview
This analysis encompasses a selection of five distinct cryptocurrencies, each representing a unique facet of the current
digital currency ecosystem. Our selected cryptocurrencies include: Bitcoin (BTC) , recognized as the original and
most well-known cryptocurrency; Ethereum (ETH) , noted for its advanced smart contract capabilities; XRP ,
developed by Ripple Labs with a focus on rapid digital payments; Dogecoin (DOGE) , which has evolved from a
meme into a cryptocurrency with practical uses in tipping and donations; and Tether (USDT) , a stablecoin that
introduces a measure of stability in the otherwise volatile cryptocurrency market. This diverse selection aims to
cover a broad spectrum of functionalities, market positions, and technological innovations within the crypto space,
providing a comprehensive overview of its varied applications and implications.
2.2 Detailed Overview and Crypto Protocol
2.2.1 Bitcoin (BTC)
Overview: Introduced in 2009 by an entity under the pseudonym Satoshi Nakamoto, Bitcoin stands as the
inaugural cryptocurrency, designed to operate as a decentralized digital currency without the oversight of a central
authority. Transactions are conducted directly between users through the peer-to-peer Bitcoin network.
Protocol: Bitcoin’s network is underpinned by a proof-of-work (PoW) protocol, wherein miners employ signif-
icant computational resources to solve intricate mathematical problems, thus validating transactions and securing
the network, with new bitcoins awarded as a mining reward.
For more details see nakamoto2009bitcoin .
2.2.2 Ethereum (ETH)
Overview: Launched in 2015, Ethereum transcends the conventional definition of a cryptocurrency. It serves as a
platform for the development of decentralized applications (DApps) through smart contracts, aiming to democratize
access to a decentralized financial system.
2
Protocol: Initially based on a proof-of-work mechanism similar to that of Bitcoin, Ethereum is transitioning
to a proof-of-stake (PoS) model with its Ethereum 2.0 update, which promises enhanced scalability and reduced
energy consumption.
Refer to buterin2015ethereum for additional insights.
2.2.3 XRP (Ripple)
Overview: Created by Ripple Labs in 2012, XRP is central to a digital payment protocol that surpasses its identity
as a mere cryptocurrency. It facilitates rapid payment settlements across the network.
Protocol: The XRP Ledger utilizes a consensus protocol that does not rely on the traditional blockchain
mining process; instead, it achieves consensus through a network of independent validating servers that constantly
compare transaction records.
Seeripple2012xrp for further information.
2.2.4 Dogecoin (DOGE)
Overview: Originating as a humorous take on the cryptocurrency phenomenon in 2013, Dogecoin was inspired by
the ”Doge” meme featuring a Shiba Inu. It has since cultivated a community focused on using the cryptocurrency
for charitable contributions and tipping online content creators.
Protocol: Dogecoin operates on a less energy-intensive proof-of-work algorithm derived from Litecoin, known
as Scrypt, facilitating faster transaction processing.
Detailed information available at dogecoin2013meme .
2.2.5 Tether (USDT)
Overview: Introduced in 2014, Tether represents a stablecoin that is tethered to the US dollar, aiming to meld
the flexibility of cryptocurrencies with the stability of fiat currency.
Protocol: Tether supports a hybrid use of protocols, operating on the Omni Layer of the Bitcoin blockchain
and as an ERC-20 token on the Ethereum blockchain, among other blockchain platforms.
Further details can be found in tether2014usdt .
These cryptocurrencies were chosen to provide a diverse perspective on the various applications, market us-
age, and technological advancements within the broader cryptocurrency environment. From January 1, 2022, to
December 31, 2022, our study observed no missing data, ensuring the completeness and reliability of the analysis
conducted during this period.
2.3 Crypto Selection Rationale
The selection of cryptocurrencies for this study was informed by a multifaceted rationale emphasizing diversity,
technological innovation, community engagement, and market stability. Each cryptocurrency was chosen not only
for its unique position within the market but also for its contribution to advancing the blockchain technology
landscape.
Diversity and Relevance: Bitcoin and Ethereum are selected as foundational pillars within the cryptocur-
rency domain, illustrating the broad spectrum of blockchain applications. Bitcoin, often hailed as the original
cryptocurrency, has pioneered the concept of a decentralized digital currency and enjoys widespread adoption and
recognition. Ethereum, on the other hand, extends the utility of blockchain beyond mere financial transactions
through its support for smart contracts, thereby catalyzing a plethora of decentralized applications (DApps). This
diversity underscores the significant role these currencies play in the ongoing development and maturation of the
cryptocurrency market.
Technological Diversity: XRP and Tether were chosen to highlight the technological diversity within blockchain
implementations. XRP, developed by Ripple, is notable for its rapid transaction capabilities and minimal energy
consumption, diverging from the traditional mining-based consensus used by currencies like Bitcoin. Similarly,
Tether introduces a model of stability in the highly volatile cryptocurrency market by being pegged to the US
dollar, showcasing a unique application of blockchain technology in creating stablecoins that mitigate the price
volatility typically associated with cryptocurrencies.
Community and Innovation: Dogecoin exemplifies the impact of community on the value and adoption of a
cryptocurrency. Originating as a meme, Dogecoin has transcended its initial novelty to foster a robust community
3
that actively engages in tipping and charitable activities through the currency. This aspect highlights the role of
societal and cultural dynamics in shaping the cryptocurrency landscape, emphasizing the importance of community-
driven development and innovation.
Market Stability and Innovations: Finally, the inclusion of Tether also addresses the critical challenge of
market stability. By anchoring its value to a stable fiat currency, Tether offers a pragmatic solution to the issue of
volatility, which is a pervasive concern for investors in cryptocurrencies like Bitcoin and Ethereum. This approach
not only facilitates greater market stability but also enhances the practicality of cryptocurrencies for everyday
transactions and financial applications.
Collectively, these selections provide a comprehensive overview of the current state and potential future direc-
tions of blockchain technology, illustrating a spectrum of use cases from foundational cryptocurrencies to innovative
adaptations addressing specific market needs.
2.4 Market Analysis
Figure 1: Standardized Daily Prices of Cryptocurrencies for 2022
2.4.1 Price Trend Analysis
The analysis of standardized price trends of Bitcoin (BTC), Ethereum (ETH), XRP, Dogecoin (DOGE), and Tether
(USDT) throughout 2022 reveals several key insights into the dynamics of the cryptocurrency market:
Correlated Movements: The data illustrates that most cryptocurrencies exhibited closely correlated movements
over the course of the year. Such correlation is indicative of the substantial influence exerted by broader market
forces and global economic events on the cryptocurrency market as a whole, driving collective swings in investor
sentiment—whether bullish or bearish.
4
Volatility Across Assets: The degree of volatility varied significantly among the analyzed cryptocurrencies.
Bitcoin and Ethereum experienced relatively moderate fluctuations, maintaining tighter price bands, while Dogecoin
displayed higher volatility, characterized by more pronounced peaks and troughs. This disparity in volatility
underscores the differential market perceptions and investor bases of these assets.
Stablecoin Anomaly: An unexpected anomaly was observed in the price trend of Tether (USDT), particularly
in May 2022, where it deviated markedly from its expected stable trajectory. Such a divergence, given the design
of stablecoins to maintain parity with a peg (e.g., USD), suggests potential extraordinary events, data reporting
inaccuracies, or underlying issues with the stability mechanisms during that period.
Market Recovery Ability: Following significant market dips, the cryptocurrencies demonstrated varying de-
grees of recovery. Bitcoin and Ethereum showed robust resilience and recovery capabilities compared to Dogecoin.
This variation could reflect differing levels of market confidence and inherent stability within these digital assets.
Stablecoin’s Peculiar Trend: Assuming the accuracy of the observed sharp decline in USDT’s value, this could
represent a period of intense market stress or a temporary disruption in the stablecoin’s dollar peg. However, such
incidents are generally ephemeral, as corrective mechanisms typically restore stability swiftly, aligning with the
observed rapid return to normalcy.
From the analysis of these price trends, it is evident that while cryptocurrencies are interconnected and re-
spond collectively to market shifts, individual assets exhibit distinct behaviors influenced by their specific market
dynamics, investor sentiment, and technological foundations. The peculiar movement observed in Tether’s price
trend during the analyzed period merits further investigation to ascertain the causes and implications of such an
anomaly.
2.4.2 Volatility Analysis
The study of volatility in cryptocurrency markets provides crucial insights into the risks and stability of digital
assets. By calculating daily returns and examining their standard deviations, we can gauge the unpredictability
associated with each cryptocurrency and identify the factors contributing to these dynamics.
Dogecoin (DOGE-USD): Dogecoin exhibits the highest volatility among the cryptocurrencies analyzed, with
a standard deviation of approximately 5.64% . This elevated volatility can primarily be attributed to its relatively
low price per unit, which renders it more susceptible to significant percentage changes on a per-unit basis. Moreover,
Dogecoin’s price is notably influenced by social media trends and possesses comparatively less market liquidity than
more established cryptocurrencies. These elements combine to increase its price volatility, reflecting the substantial
impact of retail investor sentiment and speculative trading on its market behavior.
Tether (USDT-USD): In stark contrast, Tether shows the lowest volatility, with a standard deviation near
0.03% . As a stablecoin, Tether is explicitly designed to be pegged to a fiat currency, specifically the US dollar,
and maintains a stable value through various regulatory and technological mechanisms. This stability is critical for
its role in providing a safe haven during market turbulence and for facilitating transactions where volatility can be
a deterrent.
Bitcoin (BTC-USD) and Ethereum (ETH-USD): Both Bitcoin and Ethereum exhibit moderate levels of
volatility, reflecting their established presence in the market and larger capitalizations. These factors typically
confer higher liquidity and result in less drastic percentage changes in daily prices.
Benchmark Volatility Analysis: Comparing the volatility of cryptocurrencies with traditional financial mar-
kets, such as the S&P 500, highlights the unique risk profiles inherent to digital assets. The S&P 500, with a
volatility of 1.00% , offers a contrast to the higher volatility levels seen in cryptocurrencies, underscoring the
potential for greater price stability in traditional equity markets.
5
Market Implications: This variability in volatility, especially when benchmarked against traditional indices
like the S&P 500, illustrates the diverse nature of cryptocurrency markets. While stablecoins like Tether aim to
minimize price fluctuations, other cryptocurrencies such as Dogecoin and Bitcoin exhibit a range of volatilities,
heavily influenced by investor sentiment, liquidity, and their roles within the digital economy.
The higher volatility of cryptocurrencies compared to traditional markets like the S&P 500 underscores their
speculative nature and the heightened risks they pose, which investors must navigate carefully. This analysis em-
phasizes the importance of strategic risk assessment and portfolio diversification to mitigate the inherent volatility
of cryptocurrencies.
2.4.3 Correlation Analysis
Figure 2: Correlation Matrix Heatmap of Cryptocurrencies for 2022
A comprehensive examination of the correlation matrix for daily returns of Bitcoin ( BTC ), Ethereum ( ETH ),
XRP, Dogecoin ( DOGE ), and Tether ( USDT ) in tandem with the S&P benchmark ( GSPC ) elucidates the
interrelationships among these prominent cryptocurrencies:
High Correlations:
6
•Bitcoin and Ethereum: Exhibiting a correlation coefficient of 0.90, BTC and ETH demonstrate a very
strong positive correlation, indicating that these cryptocurrencies often move in tandem. This strong linkage
is primarily due to their predominant positions in the market, where both are frequently influenced by similar
economic factors, investor sentiments, and regulatory developments.
•Ethereum and XRP: With a correlation of 0.75, movements in Ethereum frequently correlate closely with
those in XRP, suggesting overlapping functionalities and investor bases that react similarly to market stimuli
in these two platforms.
Moderate Correlations:
•XRP with Bitcoin and Dogecoin: XRP displays moderate correlations of 0.74 with BTC and 0.61 with
DOGE. These correlations suggest a level of synchronicity, albeit influenced by distinct market dynamics and
external factors specific to each cryptocurrency.
•Dogecoin with Ethereum and Bitcoin: Correlation coefficients of 0.67 with ETH and 0.65 with BTC
for Dogecoin indicate a moderate degree of correlation, influenced by broader market trends that impact all
cryptocurrencies, though each responds according to its unique market niche and investor behavior.
Lower Correlations with Tether:
•All Cryptocurrencies with Tether: Tether, being a stablecoin tied closely to the US dollar, shows
significantly lower correlation coefficients with BTC ( 0.26), ETH ( 0.25), XRP ( 0.28), and DOGE ( 0.27).
This fundamental difference in design and purpose—aimed at providing stability—results in less synchronized
movements with the more speculative cryptocurrency assets.
Benchmark Correlation Comparison: Comparison with traditional financial markets, specifically through a
correlation study with the S&P 500, reveals additional insights. While cryptocurrencies such as BTC and ETH
show the highest correlation with each other, they exhibit only moderate correlation levels with the S&P 500, with
BTC showing the highest correlation at 0.52. This suggests that while cryptocurrencies do move somewhat in sync
with traditional financial markets, they retain distinct market dynamics that set them apart.
Market Implications: These findings highlight the diverse correlation landscapes within the cryptocurrency
markets, where strong intra-crypto correlations contrast with more moderate interactions with traditional financial
indices. This divergence underscores the necessity for investors to consider the unique correlation patterns when
diversifying portfolios or implementing hedging strategies. The mixed correlation profiles suggest both opportunities
and risks, as cryptocurrencies can offer portfolio diversification benefits due to their partial independence from
traditional market movements.
2.5 FTX Delta Analysis
2.5.1 Event Overview
In mid-November 2022, the cryptocurrency exchange FTX filed for bankruptcy, triggering significant disturbances
across the cryptocurrency markets. This event was exacerbated by the resignation of its CEO, Sam Bankman-Fried,
further destabilizing the market’s confidence.
2.5.2 FTX Impact on 11/11/22
2.5.3 Immediate Impact
The immediate repercussions of the bankruptcy announcement on November 11, 2022, were starkly evident across
various cryptocurrencies:
•Bitcoin (BTC) andRipple (XRP) each faced notable declines, with Bitcoin falling by -3.14% and XRP
by-2.92% .
•Ethereum (ETH) exhibited relative resilience, with a modest decline of -0.94% , reflecting its robust market
presence and investor confidence.
7
Ticker Impact on Nov 11
BTC-USD -3.14%
DOGE-USD -5.46%
ETH-USD -0.94%
USDT-USD 0.04%
XRP-USD -2.92%
GSPC 1.00%
Table 1: FTX Impact on Cryptocurrency Prices on November 11, 2022
•Dogecoin (DOGE) experienced the most significant drop of -5.46% , illustrating its susceptibility to market
shocks.
•Tether (USDT) , maintaining its stability, changed insignificantly by +0.04% , underscoring its role as a
stabilizing force within the volatile cryptocurrency environment.
2.5.4 FTX Impact in November 2022
Ticker Change in Nov 2022
BTC-USD -16.19%
DOGE-USD -25.05%
ETH-USD -17.98%
USDT-USD 0.01%
XRP-USD -12.02%
GSPC 5.81%
Table 2: Monthly Impact of FTX Bankruptcy on Cryptocurrency Prices in November 2022
2.5.5 Long-Term Impact
The extended impact throughout November painted a grim picture of recovery challenges:
•Major cryptocurrencies like BTC ,ETH , and XRP recorded substantial declines of -16.19% ,-17.98% , and
-12.02% respectively.
•DOGE was particularly hard hit, plummeting by -25.05% , marking the highest vulnerability among the
group.
•Conversely, USDT showed remarkable stability with only a 0.01% change, reinforcing its value proposition
as a hedge against volatility.
2.5.6 Benchmark and Market Performance Comparison
The correlation and impact studies reveal that while the cryptocurrency market suffered significant losses in response
to the FTX crisis, the traditional financial markets, as represented by the S&P 500, exhibited contrasting behavior:
•On November 11, 2022, while cryptocurrencies faced sharp declines, the S&P 500 (GSPC) experienced a
rise of 1.00% , demonstrating a decoupling from cryptocurrency market dynamics.
•Over the entire month of November, the S&P 500 gained 5.81% , further highlighting the resilience and
differing risk profiles of traditional equity markets compared to the high-risk cryptocurrency sector.
2.5.7 FTX Conclusion
The FTX bankruptcy served as a critical stress test, revealing the inherent volatility and risk exposure of speculative
cryptocurrencies compared to the stability offered by stablecoins like Tether and traditional financial indices like
the S&P 500. This event underscores the need for robust risk management strategies and diversified investment
approaches to navigate the complexities of cryptocurrency investments effectively.
8
2.6 Crypto Market Analysis Synthesis
Part I of this project delved into a comprehensive analysis of the cryptocurrency ecosystem, with an emphasis on
five key cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin, and Tether) and comparisons against the S&P 500
index. Our study covered daily price behavior, volatility, correlations, and market responses to major events like
the FTX bankruptcy. Let’s synthesize our key findings:
2.6.1 Volatility and Stability
•Cryptocurrencies demonstrate substantially higher volatility than traditional markets like the S&P 500. Do-
gecoin exhibited the highest volatility due to its smaller size and speculative nature.
•Tether’s negligible volatility confirms its role as a stablecoin, offering refuge within the cryptocurrency market.
2.6.2 Market Correlations
•Bitcoin, Ethereum, and other major cryptocurrencies are highly correlated, driven by similar market forces.
•Cryptocurrencies show low-to-moderate correlation with the S&P 500, suggesting some independence and
potential diversification benefits.
2.6.3 FTX Crisis Impact
•The FTX bankruptcy severely impacted cryptocurrency prices, while the S&P 500 remained largely unaf-
fected, highlighting sector-specific risks within crypto.
•November 2022’s broader market picture reinforced this divergence. Cryptocurrencies declined significantly
(excluding Tether), while the S&P 500 grew, emphasizing a decoupling during cryptocurrency-specific crises.
2.6.4 Long-Run Market Behavior
•2022 data illustrates that while offering potential for growth, cryptocurrencies also carry substantial risks of
sharp declines.
•The S&P 500’s lower volatility and positive November performance underscore the importance of traditional
equity investments for risk mitigation in diversified portfolios.
2.7 Part I Key Takeaways
Diversification: Cryptocurrencies offer diversification potential, but investors must carefully manage their high-
risk profile.
Investment Strategy: Balancing crypto holdings with safer assets like the S&P 500 can mitigate losses during
downturns.
Regulatory and Market Sensitivity: Staying informed about regulatory developments and sector-specific
events is crucial for navigating the dynamic cryptocurrency market.
2.8 Future Implications
These insights are vital for developing robust investment strategies maximizing the potential of cryptocurrencies
while safeguarding against their risks. Monitoring evolving correlations between cryptocurrencies and traditional
markets will aid in understanding market dynamics and adapting investment strategies accordingly.
9
3 Part II: Real-Estate Business Protocol Proposal
3.1 Business Proposal
3.1.1 Overview of the Blockchain Protocol
Our business proposal introduces an innovative blockchain protocol designed to revolutionize the real estate sector.
This protocol allows homeowners to store the deeds of their houses on the blockchain and facilitates the sale of
properties without traditional intermediaries such as lawyers, brokers, or other third parties. This system not only
simplifies transactions but also enhances security, reduces costs, and increases transparency.
3.1.2 Transactional Process
The transactional process under this protocol is streamlined to ensure efficiency and security:
1.Initiation of Sale: Homeowners list their properties on the blockchain platform directly, bypassing the need
for intermediaries. This step significantly reduces the complexity and duration of property transactions.
2.Proof of Ownership: The blockchain technology inherently provides a clear, immutable record of ownership.
This proof is publicly accessible and verifiable, ensuring that the current owner has indisputable ownership
of the property before proceeding with the sale.
3.Payment and Transfer: The buyer pays the seller in cryptocurrency, such as Bitcoin. Following payment
confirmation, the property deed is automatically transferred to the buyer’s blockchain address via a smart
contract, which also handles the transaction fee, typically associated with platforms like Ethereum.
4.Final Ownership: The new owner receives the property deed securely stored on the blockchain, ensuring
both safety and accessibility. This digital deed is resistant to tampering, loss, or theft, providing a permanent
record of ownership.
3.1.3 Advantages of Blockchain in Real Estate
The integration of blockchain into real estate transactions offers several improvements over traditional methods:
Transparency: The blockchain’s immutable ledger ensures that all transactions, including historical ownership
data and property details (e.g., square footage, number of bedrooms, date of last renovation), are permanently
recorded and openly verifiable. This level of transparency significantly reduces the potential for fraud and
disputes.
Cost Efficiency: By eliminating the need for various intermediaries and reducing paperwork and manual verifi-
cation processes, the blockchain protocol cuts down on significant transactional costs. These savings make
real estate transactions more economical for both buyers and sellers.
Global Accessibility: The blockchain protocol enables international transactions without the complexities of
cross-border legalities and financial transactions, opening up the property market to global participants and
investors.
Market Liquidity: The use of blockchain can enhance market liquidity. Buyers who may not have immediate
access to traditional financing options can leverage decentralized finance (DeFi) solutions, such as Aave , for
quicker funding solutions, thereby accelerating the buying process.
3.2 Target Customers
3.2.1 Market Strategy and Consumer Segmentation
Our blockchain protocol adopts a dual-sided market strategy designed to address distinct needs within the real
estate transaction process. This approach targets two main customer segments: property sellers (and deed holders)
and property buyers, along with a third, indirect segment involving financial lenders.
10
Property Sellers and Deed Holders: The primary market segment consists of current property owners who
stand to benefit substantially from blockchain integration. Traditional methods of deed storage involve physical
documentation, which not only increases the risk of loss and damage but also complicates the verification and
transfer processes. Our protocol offers a secure and immutable storage solution on the blockchain, eliminating the
need for physical safekeeping.
In tandem with disproportionately high the current real estate market structure necessitates multiple interme-
diaries, including real estate agents, brokers, and legal advisors, each adding significant transaction costs in the
form of commissions and fees. Historical data indicates that commission rates have remained relatively stable over
the past three decades, despite substantial increases in property values, leading to disproportionately high costs
for sellers ( refer to Figure 3 ,commratetrends2013 ). Moreover, median housing prices have soared, as vividly
depicted in the provided data from the Federal Reserve Economic Data (FRED, fredmspus2024 ) (see Figure 4).
Our protocol simplifies this process, allowing sellers to initiate and complete sales directly on the blockchain,
thereby reducing or eliminating traditional commission fees.
Figure 3: Historical Analysis of Average Commission Rates in Real Estate Transactions
Property Buyers: The second primary target segment includes potential property buyers who benefit from the
streamlined purchase process. Through our protocol, buyers can directly engage with sellers, conduct swift and
secure transactions, and gain immediate access to verified property deeds, significantly speeding up the acquisition
process. The use of smart contracts ensures that all conditions of the sale are met before the transaction is finalized,
offering additional security and efficiency.
Financial Lenders: An emerging market segment within our protocol includes financial lenders, particularly
those operating in the decentralized finance (DeFi) space. With the rise of blockchain technology, platforms like
Aave have demonstrated significant demand for more dynamic lending solutions that offer higher yields compared
to traditional financial products. Our protocol can connect these lenders directly with real estate buyers, providing
a new avenue for secured lending at competitive interest rates, reflective of the increased risk profiles associated
with cryptocurrency-based transactions ( refer to Figure 5 ).
11
Figure 4: Federal Reserve Economic Data (FRED) on Median House Sale Prices
Figure 5: Top Total Value Locked (TVL) in DeFi; Growth in DeFi Loan Market Size
3.2.2 Integration of Market Sides
By effectively integrating these two sides of the market—sellers and buyers with the financial backing of lenders—our
blockchain protocol facilitates a comprehensive ecosystem that enhances liquidity, reduces transaction latency, and
improves overall market efficiency. This integrated approach not only serves the immediate participants but also
introduces a scalable model for future expansions in global real estate markets.
12
3.3 Competitive Analysis
3.3.1 Propy: A Comparative Study
Propy emerges as a significant player within the blockchain-based real estate marketplace, providing a global
platform that aligns closely with the decentralized ethos of the blockchain revolution. As a competitor, Propy’s
operational model is built upon eliminating traditional intermediaries from the property transaction process. By
leveraging smart contracts on the blockchain, Propy ensures secure and efficient property transactions.
Operational Model: The core of Propy’s proposition is its decentralized marketplace, which facilitates the
buying and selling of properties. This innovative approach circumvents the need for brokers and agents, potentially
reducing transactional friction and cost. Furthermore, Propy offers digital deeds alongside automated escrow
services, thus simplifying real estate transactions and enhancing user experience.
Economic Structure: Propy’s economic framework incorporates the use of its native cryptocurrency, PRO,
alongside a designated fee for smart contract execution, termed PGas. The integration of PRO within their
platform ecosystem not only facilitates transactional activities but also extends utility to users engaging with
Propy’s services. The current market valuation of PRO stands at $2.99, which plays a pivotal role in the cost
structure of property sales on Propy’s platform.
Figure 6: Analysis of Propy’s Transactional Fees for Property Sales
This competitive analysis provides a deeper understanding of Propy’s strategic positioning within the blockchain-
based real estate sector. By dissecting their transactional fee structure and operational model, we can assess the
potential impact on our blockchain protocol’s market penetration and user adoption.
3.4 Competitive Advantage Analysis
Our protocol presents a unique value proposition in the blockchain real estate marketplace, offering comprehensive
solutions that address various stages of the real estate transaction process. It capitalizes on the inherent advantages
of blockchain technology to deliver an end-to-end service that simplifies the complexities traditionally associated
with real estate transactions.
Comprehensive Transactional Solutions: At the heart of our protocol is the capability to facilitate complete
real estate transactions on the blockchain. This ranges from secure deed storage to the actual execution of property
sales via cryptocurrency. By providing a single, unified platform, we significantly reduce the dependency on multiple
services, thereby streamlining the transaction process for all stakeholders involved.
13
Transparency and Security: The blockchain’s immutable ledger is a cornerstone feature that enhances our
protocol’s appeal. It serves as an unalterable record of transactions, ensuring complete transparency and security
for the transaction history. This transparency is a critical factor for buyers and sellers who prioritize trust and
verifiable transparency in their transactions, eliminating the traditional concerns of fraud and ambiguity in property
ownership and history.
Cost Efficiency: By obviating the need for intermediaries such as agents, brokers, and legal consultants, our
protocol minimizes the associated transaction costs. The conventional commission-based model, which signifi-
cantly increases transaction expenses, is replaced by a more cost-effective structure that aligns with the economic
preferences of a market leaning towards efficiency and reduced overhead.
Global Market Accessibility: Our protocol removes the barriers to entry for international buyers and sellers,
thereby facilitating global transactions. Without the constraints imposed by legal and regulatory compliance typical
of centralized systems, our platform paves the way for a more inclusive and expansive real estate market, appealing
to a broader investor base and contributing to the diversity of real estate offerings.
In summary, our competitive advantage stems from a holistic approach that not only provides practical trans-
actional capabilities but also fosters trust, reduces costs, and embraces global inclusivity. This strategic positioning
is poised to disrupt the traditional real estate market, leveraging blockchain technology to its fullest potential.
3.5 Implementation Strategy
The implementation of our business model onto the blockchain comprises a systematic approach, focused on smart
contract formulation, asset tokenization, oracle integration, privacy considerations, and user interface development.
Defining Smart Contracts: The foundation of our blockchain protocol is the design of smart contracts, which
are digital representations of real estate assets, mortgages, and deed transfers. The contracts will encapsulate the
logic for buying, selling, transferring ownership, and managing mortgage payments. This will involve:
1. Structuring smart contracts to encapsulate real estate transaction requirements.
2. Integrating functions for various transaction processes within these contracts.
Tokenizing Real Estate Assets: We will transform real estate properties into digital tokens on the Ethereum
blockchain, where each token signifies property ownership. This process will:
1. Utilize established token standards, such as ERC-20, for the representation of real assets in the digital domain.
2. Deploy contracts to issue and regulate these tokens, assigning unique identifiers to each property ( erc20whitepaper ).
Integration of Oracles: Oracles will be employed to incorporate off-chain data, like property specifications and
legal documents, into the blockchain. These oracles will:
1. Source trusted data essential for executing real estate transactions.
2. Update on-chain records to reflect accurate off-chain information, ensuring the veracity and reliability of data
(blockchainoracle2020 ).
Privacy Enhancements: Our protocol will incorporate privacy measures to safeguard sensitive transaction data,
allowing for public verification while maintaining confidentiality. We will:
1. Develop smart contracts with robust privacy controls.
2. Implement encryption techniques to restrict data access to authorized entities only.
14
User Interface Development: To facilitate user interaction with our blockchain platform, we will develop
accessible and intuitive interfaces. These interfaces will:
1. Offer a seamless user experience for property searches, transaction initiation, mortgage tracking, and deed
management.
2. Provide tools that are comprehensible and efficient for users, irrespective of their familiarity with blockchain
technology.
The strategic deployment of these elements will result in a robust blockchain protocol for real estate, streamlining
the transaction process and enhancing the overall experience for users in the real estate market. The careful
orchestration of smart contracts, tokenization, oracles, privacy considerations, and user interfaces are essential
components of our strategy to integrate real estate transactions with blockchain technology effectively.
3.6 Economic Viability for Ethereum
Evaluating the economic feasibility of our business proposal on the Ethereum platform involves careful consideration
of various factors, including scalability, transaction fees, and the complexity of smart contracts.
Scalability Concerns: Ethereum’s scalability is a pivotal concern, particularly as transaction volumes escalate.
As the leading blockchain platform, Ethereum faces challenges in maintaining performance amid rising demand.
The introduction of Ethereum 2.0 promises to alleviate these issues through sharding and a proof-of-stake consensus
mechanism, potentially enhancing throughput and lowering transaction costs ( blockchaingasfees2021 ).
Transaction Fees: Gas fees on Ethereum are known for their volatility and can constitute a significant portion
of transaction costs. These fees tend to surge during periods of network congestion, impacting the cost-benefit
analysis for users engaging in real estate transactions. Monitoring the historical trends of Ethereum’s average gas
fee is crucial in forecasting and managing the financial viability of transactions ( ethereumgasprices2024 ).
Figure 7: Average Ethereum Gas Fees Over the Last Five Years
Smart Contract Complexity: The smart contracts at the core of our real estate protocol, which will enable
property transfers, loan repayments, and the enforcement of covenants, are inherently complex. The intricacy of
these contracts is directly proportional to the computational resources required, thus influencing the overall gas fees
incurred. This complexity must be carefully managed to ensure that the benefits of using the Ethereum platform
outweigh the costs for all parties involved.
The prospective enhancements with Ethereum 2.0 alongside strategic management of smart contract complexity
and monitoring of gas fees are essential for ensuring the economic viability of deploying our real estate transaction
protocol on the Ethereum blockchain. As we progress, it will be imperative to remain adaptable to the evolving
blockchain landscape to maintain a competitive and cost-effective platform for real estate transactions.
3.7 Comparative Analysis of Blockchain Platforms
In exploring the economic viability and technical suitability of our real estate transaction protocol, we extend
our analysis beyond Ethereum to other leading blockchain platforms, each with distinct attributes and potential
advantages.
15
Figure 8: Comparative Analysis of Ethereum Versus Alternative Blockchain Platforms
Binance Smart Chain: Binance Smart Chain (BSC) emerges as a viable alternative, promising higher through-
put and lower latency compared to Ethereum, which is advantageous for high-demand scenarios. Despite these
benefits, BSC’s more centralized nature may raise concerns among stakeholders seeking a fully decentralized solu-
tion.
Solana: Solana presents a compelling case for applications necessitating rapid transaction processing, offering su-
perior transaction speeds and scalability ( solana2022 ). While Solana provides an efficient alternative to Ethereum,
its developer ecosystem and tooling are less mature, potentially imposing limitations on development and integra-
tion efforts.
Polkadot: The multi-chain framework of Polkadot facilitates cross-chain interoperability, which can significantly
enhance the scope and flexibility of our protocol. Polkadot’s design allows for seamless integration with a variety
of blockchain networks, potentially expanding the protocol’s reach. Nevertheless, Polkadot’s infrastructure and
tooling are still evolving, which may introduce challenges during early adoption phases.
This comparative analysis underscores the importance of selecting a blockchain platform that aligns with our
protocol’s requirements for security, decentralization, transaction speed, and scalability. As we proceed with our
implementation strategy, ongoing evaluation of these platforms’ evolving capabilities will be imperative to maintain
an innovative and user-centric service in the dynamic real estate market.
3.8 Synthesis of the Blockchain Real-Estate Protocol
The business protocol presented in Part II encapsulates an innovative, blockchain-based approach to real estate
transactions. The proposed system introduces a transformative model that empowers homeowners to engage directly
in the sale and purchase of properties, effectively circumventing the traditional, intermediary-reliant processes that
are often cumbersome and less secure.
Overview and Efficacy of Protocol Application The cornerstone of the proposed protocol lies in its uti-
lization of blockchain’s inherent properties such as immutability, transparency, and distributed consensus. These
properties facilitate a seamless transition of deeds and payments, providing a robust proof of ownership and stream-
lining transactions. By leveraging smart contract technology, the protocol ensures that all prerequisites of a property
transaction are automatically met, heralding a new era of efficiency in property dealings.
16
Enhancing Real Estate Transaction Dynamics The protocol offers multiple benefits over conventional meth-
ods. It provides a public, transparent ledger for ownership and transaction history, reduces the costs associated
with property transactions by eliminating intermediary fees, and enables global participation in the real estate
market. Additionally, the protocol introduces greater liquidity to the market by integrating with decentralized
financial platforms, allowing prospective buyers to secure funding rapidly.
Consumer-Centric Market Strategy This protocol advocates a dual-sided market strategy that aims to reform
the real estate transaction paradigm. Property sellers are afforded the ability to secure deed storage on the
blockchain while benefiting from direct market access for sales, effectively bypassing intermediary overheads. For
property buyers, the protocol simplifies the purchase process, offering immediate access to property details and
streamlining the transfer of ownership. Moreover, financial lenders find a new marketplace in which to offer secured
loans, augmented by blockchain’s security features.
Competitive Landscape and Advantages In the competitive landscape, the proposed protocol differentiates
itself by presenting a comprehensive and integrated solution that extends beyond mere transaction facilitation.
It anticipates the current and future needs of the real estate market, focusing on user experience, transactional
integrity, and market inclusivity. Against competitors like Propy, RealT, and Deedcoin, the protocol asserts its
edge through its amalgamation of transactional efficiency, cost savings, and market reach.
Strategic Implementation and Economic Considerations The practical implementation of the protocol
on the Ethereum blockchain, and the considerations for its economic viability, are outlined with a foresight into
potential scalability issues and transaction costs. Alternative blockchains such as Binance Smart Chain, Solana,
and Polkadot are appraised for their suitability, with an emphasis on their comparative advantages in terms of
transaction speed, costs, and infrastructural development.
Implications and Future Prospects In conclusion, the blockchain real-estate protocol promises to not only
revolutionize the manner in which real estate transactions are conducted but also to serve as a blueprint for
future applications of blockchain technology in other domains. The synthesis of the protocol’s operational model,
strategic implementation, and competitive positioning underscores its potential to offer a superior alternative to the
established real estate transaction processes, setting a new benchmark in efficiency, security, and global accessibility.
4 Conclusion
This paper has presented an in-depth examination of the cryptocurrency market, followed by a pioneering proposal
for a blockchain-based real estate transaction protocol. Through meticulous analysis, it has provided evidence of
the intricate dynamics governing cryptocurrency price movements, volatility, and correlations, particularly in the
context of the FTX bankruptcy event, thereby illuminating the vulnerabilities and resilience inherent in the digital
currency landscape. In tandem, it has offered a visionary blueprint for the utilization of blockchain technology in
streamlining real estate transactions, proposing a model that is poised to redefine the sector.
4.1 Synthesis of Findings
The market analysis revealed that cryptocurrencies are not only highly volatile but are also subject to correlated
movements, which can lead to systemic risks within the digital asset class. Yet, this volatility and interconnectivity
also underscore the potential of cryptocurrencies to diversify investment portfolios when judiciously balanced with
traditional assets. The FTX bankruptcy served as a litmus test for the market, delineating the stability offered by
stablecoins and the S&P 500 in contrast to the pronounced volatility of cryptocurrencies like Bitcoin and Dogecoin.
On the frontier of innovation, the blockchain real estate proposal detailed in Part II of this paper is set to
disrupt a long-established industry. By removing intermediaries, reducing transaction costs, and enhancing trans-
parency, the protocol demonstrates a tangible application of blockchain beyond speculative trading, embodying the
technology’s transformative potential in real-world asset management and exchange.
4.2 Prospects and Implications
The synthesis of the paper’s findings paints a nuanced picture of the cryptocurrency market’s complexities and
introduces a sophisticated approach to real estate transactions that capitalizes on blockchain technology’s strengths.
17
As the cryptocurrency market continues to mature, it is anticipated that investor strategies will adapt to encompass
both traditional and digital assets, ensuring balanced portfolios that mitigate risk while capitalizing on growth
opportunities.
The real estate blockchain protocol, while nascent, holds promise for a radical shift in property ownership
transfer, marking a significant leap towards a more interconnected, efficient, and accessible global market. Its
implications extend beyond the real estate sector, signaling the advent of a broader adoption of blockchain in
various facets of commerce and governance, ushering in a new era of decentralized digital solutions.
4.3 Summary and Forward Outlook
In summary, the research presented in this paper contributes meaningfully to the understanding of cryptocurrencies
and offers a progressive application of blockchain technology. As we witness the convergence of traditional financial
methodologies with groundbreaking digital solutions, the potential for innovation in both markets and technology
is boundless. Future research and development will undoubtedly continue to expand on these foundations, further
integrating the burgeoning possibilities of blockchain technology into the fabric of societal and economic structures.
In moving forward, continuous monitoring of market trends, regulatory developments, and technological ad-
vancements will be crucial in optimizing the strategies and applications discussed herein. The cryptocurrency
market’s evolution and the blockchain real estate protocol’s maturation will undoubtedly serve as critical barom-
eters for the future trajectory of digital finance and property transactions. The journey ahead promises to be as
challenging as it is exciting, with the potential to redefine the very essence of investment, ownership, and exchange
in an increasingly digital world.
18
|
{
"id": "2405.02547"
}
|
2408.11961
|
Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain
litigation using LLM-based Thematic Factor Mapping
|
The proliferation of blockchain entities (persons or enterprises) exposes
them to potential regulatory actions (e.g., being litigated) by regulatory
authorities. Regulatory frameworks for crypto assets are actively being
developed and refined, increasing the likelihood of such actions. The lack of
systematic analysis of the factors driving litigation against blockchain
entities leaves companies in need of clarity to navigate compliance risks. This
absence of insight also deprives investors of the information for informed
decision-making. This study focuses on U.S. litigation against blockchain
entities, particularly by the U.S. Securities and Exchange Commission (SEC)
given its influence on global crypto regulation. Utilizing frontier pretrained
language models and large language models, we systematically map all SEC
complaints against blockchain companies from 2012 to 2024 to thematic factors
conceptualized by our study to delineate the factors driving SEC actions. We
quantify the thematic factors and assess their influence on specific legal Acts
cited within the complaints on an annual basis, allowing us to discern the
regulatory emphasis, patterns and conduct trend analysis.
|
http://arxiv.org/pdf/2408.11961v1
|
Junliang Luo, Xihan Xiong, William Knottenbelt, Xue Liu
|
cs.CL
|
cs.CL
|
Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain
litigation using LLM-based Thematic Factor Mapping
Junliang Luo
McGill University
Montréal, Québec, CanadaXihan Xiong
Imperial College London
London, United KingdomWilliam Knottenbelt
Imperial College London
London, United KingdomXue Steve Liu
McGill University
Montréal, Québec, Canada
ABSTRACT
The proliferation of blockchain entities (persons or enterprises) ex-
poses them to potential regulatory actions (e.g., being litigated) by
regulatory authorities. Regulatory frameworks for crypto assets are
actively being developed and refined, increasing the likelihood of
such actions. The lack of systematic analysis of the factors driving
litigation against blockchain entities leaves companies in need of
clarity to navigate compliance risks. This absence of insight also
deprives investors of the information for informed decision-making.
This study focuses on U.S. litigation against blockchain entities,
particularly by the U.S. Securities and Exchange Commission (SEC)
given its influence on global crypto regulation. Utilizing frontier
pretrained language models and large language models, we sys-
tematically map all SEC complaints against blockchain companies
from 2012 to 2024 to thematic factors conceptualized by our study
to delineate the factors driving SEC actions. We quantify the the-
matic factors and assess their influence on specific legal Acts cited
within the complaints on an annual basis, allowing us to discern
the regulatory emphasis, patterns and conduct trend analysis.
CCS CONCEPTS
•Applied computing →Law.
1 INTRODUCTION
Current laws in jurisdictions such as the U.S., Canada and EU, are
incorporating the concepts introduced by the blockchain economy
into traditional financial regulations [ 9,21,32]. The integration of
concepts concerns the recognition and regulation of crypto service
providers (CASPs), with a focus on compliance, consumer protec-
tion, and market integrity[ 32] within established or new legal frame-
works [ 42]. The enforcement of AML standards [ 8] and applicable
financial regulations is central to the regulation of cryptocurrency
tokens across various jurisdictions. These regulations are being
developed to mitigate the risks associated with the pseudonymity
and borderlessness of blockchain transactions, while leveraging
their potential to reduce inefficiencies in securities settlement [ 7,9].
Globally, regulation frameworks are being developed to address
the regulation of digital assets, such as the Regulation on the Markets
in Crypto-Assets (MiCA) [45] created by the European Commission,
the SEC’s Framework for “Investment Contract” Analysis of Digital
Assets [39] in the U.S. These frameworks target establishing guide-
lines for the definition, classification and trading of digital assets
(including crypto, the blockchain-based digital asset), maintaining
financial stability and protecting investors. Focusing on the present,
these developing regulations have inevitably led to an increase in
litigation in recent years as crypto assets challenge existing finan-
cial legal norms [ 24]. Crypto asset disputes often involve technical
issues, asymmetric information, and impacts on investors and thebroader socio-economic [ 10,19,30]. The disputes also intercon-
nected with the market volatility triggered by blockchain activities
and the ethical considerations of technology deployment that may
influence public trust and participation in blockchain systems [ 23].
Given the significant influence of U.S. financial regulations on
global crypto assets regulation, this study focuses on U.S. litigation.
In the U.S., the SEC is actively pursuing lawsuit cases to ensure
regulatory compliance and the protection of investors under the
existing security laws. The Commodity Futures Trading Commis-
sion (CFTC) also engages in litigation, regulating the crypto assets
classified as commodities to ensure compliance with the Commod-
ity Exchange Act . Given the distinct regulatory roles of the SEC and
CFTC, this study focuses exclusively on SEC lawsuit cases, leaving
CFTC-related litigation for potential future research.
The SEC published Crypto Assets and Cyber Enforcement Actions
[40], a comprehensive collection of SEC lawsuits against crypto en-
tities from 2012 to 2024. Given the collection, we obtained the com-
plaints for all the cases. The complaints enumerate a constrained
set of Acts (legal statutes enacted by legislative bodies that establish
regulatory requirements and prohibitions) in a few sentences, yet
the comprehensive content of complaints contains extensive infor-
mation. For instance, the high-profile case of SEC v. Ripple Labs,
Inc. [ 36] cites 5(a) and 5(c) of the Securities Act [37], prohibiting the
sale of unregistered securities, but the complaint itself spans over
four hundred segments containing considerable details (detailed
in Section 4). The absence of analysis translating complaint details
into clear, interpretable insights complicates understanding. This
leaves enterprises, even those with legal awareness, struggling to
navigate compliance risks due to the complexity of legal documents,
obscuring the full regulatory picture. Additionally, investors lack
the insights needed to understand regulatory trends, potentially
obstructing the growth of the crypto market. To address this, we
tackle explaining the underlying litigation drivers by employing
quantitative analytics and modeling to systematically extract and
interpret the latent factors triggering the litigation.
The research questions comprise three focused inquiries: How
can we conceptualize a reasonable categorization for limited types
of factors within the complaints, termed thematic factors, to delin-
eate the factors driving SEC enforcement actions? How can these
critical factors be extracted and quantified using existing machine
learning language models, transforming legal text into measurable
factors? How do these quantified thematic factors map to specific
regulatory Acts, and what does this reveal about enforcement trends
and regulatory priorities over time, enabling us to infer the form of
activities most likely to precipitate SEC litigation.
We initiated the study by conceptualizing a set of thematic fac-
tors inspired by multiple sources (detailed in section 4.1). To extract
and quantify the thematic factors in complaints, we proposed aarXiv:2408.11961v1 [cs.CL] 21 Aug 2024
Anonymous Author(s)
Figure 1: Association between the legal Acts and their corresponding lawsuit case groups. Larger circles in size indicate larger
number of cases mentioning the same legal Act. Instances of lawsuit cases of some large groups are also given.
method to assign each indexed segment (e.g. in Figure 2) a corre-
sponding thematic factor. The method leveraged recent advances
in pretrained language models (PLMs) and large language models
(LLMs). The PLM transformed each segment of the SEC complaints
into a vector, embedding their contextual representations within a
semantic space. Each of these vectors was mapped to the thematic
factor by its similarity to the embedding vectors of LLM-generated
seed sentences, each anchored to a specific thematic factor, and
produced using the same PLM making the same semantic space.
Subsequently, we employed a Generalized Linear Model (GLM) to
estimate the coefficients of these thematic factors with respect to
specific legal Acts cited within the complaints on an annual ba-
sis. We conducted a detailed examination of these coefficients and
identified the trends of regulatory emphases.
To the best of our knowledge, we are the first to systematically
analyze SEC complaints for blockchain entities from a data-driven
perspective. Our contribution provides CASPs and investors with a
language model-based approach to systematically analyze SEC law-
suit complaints to extract and interpret regulatory trends, assisting
their compliance strategies and decision-making. By uncovering
and quantifying the driving factors behind SEC enforcement ac-
tions, we provide valuable insights into past enforcement patterns
and potential future regulatory priorities. This work serves as a
resource for the blockchain community and the broader crypto in-
dustry, helping to clarify regulation and supporting the responsible
development of the sector. The conclusions extrapolated include:
•Practices detrimental to investors, such as fraud offering and
misappropriation of consumer funds, remain a consistent and
primary trigger for SEC enforcement actions across all years
regardless of market conditions. It can be reasonably inferred
that any entity engaging in such misconduct will continue to be
subject to SEC litigation in the future.
•During market surges (e.g., crypto surges in 2017-18 & 2021), the
SEC’s focus sharpened on the financial scale of companies’ op-
erations. Enterprises handling high market valuations of crypto
asset, particularly those involved in unregistered securities offer-
ings, are more likely to be scrutinized for non-compliance with
registration mandates under the Securities Act. Entities offering
assets during market surges involving large amounts of money
face elevated risks of SEC litigation for unregistered offerings.•The SEC’s enforcement has expanded to cover a broader range
of areas post 2020 such as tender offers, mandatory disclosures,
and annual or quarterly reporting. Misuse of funds by key indi-
viduals was notably more prevalent before 2018, and celebrity
promotions occurred in separate years, but it is reasonable to
infer that the SEC continues to monitor such activities closely.
2 PRELIMINARY
This section provides an overview of the crypto regulatory context,
the SEC crypto assets litigation and the complaint data sources.
2.1 Crypto Regulatory Context
The global regulation for cryptocurrencies varies across jurisdic-
tions [ 3,42], with regions implementing specialized frameworks
such as the EU’s Markets in Crypto-Assets (MiCA) regulation to
oversee activities related to Crypto-Asset Service Providers (CASPs)
Other jurisdictions integrate cryptocurrency regulation within their
existing legal frameworks. For example, in the U.S., the regulation
of cryptocurrencies is incorporated into the existing securities and
commodities regulatory frameworks. This divergence in regulatory
approaches presents the challenges in achieving a unified global
stance on cryptocurrency. In the U.S., the SEC and the CFTC are the
primary agencies responsible for overseeing cryptocurrencies. If a
crypto asset qualifies as a security under the Howey Test [ 15,17],
the asset falls under the jurisdiction of the SEC and must comply
with the Securities Act of 1933 [ 37] and the Securities Exchange
Act of 1934 [ 35]. Typically if a crypto asset does not fit the legal
definition of a security under the Howey Test, the asset is classified
as a commodity and regulated by the CFTC and must adhere to the
Commodity Exchange Act of 1936 [2].
2.2 SEC Crypto Assets Lawsuit Cases
The SEC has actively pursued legal actions against various crypto
entities in recent years due to violations of securities laws. One
frequent violation is the failure to register with the SEC, which can
manifest in several forms, such as unregistered brokers, traders,
and clearing agencies operating without proper authorization.
The legal actions were against companies with cryptocurrency
coin (token) offerings such as Ripple (XRP), Telegram (TON), Bi-
nance (BNB), etc., alleging violations of federal securities laws
regarding unregistered securities offerings [ 34]. Also, exchanges
Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping
like Binance, Coinbase, Kraken, and Gemini have faced lawsuits
for operating as unregistered brokers, traders, and clearing agen-
cies offering unregistered securities [ 6]. For instance, Binance and
Coinbase have been accused of several violations, such as operating
without proper registrations, mismanaging customer funds, and
lacking adequate trading controls [ 33]. Fraudulent activities are an-
other common violation that the SEC targets. A notable case is the
SEC’s lawsuit against BitConnect, which alleged a two billion USD
fraud involving a crypto investment scheme. Market manipulation
cases, such as the SEC’s litigation against The Hydrogen Technol-
ogy Corporation, et al., for manipulating the trading volume and
price of the ‘Hydro’ crypto asset.
Lawsuit cases are documented and accessible through Public
Access to Court Electronic Records (PACER)1, which provides
access to case and docket information including all documents filed
with the court, case-related transcripts, and orders from the court,
culminating in a final judgment which resolves the legal issues in
question and may include remedies such as penalties or directives
to cease certain activities. In our study, we focus on complaints
of all these cases from SEC Crypto Assets and Cyber Enforcement
Actions [40] as the complaints are publicly available irrespective
of the ongoing judicial status, and they provide a window into
the obscured litigation elements and the SEC’s adaptive regulatory
measures regarding blockchain assets.
3 DATASET
The dataset Crypto Assets and Cyber Enforcement Actions [40] con-
sists of 226 distinct lawsuit cases filed against companies and or
individuals from January, 2012, to July, 2024. The fields detail the
action name, description, and date filed, and the link to the full com-
plaint. These cases involve accusations of unregistered securities
offerings, account intrusions, insider trading, market manipulation,
regulated entities, public company disclosure, and trading suspen-
sions (official categories). We obtained the complaints of all these
cases. In each complaint, the text is organized using segment in-
dices. The indices number specific paragraphs within the complaint
document to organize the content logically. The logical organiza-
tion typically presents an introduction, background details, factual
allegations, legal charges, and the relief sought. We processed the
complaint of each case by segmenting each complaint into distinct
segments by the indices. Statistically, the derived SEC complaints
dataset consists of a vocabulary size of 39,441 and an average word
count of 6,042 per case. Each case is structured into averagely 79
segments, with an average segment length of 106 words.
4 THEMATIC SEMANTIC MAPPING
We extracted all legal Acts and associated group of lawcuit cases
mention the same Acts presented in Figure 1. For instance, the
complaint of the case: SEC v. Ripple Labs, Inc. mentioned Sections
5(a) and 5(c) of the Securities Act (of 1933) , which prohibits the sale
and delivery of unregistered securities (In this case, XRP) unless a
registration statement is in effect; Section 20(b) of the Securities Act ,
which holds individuals who control others liable for violations of
the Securities Act. The complaint mentions also the other Acts that
1https://pacer.uscourts.gov/
Figure 2: Workflow of thematic factor mapping and Act-
factor trends modeling. Models are input with multiple com-
plaints, SEC v. Ripple Labs shown as an illustrative example.
allow the SEC to investigate violations of the securities laws, and
provide the SEC with the power to enforce its provisions.
However, the SEC v. Ripple Labs exampled a complaint that en-
compasses extensive details, subdivided into over four hundred in-
dexed segments. Since the Acts provide only limited information on
legal requisites, comprehending complaints through a method that
can extract segment-level factors from complaints to gain a com-
prehensive understanding of the overall progression of blockchain
assets is needed. We demonstrate the whole workflow in Figure 2.
4.1 Thematic Factors
We initiate our workflow by operationalizing the litigation drivers
using thematic factors as quantifiable proxies for potential regu-
latory triggers. These thematic factors are drawn from publicly
available complaints involving litigation by the SEC against various
blockchain companies and entities, ranging from financial discrep-
ancies to compliance breaches—and are hypothesized to directly
correlate with litigative risks that precipitate SEC interventions.
Drawing on insights from multiple publications and articles includ-
ing Carmona et al. (2023) [ 4], Goforth (2021) [ 12], and Hennelly
(2022) [ 16], we hypothesized the thematic factors as the following:
Financial Misconduct & Investor Impact : Focuses on the
financial integrity and the direct impact on investors.
Regulatory Compliance : Examines the adherence of compa-
nies to essential securities laws and regulatory mandates.
Promotion & Misrepresentation : Concentrates on the pro-
motion and representation of information to investors.
Scope & Scale of Operations : Considers the extent and scale,
specifically monetary values, of the company’s operations.
Anonymous Author(s)
Technological Risks : Represents the vulnerabilities within the
technological deployments of the application.
Key Individuals : Probes the roles and responsibilities of core
company figures in legal compliance and possible misbehaviours.
4.2 Mapping Method
Some pretrained language models (PLM) can produce embedding
vectors given the sentences that capture the contextual and seman-
tic meanings within the text [ 26]. In our case, we apply the PLMs on
each segment of all lawsuit cases of the dataset. The segment-level
embedding vectors present specific positions within the semantic
space, situating the segment texts in a high-dimensional context
representing their semantic meanings. The segment-level embed-
ding vectors will be input to a model. In such a model we employ a
large language model (LLM) to map the embedding vector to a the-
matic semantic factor by synthesizing embedding vectors produced
using LLM as a generative synthesizer to generate seed sentences
targeting specific thematic factors.
The functions are defined as follows: L𝜇is an LLM generative
synthesizer to map N(factors in 4.1) prompts to text outputs. P𝜆
is an PLM to convert the input text into a 𝑛-dimension semantic
embedding space. 𝜇and𝜆indicate a particular kind of models.
P𝜆:Text→R𝑛,L𝜇:Prompts×N→Text
L𝜇generates a sentence seed_s𝑖of length limit ℓfrom prompt p𝑖.
seed_s𝑖=L𝜇(p𝑖,ℓ),v𝑖=P𝜆(seed_s𝑖)
P𝜆(t)converts text tinto an n-dimension embedding vector v.
v𝑗=P𝜆(s𝑗),∀s𝑗∈Segments S
The seed sentences generated are assuredly aligned with designated
thematic factors, and will be subsequently mapped into the identical
embedding space produced by the same PLM as the segments. Then
each segment embedding v𝑗will be aligned with the thematic
semantic factorFof its most similar seed sentence embedding v𝑖
using the distance function 𝛿.
F(𝑠𝑗)←F( seed_s𝑖)where𝑖=argmin
𝑖𝛿(v𝑖,v𝑗)
The aforementioned process is the mapping method performing the
alignment of text segments to their thematic factors by leveraging
the generated seed sentences and their embeddings.
4.3 Experiment
To assess the functioning of the proposed semantic embedding map-
ping, it is essential to develop a proper evaluation metric to assess
the aligned thematic factors of segments, given that the task di-
verges from conventional downstream classification. Subsequently,
we conduct empirical evaluations applying a customized perfor-
mance metric across a range of LLMs and PLMs. This is to test LLMs’
varying generalization capabilities in generating contextually ap-
propriate thematic seed sentences, and the differing capacities of
PLMs to capture semantic meaning, particularly within the special-
ized vocabulary and sentence characteristic of the complaints.4.3.1 PLM and LLM Models. We selected the PLM models from the
MiniLM, MPNet, Sentence-T5, and Generalizable T5 Retriever (GTR)
families because they are compatible with Sentence Transformers
2and are known for their efficiency and performance in semantic
search tasks [ 28,29,38,41]. We utilized the LLMs models ChatGPT-
4 [31], Meta Llama 3 (70B) [ 1], and Gemini [ 13] due to their advances
attributed to their extensive parameter numbers. The prompts used
to generate the seed sentences are detailed in Table 1 beneath.
4.3.2 Evaluation Metric. Given the lack of the ground truth labels
for each segment, we adopt an automated scoring metric using
anchored lexical assessment without reliance on extensive labels.
The anchored lexical assessment is implemented via employing
NER GLiNER model [ 44] to extract and score specialized terms that
are anchored to each thematic semantic factor. We compare the
relevance of terms linked to the defined factor against terms from
other factors within the segments.
Considering each thematic semantic factor 𝑖, the model extracts
entities𝐸𝑠𝑖from all segments 𝑆𝑖aligned with that factor.
𝑠𝑐(𝑗)
𝑖=1
N∑︁
𝑠∈𝑆𝑖∑︁
𝑒∈𝐸𝑠𝑖𝑤𝑒·𝛿𝑓𝑖(𝑒)
The score for each factor 𝑖is computed as above, where Nis
the total number of factors, 𝑤𝑒is the confidence score output
from the GLiNER model as the weight of entity 𝑒, and𝛿𝑓𝑖(𝑒)=
1if entity𝑒aligns with factor else 0. For each factor 𝑖, we obtain a
list of scores[𝑠𝑐(0)
𝑖,𝑠𝑐(1)
𝑖,...,𝑠𝑐(N)
𝑖], where each score is the average
weighted count of the relevant anchored terms by GLiNER model.
Given this list of scores, we define a normalized score 𝑅𝑖for the
factor𝑖:
𝑅𝑖=Í
𝑗≠𝑖Δ(𝑠𝑐(𝑖)
𝑖,𝑠𝑐(𝑗)
𝑖)
ÍN
𝑘=1Í
𝑗≠𝑘Δ(𝑠𝑐(𝑘)
𝑖,𝑠𝑐(𝑗)
𝑖)
The difference between two two scores 𝑠𝑐(𝑖)
𝑖and𝑠𝑐(𝑗)
𝑖is given by:
Δ(𝑠𝑐(𝑖)
𝑖,𝑠𝑐(𝑗)
𝑖)=max(0,𝑠𝑐(𝑖)
𝑖−𝑠𝑐(𝑗)
𝑖)
The rationale is that for segments aligned with the factor 𝑖, a higher
number of extracted anchored terms for factor 𝑖compared to other
factors indicates a good alignment, whereas high counts for both 𝑖
and other factors suggest inferior alignment. Therefore we focus on
calculating the positive difference as defined in the above difference
function Δ. The normalized score calculates the normalized posi-
tive differential contributions of segments aligned with one factor,
for anchored terms of that factor compared to others, ascending
positively from no to maximal dominance in the 0-1 range.
4.3.3 Results. We conducted experiments using a segment length ℓ
equivalent to the average length of segments (in section 3), and a set
of one hundred sentences (testings of additional sentence volumes
may be considered in future studies) using various GLMs and LLMs.
The experiment results are presented in Table 3. The experimental
results indicate that the ChatGPT-4 model obtained overall average
scores compared to other LLMs. The MiniLM-based models report
a stable metric distribution with minimal variance. In contrast, the
gtr-t5 variants display the highest mean performance (as the top
mean scores shown in bold), for example, with mean scores of 0.515
2https://www.sbert.net/
Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping
Table 1: Thematic Factor IDs, names, and corresponding prompts
Factor ID Factor Name Prompt
0 Financial Misconduct & Investor Impact (FM) Focus on mentions of how the complaints describe the impact on investors, such as specific harms or financial losses. This
includes improper accounting practices and bribery, highlighting the importance of compliance with internal controls and
transparency in financial dealings.
1 Regulatory Compliance (RC) Focus on mentions of companies failing to comply with regulations, such as not properly registering securities or failing to
disclose critical information, and how these shortcomings subject them to lawsuits, emphasizing the importance of adherence
to established securities laws.
2 Promotion & Misrepresentation (PM) Focus on mentions of any misrepresentations, particularly on instances of asymmetric information and over-promotion. Detail
how information was misleading, exaggerated, or deceptively presented, offering insights into subtle forms of fraud.
3 Scope and Scale of Operations (SO) Focus on mentions of how the scope and scale of the company’s operations are described. Pay attention to numeric facts such
as the amount of money involved, the number of investors, and the geographic reach of operations.
4 Technological Risks (TR) Focus on mentions of specific technological vulnerabilities or failures, such as inadequacies in the blockchain technology itself,
security breaches, or technical misrepresentations.
5 Key Individuals (KI) Focus on mentions of key individuals within the company. Observe how their actions, statements, and roles might reveal
individual culpability or highlight leadership issues that contribute to legal violations.
Table 2: Comparison of normalized evaluation scores 𝑅𝑖, where𝑖corresponds to a factor ID. Results are presented with the
score of each factor (under its abbreviation factor name as listed above) across various PLMs and LLMs.
PLMsGPT-4 Meta Llama 3 (70B) Gemini
FM RC PM SO TR KI M.V FM RC PM SO TR KI M.V FM RC PM SO TR KI M.V
all-MiniLM-L6-v2 .414 .650 .451 .372 .580 .459 .488 ±.106 .396 .446 .525 .331 .523 .454 .446 ±.075 .311 .565 .464 .332 .524 .448 .441 ±.102
all-MiniLM-L12-v2 .310 .639 .436 .388 .557 .446 .463 ±.118 .337 .463 .452 .399 .505 .491 .441 ±.063 .268 .440 .411 .317 .518 .434 .398 ±.091
all-mpnet-base-v1 .348 .684 .446 .319 .609 .398 .467 ±.147 .348 .533 .478 .380 .529 .471 .457 ±.077 .215 .548 .412 .257 .530 .429 .399 ±.137
all-mpnet-base-v2 .355 .655 .503 .308 .578 .423 .470 ±.133 .324 .507 .436 .380 .571 .504 .454 ±.091 .297 .571 .433 .310 .571 .492 .446 ±.122
sentence-t5-base .405 .620 .420 .305 .588 .434 .462 ±.119 .347 .437 .405 .348 .601 .501 .440 ±.098 .319 .617 .433 .310 .561 .488 .455 ±.125
sentence-t5-large .378 .712 .490 .289 .619 .477 .494 ±.154 .392 .601 .508 .378 .570 .463 .485 ±.091 .321 .691 .436 .332 .623 .440 .474 ±.152
gtr-t5-base .383 .694 .437 .502 .649 .425 .515 ±.128 .361 .504 .478 .436 .577 .387 .457 ±.080 .338 .679 .376 .353 .561 .457 .461 ±.135
gtr-t5-large .358 .660 .445 .348 .629 .452 .482 ±.133 .399 .585 .516 .352 .545 .493 .482 ±.089 .318 .676 .404 .315 .593 .510 .469 ±.149
for the base model. The sentence-t5-large demonstrates a mean
score of 0.494 but a higher variance among factors.
The evaluation result indicates that segments consistently show
a relatively positive contribution in the non-marginal order to-
ward their aligned thematic factors. This suggests that extracted
terms associated with the segment’s aligned factor are markedly
more prevalent compared to terms from other factors, indicating a
functioning thematic distinction for the alignments.
5 ANALYSIS
This section analyzes the coefficient modeling between thematic
factors and specific Acts, focusing on analyze the trend in the results
and the alignment of these trends across various categories.
5.1 Modeling Trends in Thematic Factor and
Act Correlations
The segment-level thematic factors were obtained and presented
their efficacy. To further uncover the research question of SEC
priorities and regulatory trends over time, we conduct analysis to
discern which thematic factors are most predictive of certain types
of SEC legal Acts during a specific year. We use the thematic factors
produced by the PLM gtr-T5-base and LLM GPT-4 , as they demon-
strated comparatively better performance than all other models
evaluated in our experiments.
We employed a Generalized Linear Model (GLM) with a logit link
function to quantitatively assess the influence of thematic factors
on the probability of SEC enforcement actions over successive
years by the interpretable coefficients the GLM produced. Denoteour dataset{𝑋𝑖,𝑦𝑖,𝑡𝑖}𝑛
𝑖=1, where𝑋𝑖represents the thematic factors
associated with the segments of the 𝑖-th complaint, 𝑦𝑖denotes the
presence of specific enforcement Acts, and 𝑡𝑖indicates the year of
the filing date. 𝑋𝑖=(𝑥𝑖1,𝑥𝑖2,...,𝑥𝑖𝑝)⊤is a vector of thematic factor
proportions for the 𝑖-th complaint, 𝑥𝑖𝑗is the proportion of factor
𝑗in complaint 𝑖,𝑦𝑖∈{0,1}is a binary indicator for the presence
of a specific Act. The GLM for binary response with a logit link
function is formulated as:
logit(𝑃(𝑦𝑖=1|𝑋𝑖))=𝛽0+𝑝∑︁
𝑗=1𝛽𝑗𝑥𝑖𝑗=ln𝑃(𝑦𝑖=1|𝑋𝑖)
1−𝑃(𝑦𝑖=1|𝑋𝑖)
,
where𝛽=(𝛽0,𝛽1,...,𝛽𝑝)⊤is the vector of coefficients for the GLM
model,𝜂𝑖=𝛽0+Í𝑝
𝑗=1𝛽𝑗𝑥𝑖𝑗as the linear predictor, and 𝜇𝑖=1
1+𝑒−𝜂𝑖
as the expected value of 𝑦𝑖using the logistic function.
The vector of coefficients 𝛽is estimated to maximize the like-
lihood of observing the given data. 𝛽0is the intercept term. The
model output denoted as ˆ𝛽, is the estimated value of the coefficient
vector𝛽, indicating the strength and direction of the influence of
each factor on the probability of a legal Act. The vectors of coeffi-
cients are Gaussian standardized, enabling categorizing the impact
levels into three categories across different acts and time periods.
These coefficients demonstrate the alignment between thematic
factors and specific regulatory Acts, confirming the relevance of
these factors in existing legal Act in a certain year. The coefficients
also offer an interpretable view of enforcement trends. The results
are summarized in Table 3.
Considering the overall results, multiple thematic factors exhibit
high coefficients with sections such as Section 20 of the Securities Act,
Anonymous Author(s)
Table 3: Coefficients output from the GLM for the thematic factor and legal Act pairs. The table lists the maximum coefficient,
the year of its occurrence, and categorizes the coefficient as high •, moderate◦, or low·on an annual basis. 2012-2016 are
merged due to few cases; 2024 into 2023 for being incomplete. The top three Act-factor pairs based on the frequency of high
coefficients are demonstrated. These metrics reflect trends in SEC enforcement actions over the analyzed period.
Act Thematic Factor Max Coef Max Coef YrAnnual Coef
2012-2016 2017 2018 2019 2020 2021 2022 2023+
Section 10(b) of the Exchange Act Financial Misconduct & Investor Impact 1.899 2018 • • • ◦ · • • •
Section 17(a) of the Securities Act Financial Misconduct & Investor Impact 1.970 2022 • • • • · • • ◦
Section 5 of the Securities Act Financial Misconduct & Investor Impact 1.408 2023 • · · • · ◦ • •
Section 14(e) of the Exchange Act Regulatory Compliance 1.550 2016 • • · · ·
Section 13(a) of the Exchange Act Regulatory Compliance 1.156 2020 · · • • ◦ ·
Section 12(g) of the Exchange Act Regulatory Compliance 1.156 2020 · · • · ·
Section 17(b) of the Securities Act Promotion & Misrepresentation 1.678 2023 · · · • • •
Section 206(4) of the Advisers Act Promotion & Misrepresentation 1.067 2018 • · · •
Section 12(k) of the Exchange Act Promotion & Misrepresentation 1.851 2018 · • • •
Section 5(a) of the Securities Act Scope and Scale of Operations 1.816 2016 • · • · · · • ◦
Section 5(c) of the Securities Act Scope and Scale of Operations 1.816 2016 • · • · · · • ◦
Section 15(a) of the Exchange Act Scope and Scale of Operations 1.582 2021 · • · • ◦ ◦
Section 13(a) of the Exchange Act Technological Risks 1.330 2023 · · · • · •
Section 15(b) of the Exchange Act Technological Risks 1.408 2019 ◦ · • · ◦ • ◦
Section 3(a) of the Exchange Act Technological Risks 1.876 2017 ◦ • • · ◦
Section 10(b) of the Exchange Act Key Individuals 1.904 2020 • ◦ · • • · ◦ ·
Section 20(a) of the Exchange Act Key Individuals 1.178 2022 • • ·
Section 12(a) of the Securities Act Key Individuals 1.270 2019 • • · ·
Section 20(e) ,22(a) of the Securities Act , and Section 21(d), 21(a) of the
Exchange Act , which presents the SEC’s broad enforcement powers
to investigate violations, seek injunctions, impose penalties, and
enforce compliance with securities laws. Since these Acts are pri-
marily related to the general enforcement mechanisms rather than
the specific thematic factors and legal provisions, these sections
are not central to our analytical focus.
Focusing on the distinct correlation, Financial Misconduct & In-
vestor Impact shows consistently high coefficients with Section
5 and Section 17(a) of the Securities Act , indicating strong enforce-
ment against unregistered securities offerings and fraud. Regulatory
Compliance shows strong associations with Section 12(k), 12(g) of
the Exchange Act suggesting heightened regulatory scrutiny on
registration requirements. Promotion & Misrepresentation exhibits
rising coefficients linked to Section 17(b) of the Securities Act ,Sec-
tion 206(4) of the Advisers Act , reflecting intensified enforcement
against misrepresentation and undisclosed promotions. Scope and
Scale of Operations shows coefficients with Section 12a, 12g of the
Exchange Act , indicating operational requirements, such as requir-
ing companies to register securities traded on national exchanges.
Technological Risks present high coefficients related to Section 3(a)
of the Exchange Act , reflecting the attention to how crypto asset
trading terms are integrated with the key concepts and definitions
from the Exchange Act. Key Individual s is notably linked to Section
10(b) of the Exchange Act forSection 206 of the Advisers Act for
prohibiting fraud by investment advisers, representing a focus on
individual misconduct and high-profile executive cases.
Beyond specific correlations, our research objective emphasizes
analyzing the temporal Act-factor trend to uncover shifts in the
SEC’s enforcement priorities. To conduct the analysis, each Act-
factor pair was ranked based on the number of years where coef-
ficients surpassed 1.0, categorizing them as high. Values over 0.5
were deemed moderate and all others low. We disregarded nega-
tive coefficients since they complicate the interpretation of direct
impacts or influences, focusing instead on positive coefficients to
provide the insights of direct regulatory emphasis.From 2012 to 2016, the SEC focused heavily on the market, in-
vestors, and regulation mentioning Section 10(b) of the Exchange
Actto combat securities fraud and deceit. The emphasis on Section
14(e) of the Exchange Act regulating tender offers, which allows
shareholders in a private company to sell some or all of their shares.
At this very early period, we saw enforcement under Section 5 of
the Securities Act , which addresses the registration of securities,
stressing the agency’s commitment to ensuring that all securities
offerings were duly registered.
In 2017, the SEC began to shift its focus toward technological
risks and further enhancing regulatory compliance, as evidenced by
increased enforcement of Section 3(a) of the Exchange Act , related
to broker-dealer registration. This focus was likely a response to
the rise of digital trading platforms and of the cryptocurrency price
in 2017. Both Section 10(b) of the Exchange Act and Section 17(a)
of the Securities Act remain high coefficients with the Financial
Misconduct & Investor Impact factor , reflecting the SEC continued
addressing securities fraud and deceptive practices.
Following the rise of trading platforms, the SEC presented the
first time focus on promotion in 2018, concerning fraudulent pro-
motional activities and misleading financial endorsements, such as
celebrities who promoted ICOs on social media without disclosing
the fact. Promotion & Misrepresentation factor highly correlated to
Section 17(b) of the Securities Act , which prohibits the promotion of
securities without full disclosure of compensation received for such
promotion, accompanied by Section 206(4) of the Advisers Act pro-
hibits false statements by investment advisers, reflecting the SEC’s
strategy to curb misleading promotions and endorsements on social
media platforms. The period likely witnessed an SEC’s response to
the burgeoning impact of social media and celebrity endorsements,
where undisclosed financial incentives could lead to conflicts of
interest. Meanwhile, the coefficient linking misrepresentation to
Section 12(k) of the Exchange Act is associated with addressing short
sale regulations in the market. Sections 5(a) and 5(c) of the Securities
Actexhibited high coefficients for Scope and Scale of Operations ,
reflecting the SEC’s intensified focus on compliance with securities
registration during the period of market expansion.
Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping
Table 4: SEC cases related to crypto and cyber assets (by year and SEC’s categorization)
Year Category Cases Notable Cases
2016 Account Intrusions 3 SEC v. Mustapha: Hacked brokerage accounts for unauthorized trades
2017 Account Intrusions 1 SEC v. Willner: Hacked brokerage accounts to manipulate stock prices
2022 Account Intrusions 1 SEC v. Mohamed, et al.: Fraudulent scheme hacking brokerage accounts
2016- Crypto Assets 6 SEC v. Garza, et al.: Bitcoin mining Ponzi scheme; SEC v. Shavers: Bitcoin Ponzi scheme with high returns
2017 Crypto Assets 5 SEC v. PlexCorps, et al.: Fraudulent ICO scheme; SEC v. REcoin Group Foundation, LLC, et al.: Fraud with ICOs backed by real estate
2018 Crypto Assets 16 SEC v. Longfin Corp., et al.: Fraudulent trading activities
2019 Crypto Assets 17 SEC v. Kik Interactive Inc.: $100 million unregistered securities offering; SEC v. ICOBox, et al.: Illegal $14 million securities offering
2020 Crypto Assets 23 SEC v. Meta 1 Coin Trust, et al.: Conducting a fraudulent initial coin offering of unregistered digital asset securities
2021 Crypto Assets 18 SEC v. BitConnect, et al.: $2 billion fraud with crypto lending platform; SEC v. LBRY, Inc.: Unregistered offering of digital asset securities
2022 Crypto Assets 23 SEC v. Wahi, et al.: Insider trading charges against a former Coinbase manager
2023 Crypto Assets 36 SEC v. Hex et al.: $1 billion raised fraudulently; SEC v. Coinbase, Inc.: Operating as an unregistered securities exchange
2024 Crypto Assets 6 SEC v. Geosyn Mining, LLC: Fraudulent securities offering; SEC v. Sanchez, et al.: $300 million Ponzi scheme
2016- Hacking/Insider Trading 4 SEC v. Dubovoy, et al.: Newswire hack for insider trading; SEC v. Hong, et al.: Law firm hack for insider trading
2019 Hacking/Insider Trading 1 SEC v. Ieremenko, et al.: Hacked SEC’s EDGAR system for illegal trading
2021 Hacking/Insider Trading 2 SEC v. Kliushin, et al.: Profited from stolen earnings announcements
2022 Hacking/Insider Trading 1 SEC v. Dishinger, et al.: Insider trading with Equifax breach data
2023 Hacking/Insider Trading 1 O. Kuprina: Alleged hacking scheme targeting the SEC’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system
2017 Market Manipulation/False Tweets/Fake Websites/Dark Web 1 SEC v. Murray: Manipulated stocks with false SEC filings
2018 Market Manipulation/False Tweets/Fake Websites/Dark Web 1 SEC v. Burns: Manipulating stock price with false EDGAR filing
2020 Market Manipulation/False Tweets/Fake Websites/Dark Web 2 SEC v. Sotnikov, et al.: Lured investors with fake websites
2021 Market Manipulation/False Tweets/Fake Websites/Dark Web 5 SEC v. Trovias: Sold "insider tips" on the Dark Web; SEC v. Gallagher: Stock manipulation using false Twitter posts
2022 Market Manipulation/False Tweets/Fake Websites/Dark Web 6 SEC v. EmpiresX, et al.: $40 million fraud with fake daily profits; SEC v. Parrino: Market manipulation with false rumors
2023 Market Manipulation/False Tweets/Fake Websites/Dark Web 1 SEC v. Patel: False rumors for illicit profits
2016- Market Manipulation/False Tweets/Fake Websites/Dark Web 3 SEC v. PTG Capital Partners LTD, et al.: False SEC filings to manipulate prices
2018 Public Company Disclosure and Controls 2 Altaba Inc., f.d.b.a Yahoo! Inc.: Concealment of a massive data breach
2021 Public Company Disclosure and Controls 2 First American Financial Corporation: Poor cybersecurity disclosures
2022 Public Company Disclosure and Controls 1 NVIDIA Corporation: Inadequate disclosures on cryptomining impact
2023 Public Company Disclosure and Controls 2 Solarwinds Corp.: Fraud and internal control failures
2016- Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 2 Morgan Stanley Smith Barney LLC: Failed to safeguard customer data
2018 Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 1 Voya Financial Advisors: Cybersecurity failures and data protection issues
2019 Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 2 Virtu Americas LLC: Violations in dark pool operation compliance
2021 Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 4 KMS Financial Services, Inc.: Cybersecurity failures exposing client data
2022 Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 4 Morgan Stanley Smith Barney LLC: Failure to protect customer PII; J.P. Morgan Securities LLC: Identity theft prevention deficiencies
2023 Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 1 Options Clearing Corporation: Non-compliance with SEC-approved stress testing rules
2024 Regulated Entities – Cybersecurity Controls and Safeguarding Customer Information 1 Intercontinental Exchange, Inc.: Failure to timely inform SEC of cyber intrusion
2016- Trading Suspensions 1 In re Imogo Mobile Technologies Corp.: Trading suspended due to questions about claims of a secure mobile Bitcoin platform
2017 Trading Suspensions 7 The Crypto Co.: Trading was suspended due to concerns about the accuracy of information regarding insider plans to sell shares
2018 Trading Suspensions 9 PDX Partners, Inc.: Trading suspended due to concerns over information accuracy and business operations.
2019 Trading Suspensions 1 Bitcoin Generation, Inc.: Trading suspended due to concerns about the impact of stock promotional activities
2021 Trading Suspensions 2 Long Blockchain Corp.: Trading suspension due to failure in timely filings
2022 Trading Suspensions 1 American CryptoFed DAO LLC: Halted registration of digital tokens
Table 5: SEC Categories, averaged alignment score for each category, percent of cases with high score (Cse. pct.). The top three
most contributive pairs are demonstrated. Category names are abbreviated for clarity.
Category Avg. Score Cse. pct. Most Contributive Pairs Act Description
Crypto Assets 1.477 .662Section 5(a) of the Securities Act
Section 5(c) of the Securities ActScope and Scale of Operations
Scope and Scale of OperationsProhibits the sale or offer of securities in interstate commerce without an effective registration statement filed.
Prohibits the sale of securities unless a registration statement is in effect.
Account Intrusions 1.647 .750Section 17(a) of the Securities Act
Section 10(b) of the Exchange ActFinancial Misconduct & Investor Impact
Financial Misconduct & Investor ImpactProhibits fraud and deceit in the offer or sale of securities.
Prohibits manipulative and deceptive practices in connection with the purchase or sale of securities.
Hacking/Insider Trading 1.371 .444Section 21(d) of the Exchange Act
Section 20(b) of the Securities ActKey Individuals
Key IndividualsGrants the SEC authority to seek court orders to enforce compliance with the Exchange Act.
Addresses the jurisdiction and venue for legal actions under the Securities Act.
Market Manipulation 1.406 .631Section 21(d) of the Exchange Act
Section 17(a) of the Securities ActKey Individuals
Financial Misconduct & Investor ImpactGrants the SEC authority to seek court orders to enforce compliance with the Exchange Act.
Prohibits fraud and deceit in the offer or sale of securities.
Regulated Entities 0.766 .263Section 203(e) of the Advisers Act
Section 203(k) of the Advisers ActRegulatory Compliance
Regulatory ComplianceAllows the SEC to censure, place limitations on, or suspend or revoke the registration of investment advisers.
Provides the SEC with authority to impose sanctions on investment advisers for violations.
Public Company
Disclosure and Controls1.059 .574Section 12 of the Exchange Act
Section 13(a) of the Exchange ActTechnological Risks
Technological RisksRequires securities to be registered with the SEC to be traded on national exchanges.
Requires issuers to file annual and quarterly reports with the SEC.
Trading Suspensions 0.673 .173Section 12(k) of the Exchange Act
Section 8(d) of the Securities ActRegulatory Compliance
Regulatory ComplianceAddresses short sale regulations and the reporting of certain trading activities.
Addresses the process and conditions under which the SEC can issue a stop order against a registration statement
From 2018 to 2020, the SEC continued to address security regis-
tration while also targeting key individuals under Section 10(b) of
the Exchange Act and Section 20(a) concerning controlling persons.
This demonstrated the SEC’s commitment to holding executives
and other high-profile figures accountable for misconduct, rein-
forcing the principle of individual accountability within corporate
governance. In 2020, the SEC focused on regulatory compliance un-
derSection 13(a) of the Exchange Act , concerning periodic financial
reporting, for companies’ transparent disclosure practices.
In 2021, the SEC’s enforcement efforts emphasized Scope and
Scale of Operations , particularly correlated with Section 15(a) of the
Exchange Act , which governs broker-dealer activities. The SEC’s
strategic intent is to oversee the operations of large-scale firms
and ensure their compliance with the registration and operational
provisions. This year was characterized by a bull market, with rising
crypto prices, which may have influenced the SEC’s scrutiny on
broker-dealer activities and high-volume market operations.From 2022 to 2023, the SEC intensified its focus back on finan-
cial misconduct and investors, notably mentioning Section 10(b),
17(a) of the Securities Act to address fraud in securities offerings.
Simultaneously in 2023, the SEC focused on enforcing Section 17(b)
of the Securities Act andSection 206(4) of the Advisers Act , showing
high coefficients for promotion and misrepresentation to target
fraudulent promotions and misleading financial advisement, while
Section 12(k) of the Exchange Act was also mentioned to authorize
trading suspensions and protect market integrity.
5.2 Trend Alignment across SEC’s Categories
We calculate an alignment score for each case to quantify how
closely the factors and Acts in a case align with the overall (all-
inclusive) trend of Act-factor coefficients obtained in the previous
section. The alignment scores of individual cases are calculated
to examine their distribution across the SEC’s official categoriza-
tion. This scores help understand whether the SEC’s categorization
Anonymous Author(s)
aligns with or diverges from broader enforcement patterns, i.e.,
whether certain categories, exhibit consistent enforcement pat-
terns, or present more variability suggesting diverse regulatory
challenges. The alignment score 𝑆𝑐of a category 𝑐is defined as
follows, where each case (compliant) 𝑐is associated with a set of
Acts𝐴𝑐and factor proportions 𝑋𝑐, as well as a specific year 𝑦𝑐.
𝑆𝑐=Í
𝑖∈𝐴𝑐Í
𝑗∈𝑋𝑐𝑝𝑗·𝐶𝑦𝑐
𝑖𝑗
Í
𝑖∈𝐴𝑐Í
𝑗∈𝑋𝑐
𝐶𝑦𝑐
𝑖𝑗
𝑛
𝑝𝑗is the proportion of factor 𝑥𝑖in the case, and 𝐶𝑦𝑐
𝑖𝑗is the overall
correlation coefficient between Act 𝑖and factor𝑗for the corre-
sponding year 𝑦𝑐. The denominator assumes an equal distribution
of factors across Acts normalizing the score. A high alignment score
(𝑆𝑐≥1.0) suggests that the specific combination of factors and Acts
in the case corresponds more closely to the overall trends than a
hypothetical scenario where factors are evenly distributed.
The average alignment scores across categories are presented in
Table 5 with the percentage of cases with high scores for the cases of
each category. The table identifies the most contributive Act-factor
pairs for each category. i.e., a pair of Act and factor for a given year
that has the highest correlation coefficients of a category. For in-
stance, the Crypto Assets category with the most cases prominently
features Section 5(a) and Section 5(c) of the Securities Act paired with
Scope and Scale of Operations indicating a persistent regulatory
focus on unregistered securities offerings and the amount of money
involved for crypto assets for all the lawsuit cases in this category.
The higher average scores and larger percentage of high-scoring
cases in categories like Crypto Assets andMarket Manipulation sug-
gest strong conformity to the overall trends. Conversely, lower
scores in categories like Regulated Entities andTrading Suspensions
may indicate more diverse or evolving challenges, suggesting at
diverse complexities. The relation between cases in each category
and the overall trend indicates that Crypto Assets andMarket Ma-
nipulation consistently align with global trends patterns due to
well-defined enforcement practice, whereas others, such as Reg-
ulated Entities and Trading Suspension show more variability in
litigation reasoning, suggesting diverse regulatory challenges.
6 RELATED WORK
This section reviews literature relevant to crypto regulation, crypto
litigation and the techniques for analyzing legal documents.
Crypto Regulation. The regulation of cryptocurrencies has been
a subject of global interest. For instance, Blandin et al. [3] analyzed
regulatory frameworks for cryptoassets in 23 jurisdictions. The
study found significant variability in regulatory approaches. Tran-
sitioning from the global perspective, many scholars have focused
on the legal framework specific to the United States. Hughes [ 18]
examined various federal and state-level enforcement actions, the
challenges of regulating decentralized digital currencies, and the
legal definitions provided by various U.S. agencies. The paper con-
cluded that while significant efforts have been made to regulate
the cryptocurrency market, the existing patchwork of laws often
creates confusion and inconsistency. Moffett [ 27] discussed the reg-
ulatory challenges and jurisdictional conflicts between the CFTC
and the SEC concerning cryptocurrencies. The article suggested adual regulatory framework and emphasized the need for coopera-
tion between the CFTC and SEC.
Crypto Litigation. Research has also investigated the wider scope
of crypto litigation beyond SEC actions. Ghodoosi [ 11] provided
an empirical analysis of crypto-related cases litigated in the United
States using the dataset from the Morrison Cohen Crypto Litiga-
tion Tracker3. This study examined the number of cases, types of
disputes, and causes of actions involving cryptocurrencies, tokens,
exchanges, and decentralized autonomous organizations (DAO). It
revealed that while early cases were dominated by securities litiga-
tion from the ICO boom, there has been a significant shift towards
private law claims involving contracts and torts. This trend, termed
the “private law pivot,” suggests future crypto litigation will focus
on more complex private law issues. Yahya and Pecharsky et al. [43]
provided an overview of crypto litigation from 2020, categorizing
cases by causes of action and highlighting the prevalence of fraud,
breach of contract, and regulatory infractions. Their findings under-
scored the diversity of legal challenges faced by the crypto industry,
from fraudulent investment schemes to criminal prosecutions.
Natural Language Processing in Legal Analysis. The applica-
tion of Natural Language Processing (NLP) techniques to analyze
legal documents has proven valuable in understanding regulatory
and litigation trends [ 14]. For instance, Chalkidis et al. [5] explored
adapting BERT models for legal tasks such as multi-label text clas-
sification of EU laws, binary and multi-label classification of Euro-
pean Court of Human Rights cases, and named entity recognition
in US contracts. The study compared using BERT out of the box,
further pre-training on legal texts, and pre-training from scratch
with a legal-specific vocabulary and found that domain-specific pre-
training improved performance. The authors introduced LEGAL-
BERT, a family of BERT models optimized for legal texts, show-
ing improved results in the aforementioned tasks. Merchant et
al.[25] used Latent Semantic Analysis (LSA) for legal text summa-
rization. The method involves pre-processing the text, creating a
term-document matrix, and applying Singular Value Decomposi-
tion (SVD) to identify key sentences. The method was tested using
a dataset of legal judgments from Indian courts and achieved a
decent average ROGUE-1 score. Jallan et al. [20] used the Lexis-
Nexis database [ 22] to automatically extract and analyze cases from
the past decade. By applying the Latent Dirichlet Allocation (LDA)
model, they identified and classified common themes and patterns
in these cases. The study concludes that the automated method can
effectively identify broad patterns in construction-defect litigation.
7 CONCLUSION
We approach a systematic investigation of the drivers behind SEC
enforcement actions against blockchain and cryptocurrency en-
tities. The drivers are substantively delineated through thematic
factors defined by our study conceptualized in response to previ-
ous publications. By leveraging pretrained language models and
large language models, we provide a data-driven semantic mapping
method for quantifying these factors and assessing their impact on
regulatory actions. By aligning thematic factors with regulatory
3https://www.morrisoncohen.com/insights/the-morrison-cohen-cryptocurrency-
litigation-tracker
Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping
Acts, we offer insights into the SEC’s evolving priorities, providing
the understanding of the SEC’s adaptive focus annually.
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|
{
"id": "2408.11961"
}
|
2302.06348
|
A Tale of Two Currencies: Cash and Crypto
|
We discuss numerous justifications for why crypto-currencies would be highly
conducive for the smooth functioning of today's society. We provide several
comparisons between cryptocurrencies issued by blockchain projects, crypto, and
conventional government issued currencies, cash or fiat. We summarize seven
fundamental innovations that would be required for participants to have greater
confidence in decentralized finance (DeFi) and to obtain wealth appreciation
coupled with better risk management. The conceptual ideas we discuss outline an
approach to: 1) Strengthened Security Blueprint; 2) Rebalancing and Trade
Execution Suited for Blockchain Nuances 3) Volatility and Variance Adjusted
Weight Calculation 4) Accommodating Investor Preferences and Risk Parity
Construction; 5) Profit Sharing and Investor Protection; 6) Concentration Risk
Indicator and Performance Metrics; 7) Multi-chain expansion and Select
Strategic Initiatives including the notion of a Decentralized Autonomous
Organization (DAO). Incorporating these concepts into several projects would
also facilitate the growth of the overall blockchain eco-system so that this
technology can, have wider mainstream adoption and, fulfill its potential in
transforming all aspects of human interactions.
|
http://arxiv.org/pdf/2302.06348v1
|
Ravi Kashyap
|
econ.GN, cs.CR, cs.CY, q-fin.EC, 90B70 Theory of organizations, 97U70 Technological tools, 93A14
Decentralized systems, 91G45 Financial networks
|
econ.GN
|
A Tale of Two Currencies: Cash and Crypto
Ravi Kashyap (ravi.kashyap@stern.nyu.edu)1
Estonian Business School / City University of Hong Kong
July 19, 2023
Keywords: Crypto; Cash; Tao; Dao; Human Capital; Decentralized Autonomous Organizations; Blockchain;
Risk Parity; Wealth Management; Universal Identity; Small; Step; Giant; Leap; Mankind
Journal of Economic Literature Codes: D7: Analysis of Collective Decision-Making; D8: Information,
Knowledge, and Uncertainty; I31: General Welfare, Well-Being; O3 Innovation ,Research and Development,
Technological Change, Intellectual Property Rights; XYZ: Creation of Universal Identities (New JEL Code
to be Added)
Mathematics Subject Classification Codes: 90B70 Theory of organizations; 68V30 Mathematical knowledge
management; 97U70 Technological tools; 68T37 Reasoning under uncertainty in the context of artificial
intelligence; 93A14 Decentralized systems; 91G45 Financial networks; 97D10 Comparative studies; XYZ:
Mathematical Techniques for Creating Universal Identities (New MSC Code to be Added)
1Numerous seminar participants, particularly at a few meetings of the econometric society and various finance organizations,
provided suggestions to improve the paper. The following individuals have been a constant source of inputs and encouragement:
Joshua Hong and the team at Formation Fi; Dr. Yong Wang, Dr. Isabel Yan, Dr. Vikas Kakkar, Dr. Fred Kwan, Dr. Costel
Daniel Andonie, Dr. Guangwu Liu, Dr. Jeff Hong, Dr. Humphrey Tung and Dr. Xu Han at the City University of Hong Kong.
The views and opinions expressed in this article, along with any mistakes, are mine alone and do not necessarily reflect the
official policy or position of either of my affiliations or any other agency.
©2021 Ravi Kashyap. All Rights Reserved. 1arXiv:2302.06348v1 [econ.GN] 13 Feb 2023
TABLE OF CONTENTS
Table of Contents
1 Abstract 3
2 MMT and MPT are Starting to Sound Empty 4
2.1 Outline of the Sections Arranged Inline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Interest in Interest Rates, Inflation and Money Machines!!! 6
4 Since Bitcoin Was Coined ... 8
5 Decrypting Crypto and DeFi Investing 9
6 Back To The Future: Decentralized to Centralized and Back 10
7 Crypto Conundrums versus MMT Mayhem 11
7.1 DeFi Yield Farming: The Fields of Gold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.2 The Decentralized Ark for The Great Flood of Post Modern Monetary Maladies . . . . . . . 12
8 Bringing Risk Parity To The DeFi Party 13
8.1 A Complete Solution To The Crypto Asset Management Conundrum . . . . . . . . . . . . . . 13
8.2 DeFi Security: Turning the weakest link into the strongest attraction . . . . . . . . . . . . . . 14
8.3 Trade Execution: To Trade or Not To Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3.1 Shakespeare As A Crypto Trader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
8.4 VVV Weight Calculations: Prepared for the Downside and Primed for the Upside . . . . . . 19
8.4.1 Tables and Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
8.5 The Risk Parity Line: Moving from the Efficient Frontier to the Final Frontier of Investments 23
8.6 Sharing is Caring: Setting Aside Profits for The Crypto Community . . . . . . . . . . . . . . 26
8.7 Raising the Bar for Portfolio Performance Measurement: The Concentration Risk Indicator . 29
8.8 Multichain Expansion and Select Strategic Initiatives: Building Bridges That Do Not Burn . 32
9 Crypto or Cash or Crypto will become Cash 36
10 End-notes 36
11 References 40
©2021 Ravi Kashyap. All Rights Reserved. 2
1 ABSTRACT
1 Abstract
We discuss numerous justifications for why crypto-currencies would be highly conducive for the smooth func-
tioning of today’s society. We provide several comparisons between cryptocurrencies issued by blockchain
projects, crypto, and conventional government issued currencies, cash or fiat. We summarize seven funda-
mental innovations that would be required for participants to have greater confidence in decentralized finance
(DeFi) and to obtain wealth appreciation coupled with better risk management.
The conceptual ideas we discuss outline an approach to: 1) Strengthened Security Blueprint; 2) Re-
balancing and Trade Execution Suited for Blockchain Nuances 3) Volatility and Variance Adjusted Weight
Calculation 4) Accommodating Investor Preferences and Risk Parity Construction; 5) Profit Sharing and
Investor Protection; 6) Concentration Risk Indicator and Performance Metrics; 7) Multi-chain expansion and
Select Strategic Initiatives including the notion of a Decentralized Autonomous Organization (DAO).
Incorporating these concepts into several projects would also facilitate the growth of the overall blockchain
eco-system so that this technology can, have wider mainstream adoption and, fulfill its potential in trans-
forming all aspects of human interactions.
©2021 Ravi Kashyap. All Rights Reserved. 3
2 MMT AND MPT ARE STARTING TO SOUND EMPTY
2 MMT and MPT are Starting to Sound Empty
There is a debate raging amongst economists and politicians that goes to the very heart of what governments
should and shouldn’t do to manage future prosperity. The monetary and fiscal policies adopted by many
nations, over the last few decades, have garnered strong support for the so called Modern Monetary Theory
(MMT)(Mankiw2020; Wray2015). MMT’sproponentsclaimthatanynationthatproducesitsownsovereign
currency (fiat or cash) cannot run out of money because it can always just print more. In other words, the
government essentially has no financial constraints.
MMT was originally a description of how spending in the economy already happens. In that sense, the
debate isn’t so much whether it should or shouldn’t be implemented, but to what degree and under what
circumstances. Challengers say MMT would be highly irresponsible mismanagement of the economy. The
policies, they say, will lead to a massive increase of the money supply that is bound to trigger inflation at
levels not seen since the seventies and eighties and perhaps even trend higher. The application of MMT
will require tax increases, to control any inflationary pressures, which can be hugely unpopular and hard to
implement.
The debate boils down to whether we believe that politicians and officials have the data, knowledge and
skills to delicately balance their spending to deliver full employment while hitting an inflation target. MMT,
coming at a time of general economic uncertainty, could have a profound impact on investment management,
Decentralized Finance (DeFi: Zetzsche, Arner & Buckley 2020; End-note 1) and how corporations and
ordinary citizens secure their wealth.
Modern Portfolio Theory (MPT) is the theory behind most current financial investment strategies. It uses
elegant mathematics to formalize many intuitive ideas about risk and return. MPT is one of the primary tools
used by fund managers to construct portfolios that match expected reward to accepted risk (Elton & Gruber
1997; Goetzmann, et al. 2014; Fabozzi, Gupta & Markowitz 2002). MPT is driven by a wide diversification
of assets to even out any downturns and achieve consistent growth. Asset classes such as bonds are usually
included to reduce risk, But when the US Treasury bond yields dip to low levels, such as the recent drop
of 10 year US Treasury bond yields to below 1% as it happened during 2020, investors are having to look
around for alternatives. Bitcoin is suddenly coming up in more financial planning conversations (End-note
3).
Despite its success, MPT is under threat (Miccolis 2012; Curtis 2002;2004). MPT is not immune from
inflation. Economicexpectationsarepricedin, butunexpectedeconomicshocksarenot. Aneconomyrunning
according to MMT principles carries a higher risk of missing the inflation target as the government tries to
juggle its spending and taxation. Bonds offer no protection here either, as witnessed during the high inflation
1970s, when the spread between bond yields and inflation converged significantly compared to previous years
(Laidler & Parkin 1975; Blinder 1982; DeLong 1997; Boschen & Weise 2003; Figure 1).
©2021 Ravi Kashyap. All Rights Reserved. 4
2 MMT AND MPT ARE STARTING TO SOUND EMPTY
Another environmental risk comes from economic growth targets. MMT’s main aim is to secure full
employment thereby maximizing productivity and GDP. Again, as governments try to spend and tax their
waytotheirobjectives, theyareunlikelytogetconsistentresults(Palley2015; Epstein2019; Baker&Murphy
2020).
Figure 1: Interest Rates and Inflation
Source: St. Louis Federal Reserve, S&P Dow Jones Indices
2.1 Outline of the Sections Arranged Inline
Section (2), which we have already seen, provides an introductory overview of the monetary policies cur-
rently being pursued by most governments and the wealth management strategies commonly used by many
traditional financial firms. Section (3), develops an analogy that helps us to understand the role of money
in society and then to see how crypto can fulfill the functions of money, as we know it, in a better way.
Sections (4; 5) look at the origins of crypto-currencies and the evolution of decentralized finance. Section (6)
looks at how money has moved from being centralized to decentralized and how there are attempts to make
it centralized again using blockchain technology. Section (7) is a discussion of how crypto can be a remedy
to several monetary maladies and the promise it holds for creating equal wealth generation opportunities for
everyone. Section (8) summarizes several innovations that would be necessary to make blockchain wealth
management accessible and safe for the masses.
Thesevensub-sectionsinSection(8)cover: 1)StrengthenedSecurityBlueprint(Section8.2); 2)Rebalanc-
ing and Trade Execution Suited for Blockchain Nuances (Section 8.3); 3) Volatility and Variance Adjusted
©2021 Ravi Kashyap. All Rights Reserved. 5
3 INTEREST IN INTEREST RATES, INFLATION AND MONEY MACHINES!!!
Weight Calculation (Section 8.4); 4) Accommodating Investor Preferences and Risk Parity Construction
(Section 8.5); 5) Profit Sharing and Investor Protection (Section 8.6); 6) Concentration Risk Indicator and
PerformanceMetrics(Section8.7); and7)Multi-chainexpansionandSelectStrategicInitiatives(Section8.8).
This present article provides a conceptual overview of these topics since these ideas will be published in seven
separate articles, with detailed mathematical formulations and software architectural design considerations
where applicable.
3 Interest in Interest Rates, Inflation and Money Machines!!!
We look at a simple analogy (Kashyap 2015) to get a better understanding of money, interest rates, inflation
and the importance of these concepts for economic growth and wealth generation including how technology
is shaping the future of money. Sweeney & Sweeney (1977) is a very interesting tale of interest rates and
inflation. Cochrane (2009) has a discussion of money machines as they are understood in finance (End-note
4).
Water gives life and sustains it (Franks 2000; Chaplin 2001; Westall & Brack 2018). It is required
everywhere for life, as we know it, to exist. In a similar vein, it is hard to imagine a modern economy without
money or money-equivalents. This comparison is only partly valid since life, as we know it, would cease to
prevail without water. While we can essentially have a barter economy without money-equivalents. Barring
this key limitation, the smooth functioning of a modern economy requires the flow of money-equivalents.
Money has three main utilities: it serves as a medium of exchange, a unit of measurement and a storehouse
for wealth (Brunner & Meltzer 1971; McLeay, Radia & Thomas 2014). Water has numerous uses, but we list
three main ones to develop our comparison: it helps to transports nutrients and minerals both within our
bodies and all around us; it regulates the temperature of our bodies and the external environment and gives
shape and structure to many things around us; and it dissolves nutrients and stores them, more than any
other substance known to mankind.
We have constructed elaborate devices and machines, to control and divert the flow of water, to maximize
the growth of life (Rogers & Fiering 1986; Pahl-Wostl 2008; Cosgrove & Loucks 2015). Likewise, we have
the financial services sector, that controls and diverts the flow of money-equivalents, to maximize the growth
of an economy. Taking the analogy a step further: our central reservoirs, irrigation canals, water tankers,
pumping stations, pipes and water sprinklers are devised to keep water flowing around. Similarly, centralized
and regional financial institutions, wire transfers, credit cards, checks, bank drafts, the internet and related
technologies are meant to keep money-equivalents sloshing around.
Whencentralbankscreateliquidityorpumpmoneyintothefinancialsystem, itislikerainfallorsnowmelt
that feeds rivers and streams which carry the water around. The centralized institutions , or monetary policy
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3 INTEREST IN INTEREST RATES, INFLATION AND MONEY MACHINES!!!
makers, then become our rain gods or water gods. We do not know exactly when and how much rain we
are likely to receive. But we have some decent expectations, which is what we call our seasons, and we have
views on what to anticipate based on previous experiences. Central bank meetings, which do have a fair bit
of regularity, to decide future monetary measures are like the seasonal patterns we have come to rely on.
Inflation, which happens when there is too much money in the system, is like a flood scenario. Drought
then becomes a recessionary episode. Clearly both are not desirable. These are unintended consequences,
both in an economy and other aspects of life, due to the nature of uncertainty around us, all of which we will
discuss in later articles as it pertains to investment management (Kashyap 2016b).
Interest rates can be viewed as the ways in which money is taken away by the system that is designed
to send it around. As water flows around, part of it is lost due to evaporation, seeping into the ground or
flowing into the ocean. This rate at which water is lost by the system is similar to the base interest rate set
by the monetary authorities. All rates (interest and water loss) and transaction costs are then modifications
of these fundamentals rates specific to different situations. As our analogy illustrates, when interest rates are
higher inflation will tend to be lower and vice versa.
There are two main issues with the central monetary or water system. One boils down to the essence of
centralization and the overt dependence on the main source of water or money, which relies heavily on what
the gods or policy makers do. Central bankers have sole control over money machines, which have become
crucial for financial well-being. The other issue is that when fresh water does find its way into the system,
the people that can collect most of it are the ones that are already connected to and well established in
the existing network. In a water system, this is simply the life around rivers and streams that benefit the
most from rainfall. Similarly new wealth ends up getting concentrated in the hands of those already well
entrenched into the current money transfer mechanisms.
Unfortunately, the money gods are also likely to be influenced (aka lobbying) by those that benefit the
most whenever new money is printed. In some ways, the vegetation around a water network precipitates
further rainfall. It would not be entirely incorrect to state that most, if not all, policy makers have good
intentions with no desire to cause monetary mutilation. We want to emphasize that there are no good or bad
people. Policy makers do what they do, in response to seemingly tough situations, based on the application
of what they have learnt from mediocre role models. Things have gone haywire due to the lack of better
solutions. The reasons for the lack of superior methods is due to the need to be conservative when tinkering
with economy wide policies as discussed in Section (5).
It is also worth mentioning that the natural system of rainfall or snowfall and the corresponding watering
network, which we should someday hope to more thoroughly emulate, has no strict parallels for now in our
economy. But the comparison we have outlined serves as a way to illustrate how the existing monetary
system works and to make a strong case for the necessity of the DeFi technological innovations, which we
©2021 Ravi Kashyap. All Rights Reserved. 7
4 SINCE BITCOIN WAS COINED ...
discuss next in Sections (4; 5).
Money to Business is as Water to Life.
4 Since Bitcoin Was Coined ...
The invention of Bitcoin in 2008, and the subsequent launch of the currency in 2009, is no doubt a landmark
event permanently etched in the history of technological innovations. This seminal event is opening frontiers
that are set to transform all aspects of human interactions (Nakamoto 2008; Narayanan & Clark 2017; Chen
2018; Monrat, et al. 2019). It has opened the floodgates for innovations seeking to add different aspects of
business and human experiences onto the blockchain. The rest, as they say, is history.
As the Bitcoin movement gained steam, adding supporters and gaining momentum as a substitute for
money as we know it, many great minds deemed several improvements as being essential to enhance this
landscape. Ethereum, which was conceived in 2013 and launched in 2015, provided a remarkable innovation
in terms of making blockchain based systems Turing complete (or theoretically being able to do what any
computer can do: Sipser 2006; End-note 5). This has now opened the floodgates for innovators seeking to
add different aspects of business and human experiences onto the blockchain.
That said, many barriers need to be scaled for the wider adoption of blockchain technologies. Some of
these limitations are: increased latency, usability limitations, security issues, size and bandwidth limitations,
all of which need to quantified and assessed from a risk perspective depending on specific use cases (Hughes,
et al. 2019). The funding needs for new projects, and innovators seeking capital for amazing novel ideas, are
creating remarkable opportunities for investors. Though, the early days of crypto investing are synonymous
with huge swings in prices or volatility, security issues or hacks, and a lack of protective mechanisms for
investors. This has stood in the way of wider inclusion of crypto currencies, and blockchain assets, in the
portfolios of individuals who do not have a stomach for roller coaster rides and fatal accidents.
As the story of Sergey Bubka illustrates (End-note 6), human ingenuity has no bounds. Many innovators
are learning from the insightful lessons offered by contemporary chains. Proof of work, as a consensus
mechanism, already has a plethora of interesting alternatives (Dimitri 2022). Massive efforts are underway
to build platforms that address issues related to high transaction costs, low throughput, scalability and also
to ensure that different chains have a greater degree of connections and interoperability.
We see this development of newer chains as a great possibility to find investment opportunities. Selection
of assets will be done across networks such that each investor can get exposure to the whole suite of assets on
multiple chains. Investing on different chains, and hence linking different networks, is one way of providing
diversified exposure to an investor base. Though, to trade numerous assets on different chains can be an
onerous task. But, many innovations in decentralized finance are clearing the way for more investors to
©2021 Ravi Kashyap. All Rights Reserved. 8
5 DECRYPTING CRYPTO AND DEFI INVESTING
enter this space. Several DeFi protocols are pioneering new methodologies to make crypto investing less
risky, secure and accessible to everyone. Better risk management techniques will ensure that the gap between
funding needs and the supply of funds will be bridged.
Rigorous risk management within the crypto landscape is something that is badly needed by all investors
seeking crypto exposure. Blockchain technology is still evolving and this new landscape presents amazing
opportunities to revolutionize all aspects of how we transact. But several issues, related to security, wild
swings in prices and diversification of assets, have to be addressed for the wider adoption of the blockchain
innovation from an investing perspective. Once investing in this sector becomes more appealing it will spur
further innovation in all other areas of blockchain technology.
5 Decrypting Crypto and DeFi Investing
The DeFi phenomenon is offering a radically different paradigm (Werner, et al. 2021; Xu et al. , 2021). The
DeFi movement is creating entirely new sources and systems of money transfer. This is like tapping into
new and alternate sources of water and building novel techniques to spread it around. Cryptocurrencies are
creating channels that can stay independent of the centralized systems in many ways. These new pathways
are more accessible for anyone to benefit from them. This plethora of wealth generation opportunities are due
to the many alternate ways to create and raise money. Technology and other innovations are also ensuring
that these new sources of money have safety measures designed to prevent inflationary scenarios and several
forms of fraudulent activity.
This does not imply that sailing on crypto waves with be completely smooth. There are likely to be
unintended consequences in DeFi, just as in every aspect of life. But a strong argument can be made that
many independently controlled systems are likely to weather tougher storms, which makes for a more robust
overall framework for financial welfare. Misdemeanour on the part of any DeFi assets will send funds fleeing
to other crypto alternatives that are already there or those that will mushroom up as needs arise.
Better solutions are obtained when we can have a trial and error approach (Kashyap 2021). Such a trial
and error approach happens naturally in the DeFi environment when compared to the full economy. The risk
of any crypto blowing up is unlikely to be fatally detrimental to the entire system. Taking fundamentally
different approaches would be extremely ill advised in a central banking atmosphere. As the crypto ecosystem
grows, innovators will have greater flexibility in trying new and unproven techniques. Everyone benefits since
the lessons learnt, even from failed projects, can be applied elsewhere. As many blockchain projects pioneer
the way in bringing sophisticated risk mitigation principles to the DeFi space, innovation will flourish and
continue to happen in an unperturbed manner.
©2021 Ravi Kashyap. All Rights Reserved. 9
6 BACK TO THE FUTURE: DECENTRALIZED TO CENTRALIZED AND BACK
6 Back To The Future: Decentralized to Centralized and Back
Money started as a decentralized unit, (in the form of animal skins, salt, shells etc., to facilitate commerce.
It then became centralized (in the form of coins and notes) when monarchs and later governments took over
the task of supplying currency. Now money, which is increasingly becoming digital, is moving away from the
control of any authority (Davies 2010; Nakamoto 2008; Reiners 2020; Yadav, et al. 2020).
History does repeat itself.
As the acceptance of crypto currency increases, and the majority of daily business transactions happen in
the alternative world of crypto, the influence of centralized banking systems and the corresponding policies
will wane. Using our water analogy in this case, as alternate water sources become important, we can see
that the system of rainfall and other water courses have little effect on our lives. It is a scenario wherein
we (all or humanity or at-least the majority) are living very far from natural water pathways so that floods
and droughts in this system, have little bearing on us. Clearly we are not there yet both in the water and
monetary system. There is no backup for national currencies right now. With crypto-currencies on the rise,
wealth will get more options to flee to an alternate asset quite easily.
Central bank digital currencies (CBDCs) or centralized decentralized currencies, oxymoronic as it sounds,
have many pros and cons (Auer, et al. 2021; Barrdear & Kumhof 2021; Agur, et al. 2022). Not wanting
to be left out, many national monetary authorities are planning or actively contemplating such a scheme.
CBDCs can offer stable diversification benefits and act as a safe haven, if they are governed like other crypto-
currencies with strict guidelines on money supply and other related aspects. But if just becomes a national
currency in digital form, it will not be very different from other conventional currencies. If participants are
required to follow extensive guidelines before they can participate, money flow patterns can be traced back
to the originators and CDBC will be less anonymous than pure crypto. Some participants might favour
CDBCs because of the extent of traceability that comes with recording and displaying all transactions in a
blockchain. But for the same reason, many might stay away from it entirely. This will be an interesting
development to watch once CBDCs become a reality and try to fit in with the rest of the crypto landscape.
A counter argument to support centralized currencies can be that it is easy to manage one currency,
whereas too many competing currencies are bound to cause chaos. To see that this argument holds little
merit, itisimportanttorealizethateachcrypto-currencyisselfgoverningwithmembershavingatransparent
view of the policies and in many cases even having a say on how things should be run. Despite these measures,
things can go wrong sporadically but money will be able to flee to other sources in such instances given the
plenty of alternatives available.
There will be turbulence when new and heavy flows of water, or money, start pouring in. We experience
this as volatility in prices when money moves in and out of crypto-assets. The next wave of innovations in
DeFi will be geared around reducing fluctuations and ensuring the adoption of crypto as a main stream asset
©2021 Ravi Kashyap. All Rights Reserved. 10
7 CRYPTO CONUNDRUMS VERSUS MMT MAYHEM
class.
Numerous startups are pioneering the way by bringing many well established techniques that have worked
well in traditional investing, including many innovations tailored for the DeFi arena, to decentralized finance.
We list below seven techniques that are essential for Decentralized Finance and blockchain investing to be
more widely adopted by individuals and businesses alike (Section 8).
7 Crypto Conundrums versus MMT Mayhem
Crypto is starting to be perceived as a hedge against a devaluing dollar (Shahzad, et al. 2020; Blau, Griffith
& Whitby 2021; Conlon, Corbet & McGee 2021; Choi & Shin 2022). Several asset management firms are
actively investing in cryptocurrencies and crypto is being deemed as an asset class (Hong 2017; Bianchi
2020; Bianchi & Babiak 2022). The majority of crypto assets are engineered to be actively deflationary with
features such as fixed supply/flow, token burns, etc. Whether it is going to be completely effective or not
is still to be seen. But the high level of volatility in Crypto assets, and the general correlation between
Bitcoin and the S&P500, suggests that a strategy of simply holding crypto assets is not necessarily a wise
move (Chuen, Guo & Wang 2017; Kosc, Sakowski & Ślepaczuk 2019; Flori 2019; Xi, O’Brien & Irannezhad
2020; Liu, Tsyvinski & Wu 2022; Troster, et. al. 2019; Figure DELETION:Add figure here). Nonetheless,
if inflation continues to rise then demand for crypto is likely to go up, driven by corporations wanting to
diversify their reserves.
There will be another side effect of MMT policies too. As unemployment falls, there will be more money
in people’s pockets and their ability to save will increase. Some will be attracted by crypto’s get rich quick
headlines, some by the stories of inflation protection. A few will be drawn by the transparency of DeFi, or
in other words, driven away from banks by the centralized and politicized feel of MMT.
7.1 DeFi Yield Farming: The Fields of Gold
In 2020, many people in the crypto space discovered yield farming: the ability to increase returns on holdings
through different combinations of staking, liquidity pools, lending, and so on (Xu & Feng 2022; End-note
2). Annualized returns of over 100%, for a short time anyways,were not uncommon. It is tempting then, to
consider if yield farming will protect against market fluctuations and environmental shocks.
In 2020 and 2021, yield farming has achieved higher real yields than can be achieved by cash or bonds,
but all assets don’t behave equally. Yield farming strategies should be considered as growth assets, highly
dependent upon crypto market volatility and volumes. Crypto deposited in liquidity pools, such as Uniswap
(Angeris, et. al. 2019), earn a fixed 0.03% of all trades pro-rata. But the total return depends on the volume
of trades going through the exchange and the capital is also at risk of impermanent loss (Aigner & Dhaliwal
©2021 Ravi Kashyap. All Rights Reserved. 11
7 CRYPTO CONUNDRUMS VERSUS MMT MAYHEM
2021). High price volatility, and other fees in some instances, also reduce the usefulness of yield farming as
an inflation hedge. Lending crypto, on the other hand, is not so volatile. Stable coin (Ante, Fiedler & Strehle
2021; Hoang & Baur 2021; Lyons & Viswanath-Natraj 2023; End-note 7) deposits earned yields higher than
bonds or cash in 2021. Rates will vary but they are likely to beat income from traditional cash deposits
under normal circumstances (End-notes 8). Lending crypto can therefore be equated to the role of bonds in
MPT.
In the year 2021 we found ourselves in an unenviable position. Permanently low interest rates had
broken monetary policy. Quantitative Easing (QE: Blinder 2010; Fawley & Neely 2013) had not reached
past the banks, forcing MMT style policies. This massive fiscal stimulus could backfire by causing steep
inflation. MMT centralizes more power in the hands of politicians who distort the spending patterns, adding
further inflation risk. Evidence of rising inflation will drive further corporate and consumer demand towards
crypto and DeFi. However, crypto and DeFi are not necessarily immune either. We need a new (or proven,
repurposed) strategy that will offer protection against environmental shocks.
Yield farming, however, even with lending, is still at risk from MMT inflation. Most yield farming is
denominated in stable-coins, which are pegged to the US dollar, so any gains will be subjected to the same
devaluation as the dollar. Cryptocurrencies and blockchain projects are recent innovations with several active
frontiers of research (Yli-Huumo, et. al. 2016; Xu, Chen & Kou 2019; Gorkhali & Shrestha 2020). They
have not yet lived through many different business cycles and stressful episodes. Reliable data on crypto
projects only goes to a little more than a decade and DeFi platforms are much younger still (Zetzsche, Arner
& Buckley 2020; Schueffel 2021) with no previous exposure to inflationary periods, so we do not know for
sure how crypto markets will behave as situations change drastically.
Some DeFi platforms are attempting to use MPT to construct balanced crypto indexes (Kim, Trimborn
& Härdle 2021; Lucey, et. al. 2022;Naeem, et. al. 2022). Although many indices are constructed such that
they can perform reasonably well during a market downturn, MPT does not defend against environmental
shocks (Lee, et. al. 2022; Briola, et. al. 2023) as we have seen in Section (2). Another strategy is required
to bring this layer of protection from external impacts and to construct more robust crypto portfolios. Risk
Parity is such a strategy, which we introduce in the next sub-section (7.2).
7.2 The Decentralized Ark for The Great Flood of Post Modern Monetary
Maladies
Risk Parity is an extraordinarily successful methodology from traditional finance pioneered by Ray Dalio at
Bridgewater Associates (Chaves, Hsu & Shakernia 2011; Clarke, De Silva & Thorley 2013). It is specifically
designedtoresistenvironmentalfactorssuchasunexpectedinflationandgrowth(Asness, Frazzini&Pedersen
2012; Fabozzi, Simonian & Fabozzi 2021).
©2021 Ravi Kashyap. All Rights Reserved. 12
8 BRINGING RISK PARITY TO THE DEFI PARTY
The traditional finance world has generated many models and innovations related to trading and risk
management. These techniques have gone several phases of iterations involving implementation and active
usage, which have resulted in many robust and improved techniques becoming a part of our lives. The
challenge is to find ways to simplify many aspects of the sophisticated techniques used by investment firms
and tailor them to the blockchain environment.
A big part of our lives revolves around seeking financial security. The existing mainstream financial
industry has done a lot to bring about financial well-being to many. But there are several issues with the
existing set up. One of the main concerns is that access to financial services that work really well are highly
restricted and not available to most people. Clearly, we are simplifying the picture significantly for the sake of
this discussion. The essence of what is needed is about creating “Equal Wealth Generation Opportunities For
Everyone,” which can be accomplished using decentralized technological innovations discussed next (Section
8).
To bring effective risk management, and to incorporate asset management technique such as Risk Parity,
to cross-chain DeFi using crypto native assets, would be to achieve what traditional wealth managers are
doing with stocks and bonds. One approach would be to engineer a set of four indexes or funds: Alpha, Beta,
Gamma and Parity (ABGP). Alpha, Beta and Gamma are funds with different levels of risk and expected
returns. The investment mandates for these three funds will be to ensure that, under most circumstances,
Alpha will be more risky than Beta and Beta will be more risky than Gamma. Investors will be able to
combine the three funds depending on their risk appetites. Mixing Alpha, Beta and Gamma will give the
RiskParityportfolio(Kashyap2021-X).RiskParitywillbetheinvestmentvehiclethatwillprovidediversified
returns, tailored to the risk appetites of each investor, entirely on a highly secure blockchain environment.
Together, ABGP will capture the market highs, track consistent growth, even out downturns and protect
against shocks. By assigning different weights to each one, it will provide the capability to offer balanced,
risk-adjusted portfolios.
8 Bringing Risk Parity To The DeFi Party
8.1 A Complete Solution To The Crypto Asset Management Conundrum
We will publish seven separate articles, that will discuss several innovations necessary to address the main
concerns and to alleviate the challenges in crypto asset management, in significant detail. These separate
articles, which are referenced in the appropriate place throughout the text below, contain mathematical
formulationsandtechnicalimplementationpointerswhereapplicable. Thesequenceofthenextsevensections
will summarize numerous conceptual ideas, in an incremental fashion, to make DeFi investing more secure
and less risky. They will provide a description of the main components that would need to be created to reach
©2021 Ravi Kashyap. All Rights Reserved. 13
8 BRINGING RISK PARITY TO THE DEFI PARTY
our goal of bringing Risk Parity to the decentralized finance world. These articles, which are summarized in
separate sections below, describe our approach to:
1. DeFi Security (Section 8.2; Kashyap 2021-I)
2. Rebalancing and Trade Execution (Section 8.3; Kashyap 2021-II)
3. Weight Calculation (Section 8.4; Kashyap 2021-III)
4. Risk Parity Construction (Section 8.5; Kashyap 2021-IV)
5. Profit Sharing and Investor Protection (Section 8.6; Kashyap 2021-V)
6. Concentration Risk Indicator and Portfolio Performance Metrics (Section 8.7; Kashyap 2021-VI)
7. Multi-chain expansion and Select Strategic Initiatives (Section 8.8; Kashyap 2021-VII)
Risk Parity will bring long-term stability to DeFi and the seven innovations we describe below
will bring Risk Parity to the Crypto Party.
8.2 DeFi Security: Turning the weakest link into the strongest attraction
•We start with the first section of this series of seven, which will focus on what we consider to be
the foremost priority for all organizations engaged in decentralized finance endeavors, to provide an
overview of a strengthened security blueprint. Kashyap (2021-I) has a detailed discussion including
the corresponding mathematical formulations and pointers for technological implementation.This first
section will focus on security and the corresponding innovation, which we are calling the Safe-house.
The Safe-house is a piece of engineering sophistication that utilises existing blockchain principles to
bring about greater security when customer assets are moved around. The Safe-house is badly needed
since there are many ongoing hacks and security concerns in the DeFi space right now.
•Any tall tower, has to withstand a lot of wind resistance. The taller a structure the stronger the wind
forces that it has to overcome. Hence, the height of the tall tower becomes its weakest aspect. But if
this weakness is addressed properly, and enough safety mechanisms are incorporated in the design, the
height of the tower becomes its greatest attraction. People flock to the top to marvel at the views and
to admire the accomplishment of having created such a safe and tall structure. Clearly, the importance
of having a solid foundation for a tall structure cannot be overlooked.
•Likewise, security is the biggest threat, or the weakest link, in DeFi right now. DeFi is nothing but the
movement of funds seeking profits. The more the funds move, the greater the security vulnerability.
But if the security concerns are adequately addressed, and appropriate features are designed to make
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8 BRINGING RISK PARITY TO THE DEFI PARTY
DeFi investing more safe, this very weakness can be turned into the greatest attraction. The captivating
fascination will then be the generation of significant wealth for all participants. The solid foundation,
in our case, is the rigorous risk management, or Risk Parity, that is an intrinsic part of the framework.
DeFi Protocols need to add a protective shield against internal theft and external intrusion. These are
proprietary innovations, entirely custom built to safeguard our workflow, and we call this, The Safe House.
The Safe House is the combination of a novel software engineering architecture and automated / manual
processes, specific to handling fund movements, with certain multi-signatory approvals required for changing
key governance policies. This approach will limit any potential one-time loss to a negligible amount and keep
a detailed history of all the transactions linked to specific internal staff responsible for fund movements and
trade execution. Needless to say, an extra layer of protection can be provided if the personnel involved in the
process are fully KYC’ed (Know Your Customer or Client; End-note 9).
Necessity is the mother of all creation / invention / innovation, but the often forgotten
father is frustration.
The enhanced security features we are describe here are, no doubt, very necessary. But the essence of
the security innovations we are creating are borne out of the numerous troubles several (all?) protocols are
encountering due to unauthorized parties trying to access their funds (Grobys 2021; Li, et al. 2020). The
same could be said about the rest of the investment vehicles discussed here. These innovations are very
necessary. But the key motivation for these mechanisms and architectural designs are due to the main issues
that one encounters while trying to obtain: 1) unencumbered access to decent investment opportunities in
the traditional financial world, and 2) peace of mind while investing in crypto assets.
In today’s blockchain environment, many protocols are constantly under threat wherein their assets can
be taken out or withdrawn by unlicensed individuals. Cryptographic methods used in blockchain protocols,
do provide a certain amount of security. But, most projects are still vulnerable either when cryptographic
keys, corresponding to fund movements, are compromised or when internal parties, who have access to the
keys, have the intention of misappropriating investor funds.
The extent of the perils are magnified in the blockchain environment, since a few parties with malicious
intentcanreachnumerousvictims,giventhedistributednatureofthistechnology. Thisaddstotheperception
that security dangers are commonplace and that hackers are ruling the roost. The many security related
incidents stand in the way of the mass adoption of blockchain technology, which otherwise has the potential
to transform all human interactions. We wish to do our part to grow this ecosystem by mitigating the harmful
influences and restoring the balance of power to groups that are actively trying to develop this landscape.
To counter these hazards, we are introducing several new innovations that will increase the overall defense
mechanisms of our protocol. The novel security innovations, which we are developing, are to ensure that
our system cannot be compromised by either internal or external actors. Our multi-pronged protection
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8 BRINGING RISK PARITY TO THE DEFI PARTY
scheme refines the existing cryptographic cover by adding extra layers of protective shields. By making these
upgrades, we are converting one of the major drawbacks of the DeFi space to one of the major strengths of
our protocol.
The central element of our security innovations is the creation of a safe house, which will be guarded
by private-public key cryptographic methods, to store all our assets. As an additional measure to enhance
the security, access to the safe house will be provided only upon verification of the identity of the person
requesting the permission. Our identity verification methodology is above and beyond the security provided
by existing blockchain public-private key cryptographic methods.We can this technique the One Time Next
Time Password (OTNTP). The OTNTP works similar to the One Time Password (OTP) mechanism. The
OTNTP concept will be used to verify the identity of the portfolio manager, trying to take out funds, and to
allowsafe-houseaccessformakingwithdrawals. Thismodifiedschemeshouldhelpwithpasswordprotectionin
decentralized environments where all transaction information has to be made public for verification purposes.
The safe house has also been designed to detect and neutralize dangers such as attempts to withdraw
by players without the right credentials. If a real threat is determined, the safe house will go into a locked
state. It will not allow anyone to take out any assets or funds from it until the severity of the danger has
been assessed and it is deemed safe to resume further operations.
In the event of an extreme situation, such as a malicious party breaching the safe house, the extent of
damage will be limited due to numerous safeguards on the mobility of funds. This scenario can occur if an
internal member, or an employee, decides to turn rogue. In such a case, the identity of the person who stole
the funds would be established with certainty, due to our identity verification methodology, and the amount
lost would be minimal. Even if the missing amount is very small, further action will be taken to recover the
lost funds since the identity of the individual, who took the funds, will be known.
While building the new security features mentioned above, the overriding challenge will be to ensure that
the improved safety procedures will not become too cumbersome. The objective is to be able to accommodate
more security guidelines and yet operate quickly and effectively to take advantage of market conditions. This
will be discussed further in the next article, where we consider our trade execution related innovations. But
to summarize, this can be accomplished by matching fund flows, which are governed by security parameters,
to asset management principles and requirements. The result is a system that will protect investor assets
and yet allow smooth functioning of our investment machinery.
8.3 Trade Execution: To Trade or Not To Trade
•The portfolio rebalancing mechanism we recommend is based on an innovative and proprietary system
called, The Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends
an ideal trade size for each asset during the periodic rebalancing process, factoring in the gas fee and
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slippage.
•In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price
movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the
buying and selling happen only when certain boundaries are crossed in order to weed out any market
noise and ensure sound trade execution.
•Careful orchestration among mathematical optimization for portfolio construction, trade automation of
the investment apparatus, and human oversight will allow one to watch out for exceptional situations
and ultimately lead to a better outcome.
8.3.1 Shakespeare As A Crypto Trader
To Trade Or Not To Trade, that is the Question,
Whether an Optimizer can Yield the Answer,
Against the Spikes and Crashes of Markets Gone Wild,
To Quench One’s Thirst before Liquidity Runs Dry,
Or Wait till the Tide of Momentum turns Mild.
This is inspired by Prince Hamlet’s soliloquy in the works of Shakespeare: "To be or not to be; that is
the question" (End-note 10; Bradley 1991).
We continue with the second of the 7-section series of blockchain innovations, describing the main com-
ponents that need to be built, to get closer to Risk Parity. . In this article, we will take a closer look at the
trade execution innovations we have brought to the DeFi space in order to rebalance portfolios on a daily
basis or even at an intraday frequency.
“Cascading Waterfall Round Robin Mechanism” are the words we use to summarize our rebalancing
algorithm. To describe how it works, we first assign a certain capacity to hold funds to each asset in our
portfolio. This capacity is the result of several calculations that depend upon:
1. The risk and return properties of each asset.
2. How the asset prices vary in comparison to other assets in the portfolio.
3. The amount of funds collected for investment (or the total requests for redemption).
Once the capacity is determined, we check how much of that capacity is utilized. This gives us an idea of
how much money we can put into each individual asset when we invest money across our assets. Likewise, it
also tells us how much to pull out of each asset if a withdrawal is needed. Next, we distribute funds across
the assets, or redeem funds from the assets, in a circular manner, or round robin fashion, till the full capacity
of each asset is reached. As the capacity on one asset reaches its full limit, the funds start trickling down to
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the next asset, similar to a waterfall. The reverse happens when redemptions are to be fulfilled. Hence the
name, “Cascading Waterfall Round Robin Mechanism”.
After the trade execution schedule is decided, we must consider the transaction costs of completing the
trade orders. There are two main implicit costs at this stage. First, there are gas fees for each transaction
we execute. Second, there is slippage or market impact. The gas fees depend on a number of factors, such as
the time of execution and the network on which a trade happens (Zarir, et. al. 2021; Donmez & Karaivanov
2022). The slippage depends on the size of our trades relative to the sources of liquidity (Kashyap 2020).
Here is a quick summary:
1. The larger the number of trades, the greater the total gas costs.
2. The larger the trade sizes, the greater the slippage.
3. The smaller the trading volume (or liquidity) at the exchange, the greater the slippage.
The quintessential trading conundrum in traditional finance is timing (when to enter a trade) and trade size.
The problem is compounded in crypto since we must factor in the gas fees, which are constantly variable
based on network congestion and type of blockchain. We will discuss market timing in a later article as it’s a
topic particularly insightful to future front-runners but generally our portfolio will rebalance daily. The trade
size is then determined by the dual objectives of minimizing both gas fees and slippage. We perform asset
level calculations which are coupled with our “Cascading Waterfall Round Robin Mechanism” to arrive at
recommended minimum and maximum trade sizes. Basically, the algorithm described in Kashyap (2021-II)
will generate (recommends) a set of min-max values for each trade.
These trade size recommendations ensure that the fund managers adhere to the security guidelines, when
funds need to be moved into and out of assets from our secure safehouse. The first section has a detailed
discussion of the security plan (Section 8.2). The goal of strengthening security is achieved without creating
bottlenecks for trading since fund movements correspond to trade size restrictions.
The calculation of asset capacities and the rebalancing methodology are among the most central elements
of any investment process. It is no different in our case. If anything, it is more important for blockchain
projects given the need to adhere to strict risk metrics and having to incorporate several new techniques
geared towards overcoming the additional challenges in the decentralized space. These two components,
asset capacities and the rebalancing methodology, can be invoked and utilized on an on-demand basis in the
initial stages. The next set of enhancements are to be able to connect them to data updates, and completely
automate them, so that these calculations can run on a daily basis or even several times during a 24-hour
period.
To govern a system with many moving parts, such as blockchain wealth management, several parameters
must be monitored and tweaked on a regular basis. The portfolio management team will have to observe
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8 BRINGING RISK PARITY TO THE DEFI PARTY
these parameters continuously and update them, as necessary, using specialized internal tools. The bulk of
the configurations that decide how the system will run are related to asset capacities and trade executions.
In addition, trade executions can be error prone wherein failures need to be monitored and intelligent cus-
tomizations to retry need to be incorporated into the process. Hence trade execution related parameters and
operational procedures will garner significant focus and a big chunk of time from the investment team.
The internal tools, to run this operation, are designed such that the flow of funds happens automatically,
for the most part, with human intervention to complement the decision making. Significant automation of
our investment apparatus will allow us to take advantage of market opportunities seamlessly and human
oversight will enable us to watch out for exceptional situations and fine-tune the decisions. This coupling of
“Man-and-Machine” will lead to a better final outcome for all our participants.
An illustration of this pairing is that our approach to investing will benefit from volatility, which is seen
as the bane of crypto markets by most players. Volatility, which is the up and down movement of asset
prices, will cause our rebalancing algorithm to buy assets that drop in prices and sell assets as they start
soaring again. But to filter out the noise, and react only to real signals, the buying and selling happens only
when certain boundaries or range thresholds are crossed. This spectrum over which transactions happen are
automatically calculated based on asset properties, but fine tuned by investment specialists. Suffice it to
say, while mathematical optimization techniques offer powerful venues to garner profits, they might fall short
of conquering the extreme scenarios that markets present. Hence mixing mathematical models with human
intuition, that takes care of exceptional cases, is the ideal recipe for wealth creation.
In the next section (8.4), the third one, we will go into greater detail regarding the use of risk and return
characteristics to arrive at the capacity for each asset.
8.4 VVV Weight Calculations: Prepared for the Downside and Primed for the
Upside
•Two of the most essential ingredients in determining weights are volatilities and variances (also covari-
ances) of assets.
•In the “Velocity of Volatility and Variance” (or VVV) crash protection mechanism, we adjust the
volatilities and the variances (including covariances) of assets depending on how fast they are likely to
change during market crashes.
•Using VVV weights, portfolios can outperform, in terms of returns, typical portfolios using more con-
ventional weighing mechanisms by almost 80% with a considerably higher sharpe ratio (e.g. VVV:
1.91 vs OTHERS: 1.44; Sharpe 1994). We achieve this by taking slightly higher risk and based on our
belief that volatility is a small price to pay for the convenience of trading anything from anywhere and
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anytime, as long as we are sufficiently equipped to deal with downward movements.
Thisthirdsection, ofsevenplannedonesinthisseries, willprovideasummaryofourassetweightcalculations.
Our novel portfolio weighting technique considers certain stylized facts about the financial markets. We have
then tweaked the weight computations to factor in the nuances of the crypto markets.
Researchers have observed and documented, over several decades, a few stylized facts about traditional
financial markets. The propensity for markets to suddenly crash is much higher than the probability of an
upward movement of similar magnitude (Hong & Stein 2003; Veldkamp 2005; Bates 2012). When markets
crash the prices of most assets move in tandem or they fall together (Ang & Bekaert 2004; Hartmann,
Straetmans & Vries 2004). That is, during market crashes assets tend to have higher correlations. This
correlated movement of prices is due to the extensive linkages that have developed between financial markets
over the years (Dungey & Martin 2007).
Volatilities tend to be higher during market crashes. We need to take note of this point about asset prices
moving a lot more, and tending to become more volatile when markets crash, as we build a weight calculation
engine.
Asset weight calculations are generally driven by many inputs, but the most essential ingredients are
volatilities and variances (also covariances) of assets. We adjust the volatilities and the variances (including
covariances) of assets depending on how fast they are likely to change during market crashes. Hence, we term
this methodology the “Velocity of Volatility and Variance” (or VVV) crash protection mechanism.
Our approach is ideally suited for crypto assets which, even during normal times, are very volatile and
are also heavily correlated (Klein, Thu & Walther 2018). Hence, we can expect much higher volatilities
and correlations in the crypto investment landscape during a downturn. Our protection scheme is tailor-
made for beating benchmarks when markets head downward. As we will demonstrate, our methodology also
performs better than other weighting schemes over an entire bull-and-bear market cycle with no significant
underperformance during upward market trends.
Returns are obtained as a direct result of bearing risk. Hence, an approach that allocates equal risk
across all assets will yield a more robust weighting scheme. This approach is also known as the risk parity
approach. Here, the weights allocated to an asset are proportional to their corresponding risk (as opposed to
their expected return) relative to that of the overall portfolio. And, this weighting mechanism is statistically
measured using volatility and also accounts for correlations between assets in a portfolio.
Implementing risk parity techniques for defi cryptoassets will require paying special attention to the
nuances of how these markets operate. Crypto markets are more volatile and highly correlated as seen in
Tables (3; 2) shown in Section (8.4.1) below, compared to traditional finance. A weighting technique designed
to outperform most benchmarks during a market crash while generating solid returns under other market
conditions should be the recommended approach. And, VVV is our recommended approach.
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8 BRINGING RISK PARITY TO THE DEFI PARTY
The asset weights are the primary constituents required to calculate asset capacities, which determine
how our rebalancing methodology would work. Section (8.3) has a discussion regarding our rebalancing
methodology. TheVVVWeightcalculationalgorithmswereamongtheearliest,ifnottheearliest,components
that we had worked on and tested using historical data. A simple way to use the weight calculation engine
would be to invoke, calculate and utilize the weights on an on-demand basis. The next set of enhancements
can be able to connect it to data updates, and completely automate them, so that these calculations can run
on a daily basis or even several times during a 24-hour period.
The VVV weighting methodology factors in several empirical observations about financial markets and
tailors them to the more volatile and correlated defi environment. This approach does well under a wide vari-
ety of market conditions and is custom built to outperform benchmarks during market downturns. Building
portfolios in this manner epitomizes our belief that upward movements will take care of themselves, but it is
the downward movements that require the most preparation. As we have discussed in (Kashyap 2021-III),
volatility is caused by the actions of traders. It is inevitable when a huge number of traders, with different
perceptions of value, transfer large sums of money. Volatility is a small price to pay for the convenience of
trading anything from anywhere and anytime, as long as we are sufficiently equipped to deal with downward
movements. VVV is the protection mechanism sorely needed for the DeFi space.
8.4.1 Tables and Explanations
Each of the tables in this section are referenced in the main body of the article. Below, we provide supple-
mentary descriptions for each table. The full data sample consists of daily observations over the previous 365
days going back from October 31, 2021. The portfolio based on VVV significantly outperforms the portfolios
using MVO (mean variance optimization) and MVO without shorts by 74-81%. Yes, VVV Portfolio takes
more risk (by 34-36%). However, for the unit of risk assumed, the portfolio based on VVV will generate a
much higher return - as demonstrated by the higher Sharpe Ratio. This is one trade-off worth the risk.
In the Table in Figure (3): Each cell represents the correlation between the asset returns in the corre-
sponding row and column over the historical period.
In the Table in Figure (3): The first column represents the name of the asset under consideration.
The next six columns represent the following information respectively:
•Volatility (annualized) of the assets calculated on a 90 day moving internal;
•vvvFactor calculated on a 90-day moving interval (i.e. volatility of volatility);
•VVV-Adj-Volatility , which is the sum of annualized Volatility and vvvFactor;
•vvvWeight calculated using VVV-Adj-Volatility;
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•mvoWeight calculated using mean variance optimization (MVO) by Markowitz;
•noShortWeight calculated using MVO with no shorts (or no negative weight).
The last three rows show the portfolio expected return calculated based on the annualized average return
over the data set horizon; the portfolio volatility; the sharpe ratio given by the portfolio expected return
minus benchmark rate of 10% divided by the portfolio volatility.
Figure 2: Correlation Matrix
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8 BRINGING RISK PARITY TO THE DEFI PARTY
Figure 3: VVV Weights Comparison to MVO Weights
8.5 The Risk Parity Line: Moving from the Efficient Frontier to the Final Fron-
tier of Investments
•Each of the sub-funds (Alpha, Beta and Gamma, ABG) we discussed in Section (7.2) can be designed
to provide risk parity because the weight of each asset in the corresponding portfolio can be set to be
inversely proportional to the risk derived from investing in that asset. This can be equivalently stated
as equal risk contributions from each asset towards the overall portfolio risk.
•Investors can select their desired level of risk or return and allocate their wealth accordingly among the
sub funds (ABG), which balance one another under different market conditions. This evolution of the
risk parity principle, resulting in a mechanism that is geared to do well under all market cycles, brings
more robust performance and can be termed as conceptual parity.
•The inclusion of newer and more diversified assets into the portfolios, as the crypto landscape expands,
can be viewed as a natural progression from the conventional efficient frontier to a progressive final
frontier of investing, which will continue to transcend itself.
Risk Parity is the holy grail that we originally set out to bring to the decentralized investment world. To
obtain parity, the amount of money allocated to the individual assets in a portfolio has to be proportional
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to the extent of risk encountered from investing in that specific asset. As the risk characteristics of an asset
fluctuate, the weight assigned to that asset has to be correspondingly modified.
AsubtleaspectofourportfolioconstructionandVVVweightcalculationmethodology(Section8.4)isthat
parity is already accomplished in each of the individual funds Alpha, Beta and Gamma. These investment
products, (Alpha, Beta, Gamma and Parity) will provide risk managed access to several crypto assets and
strategies. We have adapted many of the well known safety mechanisms and investor protection schemes that
have evolved for several decades in traditional finance, and combined them with many innovations that are
unique to crypto markets (Section 8.6).
Having mentioned that each of the sub funds already achieves risk parity, we need to draw a distinction
between mathematical parity and conceptual parity. The assets weights are calculated based on precise rules
and mathematical operations and this brings parity to each of the sub funds at the asset level. While this is
still a huge innovation to bring to the blockchain environment, we wish to proceed further and bring parity
also on a conceptual level.
To elaborate further, we create portfolios that perform satisfactorily where mathematics can fall short
of completely combating market uncertainty. Broad categories of assets have slightly different risk and
return attributes. By grouping assets with similar responses to different market regimes, we can ensure
that the various groups counterbalance one another under diverse market conditions. Hence, in addition to
mathematical parity, within each sub fund, each sub fund has an overall risk return feature which is preferable
to the other sub funds under a particular market criterion.
Another motivation for creating these groups is because even if assets at the individual level deviate from
their risk and expected return properties, such a misalignment is less likely at the group level. A few assets
in a bunch might display atypical behavior, but the majority of them will be closer to their representative
qualities. The result is that the overall group can be expected to behave in a certain way and offset other
groups, which are constructed based on the same principle of clubbing together similar assets, that have
different attributes. We term this fluctuating pseudo-equilibrium between groups of assets conceptual parity.
A remarkable idea from the financial markets is that of the efficient frontier (Elton, Gruber & Padberg
1978; Broadie 1993; Bodnar & Schmid 2009). There are many ways to combine assets to create portfolios.
Among all the possible combinations the set of combinations that are superior to the rest, in terms of risk
and expected returns, form the efficient frontier.
Despite the efficient frontier being an intriguing idea, there are many practical limitations to accomplish
this. To ensure that we are not constrained by the many reservations, our innovation has been to come
up with the idea of conceptual parity tailored for the crypto environment (Kashyap 2021-IV). With this
modification, Alpha will be a sub-fund composed of assets that provide higher returns and take on higher
risks. Beta will be representative of the larger market behavior and provide more steady returns with a
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8 BRINGING RISK PARITY TO THE DEFI PARTY
correspondingly lower level of risks. Gamma will take on the role of acting as the risk free rate, with decent
returns but with very little to no risk. Gamma will also be filled with assets that demonstrate negative
correlation to Alpha and Beta assets.
The implication of constructing the sub-funds (Alpha, Beta and Gamma) in this way ensures that when
the overall market under performs, which means Alpha and Beta will not deliver very high returns, Gamma
will still continue to provide acceptable returns because of its negative correlation to Alpha and Beta. The
manufacturing, and linking, of Alpha, Beta and Gamma will then produce the most efficient set of portfolios
in terms of risk and return characteristics. We term this collection of portfolios, the parity line.
We believe that the efficient frontier is a moving target, even in the traditional financial world, with assets
being added or removed, their risk-return properties undergoing alterations and even entire markets getting
transformed. This is all the more the case with the rapidly evolving crypto landscape, where many new
protocols and projects are appearing on the scene.
Investment funds will need to add several blockchain protocols, as they become available, transforming
themselves into highly diversified cross chain collectors of wealth appreciation venues. The plans to add more
protocols will be discussed in the last and seventh section of this series (8.8). Clearly, there will be a need
to continuously evaluate new projects, and if they pass certain due diligence standards, to include them in
the portfolios. Adding exposure to derivative instruments and physical assets such as gold, real estate, and
so on, as and when they become available. would be prudent as well The implication of this is that investors
will be getting better returns and lower risks, as we seek out varied sources of risk adjusted returns.
The user experience has to be designed such that investors can tailor their wealth allocations to their
preferred risk appetites. Users can select either their preferred level of risk or return. Investors can also
directly decide how much of their wealth they want to allocate to the three funds: Alpha, Beta and Gamma.
Once either of the three routes are selected, (Risk or Return or Weights of Alpha, Beta and Gamma), the
other parameters can be automatically calculated and saved into an NFT, which the investor will hold for
the life of the investment.
The preferences can be changed anytime by investors and this will trigger a readjustment of their sub fund
allocations. Investment specialists have to also monitor the markets and, as the relationship between risk
and return changes, fine tune the parameters of the parity line and update the parameters of the portfolio
allocations. This will guarantee that all investors are getting the best possible outcomes customized for their
desired wealth management objectives.
The challenge will be to ensure that the user interactions are intuitive, and yet their preferences are
precisely captured in the investment decisions. This can be accomplished by letting someone who does not
wish to be bothered with all the settings, or a novice investor, have the simple option of choosing the default,
depositing his funds and forgetting about everything else. If this is the option chosen, the portfolio can select
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a low level of risk and calculate the other parameters accordingly. Advanced users can choose their risk level
or their expected return, or the weights they want to assign to each of the sub funds. The other parameters
will be automatically calculated.
The outcome of these innovations is an investment machinery that responds to investor preferences and
adapts to changing market conditions. In addition, these vehicles will adhere to the core tenets of decentral-
ization and be accessible by anyone. The next, and fifth, section (8.6) will discuss plans to share a significant
portion of the profits generated with the community. All of this can be viewed as a natural progression from
the conventional efficient frontier to a progressive final frontier, which will continue to transcend itself.
8.6 Sharing is Caring: Setting Aside Profits for The Crypto Community
•A significant portfion of the trading profit will be earmarked for distribution to the investors who hold
project tokens along with investments in either of the funds: Parity, Alpha, Beta or Gamma.
•“Trickle effect” mechanism ensures that the rewards will be paid out slowly while the amount of the
profits designated as performance fees and the fraction of the fees that will be earmarked for the
community will be varied at different stages of the growth cycle of the project.
•Each of the four funds (Parity, Alpha, Beta, and Gamma) will operate as individual profit and loss
(P&L) centers. Investors in Parity will get their share of the profits which are derived from how their
investment will be split into Alpha, Beta and Gamma.
It is essential to stay close to the spirit of decentralization by setting aside a significant portion (up to 50%,
perhaps) of the trading profits generated to be paid out to long term investors. This is absolutely unheard
of in the hallowed halls of high finance and a definitive differentiator in the decentralized community as well.
The cutting edge designs we have outlined thus far, tailored to overcome the challenges in the crypto
environment, are geared to accumulate wealth through all cycles in the market. As the investments funds
grow, just like any organization, they will collect fees and generate revenues to offset the costs. We see two
clear claimants to the earnings produced: the loyal investors and the talented team.
The investors are those who hold the project tokens and also those who will put capital into Parity, Alpha,
Beta and Gamma funds. Without investors providing capital there is little that could be accomplished and
they need to be rewarded accordingly. Also, the highly skilled individuals in the project build the vehicles
to channel the funds received into pools that continue to expand. Their efforts are the key to increase the
capital received and due appreciation needs to be shown. Also, a portion of the proceeds has to be used
to develop the organization and recruit the brightest minds so that it can continue to do the best towards
creating the preeminent wealth management platform for the masses.
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8 BRINGING RISK PARITY TO THE DEFI PARTY
An extremely popular investor protection mechanism in the traditional finance world is the idea of per-
formance fees and a high water mark (Goetzmann, et al. 2003; Guasoni & Obłój 2016; Kashyap 2021-XI).
The simple summary of this concept is that performance fees are charged only when investors are entitled to
a profit off their original principal. This is perhaps best clarified with a simple numerical illustration.
For example, let us say an investor deposits 10,000 USD. After some time, the invested amount grows
to 14,000 USD, at which a high water mark is established. The profit in this case is 4,000 USD. A part of
this profit is taken as performance fees. After this, if the value of the investment goes down to say 12,000 no
performance fees are charged until the value of investment climbs back above 14,000, the high water mark.
The bottomline is that unless a tangible wealth increase is generated for every investor, at a holistic level,
no performance fees are paid. This creates a strong incentive for the team to produce solid returns for the
investors.
This simple scenario can get extremely complicated when there are multiple investors who deposit at
different levels of the fund price. Tracking all this in a smart contract, with the current state of blockchain
technology, is extremely hard and can be deemed almost impossible (Wang, et. al. 2018; Zou, et. al. 2019;
Zheng, et. al. 2020). Tobeabletoaccommodatethesecomplexitieswehavefoundanovelsolutionthatworks
elegantly, is rather straightforward to implement as a smart contract, provides the same level of protection
to every single investor and is mathematically identical, in terms of fees and proceeds, to what investment
funds in the traditional world have been doing for decades. Kashyap (2021-V) has a detailed discussion of
this topic including the corresponding mathematical formulae.
The point worthy of highlighting is that the performance fees, earmarked for community distribution, will
be directed to a separate bucket, and kept aside, to be paid out regularly to loyal investors. Loyalty here will
be measured in terms of the length of time someone holds project tokens along with either Parity, Alpha,
Beta or Gamma. If someone has to claim the full share of their reward, they need to stake project tokens
and either Parity, Alpha, Beta or Gamma tokens and keep it staked to gather rewards. Staking here means
deposting tokens into a smart contract.
The amount of project tokens and other fund tokens to be staked to claim the full rewards will be
dependent on a ratio, such as 1:1 or 2:3 and so on. This is a parameter that can be changed depending on
external factors such as the price of project token, the total investments made, the amount of profits being
generated and so on. To ensure greater equitability for all investors, who might invest a large sum into say
Parity, if they hold a certain minimum amount of project tokens they need not adhere to this ratio of project
tokens to other tokens to claim the full share of their profits. The fundamental criteria is that everyone needs
to hold project tokens, in addition to any other investments they make and they need to continue holding
project tokens, to be eligible to earn their rewards.
Another innovation we have designed, to ensure that investors are motivated to continue to hold project
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tokens can be termed the “Trickle effect”. This mechanism will not pay out the bulk of the profits as and
when they are generated, but the rewards will be paid out slowly. For example, if the profits we have put
into the community pot today is 100,000 USD. All of this will not be given away on the same day. Rather, a
certain percentage will be paid out today, and a percentage of what remains will be given out the next day.
Let us say, this payout percentage is 50%. Then the first day, USD 50,000 will be distributed to investors
as rewards. If no additional profits are generated the next day, 50% of what is left will be distributed. So
investors will get 25,000 on the second day and so on. If additional profits are added to the pool only a
percentage of the total accumulated amount that can be shared will be paid out immediately and the rest
will be retained for subsequent payouts. The result is that there is a strong incentive to continue to invest in
the funds and hold the project token to be eligible to claim the full stream of profits.
In addition to the above primary utility of the project token, which enables one to have substantial
participation in our upside, there are two additional reasons for someone to buy and keep the project token.
Holding the project token gives someone the right to participate in the governance process when the protocol
starts to operate as a DAO, Decentralized Autonomous Organization (End-note 11). Also, project tokens
owners could be given access to hot, and upcoming projects with significant upside potential. This would like
having a sub-fund which invests in special projects and accept investments only from project token owners.
Hence the project token will be a triple utility token.
Theamountoftheprofitsdesignatedasperformancefeesandthefractionofthefeesthatwillbeearmarked
for the community can be varied at different stages of the growth cycle. When significant profits are being
generated, the possibility of using those proceeds to burn (retire) some of the project tokens (from the
circulation) will be pursued so that it might act as a deflationary mechanism and prop up the token price.
When the platform starts to function as a DAO, some of these governance parameters will be subject to
community input.
The bulk of the revenue generated will be from performance fees. Setting aside, a big chunk of this
for the community might seem excessive. To toe the fine line between growth (investment for future) and
decentralization (distribution of profit to the community for now), during the initial stages of the lifecycle
before transforming into a DAO, the parameters can be skewed towards favoring growth. And even at a
later stage, there can be a threshold in the distribution bucket, so that profits only in excess of that level
will be handed out using the trickle effect. This threshold can be varied depending on the magnitude of
profits, the stage of growth and future plans, risk provisions to accommodate unforeseen emergency funding
requirements, and to ensure that both investors and employees are compensated fairly for their contributions.
Since Parity is a combination of Alpha, Beta and Gamma. Investors in Parity will get their share of the
profits which are derived from how their investment will be split into Alpha, Beta and Gamma. Each of the
four funds will continue to operate as individual profit and loss (P&L) centers. Since claiming profits requires
©2021 Ravi Kashyap. All Rights Reserved. 28
8 BRINGING RISK PARITY TO THE DEFI PARTY
holding both project tokens and one of the other tokens, the total rewards will match and exceed the rewards
from holding project tokens alone. In the initial phases when very little profits are being generated investors
can deposit project tokens in a single sided staking pool, which can eventually be phased out entirely and
replaced by the enhanced profit sharing plan discussed above.
This profit sharing mechanism, and related innovations, will ensure that we are creating a strong economic
incentive for investing in and holding fund tokens (Alpha, Beta, Gamma, and Parity), and especially the
project governance token. The other unintended, yet perhaps welcome consequence, will be that such an
approach might become the trendsetter clearing the path for other enterprises to be able to share their
proceeds with all their stakeholders, which is the true hallmark of decentralization.
8.7 Raising the Bar for Portfolio Performance Measurement: The Concentra-
tion Risk Indicator
•CRI (the concentration risk indicator), modified and adapted from the Herfindahl-Hirschman (HH)
index, is a novel risk measurement measure we have developed. Supplementing the CRI with other
metrics allows us to gauge how portfolios are performing and to compare them to the wider set of
crypto investment opportunities.
•Asset selection guidelines, due diligence process, risk management oversight and the VVV weighting
methodology (Section 8.4) take care of monitoring the many other factors that dictate whether an asset
makes a good investment.
•Continuous innovation, inspired by how world class athletes deal with new record settings, is the
hallmark of any outstanding investment management approach.
Bringing Risk Parity to the DeFi Party has been the impetus for numerous innovations and designs de-
scribed here. Once these novel techniques, are implemented in the blockchain environment, it will create an
unparalleled platform for wealth generation accessible by anyone.
In this sixth section, we will discuss a new metric we have developed, termed the concentration risk
indicator (CRI), that will allow us to gauge how portfolios are performing and compare them to the wider
set of crypto investment opportunities. This metric is focused on the current facet of the decentralized
terrain, wherein the majority of the wealth is restricted to a small number of tokens. Our new measure, when
supplemented with other well known portfolio measurement yardsticks, will give a complete picture of how
well any investment machinery is working.
The crypto landscape has many individuals who invested early in projects such as Bitcoin and Ethereum,
when they were up and coming prospects. These holdings have grown significantly to become fairly large
positions. From a portfolio perspective, their wealth is heavily concentrated in a few names. This is also the
©2021 Ravi Kashyap. All Rights Reserved. 29
8 BRINGING RISK PARITY TO THE DEFI PARTY
very nature of the crypto markets, where bitcoin and ethereum command more than 60% of the total market
capitalization.
The number of tokens listed now on major data providers, such as coinmarketcap, is around 19,500+ as
of May-25-2022 (End-note 13). This figure has more than doubled within the last one year. With a trend
where several tokens appear, and an equal or greater number disappear, choosing the right investments is an
arduous task. Proper due diligence and research procedures need to be utilized for forming portfolios.
Having the right selection methodology is crucial and, once a selection is made, evaluating the corre-
sponding performance is equally important. To address drawbacks with prevailing methodologies, and to
supplement existing methods, we had to come up with the CRI.
The concentration risk indicator is meant to indicate how diversified the holdings in a portfolio are. This
is a modification of the Herfindahl–Hirschman (HH) index, (Rhoades 1993; End-note 12), which is widely
used as a measure of the size of firms in relation to the industry they are in and an indicator of the amount of
competition among them. We tailor the HH-Index to the crypto markets based on the following two features:
1) the larger the market cap of an asset, the lesser the risk of holding it; 2) the more volatile an asset, the
higher the risk of holding it. The amount of money invested in an asset as a fraction of the overall wealth
held by an investor, which is also the weight of the asset within the portfolio, is also factored in this metric.
The concentration risk indicator can be calculated for individual assets and for portfolios of assets as
well. When comparing two investments, the lower the concentration risk, the better the investment from
a diversification point of view. If two assets have comparable market cap then the asset with lower price
volatility would be preferred. Instead of using the raw market cap values, we normalize and express it as
a fraction to the total crypto market cap before including this factor in the concentration risk indicator.
Similarly, if two assets have comparable levels of volatility, the asset with a greater share of the market would
be preferred.
As an illustration, given a choice between holding BTC or ETH, if we need to isolate the effect of size on
our investment, BTC with its higher market capitalization would seem as a better alternative (End-note 14).
Likewise, SOL, XRP and ADA have a similar level of market share and hence their price volatility determines
how concentrated an investment in these assets would be. This simplified example is meant only to illustrate
the influence of size and volatility. Clearly, ETH has many other features that could potentially qualify it
as a more desirable investment than BTC. An argument can also be made that tokens with higher market
cap will have lower volatility than the ones with lower market cap (Fama & French 1992; Perez-Quiros &
Timmermann 2000; Van Dijk 2011; Fama & French 2018).
The first draft of this article originally included LUNA along with SOL, XRP and ADA in the above
example. But the events of the past few weeks are a wake up call to all players in the Crypto landscape.
Better risk management, stress testing and checking numerous seemingly unlikely scenarios are an absolute
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8 BRINGING RISK PARITY TO THE DEFI PARTY
necessity. The recent LUNA / UST episode on the Terra network, from May 8 to May 13 2022 and beyond,
is a demonstration of the risk of holding concentrated portfolios (Uhlig 2022; Lee, et. al. 2022; Briola, et. al.
2023).
We have been developing and testing this new CRI metric for several months now. The creation of this
measure was to have a numeric score to show people that no concentrated holding is safe even if it is as large
as BTC or ETH. Clearly the recent events, surrounding LUNA, have not been easy for many of us. But it
affirmsourlongheldbeliefthatnothingcanbetakenforgrantedincrypto, andforthatmatteranywhere, and
suitable risk mitigation plans have to be made even for rather extreme scenarios. These beliefs are encoded
in the risk management guidelines, espoused in Kashyap (2022), that investment teams have to adhere to.
Going beyond just this new metric, a rigorous approach to investing and risk management is what investing
on blockchain needs. Risk parity and the whole suite of tools we are describing are exactly the need of the
hour.
While the CRI metric gives preference for larger and more stable assets, smaller and newer assets will be
the drivers of growth. Hence, adding a greater number of smaller assets can compensate for the risk they
bring in terms of size. Asset selection guidelines, due diligence processes, proper risk management oversight
and our VVV weighting methodology take care of monitoring the many other factors that dictate whether
an asset makes a good investment.
We are deeply cognizant of the delicate necessity that to keep improving the performance of our portfolios,
our tools to assess performance need to keep improving as well. Volatility, returns and other metrics are
generally more meaningful when evaluated on a comparative or relative basis. Since Crypto investments are
deemed riskier, to perform a proper comparison for risk and return, it will be helpful to try to incorporate
benchmarks external to the crypto world. Initially it would be easier to start displaying returns, volatility and
the concentration risk indicator, over different time intervals, comparing the main funds we have discussed,
Alpha, Beta and Gamma, with several other prominent crypto assets. Parity investments at different risk
levels can also be viewed as different crypto funds and similar comparisons can be performed.
At a later stage, we can compare the volatility of crypto investment funds to an external benchmark such
as the VIX volatility index (End-note 15; Wang 2019). Also returns can be benchmarked against returns from
other asset classes outside the crypto landscape. External indices across asset classes such as stocks, bonds,
commodities and so on could be useful for this purpose. There are further improvements we are planning to
the CRI, so that it will take into account the proportion of assets invested on different chains. Similar to
the basic CRI discussed earlier, the enhanced CRI will reflect the diversification benefits of amounts invested
across multiple chains (Kashyap 2021-VI).
Abigpartofcryptoinvesting, andalsoperhapsmanyotheraspectsofourlives, isdealingwithuncertainty
and our struggle to overcome it. Sergey Bubka is our Icon of Uncertainty. As a refresher, he broke the pole
©2021 Ravi Kashyap. All Rights Reserved. 31
8 BRINGING RISK PARITY TO THE DEFI PARTY
vault world record 35 times (End-note 6). Pole vault is a simple sport, where you use a long pole to jump
over another long pole, which is placed on top of two other long poles.
Applying the central idea from pole vault to the crypto landscape, we can view the introduction of any
new trading strategy or innovation or even regulatory change as equivalent to the raising of the bar in the
game of pole vault. Once a new innovation starts becoming popular others imitate it or come up with other
wonderful ideas, and we need to find ways to better ourselves. Each time the bar is raised the spirit of Sergey
Bubka, whom we admire a lot and who is a huge inspiration for us, will help us to reach higher and find a
way over the raised bar.
This anecdote, about Sergey Bubka and overcoming uncertainty, forms our fundamental belief that gal-
vanizes us to constantly innovate and find better models, metrics, trading strategies and ways to generate
wealth for all investment participants.
Figure 4: Sergey Bubka: Icon of Uncertainty
8.8 Multichain Expansion and Select Strategic Initiatives: Building Bridges
That Do Not Burn
•Selection of assets can be done across networks in such a way that each of the investors will get
exposure to the whole suite of assets we have on all chains. Investing on different chains, and hence
linking different networks, is a way of providing diversified exposure to the investor base. The fund
prices can be the same across all the networks where the investments are deployed.
•Use of bridges should be cautious at first and depending on asset flow requirements, and improvements
to the corresponding infrastructure, we can readjust the fund transfer limits.
•Our all inclusive approach is to recognize the team and community as one group: Our Human Capital.
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8 BRINGING RISK PARITY TO THE DEFI PARTY
Essentially what this means is that we will not differentiate between the team and community but
instead view them simply as different subunits or divisions within our organization.
In this seventh and the final section of the series, we will touch upon some strategic plans, that projects can
consider over the long term horizon, and our motivations for choosing this particular set of initiatives. A key
focus that will be highlighted are the efforts and the rationale for rolling out various investment products on
different chains.
As discussed in Section (4), many protocols with wonderful possibilities are being developed. At this
time, ETH, BSC and Polygon are good candidates for an initial launch of the investment funds, including
riskparitycomponentsandasafehouse. Lauchingtheproductinphasesispracticalsothatwecanthoroughly
test on each platform and resolve any issues related to each blockchain system. These three protocols are
good candidates for starting out given the remarkable progress they have made, the stability they bring
to this space and the similarity they offer in terms of technological requirements. All three of them are
EVM (Ethereum Virtual Machine) compatible, making it relatively straightforward to start using another of
these platforms once a product is built for one of these chains (Jia & Yin 2022).That said: ETH with high
gas fees, BSC with some vulnerabilities in its choice of validators, Polygon with scalability issues at times
represent challenges that are inherent in any technology saga. Numerous small tweaks and entire redesigns
of architectural frameworks are being undertaken with these networks and their future looks promising.
To elaborate on this further, fund prices will be the same across all the networks on which the investment
infrastructure will be deployed. To arrive at a fund price, we will consider two factors: 1) the combined total
value locked (total investment funds received) across all networks for that fund, and 2) the number of tokens
issued for that fund across all networks. For example, if an investor invests $50,000 USD on only one network,
say BSC for example, he / she will be getting exposure to the performance and diversification benefits of all
the assets held across all networks in that fund. For an investor to do this by himself would be an extremely
arduous task. For any one person to continually monitor such a portfolio spread across networks, and change
it based on market conditions, would be almost impossible.
Solana, Fantom, Harmony One, Avalanche are some chains, which are showing a lot of promise, and
should feature actively in any plans to deploy products and invest in assets on these platforms. Several other
platforms could also be on the immediate radar. As and when promising investment opportunities arise on
newer chains, it is prudent to be prepared to monetize that.
From a network exposure point of view, the entire amount of funds under management will be seen
from two perspectives: one is the network portfolio and a global portfolio that aggregates all of the network
portfolios. We need to monitor the weights of assets globally and strict risk management limits have to
apply to the global portfolio. This global capacity on each asset will be filled by positions on each network
depending on how easily funds can flow between networks. The amount of funds we transfer across networks
©2021 Ravi Kashyap. All Rights Reserved. 33
8 BRINGING RISK PARITY TO THE DEFI PARTY
via bridges will depend on the capacity of the bridge that spans across networks, the relative gas fees of the
networks, the amount of funds we receive, the asset availability, and the exposure we assign to each network.
Right now, “Bridges” built between various networks are both a “Bottleneck” and an “Achilles heel”. They
limit the amount of funds that can move between networks and they also become an attack vector for hackers
to target, resulting in the loss of funds (End-note 16; Belchior, et. al. 2021; Lee, et. al. 2022; Li, Liu & Tan
2022). Hence the use of bridges should be cautious at first and depending on asset flow requirements, and
improvements to the corresponding infrastructure, we need to readjust our fund transfer limits.
Therewillbedifferentdegreesofcorrelationbetweenpricesacrossdifferentchainsdependingontheextent
of inter-connectedness between them. As the fund flow increases across existing chains, it is highly likely that
the movements will increase in lock steps. The greater overlap between chains in terms of asset movements
will also bring about the risk for a drastic drop in total value invested in on any chain, if that particular
network starts to lose trust and get abandoned. Initially, frictions that will impede fund movements will
serve the best interest of certain parties. But as competitive pressures erode the frictions, they will later
exacerbate certain other risks.
Trials with small errors are the key to sustained progress. As product iterations happen we have to
continuously assess what we have learned so far and look to make improvements. Huge mistakes such as
Terra / Luna, unintended and unwanted as they might be, can be quite costly to the system. It is reassuring
to see that efforts are being made towards a recovery and the community of blockchain enthusiasts are not
deterred by this setback. This resilience is to be whole heartedly applauded. One of the core reasons why we
have written this article is to be prepared for such drastic incidents by incorporating more robust mechanisms.
Despite the unfortunate losses for many victims it serves to affirm that better risk management principles,
and the other benefits described here, are sorely needed by the blockchain community.
Another set of bridges that need to be actively built are strategic partnerships to ensure that the crypto
environment can be highly inclusive, and connect investing to several real world platforms, solving many
problems that plague humanity along the way. These will be ongoing and some focus on these initiatives will
be required once the main products are tested thoroughly and deployed.
Moving on to the investing activities and plans for seeking additional returns. As time goes on several
overlay strategies can be added, to the basic funds (ABG) we have discussed in Section (7.2), and we can
seek to benefit from any potential opportunities that open up (Mulvey, Ural & Zhang 2007). A team of
researchers and investment specialists need to continually scour the landscape to identify ways to generate
profits. The development of new networks, and derivative providers within networks, will enable us to use
options as a hedging mechanism (Hull 2003). This will help to protect from market crashes and can be used
to reduce the risk in Gamma. Also, derivative strategies combined with rigorous risk management can help
to gain additional returns (Huberts 2004; Madan & Sharaiha 2015). These will be considered for Alpha.
©2021 Ravi Kashyap. All Rights Reserved. 34
8 BRINGING RISK PARITY TO THE DEFI PARTY
Further overlays can be based on specific allocations to sectors we see as promising. This would be similar
to sector themed sub-indices or ETFs but within a larger grouping of assets (Healy & Lo 2009; Mohanty,
Mohanty & Ivanof 2021). These developments can be part of Beta, including allowing investors to customize
their preferences in a basket or theme. Initially it will be easier to accept investments made only in stable
coins (USDT, USDC and BUSD). We are developing mechanisms through which investors can participate in
these investment vehicles by making deposits denominated in a larger set of assets (Kashyap 2021-IX).
DAOs (Decentralized Autonomous Organizations: End-note 11; Kashyap 2021-VIII) are seen as the way
forward for blockchain systems. Tao or Dao is the natural order of the universe (End-note 17). This intuitive
knowing of life cannot be grasped as a concept. Rather, it is known through actual living experience of one’s
everyday being.
We draw inspiration from the natural order of the universe, as we know it, and try to incorporate these
principles into the DAO, as wish to create it. The simple message that we arrive at is this: our engagement
with the DAO has to be one of absolute involvement, just like our experience with the universe has to be. A
belief that a DAO is not just an organization but a way of life is necessary to ensure that we can make the
most of the fascinating capabilities of the decentralized realm. We propose several mechanisms in Kashyap
(2021-VIII) by which such an attitude can be developed among all the participants and how technological
systems can facilitate that.
The topic of the DAO can be a seven hundred part, or more, series of articles. But in this article, we
summarize the most essential ingredients for a DAO. The core philosophy we espouse regarding this topic
is that we need to have similar principles, and the same group of people in the organization, for all human
touch points and to handle the policies for both the Team and the Community. Having such a structure
would be a huge first step towards the establishment of a DAO.
To the best of our knowledge every organization thus far, both within the crypto terrain and outside,
has viewed employees and clients, including external stakeholders, as two separate entities. The all inclusive
approach that we recommend is to recognize the team and community as one group, the human capital.
Essentially what this means is that we should not differentiate between the team and community but instead
view them simply as different subunits or divisions within the organization. The team and community will
include all human (and perhaps, even non-humans at a later stage) actors that are participating in some
aspect of the DAO.
It is important to have similar principles, but not the same ones, for all participants. Though, similar to
any other organization, there will be different sets of responsibilities and rewards for different departments
within the organization. We need to have different incentives, and guidelines, for the many different duties
that members of the human capital perform. Surely, such a unique approach might bring conflicts that are
inherent when attempting such a radical change. The objective should be to establish this paradigm as an
©2021 Ravi Kashyap. All Rights Reserved. 35
10 END-NOTES
intrinsic part of the culture and revise policies to ensure conflicts are minimized.
9 Crypto or Cash or Crypto will become Cash
We have considered the many challenges in blockchain projects and decentralized finance. We have discussed
several innovations that will aid DeFi projects in their efforts to become more widely adopted by the general
public. The innovations we have described here are covered in greater detail, including mathematical formu-
lations and technical implementation pointers in separate articles, in separate articles which are referenced
at the appropriate places. As with any technology that holds vast promise, it is hard to accurately pin down
exactly how it will shape our lives. That said, money is likely to end up almost entirely in a digital format.
How soon will the corresponding developments democratize wealth management is a question that we need
to ponder further upon?
Blockchain technology is creating a fascinating marketplace where any-one can participate from any-where
and at any-time to trade any-instrument to full-fill almost any-desire. Preparation for the unintended and
unwelcome outcomes, by coupling rigorous risk mitigation with continuous innovation, will ensure that this
technology fulfills the massive potential it holds. The possibilities are endless.
10 End-notes
1. Decentralized finance (often stylized as DeFi) offers financial instruments without relying on intermedi-
aries such as brokerages, exchanges, or banks by using smart contracts on a blockchain. Decentralized
Finance (DeFi), Wikipedia Link
2. The following are the four main types of blockchain yield enhancement services. We can also consider
them as the main types of financial products available in decentralized finance:
(a) Single-Sided Staking: This allows users to earn yield by providing liquidity for one type of asset, in
contrast to liquidity provisioning on AMMs, which requires a pair of assets. Single Sided Staking,
SuacerSwap Link
i. Bancorisanexampleofaproviderwhosupportssinglesidedstaking. Bancornativelysupports
Single-Sided Liquidity Provision of tokens in a liquidity pool. This is one of the main benefits
to liquidity providers that distinguishes Bancor from other DeFi staking protocols. Typical
AMM liquidity pools require a liquidity provider to provide two assets. Meaning, if you wish
to deposit "TKN1" into a pool, you would be forced to sell 50% of that token and trade it for
"TKN2". When providing liquidity, your deposit is composed of both TKN1 and TKN2 in
the pool. Bancor Single-Side Staking changes this and enables liquidity providers to: Provide
©2021 Ravi Kashyap. All Rights Reserved. 36
10 END-NOTES
only the token they hold (TKN1 from the example above) Collect liquidity providers fees in
TKN1. Single Sided Staking, Bancor Link
(b) AMM Liquidity Pairs (AMM LP): A constant-function market maker (CFMM) is a market maker
with the property that that the amount of any asset held in its inventory is completely described by
a well-defined function of the amounts of the other assets in its inventory (Hanson 2007). Constant
Function Market Maker, Wikipedia Link
This is the most common type of market maker liquidity pool. Other types of market makers are
discussed in Mohan (2022). All of them can be grouped under the category Automated Market
Makers. Hence the name AMM Liquidity Pairs. A more general discussion of AMMs, without
being restricted only to the blockchain environment, is given in (Slamka, Skiera & Spann 2012).
(c) LP Token Staking: LP staking is a valuable way to incentivize token holders to provide liquidity.
When a token holder provides liquidity as mentioned earlier in Point (2b) they receive LP tokens.
LP staking allows the liquidity providers to stake their LP tokens and receive project tokens tokens
as rewards. This mitigates the risk of impermanent loss and compensates for the loss. Liquidity
Provider Staking, DeFactor Link
i. Note that this is also a type of single sided staking discussed in Point (2a). The key point to
remember is that the LP Tokens can be considered as receipts for the crypto assets deposits in
an AMM LP Point (2b). These LP Token receipts can be further staked to generate additional
yield.
(d) Lending: Crypto lending is the process of depositing cryptocurrency that is lent out to borrowers
in return for regular interest payments. Payments are typically made in the form of the cryp-
tocurrency that is deposited and can be compounded on a daily, weekly, or monthly basis. Crypto
Lending, Investopedia Link; DeFi Lending, DeFiPrime Link; Top Lending Coins by Market Cap-
italization, Crypto.com Link.
i. Crypto lending is very common on decentralized finance projects and also in centralized ex-
changes. Centralized cryptocurrency exchanges are online platforms used to buy and sell
cryptocurrencies. They are the most common means that investors use to buy and sell cryp-
tocurrency holdings. Centralized Cryptocurrency Exchanges, Investopedia Link
ii. Lending is a very active area of research both on blockchain and off chain (traditional finance)
as well (Cai 2018; Zeng et al., 2019; Bartoletti, Chiang & Lafuente 2021; Gonzalez 2020;
Hassija et al., 2020; Patel et al. , 2020).
3. United States Department of Treasury provides daily interest statistics for the past several decades:
US Department of Treasury, Interest Rate Satistics.
©2021 Ravi Kashyap. All Rights Reserved. 37
10 END-NOTES
4. Money Machines will get turned off, as soon as people step in to take advantage of it. This is also know
as arbitrage (Shleifer & Vishny 1997) and it is possible when the law of one price is violated (Isard
1977; Crouhy-Veyrac, Crouhy & Melitz 1982).
5. In computability theory, a system of data-manipulation rules (such as a computer’s instruction set,
a programming language, or a cellular automaton) is said to be Turing-complete or computationally
universal if it can be used to simulate any Turing machine. Turing Completeness, Wikipedia Link
A Turing machine is a mathematical model of computation describing an abstract machine that ma-
nipulates symbols on a strip of tape according to a table of rules. Despite the model’s simplicity, it is
capable of implementing any computer algorithm. Turing Machine, Wikipedia Link
6. Any attempt at regulatory change is best exemplified by the story of Sergey Bubka, the Russian pole
vault jumper, who broke the world record 35 times. Attempts at regulatory change can be compared
to taking the bar higher. Similarly, when faced with obstacles, or constraints or problems, the spirit of
Sergey Bubka within all of us will find a way to surmount those challenges and sail over them.
•Sergey Nazarovich Bubka (born 4 December 1963) is a Ukrainian former pole vaulter. He rep-
resented the Soviet Union until its dissolution in 1991. Sergey has also beaten his own record
14 times. He was the first pole vaulter to clear 6.0 metres and 6.10 metres. Bubka was twice
named Athlete of the Year by Track & Field News and in 2012 was one of 24 athletes inducted as
inaugural members of the International Association of Athletics Federations Hall of Fame. Sergey
Bubka, Wikipedia Link
7. A Stablecoin is a type of cryptocurrency where the value of the digital asset is supposed to be pegged
to a reference asset, which is either fiat money, exchange-traded commodities (such as precious metals
or industrial metals), or another cryptocurrency. Stable Coin, Wikipedia Link
8. Lending rates on Stable coins have fallen down from around 12% in 2021 to less than 3% in 2022 after
the crash of LUNA and FTX (Uhlig 2022; Fu, Wang, Yu & Chen 2022). The stable coin lending rates
mentioned here are from Aave, a DeFi protocol on the Ethereum network (Ao, Horvath & Zhang 2022).
9. The know your customer or know your client (KYC) guidelines in financial services require that profes-
sionals make an effort to verify the identity, suitability, and risks involved with maintaining a business
relationship. Know Your Customer, Wikipedia Link; Know Your Client, Investopedia Link
©2021 Ravi Kashyap. All Rights Reserved. 38
10 END-NOTES
10. To be, or not to be" is the opening phrase of a soliloquy given by Prince Hamlet in the so-called
"nunnery scene" of William Shakespeare’s play Hamlet, Act 3, Scene 1. (William Shakespeare: William
Shakespeare, Wikipedia Link)
To be, or not to be, that is the question:
Whether ’tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take Arms against a Sea of troubles,
And by opposing end them: to die, to sleep ...
11. A decentralized autonomous organization (DAO) is an organization constructed by rules encoded as a
computer program that is often transparent, controlled by the organization’s members and not influ-
enced by a central government. Decentralized Autonomous Organization, Wikipedia Link
12. The Herfindahl index (also known as Herfindahl–Hirschman Index, HHI, or sometimes HHI-score) is a
measure of the size of firms in relation to the industry they are in and is an indicator of the amount of
competition among them. Herfindahl–Hirschman Index, Wikipedia Link
13. CoinMarketCap is a leading price-tracking website for cryptoassets in the cryptocurrency space. Its
mission is to make crypto discoverable and efficient globally by empowering retail users with unbiased,
high quality and accurate information for drawing their own informed conclusions. It was founded in
May 2013 by Brandon Chez. CoinMarketCap, Website Link
14. A ranking of cryptocurrencies, including symbols for the various tokens, by market capitalization is
available on the CoinMarketCap website. We are using the data as of May-25-2022, when the first
version of this article was written. CoinMarketCap Cryptocurrency Ranking, Website Link
15. Chicago Board Options Exchange (CBOE) Global Markets revolutionized investing with the creation
of the CBOE Volatility Index ®(VIX®Index), the first benchmark index to measure the market’s
expectation of future volatility. The VIX Index is based on options of the S&P 500 ®Index, considered
the leading indicator of the broad U.S. stock market. The VIX Index is recognized as the world’s
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|
{
"id": "2302.06348"
}
|
1805.01081
|
LedgerGuard: Improving Blockchain Ledger Dependability
|
The rise of crypto-currencies has spawned great interest in their underlying
technology, namely, Blockchain. The central component in a Blockchain is a
shared distributed ledger. A ledger comprises series of blocks, which in turns
contains a series of transactions. An identical copy of the ledger is stored on
all nodes in a blockchain network. Maintaining ledger integrity and security is
one of the crucial design aspects of any blockchain platform. Thus, there are
typically built-in validation mechanisms leveraging cryptography to ensure the
validity of incoming blocks before committing them into the ledger. However, a
blockchain node may run over an extended period of time, during which the
blocks on the disk can may become corrupted due to software or hardware
failures, or due to malicious activity. This paper proposes LedgerGuard, a tool
to maintain ledger integrity by detecting corrupted blocks and recovering these
blocks by synchronizing with rest of the network. The experimental
implementation of LedgerGuard is based on Hyperledger Fabric, which is a
popular open source permissioned blockchain platform.
|
http://arxiv.org/pdf/1805.01081v1
|
Qi Zhang, Petr Novotny, Salman Baset, Donna Dillenberger, Artem Barger, Yacov Manevich
|
cs.DC, cs.CR
|
cs.DC
|
LedgerGuard: Improving Blockchain Ledger
Dependability?
Qi Zhang1, Petr Novotny1, Salman Baset1Donna Dillenberger1
Artem Barger2, Yacov Manevich2
1IBM Research, Yorktown, USA
2IBM Research, Haifa, Israel
Abstract. The rise of crypto-currencies has spawned great interest in
their underlying technology, namely, Blockchain. The central component
in a Blockchain is a shared distributed ledger. A ledger comprises series of
blocks, which in turns contains a series of transactions. An identical copy
of the ledger is stored on all nodes in a blockchain network. Maintaining
ledger integrity and security is one of the crucial design aspects of any
blockchain platform. Thus, there are typically built-in validation mecha-
nisms leveraging cryptography to ensure the validity of incoming blocks
before committing them into the ledger. However, a blockchain node may
run over an extended period of time, during which the blocks on the disk
can may become corrupted due to software or hardware failures, or due
to malicious activity. This paper proposes LedgerGuard , a tool to main-
tain ledger integrity by detecting corrupted blocks and recovering these
blocks by synchronizing with rest of the network. The experimental im-
plementation of LedgerGuard is based on Hyperledger Fabric, which is a
popular open source permissioned blockchain platform.
Keywords: Blockchain Ledger Dependability Fault tolerance Hy-
perledger Fabric.
1 INTRODUCTION
A distributed ledger is the central component of any blockchain platform. Each
peer in the Blockchain network maintains its own replica of the ledger. The
ledger is an immutable append-only data structure, which contains a sequence
of historical transactions grouped into blocks. The ledger is formed by chaining
the blocks together with hash pointers (i.e., a subsequent block contains the hash
of its previous block).
The integrity of the ledger is essential for correct functioning of the peer. With
corrupted ledger, the peer is not able to generate valid transactions when the
smart contract needs to retrieve historical transactions from the ledger. More-
over, when historical transactions recorded in the ledger are requested by ex-
ternal tools such as analytical or auditing applications, the peer rst veri es
?This paper has been accepted by 2018 International Conference on Blockchain
(ICBC)arXiv:1805.01081v1 [cs.DC] 3 May 2018
2 Qi Zhang et al.
the integrity and validity of the relevant blocks before extracting the transac-
tions. Any corruption of blocks discovered by these operations leads to signi cant
degradation of the peer functionality. Typically, the peer will cease to function
till the correct ledger is available. Furthermore, the applications accessing the
corrupted data may become signi cantly impaired as well.
The peer protects its ledger from introducing corrupted data. When a new
block is received the peer validates the integrity of the block before appending
the block to the ledger. However, the peer lacks the capability of detecting and
recovering the corrupted blocks existing in the ledger during its runtime.
A corruption of the ledger may have one of several di erent causes. Vari-
ous types of ledger corruptions have been observed on the public Blockchain
platforms, such as Bitcoin [16] and Ethereum [10]. For instance, on Ethereum
platform users reported corrupted data les due to false positives of antivirus
software [5]. Ledger corruptions were also reported by Bitcoin users due to block
checksum mismatch [6] . In private Blockchains such as Hyperledger Fabric [7]
or R3 Corda [9], it is critical to maintain the nodes hosting peers highly secure.
However, when a peer is hosted in a less secure environment, an external at-
tacker or malicious user can hack into the peer node and modify the content of
the ledger les. Moreover, since the ledger les are typically stored on a storage
medium such as magnetic disks or SSDs, a hardware failure [13] [17] [15] may
also cause a corruption of the ledger les.
In this paper, we introduce LedgerGuard , a mechanism that enables the peer
to maintain the integrity of its ledger. LedgerGuard enforces the integrity of
the ledger with the following two techniques. First, it validates the content of
each block and the hash links between blocks. Second, if corrupted block is
identi ed, LedgerGuard recovers the block and corrects the a ected part of ledger
without the need for rebuilding the whole ledger. LedgerGuard is designed in a
highly con gurable manner. It can be used as a tool (e.g., by an operator) to
validate and correct online or oine ledger. It can also be used as a service of
the peer node, to continuously monitor and correct the ledger and thus increase
the resiliency and availability of the peer.
2 BACKGROUND
In this section, we brie
y describe the ledger design of Hyperledger Fabric. Al-
though the ledger design varies among di erent Blockchain platforms, they follow
the same principles. Due to the space limitation, we do not describe the details
of Hyperledger Fabric design in the paper, but we recommend readers to refer
to [11] [20] [19] for more information.
In Hyperledger Fabric, a transaction is submitted by the client and endorsed
by multiple peers. If being successfully endorsed, the transaction with its en-
dorsements will be further sent to the orderer, who collects transactions from
multiple clients and organizes them into blocks. After that, the orderer delivers
the block to the peers, who nally validates the block and commits it into ledger.
LedgerGuard: Improving Blockchain Ledger Dependability??3
Fig. 1: Hyperledger Fabric Blockchain ledger
Figure 1 shows the design of the Blockchain ledger in Hyperledger Fabric,
which consists of a chain of blocks that are connected by hash pointers. Normally,
a block will never be changed after being committed into the ledger. The rst
block is called the genesis block, which contains con guration information of
this Blockchain network. Blockchain con guration can be changed over time, for
example, when a new peer joins or an existing peer leaves. The new con guration
transactions will be recorded in the other blocks.
Each block has three sections: block header, block data, and block metadata.
The block header section includes the sequence number of this block, the hash
value of the previous block, and the hash value of the data section in the cur-
rent block. The block data section contains a series of transactions with some
additional information such as the read/write sets and the endorsers' signatures.
For the medadata section, it incorporates the certi cate, public key and the sig-
nature from the orderer. When creating the block, the orderer signs the block
header and stores the signature into the metadata section. Depending on the
architecture, a block can be signed by a single or multiple orderers. The meta-
data section also contains information such as the
ags of the validity of each
transaction in the block.
3 LEDGER CORRECTION
In this section, we describe LedgerGuard , which improves the Blockchain ledger
dependability by providing a runtime self-correction mechanism for ledger.
Approach overview. In order to minimize the negative impact brought by
the corrupted ledger, we introduce a runtime self-correction mechanism, Ledger-
Guard , for the Blockchain ledger. LedgerGuard runs as a service on each peer,
checks the integrity of the ledger on the peer, and recovers the corrupted block
if there is any. We provide several options for the users to activate LedgerGuard .
First, it can be setup as a periodically running process in the peer, which is
initialized when the peer starts and runs after every period of time. Second, in
order to not a ect the peer performance, LedgerGuard can be triggered by a
resource monitor in the peer when the hardware resource utilization, such as
4 Qi Zhang et al.
CPU, is under a pre-con gured threshold. Third, LedgerGuard can be provided
as a peer built-in tool and explicitly activated by the user when he or she wants
to know the integrity of the ledger.
Ledger corruption detection As shown in Figure 1, the blocks in the ledger
are connected by the hash pointers. LedgerGuard validates the ledger integrity
from two aspects: (1) each single block in the ledger is not corrupted, and (2)
the hash pointers between the blocks are valid. In Hyperledger Fabric, a block
is created by the ordering service, which signs the block header and stores the
signature in the block metadata. Therefore, LedgerGuard uses the certi cate
of the ordering service to validate the correctness of each block header. Since
the block header contains the hash value of the block data section and the
signature is collected from the block metadata section, a successfully veri ed
signature indicates the block has not been tampered with. We assume the root
Certi cate Authority in the Hyperledger Fabric Blockchain platform is trusted,
thus LedgerGuard can get a valid ordering service certi cate to validate the
blocks. To validate the correctness of the hash pointer, LedgerGuard calculates
the hash value of the current block (e.g. Hash(block X)), and compares this hash
value with the value of "PreviousHash" in the header of block X+1. The hash
pointer is integrated if these two value matches. Otherwise, at least one of the
two blocks are corrupted.
Corrupted ledger recovery Once a corrupted block is detected by a speci c
peer (e.g. peer 1), LedgerGuard on this peer will send a request to the other
peers (e.g. peer 2) in the same Blockchain network, and ask for the block with
the same ID as the corrupted block. Since all the peers have the same copy
of the ledger, after peer 1 obtains the block from peer 2, it use the approach
described in the previous subsection to validate the correctness of this newly
received block. If this block is invalid, peer 1 will keep asking the other peers
for the same block. Otherwise, peer 1 uses this newly received block to recover
the corrupted ledger. Sometimes multiple blocks need to be retrieved to x the
Fig. 2: Blockchain ledger stored in les
ledger even though only one block is corrupted. In Hyperledger Fabric, a ledger
consists of one or multiple x sized les, and each le contains a continuous
LedgerGuard: Improving Blockchain Ledger Dependability??5
series of blocks. A corrupted block can be either larger or smaller than the
original block, thus simply replacing the corrupted block with a correct block
still breaks the integrity of the ledger. As an example depicted in Figure 2, block
A in le 2 is detected as a corrupted block. Block A' is a correct block retrieved
from another peer. If the size of block A is not equal to that of block A', simply
replacing block A with block A' will either overwrite part of block B or leave a
gap between block A and block B. In order to solve this problem, LedgerGuard
rst checks whether the size of block A has changed. If it is, as shown in Figure
2, the process will replace all the blocks in le 2 that are subsequent to block A
(case1 and case2). Otherwise, only block A needs to be overwritten (case3).
Optimization As an in progress work, we are exploring optimizations for Ledger-
Guard . For example, since hash value calculation and signature veri cation are
CPU intensive, LedgerGuard can use le level veri cation to decrease its CPU
resource consumption. Concretely, when LedgerGuard validates the ledger for
the rst time, it temporarily keeps the validated blocks in memory until all the
blocks in a le have been validated. If all the blocks as well as the hash pointers
are correct, LedgerGuard calculates the hash value of the whole le. The hash
values of the les will be kept by system administrators in a separate secure
storage. Therefore, when the same portion of the ledger needs to be checked for
a second time, only the hashes of the les need to be calculated and compared.
Since a le usually contains many blocks, this will largely reduce the amount of
hash value calculations. The linkage between the two les can be validated by
checking hash pointer between the last block of the previous le and the rst
block of the next le.
4 Evaluation
In this section, we evaluate the e ectiveness of LedgerGuard on a 4-core VMWare
virtual machines, with Intel(R) Xeon(R) CPU E5-2698 2.20GHz with 4GB of
RAM. The ledgers used in this section is generated by a tool, which closely
simulates the blocks generation on a real Hyperledger Fabric Blockchain network.
The tool rst loads peer and orderer private keys and certi cates, then crafts
transaction proposals and endorsements signed by the peer private key, and
nally batches the resulting transactions into blocks signed with the orderer
private key. A Hyperledger Fabric Blockchain network used in this section is
setup with 4 peers, and each peer loads the generated ledger.
Figure 3 shows how much time LedgerGuard takes to validate all the blocks
in the ledger. The ledger size of 1000 blocks, 2000 blocks, 5000 blocks, and 10000
blocks are used. For each ledger size, we vary the block size from 50 transactions
per block to 150 transactions per block, and each transaction is 3KB. We observe
that rst, with di erent ledger sizes but the same block size, the larger the ledger
is, the longer it takes to nish validation. For example, with 50 transactions
per block, it takes 69 seconds to nish the validation of the ledger with 10000
blocks, while it takes 69 seconds when there are 5000 blocks in the ledger. This
is because LedgerGuard sequentially scans through each block in the ledger, the
6 Qi Zhang et al.
Fig. 3: LedgerGuard ledger validation time
more blocks the ledger contains, the longer time LedgerGuard takes to nish
validation. Second, with the same ledger size but di erent block sizes, the larger
the block size is, the longer it takes for validation. Taking the ledger with 5000
blocks as an example, it takes 39 seconds to nish validation when each block
contains 50 transactions, while that number increases to 110 seconds when each
block includes 150 transactions. Our measurement shows that the block hashing
time does not vary much when the block size increases from 50 transactions to
150 transactions, and also the order signature veri cation time is independent of
the block size. Therefore, the di erence in the ledger validation time is mostly
because the larger the block size is, the more time is spent on I/O to read the
blocks.
(a) 50 trans/block
(b) 100 trans/block
(c) 150 trans/block
Fig. 4: LedgerGuard CPU and memory utilization(ledger size is 10000 blocks)
We also measure how much CPU and memory does LedgerGaurd consume
during ledger validation, and the results are depicted in Figure 4. It shows that,
no matter for what block size, LedgerGuard uses about 60MB memory, and the
CPU utilization of LedgerGuard starts with around 110%, then drops to around
20% and stays stable. The reason for the initial CPU utilization spike is Ledger-
Guard needs to do some initialization work such as opening the ledger, reading
con guration of the blockchain network, and initializing the MSP (Membership
Service Provider) manager. After that, the LedgerGuard works as a single process
to scan through the ledger and validate each block. Since calculating the block
LedgerGuard: Improving Blockchain Ledger Dependability??7
hash value and verifying the signature are both CPU intensive, the LedgerGuard
occupies the whole CPU core, which leads to around 20% CPU utilization in a
4 core machine.
Moreover, we measure the speed of recovering the ledger. It shows that with
the size of 150 transactions per block, a peer node can fetch the block from
the other peer, validate and commit it in a speed of 8.5 blocks per second. As
part of our on going research, we are working on creating a ledger with di erent
distribution of corrupted blocks, and measure the e ectiveness of LedgerGuard
to recover the corrupted ledger.
5 Related Work
As blockchain technologies gain popularity, issues about the Blockchain plat-
form reliability and security have been observed. Some Ethereum users reported
that the Blockchain ledger on his or her machine has been corrupted due to
a false positive of antivirus software [5]. This was con rmed by another user
who has seen report saying that an antivirus software corrupted an Ethereum
Blockchain by deleting some le from the ledger. The suggested solution was to
delete the ledger data, and restart the client to re-download the whole ledger.
Error of "block checksum mismatch" has also been observed by users of Bit-
coin [16], Litecoin [3], and Dogecoin [2] when Btrfs [18] is used. The reason was
due to single-bit errors when reading from disk, and the proposed solution was
to change the lesystem to EXT4 [12] and re-downloading the whole ledger [6].
Moreover, since smart contracts are programs that could move large value assets
on the Blockchain, they always become the victims of attackers who want to steal
the assets. The DAO attack [4] showed that a program built on the Ethereum
Blockchain platform was breached in a case that results in $50 million worth
of Ether being stolen. Researchers and practitioners are making great e orts to
improve the reliability and security of the Blockchain platform. Nicola [8] did
a survey of the attacks on Ethereum smart contacts by exploiting a series of
attacks and providing a taxonomy of programming pitfalls which can lead to
such vulnerabilities. Zcash [14] was invented by creating transactions that re-
veal neither the payment's origin, destination, nor the amount. This approach
prevents leakage of the users' spending habits by Blockchain mining [1].
6 Conclusions and Future work
Blockchain ledger can be corrupted due to many reasons, and ensuring the in-
tegrity of the ledger is critical to the functionality and the performance of the
Blockchain platform. In this paper, we propose LedgerGuard - a mechanism
to keep track of ledger integrity by detecting corrupted blocks and recover the
ledger by synchronizing it with rest of the network, implement a preliminary pro-
totype, and evaluate its e ectiveness and overhead. As the on-going and future
work, we are extending and improving the LedgerGuard from multiple aspects.
For example, we are exploring algorithms to enable the LedgerGuard to smartly
8 Qi Zhang et al.
select the other peers based on the network connection quality when it tries
to fetch a block, which will further improve the performance of recovering the
ledger. Furthermore, we are investigating more alternative approaches to detect
corrupted blocks other than sequential scan.
References
1. Chainalysis. https://www.chainalysis.com/
2. Dogecoin. http://dogecoin.com/
3. Litecoin. https://litecoin.org/
4. Understanding the DAO attack. https://www.coindesk.com/
understanding-dao-hack-journalists/
5. Antivirus corrupting ethereum block. https://github.com/ethereum/mist/
issues/581 (2018)
6. Bitcoin Block Checksum Mismatch. https://github.com/bitcoin/bitcoin/
issues/6528 (2018)
7. Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro,
A., Enyeart, D., Ferris, C., Laventman, G., Manevich, Y., et al.: Hyperledger fab-
ric: A distributed operating system for permissioned blockchains. arXiv preprint
arXiv:1801.10228 (2018)
8. Atzei, N., Bartoletti, M., Cimoli, T.: A survey of attacks on ethereum smart con-
tracts (sok). In: International Conference on Principles of Security and Trust. pp.
164{186. Springer (2017)
9. Brown, R.G.: Introducing r3 corda: A distributed ledger for nancial services. R3,
April 5(2016)
10. Buterin, V., et al.: A next-generation smart contract and decentralized application
platform. white paper (2014)
11. Cachin, C.: Architecture of the hyperledger blockchain fabric. In: Workshop on
Distributed Cryptocurrencies and Consensus Ledgers (2016)
12. Cao, M., Bhattacharya, S., Ts'o, T.: Ext4: The next generation of ext2/3 lesystem.
In: LSF (2007)
13. Gray, J., Van Ingen, C.: Empirical measurements of disk failure rates and error
rates. arXiv preprint cs/0701166 (2007)
14. Hopwood, D., Bowe, S., Hornby, T., Wilcox, N.: Zcash protocol speci cation. Tech.
rep., Tech. rep. 2016-1.10. Zerocoin Electric Coin Company (2016)
15. Meza, J., Wu, Q., Kumar, S., Mutlu, O.: A large-scale study of
ash memory fail-
ures in the eld. In: ACM SIGMETRICS Performance Evaluation Review. vol. 43,
pp. 177{190. ACM (2015)
16. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)
17. Pinheiro, E., Weber, W.D., Barroso, L.A.: Failure trends in a large disk drive
population. In: FAST. vol. 7, pp. 17{23 (2007)
18. Rodeh, O., Bacik, J., Mason, C.: Btrfs: The linux b-tree lesystem. ACM Trans-
actions on Storage (TOS) 9(3), 9 (2013)
19. Sousa, J., Bessani, A., Vukoli c, M.: A byzantine fault-tolerant ordering service for
the hyperledger fabric blockchain platform. arXiv preprint arXiv:1709.06921 (2017)
20. Vukoli c, M.: Rethinking permissioned blockchains. In: Proceedings of the ACM
Workshop on Blockchain, Cryptocurrencies and Contracts. pp. 3{7. ACM (2017)
|
{
"id": "1805.01081"
}
|
2103.02606
|
Blockchain in Cyberdefence: A Technology Review from a Swiss Perspective
|
Since the advent of bitcoin in 2008, the concept of a blockchain has widely
spread. Besides crypto currencies and trading activities, there is a wide range
of potential application areas where blockchains are providing the main
building block for secure solutions. From a technical point of view, a
blockchain involves a set of cryptographic primitives to provide a data
structure with security and trust properties. However, a blockchain is not a
golden bullet. It may be well suited for some problems, but often an
inappropriate data structure for many applications. In this paper, we review
the high-level concept of a blockchain and present possible applications in the
military field. Our review is targeted to readers with little prior domain
knowledge as a support to decide where it makes sense to use a blockchain and
where a blockchain might not be the right tool at hand.
|
http://arxiv.org/pdf/2103.02606v1
|
Luca Gambazzi, Patrick Schaller, Alain Mermoud, Vincent Lenders
|
cs.CR
|
cs.CR
|
Blockchain in Cyber defence:
A Technology Review from a Swiss P erspective
Luca Gambazzi, Patrick Schaller, Alain Mermoud , Vincent Lenders
Cyber Defence Campus
armasuisse, Science and Technology, Switzerland
Abst ract:
Since the advent of bitcoin in 2008, the concept of a blockchain has widely spread. Besides
crypto currencies and trading activities , there is a wide range of potential application areas
where blockchains are providing the main building block for secure solutions. F rom a
technical point of view, a blockchain involves a set of cryptographic primitives to provide a
data structure with security and trust properties. However, a blockchain is not a golden bullet .
It may be well suited for some problems, but often an inappropriate data structure for many
applications . In this paper , we review the high-level concept of a blockchain and present
possible applications in the military field. Our review is targeted to readers with little prior
domain knowledge as a support to decide where it makes sense to use a blockchain and where
a blockchain might not be the right tool at hand.
3 March 2021
Corresponding author: luca.gambazzi@ ar.admin.ch
Disclaimer: Opinions expressed in this report are solely our own and do not express the views
or opinions of our employer, the Swiss government .
2/22
1 What is a Blockchain?
In 2008, in his seminal work on Bitcoin [1] Satoshi Nakamoto introduced a data structure ("a
chain of blocks") as well as a consensus mechanism that enables a set of entities to maintain
the general ledger of a currency in a distributed manner . The construction provides security
guarantees as long as more than half of the entities participating are honest.
Parts of the difficulty and confusion when talking about "blockchains" stems from the fact that
there is no precise definition of what a "blockchain" is. S ome consider the whole ecosystem ,
including all its compon ents, such as the consensus mechanism, the execution environment for
a scripting language running on the participating nodes, etc. as "the blockchain", others restrict the focus on the underlying data structure that consists of blocks that contain the data and build
a chain. Blocks are tied -up using cryptographic primitives in such a way that it is impossible
to modify the blocks' content or to rearrange the blocks, thus resulting in an immutable chain
(Figure 1).
Figure 1 Immuta ble Chain of Blocks .
First application of the blockchain: B itcoin
Nakamato has implemented the first blockchain in 2009 as part of the bitcoin system, where
the blockchain represents the core component of the cryptocurrency. In terms of a distributed
cryptocurrency such as bitcoin, where the blockchain (here the data structure) is supposed to
provide a ledger archiving every single transaction of bitcoins, the requirements are rather obvious:
• Consensus on the extension of the blockchain among honest players: every new block
added to the chain of blocks must fulfill a set of consistency rules, e.g., transactions
contained in a new block have to be valid transactions.
• Public verifiability : given data with in the blockchain, it is easily verifiable, that the data
is indeed part of the blockchain.
• Immutability of block chain data: Once players have reached consensus about the
extension of the chain of blocks and once a block was appended to the chain, it shoul d be
impossible to change the content s of the block or to rearrange the sequence of the blocks
in the chain.
These are probably the most striking properties introduced by the blockchain construction. Of course, there are many other properties to be met, su ch as efficiency, authenticity, non -
repudiation; however, the other properties are more standard, in the sense that there exist other well-established database solutions.
3/22
2 How a Blockchain operates
2.1 Architecture of a blockchain
In the following, we provide more background on the blockchain architecture from Hardware
to Application layer (Figure 2) as suggested in [2].
Figure 2 Blockchain architecture .
Hardware l ayer
The hardware layer specifies the characteristics necessary for a node to perform blockchain
operations efficiently and safely. T he requirements to be considered are mai nly related to the
performances required by the consensus layer, the execution security (e.g., smart contracts, but
also the secure use of all the private cryptographic elements) and the connectivity, including
the performance requirements of the network layer.
Data l ayer
The data layer specifies a data structure that holds the blockchain information . It defines how
a valid element of a blockchain (i.e., a "block") looks like, the data structure in a block, and a
set of conditions that makes a block a valid element of the blockchain (Figure 3).
Consider for example an element (a block) of the bitcoin blockchain1. It contains a set of
parameters (e.g., the reference to previous block), as well as the transactions validated in this
block (2'580 in the referenced example).
1
https://www.blockchain.com/btc/block/000000000000000000301fcfeb141088a93b77dc0d52571a1185b425256
ae2fb
4/22
Figure 3 A simplified view of a blockchain.
The main characteristic of the data structure used in blockchains is that each block has a unique
predecessor and a unique successor, thus building a chain of blocks. The linkage of the data -
blocks is implemented using cryptographic tools , such as hash f unctions and digital signatures,
that guarantee that the order of the blocks in the chain is preserved and that the content of the blocks cannot be modified once a block is part of the chain.
Network l ayer
The Network Layer defines how elements (nodes) of the blockchain ecosystem communicate
and what kind of information they exchange. In the case of the Bitcoin or Ethereum blockchain,
the network is a peer -to-peer network
2. As of October 2020, the Bitcoin network consisted on
average of 10'500 nodes reachable from the Internet3 (Figure 4), the Ethereum network of
83004 nodes. A large number of nodes are not accessible directly from the Internet, either
because the nodes are elements of private networks (NAT), or because they are part of the Tor
anonymity network5. In this regard, a work was presented at the Internet Mea surement
Conference [ 3].
Figure 4 Reachable bitcoin nodes around the world. Source: bitnode s.io.
Consensus
The previous paragraphs have described properties of the underlying data structure and the
necessity of a network to build a blockchain ecosystem. Given that we have defined an underlying data structure and network protocols that enable nodes to exchange information, an
2 https://bitcoin.org/en/p2p -network -guide
3 https://bitnodes.io/
4 https://www.ethernodes.org/
5 https://www.torproject.org /
5/22
important follow -up question is how one could achieve agreement about the state and the
correctness of the content of the blockchain among the participating nodes especially in the
presence of potentially malicious nodes. This problem is trivially solved, if there is a trusted
and accepted authority that checks the correctness of the content and returns the current state of the data upon request. One of the strongest properties of the Bitcoin blockchain is the fact
that it allows to achieve agreement among honest no des and correctness of the data in the
blockchain in the presence of potentially malicious nodes in a decentralized setting. The
keyword here is consensus.
"Classic" consensus protocols achieve consensus among honest agents in the presence of
malicious nodes , as long as the fraction of malicious nodes does not exceed a certain threshold.
A famous result in computer science
6 shows that consensus can be achieved among honest
agents if the fraction of malicious agents does not exceed 1/3.
One of the central bu ilding blocks of the bitcoin ecosystem and one of the main contributions
of Nakamoto [1] is the introduction of a new consensus mechanism, the so- called "Nakamoto
consensus". This type of consensus allows the blockchain ecosystem to provide guarantees
about the state and the correctness of the blockchain in a decentralized setting, i.e., in a peer -
to-peer setting, where there is no central, trusted authority.
"Permissionless blockchains" (decentralized blockchains, presented in detail below ) such as
Bitcoin or Ethereum rely on this type of consensus mechanisms , the two most well -known are:
• Proof of work (PoW) : Extending the blockchain with a new valid block requires a
predefined amount of computational work to be completed. The work is desi gned in way
that is hard to be done , but can be easily verified . Similar to solving a puzzle, where
assembling the puzzle is hard, checking the correctness of an assembled puzzle is easy. Completing the work and extending the blockchain with a valid block is rewarded (mining). Creating valid blocks for the blockchain thus requires computational power. Given the chain structure of the blockchain, changing a block within the blockchain requires changing all subsequent blocks and thus requires even more work. The simple rule "the longest blockchain is the valid blockchain" creates a race between malicious nodes and honest nodes. It can be argued that as long as more than half of the computational power is controlled by honest players, the correct blockchain grows faster than any maliciously
modified version of the blockchain. Nevertheless, it is worth remembering that selfish
mining attacks could reduce the tolerable number of malicious nodes in Bitcoin network
[4], [5].
• Proof of stake (PoS) : Here the node to ex tend the blockchain with a new block is selected
from the set of participating nodes through a combination of earned credits and
randomness. The intention is to design or tune the selection criteria in a way such that correct behavior is rewarded, malicious behavior is penalized. Finally, malicious behavior
should not be profitable. Note that most of the real- world and open blockchains currently
rely on PoW.
Incentive l ayer
To guarantee persistence and liveness of a network, it is crucial to attract as many nodes as
possible willing to participate in the consensus protocol. T o achieve this goal, there have to be
incentives for nodes to participate in the blockchain extension process.
In PoW and PoS mechanisms mentioned above, nodes that ex tend the blockchain and thereby
contribute to correct functioning of the system, receive a reward for their contribution.
6 https://en.wikipedia.org/wiki/Byzantine_fault
6/22
In the Bitcoin ecosystem this process is called "mining", since with the creation of a new block
the creator is rewarded with a predef ined number of newly created (mined) Bitcoins. The
reward rule was defined by the Bitcoin creator and is a central rule in the system. The rule
cannot be changed without agreement of the entire Bitcoin network. The block reward started
at 50 BTC in block #1 and is defined to halve every 210,000 blocks. Thus, every creation of a
valid block up to block #210,000 has been reward 50 BTC, while block 210,001 has been
rewarded with 25 BTC. This is the only way Bitcoins are created. Consequently, the total
number of Bitcoin cannot exceed the limit of 21 million BTC.
Besides the "mining" of fresh coins, creators of valid blocks are paid transaction fees, such that
in case all coins have been mined, there is still an incentive to participate and contribute to the ecosystem.
Contract l ayer
Transactions of Bitcoins consist of one or more inputs and outputs. An input denotes a previous
transaction that transfers Bitcoins to the payer (of the current transaction ), an output assign s
Bitcoins to the payee of the transaction. In the Bitcoin ecosystem inputs/outputs of transactions
are defined as function associated with the transaction, so called scripts.
For example, a transaction could have a timestamp associated with it, that allow s the
transaction to be pending and replaceable until an agreed -upon future time, specified either as
a block index or as a timestamp.
Bitcoin has a basic set of instructions that can be used to define constraints/conditions on the execution of the corresp onding transaction. These scripting languages enable more
sophisticated smart contracts. Smart contracts are programs stored in blocks of a blockchain
that are executed if a set of conditions is fulfilled. This enables contractual partners to automate
the execution of tasks without a third -party intermediary.
Technically Bitcoin supports smart contracts too, but the scripting language is extremely
limited making this type of feature impractical. Other blockchain frameworks contain rich
scripting languages: for example, Solidity on Ethereum. Smart contract technology could
therefore speed up business processes, reduce operational errors, and improve cost efficiency.
Application l ayer
The application layer is the point where users interact with the blockchain. In case of the
Bitcoin framework, this would for example be a Bitcoin- Wallet that enables a user to transfer
money to another user's Bitcoin- Wallet.
2.2 Changes in the architecture
The s pecification and implementation of the layers presented in the previous paragraphs of this
chapter define a blockchain system. As we had explained , it is not enough to define the data
structure of a block only, but also requires the definition of the consensus mechanis m, the way
nodes communicate, a language to define contracts, etc. This defines the rules of the game,
which have to be followed by the nodes participating in the system. As such, the rules
define/specify the elements in the blockchain system, for example, the client- software that
allows end users to take part of the system.
As it is the case for all data processing systems, the requirements might change over time or there may be bugs in the implementation that need to be fixed. As a consequence, data
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struc tures or code may need to be changed and the system needs to be updated. Especially in
the case of decentralized systems ( e.g., Bitcoin) this kind of changes/updates may be
challenging. Changes/updates that affect the consensus rules of the system are call ed forks . In
some sense they introduce "forking points" into the chain of blocks since after the
change/update the governing rules of how the blockchain is extended chances.
One differentiates between soft forks and hard forks . In case of a soft fork, node s do not require
to update to maintain consensus, because blocks of the "new"/forked blockchain follow the old
as well as the new consensus rules. However, nodes that did not update might not be able to
produce new, valid blocks since the old set of consensus rules may violate the new set of
consensus rules. In case of a hard fork, the new set of consensus rule s is not compatible with
the old set of consensus rules. Consequently, all participating nodes are required to upgrade to the latest version in order to be compatible with the new version of the consensus rules.
2.3 Types of b lockchains
Considering a blockchain as a distributed ledger [1], there exist two main types of blockchains:
• Permissionless b lockchains : Examples of this type of blockchain are Bitco in [1] and
Etherum [ 6]. The system is open and decentralized and allows any peer to join the network
as a reader or writer. There is no membership management, which would ban malicious
readers or writers. Typically, the consensus mechanism prevents malicio us behavior and
guarantees security requirements up to a certain threshold of malicious peers.
• Permissioned b lockchains : In this type of blockchains only an authorized set of writers
and readers is admitted participating in the system. Examples of this type of bl ockchains is
Hyperledger Fabric
7. Here a consortium of peers or a central entity assigns rights to peers
that want to participate in the network.
In terms of permissioned blockchains, we further distinguish between public permissioned
blockchains and private permissioned blockchains. Whereas private permissioned blockchains restrict access to stored data to a set of peers that hold the necessary access rights, public permissioned blockchains allow every to access t he stored data and thereby
to verify that the data has been stored according to the consensus rules of the blockchain.
Note at this point, that verifiability of the content of a blockchain may interfere with privacy requirements. However, in many cases pri vacy is achieved by the use of cryptographic
techniques to prevent leakage of private data when public verifiability is a requirement.
7 https://www.hyperledger.org/projects/fabric
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3 Blockchain market analysis
Blockchain technology can be integrated into multiple application areas. The primary use of
blockchains today is as a distributed ledger for cryptocurrencies, most notably bitcoin. There
are a few operational products maturing from proof of concept s [7] [8]. Here follows a market
analysis with the aim of measuring the presence of this technology on the innovation sector
and more specifically in Switzerland .
3.1 Blockchain's emergence
Various studies estimate the future potential of blockchains and distributed ledgers as high to
very high. In a 2018 study, the World Economic Forum (WEF)8 calculated world -wide
efficiency gains of around 1‘000 billion USD for trade finance alone. Other areas of application
include logistics (Maersk plans to optimize its container logistics), retail (IBM and Walmart
are developing a solution for food safety), insurance (B3i is developing a smart contract
solution for insurance contracts), energy (Axpo is developing a solution for peer -to-peer energy
markets), transportation (Novotrans stores inventory level data for railway repairs) or public
administr ation (the Netherlands are developing a border control system for passenger data).
However, businesses have been thus far reluctant to place blockchain at the core of the business
structure
9. Despite the hype, blockchain is still an immature technology, w ith a market that is
still nascent and a clear recipe for success has not yet emerged. Unstructured experimentation of blockchain solutions , without strategic evaluation of the value at stake or the feasibility of
capturing , means that many companies will not likely see a return on their investments.
A study of the Internet of blockchain foundation
10 summarizes several applications using
blockchain. It should be noted that, as will be discussed in the following chapters, the use of
the blockchain is rarely essential : in most of these applications, the blockchain is used as a
distributed logbook.
Such applications can also be transposed to Switzerland. Further examples in this country
include Modum (pharmaceutical supply chain), Swiss Prime Site (property management and
rentals) and UBS (Utility Settlement Coin, trade finance, etc.). Next to improving efficiency,
blockchains and distributed ledgers also open up numerous new fields of business, including
new services ( e.g., digital identity), software development (e.g. new web services or so- called
“distributed apps” or “dApps”) and specialist services (e.g. legal). Their successful implementation hinges on at least three critical factors: the availability of talents and their
training at institutions of higher education, a well a functioning ecosystem of institutions of
higher education, established players and startups (with good access to venture capital), as well as a flexible regulatory and legal framework. While Switzerland has a well -functioning
ecosystem, it needs to catch up as regards the education of talents and access to venture capital.
11
8 https://www.weforum.org/agenda/archive/blockchain/
9 https://www.ft.com/content/c905b6fc -4dd2- 3170- 9d2a -c79cdbb24f16
10 https://medium.com/@essentia1/50 -examples -of-how-blockchains -are-taking -over-the-world -4276bf488a4b
11 https://www.satw.ch/en/cybersecurity/technology -outlook -2019/
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Some probably overoptimistic financial forecasts on statista.com12 suggest that global
blockchain technology revenues will experience massive growth in the coming years, with the
market expected to climb to over 39 billion U.S. dollars in size by 2025. The financial sector has been one of the quickest to invest in blockchain, wi th over 60 percent of the technology’s
market value concentrated in this field. The United States, Russia, China, and most of the G20 countries have devoted resources to
blockchain solutions. The United Arab Emirates, led by its tech- hub in Dubai, aims to be the
world’s first blockchain powered government; and the Australia National Blockchain aims to
move the nation towards blockchain immersion. Many industry leaders have pooled into consortia — technology -specific, such as R3 and the Ethereum Enterprise Alliance; or business
specific, such as the Hyperledger, Bankchain, TradeLens, and MediLedger. The goal for such
co-opetition is to bring standards that lifts all boats, such as a Blockchain in Transport Alliance
for supply chain management, or a Blockchai n Law Consortium for the legal industry.
3.2 Blockchain in a cademia
Clarivate Analytics' Web Of Science
13 provides ac cess to multiple databases including
comprehensive citation data for many different academic disciplines. Data retrieved from Web
of Science indicate a clear interest and important volume of investment from Chinese
institutions. While the number of academic publications from North American, European and
Chinese institutions is comparable (~ 1'500 publication / year) , it is impressive observing the
massively higher amount of Chinese investment : almost 10 times larger than European or North
American countries . This could be interpreted in two ways: either as a brute force approach to
generate scientific success by funding local institutions dispr oportionately, or as a strong signal
to the world about the means available for Chinese academic development and innovation.
In addition to this information , a quantitative analysis based on title and abstract of the
publications on a rXiv
14 allows comparin g the number of publications covering blockchain or
blockchain and cyber (Figure 5 ).
As of 2018, a clear blockchain- hype is visible, and the first publications related to cybersecurity
of blockchain appear. T his can be explained by the fact that the security concerns of an
emerging technology often appear i n a second phase of development , or simply that other
application domains, such as cybersecurity or cyberdefence are not correlated with blockchain interest.
12 https://www.statista.com/statistics/647231/worldwide -blockchain -technology -market -size/
13 https://clarivate.com/webofsciencegroup/solutions/web -of-science/
14 https://arxiv.org/
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Figure 5 arXiv p ublications containi ng the keywords “Blockchain” (in blue) and
“Blockchain AND Cyber*” (in red). Source: S. Gillard , T. Maillart and D . Percia David .
3.3 Blockchain in Switzerland
It is possible to observe that to date, blockchain innovation in Switzerland primarily originates
from academia and industry, which in turn are stimulated by the financial sector. The defence
sector, on the other hand, does not seem particularly active. Swiss institutions of higher
education operate various research centres on the topic of “blockchain”; the EPFL and the ETH
Zurich as well as the Universities of Basel, Lucerne and Zurich are very active in this field.
The Swiss Confederation’s press release “Federal Council wants to further improve framework
conditions for bl ockchain/DLT”, published on 14 December 2018, also triggered many social
media posts.15 During its session on 19 June 2020, the Federal Council took note of the report
on the need to amend tax law with regard to blockchain. The report concluded that no spec ial
legislative amendments to tax law are necessary.16 The interest in blockchain in Switzerland is
presented in the following indicators: analysis of jobs vacancies in Switzerland (Figure 6 ) and
by geographical region (Figure 7)17.
15 https://www.admin.ch/gov/en/start/documentation/media -releases.msg -id-73398.html
16 https://www.efd.admin.ch/efd/en/home/dokumentation/nsb -news_list.msg -id-79513.html
17 Figure 6 and 7 are based on the automated Technology and Market Monitoring (TMM) system developed by
armasuisse S+T. The data is collected by a web crawler that searches through publicly available sources, such as
commercial registers, company w ebsites and social media channels. The data is searched at regular intervals and
updated monthly by TMM. Thanks to TMM, companies can be located along with relevant information such as
the products, services and technologies that they offer.
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Figure 6 N umber of bl ockchain jobs opening in Switzerland.
Source: TMM armasuisse S+T
Figure 7 Blockchain activities based on companies’ websites in Switzerland.
Source: TMM armasuisse S+T
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3.4 A brief h istory of the Crypto Valley
Switzerland has become a center for new business ideas in the field of blockchain and
distributed ledger technology (DLT). In particular, originally based in the canton of Zug, Crypto Valley has established a worldwide reputation as a hub for global growth, resulting in
a high density of blockc hain and DLT companies throughout Switzerland. The country is
known worldwide for its privacy -conscious legislation, world- class talent and its openness.
Like blockchain, Switzerland is also organized in a decentralized way, which has a positive
effect on understanding this new technology. The state government’s open and proactive
attitude has led to favorable conditions for blockchain companies, which has created an ecosystem that produces world premieres: in 2016, Zug became the first city in the world to
accept Bitcoin payments for tax purposes; in 2017, Crypto Valley announced the introduction
of a decentralized Ethereum -based digital ID system; and in 2018, the fintech company Amun
launched the world’s first crypto index product on the SIX Swiss Exchang e.
3.5 Blockchain and patents
In this market analysis, we have deliberately omitted patent data analysis. Well aware that i n
order to identify new competitive products and processes it is necessary to have access to detailed informatio n on technological innovations, but analysis of software patents is often
misleading. While for other products analysis on filed patents could be a n effective method to
obtain such information, for example by indicating the types of products and processes that
companies are plan ning to introduce to the market; w hen it comes to software, quantitative
patent analysis distorts the reality . In fact, the vast majority of blockchain- based solutions are
based on open -source technologies, and the legislation governing the patentability of software
is extremely heterogeneous globally [9]. In this sense, we observe the largest number of patents
in countries (such as China
18) where patenting software is simple.
18 We observe t hat China fills most of the blockchain -related patents (49%). The US is the second chosen
jurisdiction for filing blockchain patents (19%); a 12% of patents documents are worldwide applications that have
gone via Patent Cooperation Treaty (PCT) through the Word Intellectual Property Office (WIPO). Korea and
Japan are also leading countries for blockchain patent filings; other key regions where protection is sought are
Australia, Canada, Taiwan, India and Singapore. Great Britain is the first European countr y for blockchain patents,
followed by Germany.
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4 Can a b lockchain solve your problem?
In the previous chapters, we provided a brief description of the components and properties of
a blockchain and a market analysis as well . Many of the component observed are not exclusive
to the blockchain technology: distributed databases exist in many for ms, cryptographically
signed chain of events too, etc. Here we present three steps that allow you to decide whether a
blockchain is the right technology to solve your problem or not.
4.1 Storing i nformation in an untrusted e nvironment
In [10] the authors present a decision graph that allows you to decide whether a Blockchain is
the right tool to solve a given problem. They consider the three main types of blockchain
(permissionless, public permissioned and private permissioned) and a guide for decision -
making (Figure 8 ).
Figure 8 Decision graph: choosing the right Blockchain type in an untrusted environment .
Necessity to store s tate changes : is state essential for your stored data?
A blockchain, as a form of a database, stores information in sequential order and ensure the
integrity of the data and of its order. In this context , "is state essential" indicates the need to
store information in a given sequential order and to protect it s integrity . This must be an
essential requirement in order for the blockchain data structure to make sense.
Multiple w riters : are there multiple writers?
Although straightforward , it is worth remembering that if only a single entity is responsible for
writing data, the integrity and authenticity of the data can be guaranteed without the use of a
blockchain.
Trusted t hird p arty: can you use an always -online TTP?
If there is a trus ted third party (TTP), this can verify the state changes and guarantee transitions
correctness. The TTP could be used as a trusted writer in case the TTP fulfills the availability
requirements. In this case, from a purely technical point of view, a databas e would be a better
and more performing solution.
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In case the TTP does not meet the availability requirements, the TTP, acting – for example –
as a Certificate Authority, can establish a group of trusted writers. The TTP in that sense
ensures that the secu rity requirements are met.
Known writers : are all writers known?
If participants do not know each other, and there is no agreement on a common trusted third
party, the solution – as in the case of cryptocurrencies – is an open blockchain.
Trusted w riters : are all writers trusted?
If all the writers are trusted there is obviously no need for a system that guarantees integrity
and sequential order of entries, since by definition writers act according to the rules.
On the other hand, if we consider the case that writers could be malicious and try to compromise
the integrity of the stored data and the correctness of each transaction, a blockchain could be
the ideal solution.
Public verifiability : is public verifiability required?
Finally, it is possible to allow access to the blockchain, without the possibility of changing the
chain status, to allow third parties to verify the state of the data saved, and consequently their
correctness at all times.
4.2 Interaction with the physical world
Depending on the type of blockchain chosen with the help of the decision graph previously
presented, we find ourselves in a situation where writers are either unknown, not trusted and
therefore potentially malicious. Hence, we provide an additional set of observations with the
aim of making the reader aware of the limits of the blockchain when interaction with the physical world becomes necessary (Figure 9) .
Figure 9 Using a blockchain when interacting with the physical world.
Can state be validated wit h information available on the chain ?
It is necessary to be aware of one additional, fundamental aspect before choosing a blockchain
as a technical solution: a blockchain can guarantee that an entry in the ledger reflect s the
corresponding state only if data and its state changes can be validated with information
available on the chain itself . This is the typical scenario i n financial applications, where for
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instance the creation of crypto currencies are the product of the mining process and therefore
no interaction with the physical world is ever required.
Consensus and integrity : must state veridicity be guarantee?
Consequently, if someone wants to capitalize on the characteristics of a blockchain as a whole,
he has to find a use case where both data and its state changes can be validated with information
available on the chain and they do not depend from external sources.
If we consider the two main security properties provided by a blockchain as
1. consensus about the current state of the blockchain
2. guarantee that the blockchain cannot be changed afterwards
there is an interesting requirement of cryptocurrencies: in the cryptocurrency case, where the
blockchain provides some sort of a ledger, the user does not care about the honesty of the
writers. If the ledger contains a transaction according to the user expectations, he has by
property 1 the guarantee that everybody agrees that he owns now the money and by property 2
that there is no way the money could be taken from his wallet. In that sense the main value is that it is in the blockchain and that nobody can cha nge it.
Following the decision graph presented above, as soon as this kind of "guarantee of veridicy" or "trust" exists, there is the question if the blockchain is then still the right tool, since then one
could most probably also trust that the writer doe s not change it afterwards.
Decentralized logbook
On the other hand, in m any non-cryptocurrency applications , the main role of the blockchain
is to provide some kind of a verifiable decentralized logbook where information originated in
the physical world are stored on the chain.
Typically, the content of a block is reserved to transactions and smart contracts, but this is not
mandatory. It is possible to use a blockchain as distributed storage by adding arbi trary data to
each transaction.
Although it is not possible for a blockchain to provide any guarantee that this arbitrary data in
the ledger reflects a corresponding state of the reality, this immutable, distributed and secure
data mod el makes it very attractive to add arbitrary information form physical world on a
blockchain.
In contrast to the cryptocurrency case, where the main value is the entry in the bl ockchain itself,
in these scenarios, the main value lies in the fact that the content in the blockchain reflects the
corresponding state in the physical world. Thus, it requires trust into the one who reported the
physical value written in the blockchain.
In case of a violation of the rules in the physical world, the blockchain allows detecting and
tracing previous operations, but does not prevent an incorrect write to happen.
4.3 Starting a new blockchain or relaying to an existing one
By separating out the architecture of the Blockchain into multiple layers, we could better study
the various properties that we want the Blockchain to enjoy and where they need to be
implemented. Lastly, before choosing the right solution for a blockchain other aspects ha ve to
be taken in account:
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• Security : no party should be able to control a majority of some scarce resource (typically
computing power) , and therefore to convince nodes that an alternate version of the ledger
is the valid one,
• Liveness : nodes can add new blocks to the ledger with acceptable latency,
• Stability : nodes in the network should not alter their opinion of the consensus ledger
(except in very rare cases),
• Correctness : only blocks that represent valid transactions (i.e. they conform t o a
specification of how new blocks may relate to previous blocks) may be added to the ledger.
Because of all these characteristics, when blockchain fits your requirements, you will have to
choose carefully if you want to create your own new blockchain or to use an existing live, solid
and stable chain.
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5 Blockchain in d efence: Military application of blockchain
Based on the elements and decision- making processes presented above, it is now possible to
assess whether the blockchain is the right solution for an application. In this chapter, we carry
out this type of analysis systematically.
As presen ted in the previous chapter, using an open or permissioned blockchain is justifiable
when multiple mutually mistrusting entities want to interact in order to change the state of a system, and are not willing to agree on an online trusted third party.
Among the most cited applications in the military press, we present three of them covering
supply chain, detached labels and messaging applications. Even for these specific examples, the three applications have not reached a sufficient stage of maturity into the military domain
today.
Before analyzing individual cases, it is important to revisit two fundamental concepts, as stated
in [10] and [ 11]; using a blockchain in a particular application scenario make sense when:
1) the scenario requires multiple mutually mistrusting entities to interact and record or change some state of a system, and
2) in respect to use of a common online Trusted Third Party (TTP): the entities are not able to agree on such TTP or the implementation o f such a common TTP is not possible.
It is obvious that the first condition is not the case in most of the national and alliance peacetime operations, as usually there are some pre -existing trust arrangements between the parties
involved in the interactions. Nevertheless, situation is different when it comes to federated mission operations.
5.1 Supply c hain for logistics and p rocurement
Scenario: The modern military logistics and supply chain brings together hundreds of different
military and private sector c omponents [12]. With so many participants, there are numerous
points of friction that introduce numerous failure points, unnecessary costs, and result in
inaccuracies and misrepresentation. By providing a single source of truth and supporting intelligent a utomation, blockchain is a technology candidate to address these challenges and
allows keeping track the origins and history of transactions in various commodities.
Moreover, integrating blockchain within each step of an operation to secure and share data throughout the manufacturing process, including design, prototyping, testing, and production; deployment blockchain may offer the defense procurement a solution
19.
Using a public infrastructure such a blockchain could ease integration with non- military
partners, as well as facilitates civil -military cooperation without depending of a common
trusted authority (or cross -signed PKI certificates sharing multiple authorities).
Choosing a b lockchain: in a logistic and procurement environment, storing state (e .g.
equipment inventory) is critical; moreover, a supply chain can be complex and requires
multiple writers. If a trusted third party is agreed among all the writers, a blockchain is not necessary; on the other hand, if it is not possible to agree on a TTP a blockchain- based solution
might make sense. In case of a supply chain, depending on the confidentiality of the saved
information, you can choose the blockchain model.
19 https://www.army.mil/article/227943/blockchain_for_military_logistics
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Interaction with the physical world : in this particular case, the correctness of the information
entered in the blockchain is crucial; it would be a failure if the information stored did not
correspond to reality. To solve this problem, we could then delegate the responsibility to
inventory the assets to an external body or use certified s ensors, which ensure - for example -
the inventorying of the assets. In the first case, this means we would agree on a TTP responsible
for data entry; in the second case we would accept data entry only by certified sensors (trusted
writers): in both cases, we would find ourselves in a situation where the blockchain is no longer
needed.
For this type of application, the use of a database (centralized or decentralized) would be more
effective and performant.
5.2 Detached l abels and p roof of o wnership in a federa ted e nvironment
Scenario: in [11], the author suggests using a distributed ledger for implementing a distributed
solution for storing detached labels. The proposed solution aims to add a tool for information
security in organization. Hence, ensure traceability and offer a transparent service to verify a metadata co llection for classified information (e.g. ownership, classification, expiration).
Such labels include metadata- describing data objects as defined in STANAG 4774. Metadata
are neither directly attached to nor stored with the data objects. For such applicati on, it seems
to be efficient to write this information in a blockchain as detached metadata instead of depending of a PKI infrastructure.
In particular, authors propose to allow only data originators to assign security marking to a
document (proof of owne rship). Moreover, the blockchain shall be publicly readable, so that
any party requiring access to the data or receiving the data could be able to verify the security
requirements for handling and protection of a particular data object. The security classi fication
itself can change over time: for example, due to a de- classification of information after some
period of time.
As an additional application in the physical world, the same infrastructure could be used to keep an inventory of the physical copies of a sensitive document, recording new item creation
(e.g. reproducing documents), as well as their destruction and confirmation of destruction.
Choosing a blockchain: In this specific case, it is essential to save the status of the information,
allowing th e different data originators to enter information. Moreover, the author assumes that
it is not possible to have a TTP always online.
The choice of an open or permissioned blockchain is left to the user, as long as it is possible to
verify the information ( unrestricted or limited to a restricted group of clients). Regardless of
the solution chosen, it will be necessary to assess the impact of an open or public blockchain
on the confidentiality of the metadata entered.
Here, it is assumed that only the data originator, who first registered an object on the
blockchain, or the users to whom he delegated the responsibility, can update its state. This is
interesting, because it increases - without certain guarantees - the feeling of correctness of the
information entered.
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Interaction with the physical world : the main point in this scenario is the proof of ownership
and the consequent exclusive responsibility to update the state of an object in the distributed
ledger assigned to the first user who claims its ownership.
To avoid with certainty that a malicious w riter cannot claim ownership of an asset that does
not belong to him, we should delegate the writes to a TTP or trust all writers, in both cases a
blockchain would then not be necessary.
Moreover, it is realistic to think that the status associated with th e assets described in the
blockchain changes regularly over time: what to do if the data originator can no longer change
the status of an asset (for example, no longer having access to the credential used to
demonstrate its ownership)? Probably you should decide to have a super partes TTP capable
of reassigning the ownership of an asset or instead to give up updating the status of an asset.
5.3 Messaging
Scenario: Since 2016 DARPA invested about 1.8 mio. dollars to study public blockchains use
cases20,21. DARPA asked experts to develop efficient methods to use blockchain technology to
support messaging application.
Objective of the mandate is to use the stable and reliable infrastructure of existing blockchain
such Bitcoin or Ethereum as a transport layer for m essages. In this specific scenario, DARPA
does not aim to use blockchain properties as in the case of crypto currencies, but rather seeks to exploit existing blockchain as a method of transporting messages.
Choosing a b lockchain: Now, aware that for this type of approach, according to the decision
graph in chapter 4, an open blockchain is the solution sought (as independent from an
authorization system such as a certificate authority) there are two challenges to solve:
• The first one concerns anonymity: it must not be possible to trace and identify who pushed a message to a node participating in a blockchain. It is good to remember the communication between nodes (miners) participating in a blockchain is different for each solution, as is the fee required for each transaction. As an example, the bitcoin communications are not encrypted, so it would be possible, by analyzing the network traffic on a large scale, to trace the source of a message. On the other side, Ethereum communication between nodes is encrypted (while it is not for the discovery of nodes); in this case, it would probably be possible to leave a message without third parties tracing its origin.
• Secondly, it must not be possible for third parties to retrieve the content of the message. Encrypti ng a message and depositing it on the blockchain is not enough, as the encrypted
message would remain potentially available to analysts forever. In the future, it will be possible to decipher it with the help of more powerful computers, or by exploiting cy pher
vulnerabilities unknown today.
Based on this latest observation, one can envision exploiting the temporary information used by a network of miners (e.g. discovery protocol) to insert obfuscated messages in the transmission of data between nodes. In t his case, the use of the blockchain might actually be
interesting. Even so, at this very moment, no pragmatic and efficient solution has been released.
20 https://sociable.co/technology/darpa -explores -blockchain- to-develop -unhackable -code -for-military/
21 https://media.consensys.net/why -military -blockchain -is-critical -in-the-age-of-cyber -warfare- 93bea0be7619
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5.4 Further b lockchain use -cases in D efence
In 2016, the US Department of Homeland Security (DHS) announce d a project that would use
blockchain as a means of securely storing and transmitting the data it captures. Using the
Factom22 blockchain, data retrieved from security cameras and other sensors are encrypted and
stored, using blockchain as a means to guaran tee data distribution, integrity and traceability .
The project is still ongoing.
Many other proposals for military applications were analyzed in the preparation of this technology review [13] [14] , but none of them essentially requires the fundamental pr operties
to be found in a blockchain.
22 https://www.coindesk.com/factom -blockchain- project -wins -grant -to-protect -us-border -patrol -data
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6 Conclusion
In this review, we presented the architecture elements composing a blockchain environment, a
series of questions guiding the reader to choose , or not to choose a blockchain technology stack .
Moreover, we presented how to select the right blockchain between permissionless , public
permissioned and private permissioned. We also brought to light the benefits and limitations
of blockchains, particularly with regard to the integration of information from external sources
that are not trusted.
A market analysis from a global and regional perspective highlights many initiatives, these are
not related to defence but mainly to the financial world. In addition, there is great interest in using the blockc hain as a transparency tool where there is a need to publish data, guaranteeing
its integrity and immutability without necessarily being linked to a reference authority.
Many experts bet on the future stability and performance of blockchains, allowing gene ric
information storage and distributed traceability. Especially in government applications, the need of public decentralized verifiability of information where the government itself cannot act as a single, perpetual trusted authority, a distributed ledger could provide a solid base for shared
democracy data.
Most of the applications depend on existing chains such as Ethereum , because of its stability,
liveness and the ability to integrate complex smart contracts. Nevertheless, solutions like the ones proposed in the Hyperledger umbrella project could dramatically increase the performances of a blockchain network, giving the ability to script more complex smart
contracts (e.g. Go, JavaScript) and become the reference in the open source community.
Finally, we presented three interesting examples for defense, extremely different from each other: a supply chain scenario for logis tics management, a metadata repository with a focus on
information ownership, and the exploitation of a blockchain to convey messages.
In all cases the use of the blockchain is possible but not necessary. In cases where a form of
"trust" is necessary, th e question always comes back if the use of the blockchain is essential or
if a central or distributed database can be a more efficient solution.
From a military point of view, despite the interest aroused by this technology, to date there are
no blockchai n-based products integrated into defense systems. In particular, the need to have
no trusted authority and the awareness that information stored on a blockchain cannot be removed in the future, makes this type of solution not suitable for tactical applicat ions where
the control of the life cycle of an information is crucial. The focus goes on federated environments where mistrusting entities need to share information or proof some sort of ownership or presence, but advantages are not evident compared to exi sting information
sharing solutions and could have a negative impact due to the overhead introduced (e.g. infrastructure, responsiveness, energy consumption).
We are convinced that information decentralization, distributed databases and a zero -trust
archit ecture are fundamental elements for future interconnected tactical systems; however,
with the elements presented in this document, we can say that the blockchain is not necessarily
required to address these design goals .
22/22
7 Acknowledgements
We would like to thank Arthur Gervais for his feedback and suggestions for this technology
review . Moreover, we would like to thank Sébastien Gillard, Thomas Maillart and Dimitri
Percia David for Figure 5.
8 References, bibliography and additional resources
[1]: S.Naka moto, "Bitcoin: A Peer -to-Peer Electronic Cash System",
https://bitcoin.org/bitcoin.pdf, 2008. [2]: R. Zhang, R. Xue, L. Liu, "Security and Privacy on Blockchain", ACM Computing
Surveys, 2019.
[3]: Seoung Kyun Kim et al., " Measuring Ethereum Network Peers ", Internet Measurement
Conference , 2018.
[4]: T. Locher, S. Obermeier, Y. Pignolet, "When Can a Distributed Ledger Replace a
Trusted Third Party?", IEEE Blockchain, 2018.
[5] Ittay Eyal, Emin Gü n Sirer , Majority is not Enough: Bitcoin Mining is Vulnerable ,
International Conference on Financial Cryptography and Data Security , 2013.
[6]: V. Buterin , "Ethereum Whitepaper ", https://ethereum.org/en/whitepaper/, 2013.
[7]: M., K rótkiewicz M., Srinilta C. Intelligent Information and Database Systems. ACIIDS
2020. Communications in Computer and Information Science, vol 1178. Springer, Singapore
[8]: Ford, B . Technologizing Democracy or Democratizing Technology? A Layered-
Architecture Perspecti ve on Potentials and Challenges, 2020.
[9]: Bhatt P.C., Kumar V., Lu TC., Cho R.LT., Lai K.K. Rise and Rise of Blockchain: A
Patent Statistics Approach to Identify the Underlying Technologies. 2020.
[10]: K. Wüst, A. Gervais, "Do you need a Blockchain?", Crypto Valley Conference on
Blockchain Technology (CVCBT), 2018.
[11]: K. Wronay, M. Jarosz, "Does NATO need a blockchain?", NATO Communications and Information Agency, Milcom, 2018.
[12]: Hsieh, M., & Ravich, S.. Leveraging Blockchain Technology to Protect the National
Security Industrial Base from Supply Chain Attacks. Researc h memo, Founda tion for
Defense of Democracies, 2017.
[13]: Sudhan, A., & Nene, M. J . Employability of blockchain technology in defence
applications. In 2017 International Conference on Intelligent Sustainable Systems (ICISS)
(pp. 630- 637). IEEE.
[14]: Tarhini, A., & Chedrawi, C. Blockchain in the security and defense sector, 2019.
|
{
"id": "2103.02606"
}
|
2103.16216
|
A Regulatory System for Optimal Legal Transaction Throughput in
Cryptocurrency Blockchains
|
Permissionless blockchain consensus protocols have been designed primarily
for defining decentralized economies for the commercial trade of assets, both
virtual and physical, using cryptocurrencies. In most instances, the assets
being traded are regulated, which mandates that the legal right to their trade
and their trade value are determined by the governmental regulator of the
jurisdiction in which the trade occurs. Unfortunately, existing blockchains do
not formally recognise proposal of legal cryptocurrency transactions, as part
of the execution of their respective consensus protocols, resulting in rampant
illegal activities in the associated crypto-economies. In this contribution, we
motivate the need for regulated blockchain consensus protocols with a case
study of the illegal, cryptocurrency based, Silk Road darknet market. We
present a novel regulatory framework for blockchain protocols, for ensuring
legal transaction confirmation as part of the blockchain distributed consensus.
As per our regulatory framework, we derive conditions under which legal
transaction throughput supersedes throughput of traditional transactions, which
are, in the worst case, an indifferentiable mix of legal and illegal
transactions. Finally, we show that with a small change to the standard
blockchain consensus execution policy (appropriately introduced through
regulation), the legal transaction throughput in the blockchain network can be
maximized.
|
http://arxiv.org/pdf/2103.16216v1
|
Aditya Ahuja, Vinay J. Ribeiro, Ranjan Pal
|
cs.GT, cs.CR
|
cs.GT
|
A Regulatory System for Optimal Legal Transaction Throughput
in Cryptocurrency Blockchains
Aditya Ahuja
Indian Institute of Technology
Delhi
New Delhi, India
aditya.ahuja@cse.iitd.ac.inVinay J. Ribeiro
Indian Institute of Technology
Bombay
Mumbai, India
vinayr@iitb.ac.inRanjan Pal
University of Michigan
Ann Arbor, USA
palr@umich.edu
ABSTRACT
Permissionless blockchain consensus protocols have been designed
primarily for defining decentralized economies for the commercial
trade of assets, both virtual and physical, using cryptocurrencies.
In most instances, the assets being traded are regulated , which man-
dates that the legal right to their trade and their trade value are
determined by the governmental regulator of the jurisdiction in
which the trade occurs. Unfortunately, existing blockchains do not
formally recognise proposal of legal cryptocurrency transactions, as
part of the execution of their respective consensus protocols, result-
ing in rampant illegal activities in the associated crypto-economies.
In this contribution, we motivate the need for regulated blockchain
consensus protocols with a case study of the illegal, cryptocurrency
based, Silk Road darknet market. We present a novel regulatory
framework for blockchain protocols, for ensuring legal transaction
confirmation as part of the blockchain distributed consensus. As
per our regulatory framework, we derive conditions under which
legal transaction throughput supersedes throughput of traditional
transactions, which are, in the worst case, an indifferentiable mix
of legal and illegal transactions. Finally, we show that with a small
change to the standard blockchain consensus execution policy (ap-
propriately introduced through regulation), the legal transaction
throughput in the blockchain network can be maximized.
CCS CONCEPTS
•Security and privacy →Distributed systems security ;•Ap-
plied computing →Electronic commerce ;Digital cash ;Se-
cure online transactions ;Electronic funds transfer ;E-commerce
infrastructure .
KEYWORDS
Legal Cryptocurrency Transactions, Regulated Blockchain Con-
sensus Protocols, Regulated Blockchain Stochastic Games, Nash
Equilibria
1 INTRODUCTION
Decentralized financial institutions are a novel, emerging economic
infrastructure. These institutions are based predominantly on cryp-
tocurrencies for the trade of assets, both physical and virtual. It has
been established that the collective market capitalization of cryp-
tocurrencies is over $1 trillion (as of early 2021) [ 51]. This shows
promise in the long term presence and viability of cryptocurrency
based markets, in competition with (and possibly as a replacement
of) federally administered centralized financial institutions. How-
ever, these cryptocurrency based markets survive solely on thecorrectness of the underlying computational principles, which are a
basis of the efficacy of these economies. More specifically, in order
to sustain these cryptocurrency based decentralized economies,
blockchain consensus protocols serve as a technical foundation.
Existing blockchain protocols for cryptocurrencies address one
of (or any combination of) the following system goals: speed, se-
curity and decentralization . Unfortunately, these system goals are
necessary but insufficient. Illegal activities propelled through the
strategic use of blockchain based cryptocurrencies, is a serious
problem staring at the face of many world governments today
[47]. These illegal activities exploit the permissionless nature of
the blockchain networks for illegal trade, to strategically defeat
regulation by obfuscating the jurisdictions of the blockchain users
through anonymity, making federal legal rules inapplicable [ 36].
In this contribution, we introduce a fourth pillar of correctness for
ensuring confidence in blockchain based cryptocurrencies: legality .
More specifically, we address the problem of legal cryptocurrency
based trade of regulated assets: assets whose value and trade terms
are overseen by the respective governments1.
The Problem Setting
For fear of legal scrutiny and persecution, law abiding cryptocur-
rency blockchain users may sign up and get authorization from
their respective governments (for instance, through the issuance
of the BitLicense in New York, USA [ 8]) to commit solely to legal
cryptocurrency based trade of regulated assets, and the respec-
tive regulator (for instance, the New York State Department of
Financial Services in the case of BitLicense [ 8]) may license these
law abiding blockchain users to legally participate in the crypto-
economy. However, this does not preclude illegal transactions going
on chain. Remembering that the blockchain network is permission-
less, there will always exist unregulated transactors and executors
of the consensus protocol that propose and mine/validate dubious
transactions having an indeterminate legal status, while faithfully
following the consensus protocol. Consequently, there opens up a
competition in dubious versus legal block proposal between unreg-
ulated and regulated consensus protocol executors (respectively),
and that competition is dependent on how much consensus re-
source (for instance, mining power in proof-of-work blockchains
and stake in proof-of-stake blockchains) in the blockchain network,
do the regulated consensus protocol executors possess as a whole.
This finally results as a problem for the federal regulatory body to
1Regulated assets include but are not limited to the following asset classes: cryptocur-
rencies (evidence of regulation: [ 52]), alternate virtual assets (for example copyrighted
digital content), physical commodities (for example gold, water, energy), real-estate,
etc.arXiv:2103.16216v1 [cs.GT] 30 Mar 2021
, , Aditya Ahuja, Vinay J. Ribeiro, and Ranjan Pal
strategically decide on how much consensus resource to license
in the blockchain network, and what block proposal strategies to
advise to the regulated consensus protocol executors, for reasonable
guarantees on legal transaction throughput.
The resultant open questions that we address, are: (i) given a per-
missionless blockchain network for cryptocurrency based regulated
asset trade, can there exist a framework where, without degrading
the anonymity/privacy of the blockchain users, legal transactions can
be clearly identified and confirmed in the blockchain network?; and
(ii) can we derive conditions, as a function of the regulated consen-
sus resource in the blockchain network, in which legal transaction
throughput supersedes dubious transaction throughput?
Our Research Contributions
In this paper, we first motivate the need for designing regulatory
frameworks for legal cryptocurrency based trade of regulated assets,
through the study of the Silk Road darknet online market [45–47]
(Section 2). We then make the following research contributions.
(1)(A Regulatory Framework) We define a regulated blockchain
consensus system for existing permissionless blockchain
protocols that support cryptocurrencies (Sections 3 and 4),
where the goal of the consensus protocol executors is to
maximize their rewards from participating in the consensus
protocol, and the mandate of the regulator is to maximize
the legal transaction throughput in the blockchain protocol.
(2)(Block Proposal Competition) Consequent to our regulated
consensus framework, there ensues a competition in block
proposal between regulated and unregulated consensus ex-
ecutors. We formalize this competition through a two player
stochastic game [39, 40, 44] (Section 5), and, we show that:
(a)(Under Immediate Block Release [ 39])When the regulator
licenses between 58%and100% of the consensus resource,
the unregulated executors can do no better than adding
legal blocks at the end of the longest unconfirmed branch
of legal blocks (Section 5.2), thereby maximizing the legal
transaction throughput.
(b)(Under Immediate Block Release [ 39], With An Oversight
Compliance Fee [ 40])When the regulator licenses be-
tween 50%and58%of the consensus resource, given that
the regulator incentivizes to build on the branch of legal
blocks with a pay forward scheme (similar to [ 40]), the
unregulated executors can do no better than adding le-
gal blocks at the end of the longest unconfirmed branch
of legal blocks (Section 5.3), again maximizing the legal
transaction throughput.
(c)(Under Strategic Block Release [ 37,39])When the reg-
ulator licenses between 33% and50% of the consensus
resource, given that regulated executors strategically re-
lease a subset of the blocks notarized by them (similar to
the selfish mining attacks [ 37,38]), and the unregulated
executors faithfully follow the longest branch rule, the
regulated executors can prune/orphan some notarized but
unconfirmed dubious blocks to increase the legal transac-
tion throughput, beyond their fair share of legal transac-
tion throughput. However, when the regulator licenses be-
tween 0%and33%of the consensus resource, the regulated
NetworkBlock ConsensusSmart Contract / Virtual MachineApplication
Blockchain System Stack
executors + regulatorsagreement on
legal transactionsdecentralized ,
regulated economy
consensus executorsagreement on
transactionsdecentralized
economy
Figure 1: Traditional vs Regulated Blockchain Systems
consensus executors can do no better than building on the
longest unconfirmed branch of dubious blocks (Section
5.4), resulting in legal transaction throughput proportional
to their consensus resource.
Blockchain based crypto-economies are defined using a four layer
system stack [ 27]. The difference between a traditional instantiation
of the blockchain stack and an instantiation corresponding to our
regulated blockchain system is depicted in Figure 1.
2 CASE STUDY: THE SILK ROAD DARK WEB
MARKET
We first give a brief study on the operation of the Silk Road Darknet
Market, which flourished through the illegal use of the Bitcoin
cryptocurrency.
2.1 An Overview of the Silk Road Marketplace
Anonymity is the most prominent institution of the Deep Web. Each
user (buyer and seller) is identified by a username, with a secret
true identity. Deep Web users record market interactions through
forums and blogs. Consequent to the anonymity it provides, black
market activity over the Deep Web is highly feasible and attrac-
tive. Web traffic is anonymized through TOR, and Bitcoin serves
as an untraceable virtual currency. Email interactions to discuss
illicit transaction details are encrypted through PGP. These three
network, email and currency elements serve as the technological
foundation to build an illegal market, with low cost illegal trans-
actions. Given that previously illegal transactors relied heavily on
in-person deals and reputations built on personal encounters, Deep
Web based illicit markets resulted in a paradigm shift for illegal
activities on a global scale [45].
The Silk Road online (mostly) narcotics trade marketplace has flour-
ished anonymously on the Deep Web since 2011. A study aimed
at the discovering the realities and motives of operations (naviga-
tion and purchase) by drug users on the Silk Road marketplace has
been conducted [ 46]. The study was conducted through strategic
online observations, four month long fieldwork on marketplace site
discussion threads and twenty anonymous online interviews of a
sample of adult users.
A Regulatory System for Blockchains , ,
2.2 Illicit Transactions employing Bitcoin
The Silk Road marketplace is inferior to traditional online sites
(such as eBay), due to its bad Bitcoin escrow implementation : the
ability to undo a transaction. Standard escrow requires that if the
trade is deemed fraudulent, the traded assets are returned to the
seller, and the escrow service refunds the currency to the buyer. In
case of fraud, market users only lose their service fee. Typical of
illegal marketplaces, Silk Road purchases cannot be undone: drug
sellers do not provide return addresses, and a perfect escrow service
cannot exist (although a rudimentary escrow can) that satisfies both
the buyer and the seller, simultaneously [45].
A prominent drug study [ 46] concluded that many narcotics buy-
ers were not technically proficient and faced trouble in arranging
for Bitcoin credit and accessing the Silk Road website via the Tor
browser. An anonymous interview of an adult drug user revealed
his drug procurement process over Silk Road. The said user was
able to procure Bitcoins with minimal paperwork from a particular
bank, with no self identifying information submitted to the bank.
By the end of the day, the user had Bitcoins credited to his Silk
Road account. (S)He then went to the narcotics’ vendor webpage,
added the drugs to his shopping cart and entered an encrypted
postal address for the drug delivery, thereby confirming the order.
The user even employed an escrow system to avoid being scammed
by the vendor. Consequently, the Bitcoins are not delivered to the
vendor until the drug package reached the user and the order was
finalized.
Thousands of Bitcoin worth approximately a billion US dollars
connected with Silk Road based drugs and goods trade have been
confiscated by the United States Justice Department, the biggest
seizure in history of the agency [ 48]. This motivates the need for
preemptive monetary investment by the regulator to ensure only
legal transactions are confirmed on-chain (as is suggested in Sec-
tion 5.3) to minimize the cost associated with law enforcement and
the recovery of illegal cryptocurrency, and reduce illegal activity
through the blockchain.
Very recently, senior authorities from the US Treasury Depart-
ment and the European Central Bank have also formally recognised
the use of Bitcoin and other cryptocurrencies for illegal activities,
and are strongly considering regulation in these digital economies
[24, 25].
The drug study, seizures and recent concerns raised by authoritative
figures in prominent financial bodies advocate that, at least the
Bitcoin protocol, is rife with issues of confirmed illegal transactions,
and appropriate regulatory mechanisms are need to be enforced to
define legal, decentralized crypto-economies.
2.3 Motivation for a Regulated Blockchain
Protocol
Blockchain systems are defined with a four layer system stack (from
bottom to top): the network layer, the consensus layer, the smart
contract layer, and the application layer [ 27]. Given the pressing
need for a regulatory framework for blockchains, we now moti-
vate how introducing regulation for blockchain transactors and
consensus protocol executors is a prudent choice for the regulatory
body.Problems with Super-Consensus Layer Solutions. Without consid-
eration of the network and consensus layer, regulatory policy can
only be enforced at the smart contract layer, given that the ap-
plication layer is confined to the decentralized trade of regulated
assets. Unfortunately, there are two problems associated with reg-
ulatory policy enforcement at the smart contract layer: (i) As it
has been observed in Section 2.1, most times when a blockchain
user is involved in illegal trade, its digital identity is difficult to
map to a specific federal jurisdiction. This makes it hard to enforce
regulation rules as a distributed program (which is the executed at
the smart contract layer). (ii) A smart contract layer enforcement
of regulation will take time to achieve consensus + time to execute
the smart contract . This would be slower in contrast to consensus
layer only enforcement of regulation.
Regulating Blockchains at the Consensus Layer. One approach to
regulate the blockchain system, to ensure legal transaction proposal
and confirmation, is to license the blockchain consensus protocol
executors who take it upon themselves to add (provably) legal
transactions to their blocks, and run the consensus protocol on
these blocks. We give details of this regulatory approach, next.
3 THE REGULATED BLOCKCHAIN SYSTEM
MODEL
We now detail our regulated blockchain system model. We first
give a short summary on the principles of blockchain consensus,
and give some foundational definitions and assumptions. We then
give the design goals and protocol features (with their motivations)
for our regulatory framework. Finally, we give the threat model in
our setting.
3.1 A Brief on Blockchain Consensus
The original consensus algorithm in a blockchain network is proof-
of-work (PoW), where the protocol participants, called miners , solve
a crypto-puzzle requiring a significant amount of computation, to
add (notarize) blocks as part of the blockchain. On successfully
adding a block to the blockchain, miners are eligible for a block
reward in the form of cryptocurrency tokens, for their effort. Given
that computation is the only resource that dominates the result of
consensus, miners might form a pool of their computation power
together [ 37] to increase their chances of solving the puzzle, adding
their blocks to the blockchain, and consequently winning the block
reward.
An alternate, dominant family of blockchains follow a proof-of-
stake (PoS) consensus regime. In proof-of-stake, blockchain protocol
executors validate the correctness of blocks as a function of the
number of coins / digital tokens they possess in the blockchain
network, which is referred to as their stake . Remembering that
digital tokens are a replicatable resource, one possible attack in
these blockchain systems is that protocol executors can duplicate
and pitch their coins in every block competing to be added to the
blockchain, in order to maximize their chances of winning the block
reward. This attack is called a nothing-at-stake attack [31].
, , Aditya Ahuja, Vinay J. Ribeiro, and Ranjan Pal
3.2 Stakeholders, Terminology, Assumptions,
and Notation
We define the stakeholders in our regulated decentralized economy,
and give the associated terminology and assumptions, first.
•Transaction and Block Types: We will assume there are two
types of transactions: legal ordubious . Transactions whose
legal status can be established for certain will be called legal.
All other transactions would be called dubious. A block that
contains at least one dubious transaction will be called a
dubious block . A block that contains only legal transactions
will be called a legal block . A legal block generated by a
regulated consensus protocol executor (defined later) will be
called a regulated block .
•A Body of Regulators: We will assume the existence of a cross-
jurisdictional network of regulators, which are federal asso-
ciations in-charge of ensuring legal and non-discriminatory
practices in the institutions they oversee [ 2]. In our case the
institution is the blockchain network.
•Blockchain Transactors: We will refer to blockchain users that
define and propose cryptocurrency transactions as transac-
tors. The transactors can be of two types: regulated and un-
regulated. Regulated transactors will always propose trans-
actions that are legal and verifiable within the blockchain
network ( assumption: The regulator will announce a li-
cense corresponding to each regulated transactor so that the
blockchain network can verify the legality of transactions
proposed by them). Unregulated transactors can propose
either of legal and dubious transactions.
•Blockchain Executors: We will refer to blockchain users that
execute the consensus protocol as consensus executors , or
executors for short. These executors will be miners in proof-
of-work (PoW) blockchains, or validators in proof-of-stake
(PoS) blockchains. We will assume that all executors are ratio-
nal, and want to maximize their reward / revenue resulting
from their participation in the consensus protocol (identical
to the standard notion of rationality in [ 39,40]). We will also
refer to the consensus resource associated with each execu-
tor, where the resource is hash power in PoW blockchains, or
stake in PoS blockchains. Here too, we will assume executors
will be of two types: regulated and unregulated. Regulated
executors will propose blocks that contain only verifiably
legal transactions, with an evidence of regulation of the said
executor, and so their blocks will be called regulated blocks.
Unregulated executors can propose blocks that may be ei-
ther legal or dubious. When specifically dealing with PoS
blockchains, we will consider Byzantine behaviour by un-
regulated validators through a nothing-at-stake attack [ 31].
Finally, we assume that unregulated executors cannot form
selfish notarization pools, due to lack of trust in fair block re-
ward distribution in the unregulated setting, given the pool
administrator can be dishonest [ 41]. However unregulated
executors can coordinate amongst themselves for deciding
individual notarization strategies that may maximize their
individual expected block rewards.
Figure 2: Notation for our Regulatory Framework
•Block Notarization and Confirmation: We say that a proposed
block is notarized2(similar terminology in [ 35]), once it is
successfully mined by a PoW executor, or successfully vali-
dated by a PoS executor (for instance, on receiving the thresh-
old of validation votes in Algorand [ 32]). We say that a block
isconfirmed once it is sufficiently deep in the blockchain and
all the transactions contained in it are confirmed/finalized
(for instance, transactions in Bitcoin are confirmed once they
are six blocks deep [30]).
•Block Proposal in Discrete Time: We assume that the consen-
sus protocol executors have loosely synchronized clocks by
employing protocols such as NTP [ 32]. We will ignore net-
work delays, and assume that blocks are proposed in discrete
sequential epochs of time, with the time difference between
two consecutive epochs equal to the block proposal time of
the base protocol (for instance, 10minutes in Bitcoin [ 30]
and22seconds in Algorand [ 32]). Also, the epoch number
of every block will also be equal to the block’s height.
•Dubious and Legal Block Branches: Given the block proposal
competition ensuing between two categories (regulated and
unregulated) of consensus executors, there would exist a
fork in the blockchain with block branches corresponding
to each category of executors. We would refer to the block
where this fork originates as the root block (this would be the
block at the end of the trunk of the unambiguous part of the
blockchain). The block branch corresponding to regulated
executors will be referred to as the legal branch (as all blocks
in this branch will be verifiably legal), and that corresponding
to unregulated executors will be referred to as a dubious
branch . Given any one of the legal or dubious branches, we
would refer to the most recently notarized block in a branch
as afrontier block for that branch, and every other notarized
block as an interior block for that branch.
We present the notation that will be used throughout the paper.
Given𝑛blockchain consensus protocol executors, we will denote
them using[𝑛]:={1,2,...,𝑛}. The consensus resource of each
executor𝑖∈[𝑛]will be given by 𝛼𝑖. Given any blockchain user 𝑖
2In the context of dubious blocks, the word ‘notarized’ will bring out the violation in
how unregulated executors validate/attest blocks with possibly illegal transactions.
A Regulatory System for Blockchains , ,
(may it be a transactor or executor), its private signing key will be
denoted by𝑠𝑘𝑖and its public verification key will be denoted by 𝑣𝑘𝑖.
We will assume the existence of a signature scheme (sign,ver),
where, for any message 𝑚, given Sig𝑖(𝑚):=(𝑚,sign𝑠𝑘𝑖(𝑚)), it is
true that∀𝑖,ver𝑣𝑘𝑖(Sig𝑖(𝑚))=1(verification succeeds for a signed
message). We will also assume the existence of a random oracle
𝐻∗, realizable through an ideal collision resistant hash function.
We will denote the set of networked, cross-jurisdictional regulators
byF. Each executor in [𝑛]will be under the jurisdiction of some
regulator inF. We will denote the set of regulated executors by
𝑅(⊆[𝑛]). We will denote the set of unregulated executors with
𝑅:=[𝑛]\𝑅. We will denote the regulatory licenses for legal
transaction proposal by 𝜎F
𝑗(where𝑗is a transactor) and legal block
notarization by 𝛽F
𝑖(where𝑖is an executor). We will denote the
block proposal rounds/epochs by 𝑒. Finally, we will denote the
regulatory oversight window, as the number of sequential block
proposal rounds for which the regulatory licenses are valid3, by𝐸.
Our notation is summarized in Figure 2.
3.3 Regulated Blockchain System Design Goals
1. Enforcing legal policies on blockchain users while preserving anonymity.
A1. As a general rule, the regulators need not intervene in the
blockchain network beyond their policymaking role, as is their
purview in existing financial institutions [ 3] and computational
systems [2].
A2. The identity of the regulated users in the blockchain network
should not be unanonymized beyond their legal status in the net-
work: this means that the regulated users preserve their anonymous
digital identity, but their digital identity is mapped to a policy state-
ment dictating what are the legal rules applicable to the said identity
when employed for cryptocurrency based trade of regulated assets.
A3. Regulated users should not be able to participate in the regu-
lated version of the consensus protocol prior to a sanction of their
license by the regulatory body.
2. Requirement of regulatory policy enforcement in lock-step with
transaction agreement (blockchain consensus).
As a pre-emptive measure to eliminate illegal transactions within
the crypto-economy, regulatory policy enforcement should be in
lock-step with the execution of the consensus protocol by regulated
executors, so that legal transactions are preferentially agreed over
and above the dubious/illegal transactions, and illegal activities
through cryptocurrencies are minimized in this way, if not elimi-
nated. This requires re-engineering of existing blockchain protocols,
and leads to the following goals:
B1. The re-engineered protocol should be different from the original
protocol to enable the network to distinguish protocol execution
and blocks coming from regulated users following the re-engineered
protocol, as opposed to protocol execution and blocks from unreg-
ulated users following the original protocol.
B2. The design philosophy of the re-engineered blockchain proto-
col should be same as that of the original blockchain protocol, and
3Many regulated assets exist whose monetary value and trading terms vary over time.
Consequentially, licenses corresponding to these assets may have a date of expiry. The
regulator determines this expiry window.the two protocols should be statistically equivalent4, so that the
re-engineered protocol retains the speed, security and decentraliza-
tion guarantees of the original protocol.
B3. Assuming there are always some legal transactions eligible to be
added to the blockchain, the blockchain network being permission-
less, must have public guarantees of legal transaction throughput :
number of legal transactions distributively agreed per unit time.
The throughput of legal transactions generated by regulated users
should supersede the throughput of dubious transactions generated
by unregulated users, in instances of race conditions in block pro-
posal. These guarantees would increase confidence in the legality
of the associated crypto-economy.
3.4 Regulated Blockchain Protocol Features
Given an existing blockchain consensus protocol BChain , we re-
engineer the same to define a regulated blockchain protocol RBChain .
Under RBChain , the regulatory body Fonly licenses (consistent
with A1,A2,A3 ) the consensus protocol transactors and executors
to ensure that these blockchain users undertake the responsibility
of distributed consensus on legal transactions. Also, the RBChain
protocol (defined in Section 4) must have the following features:
•Transactions under RBChain should be distinguishable from
those under BChain .This feature would allow the blockchain
network to distinguish between legal and dubious transac-
tions by regulated and unregulated transactors respectively.
•Block structure under RBChain should be different from that
under BChain .This feature would allow the blockchain net-
work to distinguish between regulated blocks and unreg-
ulated (which could be either of legal or dubious) blocks
proposed by regulated and unregulated executors respec-
tively (consistent with B1).
•The execution of RBChain andBChain , when viewed as prob-
ability distributions, should be statistically indistinguishable.
This would guarantee that the regulated executors do not
have an unfair advantage over unregulated executors, in
speed and security during protocol execution (consistent
with B2).
•The consensus resource of executors under RBChain should
be same as that when they were executing BChain .This fea-
ture would allow the regulated executors to have the same
consensus as when they were unregulated, in order to pre-
serve the decentralization status of the blockchain network
(consistent with B2).
We address system design goal B3in Section 5.
3.5 The Threat Model in a Regulated Setting
We discuss briefly the threat model inherited by RBChain , on being
re-engineered from BChain .
Security under the Traditional Byzantine Adversary. An artefact of
the statistical properties of RBChain andBChain being equivalent,
we can conclude that RBChain will be secure under a Byzantine
fault adversary, under the same security assumptions as applicable
4If both protocol states are observed as probability distributions, then the two distri-
butions should be statistically indistinguishable [42].
, , Aditya Ahuja, Vinay J. Ribeiro, and Ranjan Pal
Figure 3: Toy Model of a Permissionless Regulated
Blockchain Network. Regulatory body (center) licenses
four executors (highlighted with a stamp on the associated
coin miner) to propose regulated blocks. The remaining
executors are free to propose any type of blocks.
toBChain (for example, majority of miners being honest in Bitcoin
[30], or fraction of money held by honest users being >2
3rd of the
total wealth in Algorand [32]).
Block Proposal Competition between Regulated and Unregulated Ex-
ecutors. Given the permissionless nature of cryptocurrency blockchain
networks, these networks can never be wholly regulated. This
would entail a strategic competition between legal and dubious
block proposal.
In Section 5, we study legal transaction throughput as a function
of the cumulative consensus resource licensed by the regulatory
bodyF. We derive conditions under which unregulated executors
‘defect’ to a legal block proposal, (i) to avoid legal scrutiny and
prosecution; and more importantly, (ii) to have monetary benefit
in consensus protocol participation: incentivized to participate in
legal block proposal, given dubious block proposal does not give the
best reward. We will also assume that regulated executors can form
notarization pools and keep notarized blocks private, whereas un-
regulated executors cannot form such pools due to mistrust among
them towards a fair distribution of block notarization reward (con-
sistent with the study of prevalent mistrust among unregulated
miners in [41]).
A toy model of our regulated blockchain system is given in Figure
3.
4 REGULATED CONSENSUS PROTOCOLS
We now give exact construction of the regulated version of two
popular regimes of blockchain consensus protocols. We will use
◦as a bit-string concatenation operator. To denote statistical in-
distinguishability of two probability distributions, we will use the
notation≈𝑠.4.1 Regulatory Licenses
Prior to generating verifiably legal transactions and notarizing reg-
ulated blocks, regulated blockchain users5wait for license from the
regulatory bodyFto define the terms for legal trade and regulated
block proposal in the blockchain network.
The regulator first announces the legal rules applicable to the cryp-
tocurrency based trade of asset classes Ain regulatory jurisdic-
tionsF, through a signed document ΓF
A. More specifically, ΓF
Ais
a|F|×|A| matrix where each entry (𝑓,𝑎)∈F×A is a list of
legal rules pertaining to cryptocurrency based trade of asset 𝑎in
jurisdiction 𝑓. These rules may pertain to the legality of asset 𝑎in
jurisdiction 𝑓, or the (time varying) valuation of asset 𝑎in juris-
diction𝑓. Next, the regulator licenses each authorized transactor
𝑗by announcing signed permissions for the transactor to trade in
jurisdictions 𝐹𝑗(⊆F) with asset classes 𝐴𝑗(⊆A) . This essentially
means that under license (denoted by 𝜎F
𝑗), transactor 𝑗is only per-
mitted to trade under rules Γ𝐹𝑗
𝐴𝑗. Finally, given that the regulator
approves executors 𝑅(⊆[𝑛]), the regulator licenses each autho-
rized executor 𝑖∈𝑅by announcing signed permissions (denoted
by𝛽F
𝑖) that𝑖is to include only transactions consistent with ΓF
A
in the blocks that it proposes. All the licenses announced by the
regulator are only valid for an oversight window of 𝐸contiguous
blocks, starting from some root block 𝐵𝑒0proposed in epoch 𝑒0.
The announcement of regulatory licenses is highlighted in Algo-
rithm 1. Note that the legal rules ΓF
A, and the licenses 𝜎F
𝑗for trans-
actors𝑗and𝛽F
𝑖for executors 𝑖∈ [𝑛]are only relevant for the
regulated protocol specification. They have no bearing on the block
proposal competition in the next section, as the said competition
only depends on the consensus resource (𝛼𝑅,𝛼𝑅), and the oversight
window𝐸(please see Section 5 for details).
Algorithm 1: Regulatory Licenses
procedure RegLicenses (BChain )
L0.Regulator announces over [𝑛]:SigF(RBChain,𝐵𝑒0,𝐸,ΓF
A).
L1.For each transactor 𝑗, regulator issues
𝜎F
𝑗:=SigF(RBChain,𝐵𝑒0,𝐸,𝑣𝑘𝑗,transactor ,𝐹𝑗,𝐴𝑗).
L2.For each executor 𝑖∈𝑅, regulator issues
𝛽F
𝑖:=SigF(RBChain,𝐵𝑒0,𝐸,𝑣𝑘𝑖,executor,𝐻∗(ΓF
A)).
end procedure
4.2 Regulating Proof-of-Work Consensus
Legal Transaction Structure: Consider the off-chain transfer of a reg-
ulated asset of type 𝑎(∈𝐴𝑗), in jurisdiction 𝑓(∈𝐹𝑗), to transactor
𝑗with a corresponding receipt 𝛿𝑓,𝑎
𝑗. A regulated Bitcoin transaction
for transactor 𝑗includes(𝜎F
𝑗◦𝛿𝑓,𝑎
𝑗)in the transaction script.
The Regulated Bitcoin Protocol: The Bitcoin [ 30] consensus protocol
requires the executors (called miners) to solve a compute inten-
sive crypto-puzzle, to notarize a block. Our proposed regulated
Bitcoin protocol RBitcoin is given in Algorithm 2, where the regu-
lated miner adds evidence of its license in the coinbase transaction
5We use the term ‘users’ to refer to both transactors and executors collectively.
A Regulatory System for Blockchains , ,
script before trying to solve the crypto-puzzle associated with the
regulated block that it wishes to go on-chain.
Algorithm 2: Regulated Bitcoin Consensus Protocol
Given:
TheBitcoin Consensus Protocol [30].
procedure RBitcoin
Given a legal block LB, regulated miner 𝑖∈𝑅generates a
regulated blockRBby adding𝐻∗(𝛽F
𝑖)to the coinbase
transaction ofLB, and finds nonce 𝜂such that𝐻∗(𝜂◦RB) lies
in the target window.
end procedure
Security of RBitcoin (Consistent with B2in Section 3.3): It is known
that the image distribution of the random oracle (ideal CRHF) 𝐻∗is
uniform, for any pre-image distribution. So, for any block Bmined
on for an appropriate nonce 𝜂′in the traditional Bitcoin consensus
protocol, it is true that: 𝐻∗(𝜂′◦B)≈𝑠𝐻∗(𝜂◦RB) . This implies
that mining under RBitcoin is statistically equivalent to mining
under Bitcoin .
4.3 Regulating NxtProof-of-Stake
TheNxtProof-of-Stake [ 31] consensus protocol uses the IsEligible
deterministic algorithm to elect a validator for notarizing a block.
We propose the regulated version of the eligibility algorithm, to
validate a regulated block, called RIsEligible , using the regulatory
license in the hash pre-image for eligibility proof generation. RIsEl-
igible is given in Algorithm 3. The proof of security of RIsEligible
is identical to that of RBitcoin .
Algorithm 3: Regulated NxtProof-of-Stake Protocol
Given:
TheNxtConsensus Protocol [31].
procedure NxtPoS-RIsEligible
Given a legal block LB, regulated validator 𝑖∈𝑅receives a
regulated blockRB(formed by adding 𝛽F
𝑖toLB).𝑖then finds
nonce𝜂such that𝐻∗(𝑣𝑘𝑖◦𝛽F
𝑖◦𝜂)lies in the target window
which is a function of time and 𝛼𝑖, to become eligible to validate
RB.
end procedure
5 BLOCK PROPOSAL COMPETITION
BETWEEN REGULATED AND
UNREGULATED EXECUTORS
We now analyze the competition between regulated and unregu-
lated consensus protocol executors (as per goal B3in Section 3.3)
as a two player stochastic game, inspired from [ 39,40]. We for-
mally define the stochastic game, and state best responses by the
regulated executors 𝑅and unregulated executors 𝑅(in the form of
their Nash Equilibria [ 39,44] strategies), as a function of the total
consensus resource regulated by F.
The proof of each theorem in this section, is given in Appendix A.5.1 The Regulated Blockchain Game Features
We give the general characteristics and an overview of our regulated
blockchain stochastic games, in this subsection.
Generality of Blockchain Mining Games. The blockchain mining
games introduced by Kiayias and Koutsoupias [ 39,40], atop which
our regulated blockchain stochastic games are constructed, allow
a minority resource consensus executor p(⊆[𝑛], with𝛼p<0.5)
to consider switching to an interior block (a block that is not the
most recent block), or the frontier block, in the competing chain, as
a better response than its present notarization strategy. However,
the pioneering selfish mining strategy SM1 [ 37] (named in [ 38])
only allows pto switch to the frontier block in the competing chain.
Consequently, the mining games introduced in [ 39,40] are more
general in their strategy than the original selfish mining proposal.
Preliminaries. Our blockchain notarization stochastic game defines
competition between two player categories: the regulated execu-
tors𝑅and the unregulated executors 𝑅. We assume that the to-
tal consensus resource in the blockchain network is normalized:Í
𝑖∈[𝑛]𝛼𝑖=1; and the expected revenue / reward per epoch of
the blockchain for any category of players p∈{𝑅,𝑅}is denoted
by𝑔p, and is a function of 𝛼p=Í
𝑖∈p𝛼𝑖. Unless the regulator F
adds extra transactions to the blockchain (Section 5.3), 𝑔𝑅+𝑔𝑅=1.
Our game evolves as a block tree of width two, with one branch
consisting of regulated blocks notarized by 𝑅, and the other branch
consisting of legal or dubious blocks notarized by 𝑅, and the fork-
ing point of the block tree has a root block, say 𝐵𝑒0. If either of
𝑅or𝑅abandons its branch for notarizing on a block linked to its
competing branch, the root block 𝐵𝑒0moves accordingly to newly
selected block for notarization, and the game starts afresh. We
also allow the regulator Fto add an additional reward 𝜌Fin the
regulated blocks, when 𝑅is in a small majority in the blockchain
network ( 0.5<𝛼𝑅<0.58), resulting in 𝑔𝑅+𝑔𝑅≥1. Our game
has depth𝐸, equal to the oversight window and the window for
collecting the notarization reward (coinbase reward in Bitcoin [ 40]).
The game depth 𝐸means that the first branch originating from 𝐵𝑒0
that achieves height 𝐸is confirmed as part of the blockchain, the
other competing branch is orphaned, and again the game starts
afresh. Finally, we assume that the executors collect their block
notarization reward at the end of the game depth (for example, for
𝐸=100in Bitcoin [ 39]). For a simplified analysis of the game, we
assume𝐸=∞, unless stated otherwise.
We will denote the normalized legal transaction throughput, which
can also be interpreted as the expected number of legal blocks
agreed at each epoch of the blockchain, by 𝑡F. Given that regulated
executors only propose legal transactions, and unregulated execu-
tors may propose either of legal or dubious transactions, it is easy
to see that𝑡F≥𝑔𝑅.
Stochastic Game Summary . Our results on normalized legal trans-
action throughput 𝑡Fand normalized expected block rewards for
regulated executors 𝑔𝑅and unregulated executors 𝑔𝑅are summa-
rized in Figure 4. These results are formally detailed in Section 5.2,
Section 5.3, and Section 5.4.
We now define how executors choose to release their notarized
blocks, and which blocks they may select to notarize future blocks
on.
, , Aditya Ahuja, Vinay J. Ribeiro, and Ranjan Pal
Figure 4: A summary of the results of our regulatory sys-
tem. When 𝛼𝑅≤0.5, the legal transaction throughput is
sub-optimal under the longest branch rule. However, when
𝛼𝑅>0.5, the longest legal branch rule wins, unregulated ex-
ecutors can do no better than proposing legal blocks, and
the legal transaction throughput is maximized. Consensus
thresholds ℎ𝐼𝑅,ℎ𝑜𝑐𝑓
𝐼𝑅, and ˆℎ𝑆𝑅are formally defined later.
Block Release Models. We first define the block release models (from
[39,40]), which elucidate when consensus executors choose to re-
veal information about their notarized blocks.
The first block release model stated below can be adopted by both
regulated executors 𝑅and unregulated executors 𝑅.
Definition 1 (Immediate Release Model). A consensus executor
follows the immediate block release (IR) model when, any block
notarized by it is immediately released and added to the blockchain
for use by other executors.
Notarization rewards earned by a notarization pool must be dis-
tributed among pool members appropriately. Due to prevalent dis-
trust between unregulated executors 𝑅in the fair distribution of
notarization rewards among members of a secret notarization pool
created by them [ 41], the second block release model stated below
can be adopted by regulated executors 𝑅alone. Here, the adminis-
trator denotes the pool leader.
Definition 2 (Strategic Release Model). A consensus executor fol-
lows the strategic block release (SR) model when, on successful
notarization of block(s) by it, the (block administering) executor
announces its existence, but the block(s) can only be used by other
executors when the administrator decides to release them.
Consensus Execution Strategies. The honest strategy where a con-
sensus executor notarizes blocks at the end of the longest existing
unconfirmed branch of the blockchain, is traditionally referred to
as the Frontier strategy [ 39,40]. Under the Frontier strategy,
the expected gain per epoch of the associated executor is equal to
the consensus resource possessed by it. We now give definitions
of equivalent (to Frontier ) consensus execution strategies in ourregulated setting. We will use DB to denote dubious blocks, LB
to denote legal blocks, and RBto denote regulated blocks.
We first define the frontier block notarization strategies that the
unregulated executors 𝑅can adopt.
Definition 3 ( DubFrontier ).An unregulated consensus executor
follows the DubFrontier strategy, when it notarizes legal ( LB) or
dubious (DB) blocks chained at the frontier block of the longest
dubious branch of the blockchain.
Definition 4 ( LegFrontier ).An unregulated consensus execu-
tor follows the LegFrontier strategy, when it notarizes legal ( LB)
blocks chained at the frontier block of the longest legal branch of
the blockchain.
We now define the frontier block notarization strategies that the
regulated executors 𝑅can adopt. We will also assume that the
regulatory bodyFmay adopt a pay-forward scheme (explained in
Section 5.3) in the regulated blocks.
Definition 5 ( RegFrontier ).A regulated consensus executor
follows the RegFrontier strategy, when it notarizes regulated ( RB)
blocks chained at the frontier block of the longest legal branch of
the blockchain.
Definition 6 ( RegFrontier (𝜌F)).A regulated consensus execu-
tor follows the RegFrontier (𝜌F) strategy, when it notarizes regu-
lated (RB) blocks chained at the frontier block of the longest legal
branch of the blockchain, with a pay-forward of 𝜌F(which is a
function of 𝛼𝑅) by the regulatory body Fin the regulated blocks,
as an oversight compliance fee (OCF).
Definition 7 ( RDubFrontier ).A regulated consensus executor
follows the RDubFrontier strategy, when it notarizes regulated
(RB) blocks chained at the frontier block of the longest dubious
branch of the blockchain.
The longest legal branch rule. Block notarization strategies LegFrontier ,
RegFrontier andRegFrontier (𝜌F) constitute notarization on the
longest legal branch: at every epoch, all executors following these
strategies add legal blocks to the longest existing branch of legal
blocks.
Our competition analysis. In the following analysis, the first two
games (in Section 5.2 and Section 5.3), deal with deriving the condi-
tions under which attacks by the unregulated executors on the legal
branch fail, given that the regulator licensed the majority of the
consensus resource in the blockchain network. The third and final
game (in Section 5.4) deals with deriving conditions under which
attacks by the regulated executors on the dubious branch succeed,
given that the unregulated consensus resource in the blockchain
network is in a majority.
5.2 A Stochastic Game with Immediate Block
Release
Our first two player game consists of competition between the
regulated executors 𝑅and the unregulated executors 𝑅, when both
players notarize and release their blocks immediately, the regulator
A Regulatory System for Blockchains , ,
time
e0
anye0+ 1
R
(αR≥58%)e0+ 2e0+ 3e0+ 4
e0+ 1
R
(αR≤42%)e0+ 2e0+ 3
join←join
→
Be0
RB
RB
RB
RB
LB
DB
LB
defect
Figure 5: When ≥58% of the consensus resource in the
blockchain network is regulated, the legal branch (right) cor-
responding to the regulated executors 𝑅wins, by forcing 𝑅
to abandon and defect from their branch.
Flicenses more than 1−ℎ𝐼𝑅=58%of the consensus resource in
the blockchain network, and the regulated executors add regulated
blocks to the existing longest legal branch. The best responses (in
terms of expected gain maximization) for both players are formal-
ized in the following theorem (depicted in Figure 5).
Theorem 8 (Eqilibrium under IR). In the immediate block
release (IR) model, given regulated executors 𝑅follow the ‘longest legal
branch’ rule, and 𝛼𝑅<ℎ𝐼𝑅=0.42, then𝑅playing RegFrontier
and𝑅playing LegFrontier is a Nash Equilibrium.
Theorem Implication: This theorem implies that, if the regulatory
bodyFis successful in licensing more than 58%of the total con-
sensus resource, then no executor can do better than adding legal
blocks at the frontier block of the legal notarized but unconfirmed
branch in the blockchain, resulting in a fair block reward for each
executor type: 𝑔𝑅=𝛼𝑅,𝑔𝑅=𝛼𝑅, and a 100% legal transaction
throughput in the blockchain network: 𝑡F=1.
The consensus resource bound ℎ𝐼𝑅on unregulated executors un-
der immediate release, is a function of the game depth (oversight
window)𝐸, with experimental values given in Figure 6, with an
approximate value of 0.42(please see Section 5.5 for details).
5.3 A Stochastic Game with Immediate Block
Release and an Oversight Compliance Fee
Our second two player game consists of competition between the
regulated executors 𝑅and the unregulated executors 𝑅, when both
players notarize and release their blocks immediately, the regulator
Flicenses more than ℎ𝑜𝑐𝑓
𝐼𝑅=0.50of the consensus resource in the
blockchain network. In this game, the regulator additionally re-
mits an oversight compliance fee (OCF) 𝜌Fas an extra transaction
in each regulated block (to incentivize legal block proposal over
Figure 6: Upperbound on the Unregulated Consensus Re-
source for the RegFrontier/LegFrontier strategies, as a func-
tion of the Game Depth.
dubious block proposal), which is claimed by the executor corre-
sponding to the confirmed block following the said regulated block:
given that some block 𝐵𝑒is regulated and contains the OCF, then
notarizer of block 𝐵𝑒+1claims the said OCF. This OCF is a function
of𝛼𝑅. Here again, the best responses for both players, given that
the regulated executors add regulated blocks to the existing longest
legal branch, are formalized in the following theorem (depicted in
Figure 7).
Theorem 9 (Eqilibrium under IR with an OCF). In the im-
mediate block release (IR) model, given regulated executors 𝑅follow
the ‘longest legal branch’ rule, and 𝛼𝑅>ℎ𝑜𝑐𝑓
𝐼𝑅=0.50, then𝑅playing
RegFrontier( 𝜌F)and𝑅playing LegFrontier is a Nash Equilib-
rium.
Theorem Implication (in conjunction with Theorem 1): As a con-
sequence of this theorem and the previous one, if the regulatory
bodyFis successful in licensing between 50%and58%of the total
consensus resource, andmandates legal blocks to an oversight com-
pliance transaction fee 𝜌F(which is paid byFand is a function of
𝛼𝑅), then again, no executor can do better than adding legal blocks
at the frontier block of the legal branch in the blockchain, resulting
in a fair block reward for each executor type: 𝑔𝑅≥𝛼𝑅,𝑔𝑅≥𝛼𝑅
(inequality due to 𝜌F), and a 100% legal transaction throughput in
the blockchain network: 𝑡F=1. Note that when the regulated con-
sensus resource in the blockchain network is between 50%and58%,
if the regulatorFdoes not include the OCF in the regulated blocks,
it would still be true that 𝑡F=1, but the unregulated executors 𝑅
can attack and successfully add legal blocks linked to an interior
block in their competing branch, resulting in 𝑔𝑅<𝛼𝑅. Thus it is
imperative that the regulator adds the OCF in the regulated blocks,
to save the expected gain 𝑔𝑅(≥𝛼𝑅)of the regulated executors 𝑅.
Justification for a regulatory pay-forward. We justify this model, as
an answer to the following question: given that regulated execu-
tors follow the pay-forward scheme with money pitched in by the
, , Aditya Ahuja, Vinay J. Ribeiro, and Ranjan Pal
time
e0
anye0+ 1
Rw/ OCF
(αR>50%)e0+ 2e0+ 3e0+ 4
e0+ 1
R
(αR<50%)e0+ 2e0+ 3
join←join
→
Be0
RB
RB
RB
RB
DB
DB
LB
defect
Figure 7: When >50% of the consensus resource in the
blockchain network is regulated, and the regulator adds an
OCF (denoted by the money bag in the RBblocks), the legal
branch (right) corresponding to the regulated executors 𝑅
wins, by forcing 𝑅to abandon and defect from their branch.
regulators on the legal branch, why cannot there be a similar pay-
forward scheme enforced by unregulated executors on the dubious
branch?
Here we will assume that world governments, and consequently
the regulatory bodies, are richer than the executors and want to
preserve the revenue generated by their regulated executors (in
order to motivate licensing within the blockchain network). If the
unregulated miners even decide to pitch some money per block
for the unregulated branch, say 𝑤𝑅, then the regulators can coun-
terbalance the legal branch by pitching 𝑤𝑅=𝑤𝑅+𝜌F, where𝜌F
is defined as a function of 𝛼𝑅before. This way, the branch of the
regulated executors will have an additional weight of 𝜌F, and our
model is equivalent to the blockchain pay-forward mining game in
[40]. Consequently the results of that game apply.
5.4 A Stochastic Game with Strategic Block
Release by Regulated Executors
Our final stochastic game considers best responses by 𝑅and𝑅,
given that the regulator Flicenses less than one-third ( ˆℎ𝑆𝑅= 0.33)
of the total consensus resource, the unregulated executors (being
in a majority) follow the longest branch rule, and the regulated
executors can follow the strategic block release (SR) model. The
best responses are given in the following theorem.
Theorem 10 (Eqilibrium under SR from 𝑅).In the strategic
block release (SR) model for regulated executors 𝑅, given unregulated
executors𝑅play DubFrontier with immediate block release (IR), 𝑅
playing RDubFrontier is a Nash Equilibrium only if 𝛼𝑅≤ˆℎ𝑆𝑅=
0.33.
Theorem Implication (in conjunction with Theorem 2): The legal
transaction throughput in the dubious branch of the blockchain,
without loss of generality, would be less than 100% . In the case that
time
e0
anye0+ 1
R
(αR>33%)e0+ 2e0+ 3e0+ 4
e0+ 1
R
(αR<67%)e0+ 2e0+ 3
join←join
→
Be0
RB
RB
RB
RB
LB
LB
DB
defectFigure 8: When >33%but less than a majority of the con-
sensus resource in the blockchain network is regulated, the
regulated executors 𝑅can force the unregulated executors
𝑅to abandon and defect from their branch through strate-
gic release (unreleased RBblock denoted by a dashed box
in epoch𝑒0+4).
the regulatory body Flicenses less than 33%of the total consensus
resource, the regulated executors can do no better than adding legal
blocks at the tip of the dubious branch, inducing a small increase in
the legal transaction throughput. However, if the regulatory body
Fis successful in licensing more than 33%(and less than 50%) of
the total consensus resource, it can orphan some of the dubious
blocks through strategic release (or equivalently using the original
selfish mining attack [ 37]), thereby inducing a higher increase in
the legal transaction throughput: 𝑡F≥𝑔𝑅>𝛼𝑅(and also𝑔𝑅<𝛼𝑅).
This scenario is depicted in Figure 8.
Justification of SR by regulated executors alone. Given that the reg-
ulated executors follow strategic release, we argue next that the
unregulated executors cannot form a competing pool of their own
and also follow strategic release.
Remember that executors on the unregulated branch are of two
types: law-abiding (but not licensed by the regulator), and law-
breaking. It is clear from the Silk road study (Section 2), that a
major reason why law breakers choose Bitcoin for illegal trade is
to misuse the anonymity it provides. If the law breaking execu-
tors decide to form an untrustworthy notarization (mining) pool
with unlicensed law abiding executors for strategic release, it has
been established in a study [ 41] that such pools will not sustain
due to mistrust that a dishonest notarization pool administrator
may not fairly distribute notarization rewards to pool members. So
the chances of sustainable mining/validation pools forming on the
dubious branch among these untrustworthy executors are low.
Regulated Executors (Righteously) Attacking the Blockchain. In the
instance that the regulated executors 𝑅are in a majority in the
blockchain network, with 𝛼𝑅>0.5, these executors might attack
the blockchain by using selfish mining strategies from [ 37–39],
thereby ensuring 𝑔𝑅=1>𝛼𝑅, and consequently achieving the
A Regulatory System for Blockchains , ,
regulatorF’s goal of𝑡F=1. However, this policy is controversial
and socially unacceptable for two reasons: (i) this requires hijacking
the blockchain by a majority of the consensus executors, and more
importantly (ii) it kills the legal transactions notarized in any com-
peting branch. So it is prudent to employ the ‘longest legal branch’
rule when the regulated executors are in a majority, thereby fairly
maintaining 𝑔𝑅=𝛼𝑅, and achieving 𝑡F=1.
In the instance 0.33<𝛼𝑅<0.5as discussed in this subsection,
assuming that the unregulated executors 𝑅might misuse their dom-
inance in the blockchain network to notarize dubious transactions,
the regulated executors may resort to a white hat attack on the
blockchain by adopting one of the selfish mining strategies from
[37–39] to ensure 𝑡F≥𝑔𝑅>𝛼𝑅.
5.5 The Consensus Resource Thresholds
Our stochastic game for block notarization competition between
regulated and unregulated executors is equivalent in analysis to
the seminal blockchain mining games proposed in [ 39,40], and
consequently the results of those game models directly apply. The
consensus resource thresholds ℎ𝐼𝑅,ℎ𝑜𝑐𝑓
𝐼𝑅, and ˆℎ𝑆𝑅are rigorously
established in [ 39,40]. In [ 39], it was proven that 0.361≤ℎ𝐼𝑅≤
0.455, but experimentally ℎ𝐼𝑅≈0.42. Similarly [ 39] proves ˆℎ𝑆𝑅≥
0.308, but in [ 38], it is experimentally shown that ˆℎ𝑆𝑅≈0.33. Finally,
it is proven in [40], that ℎ𝑜𝑐𝑓
𝐼𝑅=0.5.
5.6 Tying the Results with Existing Protocols
We now interpret our blockchain notarization stochastic game in
the context of two prominent consensus protocol regimes: Proof-
of-Work from Bitcoin and vanilla Proof-of-Stake.
Competition in Bitcoin. Our regulated blockchain stochastic game
is inspired from [ 39,40] where the analysis is focused on game-
theoretically determining optimal mining strategies for the Bitcoin
consensus protocol in an unregulated setting. In the Bitcoin con-
sensus protocol, 𝛼𝑅would represent the total hash power regulated
byFin the Bitcoin network. Although the game depth for Bitcoin
is100blocks as determined by the coinbase reward rule of the
protocol [ 39], all the consensus resource thresholds apply, verifi-
able through results in [ 38–40], by replacing 𝐸=∞with𝐸=100.
For example, for an approximate hash power upper-bound ℎ𝐼𝑅on
𝛼𝑅, extrapolated for 𝐸=100through experimental results in [ 39],
please see Figure 6.
Competition in vanilla Proof-of-Stake. Proof-of-Stake (PoS) blockchain
systems (unlike PoW) have a replicable consensus resource: the
executor (validator) stake can be invested in multiple blocks at
the same time. Without risking their stake, executors in PoS sys-
tems can notarize (validate) conflicting blocks, thereby launching a
nothing-at-stake attack [ 31] and compromising the consistency of
the blockchain.
Fortunately, in a regulated PoS blockchain, the regulated executors
𝑅cannot mount a nothing-at-stake attack as they are mandated by
the regulator to notarize blocks in the regulated branch, exclusively.
However, there is a possibility that unregulated executors 𝑅are the
sole party that can do a costless simulation with a nothing-at-stake
attack. This implies that the consensus resource invested on the reg-
ulated branch is≥𝛼𝑅and that on the unregulated branch is ≤𝛼𝑅,and all the results from our regulated blockchain stochastic games
where the regulated branch weight has a known lower-bound on
𝛼𝑅(implying a lower-bound on the regulated branch weight in the
PoS blockchain), directly apply.
The Practical Implication of our Results
We assume that the regulator Festimates𝛼𝑅as part of its licensing
practice, and accordingly conveys optimal consensus execution
strategies to 𝑅. The blockchain network stabilizes once 𝑅adopts
best responses to the strategies chosen by 𝑅(given by the Nash
equilibria of our regulated blockchain stochastic games).
We are proposing a general regulatory framework for permission-
less blockchains to give guarantees on legal block throughput as
a function of the regulated consensus resource in the blockchain
network. World governments can adopt this framework in general
commercial cross-border cryptocurrencies like Bitcoin or Algorand,
or setup localized digital currency networks in jurisdictionally con-
strained but permissionless ‘regulatory sandboxes’6.
6 RELATED WORK
We briefly review related work with respect to regulated asset
trade using existing blockchain protocols designed for crypto-assets.
Since none of the existing protocols have all the principles of regu-
lation enforced within the consensus mechanism, we elucidate the
legal and technical aspects of regulation enforced separately from
the consensus mechanism.
Susceptibility of existing blockchains towards illegal transactions.
Regulators have identified that the illegal use of blockchain based
virtual currencies includes money laundering and terror financing
[9]. There have been studies conducted for establishing emerging
regulatory approaches for the future of blockchain based token
economies [ 21]. None of the existing blockchain protocols, be them
permissionless or permissioned, are designed with regulation in
the consensus mechanism itself [ 26,28,29]. This has introduced
skepticism in the minds of regulatory authorities in the adoption
of these protocols as is [ 11], and introduced regulation as a sep-
arate mechanism to be enforced by legal authorities. Even given
separately enforced regulation, no comprehensive international
regulatory framework exists for blockchain technology [ 5], and
cross jurisdictional governmental collaboration for regulated trade
via legal transactions over blockchains is needed [ 4]. Our regula-
tory framework achieves the said collaboration through support
for cross-jurisdictional regulation.
Pocketed adoption of regulation on blockchains across the world. Li-
censing for legal use of cryptocurrencies has been initiated by the
New York financial services department, with the issuance of the
‘BitLicense’ [ 8] virtual currency business license. Many nations
across the world, including U.K., Australia, the U.S., Hong Kong,
Malaysia, Singapore, Switzerland, Thailand, and United Arab Emi-
rates are either exploring or have implemented ‘regulatory sand-
boxes’ for popular blockchains [ 10]. More specifically in Europe,
6A regulatory sandbox is a framework set up by a regulator to allow small scale and
live testing of innovations by private firms in a controlled environment under the
regulator’s oversight [12].
, , Aditya Ahuja, Vinay J. Ribeiro, and Ranjan Pal
federal banks [ 14] and governmental bodies [ 13] are contemplat-
ing (geographically constrained) oversight on existing blockchains.
Unfortunately, these separate regulatory enforcements imply that
cross-jurisdictional transaction conflict resolution would still re-
quire tenuous negotiations for (cross-jurisdictional) agreements on
the price / commodity quantity being traded as part of the transac-
tion. Such negotiations are trivial under our regulatory framework,
owing to a cross-jurisdictional network of regulators overseeing
the consensus on regulated asset trade.
Permissionless blockchains introduce an untrusted decentralized fi-
nancial system in general. Apart from jurisdiction, blockchain tech-
nology can be an enabler of decentralized autonomous organisa-
tions (DAOs), which have an uncertain legal status [ 15], and so
these DAOs constitute an untrusted financial system. These sys-
tems facilitate illegal transactions for which conflict resolution or
penalisation claims cannot be legally enforced with complete au-
thority [ 16]. This drawback of blockchains has already resulted in
a fallout for the acceptance of Bitcoin.
Bitcoin has suffered a blow to being a trustworthy trade plat-
form due to the large scale illegal trading in the Silk Road darknet
market (as highlighted in Section 2). Given this illegal trade, and
the existing mistrust in the deployment of Bitcoin for financial
services in general, Bitcoin has been banned in many countries
[17], and federal authorities in the US see more oversight coming
on most cryptocurrencies in the future [ 18]. Our regulatory frame-
work, on the other hand, provides a trustworthy, decentralized,
cross-jurisdictional financial system by curtailing trader autonomy
through regulation.
Consensus in State-of-the-art Permissionless Blockchains is insuffi-
cient. Developers at Algorand admit [ 19] that developing a fast
and secure permissionless blockchain is insufficient for a cross-
jurisdictional economy, and there is a need to enforce regulation
by leveraging sophisticated cryptographic constructions atop the
consensus mechanism, such as verifiable random functions, Boneh-
Lynn-Shacham aggregate signatures, and new Pixel multi-signatures
[19]. These constructions introduce extraneous layers of computa-
tion apart from the consensus mechanism, and still are susceptible
to attacks as long as their network is permissionless [ 33]. Our
regulatory framework does not need non-trivial cryptographic con-
structions, and thus is more suitable for regulated decentralized
trade.
Our regulatory framework differs from federated blockchains. Feder-
ated blockchains [ 20], have a permissioned network where certain
pre-selected nodes, from each organization that is maintaining a
distributed ledger, have the authority to participate in the consensus
protocol for agreeing on transactions. Unlike our regulatory frame-
work, where a trader has the authority to participate in consensus
under the oversight of the regulator, federated blockchains have no
notion of regulatory intervention for oversight in the transaction
agreement process.
7 CONCLUSION AND FUTURE DIRECTIONS
In this contribution, we have proposed a framework for the legal
trade of regulated assets via cryptocurrencies through the appro-
priate regulation of the consensus resource of existing blockchainconsensus protocols. First, we have motivated the need for our
framework through a case study of the Silkroad deepweb cryp-
tocurrency based market. Next, we have formally presented our
regulatory framework and have given guarantees of legal trans-
action throughput as a function of the total fraction of the regu-
lated consensus resource. We have shown that the legal transaction
throughput can be maximized when the regulated consensus re-
source is in a majority in the blockchain network, and the regulated
protocol executors follow a ‘longest legal branch’ block notariza-
tion rule.
In future, we would like to analyze competition between regulated
and unregulated executors, when the unregulated executors play
theDubFrontier strategy with a non-zero pay-forward scheme, but
the regulated executors are not supported by the regulator through
an oversight compliance fee. We would like to derive conditions for
a successful long range attack by regulated validators on a vanilla
proof-of-stake blockchain to confirm a legal branch originating
from the genesis block. Finally, we would like to perform a formal,
novel Markov Decision Process (MDP) analysis for block proposal
competition between regulated and unregulated executors, in both
proof-of-work and proof-of-stake regimes, similar to the recent
proposal in [54].
ACKNOWLEDGEMENT
We would really like to thank Leana Golubchik (Professor, USC) for
her comments on improving the readability of a previous version
of this paper.
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A THEOREM PROOFS
We now give the construction of our regulated blockchain stochastic
games, reduce their construction to existing blockchain mining
games, and prove the theorems corresponding to the main results
of our framework.
A.1 Preliminaries
Blockchain Game with Regulated Executor Dominance. We define
our first regulated blockchain stochastic game RBChain-Rdom-
SGame , applicable when the regulated executors are in a majority
in the blockchain network ( 𝛼𝑅>0.5). Given a root block, the
blockchain state is given by (𝑏𝑅,𝑏𝑅), where𝑏𝑅blocks are proposed
by the unregulated executors, and 𝑏𝑅blocks are proposed by the
regulated executor. The regulator may include an OCF in all of 𝑏𝑅
blocks.
RBChain-Rdom-SGame States:
•Mining States. This set of states, denoted by 𝑀, is a collec-
tion of states(𝑏𝑅,𝑏𝑅)where both 𝑅and𝑅notarize blocks
on their own branch. Note that (0,0)∈𝑀.
•Defection/Capitulation States. This set of states, denoted by
𝐶, is when executor 𝑅defects and abandons its own branch,
and adds (legal or dubious) blocks linked to some regulated
block in the competing regulated branch, transitioning
the game from state (𝑏𝑅,𝑏𝑅)to state(0,𝑠𝑅)where𝑠𝑅∈
{0,1,...,𝑏𝑅−1}.
•Legal Winning States. This set of states is given by 𝑊𝑙𝑒𝑔:=
{(𝑏𝑅,𝑏𝑅):𝑏𝑅=𝑏𝑅+1}and all𝑏𝑅blocks in each state
of𝑊𝑙𝑒𝑔are legal. Under 𝑊𝑙𝑒𝑔, when executor 𝑅overtakes,
the game transitions to state (0,0).
Blockchain Game with Unregulated Executor Dominance. We define
our second regulated blockchain stochastic game RBChain-URdom-
SGame , applicable when the unregulated executors are in a majority
in the blockchain network ( 𝛼𝑅>0.5). Given a root block, the
blockchain state is given by (𝑏𝑅,𝑏𝑅), where𝑏𝑅blocks are proposed
by the unregulated executors, and 𝑏𝑅blocks are proposed by the
regulated executors. The regulated executors release 𝑚𝑖𝑛(𝑏𝑅,𝑏𝑅)
blocks.
RBChain-URdom-SGame States:
•Mining States. This set of states, denoted by 𝑀, is a collec-
tion of states(𝑏𝑅,𝑏𝑅)where both 𝑅and𝑅notarize blocks
on their own branch. Note that (0,0)∈𝑀.
•Dubious Cut Defection/Capitulation States. This set of states,
denoted by𝐶𝑑𝑢𝑏, is when executor 𝑅defects and abandons
its own branch, and adds regulated blocks linked to some
regulated block in the competing dubious branch, transi-
tioning the game from state (𝑏𝑅,𝑏𝑅)to state(0,𝑠𝑅)where
𝑠𝑅∈{0,1,...,𝑏𝑅−1}, and all𝑠𝑅blocks are dubious.
•Winning States. This set of states is given by 𝑊:=
{(𝑏𝑅,𝑏𝑅):𝑏𝑅=𝑏𝑅+1}. Under𝑊, when executor 𝑅
overtakes, the game transitions to state (0,0).
Original Blockchain Stochastic Games. We will refer to the original
blockchain mining game under the immediate release model, pro-
posed by Kiayias et.al. (Sections 2 and 3 in [ 39]) as the K1-IR-SGame .
We will refer to the blockchain mining game with a pay-forwardscheme, under the immediate release model, proposed by Kout-
soupias et.al. (Sections 2 and 3 in [ 40]) as the K2-IR-SGame . Also,
we will refer to the original blockchain mining game under the
strategic release model (Section 4 in [39]) as the K1-SR-SGame .
Both blockchain mining games [ 39,40] do not consider a distinction
between legal and dubious blocks, and define the Frontier mining
strategy as a choice to mine on the deepest block in the blockchain
among all competing branches (with or without a pay-forward).
A.2 Proof of Theorem 8
The K1-IR-SGame considers two miners, named 1and2, where
miner 2is in a majority in the Bitcoin network ( 𝛼2>0.5), and is
always following the Frontier strategy. Theorem 3.2 [ 39] from
K1-IR-SGame proves that when miner 1has hash power 𝛼1less
than the root of the polynomial 2𝛼2−(1−𝛼)3≈0.361, then
Frontier is a Nash equilibrium strategy for miner 1. Theorem 3.2
is proven by eliminating the possible set of mining states under
the given condition. Next, through Theorem 3.12 [ 39] in the K1-IR-
SGame , it has been proven that, for game depth E = 3, the expected
gain𝑔1per epoch of miner 1is equal to𝛼2
1(2+2𝛼1−5𝛼2
1+2𝛼3
1)
1−𝛼2
1+2𝛼3
1−𝛼4
1>𝛼1,
by demonstrating strategies more rewarding than the Frontier
strategy for 𝛼1>0.455. By considering alternate game depths 𝐸,
theK1-IR-SGame experimentally establishes (Table 1 in [ 39]) the
lower bound for a deviating strategy to be 𝛼1>ℎ𝐼𝑅≈0.42.
TheRBChain-Rdom-SGame reduces to an instance of the K1-IR-
SGame , when the first miner is the unregulated executor 𝑅, the
second miner is the regulated executor 𝑅, the first miner’s Frontier
strategy is replaced by the LegFrontier strategy, and the second
miner’s Frontier strategy is replaced by the RegFrontier strategy.
Consequently, the results on the threshold ℎ𝐼𝑅from the K1-IR-
SGame directly apply to the RBChain-Rdom-SGame .
A.3 Proof of Theorem 9
TheK2-IR-SGame has an identical setting to the K1-IR-SGame in
terms of defining the miners, and their best response strategies.
However, K2-IR-SGame allows miner 1to add a pay-forward reward
𝑤(as some unknown function of 𝛼1) to the blocks mined by it.
This reward 𝑤is collected by the miner who confirms a block
immediately succeeding the block that contains the announcement
of𝑤. In this setting, it is proven through Theorem 3.2 [ 40], that
Frontier is a Nash equilibrium strategy for miner 1, when miner
2has consensus resource 𝛼2>ℎ𝑜𝑐𝑓
𝐼𝑅=0.5.
TheRBChain-Rdom-SGame with a regulator contributed OCF 𝜌F
in the regulated blocks, reduces to an instance of the K2-IR-SGame ,
when the first miner is the unregulated executor 𝑅, the second miner
is the regulated executor 𝑅, the first miner’s Frontier strategy
is replaced by the LegFrontier strategy, and the second miner’s
Frontier strategy is replaced by the RegFrontier(𝜌F)strategy.
Consequently, the results on the threshold ℎ𝑜𝑐𝑓
𝐼𝑅from the K2-IR-
SGame directly apply to the modified RBChain-Rdom-SGame .
A.4 Proof of Theorem 10
The K1-SR-SGame again considers two miners, named 1and2,
where miner 2is in a majority in the Bitcoin network ( 𝛼2>0.5),
and is always following the Frontier strategy while immediately
A Regulatory System for Blockchains , ,
releasing blocks. However, when it comes to miner 1, in the K1-SR-
SGame , the said miner only releases 𝑚𝑖𝑛(𝑏1,𝑏2)blocks, where 𝑏𝑖
blocks are successfully mined by miner 𝑖∈{1,2}. In this regime, it
is proven in Theorem 4.1 [ 39], that the best response for miner 1
isFrontier only when𝛼1is less than the root of the polynomial
𝛼3−6𝛼2+5𝛼−1≈0.308, but this bound can be improved to 0.33
through results in [38, 40].The RBChain-URdom-SGame reduces to an instance of the K1-
SR-SGame , when the first miner is the regulated executor 𝑅fol-
lowing strategic release, the second miner is the unregulated ex-
ecutor𝑅, the first miner’s Frontier strategy is replaced by the
RDubFrontier strategy, and the second miner’s Frontier strategy
is replaced by the DubFrontier strategy. Consequently, the results
on the threshold ˆℎ𝑆𝑅from the K1-SR-SGame directly apply to the
RBChain-URdom-SGame .
|
{
"id": "2103.16216"
}
|
2305.14748
|
Towards Understanding Crypto Money Laundering in Web3 Through the Lenses
of Ethereum Heists
|
With the overall momentum of the blockchain industry, crypto-based crimes are
becoming more and more prevalent. After committing a crime, the main goal of
cybercriminals is to obfuscate the source of the illicit funds in order to
convert them into cash and get away with it. Many studies have analyzed money
laundering in the field of the traditional financial sector and
blockchain-based Bitcoin. But so far, little is known about the characteristics
of crypto money laundering in the blockchain-based Web3 ecosystem. To fill this
gap, and considering that Ethereum is the largest platform on Web3, in this
paper, we systematically study the behavioral characteristics and economic
impact of money laundering accounts through the lenses of Ethereum heists.
Based on a very small number of tagged accounts of exchange hackers, DeFi
exploiters, and scammers, we mine untagged money laundering groups through
heuristic transaction tracking methods, to carve out a full picture of security
incidents. By analyzing account characteristics and transaction networks, we
obtain many interesting findings about crypto money laundering in Web3,
observing the escalating money laundering methods such as creating counterfeit
tokens and masquerading as speculators. Finally, based on these findings we
provide inspiration for anti-money laundering to promote the healthy
development of the Web3 ecosystem.
|
http://arxiv.org/pdf/2305.14748v1
|
Dan Lin, Jiajing Wu, Qishuang Fu, Yunmei Yu, Kaixin Lin, Zibin Zheng, Shuo Yang
|
cs.CR, cs.SI
|
cs.CR
|
Towards Understanding Crypto Money Laundering in Web3 Through
the Lenses of Ethereum Heists
DAN LIN, JIAJING WU*, QISHUANG FU, YUNMEI YU, KAIXIN LIN, ZIBIN ZHENG, SHUO
YANG
With the overall momentum of the blockchain industry, crypto-based crimes are becoming more and more prevalent. After
committing a crime, the main goal of cybercriminals is to obfuscate the source of the illicit funds in order to convert them into
cash and get away with it. Many studies have analyzed money laundering in the field of the traditional financial sector and
blockchain-based Bitcoin. But so far, little is known about the characteristics of crypto money laundering in the blockchain-
based Web3 ecosystem. To fill this gap, and considering that Ethereum is the largest platform on Web3, in this paper, we
systematically study the behavioral characteristics and economic impact of money laundering accounts through the lenses
of Ethereum heists. Based on a very small number of tagged accounts of exchange hackers, DeFi exploiters, and scammers,
we mine untagged money laundering groups through heuristic transaction tracking methods, to carve out a full picture
of security incidents. By analyzing account characteristics and transaction networks, we obtain many interesting findings
about crypto money laundering in Web3, observing the escalating money laundering methods such as creating counterfeit
tokens and masquerading as speculators. Finally, based on these findings we provide inspiration for anti-money laundering to
promote the healthy development of the Web3 ecosystem.
CCS Concepts: •Applied computing →Electronic commerce ;•Mathematics of computing →Exploratory data analysis ;•
Information systems →Information systems applications .
Additional Key Words and Phrases: Money laundering, Blockchain, Cybercriminal, Web3, Transaction behavior
ACM Reference Format:
Dan Lin, Jiajing Wu*, Qishuang Fu, Yunmei Yu, Kaixin Lin, Zibin Zheng, Shuo Yang. 2022. Towards Understanding Crypto
Money Laundering in Web3 Through the Lenses of Ethereum Heists. In Proceedings of the ACM SIGMETRICS Conference 2023
(SIGMETRICS’23), June 19–23, 2023, Orlando, Florida, USA. ACM, New York, NY, USA, 21 pages. https://doi.org/XX.XXXX/
xxxxxxx.xxxxxxx
1 INTRODUCTION
The past decade has witnessed the rapid growth of blockchain and the blockchain-based cryptocurrency ecosystem.
The market capitalization of cryptocurrencies has reached a staggering scale, with Bitcoin reaching a market
capitalization of $385 Billion [ 24]. Meanwhile, with the further development of blockchain technology, there
is a global wave of the third iteration of the Internet (Web3). Web3’s disruption is built on three essential
fundamentals [ 7]: an underlying blockchain that stores transaction records and ensures the decentralized nature
of Web3, smart contracts that represent the logic of the application, and crypto assets (also called digital assets)
that can represent anything of value. The shared, co-constructed, assemblable economic system on Web3 brings
a richer application ecosystem, a more open economic system, and a larger transaction volume than traditional
financial and public blockchains.
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SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
©2022 Association for Computing Machinery.
ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00
https://doi.org/XX.XXXX/xxxxxxx.xxxxxxx
1arXiv:2305.14748v1 [cs.CR] 24 May 2023
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
However, any new technology, especially those with a lack of regulation, can be exploited for unscrupulous gain.
Since blockchain transactions do not require user identification information, blockchain and its ecosystem have
become a hotbed of various cybercrimes and illegal financial activities [ 33], and the still-developing blockchain-
based Web3 is no exception. According to blockchain security firm Certik [ 26], in the first half of 2022 alone, more
than $2 billion was stolen from Web3 projects as a result of hacking and vulnerabilities. After stealing crypto
assets, cybercriminals conceal and disguise them through different channels to make them appear legitimate and
then withdraw them, a process known as money laundering. So it is said that money laundering is the subsequent
part of all other forms of crypto-based crimes [ 22,23]. Therefore, with the frequent occurrence of Web3 security
incidents, crypto anti-money laundering (AML) is in a crucial position to be the last line of defense to stop hackers
from successfully cashing out and also to deter hackers from committing Web3 crimes at the same time.
Anti-money laundering (AML) is not a new issue, and a wealth of research on the AML issue in traditional
financial scenarios have been proposed [ 8,11,13,19,32]. In the field of cryptocurrency, the Elliptic dataset [ 30] is
the first open-source Bitcoin money laundering dataset that labels abnormal/normal Bitcoin transactions roughly.
But for one, this dataset only has binary labels for money laundering with no other business details, and for
two, the Bitcoin platform this dataset focuses on is very different from the Web3 ecosystem which contains rich
decentralized applications (DApps). To the best of our knowledge, there is currently no public dataset on Web3
money laundering in academia, nor is there a systematic description and analysis of money laundering on the
Web3 ecosystem. It is not clear what the transaction characteristics of these Web3 money laundering accounts
are, how the flow of illegal funds in the money laundering ring has achieved the effect of obfuscating the source,
and what kind of impact it has on the economy of the Web3 ecosystem. Therefore, this is the question that this
paper wants to explore.
However, due to the unique characteristics of Web3 and money laundering practices on it, AML approaches on
traditional financial scenarios or bitcoin cannot be directly applied to Web3 due to the following three challenges.
(i)The underlying blockchain. Compared with traditional finance, blockchain is decentralized, borderless, and
anonymous, without limiting the number of accounts each user can create. This allows cybercriminals to conduct
a large number of frequent transactions between accounts under their control, leading to difficult identification
of account entities and a large number of anonymous transfers. (ii)Smart contracts and digital assets. Based on
blockchain, smart contracts enable various types of digital assets which can be exchanged in the trading platforms.
At the same time, Turing-complete smart contracts can represent and execute more complex application logic and
functions, leading to more complex transaction patterns. (iii)Decentralized finance (DeFi) [ 31]. On the one hand,
immature DeFi applications gather a large number of assets, attracting the attention of criminals and becoming
the hardest hit by asset theft; on the other hand, DeFi services lacking anti-money laundering compliance bring
ever-changing means of exchanging coins while also fueling crypto criminals to launder dirty money.
In this paper, we go for the first time to characterize and analyze the crypto money laundering behavior in
Web3, taking the largest blockchain platform of Web3 [ 24], Ethereum, as an entry point. Note that only the
information of the accounts where the security incidents occurred is publicly reported, whereas the money
laundering accounts where the stolen money is transferred are usually unknown. To this end, we start from
the tip of the iceberg - a very small number of accounts of known security incidents - and then dig and expand
the malicious addresses of money laundering, in order to carve out the full picture of the security incidents and
complete the chain of evidence for the transfer of stolen assets.
Specifically, we first propose an abstract model to describe the process of money laundering and present a
heuristic tracing algorithm based on this abstract model to extract money laundering transactions from the
massive amount of anonymous blockchain data ( Section 4 ). We construct the first money laundering dataset
(containing over 160,000 addresses) in Web3, called EthereumHeist , and also use a case study to illustrate the
effectiveness of the tracing method. With these real data, we conduct in-depth empirical analysis from micro to
macro perspectives. (i) From the perspective of individual laundering accounts, we count and analyze what are
2
Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
the characteristics of accounts and their transaction behavior in the money laundering process ( Section 5 ). (ii)
From the perspective of a gang, we model and measure the network of transactions involved in the cases and
analyze the difference between money laundering transaction networks and the entire Ethereum transaction
network ( Section 6 ). (iii) From a more macro perspective, we explore how the flow of money laundered funds
affects the economy of the Web3 ecosystem ( Section 7 ). Finally, we discuss the AML implications, limitations,
and ethical issues of this paper, as well as possible directions for future research. The main contributions are
as follows.
•To our best knowledge, we present the first dataset and the first systematic analysis on crypto money
laundering in Web3 through the lenses of Ethereum heists from 2018-2022. Based on a very small number
of tagged accounts of hackers, exploiters, and scammers, we adopt an augmented poison policy to trace the
untagged money laundering process, which provides a full picture of incidents. Methods for tracing, data
collection, and measurement of EthereumHeist can also be reused for other cases. We present the money
laundering dataset which can be found at https://www.dropbox.com/.
•We obtain many interesting findings of crypto money laundering in Web3 by adopting feature analysis,
graph analysis, and other methods. These findings help us gain new knowledge about the crypto money
laundering behaviors in Web3. Particularly, we find that it is common for exploiters to obfuscate stolen
funds by swapping tokens through DeFi platforms, and hackers even launder money by creating counterfeit
tokens for higher anonymity.
•We conduct an empirical study to understand the economic impact of laundering in the Web3 ecosystem
by investigating the evolution of money laundering destination service providers and the market price of
crypto assets. Moreover, we present insights for anti-money laundering in the Web3 ecosystem based on
trends of money laundering techniques and service providers, in order to promote the healthy development
of Web3.
2 BACKGROUND
2.1 Stolen Funds From Web3
The boom in the Web3 ecosystem is driving demand for trading platforms. However, where there is money, that
is where thieves are attracted. The sources of stolen funds on Web3 can be broadly classified into three types.
Centralized exchanges (CEXes), which gather large amounts of money but in most cases have weak defenses,
have been coveted by hackers. DeFi projects , which are still in the early stage of development, have also been a
prime target for hackers in recent years, with DeFi digital assets stolen mainly due to contract vulnerabilities,
flash loan attacks, and private key leaks. Scams are a more common but under-disclosed type of asset theft.
Scammers commit theft of cryptocurrency personal holdings through malicious emails or false propaganda, such
as phishing scams, Ponzi scams, etc.
2.2 Money Laundering
Money laundering is the illegal process of transferring funds generated by ill-gotten gains or criminal activities
(such as drug trafficking or terrorist financing) in order to conceal and hide the source of the funds. Money
laundering typically consists of three main stages: (i) Placement : The process of putting the proceeds of crime
into the “laundering system”. These illicit proceeds are often divided into smaller amounts and placed in multiple
accounts to prevent detection by AML systems. (ii) Layering : Separation of illicit proceeds from their sources
and maximum dispersion through complex multiple, multi-layered financial transactions to disguise leads and
hide identities. The higher the frequency of diversion, the more difficult it is for investigators to trace the source
through network paths. (iii) Integration : This is the final stage of money laundering, which is graphically
described as “draining”. The funds are integrated into the financial system as if they were legitimate.
3
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
3 RELATED WORK
3.1 Anti-Money Laundering Techniques
In traditional financial scenarios, AML techniques can obtain and analyze money laundering data through identity-
linked information, as well as various modeling and learning approaches. However, in anonymous blockchain
systems, the identity information and the association between accounts are usually not easily accessible. In
the world of cryptocurrencies, the first publicly available dataset related to money laundering was the Elliptic
dataset, classifying Bitcoin transactions into licit and illicit categories. The Elliptic dataset has attracted much
attention and has been widely followed and used in a number of studies [ 1,14,17,28]. However, the Elliptic
dataset remains inappropriate for developing and validating AML techniques on Web 3 for two reasons. First, the
Elliptic dataset only has binary labels for money laundering transactions and does not contain further details
on the events and stages of money laundering; second, the bitcoin platform that this dataset focuses on has
very different transaction behaviors than Web3 because it does not support smart contracts and decentralized
applications. Therefore, for AML on Web3, a dataset that represents diverse transactions and behaviors on Web3
is urgently needed to be proposed.
3.2 Financial Security Issues on Blockchain
Security issues abound in the blockchain ecosystem, such as phishing scams, Ponzi schemes, wash trading and
DApp attacks, etc. [ 3–5,12,16,18,21,29,34,35]. There exist several datasets for anomaly detection and a series
of approaches have been put forward to solve these issues. For example, Chen et al. [5] collect Ponzi schemes
labels1and propose a Ponzi contract detection approach. Wu et al. [34] propose a network-embedding based
method for phishing identification and disclose a phishing scam dataset2. Existing efforts are usually focused on
the beginning of the security incidents without digging deeper into the money laundering behind them. It has
been reported [ 23] that many security incidents are followed by money laundering to withdraw cash through
service providers such as exchanges. As a result, existing research cannot fully understand the whole story of
security incidents.
4 STUDY DESIGN & DATA COLLECTION
Our research aims to systematically investigate the characteristics of crypto money laundering in Web3, from an
individual account, to the transaction networks formed by money laundering groups, and further, their resulting
economic impact on the Web3 ecosystem. To this end, our research is driven by the following research questions
(RQs):
RQ 1 From a micro perspective, what are the characteristics of accounts and their transaction behaviors
in the process of crypto money laundering in Web3? Previous work lacks the collection of data on
crypto money laundering in Web3. We strive for a complete picture of cryptocurrency money laundering
accounts that cover each suspicious path and compare their trading features with normal accounts.
RQ 2 From a meso perspective, what are the properties of the complex network of transactions formed by
crypto money laundering groups? As previous work has conducted network-based measurements and
investigations on the entire Ethereum blockchain [ 15], we wish to perform network modeling of money
laundering groups’ transactions to investigate the differences in money laundering networks compared to
the entire transaction network.
RQ 3 From a macro perspective, what is the impact of the flow of crypto money laundering on the economy
of the Web3 ecosystem? Several reports [ 23,25,27] have revealed that a large number of stolen assets
1https://www.kaggle.com/datasets/xblock/smart-ponzi-scheme-labels
2http://xblock.pro/#/dataset/6
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Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
have flowed into the Web3 trading platform. Therefore, it is interesting to explore how the inflow of stolen
assets will affect the Web3 ecosystem.
4.1 Abstraction Model for Money Laundering
Since our goal is to measure the money laundering process of stolen funds in Web3, we first propose an abstract
model of the crypto money laundering process. Formally, the money laundering process of a heist can be defined
as a four-tuple:(P,L,I,T), whereP,LandIrepresent the address sets of placement ,layering , and integration ,
respectively (corresponding to the three phases of money laundering mentioned in Section 2.2). Tis a transaction
set which represents the involved transactions during the money laundering process, including external, internal
and ERC20 token transactions.
Placement Layering IntegrationMixing
ExchangeStablecoin
Cross-chain
Lending DEXAttacked
account
Tagged
hackerUntagged
accounts
Stolen
funds
Service providers
Fig. 1. Illustration of crypto money laundering phases in the Web3 ecosystem.
Figure 1 shows a toy example of crypto money laundering in Web3. Specifically, the hacker performs an attack
to steal assets and place them in P, i.e. placement address set. The addresses in Pis the source of the stolen funds,
whose tagges can be obtained by consulting the blockchain browser (e.g. Etherscan) or official announcements.
After takingP, the hacker initiates multiple transactions of Ether or ERC20 tokens, passing the money in Player
by layer into the untagged layering address set Lin the layering phase, cycling back and forth, obfuscating the
source. Finally, the stolen funds are aggregated to integration address set Ifor cash out. The addresses in Iare
usually service providers such as exchanges, DeFi platforms, etc.
4.2 Framework of Dataset Construction
4.2.1 Target Incident Selection. Ethereum has seen many security incidents of crypto asset theft each year since
its creation. As of April 27, 2022, the “Label Word Cloud” service on Etherscan has flagged 115 addresses as
“Heist”3related to stolen assets from exchange hacks, scam projects, DeFi exploits, and more. It should be noted
that the statistics of Etherscan only account for incidents from “cryptocurrency-native" crimes (i.e. on-chain
crimes), in which illicit profits are almost always obtained in the form of cryptocurrency rather than fiat currency.
Based on the “Heist” list marked by Etherscan, we selected a number of representative incidents by year and
amount stolen, and obtained the placement address set Pfor each incident. The list of incidents selected by us
for this study is shown in Table 1 in Section 4.4.
3https://etherscan.io/accounts/label/heist
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SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
Algorithm 1: Heuristic Transaction Tracing Algorithm
Data: placement address set P, address label library 𝐿𝑖𝑏
Input: max. depth of traced layers 𝐾, max. number of addresses per layer Ψ, threshold transaction number
for unknown services Ω
Result: layering address set L, integration address set I, involved transaction set T
1𝑘←0;// The tracing depth
2𝐶𝑢𝑟𝑘←P ;// The current suspicious address set
3while𝑘≤𝐾and 0<|𝐶𝑢𝑟𝑘|<Ψdo
4 for𝑎∈𝐶𝑢𝑟𝑘do
5T𝑎←QuaryTxns( 𝑎);
6 ifDirtyAmount (T𝑎,Ð𝑘
𝑖=0𝐶𝑢𝑟𝑖) >𝛽then
7 if|𝑇𝑎|>Ωthen
8I←I∪{𝑎};
9 𝐶𝑢𝑟𝑘←𝐶𝑢𝑟𝑘−{𝑎};
10 𝑇𝑎←FilterTxns (𝑇𝑎,Ð𝑘
𝑖=0𝐶𝑢𝑟𝑖);
11 else
12 𝐶𝑢𝑟𝑘+1←𝐶𝑢𝑟𝑘+1∪GetUnfamiliar (T𝑎,𝐿𝑖𝑏);
13I←I∪ GetServices (T𝑎,𝐿𝑖𝑏);
14 end
15T←T∪T 𝑎;
16 end
17 end
18𝑘←𝑘+1;
19end
20L←Ð𝑘
𝑖=1𝐶𝑢𝑟𝑖;
4.2.2 Tracing Method. In order to build a model of the above-mentioned money laundering process of Web3
heists, we need to design strategies to trace transactions and sample the transactions used for money laundering.
To this end, we propose a heuristic-based algorithm to identify the money laundering transactions of the heists,
as shown in Algorithm 1. The basic idea of the algorithm is the Augmented Poison Policy [ 18]. That is, the
downstream accounts for money laundering are usually also money laundering accounts, only to the service
provider as an exit.
Next, we briefly explain each part of the tracing algorithm. The input of the algorithm includes the placement
address setPfor each incident, and the tracing parameters: the maximum depth of traced layers 𝐾, the maximum
number of addresses per layer Ψ, and the threshold transaction number for unknown services Ω. The purpose of
setting these parameters is to control the scope of transaction tracing, to avoid an explosion in the number of
downstream addresses, and to delineate the conditions for terminating tracing. Transaction tracking starts with
the placement address set P(line 1–2). For any address 𝑎in the current address set 𝐶𝑢𝑟𝑘at layer𝑘, query its
external, internal and ERC20 transactions, and get the transaction record T𝑎(line 5). We assume that the purpose
of money laundering is to conceal the origin of illicit funds and thus the process tends to be very low profile
and avoids using one address for a large number of transactions. Therefore, money laundering usually involves
intensive and large-amount transactions between a group of accounts. We consider the address 𝑎with a large
number of transactions to be an unknown service address in the aggregation phase rather than the layering phase
6
Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
(a)
0123456789
Depth0200040006000800010000# Predicted
1411461653911760384358158770
020406080100
Prediction (%)
(b)
Fig. 2. (a) The simplified money flow graph of the Upbit Hack case. Nodes represent accounts, and edges represent transactions.
(b) Evaluation of Upbit Hack case.
(lines 6–9), after filtering transactions containing small amounts of dirty money ( ≤threshold𝛽). We then retain
the transactions between unknown service providers and upstream laundering accountsÐ𝑘
1𝐶𝑢𝑟𝑖within one
week as suspected money laundering transactions (line 10). For address 𝑎with a small number of transactions,
we select the next level of suspicious addresses 𝐶𝑢𝑟𝑘+1from recipient addresses of 𝑎’s outgoing transactions. In
particular, we check whether these recipient addresses are known service providers, according the address label
library𝐿𝑖𝑏If they are, they are added to I. Otherwise, they are included in the 𝐶𝑢𝑟𝑘+1crawled in the next layer
(line 12-13). Then, the transactions of address 𝑎are added to the transaction set T(line 15). We keep increasing
the depth𝑘(line 18) until the depth exceeds the maximum number of layers 𝐾, or the size of the current addresses
set𝐶𝑢𝑟𝑘exceeds the range[0,Ψ](line 3). Finally, we merge the addresses of each layer to obtain the final layering
address setL(line 20).
4.2.3 Data Crawling Tools. Crawlers are important means to accomplish the collection of the dataset in this
work. Specifically, we used BlockchainSpider [36], an open source crawler toolkit implemented based on the
Etherscan API, to obtain the transaction records of accounts, i.e., the QuaryTxns function in line 4, Algorithm 1.
Moreover, we utilize the address label library 𝐿𝑖𝑏in line 12-13 of Algorithm 1. The label library 𝐿𝑖𝑏consists of
two parts: the labels of the service platforms and token contracts. To determine the service providers, we employ
“Label Spider” of BlockchainSpider [36] to crawl label addresses associated with exchanges (e.g. “Exchange”,
“DEX”, etc.), mixing services (e.g. “Tornado.Cash”) and other label addresses that appear in connection with
actual money laundering activities. We obtain more than 260,000 items, which is sufficient to cover money
laundering destinations. The token contracts refer to the “ERC20TokenInfo” dataset with more than 313,000
ERC20 tokens, and the “ERC721TokenInfo” dataset with more than 15,000 ERC721 tokens, published by Zheng
et. al. [38], including contract addresses, token names, token symbols, etc. These can help identify the types of
tokens being used for money laundering in token transactions.
4.3 Example: Upbit Hack Case
To show the complexity of crypto money laundering, we visualize the simplified money flow graph of Upbit Hack
(without round-trip transactions) in Figure 2(a). Specifically, the root node (i.e., the leftmost node) represents the
source of money laundering, (i.e., P), the following nodes show the layering addresses (i.e., L), and the links
show the tainted money flow through multiple transactions (i.e., T). Through this case, we can see that crypto
money laundering flows are massively intertwined.
7
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
Table 1. The selected incidents in Web3 from 2018-2022.
Case Name Case Type Year Case Name Case Type Year
CoinrailHacker CEX Hack 2018 LiquidExchangeHacker CEX Hack 2021
BancorHacker DeFi Exploit 2018 AlphaHomoraV2Exploiter DeFi Exploit 2021
SpankChainHacker Others 2018 bZxPrivKeyExploiter DeFi Exploit 2021
FakeMetadiumPresale Scam 2018 CreamFinanceExploiter DeFi Exploit 2021
BitpointHacker CEX Hack 2019 EasyfiHacker DeFi Exploit 2021
CryptopiaHacker CEX Hack 2019 PolyNetworkExploiter DeFi Exploit 2021
DragonExHacker CEX Hack 2019 UraniumFinanceHacker DeFi Exploit 2021
UpbitHacker CEX Hack 2019 BadgerDAOExploitFunder Others 2021
PlusTokenPonzi Scam 2019 VulcanForged Others 2021
KucoinHacker CEX Hack 2020 ATOStolenFunds CEX Hack 2022
AkropolisHacker DeFi Exploit 2020 LCXHacker CEX Hack 2022
HarvestFinanceExploiter DeFi Exploit 2020 CashioAppExploiter DeFi Exploit 2022
Lendf.MeHacker DeFi Exploit 2020 FloatProtocolFuseExploiter DeFi Exploit 2022
WarpFinanceHacker DeFi Exploit 2020 DEGOandCocosExploiter Others 2022
NexusMutualHacker Scam 2020 Arthur0xWalletHacker Scam 2022
AscendEXHacker CEX Hack 2021 Fake_Phishing5041 Scam 2022
BitmartHacker CEX Hack 2021
The lack of ground truth for money laundering addresses makes it difficult to show the effectiveness of our
tracing method through large-scale experiments. But the good thing is that Etherscan has a unique case of
flagging a hacker’s money laundering account, which is the Upbit Hack case4discussed here. Therefore, we start
from the source of Upbit Hack case and obtain the suspicious laundering addresses with Algorithm 1 (Here we
choose conservative parameters 𝐾=20,Ψ=10,000,𝛽=0.01, andΩ=1,000.)
Then, as given in Figure 2(b), we calculate the precision values with varying tracing depth to verify the
effectiveness of our tracing method. We observe that as the depth increases, the number of detected money
laundering addresses grows exponentially. Even when the depth reaches 8, the precision is still over 90%. This
result suggests that our proposed tracing method is somewhat convincing.
4.4 EthereumHeist Dataset Overview
In this work, based on the above data collection method, we collect a total of 33 representative security incidents
that occurred in the Web3 ecosystem from 2018 to 2022 based on Etherscan’s “Hesit” tag. As shown in Table 1,
there are four main types of Web3 cases collected in our dataset: CEX hack, DeFi exploits, Scams and Others, e.g.
exploits of Decentralized Autonomous Organization (DAO), Game Finance (GameFi) and NFT Finance (NFTFi),
etc. We take a preliminary data analysis and exploration of the money laundering dataset as follows (The complete
statistical table of case information is shown in Appendix):
(i)In terms of duration, these cases range from less than 1 day to about 3 years. It can be found that some of
the cases in early years usually last longer, e.g., the Upbit Hack laundering lasted for more than 2 years,
while all the cases that lasted as short as one day occurred in 2022. This may be related to the newer means
in Web3 - mixing service. For example, in the LCX exchange hack that occurred in 2022, the hacker took
4https://etherscan.io/accounts/label/upbit-hack
8
Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
(a) Lifespan distribution
(b) Degree distribution
(c) Frequency distribution
Fig. 3. Comparison of trading characteristics.
only about a day to exchange the stolen ERC20 tokens for Ether through a decentralized exchange (DEX),
and eventually transferred them all to a mixing service named Tornado.Cash.
(ii)In terms of the amount involved, the average amount of money laundered in these cases ranges from $100
thousand to $1 million, and the highest value reaches $10 million, which shows that the financial loss due to
the cases is still very huge.
(iii)In terms of the complexity of the cases, we calculate the number of layers (i.e., tracing depth), transaction
fees (i.e., gas cost), and the size of transaction set Tof each case. In general, cases with more layers of money
laundering have more accounts in Land transactions in T, resulting in a larger and more complicated case
data, which makes it easier for hackers to hide and conceal the source of stolen funds, but also costs hackers
more in transaction fees.
5 RQ1: TRADING FEATURES OF ACCOUNTS
For the laundering accounts of our dataset, we investigate their trading features such as transaction amounts,
frequencies, lifespan, etc. To highlight the differences between layering and normal accounts, we also randomly
sample the same number of normal accounts as reference objects. After comparing and observing the collected
data, we obtain the most significant findings as follows.
5.1 Lifespan
As shown in Figure 3(a), on the one hand, many money laundering accounts have extremely short lifespans,
exhibiting a “used-and-dumped” characteristic. Compared to normal accounts, the peak of the lifespan distribution
for the money laundering account is more to the left. On the other hand, money laundering accounts with larger
lifespans show an irregularly high percentage of jumps, which is because some of the more careful hackers do
not transfer stolen funds immediately, but lurk until the wind passes before laundering. For example, the Coinrail
hacker5stole assets in 2018 and then lurked for two years until 2020 when the stolen money was transferred out.
5.2 Degree and Frequency
Degree indicating the transaction activeness, i.e., the sum of in-degree and out-degree. It can be concluded from
Figure 4(a) that the net transaction follows power law distribution, we also plot the fitted line 𝑦=𝑥−𝛼(𝛼=1.6) to
5https://cn.etherscan.com/address/0xf6884686a999f5ae6c1af03db92bab9c6d7dc8de
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SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
Avg.NetInflow Avg.inflow
Outflow
Avg.outflow Net
Avg.Net
1.4212.9 56.8
204.6
56.38.31.0138.6
12.7
125.7
19.9
12.9Heist
Normal
Fig. 4. Comparison of transaction amount (ETH).
prove this. Due to the threshold we set is 103, we can see from the figure that our data cut at the same point. As
for the several points, that fall around 104is because they get a big degree in the placement phase the very first
time and escape from our threshold control. Frequency is the number of transactions that an account is involved
in per day. Generally, the frequency of a Heist account is evenly distributed over all magnitudes, but when it
comes to the account that has a high proportion, we can see they normally have a high transaction frequency.
5.3 Transaction Amount
We calculate and count the inflow, outflow, and net value of layering accounts in each heist, as well as the
corresponding average value per transaction of accounts, as shown in Figure 3(b). Note that we filter the account
whose transaction value is larger than 1,000 to reduce the bias caused by exchanges with frequent transactions [ 3].
We find that the transaction amount of layering accounts is significantly larger than normal accounts in almost
every indicator. This shows that even though hackers can create accounts without restrictions, the amount of
stolen funds is so large that the amount per transaction in the laundering process remains large. As shown in
Figure 3(b), the average inflow and outflow value of layering accounts reach over 50 ETH, which is about 3-5
times higher than that of normal accounts. The reason for the smaller net value of laundering may be that as
little as possible money is left hackers usually leave as little money in the layering account as possible to reduce
the risk of being frozen.
Finding 1. The laundering accounts usually present an extremely short or long lifespan, reflecting the “use-
and-dump” or “wait-and-see” strategies, respectively. Hackers tend to engage in higher value transactions,
but their net transaction amount is smaller. The value flow in and out heist account reaches around 200
ETH on average.
6 RQ2: NETWORK FEATURES OF GROUPS
In the previous section, we describe the similarities and differences in the characteristics of isolated money
laundering accounts and normal accounts, so naturally we have the next question, what are the similarities and
differences between these money laundering transaction networks and normal transaction networks?
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Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
Table 2. Comparison of network properties. (“Med.” means median. “Avg.” means average).
Self-loop Reciprocity Density
(s.,undi)7Density
(multidi)Global
clusterAvg.
pathlen
HeistEthNet (Med.) 0.06% 4.62E-02 2.59E-02 2.67E-02 1.37E-02 2.47
HeistEthNet (Avg.) 0.64% 9.78E-02 1.86E-01 7.70E-01 2.71E-02 5.56
TransactionNet [15] 0.13% 3.00E-02 1.24E-07 1.87E-07 1.00E-01 5.33
HeistTokenNet (Med.) 0.02% 4.21E-02 2.83E-02 3.36E-02 7.08E-03 2.44
HeistTokenNet (Avg.) 0.03% 1.01E-01 1.78E-01 3.80E-01 1.15E-02 4.97
TokenNet [15] 0.19% 3.00E-02 2.03E-07 1.87E-07 1.75E-01 3.87
6.1 Network Modeling
If criminal groups initiate transactions for the purpose of money laundering, then these transaction networks
may differ from the normal transaction network of Ethereum. To this end, we first model the money laundering
transaction of each case as a network, 𝐺=(𝑉,𝐸),𝐸is the edge set containing all transactions in the case, i.e. T,
and𝑉is a node set which denotes the accounts involved in these transactions.
6.2 Global Network Properties
For each money laundering case, we model two networks: the Ether transactions (including external and internal
transactions) as HeistEthNet , and the ERC20 token transactions as HeistTokenNet , in order to compare with
TransactionNet [15] and TokenNet [15] of the entire Ethereum, respectively. It is worth noting that the entire
network [ 15] may reflect the nature of the normal transaction network, as our money laundering accounts
represent a minuscule 0.3% of the entire transaction network (46 million). Comparative results of graph properties
are shown in Table 2.
6.2.1 Basic Features. First, we count the self-loop ratio of each money laundering network and calculate their
statistic. When compared to TokenNet , as expected, the self-loop ratio of HeistTokenNet is smaller because
self-loop transactions are not consistent with the purpose of money laundering, i.e., no splitting and diverting.
Surprisingly, the average self-loop ratio of HeistEthNet is higher than that of TransactionNet . Our further
analysis reveals that it is because the CashioApp Exploiter6left messages to the community through several
self-transactions in the input data area, resulting in the high self-loop ratio in this case.
Reciprocity is defined as the ratio of the number of edges pointing in both directions to the total number of
edges. We find that the reciprocity of money laundering networks is higher than that of the entire network,
which is likely related to the high activity of token swaps in the Web3 money laundering process.
Finally, we report the density of networks, following the formulas in the exiting research [ 15]. As shown in
Table 2, both HeistEthNet andHeistTokenNet have more than twice the average density in multidigraph than
they have in simple undigraph, with HeistEthNet even reaching 4 times. But in the entire network, the density
ofTransactionNet in multidigraph is only 1.5 times of that in simple undigraph. This indicates crypto money
laundering networks are frequent and dense sub-networks.
6.2.2 Small-world Behaviour. Researchers refer to the property of large network size but small average distance as
small world effect. Analogous to social networks, the entire Ethereum blockchain graphs are also small-world [ 15].
6https://etherscan.io/address/0x86766247ba3405c5f15f06b895294200809e9cfb
7simple, undirected graph
11
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
M4 M3 M2M1
M 6 M 5 M 7
M8 M9M 10
M1 1 M12 M13
M14 M15 M16
(a)
FloatProtocolFuseExploiterBadgerDAOExploitFunderCreamFinanceExploiterDEGOandCocosExploiterHarvestFinanceHackerLiquidExchangeHackerUraniumFinanceHackerPolyNetworkExploiterbZxPrivKeyExploiterFakeMetadiumPresaleVulcanForgedHackerCashioAppExploiterWarpFinanceHackerNexusMutualHackerSpankChainHackerLendf.MeHackerAscendEXHackerBitpointHackerATOStolenFundsEasyfiHackerBancorHackerBitmartHack1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16Motif
LCXHacker (b)
Fig. 5. (a) Directed motifs: 𝑀1and𝑀2are all connected two-node motifs; 𝑀3–𝑀15are all 13 connected three-node motifs;
𝑀16is the four-node bi-fan motif. (b) Distribution of the various motifs in money laundering networks.
In Table 2, we find that the average shortest path length ofHeistEthNet andHeistTokenNet is 4-6, the same
as that of the entire Ethereum transaction network. However, by calculating the average clustering coefficient
of the money laundering network corresponding to each case, we find that the money laundering network
(both HeistEthNet andHeistTokenNet ) has a smaller clustering coefficient than the entire network. This may
be because the special purpose of money laundering makes it lose the multi-hub and social characteristics of
the entire network. Therefore, although the average shortest path length of the money laundering network is
small enough, its clustering coefficient is small, so the money laundering networks do not exhibit the small world
phenomenon.
6.3 High-order Motifs Counting
Higher-order structure of networks can be captured by network motifs [ 2] which are recurring small subgraphs
in the network. To characterize higher-order patterns, we count the percentage of directed motifs (described in
Figure 5(a)) of the simple, directed money laundering network of each case. Figure 5(b) shows the results for the
percentage of each motif in 23 cases (the others encountered out-of-memory errors). Then, we compare with the
entire Ethereum blockchain network [15] and obtain some interesting observations:
(i)The fractions of closed triangular motifs are quite low ( 𝑀3–𝑀9) in money laundering networks. This may be
because the pattern of closed triangle motifs is a manifestation of assets circulating internally, such as wash
trading behavior [29], which is not consistent with the intent to launder assets.
(ii)On the contrary, open triangle motifs are the most frequent motifs that appear in the money laundering
network, of which there are three most, i.e. 𝑀10-𝑀12. These three motifs correspond exactly to three phases
of money laundering: 𝑀10belongs to the placement phase, which spreads the illegally obtained stolen money
and extends the money path; 𝑀11belongs to the layering phase, which continuously passes stolen funds and
12
Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
makes it more difficult to trace; 𝑀12belongs to the aggregation phase, which collects the scattered laundered
stolen money for withdrawal.
(iii)In particular, the money laundering network of DeFi exploit cases has more 𝑀13-𝑀15motifs, in which the
bidirectional edges are most likely related to a classic DeFi action – token trade (also called exchange), i.e., the
trader’s account sells a certain amount of a certain token in exchange for a certain amount of another token
in a liquidity pool of an Automated Market Maker (AMM). To this end, we identify the DeFi token swap
action in each case, referring to DeFiRanger [20]. We find that the number of 𝑀13-𝑀15motifs does have a
strong correlation with the DeFi token swap action. For example, at least 70 token swaps were identified in
money laundering of Cream Finance Exploiter8, and its𝑀13-𝑀15motif fractions are also relatively high.
To further understand the criminal activities of hackers using DEXs/AMMs, we explore and analyze the
cross-asset behavior of hackers in the money laundering process and its purpose, and attribute it to the following
activitie:
(i)Swapping tokens to non-freezable assets. For example, Tether (USDT) is a stablecoin pegged to the US
Dollar, operated by Tether Limited Inc. USDT issuers may freeze assets held by illegal addresses. As a result,
criminals use DEXs to swap freezable assets for non-freezable ones. For example, in the AscendEX Exploit9
event that occurred in December 2021, the attackers quickly exchanged $5.7million worth of USDT stolen
through the Curve.Fi service for DAI, USDC, in about two hours and 40 minutes.
(ii)Swapping tokens for mixing. Many criminals make use of DEXs to swap their stolen tokens to ETH for
mixing. For example, in the Bitmart Hack10event that occurred in December 2021, the criminal swapped
MANA token for ETH in 1 inch DEX, then sent swapped ETH to Tornado.Cash for mixing.
(iii)Swapping tokens to bridge them to other blockchains. Cross-chain transactions of criminals are cunning
behavior to confuse the flow of dirty money. Before Cross-chain transactions, criminals need to swap assets
for tokens convertible on bridges. For example, in the Nexus Mutual Hacker event that occurred in December
2020, the stolen ETH was swapped for renBTC, then the renBTC was bridged to the Bitcoin blockchain.
RenBTC is a wrapped version of bitcoin on Ethereum which can then be bridged across to the Bitcoin
blockchain using RenBridge.
We go a step further to explore Illicit Token Flows for money laundering. We analyze these 33 cases in this
paper and find that criminals stole 923 different types of token assets. These different types of assets went through
multiple DEXs token swaps (in some cases occurring multiple times), with the more popular destination tokens
being: ETH, USDT, WETH, and DAI. the average time for cross-asset behavior to occur was 15 hours after the
start of laundering the stolen assets. Some of the more popular DEXs services include Uniswap, 1 inch, etc.
Finding 2. In general, the self-loop ratio of crypto money laundering networks is lower than that of the
entire network and the reciprocity is the opposite. The crypto money laundering network in Web3 is a
frequent and dense subnetwork, but does not exhibit the small world phenomenon. There exists a large
number open triangle interaction patterns but few closed triangle patterns in crypto money laundering
networks. Particularly, the open triangles in DeFi exploit cases contain more bidirectional edges, reflecting
the method of further obfuscating stolen assets through Defi’s token exchange. The use of DEXs/AMMs by
crypto criminals is closely associated with exploits in the DeFi projects and hacks of exchanges.
8https://etherscan.io/address/0x24354d31bc9d90f62fe5f2454709c32049cf866b
9https://etherscan.io/address/0x2c6900b24221de2b4a45c8c89482fff96ffb7e55
10https://etherscan.io/address/0x39fb0dcd13945b835d47410ae0de7181d3edf270
13
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
(a) Word cloud of service providers
2018 2019 2020 2021 2022
Year0%20%40%60%80%100%PercentCentralized Exchanges
Decentralized Exchanges
Crossing chain Services
Loan Services
Others
Mixing Services (b) Evolution of destination service providers
Fig. 6. Illustration of the economic impact of money laundering in terms of destination service providers and marketplace.
7 RQ3: ECONOMIC IMPACT OF LAUNDERING
7.1 Service Providers of Laundering Exit
In the aftermath of Web3 security incidents, almost all black money flows to service providers to be washed.
Thus, it is necessary to present the percentage and changes of various service providers. For a first impression, in
Figure 6(a), we draw a word cloud graph of the service providers involved in collected events. As we can see, the
most frequent word is “Uniswap”. Uniswap is a decentralized exchange of great name that enables peer-to-peer
market making and enables users to trade or swap cryptocurrencies without any involvement with a centralized
third party, so it provides a wide platform for criminals to money laundering. Additionally, there are some other
typical service providers popular among crypto laundering in Web3. For example, Binance (an eminent centralized
exchange), Opensea (the largest NFT marketplace), and SushiSwap (a decentralized exchange similar to Uniswap).
To further explore the evolution of service providers involved in money laundering over time, we first divide
the service providers into six categories, which are centralized exchanges (CEXes), decentralized exchanges
(DEXes), crossing chain services, loan services, mixing services, and others. Then, we draw a stacked bar chart
displaying the percentage of various service providers change over time as shown in Figure 6(b).
As can been seen, the preferences of the service providers to which this dirty money goes change over time.
On the one hand, centralized exchanges, once the top destination for stolen funds in 2018, phased down in 2019.
The reason may be that CEXes have enhanced AML and KYC procedures at the request of the regulatory section
in recent years [ 9,10]. On the other hand, there is an increase in the share of DEXes, which can infer that DEXes
without a centralized third party is more likely to escape law enforcement investigations. Moreover, the share of
crossing chain services is growing year by year since 2019, which allows black money to circulate and confuse
on multiple chains, indicating that criminals are becoming more crafty. There is even dirty money flowing to
lending services such as Aave, Compound, Dydx, etc. By using the liquidity pool of lending services, criminals
can not only conceal the source of dirty money and reduce the possibility of being traced, but also earn extra
income via providing huge amount dirty money to liquidity pools. In addition, since the inception of the mixing
service, Tornado.Cash, in 2019, it has been one of the destinations of dirty money. It can be presumed that it is a
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Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
Fig. 7. Transaction volume from Kucoin Hacker vs. ETH price
classic and effective money laundering service, no wonder Tornado.Cash was recently sanctioned by the U.S.
OFAC11. In addition, there exist some other kinds of service providers. For example, criminals deposit crypto to
Air Wallet, a kind of distributed airdrop and digital wallet platform.
7.2 Crypto Price Drop Caused by Cash-outs
In the previous section, we find that various trading platforms are destinations for dirty money. There is a great
possibility that money laundering accounts selling stolen crypto in large quantities to withdraw cash will affect
the volatility of the crypto price. Therefore, in this part, we further explore whether hackers sending ETH to
various service providers affects the price of ETH. Due to space limitation, here we only show the result of one of
the typical cases - Kucoin Hacker12. From Figure 7, we see that KucoinHackers sent ETH to service providers from
April 2021 to May 2022 and there exist 5 apparent spikes of transaction volume, for example, May 2021, December
2021, March 2022 and May 2022. When a transaction spike occurs, which means that the hacker is withdrawing a
lot, the price of ETH drops significantly. Therefore, we can presume that a large number of cashouts correlate
significantly with the price of ETH drops. This may be because hackers are eager to withdraw cash and then sell
ETH at low prices, resulting in a significant drop in the price of ETH.
In addition, we find that the stolen NFTs also face the fate of being sold at low prices. On the day of the Arthur
Hot Wallet heist, the hacker directly sold or auctioned off the 17 stolen Azuki NFTs for around 10 ETH, which
was significantly lower than the average market price (13 ETH) at the time. One of the biggest price drops was
Azuki#60613- from 78 ETH before the heist to 50.15 ETH when it was sold off. Moreover, the hacker transferred
the stolen NFT to other wallets before dumping it in order to prevent it from being frozen. On the one hand,
11https://home.treasury.gov/news/press-releases/jy0916
12https://etherscan.io/address/0xeb31973e0febf3e3d7058234a5ebbae1ab4b8c23
13https://etherscan.io/nft/0xed5af388653567af2f388e6224dc7c4b3241c544/606
15
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
② Transfer
ETH...
① Stole Funds③ Create
RZN & Mint
④ Add
Liquidity
CreatorHackersAkropolis Fake RZN
⑥ Remove
Liquidity⑤ Swap
Token
Liquidity
PoolParticipants
(RZN-ETH)
Fig. 8. The Role Fake Tokens Play in Money Laundering.
it is not obvious to distinguish whether the purchaser of the stolen NFT is a hacker-controlled account or an
ordinary user, increasing the difficulty of tracing the hacker’s money laundering transactions. On the other hand,
ordinary users are likely to accidentally purchase these stolen NFTs, resulting in their subsequent blacklisting by
the trading platform, which is not conducive to NFT market stability.
Finding 3. A large number of cashouts of hackers correlate significantly with ETH price drops. The hackers
send lots of stolen ETH to service providers in order to get back clean fiat currency. Hackers dumping stolen
NFTs at low prices could lead to a crisis of trust in the NFT programs and increases the risk of freezing the
crypto assets of a genuine user of NFT trading platforms. The service providers being used to launder money
are changing over time. The phenomenon of decentralized exchanges, cross-chain services, and lending
services increasing their share year by year indicates that criminals are becoming more anti-regulatory and
are constantly seeking more cunning and stealthier means of money laundering.
8 COUNTERFEIT TOKEN DEPLOYMENT FOR CUNNING LAUNDERING
In addition to money laundering techniques such as layered transfers and cross-chaining, more cunning hackers
may disguise themselves as other common players to evade detection in the Web3 ecosystem, where the existence
of counterfeit tokens provides the perfect opportunity for hackers to launder money. Researchers [ 12,37] have
found that counterfeit tokens are prevalent in the Web3 ecosystem because most DEXes do not enforce any rules
for token listing. Hackers can easily create counterfeit tokens and liquidity pools, and even disguise themselves
as ordinary speculators to launder illicit funds from liquidity pools of counterfeit tokens. To this end, we conduct
an empirical analysis based on the counterfeit token dataset provided by Gao et. al. [12], and are surprised to find
that in 13 of the 33 cases in this paper are related to counterfeit tokens some way.
One notable case is Akropolis Hacker14(a DeFi Hacker). By tracing downstream laundering transactions of
Akropolis Hacker, we find evidence that this hacker was laundering money by creating counterfeit tokens. As
plotted in Figure 8, the main procedures are as follows:
14https://etherscan.io/address/0x9f26ae5cd245bfeeb5926d61497550f79d9c6c1c
16
Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
(i)Using the tracing method mentioned earlier, we see that the hacker cascaded the stolen funds from Akropolis
to several accounts under his control (0x1c8015and 0x982d16identified as “Heist” in our dataset), and
transferred funds to another controlled account 0x1f817.
(ii)Subsequently, address 0x1f8 created the fake token RZN (Rizen Token)18, and a liquidity pool on Uniswap19
with 594 fake RZN and 0.877 ETH.
(iii)The hackers then manipulated the liquidity pool through multiple accounts, posing as ordinary speculators
and participating in the trading of counterfeit tokens, e.g. address 0x8c4d20sold 1000 fake RZN and got 300
clean ETH.
(iv)Finally, the RZN creator removed the liquidity of 2144 RZN coins and 0.25 ETH. The hackers successfully
laundered the illegal funds by disguising their addresses as ignorant participants.
Finding 4. Hackers launder money anonymously by creating fake tokens and disguising their addresses as
ignorant participants, which is an upgraded method of money laundering in Web3. At the same time, the
fake tokens created by hackers simultaneously increase the risk of ordinary users falling for the scams.
9 MONEY LAUNDERING CORE GROUP IDENTIFICATION.
In the previous section of dataset construction, we introduce how to mine downstream unknown money laun-
dering accounts and networks based on the starting hackers, exploiters, and scammers. Here, the target of
the identification of core money laundering organizations is to find core account groups with more intensive
interactions and frequent capital transactions in the relatively sizeable downstream money laundering network,
as a supplement to criminal evidence collection.
In order to achieve this target, on the basis of the money laundering network initially crawled in this paper,
we first define the money laundering suspiciousness of an account, then use the approximate greedy algorithm to
prune the original downstream network and build a minimum priority tree to speed up the iterative process.
Through this process, the most suspicious subnetwork can be obtained as the core money laundering network.
The main idea of suspiciousness comes from the money laundering characteristics observed in RQ1 and RQ2:
Money launderers will create a large and dense subgraph of transfers because money laundering accounts need
to transfer a large number of funds in a short period of time to avoid being detected and frozen, resulting in a
dense transfer subnetwork.
The suspiciousness of subgraph 𝑆=(𝑁,𝐸), where𝑁denotes nodes and 𝐸denotes edges, is defined as:
𝑔(𝑆)=𝑓(𝑆)
|𝑆|,
which can be regarded as the result of taking the average value after summing the suspiciousness of each node
and edge in the subgraph structure. Generally, 𝑓(𝑆)is defined as follows:
𝑓(𝑆)=𝑓𝑁(𝑆)+𝑓𝐸(𝑆)
=∑︁
𝑖∈𝑁𝑎𝑖+∑︁
𝑖,𝑗∈𝑁,(𝑖,𝑗)∈𝐸𝑐𝑖𝑗,
15https://etherscan.io/address/0x1c80f8670f5c59aab8e81e954aabb64dabde2710
16https://etherscan.io/address/0x982dd33d6bc5bf83eedcbcab92e4899c7a
17https://etherscan.io/address/0x1f84ba7bacd29e875367688b38ecccb7849b50fa
18https://etherscan.io/token/0x9c91310c9bf1c779b667f46322d33bfdc96c1a07
19https://etherscan.io/address/0x658b4a15aae288757c41a9b074ab1881d3ecad0c
20https://etherscan.io/address/0x8c4dedecbe3e8fbcc0501599cb59e7feadd99ffc
17
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
Algorithm 2: An approximate greedy algorithm based on a Minimal priority tree.
Data: Initially crawled Laundering Transaction newtwork 𝐺=(𝑈∪𝑉,𝐸)by Algorithm 1, suspiciousness
𝑔is defined before.
1𝑋denotes the subgraph of 𝐺.Result: A densest subgraph with maximum suspiciousness
2initialization(𝐺,𝑔);
3construction of priority tree 𝑇of𝑈∪𝑉;
4𝑋0←𝑈∪𝑉;
5for𝑡=1,...,𝑚+𝑛do
6𝑖∗←𝑎𝑟𝑔𝑚𝑎𝑥𝑖∈𝑋𝑖𝑔(𝑋𝑖\{𝑖});
7 update the priorities in tree 𝑇for all neighbors of 𝑖∗;
8𝑋𝑡←𝑋𝑡−1\{𝑖∗});
9end
10𝑋∗←𝑎𝑟𝑔𝑚𝑎𝑥𝑋𝑖∈{𝑋0,...,𝑋 𝑚+𝑛}
where𝑎𝑖denotes node suspiciousness, 𝑐𝑖𝑗denotes edge suspiciousness. For simple calculation, we set edge
suspiciousness 𝑐𝑖𝑗=0and𝑎𝑖=𝛼when node𝑖is labeled as a heist. In our experiment, we set 𝛼=49to achieve
the best result according to the experiment. Note that when it comes to application, the formula of 𝑓(𝑆)is general
and can be replaced with other formulas according to needs.
The algorithm2 for identification of the money laundering core network is shown as follows:
Step 1: Built a transaction network 𝐺=(𝑈∪𝑉,𝐸), where𝑈is a set of transaction sender with size 𝑚, and𝑉is a
set of recipients with size 𝑛,𝐸indicates transaction records.
Step 2: Build a priorities tree to restore the priorities 𝑃of each node 𝑖, calculated as:
𝑃𝑖=𝑓(𝑋𝑡\{𝑖})−𝑓(𝑋𝑡).
Step 3: Traverse all nodes in 𝑈and𝑉, and calculate the suspiciousness after removing the current node 𝑖∗.
Step 4: Find out the subgraph with the largest suspiciousness.
Step 5: Update the priorities of nodes.
Step 6: Iterate accordingly until all nodes have been traversed.
Step 7: Get the subgraph 𝑋∗with the largest suspiciousness.
Due to limited space, we only show the experimental process and results of the Upbit Hack money laundering
case here. First, We built a bipartite graph based on the transaction data. In the case of Upbit Hack, there are
131,654nodes in𝑈,16,138of which are labeled as heists, and 482,775nodes in𝑉with 16,536heists in them.
The heists are marked according to the labels we get in the previous experiment. That is, we get a matrix of size
|𝑈|×|𝑉|=131,654×482,775. After implementing the algorithm, we get a core network of size 200×1,332. For
the200and 1,332nodes extracted from 𝑈and𝑉respectively, we calculated the classification results of whether
these account has been marked as money laundering: it turns out that the precision of our experiment reached
82.5%and 100% in𝑈and𝑉respectively. Next, we collect the transaction data from our core network. The original
transaction network has 2,348,180transactions while the core network we extract has only 45,811transactions.
In all, through this method in this article, the money laundering core network of size 200×1,332can be
extracted from the original Upbit Hack money laundering network of size 16,138×16,536, which narrows the
scope of suspicion for criminal evidence collection and investigation.
18
Money Laundering SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA
10 ETHICAL CONSIDERATIONS
In this paper, we reveal the first crypto money laundering dataset in Web3, investigating and analyzing the money
laundering techniques of hackers, exploiters, scammers, and others. The disclosure and investigation may cause
the community to worry about contributing to the “copycat crime” effect, but actually, our research motivation
is similar to the studies of Ponzi contracts [ 5], phishing scams [ 16], DApp attacks [ 21], counterfeit tokens [ 12],
etc. The money laundering transactions published in EthereumHeist are only the tip of the iceberg. As shown
in this paper, cybercriminals are improving their methods and techniques year by year in the “cat-and-mouse”
game of Web3 anti-money laundering, reinforcing the need for investigation and understanding of crypto money
laundering in the Web3 ecosystem. This work will facilitate more effective designs of anti-money laundering
algorithms based on our interesting findings in the laundering accounts and networks, and further promote the
healthy development of the Web3 ecosystem.
As for whether our research involves privacy issues, the answer is NO. First, the data we collect is completely
public and can be accessed by anyone on blockchain. Second, our dataset only includes anonymous transaction
data on blockchain, but not other data associated with real-life personal information. Therefore, based on these
two points, we do not consider that this work will invade the privacy of others, or directly lead to the arrest or
prosecution of individuals.
11 DISCUSSION & CONCLUSION
In this paper, we conduct the first systematic study to characterize the crypto money laundering in the Web3
ecosystem. We start from a very small number of security incident accounts, collect abundant money laundering
transactions, and build a dataset named EthereumHeist . Based on the dataset, we obtain a series of interesting
findings of crypto money laundering in Web3 via answering three research questions from micro, meso to macro
perspectives, reflecting the feasibility and necessity of Web3 AML. By answering RQ1 and RQ2, we summarize the
characteristics at the account level and the network level, e.g. the lifespan and transaction amounts of accounts,
and higher order patterns of sub-networks. These findings can help design effective red flag indicators and
detective methods for Web3 AML. Furthermore, by answering RQ3 in a data-driven manner, it can be observed
that DApps on Web3 such as DEXes, lending services, etc. have been increasingly involved in money laundering
activities in recent years. There is also evidence that dumping stolen money in the money laundering process
affects price volatility. Therefore, money laundering is detrimental to the stability of the Web3 market, and
it is necessary to develop decentralized security protocols based on economic incentives to achieve effective
regulation of decentralized platforms in Web3. Coincidentally, the EU Commission has recently launched a public
call for tender for a study on “embedded supervision” of DeFi [6].
As a preliminary exploration of Web3 money laundering and limited by space, this paper also has some
limitations (discussed in the Appendix), and there are ample opportunities for future work following our dataset
and analysis. As the proposed dataset contains lots of accounts and transactions, and empirical results, researchers
are able to propose intelligent tracing methods and money laundering subgraph detection based on this dataset,
as those based on Elliptic dataset. Moreover, it is interesting to investigate the correlations and interactions
between money laundering transactions in different cases, such as the hacks on Upbit and Kucoin, which are
both responsible for Lazarus Group [ 22]. This may provide further insights into the evolution of their money
laundering strategies and lead to more accurate money laundering tracing. Last but not the least, researchers can
also design strategies and methods based on cross-chain tracing in the Web3 ecosystem to extend the dataset of
this paper.
19
SIGMETRICS ’23, June 19–23, 2023, Orlando, Florida, USA Anonymous author(s)
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21
|
{
"id": "2305.14748"
}
|
2409.10031
|
Assessing the Impact of Sanctions in the Crypto Ecosystem: Effective
Measures or Ineffective Deterrents?
|
Regulatory authorities aim to tackle illegal activities by targeting the
economic incentives that drive such behaviour. This is typically achieved
through the implementation of financial sanctions against the entities involved
in the crimes. However, the rise of cryptocurrencies has presented new
challenges, allowing entities to evade these sanctions and continue criminal
operations. Consequently, enforcement measures have been expanded to include
crypto assets information of sanctioned entities. Yet, due to the nature of the
crypto ecosystem, blocking or freezing these digital assets is harder and, in
some cases, such as with Bitcoin, unfeasible. Therefore, sanctions serve merely
as deterrents. For this reason, in this study, we aim to assess the impact of
these sanctions on entities' crypto activities, particularly those related to
the Bitcoin ecosystem. Our objective is to shed light on the validity and
effectiveness (or lack thereof) of such countermeasures. Specifically, we
analyse the transactions and the amount of USD moved by punished entities that
possess crypto addresses after being sanctioned by the authority agency.
Results indicate that while sanctions have been effective for half of the
examined entities, the others continue to move funds through sanctioned
addresses. Furthermore, punished entities demonstrate a preference for
utilising rapid exchange services to convert their funds, rather than employing
dedicated money laundering services. To the best of our knowledge, this study
offers valuable insights into how entities use crypto assets to circumvent
sanctions.
|
http://arxiv.org/pdf/2409.10031v1
|
Francesco Zola, Jon Ander Medina, Raul Orduna
|
cs.CR, cs.CE
|
cs.CR
|
arXiv:2409.10031v1 [cs.CR] 16 Sep 2024Assessing the Impact of Sanctions in the
Crypto Ecosystem: Effective Measures or
Ineffective Deterrents?
Francesco Zola1[0000−0002−1733−5515], Jon Ander Medina1[0009−0008−1107−0617],
and Raúl Orduna1[0000−0002−5932−0987]
Vicomtech Foundation, Basque Research and Technology Alli ance (BRTA);
Paseo Mikeletegi, 57, Donostia 20009, Spain
{fzola, jmedina, rorduna}@vicomtech.org
Abstract. Regulatory authorities aim to tackle illegal activities by tar-
geting the economic incentives that drive such behaviour. T his is typi-
cally achieved through the implementation of financial sanc tions against
the entities involved in the crimes. However, the rise of cry ptocurrencies
has presented new challenges, allowing entities to evade th ese sanctions
and continue criminal operations. Consequently, enforcem ent measures
have been expanded to include crypto assets information of s anctioned
entities. Yet, due to the nature of the crypto ecosystem, blo cking or
freezing these digital assets is harder and, in some cases, s uch as with
Bitcoin, unfeasible. Therefore, sanctions serve merely as deterrents. For
this reason, in this study, we aim to assess the impact of thes e sanctions
on entities’ crypto activities, particularly those relate d to the Bitcoin
ecosystem. Our objective is to shed light on the validity and effective-
ness (or lack thereof) of such countermeasures. Specificall y, we analyse
the transactions and the amount of USD moved by punished enti ties that
possess crypto addresses after being sanctioned by the auth ority agency.
Results indicate that while sanctions have been effective fo r half of the
examined entities, the others continue to move funds throug h sanctioned
addresses. Furthermore, punished entities demonstrate a p reference for
utilising rapid exchange services to convert their funds, r ather than em-
ploying dedicated money laundering services. To the best of our knowl-
edge, this study offers valuable insights into how entities u se crypto assets
to circumvent sanctions.
Keywords: Sanctions circumvention, Money laundering, Flow analysis ,
Behavioural analysis, Cryptocurrency, Traceability
1 Introduction
Understanding the multidimensional nature of crime is cruc ial for developing
effective strategies to prevent and combat these illicit act ivities. Crimes often
manifest in various forms and domains, like drug trafficking, human trafficking,
and other types of organised crime. Nevertheless, in all the se cases, the main
2 F. Zola et al.
goal of the criminal networks is still to make a profit [22]. Fo r this reason, dis-
rupting the economic incentives driving illicit behaviour has become the primary
objective in tackling these crimes [28]. This task requires cooperation and co-
ordination among governments, law enforcement agencies, fi nancial institutions,
regulatory bodies, and other stakeholders at national and i nternational levels.
This is the case of authority agencies such as the Office of Fore ign Assets
Control (OFAC)1, Office of Financial Sanctions Implementation (OFSI)2, Eu-
ropean External Action Service (EEAS)3and United Nations Security Council
(UNSC)4, that aim to implement and enforce financial sanctions direc tly to the
entity behind some violations and crimes. In fact, these age ncies have the au-
thority to freeze, block or restrict access to sanctioned en tities’ assets such as
banking accounts, real estate, vessels, etc.
However, with the advent of virtual currencies such as crypt ocurrencies, sta-
blecoins, and Non-Fungible Tokens (NFTs), sanctioned enti ties have discovered
new opportunities for circumventing sanctions and continu ing their illicit activi-
ties [29]. These digital assets promote decentralization a nd offer varying degrees
of anonymity or pseudo-anonymity, creating a borderless ec osystem ideal for the
proliferation of illicit activities [14][24]. According t o “The 2024 Crypto Crime
Report ” [12], although illicit crypto-transactions constitute l ess than 0.5% of the
total on-chain transaction volume, they accounted for near ly 40 billion USD in
2022 and over 24 billion USD in 2023.
The significant size of the crypto market, the opportunities crypto assets
present, and their proven involvement in illicit activitie s [9][16] have raised con-
cerns about ensuring compliance with existing financial reg ulations. While some
countries such as Tunisia, Nepal, Libya, Iraq, Bolivia, and Algeria have banned
the use of cryptocurrency [7], others have directed their eff ort to implement
new frameworks and technology to increase their control deg ree over the crypto
ecosystem. Thus, regulatory agencies have intensified thei r enforcement actions
against sanctioned entities, including tracking informat ion about their crypto
assets whenever possible. However, due to the nature of the c rypto ecosystem,
they still face limitations in blocking these digital asset s. As a result, sanctions
solely serve as a deterrent, aiming to discourage other indi viduals and companies
from engaging in transactions with punished entities. Yet, this limitation makes
crypto assets the perfect facilitator for ongoing illicit o perations and circumvent-
ing sanctions.
For this reason, in this work, we aim to examine the impact the se sanctions
generate in the crypto activities of punished entities and t heir related violations.
Our objective is to evaluate the validity and effectiveness ( or lack thereof) of such
countermeasures. Specifically, we analyse the transaction s and the amount of
USD moved by punished entities that possess crypto addresse s before and after
being sanctioned by the authority agency. Furthermore, we i nvestigate which
1https://ofac.treasury.gov/
2https://sanctionssearchapp.ofsi.hmtreasury.gov.uk/
3https://www.eeas.europa.eu/
4https://www.un.org/securitycouncil/content/un-sc-co nsolidated-list
Assessing the Impact of Sanctions in the Crypto Ecosystem 3
type of known crypto entities (Exchange, Mixers, Gambling, etc.) are typically
engaged with by the punished entities post-sanction. Thus, we investigate if
they are attempting to connect with other entities involved in illicit operations
or are employing strategies such as money laundering, on-ra mps and off-ramps
operations, funding raise campaigns, etc.
More specifically, this work analyses entities (individual s and companies)
sanctioned by the OFAC agency that have information about Bi tcoin (BTC)
assets. These decisions were taken for two specific reasons: a) Bitcoin (together
with stablecoins) is widely recognised among the most popul ar cryptocurrencies
for illicit activities [12], primarily due to its high marke t value and accessibility,
even for users without technical background; b) other autho rity agencies (OFSI,
EEAS, UNSC) do not have a comprehensive list of sanctioned cr ypto-related
entities or do not release it publicly.
The results suggest that sanctions have been effective for ro ughly half of the
sanctioned entities, while the others continue to engage in transactions through
sanctioned Bitcoin addresses. Additionally, sanctioned e ntities prefer to directly
convert their cryptocurrencies using dedicated services ( Exchanges ), rather than
apply money laundering strategies using services like Mixers orGambling .
To the best of our knowledge, this study offers a first step towa rds determin-
ing the effectiveness of sanctions within the crypto ecosyst em and how sanctioned
entities used them to circumvent sanctions.
2 Background
This section presents an overview of the crypto ecosystem, s howing regulations,
directives, and literature approaches. Specifically, in Se ction 2.1 a review of Eu-
ropean Union regulations related to the crypto ecosystem is presented, while
Section 2.2 is focused on presenting the United States OFAC a gency and its
crypto sanctions. Finally, Section 2.3 describes related r esearch in the field.
2.1 EU Directive review
As mentioned, crypto assets can be harder (or in some cases un feasible) to freeze
or block directly. However, governments and regulatory aut horities worldwide
have been increasingly implementing measures to monitor an d regulate crypto
markets to tackle concerns related to illicit activities. T hus, these regulations
aim to facilitate the imposition of sanctions when necessar y.
The European Union (EU) has established and reviewed severa l directives
aimed at tackling issues such as money laundering, fraud, te rrorism financing,
and other emerging challenges related to non-cash payments . For instance, in
2015, the 5th Anti-Money Laundering Directive (5AMLD) [1] w as introduced
to address new trends in terrorist financing, building upon t he provisions of the
4AMLD. Notably, under the 5AMLD, the role of cryptocurrency Exchanges has
changed, they are now considered equivalent to financial ins titutions. There-
fore, they are required, among other measures, to adhere to K now Your Cus-
4 F. Zola et al.
tomer (KYC) requirements, implement Anti-Money Launderin g (AML) mecha-
nisms, and register with national regulatory authorities. This directive has been
amended to include provisions regarding information accom panying transfers of
funds and certain crypto-assets, through the Regulation (E U) 2023/1113 [6]. The
amendment fosters international cooperation within the Fi nancial Action Task
Force (FATF) and the global implementation of its recommend ations [4]. Fur-
thermore, it sets the obligation for virtual asset service p roviders (VASPs) and
crypto asset service providers (CASPs) to collect informat ion about the person
who uses their services. Together with these directives, an other pivotal regula-
tion is the Markets in Crypto-Assets (MiCA) [5], which clear ly distinguishes be-
tween different types of crypto-assets (asset-referenced t okens, electronic money
or e-money, and other crypto assets). It then imposes constr aints on CASPs
to ensure market integrity and financial stability. One of it s key provisions in-
volves significant disclosure and transparency rules aimed at better informing
consumers about associated risks, as well as mandating the i mplementation of
security measures and anti-money laundering compliance.
Fraud and counterfeiting of non-cash means of payment is reg ulated through
the EU directive 2019/713 [3]. The framework establishes me asures aimed at
preventing and detecting fraud and counterfeiting of non-c ash payment instru-
ments, such as security requirements for payment service pr oviders, customer
authentication procedures, and usage of secure technologi es (encryption and to-
kenization). This directive aimed to cover also new types of non-cash payment
instruments such as e-money and virtual currencies, since t hey have a significant
cross-border dimension. Another interesting EU framework , although it doesn’t
specifically mention digital currency or cryptocurrencies due to its general aim,
is the Directive (EU) 2017/1371 [2] that defines legal framew ork and measures
to combat any fraud against the financial interests of EU.
Despite the revision, update, and implementation of these p olicies, freezing
or blocking crypto assets remains harder to accomplish by te chnical design. Its
boundary-less structure, the availability of services in j urisdictions where these
policies don’t apply, or in countries unwilling to cooperat e in criminal investi-
gations, the anonymity of users, and the ease of conducting t ransactions - these
properties collectively enable users to circumvent sancti ons and persist in their
illicit activities.
2.2 US Office of Foreign Assets Control
The Office of Foreign Assets Control (OFAC) is part of the Depar tment of the
United States (US) Treasury, and it is in charge of implement ing economic and
trade sanctions in accordance with US foreign policy and nat ional security objec-
tives. These sanctions are directed towards specific foreig n countries and regimes,
terrorists, international narcotics traffickers, individu als involved in the prolif-
eration of weapons of mass destruction, and other actors pos ing threats to the
national security, foreign policy, or economy of the United States. These actors
and their blocked assets are included in a Specially Designated Nationals and
Assessing the Impact of Sanctions in the Crypto Ecosystem 5
Blocked Persons (SDN) list5. Consequently, US individuals and companies are
generally prohibited from dealing with them. The actors rep orted in the SDN
list, which we refer to as entities in this paper, are sanctioned due to the vio-
lation of one (or more) Executive Orders and/or Code of Feder al Regulations
(CFR)6. Since 2018, OFAC has included cryptocurrency-related inf ormation in
the SDN list as blocked assets for sanctioned entities whene ver such information
is available. In some cases, one entity can have multiple san ctioned addresses.
To date, the violations that have led to sanctions against en tities with crypto
addresses involved, are detailed in Table 1. In this paper
# Code Description Executive Order N.
1 CYBER2 Malicious Cyber Activities 13694, 13757
2 DPKR3 Blocking Property of the Government of North Korea 13 722
3 DPKR4 Additional Sanctions With Respect to North Korea 138 10
4 ELECTION Foreign Interference in the US Election 13848
5 IFSR Iranian Financial Sanctions Regulations 31 CFR part 5 61
6 ILLICTI-DRUGS Illicit Drug Trade 14059
7 IRGC Iranian Financial Sanctions 31 CFR Part 561
8 NPWMD Weapons of Mass Destruction Proliferators Sanction s 31 CFR part 544
9 RUSSIA Blocking Harmful Activities of the Russian Federat ion 14024
10 SDGT Narcotics Trafficking Sanctions 31 CFR part 594
11 SDNTK Foreign Narcotics Kingpin Sanctions 31 CFR part 598
Table 1. Violations reported in the SDN list that have generated sanc tions against
cryptocurrency related entities.
2.3 Cryptocurrency and Cybercrime
Cryptocurrencies have created a convenient ecosystem for t he permanence and
movement of illicit cybercrime-related activities, with c urrencies such as Bitcoin,
Ethereum, and Monero becoming their operational space. The evolution of illicit
activities within the cryptocurrency ecosystem has been st udied extensively [13]
[11]. For instance, Hornuf et al. [17] analysed cybercrime r elated to Ethereum
transactions. In particular, they identified over 1.78 mill ion transactions related
to 19 categories of cybercrime, with losses amounting to $1. 65 billion up to
the year 2021, posing the focus on estimating how these cyber crimes impact
victims’ risk-taking, risk-adjusted returns, and investo r behaviour. In the same
line, in [25], authors employ the Generalized Autoregressi ve Score (GAS) model
to examine the impact of cybercrime on cryptocurrency retur ns in South Africa.
On the other hand, in [10], authors attempted to correlate th e expansion of
ransomware activities with transactions performed in thes e cryptocurrencies be-
tween 2015 and 2020. However, although it is clear that these cryptocurrencies
5https://sanctionssearch.ofac.treas.gov/
6https://ofac.treasury.gov/specially-designated-nati onals-list-sdn-list/program-tag-
definitions-for-ofac-sanctions-lists
6 F. Zola et al.
facilitate the growth of ransomware revenue [12], authors d id not find an evident
correlation. In [8], traditional machine learning algorit hms are used to detect
illegal activities using a Bitcoin dataset, while in [18] gr aph-based networks are
used for a similar task. Similar applications are also explo red in [19] for defining
a method to identify and trace illicit activities in the Ethe reum blockchain.
Among the most relevant cybercrimes, cryptocurrencies hav e become the per-
fect solution for laundering illegal funds [26]. By fosteri ng decentralization, user
anonymity, and the ease of making cross-border transaction s, they have led to the
proliferation of dedicated services. Achraf Guidara [16] e xamines the relation-
ship between cryptocurrencies and money laundering, empha sising the urgent
need to develop a robust and internationally coordinated re gulatory framework
to mitigate these risks. In [20], 182 Bitcoin addresses belo nging to 56 mem-
bers of the Conti ransomware group are analysed with the aim o f identifying if
money laundering mechanisms are applied. They conclude tha t cryptocurrency
exchanges and dark web services are involved in 71% and 30% of transactions,
respectively, while only 8% utilized mixers. These findings challenge the pre-
vailing notion that cybercriminals employ sophisticated m ethods, highlighting
instead the simplicity of their tactics [21].
At the same time, as introduced in the previous sections, the lack of market
control has turned these cryptocurrencies into a means of ev ading sanctions, al-
lowing entities to continue their illicit activities. For t his reason, in this work,
we aim to analyse how punished entities use crypto assets to c ircumvent sanc-
tions and whether they also employ laundering mechanisms to increase their
anonymity.
3 Experimental Framework
In this section, the dataset and the approach followed in thi s study are presented.
More specifically, Section 3.1 reviews the sanctioned list u sed, while Section 3.2
introduces the Bitcoin dataset. Finally, the guidelines an d key concepts used
during the experiments are reported in Section 3.3.
3.1 Sanctions List
As of February 2024, the SDN list contains information about 600 crypto ad-
dresses related to 17 different cryptocurrencies, as shown i n Figure 1a. The figure
shows that the majority of reported addresses ( ∼65%) belong to the Bitcoin
(BTC) network, while another ∼25% are from Ethereum (ETH). The remain-
ing addresses, representing just 10%, are divided across 15 cryptocurrencies.
Furthermore, analysing the composition of the sanctioned e ntities (Figure 1b)
that belong to the top-5 sanctioned cryptocurrencies, it be comes evident that
while there are more punished individuals than companies fo r both BTC and
ETH, companies possess a greater number of punished address es. These results
lead us to focus the analysis only on the BTC-related entitie s, as anticipated
Assessing the Impact of Sanctions in the Crypto Ecosystem 7ARB
BCH
BSC
BSV
BTC
BTG
DASH
ETC
ETH
L TC
TRX
USDC
USDT
XMR
XRP
XVG
ZEC
Cryptocurrency050100150200250300350400# Sanctioned Addresses
(a) Distribution of sanctioned cryptocurren-
cies in the SDN list.BT C ETH USDT L T C BCH050100150200250300350400P opulationEntities
Individuals
CompaniesAddresses
Individuals
Companies
(b) Addresses and entities distribution in
the SDN list (top-5 populated cryptocur-
rencies).
Fig.1. Overall statistics of sanctioned entities with cryptocurr ency information ex-
tracted from the SDN list (February 2024).
Canada
China
Czech Republic
Iran
Israel
Netherlands
None
North Korea
Pakistan
Region: CIS
Region: Gaza
Russia
St. Vincent
T ajikistan
Ukraine
United Kingdom024681012# Distinct EntitiesIndividuals
Companies
(a) Distribution of BTC-related entities in
the SDN list per country/region.
CYBER2
DPRK3
DPRK4
ELECTION
IFSR
ILLICIT -DRUGS
IRGC
NPWMD
RUSSIA
SDGT
SDNTK05101520# Entities
(b) Distribution of BTC-related entities in
the SDN list per violation.11-2018
08-2019
03-2020
09-2020
04-2021
07-2021
09-2021
11-2021
12-2021
04-2022
05-2022
09-2022
11-2022
02-2023
04-2023
05-2023
10-2023
11-2023
02-2024024681012
CYBER2
SDNTK
DPRK3
ELECTIONNPWMD
SDG T
ILLICIT -DRUGS
RUSSIAIFSR
IRGC
DPRK4
(c) BTC-related entities per violation over
time.
Fig.2. Overview about violations of sanctioned entities with BTC i nformation ex-
tracted from the SDN list (February 2024).
8 F. Zola et al.
in Section 2.2. In particular, this constraint leaves us to s tudy 43 out of the 56
available entities in the SDN list.
Figure 2a details that most of the BTC-related sanctioned en tities (both
companies and individuals) are located in China and Russia. In fact, of the
43 punished entities, 13 are from China and 10 from Russia ( ∼54%). In both
cases, the number of sanctioned individuals overwhelms the number of compa-
nies, while there are only sanctioned companies in Canada, t he Czech Republic,
St. Vincent, and in the regions of Gaza and the Commonwealth o f Independent
States (CIS). Figure 2b shows the distribution of these sanc tioned entities with
respect to their violations. It is to be noted that one entity can face sanctions for
multiple violations. Consequently, the most prevalent cat egory, with 23 entities,
pertains to malicious cyber-enabled activities since they include a broad spec-
trum of crimes. Furthermore, among the most populated categ ories, 10 entities
are sanctioned for illicit drug trading, while 5 entities ar e linked to interference
in the US election. Finally, Figure 2c analyses the temporal ity of these sanctions
and the number of entities involved. The figure shows that US a uthorities led
a significant operation on illicit drug trading in October 20 23, resulting in the
sanctioning of 6 entities. Another interesting finding is th at sanctioning opera-
tions against specific violations tend to occur only on speci fic dates. Specifically,
violations related to CYBER2 ,ILLICIT-DRUGS , andRUSSIA are consistently
detected over time, while the others are detected only on spe cific dates.
3.2 Bitcoin Dataset
In this paper, the entire Bitcoin blockchain data until the b lock 830,000 are
downloaded, i.e., all the transactions until February 11th , 2024 (more than
900M transactions). On the other hand, to have more informat ion about real-
world entities, labelled (tagged) addresses are gathered f rom multiple reliable
sources, such as WalletExplorer7and the tagpacks provided by Graphsense8.
Indeed, these sources represent valid solutions used in man y previous researches
[23][27][31], and allowed us to gather more than 38M address es of almost 400
entities labelled as Exchanges, Gambling, Marketplaces, Mining Pools, Mixers,
Services, Trading platforms, eWallet, Ransomware, Sextortio n,andExtremist .
3.3 Proposed Analysis
As mentioned in Section 3.1, the BTC addresses included in th e SDN list rep-
resent the starting point of our investigation. More specifi cally, from each of
them, we analyse the address-transaction graph [15][31]. T his graph is directly
built using the information available in the BTC blockchain , where nodes are
BTC addresses and transactions. Then directed edges (arrow s) from addresses
to transactions represent incoming relations, while edges from transactions to
addresses are outgoing relations, as shown in Figure 3. Furt hermore, the edge
7https://www.walletexplorer.com/
8https://graphsense.info/
Assessing the Impact of Sanctions in the Crypto Ecosystem 9
may incorporate BTC information like amount, fee, timestam ps, etc. With these
principles, it is possible to define the n-step address-transaction graph of a sanc-
tioned address X1, as a graph in which all the paths from X1involve maximum
ntransactions. Thus, the paths from X1have a maximum length of 2n(Figure
3).
Fig.3. An example of a 1-step address-transaction graph
In this work, we present two different analyses: the first one i s based on
assessing the effectiveness of the sanctions by analysing th e activities of the
entities ( flow analysis ), and the second has the aim to detect the relations that
entities have after being sanctioned ( behavioural analysis ).
For the flow analysis , a temporal aspect is introduced in the address-transactio n
graph. Specifically, for each address of each entity, multip le 1-step address-
transaction graphs are created, considering 4 different tem poral ranges: a) all
the transactions prior to the imposed sanction ( pre-sanction ); b) transactions
achieved immediately after the sanctions within the subseq uent 7 days ( 7 post-
sanction ); c) transactions achieved immediately after the sanction s within 30
days post-sanctions ( 30 post-sanction ); d) and finally all the activities post-
sanctions up to February 11th, 2024 ( up-to-date ). These ranges allow us to eval-
uate the behaviour of the sanctioned entity and detect how th ey react to this
situation in short, medium, and long terms.
Once these graphs are built, from each one, several metrics s uch as the num-
ber of input and output transactions, the overall balance of the entity after each
temporal range, and the amount in USD of money sent and receiv ed by the
entity (the BTC/USD value is fixed on the day the transaction i s performed),
are extracted and used for evaluating the trends and the effec tiveness of the
sanctions. It is to be noted that one entity may possess multi ple sanctioned ad-
dresses. Therefore, metrics computed from each of its addre sses are aggregated
to provide a comprehensive overview of the entity’s behavio ur.
On the other hand, the behavioural analysis is based on analysing a single
address-transaction graph for each entity, that is created using data from im-
10 F. Zola et al.
mediately after the sanctions until the end of the dataset ( up-to-date ). Further-
more, this graph is enriched with real-world entity informa tion, i.e., with labels
gathered from the external sources mentioned in Section 3.2 . This approach
enables us to identify whether the sanctioned entity engage s in transactions
with other known entities, which could include potential ac tors related to illicit
operations (e.g. other sanctioned entities, ransomware, e tc.) or it tries to ap-
ply strategies for conducting activities such as money laun dering (e.g. involving
mainly mixers, gambling or other services), on-ramps and off -ramps operations
(e.g. involving exchanges), funding raise campaign (e.g. i nvolving mining pool
or marketplace). This behavioural analysis is performed considering both 1-step
and 2-step address-transaction graphs. This approach give s us a deeper view of
the entity strategy, since the 1-step analysis only provide s information about its
direct relations, while the 2-step also includes undirecte d transactions (reached
in two steps).
4 Current Study
In this section, the results obtained in the two proposed ana lyses are presented.
In particular, Section 4.1 describes the results obtained a nalysing transactions
and money flow of the entities before and after their sanction s, while Section
4.2 details the relations that the sanctioned entities have had with other known
type of entities. Finally, discussions and limitations are reported in Section 4.3.
4.1 Flow Analysis Results
Figure 4 shows the number of entities that received and sent m oney through
crypto transactions post-sanctions. Specifically, the figu re illustrates that only
half of all the sanctioned entities were effectively discour aged from engaging in
transactions. In particular, only 21 entities stopped to re ceive money, and 25 to
send funds. On the other hand, the figure reveals that despite the sanctions, some
entities (7) continued to move funds within 7 days of the OFAC sanction. For this
reason, Figure 5 enables us to comprehend how these funds are being moved,
analysing the balance in terms of BTC held by each entity in it s sanctioned
addresses before and after the sanctions.
Although Figure 4 indicates that some entities were not dete rred from con-
ducting transactions, Figure 5 emphasises a general trend o f maintaining at least
a minimal balance in the sanctioned addresses, excluding th e two entities with
the highest balance ( ≥50BTC) who adopted an off-ramp strategy. Yet, the
number of entities with a balance of 0 decreased, while the nu mber of entities
with a balance in a range >0 and≤0.1 BTC increased.
Table 2 reports the number of transactions that involve sanc tioned entities,
categorised by the violation they are convicted of. Additio nally, the table in-
cludes the USD volume moved by the entities for each violatio n, both before
and after the sanctions. In particular, the volume indicate s both incoming and
Assessing the Impact of Sanctions in the Crypto Ecosystem 11
7 post-sanction 30 post-sanction up-to-date010203040# Entities
Entities:
no receive money
receive money
no send money
send money
Fig.4. Number of sanctioned entities that perform transactions (r eceived and sent) in
the different post-sanction intervals.
pre-sanction 7 post-
sanction30 post-
sanctionup-to-date051015202530354045# EntitiesBalance in BTC:
= 0
(0 - 0.0001]
(0.0001 - 0.001]
(0.001 - 0.1]
(0.1 - 1]
(1 - 5]
(5 - 10]
(10 - 50]
> 50
Fig.5. Entity balance (in BTC) considering the sanctioned address es at the four stages:
pre-sanction, 7 post-sanction, 30 post-sanction, andup-to-date .
12 F. Zola et al.
outgoing transactions. Results in Table 2 show that CYBER2 represents the vi-
olation with the highest number of transactions pre-sancti on (more than 150K)
and the highest USD volume of about 8,300 million. The result is expected since
this violation also has the highest number of sanctioned ent ities (Figure 2b).
However, what is interesting, is that after the sanctions, e ntities related to this
violation still perform 305 transactions for a market of abo ut 3 million USD. On
the other hand, entities related to DPRK3 andILLICIT-DRUGS violations per-
form a high number of transactions and show a USD volume of 240 million and
120 million pre-sanction, respectively. Although these nu mbers decrease in the
post-sanction phase, they remain consistent, with 209 thou sand USD for DPRK3
and 8 million USD for ILLICIT-DRUGS . Also, the market related to RUSSIA
violation is pretty high pre-sanctions, with almost 40 mill ion USD moved in just
553 transactions. However, it seems strongly affected by the sanctions, result-
ing in just 4 transactions with an overall amount of 162 USD. F inally, entities
related to violations such as IFSR, IRGC andSDGT are indeed deterred from
performing transactions; in fact, they achieve only 1 trans action post-sanctions,
moving just a few dollars (or less). Notably, the entities in volved in DPRK4 ac-
tivities are shut down. More precisely, they have not achiev ed transactions from
May 2023 (Figure 2c) until the date.
# Transactions USD Volume
# Violation Pre-Sanction Up-to-date Pre-Sanction Up-to-date
1CYBER2 153 K 305 8,300 M 3 M
2DPRK3 2 K 63 240 M 209 K
3DPRK4 62 0 10 M 0
4ELECTION 46 K 8 6 M 1 K
5IFSR 182 1 461 K ≤1
6ILLICIT-DRUGS 9 K 747 120 M 8 M
7IRGC 182 1 461 K ≤1
8NPWMD 97 8 26 K 1 K
9RUSSIA 553 4 39 M 162
10SDGT 18 K 1 42 M 103
11SDNTK 354 42 104 K 12 K
Table 2. Number of transactions achieved and estimation of USD moved before and
after the sanctions for each violation.
4.2 Behavioural Analysis Results
Table 3 reports the results gathered during the behavioural analysis, involving
1-step and 2-step address-transaction graphs. In particul ar, by creating a 1-step
graph from each of the 43 entities (387 BTC-sanctioned addre sses) it is possible
to reach about 4K addresses, of which only 340 addresses (8.4 8%) are related
to known entities and have an external label. On the other han d, increasing
the analysis considering also undirected connections (2-s tep address-transaction
Assessing the Impact of Sanctions in the Crypto Ecosystem 13
graphs), it is possible to reach more than 10M addresses, of w hich just 175,444
(1.67%) are labelled. These outcomes confirm that, although the 1-step analysis
has more labelled information, it identifies only 15 known en tities of 4 different
behaviours, while the 2-step analysis enriches the investi gation by uncovering 67
entities with 9 different behaviours.
The results reported in Table 3 highlight that, in both the 1- step and 2-step
analyses, the majority of labelled addresses belonged to Exchanges with 97% and
86%, respectively. At the same time, in the 2-step analysis, 13.5% of the labelled
addresses are linked to 11 crypto services (trading, eWalle t, banking, etc.). This
enhanced scenario also highlights connections between san ctioned entities and
addresses associated with Sextortion, Ransomware andExtremism crimes.
1-step analysis 2-step analysis
# Behaviour# Distinct
Entities# Distinct
Address# Distinct
Entities# Distinct
Address
1Service 3 5 13 23,727
2Mixer 1 1 2 14
3Exchange 9 331 37 151,385
4OFAC Sanctioned 2 3 6 33
5Gambling - - 1 249
6Extremism - - 1 13
7Ransomware - - 2 9
8Mining Pool - - 4 13
9Sextortion - - 1 1
Labelled 15 340 67 175,444
No Labelled - 3,668 - 10,356,787
Table 3. Behavioural analysis of output entities related to sanctio ned actors consid-
ering 1-step and 2-step address-transaction graphs.
4.3 Discussion and Limitations
Discussion. New regulations have started to treat Exchanges as financial ser-
vices, requiring any Crypto Asset Service Provider or Virtu al Asset Service
Provider to implement anti-money laundering control measu res such as Know
Your Customer (KYC) policies. Nevertheless, criminals hav e started to explore
new methods to obtain money fraudulently and to launder and u se it, e.g., includ-
ing dedicated services and other digital assets that curren t solutions/directives
do not adequately supervise. Indeed, these new money launde ring methods ex-
ploit gaps in existing legislations, which in turn require f requent updates and
make them challenging to enforce and follow. In this context , it is crucial to con-
sider and incorporate also technical connectivity to the cr ypto ecosystem, i.e.,
ensuring the technology used by both users and service provi ders is connected
and operates under unified legal standards. This alignment w ill help enforce the
14 F. Zola et al.
law and catch those who commit crimes, making it more difficult for criminals
to exploit any legal loopholes.
Aligned with the findings presented in [26], this study, usin g OFAC infor-
mation, shows that Bitcoin is among the most used cryptocurr encies for various
crimes, not limited to the cyber ecosystem. The results indi cate that, despite
being sanctioned, entities still perform operations with t heir blocked or frozen
cryptocurrencies without employing complex tactics or ded icated money laun-
dering services in their transactions. In fact, entities pr efer to achieve off-ramp
activities through known and reliable exchanges. These res ults are aligned with
the outcomes reported in [20] and [21] regarding ransomware funds. However,
the results also show that, in some cases, sanctioned entiti es maintain relation-
ships with other sanctioned entities or entities involved i n other crimes, such as
sextortion, ransomware, and extremism.
This paper shows that a 1-step analysis is not sufficient for un derstanding
and tracing the operations of sanctioned entities. Indeed, the analysis reveals
that only a few sanctioned entities can be traced to known ser vices, while a 2-
step analysis provides enhanced context to their operation s. However, although
expanding the analysis to include more steps seems beneficia l, it should be noted
that this approach also introduces more unlabelled address es, increasing the
uncertainty in the "follow-the-money" investigation. The refore, the number of
steps to be included must be determined on a case-by-case bas is.
Limitation. When interpreting the results, it is important to take into a c-
count some assumptions/constraints considered during thi s investigation. Firstly,
the results of the behavioural analysis strongly depend on the quantity and qual-
ity of labelled data available in the literature. The outcom es of this work are
strictly related to the information provided by the US OFAC a uthority and
need to be verified when information from new authorities bec omes available.
At the same time, regarding the quantity of the data, as intro duced in Section
3.2, this work has gathered 38M addresses of almost 400 entit ies used in many
state-of-the-art works [23][27][31]. Yet, these 38M repre sent only 2.9% of the
addresses used in the BTC blockchain, which counts about 1,3 00M addresses as
of February 2024. Additionally, the OFAC SDN list being cons idered includes
40 sanctioned entities with Bitcoin addresses involved. Wh ile one might argue
that this number is insufficient for comprehensive trend anal ysis, it should be
noted that this is the maximum number available in the real ec osystem. On the
other hand, regarding the quality of the information, we rel y on the fact that
the labelled datasets are used in many research investigati ons, as mentioned in
section 3.2, and in some cases, they are extracted by tools th at are used by LEAs
in real investigations [ ?]. Moreover, although these datasets are generated and
gathered from different sources, they do not present inconsi stencies among them
in the provided data.
Furthermore, it is to be noted that some entities were sancti oned in late 2023
or early 2024, meaning that the gathered crypto transaction s (until February
2024) might not fully capture their activities. However, we decided to focus the
analysis on the type of violation rather than concrete entit ies (Table 2). In fact,
Assessing the Impact of Sanctions in the Crypto Ecosystem 15
looking at the distribution analysis provided in Figure 2c, it is possible to see that
the majority of the entities for each violation are prior to M ay 2023 - excluding
theILLICIT-DRUGS crime - allowing us to analyse 9 months of transactions.
Finally, in the context of the 2-step analysis, this study ha s assumed that
there has been no change in ownership of the funds. However, r elying just
on blockchain information, the validity of this assumption cannot be ensured.
Nonetheless, we acknowledge this risk because limiting the analysis solely to
1-step graphs would be excessively restrictive, generatin g a skewed and poor
view of the impact of the sanctions. Furthermore, a change in the ownership of
the funds should not generate high-impact deviations in the proposed analysis,
since we are just analysing the reached entities using a basi c "follow-the-money"
approach.
5 Conclusion
The present study represents a first step and provides an inte resting yet partial
understanding of the impact of sanctions on the crypto ecosy stem, with a focus
on the Bitcoin cryptocurrency. The first point to emphasise i s that sanctions
are inherently tied to the prevailing regulations at any giv en time. As context,
this study solely reports on European policies aimed at regu lating the crypto
market and preventing financial fraud. However, the analysi s is conducted using
data provided by the US OFAC authority, as it is the only entit y that releases a
comprehensive list of economically and trade-sanctioned e ntities.
Theflow analysis shows that in general, sanctions have been effective on at
least half of the sanctioned entities, while the other half h as continued to move
(receive and send) money through the sanctioned BTC address es, although they
do not show huge changes in their current balance. In particu lar, sanctions seem
to be not very effective against entities related to specific v iolations, like CY-
BER2 andILLICIT-DRUGS which are the ones that still make transactions and
move high quantities of USD in proportion with their activit ies pre-sanctions.
On the other hand, the behavioural analysis highlights that sanctioned entities
tend to prefer reaching out directly to Exchanges to convert their cryptos, rather
than use dedicated services for money laundering (mixers or gambling).
With the aim of deepening that understanding, further analy sis should in-
clude heuristics assumptions in the loop [30] as well as auto matic labelling strate-
gies to generate clustered entities. Yet, in this way, it wou ld be possible for each
punished entity, not only to consider the actual sanctioned addresses reported in
the list, but also other addresses that are likely to belong t o the same wallet. At
the same time, it will be interesting to scale up our approach by incorporating
information from other cryptocurrencies (Ethereum) and st ablecoins. In fact, as
reported in [12], they represent a good alternative - especi ally in the last three
years - through which criminals engage in illicit activitie s and sanctions circum-
vention. This approach will allow Law Enforcement Officers to have a complete
picture of criminal modus operandi .
16 F. Zola et al.
Acknowledgments. This work has been partially supported by the European
Union’s Horizon 2020 Research and Innovation Program under the project FAL-
CON (Grant Agreement No. 101121281)
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and its arrangements in money laundering act. Lex Publica 9(2), 49–66 (2022)
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to attack bitcoin anonymity. In: 2019 IEEE International Co nference on Blockchain
(Blockchain). pp. 10–17. IEEE (2019)
|
{
"id": "2409.10031"
}
|
2207.07002
|
Applications of Blockchain for the Governance of Integrated Project
Delivery: A Crypto Commons Approach
|
This paper outlines why and how blockchain can digitally support and evolve
the governance of collaborative project deliveries, such as integrated project
deliveries (IPDs), to provide the foundation for novel and disruptive forms of
organizational collaboration in the construction industry. Previous work has
conceptualized IPDs as a common pool resource (CPR) scenario, where shared
resources are collectively governed. Through the use of blockchain and smart
contracts for trustworthy peer-to-peer transactions and execution logic,
Ostrom's design principles can be digitally encoded to scale CPR scenarios.
Building on the identified connections, the paper 1) synthesizes fourteen
blockchain-based mechanisms to govern CPRs, 2) identifies twenty-two
applications of these mechanisms to govern IPDs, and 3) introduces a
conceptualization of the above relationships towards a holistic understanding
of collaborative project deliveries on the crypto commons for novel collective
organization of construction project delivery between both humans and machines.
|
http://arxiv.org/pdf/2207.07002v1
|
Jens J. Hunhevicz, Pierre-Antoine Brasey, Marcella M. M. Bonanomi, Daniel M. Hall, Martin Fischer
|
cs.CR
|
cs.CR
|
Page 1 of 31
Applications of Blockchain for the Governance of Integrated Project
Delivery: A Crypto Commons Approach
Jens J. Hunhevicz1*, Pierre-Antoine Brasey1,2, Marcella M. M. Bonanomi1,3, Daniel M. Hall1, Martin
Fischer4
1 ETH Zurich, Institute of Construction and Infrastructure Management, Chair of Innovative and
Industrial Construction, Zurich, Switzerland
2 INGPHI Ltd, Concepteurs d’ouvrages d’art, Lausanne, Switzerland
3 Polis Lombardia, Milan, Italy
4 Stanford University, Department of Civil and Environmental Engineering, Stanford, United States
* Corresponding author: hunhevicz@ibi.baug.ethz.ch
Abstract
This paper outlines why and how blockchain can digitally support and evolve the governance of
collaborative project deliveries, such as integrated project deliveries (IPDs), to provide the foundation
for novel and disruptive forms of organizational collaboration in the construction industry. Previous
work has conceptualized IPDs as a common pool resource (CPR) scenario, where shared resources are
collectively governed. Through the use of blockchain and smart contracts for trustworthy peer-to-peer
transactions and execution logic, Ostrom’s design principles can be digitally encoded to scale CPR
scenarios. Building on the identified connections, the paper 1) synthesizes fourteen blockchain-based
mechanisms to govern CPRs, 2) identifies twenty-two applications of these mechanisms to govern
IPDs, and 3) introduces a conceptualization of the above relationships towards a holistic
understanding of collaborative project deliveries on the crypto commons for novel collective
organization of construction project delivery between both humans and machines.
Keywords: Integrated Project Delivery (IPD), Common Pool Resource (CPR), Ostrom Principles, Blockchain, Distributed Ledger
Technology (DLT), Smart Contracts, Decentralized Autonomous Organization (DAO), Construction Automation
1. Introduction
Construction project delivery models (PDMs) describe how the multiple parties involved in a project
are organized and managed to create and capture value (Davies et al. 2019). Even though the
construction industry has been slow in adopting digitalization, new digital technologies and processes
slowly make their way into the construction industry (Singh 2019). Digital information is changing how
projects are delivered (Whyte 2019); it can motivate the development of novel collaborative PDMs
with new incentive structures, procurement methods, and approaches to communication.
Meanwhile, digital information technologies in the construction industry are also rapidly changing.
One technology that is increasingly researched for the construction industry is blockchain (Li et al.
2019; Li and Kassem 2021; Nawari and Ravindran 2019a; Perera et al. 2020; Wang et al. 2017).
Blockchain is a particular design option of distributed ledger technology (DLT) (Ballandies et al. 2021;
Tasca and Tessone 2019) that enables direct peer-to-peer transactions of value without relying on
trusted facilitators.
The first ever blockchain created is Bitcoin (Nakamoto 2008). Since then, many new blockchains
iterated on the approach of Bitcoin to enable new features and infrastructure (Spychiger et al. 2021a).
Most notable, the Ethereum blockchain (Buterin 2014) made it possible that Turing-complete code
pieces termed smart contracts could be executed on a blockchain. Smart contracts allow for the coding
of interaction rules with blockchain transactions for digital workflows to coordinate economic activity
Page 2 of 31
of actors in a decentralized and borderless way. In addition, smart contracts can encode containers of
value, so-called tokens, such as currencies, securities, or utilities (Ballandies et al. 2021; Mougayar
2017). Tokens can then be transferred among blockchain users.
Blockchain has been repeatedly theorized as promising to improve construction project management
practices (Sonmez et al. 2021), especially to support financial management, automatic contract
administration, and tracing and securing data along the supply chain (Hewavitharana et al. 2019; Kim
et al. 2020). This also aligns well with the most often explored use cases for the construction sector
(Hunhevicz and Hall 2020; Li et al. 2019; Li and Kassem 2021; Perera et al. 2020; Scott et al. 2021).
However, scholarship also suggests that the impact of blockchain is highly disruptive to the
coordination of existing economic systems (Davidson et al. 2018; Miscione et al. 2019). Smart
contracts can create new organizational systems, incentivizing individual actors towards intended
collective behaviour (Voshmgir and Zargham 2019). Therefore, blockchain can be an opportunity for
new organizational designs governing the upcoming digital reality of PDMs (Hunhevicz et al. 2022a;
Sreckovic and Windsperger 2020). Since construction PDMs are already transforming due to increasing
digital information (Whyte 2019), there is need to investigate further the impact of the potentially
disruptive impact of blockchain.
In this paper, we conceptualize why and how blockchain can digitally support and evolve the
governance of PDMs. To build this conceptualization, we specifically make use of ideas about
governance of common pool resource (CPR) scenarios (i.e., the “commons”) (Ostrom 2015a). Scholars
argue that blockchain-based mechanisms can scale CPR scenario governance (Fritsch et al. 2021; Rozas
et al. 2021b; a). Such “crypto commons“ (Maples 2018) build digital governance structures for
commons by leveraging blockchain-based market mechanisms and economic incentives to reward
contributions to the common good (Crypto Commons Association 2021).
This is interesting because there is strong theoretical alignment between Integrated Project Delivery
(IPD) and the management of CPR scenarios (Hall and Bonanomi 2021). IPD is a new collaborative PDM
that uses a relational contracting approach to manage large and complex construction projects. One
driver for the development of IPD was the need for more flexible and collaborative organizational
structures to gain benefit from digital building information modelling (Hall and Scott 2019). To do this,
IPD uses a financial pool to share risk and reward among project participants depending on the
outcome of the project. IPD also emphasizes decentralized, agile, and self-organized project
governance arranged by the project participants. Collaborative PDMs such as IPD can better deal with
the complexity and ever-changing nature of modern construction projects (Levitt 2011; Luo et al.
2017).
The strong alignment of the collective nature of blockchain and collaborative approach of IPD has not
escaped the attention of researchers. Nawari and Ravindran (2019b) theorize blockchain as a
“evidence of trust” for IPD. Elghaish et al. (2020) and Rahimian et al. (2021) have developed a
blockchain prototype for the IPD financial risk-and-reward system. However, these works mainly apply
blockchain to improve existing financial processes. As stated above, blockchain has the potential to
lead to new forms of organization and governance (Davidson et al. 2018; Jacobo-Romero and Freitas
2021; Miscione et al. 2019), but no work yet has explained how this might occur for construction
PDMs.
Therefore, this paper now explores how the relationships of blockchain, CPR theory, and IPD can be
used as a theoretical foundation to inform which specific blockchain applications can be developed to
evolve and redesign PDMs. This is achieved through systematically exploring the connections between
Page 3 of 31
blockchain, CPRs, and IPD. The results of this work can help to conceive the opportunity of blockchain
for IPDs to evolve, or even enable the formulation of new digitally supported PDMs on the crypto
commons.
2. Methodology and Structure of the Paper
An overview of the research approach and contribution is presented in Figure 1. The methodology
contained three main steps: 1) We outlined established connections between CPRs, the Ostrom design
principles (OPs), and IPDs to manage construction resources; 2) we conducted a state-of-the art review
of all papers and articles that propose to use blockchain to manage CPR and identify the proposed
mechanisms for the respective OPs; 3) we identified applications of those mechanisms for
collaborative construction projects through using the link between IPDs and the OPs. Each of these
steps is now described in more detail.
In the departure section 3, we introduce relevant established concepts between CPR theory and IPD
that act as basis for our research. First, we introduce Ostrom’s design principles (OPs) for the
management of CPR scenarios (Section 3.1). Second, we explain the high-level concepts of IPD to
manage construction project resources (Section 3.2). Finally, we outline the recently established
connection between the OPs and IPD practices (Section 3.3).
To verify the link between blockchain and CPR theory (Section 4), we conducted a comprehensive
literature review of all papers and articles that proposed blockchain for the management of CPRs. We
identify four journal papers (Fritsch et al. 2021; Pazaitis et al. 2017; Rozas et al. 2021b) and five articles
(Dao 2018; Decoodt 2019; Emmett 2019a; de la Rouviere 2018; Schadeck 2019) proposing to govern
real-world commons with blockchain-based mechanisms. Building on these works, we cluster and
categorize fourteen blockchain governance mechanisms encoding the OPs for the governance of
crypto commons.
We then use abductive analysis (Timmermans and Tavory 2012) to theorize how blockchain
governance mechanisms can be transferred to the governance of IPDs. Abduction is making a probable
conclusion from what is known by systematically interpreting, matching, or re-contextualizing
phenomena within a contextual framework, from the perspective of a new conceptual framework
(Dubois and Gadde 2002b; Kovács and Spens 2005). An abductive approach is fruitful if the objective
is to develop the understanding of a “new” phenomenon or new insights about existing phenomena
by examining these from a new perspective (Dubois and Gadde 2002b; Kovács and Spens 2005).
To do this, we first synthesized applications based on observed alignment between the blockchain
mechanisms for the OPs and IPD practices in line with the OPs (Hall and Bonanomi 2021). We then
refined and complemented applications based on supporting blockchain research both from within
and outside the construction industry. In total, we identified 22 blockchain applications that can be
used to build IPD governance on the crypto commons (Section 5).
A holistic overview of the proposed conceptualization of IPD on the crypto commons demonstrates
the cohesiveness between the relationships of the OPs, the blockchain governance mechanisms, and
the specific blockchain applications to build novel governance mechanisms for IPDs (Section 6).
The paper ends with a discussion of the opportunities for blockchain to be applied to IPD and other
future forms of project delivery, as well as the challenges for governance design and for industry
implementations to facilitate next research steps (Section 7).
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Figure 1: Schematic representation of the research approach and contribution. CPR related boxes are pictured in white, the
IPD related boxes in grey. The paper builds on the existing conceptualization between CPR, OPs, and IPD (see “Point of
Departure”, Section 3.1 – 3.3. Afterwards, the paper comprehensively reviews literature proposing blockchain for CPRs and
the OPs and summarizes the proposed mechanisms (Section 4). Finally, the paper identifies applications of blockchain
governance mechanisms for IPD (Section 5) towards a holistic conceptualization of IPD on the crypto commons (Section 6)
through abductive reasoning using the connection between the mechanisms, OPs, and IPD practices (see red arrow).
3. Point of Departure
3.1. Governing CPR Scenarios
CPRs are natural resources, which are freely shared among many users (Ostrom 1990). Examples
include forests, pastures, fishing grounds, parking lots or wiki libraries. In a CPR scenario, users might
appropriate resources at a higher than optimal rate, resulting in a downward spiral of total resource
availability (Hardin 1968). This is known as the tragedy of the commons . For decades, scholars argued
that centralized control was the only way to coordinate optimal resource appropriation in CPR
scenarios.
However, more recent work pioneered by economist Elinor Ostrom (Ostrom 2010, 2015a; Ostrom et
al. 1994) and others (Gardner et al. 1990) overturned these beliefs. Ostrom used case studies to
demonstrate that local actors are often successful at self-organizing to better sustain CPR scenarios
when compared to centralized interventions. Ostrom identified eight design principles – the OPs –
that can guide effective governance of CPR scenarios (Table 1). The OPs explain necessary conditions
that should be achieved, to facilitate trust and reciprocity and to sustain collective action in long-
lasting CPR scenarios (Cox et al. 2010).
3.2. IPD Governance of Construction Projects
IPD is a project delivery model that formally multiple, independent firms to collectively share financial
risk and reward among themselves and with the project sponsor during the design and construction
of a facility (Lahdenperä 2012). IPD governance today can be best described as the combination of
multiple formal and informal practices (Bygballe et al. 2015; Hall and Scott 2019). Such practices
include early involvement of key stakeholders, risk and reward mechanisms, joint project control, and
target value design (Cheng et al. 2016; Hall et al. 2018).
IPD departs from the traditional model of project delivery in three notable ways (Hall and Bonanomi
2021). First, the multiparty contract of the IPD model creates a shared financial resource pool for the
project. The project resources become contractually available for free use by any of the project
signatory parties. Second, the participants of IPD projects share decision-making rights over the
project governance structures. Decision-making is no longer centralized (Tillmann et al. 2014). Third,
the project team shares the financial risks and rewards of the project. Positive outcomes are split
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among participants. The project teams must self-organize (Bertelsen 2003) and determine who has
access to the shared pools and who is allowed to withdraw from this pool.
3.3. Governing IPD using CPR Design Principles
Recent work has proposed a conceptualization bridging the governance of IPD projects and the OPs
(Table 1) (Hall and Bonanomi 2021), suggesting that the IPD project environment resembles a CPR
scenario (Hall and Bonanomi 2021). Project resources are “pooled” together through a multi-party
contract which shares risk and reward (Darrington and Lichtig 2010; Thomsen et al. 2009).
Similar as CPR scenarios must avoid the tragedy of the commons , IPD projects must then avoid the
tragedy of the project – where the project budget and schedule can be subject to over-appropriation
by the project stakeholders to the long-term detriment of the project resource system (Hall and
Bonanomi 2021). To avoid the tragedy of the project, project managers create effective self-
governance structures manifesting in specific management practices for IPDs, which demonstrate
many shared characteristics to the OPs. Additional work has validated this connection with examples
from IPD project practices (Bonanomi et al. 2019, 2020). Table 1 lists such example practices for IPDs
aligned with the OPs for CPRs.
Page 6 of 31
Table 1: The eight Ostrom principles and their connection with IPD practices (Source: Hall and Bonanomi (2021)).
Ostrom Principle (OP) Description of OP (Cox et al. 2010; Ostrom 2015b) Example Practice(s) for IPD (Hall and Bonanomi 2021)
1 Clearly
Defined
Boundaries a) For the users The boundaries between legitimate and non-users who have right to withdraw
resource units from the CPR must be clearly defined. The participating firms collectively determine who is a risk and reward “partner” and who
is not in the multiparty contract.
b) For the
resources Resource boundaries of the system must be clearly defined and separated from
the larger socio-economic system. The project sponsor and project team collectively define which specific aspects of project
scope and budget are open to all and which are not.
2 Ensure
congruence a) With local
conditions CPR scenarios should ensure congruence with local conditions of appropriation
rules restricting time, place, technology, and/or quantity of resource units. Trade contractors are engaged early in the project, because they have knowledge of local
conditions, such as availability of labor, material, work routines, and other resources.
b) Between
appropriation &
provision rules The benefits obtained by users from a CPR, as determined by appropriation rules,
should be proportional to the amount of inputs required in the form of labor,
material, or money, as determined by provision rules. The level of participation in the risk/reward pool is weighted according to a firm’s individual
cost structure or accounting practices, its period of involvement, and/or influence on the
outcomes.
3 Collective-choice arrangements Most individuals affected by the operational rules can participate in modifying
the operational rules. Firms that have signed the multiparty contract are entitled to participate in management
group functions and to vote on decisions that directly concern their work and area of
expertise.
4 Monitoring
of the users
and the
resources a) Presence Monitors are present to actively audit CPR conditions and appropriator behavior
of the users to ensure that all parties are adhering to agreed-upon tasks. Participants share information on resources, costs, profit, and performance openly and
transparently. Teams also create cost targets and then track the weekly withdrawals of
resource units, monitoring for deviations (Target Value Design).
b)
Accountability Monitors are accountable to or are the appropriators. Participants make commitments about the work to be completed. The Planned Percent
Completed (PPC) metric tracks the percentage of items promised last week that were
completed and is publicly reported to all team members.
5 Graduated sanctions Appropriators who violate operational rules are assessed graduated sanctions
(depending on the seriousness and context of the offense) by other
appropriators, officials accountable to these appropriators, or both. Sanctions can increase due to continuous non-conformance or underperformance of PPC,
leading to the removal of individual participants and/or firms if necessary.
6 Conflict-resolution mechanisms Appropriators and officials have rapid access to low-cost local arenas to resolve
conflicts among appropriators or between appropriators and officials. Project participants craft conflict resolution mechanisms that include clear dispute
resolution strategies intended to avoid costly litigation proceedings.
7 Minimal recognition of rights to
organize The rights of appropriators to devise their own institutions are not challenged by
external governmental authorities. Conflict resolution mechanisms allow participants to make collective decisions, including
procedures for the team to override the wishes of the project sponsor.
8 Nested enterprises Appropriation, provision, monitoring, enforcement, conflict resolution, and
governance activities are organized in multiple layers of nested enterprises. Governance activities of IPD projects are organized into multiple layers of hierarchy using
a nested enterprise design.
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4. Blockchain and the Crypto Commons: a Review
4.1. Blockchain as an Institutional Innovation
The dominant narrative for economic coordination through the blockchain argues that blockchain
enables increased productivity of existing processes by lowering transaction costs through costless
verification and without the need for costly intermediation (Catalini and Gans 2020). However, some
scholars argue the true potential of blockchain is the development of new types of institutional
organization with the potential to disrupt and substitute existing economic coordination (Davidson et
al. 2018; Jacobo-Romero and Freitas 2021; Miscione et al. 2019). Blockchain is a new way to reach
consensus about a shared truth without requiring centralized trust (Davidson et al. 2018). The
innovation of blockchain is the consensus protocols using cryptoeconomic mechanisms to reward
honest parties to reach consensus on network transactions, e.g. in Bitcoin with proof-of-work (Gervais
et al. 2016; Nakamoto 2008). Blockchain disintermediates transactions with a new form of
organizational design, and as a consequence can lower transaction costs (Davidson et al. 2018).
As a consequence, applications can leverage the innovation of cryptoeconomic mechanisms of
blockchains for trust-minimized social coordination to build new forms of economic activity on top of
blockchains. Such cryptoeconomic systems can provide an institutional infrastructure that facilitates
a wide range of socio-economic interactions to influence participants in their behavior (Voshmgir and
Zargham 2019). There is ongoing exploration of what forms of organization and governance can be
supported or replaced through blockchain. Within this paper, we focus on blockchain as a possibility
to scale CPR scenarios on the crypto commons.
4.2. The Connection of Blockchain and CPR Governance
The OPs describe how commons-based communities can create effective bottom-up governance rules
(Cox et al. 2010). However, a major limitation is scaling community governance to large and global
systems (Ostrom et al. 1999).
Recent scholars point out that blockchains can be assessed through the lens of CPR theory and the
OPs. This can enable the creation of effective bottom-up governance rules for decentralized peer
production of the network without any centralized coordination (Red 2019; Shackelford and Myers
2016; Werbach 2020). There is growing recognition that the underlying system governance
mechanisms are the key to long-term success of blockchain networks (Beck et al. 2018; Machart and
Samadi 2020; Red 2019; Werbach 2020). CPR theory and the OPs are a repeatedly mentioned concept
to guide the development of blockchain governance (Shackelford and Myers 2016; Werbach 2020).
Fritsch et al. (2021) find now that blockchain and other DLTs can enable scaling of a new generation
of commons-oriented economies, both for digital and physical commons. On the one hand,
cryptoeconomic mechanisms decrease the cost of information exchange through minimizing
opportunism and uncertainty trough transparency and cryptographic enforcement (Machart and
Samadi 2020; Schmidt and Wagner 2019). On the other hand, blockchain provides reliable
organizational means to equitably produce and distribute resources in accordance with the shared
values of productive communities (Fritsch et al. 2021). The transparent decision-making procedures
and decentralized cryptoeconomic incentive systems help avoid the tragedy of the commons (Bollier
2015). The idea is to craft blockchain-based governance mechanisms by encoding the OPs (Rozas et
al. 2021b; a). Blockchain could create networked governance to scale real-world commons, similar to
how the stock market enabled corporations to scale (Maples 2018). Such crypto commons could allow
new types of value creation with crypto assets rather than shares of stock, contributors rather than
employees, and decentralized collaboration rather than centralized ownership (Maples 2018).
Page 8 of 31
4.3. Blockchain Governance Mechanisms for the Commons
As a basis to later investigate potential applications of blockchain mechanisms for IPD, we reviewed
blockchain governance mechanisms proposed for CPRs (see also Section 2). Most notably, Rozas et al.
(Rozas et al. 2021a) assesses the relationship between blockchain affordances and the eight OPs to
support peer production of real-world commons. Rozas et al. (2021b) explore then how those can be
applied to scale-up CPR governance of global software commons to address limitations identified by
Stern (2011). Even though IPD can be characterized as a real world common, it hardly falls into the
same category of global real world commons. Therefore, we clustered proposed mechanisms from all
identified articles into 14 high level mechanisms for the eight OPs (Table 2), instead of just relying on
the categorization of Rozas et al. (2021a).
Table 2: Clustered blockchain governance mechanisms based on reviewed literature.
Blockchain Governance Mechanisms OP Sources
M1: Identity, ownership, and access rights based on
addresses and tokens 1a (Dao 2018; Rozas et al. 2021b; a; Schadeck 2019)
M2: Tokenization of the resources 1b (Decoodt 2019; Emmett 2019a; Fritsch et al. 2021;
de la Rouviere 2018)
M3: Decentralized markets to match supply and
demand of local needs and conditions 2a (Schadeck 2019)
M4: Formalizing appropriation and provision rules
with smart contracts 2b (Dao 2018; Rozas et al. 2021b; a)
M5: Decentralized proposal and voting platforms 3 (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a;
Schadeck 2019)
M6: Decentralized prediction markets 3 (Dao 2018)
M7: Transparent record and automation of
transactions 4a (Emmett 2019a; Rozas et al. 2021b; a; Schadeck
2019)
M8: Digital signatures for tamper-proof
commitments 4b (Dao 2018; Rozas et al. 2021b; a)
M9: Decentralized peer-review mechanisms 4b (Pazaitis et al. 2017; Rozas et al. 2021b)
M10: Reputation tokens 4b (Pazaitis et al. 2017; Schadeck 2019)
M11: Transparent and self-enforcing sanctions 5 (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a;
Schadeck 2019)
M12: Decentralized jurisdiction systems 6 (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a;
Schadeck 2019)
M13: Ensure decisions are made by affected parties 7 (Rozas et al. 2021b; a)
M14: Bottom-up interaction among multiple
hierarchical levels 8 (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a;
Schadeck 2019)
4.3.1. OP1 – Clearly Defined Boundaries
a) For the Users
According to OP 1a, the boundaries between legitimate and non-users who have right to withdraw
resource units from the CPR must be clearly defined (Cox et al. 2010; Ostrom 2015b) . The main
identified blockchain mechanism for OP 1a is to govern CPR boundaries through blockchain addresses
and tokens to control identity, ownership, and access rights (Table 2, M1). Blockchain identifies users
with a blockchain address, so there is no need to know the human or machine controlling the address.
Access rights and ownership can be assigned to addresses either through smart contract logic that
defines roles with specific permissions, or through membership or utility tokens that can be
transferred between users (Dao 2018; Rozas et al. 2021b; a; Schadeck 2019). While the second allows
to trade these rights with other addresses by transferring the token, the address based roles stays
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with that address until revoked. In both cases, blockchain controlled ownership and access rights can
be more easily and granularly defined, propagated, and revoked (Rozas et al. 2021b).
b) For the Resources
OP 1b states that resource boundaries of the system must be clearly defined and separated from the
larger socio-economic system (Cox et al. 2010; Ostrom 2015b) . Within the context of CPRs,
tokenization of the resources (Table 2, M2) can help to achieve clearly defined resource boundaries
on the crypto commons. Once resources are tokenized, cryptoeconomic mechanisms through smart
contracts can facilitate a wide range of interaction patterns. Tokenization can be in the form of asset-
backed currencies or commodity tokens representing the resource, good, or service in the commons
(Fritsch et al. 2021). New mechanisms such as bonding curves (Balasanov 2018; Titcomb 2019) can
incentivize early protectors of CPR scenarios (Decoodt 2019; Emmett 2019a; de la Rouviere 2018).
Bonding curves allow investors to buy a resource token by locking up their investment. Investors can
later sell back these tokens according to the new price determined by the bonding curve. The bonding
curve increases price with issued supply, and therefore rewards early investors. Bonding curves have
been proposed for “continuous organizations”, where the underlying tokens represent rights to future
revenues (Favre 2019). Augmented bonding curves introduce additional functionalities to create a
more robust system that is less subjective to speculation and manipulation (Titcomb 2019). They act
simultaneously as means of funding, liquidity provider and market maker, while the issued tokens
represent access or voting rights to the resource (Zargham et al. 2020). Therefore, augmented bonding
curves combine access rights through tokens (M1) with the idea of tokenizing the resource. The
interplay between the interests of token holders to sell when token price rises and buy as price drops
to claim additional governance power over a growing treasury, creates a negative feedback loop that
leverages speculative behavior into a continuous source of income for the commons (Fritsch et al.
2021). The Commons Stack implemented such an augmented bonding curve based on research of
Zhargam et al. (2020).
4.3.2. OP2 – Ensure Congruence
a) With Local Conditions
OP 2a states that CPR scenarios should ensure congruence with local conditions of appropriation rules
restricting time, place, technology, and/or quantity of resource units (Cox et al. 2010; Ostrom 2015b).
Decentralized markets to match supply and demand of local needs and conditions (Table 2, M3) are
proposed as a blockchain mechanism (Schadeck 2019). Smart contracts encode the rules to trade with
other actors not controlled by any intermediary, so the community using the decentralized
marketplace can benefit from unrestricted mutual trading. At the same time, the market place can be
tailored to comply with the formalized appropriation rules.
b) Between Appropriation & Provision Rules
According to OP 2b, the benefits obtained by users from a CPR, as determined by appropriation rules,
should be proportional to the amount of inputs required in the form of labor, material, or money, as
determined by provision rules (Cox et al. 2010; Ostrom 2015b). Formalizing appropriation and
provision rules (Table 2, M4) with smart contracts can make sure these agreements get obeyed (Dao
2018; Rozas et al. 2021b; a). The transparency of rules also promotes an active discussion of the notion
of value in the community (Rozas et al. 2021a). The community can then collectively decide which
contributions to recognize, as well as suited local appropriation rules (Rozas et al. 2021b).
4.3.3. OP3 – Collective Choice Arrangements
OP 3 states that individuals affected by the operational rules can participate in modifying the
operational rules (Cox et al. 2010; Ostrom 2015b). Decentralized decision making and voting are often
Page 10 of 31
discussed topics to govern blockchain networks and decentralized applications. It is therefore not
surprising that smart contract based decentralized proposal and voting platforms (Table 2, M5) are
suggested to govern real world commons (Dao 2018; Emmett 2019a). Tokens could grant rights for
decision making, either to ensure equal power distribution by design (Rozas et al. 2021b; a), or based
on the contribution and reputation of parties (Emmett 2019a; Schadeck 2019).
Furthermore, decentralized prediction markets (Table 2, M6) are proposed as a way to establish a
trusted knowledge base (Dao 2018). Prediction markets were introduced by Hanson (2013) to
establish a more representative picture of a future outcome by using a betting platform. The
underlying idea is that predictions made by people willing to risk a loss are more likely to be well-
informed.
4.3.4. OP4 – Monitoring
a) Presence
OP 4a states that monitors should be present to actively audit CPR conditions and appropriator
behavior of the users to ensure that all parties are adhering to agreed-upon tasks (Cox et al. 2010;
Ostrom 2015b) . Blockchain allows a transparent record and automation of transactions (Table 2, M7)
through smart contracts of user behavior and participation in the commons observable by all
community members (Emmett 2019a; Rozas et al. 2021b; a; Schadeck 2019).
b) Accountability
OP 4b states monitors are accountable to or are the appropriators of a CPR (Cox et al. 2010; Ostrom
2015b). Multiple blockchain mechanisms are proposed to help ensure accountability within CPR
scenarios. Every blockchain transaction is signed by a valid private key creating digital signatures for
tamperproof commitments (Table 2, M8). The immutability and censorship resistance of blockchain
ensures that decisions and transactions are accountable since all transactions are transparent and
verifiable on the blockchain (Dao 2018; Rozas et al. 2021b; a).
In cases were no automatic checking of work and contributions to the commons is possible,
decentralized peer-review mechanisms (Table 2, M9) facilitated by smart contracts allow to review
the status of work or the perceived value of contributions (Pazaitis et al. 2017; Rozas et al. 2021b).
Pazaitis et al. (2017) proposed then reputation tokens (Table 2, M10) to represent the perceived value
of contributions in the blockchain system. They can be earned by users through complying with the
CPR rules and are hence a measure of accountability (Schadeck 2019).
4.3.5. OP5 – Graduated Sanctions
According to OP 5, appropriators who violate operational rules are assessed graduated sanctions
depending on the seriousness and context of the offense (Cox et al. 2010; Ostrom 2015b) . Blockchain
allows for transparent and self-enforcing sanctions (Table 2, M11). Sanctions can be made
transparent to the whole community (Schadeck 2019), while smart contracts can self-enforce token-
based sanctions (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a; Schadeck 2019) through the loss of
either financial or reputation tokens (Dao 2018; Emmett 2019a; Schadeck 2019), or a value-decrease
of tokens (Schadeck 2019).
4.3.6. OP6 – Conflict Resolution Mechanisms
OP 6 states that appropriators and their officials should have rapid access to low-cost local arenas to
resolve conflicts among appropriators or between appropriators and officials (Cox et al. 2010; Ostrom
2015b). Blockchain offers the possibility for faster conflict resolutions with decentralized jurisdiction
systems (Table 2, M12) (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a). Tokens ensure skin in the
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game in disputes, as well as incentivize game theoretic proofs (Schadeck 2019). Such protocols must
integrate with existing legal and regulatory systems (Emmett 2019a; Schadeck 2019).
4.3.7. OP7 – Minimal Recognition of Rights to Organize
OP 7 states that the rights of appropriators to devise their own institutions should not be challenged
by external governmental authorities (Cox et al. 2010; Ostrom 2015b). Within crypto commons, smart
contract mechanisms were proposed to ensure decisions are made by affected parties (Table 2, M13)
(Rozas et al. 2021b; a), e.g. local community rules can only be enforced locally.
4.3.8. OP8 – Multiple Layers of Nested Enterprises
OP 8 states that the rules for appropriation, provision, monitoring, enforcement, conflict resolution,
and governance activities should be organized in multiple layers of nested enterprises (Cox et al. 2010;
Ostrom 2015b) . Smart contracts can facilitate coordination across nested enterprises (Table 2, M14)
between various hierarchical levels of participants to realize shared objectives in the best interest of
the commons (Dao 2018; Emmett 2019a; Rozas et al. 2021b; a; Schadeck 2019).
5. Applications of Blockchain Governance Mechanisms for IPD
Based on the 14 blockchain mechanisms for CPR scenarios (Table 3), we identified 22 potential
applications of blockchain mechanisms for IPDs (Table 3) to govern IPDs as a CPR scenario (Hall and
Bonanomi 2021). The methodological approach is explained in Section 2.
We discuss here for each of the 22 identified applications the potential to improve or extend the IPD
practices, either based on already existing practices or for potentially new mechanisms not yet applied
within IPD. Moreover, we collate the applications with existing blockchain research in the construction
industry to indicate their novelty, or if already realized, their alignment.
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Table 3: The 22 identified IPD applications based on the blockchain governance mechanisms. Some were already explored in existing construction blockchain literature.
IPD Application of Blockchain Mechanism Mecha-
nism Ostrom
Principle Excerpt of Related Blockchain Research in the Construction Industry
M1-1: Scalable management of user identities and rights M1 1a Current research uses address-based rights as a prerequisite for blockchain applications.
None investigate token-based rights.
M1-2: Machine participation M1 1a Robot participation (Lee et al. 2021); Self-owning house (Hunhevicz et al. 2021).
M2-1: Representation and ownership of project resources M2 1b Project bank accounts (Li et al. 2019; Tezel et al. 2021).
M2-2: Decentralized funding and investment mechanisms M2 1b Tokenized investment mechanisms (Tezel et al. 2021; Tian et al. 2020).
M3-1: Non-rent seeking and unrestricted matching of project needs with local
conditions M3 2a Reverse auction-based tendering (Tezel et al. 2021); Decentralized design competition
(Dounas et al. 2020; Lombardi et al. 2020a).
M4-1: Transparent logic for the appropriation and access to resources M4 2b Financial mechanisms for IPD projects (Elghaish et al. 2020; Rahimian et al. 2021).
M4-2: Scalable and self-enforcing shared risk and rewards M4 2b -
M4-3: New incentive structures M4 2b Token-based incentives for data records (Hunhevicz et al. 2020; Mathews et al. 2017);
performance-based life cycle incentives (Hunhevicz et al. 2022b; O’Reilly and Mathews 2019)
M5-1: Scaling of collective choices M5 3 -
M5-2: Definition of voting rights for intended power distributions M5 3 -
M6-1: Gamified and scalable sourcing of local actors’ knowledge M6 3 -
M7-1: More trust because of transparent user actions and resource flows, as well as
predictive automation with smart contracts M7 4a Blockchain increases trust in supply chains through data tracking, contracting, and
transferring resources (Qian and Papadonikolaki 2020). Many papers focus on these aspects.
M7-2: Transaction history enables reaction to events and learning from past decisions M7 4a Many papers focus on triggering financial transactions based on events, e.g. as in (Elghaish et
al. 2020; Hamledari and Fischer 2021a). None focus on learning aspect based on transaction
history.
M8-1: Smart legal contracts M8 4b Theoretical investigation of intelligent contracts (Mason 2017; McNamara and Sepasgozar
2020, 2021). Performance based smart contracts (Hunhevicz et al. 2022b).
M9-1: Reputation tokens for special rights or for credentials M9 4b -
M10-1: Peer-review for project progress, quality, and cost M10 4b -
M11-1: Token-based sanctioning M11 5 -
M11-2: Social sanctioning through transparent action M11 5 -
M12-1: Smart contract based “mini courts” for fast and transparent conflict resolution M12 6 Blockchain-based dispute resolution platform (Saygili et al. 2021).
M12-2: Token-based dispute participation to ensure “skin in the game” M12 6 -
M13-1: Smart contracts ensure that powerful parties cannot solely enforce collective
choices and conflict resolution M13 7 -
M14-1: Smart contracts coordinate decision making among organizational tiers M14 8 -
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5.1. M1 – Identity, ownership, and access rights based on addresses and tokens (for OP1a)
IPD projects need clearly defined rights for each actor based on their role in the project (Cheng et al.
2016).
Blockchain allows scalable management of user identities and rights (Table 3, M1-1) through address-
based identity and/or transferable tokens. Control of access and rights is the foundation for most
blockchain applications proposed for the construction industry, as well as all of the other blockchain-
based governance mechanisms, such as access to resources (M2-1), access to decentralized markets
(M3-1), access to resources (M4-1) as well as shared risk and rewards (M4-2), access to proposal or
voting platforms (M5-2), tracking of user and resource actions (M7-1), participation in smart legal
contracts (M8-1), peer-review mechanisms (M10-1), jurisdiction systems (M13-1), or coordination
among organizational tiers (M14-1). Smart contract logic ensures that only allowed participants can
perform certain actions based on their addresses or token-ownership, providing a scalable approach
to define user boundaries in IPD multi-party contracts. Research should investigate whether addresses
in IPD projects should be controlled at an individual level or by organizational entities. This depends
on many aspects, e.g. how profit, liability and risk should be distributed, or whether an incentive
system targets individual actors or firms.
Moreover, blockchain only identifies actors through their addresses, therefore allowing for machine
participation (Table 3, M1-2), e.g. to tender (M3-1) and sign (M8-1) work packages, as well as giving
them access to resources (M3-1) and compensating them for their work (M4-2). We see already
example of this in research of Lee et al. (2021) where robots get incremental payments for performed
work, as well as in the case of no1s1, a self-owning house that can receive funds for provided services,
as well as spend funds for maintenance and operations (Hunhevicz et al. 2021). For now, we assume
that decision making for IPD will be still human-based, so collective choice mechanisms (M5-1), peer-
review mechanisms (M10-1), conflict resolution mechanisms (M12-1), and coordination among
organizational tiers (M14-1) does not involve machine participation.
5.2. M2 – Tokenization of the resources (for OP1b)
IPD projects require clearly-defined boundaries for the resources, i.e. which specific aspects of project
scope and budget are open to all, and which are not (Hall and Bonanomi 2021).
With tokenized project resources, e.g. the project budget, representation and ownership of project
resources (Table 3, M2-1) can be clearly defined, also allowing monitoring of resources (M7-1). We
are so far not aware of any research that proposes tokenization of physical resources in a construction
industry context. Some research goes in this direction by exploring how crypto assets can integrate
the physical and financial supply chains (Hamledari and Fischer 2021b), or suggesting non-fungible
tokens (NFTs) to represent building components (Dounas et al. 2020). Inspiration how and which
physical resources to tokenize could also come from the asset-backed tokenization of “Holochain’s
Commons Engine” or the commodity tokens of the “Economic Space Agency” (Fritsch et al. 2021).
Tokenization of the resource would allow to manage digitally one or multiple resource pools with
distinct appropriation and payoff functions. Related to the project budget, this relates to the
suggestion of blockchain-based project bank accounts for construction projects (Li et al. 2019; Tezel
et al. 2021).
In addition, decentralized funding and investment mechanisms (Table 3, M2-2) leveraging
cryptoeconomics can be explored to extend incentive structures (M4-3). Augmented bonding curves
could be one such mechanism to extend current risk/reward structures (M7-1) in IPD by yielding
additional profit for invested stakeholders. Having said that, even though tokenized investment
mechanisms for construction projects were already proposed (Tezel et al. 2021; Tian et al. 2020),
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normally a client pays for the project and there is no need to raise funds. Moreover, the power
distribution to manage the resources is usually determined by the respective project roles, and not
dependent on their point in time when they invest and support the project. Nevertheless, future PDMs
might benefit from such new funding and investment mechanisms.
5.3. M3 – Decentralized markets to match resources to local needs and conditions (for OP2a)
Within IPD, key stakeholders often have experience with local conditions, such as availability of labor,
material, work routines and other resources. Their early involvement provides the rest of the project
team with a holistic understanding of the project conditions (Hall and Bonanomi 2021).
Decentralized markets could improve and extend this with non-rent seeking and unrestricted
matching of project needs with local conditions (Table 3, M3-1). Projects can find local resources and
knowledge important for the success of the project without middleman profiting from facilitating
these marketplaces, improving profitability of both the project and contributors. Decentralized
marketplaces also allow users of the marketplace, e.g. the IPD stakeholders, project suppliers, and
local residents, to collectively define rules. Furthermore, blockchain-based marketplaces can
introduce new decentralized market mechanisms, only requiring a blockchain address and/or holding
credentials such as reputation tokens. This could lead to more inclusive markets, potentially not only
restricted to humans but also machines. We are not aware of any implemented decentralized
marketplaces in the construction industry. Along these lines, Tezel et al. (2021) investigated a reverse
auction-based tendering mechanism facilitated by smart contracts, and Dounas et al. (2020) and
Lombardi et al. (2020b) analyze a decentralized design competition.
5.4. M4 – Formalizing appropriation and provision rules with smart contracts (for OP2b)
In IPD, the risk/reward pool is the main instrument to balance a firm’s required participation with the
potential reward according to their individual cost structure or accounting practices, their period of
involvement in the project and/or their influence on the project’s outcome (Cheng et al. 2016).
Smart contracts encode selection criteria and market mechanics visible to everyone and allow to
forecast expected behavior according to the formalized rules. Transparent logic for the appropriation
and access to resources (Table 3, M4-1) can be collectively ensured, especially if the resources are
represented in the system through tokens (M2-1). This has been acknowledged for mechanisms of
monetary resources in IPD projects (Elghaish et al. 2020; Rahimian et al. 2021). Moreover, smart
contracts can ensure scalable, self-enforcing, and stakeholder specific rules for shared risk and
rewards (Table 3, M4-2), hereby clearly defining provision rules of the system that confirm with the
defined appropriation rules. However, this replicates existing IPD allocation rules at the firm level.
New incentive structures (Table 3, M4-3) for IPDs only possible with blockchain mechanisms can be
created. For example, blockchain could be used to issue non-monetary reputation tokens or access-
tokens for decentralized markets (M3-1), decision making processes (M5-2), and to ensure “skin in the
game” in legal disputes (M12-2). It is also possible to create new ways of token-based rewards (M4-2)
and sanctioning (M11-1) at the individual or at the firm level.
Token-based incentives are to date rarely proposed in construction industry literature. Mathews et al.
(2017) propose a token to reward parties for maintaining and improving BIM databases. Similarly,
Hunhevicz et al. (2020) explore smart contracts and tokens to ensure high-quality data sets in a
construction project. Although not token-based, O’Reilly and Mathews (2019) and Hunhevicz et al.
(2022b) explored performance-based incentives across life-cycle phases to design and build for the
best possible energy performance across phases. Inspiration could come also from outside of the
construction industry, e.g. from the Finance 4.0 initiative that explored token-based incentives to
address sustainability (Ballandies et al. 2021; Dapp 2019).
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5.5. M5 – Decentralized proposal and voting platforms (for OP3)
In an IPD context, firms that have signed the multi-party contract are entitled to participate in
management group functions and to vote on decisions that concern their work and area of expertise
(Ashcraft 2011; Perlberg 2009).
Scaling of collective choices (Table 3, M5-1) in IPD could be achieved via decentralized proposal and
voting platforms. First, stakeholders can gather opinions and proposals on project spending and
execution. Afterwards, they allow for trusted voting on proposals to reach fast decisions even among
organizational tiers (M14-1). Finally, they could collectively decide, e.g. through peer-review
mechanisms (M10-1), on the appropriation and provision rules of the resources (M4-1) or how to
incentivize project stakeholders (M4-3). If the project uses a tokenized resource pool or rewards,
approved funds or resources could be automatically released upon approval (M2-1).
Although there are not yet examples in the construction industry, we can find multiple examples of
implemented blockchain based decision making mechanisms. Token Curated Registries (TCR)
(Asgaonkar and Krishnamachari 2018; Wang and Krishnamachari 2019) can be used to manage the
validity and functionality of tokens (de la Rouviere 2018). With a TCR users can collectively decide and
entries to lists, e.g. to decide on new tokens or changes to existing tokens. Within IPD, a TCR could
allow trustworthy and fast collective change processes after the initial project definition to existing
tokens or propose new tokens as the IPD participants see fit. Another example for a decentralized
governance platform is Politeia1 for the Decred2 blockchain, where stakeholders owning the
cryptocurrency can upload proposals for network changes and treasury spending and then vote on it.
Also, the Aragon project implemented a token based voting platform called Aragon Voice3. IPDs could
use similar decentralized applications to manage decision making.
Address or token-based access control allows fine-grained definition of voting rights for intended
power distributions (Table 3, M5-2) among organizational tiers (M14-1), while maintaining scalability
of the system. Suited voting forms and decision-making mechanisms would need to be explored in an
IPD context. In the blockchain space, various voting mechanisms are proposed that could inspire new
ways of voting within IPD. In Decred for example, holders have 1 vote per token (although pooled into
larger amounts and locked for an uncertain time period). This approach is anonymous, whereas in 1
vote per person as often used in existing democratic systems, voters need to be identifiable. Another
proposed voting mechanism for CPR governance (Dao 2018; Emmett 2019a) includes quadratic coin
lock voting (Buterin 2016) as a token-based variant of quadratic voting (Weyl and Lalley 2017). The
weight of votes is discounted by an exponential function to more prominently value the vote of
minority opinions (Fritsch et al. 2021). Finally, in conviction voting stakeholders continuously allocate
votes in form of tokens to different options that slowly decay if not renewed (Emmett 2019b). This
allows to sense user preferences over long time periods and prevent last minute vote swings by large
token holders (Fritsch et al. 2021).
5.6. M6 – Decentralized prediction markets (for OP3)
To make well informed decisions, decentralized prediction markets could be used for gamified and
scalable sourcing of local actors’ knowledge (Table 3, M6-1), maybe in combination with
decentralized markets for local actors (M3-1) and to extend present incentive structures towards
external actors (M4-3). Augur4 is likely the most common implementation of a blockchain based
1 https://proposals.decred.org/ , accessed 09.12.2021.
2 https://decred.org/ , accessed 09.12.2021.
3 https://aragon.org/aragon-voice , accessed 09.12.2021
4 https://www.augur.net /, accessed 09.12.2021.
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prediction market. To our knowledge there are so far no similar mechanisms within IPD. Nevertheless,
research can explore if decentralized prediction markets can be useful in cases where actors are
unknown or should remain anonymous, e.g. a betting platform to gauge expected costs of the project.
5.7. M7 – Transparent record and automation of transactions (for OP4a)
IPD projects make use of monitoring practices such as “open-book finances” to track financial
resources or “Big Room” to collocate stakeholders to commit publicly to work packages and
continuously report their progress to the rest of the team (Hall and Bonanomi 2021).
Blockchain creates more trust because of transparent user actions and resource flows, as well as
predictive automation with smart contracts (Table 3, M7-1). IPD stakeholders can be identified
through address-based access control and their transactions tracked visibly to all stakeholders,
creating an inherent incentive to behave trustworthy since the other stakeholders can recognize
malicious behavior (M11-2). Because of transparent financial transactions, open-book finances is
inherently ensured. Furthermore, with resource tokenization implemented, resource flows and
appropriation are observable. For example, a blockchain could help monitor the weekly withdrawals
of resource units and alert participants to deviations from the cost targets initially estimated with the
target value design process. In addition, smart contract automation gives more certainty in the
expected transaction logic. Overall, transparency and smart contract automation creates trust in
defined explicit incentive structures (M4-3), in collective choices (M5-1), in conflict resolution
mechanisms (M12-1), and for coordination among organizational tiers (M14-1). The available
transaction history enables reaction to events and learning from past decisions (Table 3, M7-2) for
the management of user identities and rights (M1-1), ownership of resources (M2-1), decentralized
market logic (M3-1), logic for the appropriation and access to resources (M4-1), refining incentive
structures (M4-3), the definition of voting rights (M5-2), and the execution of smart legal contracts
(M8-1), peer-review mechanisms (M10-1), and decentralized jurisdiction systems (M12-1).
Blockchain increases trust in supply chains through data tracking, contracting, and transferring
resources (Qian and Papadonikolaki 2020). Many papers in a construction context focus on
transparent and traceable records, e.g. of design data (Erri Pradeep et al. 2021) or construction related
quality data (Sheng et al. 2020; Wu et al. 2021). Literature also investigates how to ensure traceability
of built asset product information along the construction value chain in various contexts (Kifokeris and
Koch 2020; Li et al. 2021; Wang et al. 2020; Watson et al. 2019). Automated and traceable financial
transactions are often suggested to enhance financial processes within construction projects
(Ahmadisheykhsarmast and Sonmez 2020; Chong and Diamantopoulos 2020; Das et al. 2020; Elghaish
et al. 2020; Di Giuda et al. 2020; Hamledari and Fischer 2021a; Nanayakkara et al. 2021; Ye and König
2021).
5.8. M8 – Digital signatures for tamper-proof commitments (for OP4b)
To improve accountability within IPD, stakeholders can commit to work packages by signing a
blockchain transaction. This enables smart legal contracts (Table 3, M8-1) when linked to terms
encoded in smart contracts for trackable and automatic execution. Construction literature
theoretically suggested smart legal contracts (Maciel and Garbutt 2020; Shojaei et al. 2020), also
termed intelligent contracts (Mason 2017; McNamara and Sepasgozar 2020, 2021). Based on agreed
terms about committed work or performance data (Hunhevicz et al. 2022b), progress and completion
can be tracked and confirmed (M7) to ensure accountability. Because machines can hold access rights,
they also could commit to work packages and participate in smart legal contracts.
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5.9. M9 – Reputation tokens (for OP4b)
The concept of reputation tokens for special rights or for credentials (Table 3, M9-1) presents an
interesting approach to reward or punish IPD participants (M4-3). Instead of monetary incentives,
reputation tokens based on stakeholder accountability could give access to extended governance
functions (M5-2) or could be used for credentials in decentralized markets for later projects (M3-1).
5.10. M10 – Decentralized peer-review mechanisms (for OP4b)
Decentralized peer-review for project progress, quality, and cost (Table 3, M10-1) can be
implemented with blockchain. For example, the Covee protocol realized a smart contract based peer
review mechanism to determine fair profit distribution for decentralized collaborative teams (Dietsch
et al. 2018). Anonymous work contributors get rewarded with cryptocurreny according to their peer-
review score. A combination of blockchain-signed work packages, reputation tokens, and
decentralized peer-review mechanisms could create a digital “big room platform” to ensure presence
and accountability within IPD, and to evaluate appropriate rewards (M4-3) and sanctions (M11-1).
5.11. M11 – Transparent and self-enforcing sanctions (for OP5)
Graduated sanctions are often not explicitly implemented in IPDs (Hall and Bonanomi 2021). At most,
the weekly public reporting of “Planned Percent Complete” (PPC) (Thomsen et al. 2009) acts as an
early stage of social sanctioning (Kenig et al. 2010). In the case of continuous non-conformance or
underperformance, the removal of individual participants and/or firms can be necessary (Cheng et al.
2016).
Blockchain can be an opportunity to reimagine and improve upon graduated sanctioning for IPD
projects through decentralized and self-enforcing token-based sanctioning (Table 3, M11-1), e.g. loss
of access tokens, loss of reputation tokens, or decrease in value of monetary tokens.
Underperformance is also visible to everyone leading to social sanctioning through transparent
action (Table 3, M11-2). In many cases this might be enough to ensure accountability but could be
gradually combined with loss of tokens e.g. for access to the financial project rewards or even the
project itself.
5.12. M12 – Decentralized jurisdiction systems (for OP6)
IPD projects craft conflict resolution mechanisms such as project decision protocols (Ashcraft 2011) or
liability waivers (Sive and Hays 2009) that include clear dispute resolution strategies intended to avoid
costly litigation proceedings.
Blockchain enables smart contract based “mini-courts” for fast and transparent conflict resolution
(Table 3, M12-1) in IPDs. An exemplary decentralized jurisdiction system is already implemented in
the Aragon Court5 to resolve subjective disputes that cannot be resolved by smart contracts alone. A
global network of guardians helps intervene and arbitrate disputes. Saygili et al. (2021) already
propose a decentralized blockchain-based online dispute resolution platform to resolve construction
disputes. To incentivize compliance and accountability in a decentralized jurisdiction , token-based
dispute participation to ensure “skin in the game” (Table 3, M12-2) is suggested. In case of non-
compliance with the rules or the verdict, tokens at stake can be sanctioned.
5.13. M13 – Ensure decisions are made by affected parties (for OP7)
In IPD projects, project sponsors trade decision-making autonomy for consensus mechanisms among
project team members (Hall and Bonanomi 2021). In other words, authority is given by the project
owner to the project participants to self-organize and self-govern the project. Blockchain transparency
5 https://court.aragon.org/ , accessed 09.12.2021.
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and censorship resistance enables smart contracts to ensure that powerful parties cannot solely
enforce collective choice and conflict resolution (Table 3, M13-1) in IPDs (M5-2, M12-1, and M14-1).
Ideally, decisions should be only possible to be made and challenged by actors that are also affected.
5.14. M14 – Coordination rules across nested enterprises (for OP8)
Large IPD projects have multiple nested management levels, including senior management team for
executive leadership, a cross-functional project management team to coordinate project
management activities, and functional teams that handle the direct work execution and organization
(Ashcraft 2011; Laurent and Leicht 2019). Therefore, it will be important that smart contracts
coordinate decision making among organizational tiers (Table 3, M14-1). This is either according to
existing nested management levels of IPD or for new forms of organization better suited for fast
information propagation and reactions to local events enabled by blockchain-based project
governance.
6. IPD on the Crypto Commons: An Overview of the Conceptualization
Figure 2 summarizes the overall conceptualization, visualizing the OPs (Table 1), the 14 identified
blockchain governance mechanisms (Table 2), and the 22 proposed applications for IPDs (Table 3).
The interaction arrows indicate then graphically the described connections in Section 5 between the
different IPD blockchain applications. The arrows are either outgoing, meaning they are a prerequisite
or support other applications, or incoming, meaning they are supported or enabled by other
applications. The width of the arrows and/or number of incoming connections can give an
approximate indication of the importance of applications (outgoing) or prerequisites to build
applications (incoming) within the overall conceptualization.
Applications that stand out regarding important prerequisite for IPD on the crypto commons are: M1-
1 defining boundaries for the users through addresses and tokens, and M7-2 monitoring the presence
of users and resources for fast system reaction and learning based on transparent record of
automation and transactions. Applications that depend on many interactions of other applications in
the system are in decreasing order of incoming connections: M4-3 for new incentive structures to
influence the system participants towards collective action, M3-1 decentralized market structures to
match project needs with local conditions, M5-2 for definition of voting rights to create intended
power distributions, and M14-1 coordination among organizational tiers.
It is likely that not all connections have been identified and the interactions need to be updated when
more research investigates individual applications and/or the interaction between them.
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Figure 2: Overview of the conceptualization of IPD on the crypto commons: the 12 blockchain governance mechanisms for
the eight OPs as identified in Section 4, and the 22 identified applications for IPD and their interactions outlined in Section 5.
7. Discussion
7.1. Impact
The novel proposition of this paper is to create blockchain-based governance structures for IPD
construction projects using the OPs as design guidelines. Trusted digital processes together with
cryptoeconomic incentive mechanisms can align stakeholders, both human and machine, to better
collaborate towards the overall project success. We see two main scenarios for the application of
blockchain-based governance mechanisms as introduced in this paper.
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First, blockchain based governance could address tradeoffs of current relational contracting
approaches to improve IPDs. Relational contracting is well-suited to deal with contractual hazards of
“displaced agency” (Henisz et al. 2012) found in the fragmented (Fergusson and Teicholz 1996;
Howard et al. 1989; Levitt and Sheffer 2011) and loosely coupled (Dubois and Gadde 2002a)
construction project structures. However, relational contracts also comes at various costs (Henisz et
al. 2012) that could be improved through blockchain-based governance mechanisms, e.g. reduced
competition with scalable decentralized market structures, or lengthier processes for decision-making
with decentralized decision-making platforms.
Second, the introduced blockchain mechanisms could be used to build new forms of project delivery
coordinated on the crypto commons. Construction projects can be characterized by complexity
(Bertelsen 2003; Dubois and Gadde 2002a; Gidado 1996). Research suggests that bottom-up
management and self-organization are better suited than hierarchical approaches to manage
complexity (Bertelsen and Koskela 2004; Helbing and Lämmer 2008). The OPs introduce guidelines to
achieve this for CPR scenarios. Since IPD can be described as a CPR scenario, blockchain governance
mechanisms could improve IPD-like project deliveries by creating better bottom-up and self-
organizing project structures, while still allowing for scalable coordination mechanisms.
This is aligned with the emerging organizational form of a decentralized autonomous organization
(DAO), which is a blockchain-based system that enables people to coordinate and govern themselves
mediated by a set of self-executing rules deployed on a public blockchain, and whose governance is
decentralized (Hassan and De Filippi 2021).
Sreckovic and Windsperger (2020) already proposed the evolution of the construction industry
organization towards DAOs. Lombardi et al. (2020b) and Dounas et al. (2020) even prototyped a DAO
for decentralized coordination of the design finding process through smart contracts. Their research
suggests that construction project governance as a DAO is at least in a prototyping context technically
feasible. Other ongoing research explores how decision making of a self-owning house can be
coordinated through a DAO (Hunhevicz et al. 2021).
Emerging examples of DAO frameworks (Faqir-Rhazoui et al. 2021) resemble many of the identified
governance mechanisms. The introduced research directions based on Ostrom’s design principles
might help to design governance building blocks towards project delivery coordinated through DAOs.
7.2. Design Challenges
Designing new blockchain based governance systems is challenging. Cila et al. (2020) identified six
design challenges (see Table 4) that are further discussed below in the context of IPDs on the crypto
commons.
7.2.1. Tracking
While transparent monitoring is essential to manage commons, it could lead to privacy concerns
regarding the community-based data (Cila et al. 2020). Especially in public blockchain systems,
traditional data privacy solutions are hard or even impossible to implement. It needs to be carefully
evaluated what data needs to be transparently stored to enable IPD governance, how construction
stakeholders perceive implications of sharing this data, and potential measures to maintain a suited
level of privacy without hindering the monitoring.
7.2.2. Coding
A major challenge is decide, represent, and encode values in artificial commons (Cila et al. 2020). It
tends to be easier to focus on economic and quantifiable values in a blockchain system (e.g. monetary
values through market pricing mechanisms) than social and qualified values (e.g. reputation
Page 21 of 31
mechanisms). However, in commons non-monetary values often play an important role (Fritsch et al.
2021). Also, in IPDs, both quantitative and informal systems are used. Future research should
investigate value flows in IPD, as well as how to encode them into tokens and incentive systems
without suppressing creativity and teamwork with too rigid and inflexible smart contract structures.
Furthermore, incentives give people a sense of agency, yet at the same time they can have downsides
of forced conformity with collectively set rules (Cila et al. 2020). At some point earning rewards might
become a duty to not be excluded from the system and rewards cause efforts to shift towards the
actions that will be rewarded, causing potentially unforeseen negative secondary effects (Cila et al.
2020). This needs to be subject of further study when designing project delivery mechanisms.
Artificial commons needs to find a balance between so trading individual gains for the greater good of
the community (Cila et al. 2020). IPDs have differences to natural commons that need to be
considered. For example, resources in IPDs are consumed intentionally over time, whereas in natural
commons they are renewable. Natural commons also have an infinite lifetime (if the community is
able to sustain them), while IPDs only last for the duration of a project (although this could be several
years). And the product is owned by the project sponsor, whereas natural commons are not owned
by any of the involved parties.
Overall, there is a need for more research to thoroughly understand current and new IPD mechanisms,
how they contribute to the success of IPDs, and how they need to be set up in different project
settings. Methods used to design and test such mechanisms should be able to reflect the complex
nature of construction projects (Bertelsen 2003; Dubois and Gadde 2002a; Gidado 1996). Previous
research used game theory for the evaluation of profit distribution (Teng et al. 2019) and target value
design (Jung et al. 2012), agent based simulations to assess the evolution of collaboration (Son and
Rojas 2011), or mechanism design to investigate new incentive structures (Han et al. 2019).
7.2.3. Negotiation
The last dilemma concerns how to preserve human reasoning and debate in a system of formalized
and algorithmic logic (Cila et al. 2020). Also for IPD, there are major risks involved in ex-ante designs
of smart contract, where system engineers need to account from the beginning for all expected cases.
Therefore, the governance system should be able to adjust over time to exceptions and design errors
through community input. But even with such governance processes embedded, the process will likely
only start after the first failure already happened. A stepwise and careful adaption with extensive
testing of these systems will be desirable.
Table 4: Design challenges for crypto commons (Based on the identified blockchain governance design challenges by Cila et
al. (2020)).
Design Challenge Type Description
Transparency vs. Privacy Tracking Crypto Commons must be monitored, but transparent tracking
could lead to privacy concerns.
Economic vs. Social Values
Quantified vs. Qualified Values Coding Values of the Crypto Commons must be encoded in a
representative way. This can be especially challenging for social
or qualified values.
Incentivisation vs. Manipulation Coding Crypto Commons must encode incentives without causing
unjustified manipulation and exclusion of stakeholders.
Private vs. Collective Interests Coding Encoding rules for Crypto Commons must weigh individual
gains of stakeholders against the greater good of the
community.
Human vs. Algorithmic Governance Negotiation Crypto commons must preserve human reasoning and debate
in a system of formalized and algorithmic logic.
Page 22 of 31
7.3. Challenges related to the construction industry context
While technical and system design challenges towards a blockchain governed project delivery can be
proactively addressed, there are many inherent construction industry barriers. Other scholars have
already investigated barriers and socio-technical challenges for blockchain in the construction industry
(Li et al. 2019). The provided frameworks likely apply also for the proposed system. Below we highlight
some of the key challenges.
The level of digitalization in the construction industry is still low (Agarwal et al. 2016; Barbosa et al.
2017). Blockchain-governed PDM require an extensive digital base-line of project related data. As long
as this data is not available, the proposed mechanisms cannot make use of it to govern construction
projects.
Moreover, the fragmented construction industry structure poses major challenges in the adoption of
systemic innovations (Hall et al. 2018), e.g. as in the case of BIM (Papadonikolaki 2018). Blockchain
based governance for construction PDMs likely falls into the same category of systemic innovations,
since value of the solution only comes at scale. At the same time, cryptoeconomic governance
promises to reduce implications of fragmentation through incentives across phases and trades
(Hunhevicz et al. 2022a). Nevertheless, it will be challenging to organically grow adoption.
Finally, there are major legal implications with such new solutions. Research needs to investigate how
smart contract code can conform with law and regulations (De Filippi and Hassan 2016).
7.4. Limitations
For blockchain based governance processes, the underlying blockchain infrastructure is a key
component to success. For simplicity, this paper only refers to blockchain, but there are many kinds
of DLTs suitable for different types of use cases. The choice of the right type is not part of this work,
but should be considered once a use case will be implemented (Hunhevicz and Hall 2020). Many
parameters such as security, throughput, privacy, approaches to smart contracts, and others need to
be assessed. The still early technological state and the many different available solutions (Ballandies
et al. 2021; Spychiger et al. 2021b) make this challenging. The paper assesses blockchain for CPR and
IPD based on the assumption of public permissionless blockchains, since they align with the promise
to govern decentralized economic coordination. When using other DLT options, the affected
properties need to be adjusted. Future research should assess suited technical infrastructure to realize
the proposed blockchain governance mechanisms in an IPD context.
Furthermore, the paper acts only as a starting point to conceptualize the connections between
blockchain, CPR theory, and IPD. As a next step, further research is needed to validate the
conceptualization. This includes validation of both the individual mechanisms and applications as well
as their interaction. The contribution of this paper is limited as a proposed conceptualization, meant
to underpin future research efforts that can validate and extend the conceptualization.
8. Conclusion
The paper extends the thinking around blockchain as an institutional innovation for the delivery of
construction projects. It exploits the theoretical connection between both blockchain and IPDs as a
CPR scenario to offer a systematic starting point how blockchain can support and evolve PDMs by
creating novel governance mechanisms.
For that the paper introduces a conceptualization of blockchain-based governance applications for
IPDs on the crypto commons. Twenty-two applications for IPD were identified based on fourteen
mechanisms of blockchain for the governance of CPR scenarios proposed to encode the eight OPs. The
conceptualization is useful to think more structured and modular about blockchain building blocks to
Page 23 of 31
govern construction projects collectively on the “crypto commons”. Furthermore, the
conceptualization can support the thinking around how blockchain could improve current IPD
concepts, potentially lead to the next generation of PDMs, or ultimately end in novel project
coordination through DAOs. On the one hand, blockchain-based governance mechanisms promise to
facilitate trusted, scalable and efficient bottom-up coordination mechanisms that cope with
complexity and displaced agency in construction projects. On the other hand, blockchain-based
project delivery offers exciting new opportunities for machine participation.
Even though the paper introduced a coherent conceptualization for blockchain-based governance of
PDMs, it requires further validation through proof of concepts investigating the feasibility of individual
and combined mechanisms. For that the paper discusses challenges related to the early state of
blockchain technology, the difficulties in designing blockchain-based governance systems, and the
industry-related challenges to overcome.
The paper primarily targets the construction industry, but the identified blockchain governance
mechanisms and applications could eventually be transferred to other cases of real-world commons.
9. Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or
not-for-profit sectors.
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|
{
"id": "2207.07002"
}
|
2212.01194
|
Inheritance and Blockchain: Thoughts and Open Questions
|
Inheritance is the fundamental building block of civilization. This is the
addition of wealth, knowledge and properties over time that produce the society
in which we are living. Every generation does not have to start from zero and
can capitalize on the efforts of previous generations. Blockchain based assets
are very efficiently and securely transferred between living entities. Yet the
actual way to make heirs inherit crypto-assets is seldom discussed. It appears
that the problems linked with the inheritance of crypto-assets raise a lot of
technical, societal and legal issues. Part of those issues have to be tackled
with at the level of the blockchain infrastructure itself. The aim of this
paper is to open a research field, and to discuss some ideas, with regards to
this overlooked issue. Inheritance is neither a peripheral question nor one
that can be dodged. It comes with its own set of challenges that have to be met
if blockchain based finance, and asset management, is to be taken seriously.
|
http://arxiv.org/pdf/2212.01194v1
|
Frédéric Prost
|
cs.CR, K.4.2; K.5.m
|
cs.CR
|
arXiv:2212.01194v1 [cs.CR] 28 Nov 2022Inheritance and Blockchain:
Thoughts and Open Questions
Fr´ ed´ eric Prost
LIG - CNRS and Univ. Grenoble Alpes, Grenoble, France
frederic.prost@univ-grenoble-alpes.fr
December 5, 2022
Abstract
Inheritance is the fundamental building block of civilizat ion. This is
the addition of wealth, knowledge and properties over time t hat produce
the society in which we are living. Every generation does not have to
start from zero and can capitalize on the efforts of previous g enerations.
Blockchain based assets are very efficiently and securely tra nsferred be-
tween living entities. Yet the actual way to make heirs inher it crypto-
assets is seldom discussed. It appears that the problems lin ked with the
inheritance of crypto-assets raise a lot of technical, soci etal and legal is-
sues. Part of those issues have to be tackled with at the level of the
blockchain infrastructure itself. The aim of this paper is t o open a re-
search field, and to discuss some ideas, with regards to this o verlooked
issue. Inheritance is neither a peripheral question nor one that can be
dodged. It comes with its own set of challenges that have to be met if
blockchain based finance, and asset management, is to be take n seriously.
1 Introduction
Crypto-currencies, starting with Bitcoin [Nak09], have shaken the world of fi-
nance in less than a decade. It has moved from a pipe dream concept to an
every day tangible reality in the meantime. At the time of writing the glo bal
market capitalization of emitted bitcoins is around 10% of the global m arket
capitalization of gold. There are many discussions on the nature of m oney, and
assessing the relative merits of Bitcoin versus gold as a store of valu e is still an
ongoing discussion.
Among the mandatory properties that a store of value must have, the prop-
erty of being inheritable is a major one. Over a sufficiently long period t he
survival rate of everyone drops to zero. Transmitting wealth to t he future gen-
erations is neither a peripheral issue, nor one that can be dodged. The body of
laws, stories and traditions about inheritance is immense. In fundam ental texts
like the Bible [Bib] or the Odyssey [Hom], the question of who inherits wha t
1
from whom, and more generally all kinds of problems linked with succes sion,
are major preoccupations.
The issue of inheritance is orthogonal to the actual implementation of the
store of value. Society, in a very broad sense, is the tool tradition ally used to
transfer titles, assets, and to settle questions like: ”Who is the ne w King?”. Re-
garding material wealth, objects do not disappear when their owne r dies. These
remarks no longer hold with crypto-assets. Indeed, one fundame ntal feature
of the crypto-assets is that it is only with the knowledge of the appr opriate
keys that the assets can be transferred. The actual ownership of crypto-assets
amounts to the knowledge of the keys and vice-versa: everyone t hat knows
the keys is deemed to be a rightful owner of the associated crypto -assets. But
by definition, and under these circumstances, one cannot actually implements
his/herown succession because either the knowledgeof the keysd isappears with
their death or the ownership of those crypto-assetshas to be sh ared with a third
party. It appears that we are finding ourselves painted into a corn er: on one
hand a crypto-asset should only be transferred by its legitimate ow ner; and
on the other hand, the owner cannot transfer anything once dea d, making the
succession of crypto-assets seemingly impossible.
In this paper we discuss various challenges and ideas linked with the inh er-
itance of crypto-assets from a computer science point of view. Th e idea of the
discussion is to think about what desirable properties should be looke d for, in
the original spirit of crypto-currencies, inheritance of crypto-a ssets. Indeed, the
easiest way to ”solve” the inheritance problem would be to write the k eys into
the wills. This solution is going to be discussed further later in the pape r in
section 2.1. But clearly, it is not in the spirit of crypto-currencies be cause it
relies on a third party. It clashes with the fundamental idea of cryp to-currencies
which is to be able to establish trust without having to rely on third par ties.
In section 2, basic issues linked with the issue of crypto-asset inher itance
are discussed. Problems are addressed in a colloquial way and prese nted by
increasing level of complexity. In section 3 is done a state of the art review.
Future themes for research and reflection are pointed out in sect ion 4. We
finally conclude in section 5
2 Informal discussion on theinheritance of crypto-
assets
2.1 Delving into the complexity
Let’s examine some issues, as well as some workarounds, raised by t he issue of
the inheritance of crypto-assets step by step. In this section we explore this
question from a naive point of view in order to give an intuitive idea of th e
landscape. In this section we focus on intuitive understanding of th e issues, a
more technical/disciplinary point of view approach is considered in sec tion 4.
A one liner frame of the question to be addressed goes something like this:
2
How can my eight years old daughter inherits my crypto-asset s?
Thisisastartingpoint, therearemanymoresubtlersub-problems. Actually,
themoretheproblemofinheritanceisconsideredseriously, themor eitsinherent
complexities appear. Let’s examine a sample of those issues, togeth er with some
tentative solutions, by increased level of sophistication.
1. The first idea to solve the basic ”eight years old daughter inherita nce”
problem is:
(a) To set up a meeting with a lawyer.
(b) To write down the wills on a document, including the appropriate
private keys.
(c) To seal off the envelope.
(d) To hope for the best.
This natural solution presents many challenges. The more salient be ing
that crypto-currencies have been built precisely to provide agree ments
without having to rely on identified third parties. There is maybe noth ing
as opposed to this aim than having to go to see a lawyer, and having
to rely on the professional integrity and competence of this lawyer . This
is a poster child of all the issues linked with centralization. There are
many additional issues. One can think of anonymity related issues: f or
instance in order to establish the wills, a comprehensive list of all cryp to
wallets has to be done. Another class of problems is the risks inheren t to
this solution. Typically for lawyers that will be targeted by wrongdoe rs if
this practice becomes mainstream (because stealing the keys give e ffective
control of the crypto-assets). The saying ”not your keys not y our coins”
sums it all accurately. The simple fact that you have to give your key to a
third party amounts to lose the ownership of the associated crypt o-assets.
Essentially this solution reintroduces the single point of failure with all
the drawbacks attached to it.
2. The second idea that may come to mind is to put all the keys on a thu mb
drive, or write them down on a piece of paper, and lock them into a saf e
at home. It marginally improves on the previous point if family is more
trusted thanprofessionallawyers. Itisamoredistributed solutio nbecause
each individual implements a particular version of this protocol.
Besides the hazards that such a practice would produce if it were wid ely
adopted (basically the same issue than the one discussed with the law yer’s
solution and the incentive for wrongdoers to steal keys), it has th e follow-
ing additional drawback: actually my eight years old daughter is not m y
only heir. Let’s say there are four kids and seven nephews between whom
the inheritance is to be shared. It is not as if dramas about success ion,
struggleswithin families, andcommunities, area literarygenreunto t hem-
selves. Moreover,howcansomeonebesurethattheindividualope ningthe
3
safe will behave correctly? It is harder to cheat with a pile of physica l gold
because there may be witnesses, the material has to be physically m oved
around etc. With crypto-assets, one just has to remember a pas sphrase.
No one can stop someone knowing the correct keys from using them later.
Short of killing, it is impossible to delete a passphrase from the memory
of another person.
3. It is possible to be smarter and to write a smart contract that imp lements
the succession wills. It solves the ”four kids, seven nephews” prob lem: the
smart contract is going to perform the sharing. It can be seen as a n equiv-
alent of the wills in the blockchain world. It raises a new problem tough:
how will the blockchain be aware of the death of the owner of the sma rt
contract? This is a variant on the famous oracle problem [ABRSS20]. The
first ideas to deal with this issue would be to rely on some sort of stat e
based service reintroducing a centralization problem. This specific is sue
is discussed more thoroughly in section 4.1.
At this point another class of issues, that are not primarily technica l but
have technical implications, appears: the heirs may not be of age to un-
derstand the crypto technologies involved. Maybe the don’t have t he legal
rights to access such kind of funds either. Some of the heirs may als o not
have crypto-wallets in the first place. If so the mechanisms by which the
proper credentials could be transmitted to them, without being com pro-
mised, remain mysterious.
There is anothertype ofissue: what if the four kids andthe sevenn ephews
die with the one that they are supposed to inherit from? Let’s say, f or
instance, that they all (or any subset) disappear simultaneously in a plane
crash? It is not possible to re-write the smart contract: that is th e very
idea of smart contracts. Then the inheritance disappears (more p recisely
it becomes inaccessible) in such a scenario. In real life there are spe cific
laws and legal practices to deal with such kind of situation. This issue is
discussed more precisely in section 4.3
4. A rather simple solution is to set up an equivalent of a time capsule. I f
a date is chosen sufficiently far away in the future, then the death o f the
capsule owner becomes a certain event. It can be done via smart co ntracts
that just havetowaituntil someblocknumberisreachedin theblock chain
before being executed. The drawbacks lie in the lack of flexibility and t he
necessary approximation of the time of death. A middle-aged victim o f
a traffic accident or an unexpected death could potentially lead to an
inheritance process stalled for more than half a century. Moreove r, the
probability that the potential beneficiaries of the inheritance may h ave
died too in the meantime increases.
5. An improvement over the previous idea is to use a dead man’s switch .
Insteadofusingthemaximumageplusasafetymarginforthetimeca psule
deadline, it is possible to use a shorter frame. If necessary one has just to
4
edit the time capsule deadline before it is executed. The death of the time
capsuleownerstopsthis processofreprogramming,andthetime c apsuleis
eventually delivered. It, partially, solvesthe issue of the lag betwee n death
and succession. On the other hand it requires a constant vigilance a nd
work. It also opens possibilities of denial of service attacks. The de nial of
service can be malicious or due to life circumstances, typically the own er
of the dead man’s switch cannot access the blockchain for technica l or
medical reasons (for instance being in a coma etc.).
6. Everyone is going to die but we hope that it will happen as far away in
the future as possible. Life expectancy has improved a lot lately. Fr om
a technical point of view it is a very challenging aspect of crypto-ass et
inheritance to manage. It is very difficult to anticipate the technolog ical
environment a few decades from now. However, any credible propo sal for
the inheritance ofcrypto-assetsmustbe resistanttothe futur e. It suggests
that any solution should be integrated within the crypto platform its elf
rather than having to rely on outsourced processes. This problem is more
thoroughly discussed in section 4.5.
2.2 Decentralized Society and inheritance
It is not yet clear how much blockchain based technologies are going t o have
a fundamental impact on society. Bitcoin has changed the financial landscape
in deep. The most important central banks are considering to eithe r develop
their own crypto-currencies and on ways to regulate this new field [B ha22].
Some actors go as far as suggesting that the nation-state frame work could be
impacted [Sri22], or more modestly that a new kind of society, the dec entralized
society [WOB22] will emerge.
What is going to happen is very hard to discern precisely but it is clear t hat
the impact are not going to be restricted to the technical/technolo gical tiers of
the society. The question of inheritance of crypto assets is going t o be major
concern.
There are already tricky issues linked with death and social media: wh at
is supposed to happen when the user of a social account dies? Is th e account
frozen for ever? If people have access to this account (for insta nce because
the password was saved in a personal computer) can they use it? S hould the
account be deleted? Those questions are not light and not easy to d eal with.
There are propositions to use Artificial Intelligence in order to have a digital
clone of the dead person [Ceb21]. Is it ethical or not to impersonate a dead
person through an artificial intelligence piece of software? On what grounds?
Likewise there were studies on security implications of social media ac count of
deceased persons [DCB22]. Those accounts contain many importan t data that
can be used before the knowledge of the death has been spread.
It turns out that those questions, though difficult and important, are just a
small aspect of a larger one: what becomes the concept of legacy in the digital
space? The problem of the inheritance of crypto-assets is a part o f this larger
5
issue. It raises challenges that are both technical, technological a nd societal.
3 Existing solutions review
At the time of writing there has been very few proposals to tackle wit h the
various issues raised by the inheritance of crypto-assets. Some w orkarounds
have been proposed.
•Sarcophagus[sar20]isadeadmanswitchimplementationthatisblock chain-
enabled. It is resistant to censorship and immutable. It is done by th e
combination of Arweave [WJ17] for a permanent storage of data, and
Ethereum [But13] to support the ERC20 Sarco Token. This token is used
to pay so called ”archaeologists” which are in charge of releasing the data
(essentially an encrypted file) to the person of interest. The user have to
select one or more existing archaeologists. The archaeologist public key is
used asanouterlayerofencryption. This outerlayerhastobe re- wrapped
at predefined dates in the future. If one date expires then the ar chaeolo-
gist decrypts the outer layer. The inner layer is the data encrypte d with
the public key of the final receiver that can decrypt it.
In addition to the problems linked with the dead man switch that are
discussed in section 2.1, there is the presupposition that the receiv er of
the time capsule is already well identified and as access to the necess ary
public key infrastructure.
•Ternoa [Ter22] is a french start-up that proposes a ”death prot ocol”which
is basically a smart contract triggered by the API’s of local authorit ies
registering deaths. It presents the problem of relying on a centra lized
Oracle. One issue is that it is easier to hack the local authorities data base
(or to bribe agents working for this agency) than to break a distrib uted
solution relying on crypto technologies. Another issue is that there is no
standard API to deal with this issue that is shared among countries . Each
solution is limited to one nation-stateat best. Finally there is no warra nty
that the API are not going to change in the future.
•Casa[Cas22]isacompanythatproposessolutionsbasedonmulti-sig nature
schemes. Their primary service is to provide better resiliency for cr ypto
wallets. They also have an inheritance product that is basically a tech no-
logical implementation of the second point examined in section 2.1.
4 Themes for future research
4.1 A distributed protocol for the death announcement
The aim is to define a protocol that is safe, distributed and has some privacy
properties such that the blockchain is aware of the death of a part icular in-
6
dividual. This is the basic signal that is going to be used as a trigger for
smart-contracts, whatever they might be (see 4.3), implementing the wills.
Every solution to this problem must at least meet the following criteria :
1. It should be adaptable to any blockchain modulo an appropriate tu n-
ing of technical details and of governance peculiarities of the consid ered
blockchain.
2. It should respect privacy in the sense that before the death ha s been
enacted by the blockchain, there should be no way to link specific wills,
whatever there form are, to a specific crypto-wallet/crypto-ad dress.
3. It should present some warranties of a good execution, namely t hat the
inheritancewillbedoneasitwasplanned. Thispoint isnottrivialbecau se
the solution has to rely on a distributed system for which it is generally
hard to have hard warranties of execution.
In [Pro22], I have proposed such a protocol. The idea behind the Tales
From the Crypt Protocol (TFCP) is the following: the signal of the death is set
when a predetermined amount of coins is transferred to a special w allet after a
predetermined time has elapsed. The coins are stacked by witnesse s that tes-
tify on the death of a particular person. This stack can be lost if it tu rns out
that the information of death proved false. The proof of life is adju dicated by
the existence of a financial move on the account of interest. Once the network
has acknowledged the death signal, then another group of actors decrypt the
information allowing to make the link between the dead person and the corre-
sponding crypto-assets and smart-contracts. I refer to the p aper [Pro22] for the
technical details.
4.2 Transmitting secrets to the future
Cryptography is very efficient at allowing multiple parties to exchange secrets.
The untold assumptions of this field are that both parties have to be alive, and
most of the time identifiable . Both of those assumptions may not be necessarily
true in the case of inheritance. One edge case is that the heir might s till be in
the womb when the giver dies. There are also those stories of the re search of
the heirs that takes years or even decades. There is even a profe ssion ”probate
genealogist” whose job is precisely to solve work on those kind of puz zles.
Without havingto goasfarastheseextremecases, itisclearthat, within the
realm ofcrypto-assetsinheritance, the issue of transmitting the right credentials
to the right person is a major preoccupation. Indeed the knowledg e of the keys
amounts to the proof of ownership of those crypto-assets. Thu s the paradoxical
requests: the secrets have to be transferred to an unknown th ird party at an
unknown time, and in the meantime there should be some warranties t hat the
secret is not revealed to any other party while the owner of the sec ret have
disappeared. The fact that the time lapse can very easily be counte d in years
only makes the issue more complex.
7
A first idea would be to adapt some secret sharing scheme [Bei11], bu t it
doesn’t look a trivial endeavorat first sight. Indeed, whateverth e scheme would
be, the fact that shareholders should be forbid to collude to revea l the secret
is harder to implement: as we mentioned time lapse is very large. In fac ts
it is so large that shareholders might have to transfer their shares to another
shareholder for instance.
Another ideato developis toobjectify sharesin orderto regainsom econtrol.
The raw idea would be to find ways to produce clones of objects that do not
require any digital process. A very simple idea is the following: you can produce
material keys (pieces of metal) that are clones using key duplicating machines.
Those machines are analogical: there is no file recording how the clone can be
produced. Then these clones can be used in the future as a shared secret: by
making standard measurements on the object, it would be possible t o generate
some bits of shared information. One interesting point is that the me asure has
not to be fully specified at the time of production. The only property that
has to be met is that they are really clones from one another, meanin g that
any physical measurement on both objects will give the same result s. Another
desirable property would be that it should be materially difficult to prod uce
other clones: simply scanning the object should not be enough. The underlying
idea is that those material keys are going to be saved in a physical sa fe, and just
having access to them should not be enough to make clones. Anothe r important
propertythat such an artifact must have is the stability of the obj ect acrosslong
periodsoftime. The physicality/materialityaspect ofthis kind ofsolu tioncould
be a way to work around the issues raised by the non destructive, a nd perfect
copy of information, that digital technologies allow.
4.3 Flexibility of wills
One of the very basic motivation behind the smart contracts [HHL+21] is the
fact that once they have been enacted, it is impossible to change th em. This
feature is very interesting in a variety of application. In the case of inheritance,
typically when the wills are implemented via a smart-contract, it can be a hard
problem to solve. Indeed, as discussed above, the identity of the h eirs is not
always the one that was initially defined. There are more subtle proble ms,
like, for example, some evolution of the inheritance laws between the coding
of the wills. Those problems essentially comes from the fact that the re is an
incompressiblepartoftheinheritanceprocessthatcannotbecom pletelyforesaw
in advance. Therefore, developing smart-contracts more flexible is an important
task from this perspective.
This has not to be an evolution of the fundamental ideas behind smar t-
contracts but rather a way to develop an ecosystem that adds ov erlays on top
of the smart-contract in order to achieve more flexibility. Because death is both
unavoidable and unique it can be implemented as a set of ad hoc govern ance
rules. Yet those rules have to be clearly determined and studied. Th eir inter-
actions with the rest of the blockchain could be tricky to precisely an alyze.
8
4.4 Blockchain and civil status
The inheritance process brings to the forefront many problems ar ound links
between the virtual world and the material world. One of these issu es is known
as Oracle’s problem: how and by what rules is the blockchain ”aware” o f what
is happening in the outside world? Regarding the inheritance process there
is a dual type of issue: how is the blockchain being able to reach the ou tside
world? If you narrow the problem down to first principles, in order to perform
an inheritance one has to reach specific individuals. A first problem is t hat
people that are looked for do not necessarily have a presence in the blockchain
ofinterest. Inthebasic”8yearsolddaughter”scenariotheprob lemisillustrated
bythe factthat youngchildren don’thavea crypto-wallet. Anothe r similarissue
is that, even if such crypto-wallets exist, they might not be known b y the giver.
How can it be represented within the blockchain? There must be some kind of
link between the blockchain and the real world, i.e. the ”social secur ity name”
of heirs. Again you don’t want to depend on third parties for this link.
A recent proposal by Weyl, Ohlhaver and Buterin in [WOB22] is the idea
of ”Soul Bound Tokens” or SBDs. The idea is to have NFTs that can’t be
transferred. Their use could be to represent non-transferable and persistent
social relationships. Many legacy issues could use such a novel prop osition in
order to bridge the gap between virtual and real world.
4.5 The long term problem
Any solution to the inheritance issues of crypto-assets is going to f ace a particu-
lar challenge: the solution is not going to be used before a, hopefully, very large
lapse of time has elapsed. Since it is not possible to foresee the exact time of
death, the inheritance process of young people may have to stay p ut for many
decades before being actually used. It is always possible to rely on th e end user
to continually adapt to the new technological environment. But this is clearly
not a satisfactory answer both from a practical, societal and philo sophical point
of view.
From a practical point of view, the inheritance issue is not something that
one should have to work constantly on. It should be set once for all unless very
specific events occur: loss of an heir, new wedding in your family, app arition of
new heirs etc. Changing your wills when such extreme events happen is normal,
but otherwise one shouldn’t have to tinker with his/her wills on a regula r basis.
Remember than most of the time, in most civilizations, there are no ex plicit
wills. There is a ”by default” mode, embodied into customs and dedicat ed laws,
that applies. Wills are used when something specific has to be implement ed,
and more often than not laws restrict the extent to which wills can be differ
from the default procedure.
Inheritance is the engine behind culture building. Therefore, the pr ocess of
inheritance cannot be completely let to the hand of individuals. In the same
way that there are basic laws (murder, stealing are forbidden etc.) for a society
to work, there are basic rules regarding inheritance that do not sim ply rest on
9
subjective/personal choices. This has strong implications on the in heritance of
crypto-assets, indeed any implementation might have to change be cause legis-
lation has evolved. On the course of many decades it is not surprising , or even
unexpected. This is somewhat contradictory with the previous poin t. The fact
that maybe there are parts of the inheritance process that are g oing to evolve
do not solely rest on the whim of individuals.
From a more abstract point of view the inheritance process is by nat ure
a trade-off between individual wishes and societal rules. This trade -off slowly
evolves as time goes by. This has an impact on any technical ”solution ” to the
inheritance of crypto-assets. The whole idea of the blockchain gov ernance must
have to take this into account. How are these issues going to be solv ed remain
subject to many trial and errors, but unlike most of other functio nalities that
can be tested in real time, the inheritance functionality cannot be t ested on
a large scale quickly. The development of simulations is both mandator y and
difficult: what are the correct model of users? How the fact that r ules are going
to change over time can be coped with? Those are some of the quest ions that
have to be tackled with.
4.6 The customer is not the end user
Inheritance can be looked at both from the giver’s side and from the heir’s
side. What is unique in the ”inheritance application”, viewed as a featu re
of the blockchain, is that the giver, by definition, may never check w hether
things have unfolded as planned, and that the heirs may not even be aware that
they are heirs. Therefore the inheritance application is a very spec ific kind of
applicationforwhichincentivesareveryhardtoset upcorrectly. I ndeed, almost
by definition there are no customer feedback. Even if you inherit, m eaning
that the processed somehow worked, how do you know that the pr ocess was
correctly executed? Moreover, because the inheritance may inclu de privacy
management it is not even clear that the heirs of a single giver may kno w
each other. So they cannot regroup together to check that who le process has
worked as it was intended to. This is not intrinsic to the inheritance of crypto-
assets. Butit isrelativelynewfromacomputerengineeringperspec tive. Indeed,
in real life institutions, and society, are set to solve this issue. Some how the
community”knows”whoiswhoandwhatbelongstowho, evenifthiskn owledge
is distributed and somewhat fragmented. There are informal ways to have
feedback which is also known as ”reputation”. An equivalent has to b e found
in the world of blockchain.
Another related point is that heirs may refuse the inheritance. This is a
possibility in many cultures. Usually this choice has important legal implic a-
tions like accepting/refusingdebts ofthe giver. In the blockchainc ontext it may
translates in accepting/refusing the result of some smart contra cts that are exe-
cuting. Thiscontributestotheinherentcomplexityofinheritancep rocesswithin
the blockchaincontext. If the smart contractwait for an accept ance/refusaltick
how does it warn the interested parties? What if no one accepts? Et c. The list
of questions grows larger as closer as you look into the issues. Some problems
10
are just programming issues but others are specific to the inherita nce process.
The thing is that inheritance is a global functionality of a community an d not
just a personal issue. Just like the functionality of funds transfe r is not up to
the individuals. What is up to the individual is the definition of the receiv erand
the amount to be transferred. But the idea is not up to the whim of t he user.
Some parts of the inheritance process are of this nature. To pinpo int which ones
and to find out how they could be implemented in a blockchain world rema in
open questions.
4.7 Atomicity of wills
If only because of technical limits, the inheritance process has alwa ys been,
historically, an atomic process in the sense that it was performed loc ally and
executed as a batch. It could take time to gather all heirs and comp ute who
inherits what but the process itself is clearly identified. It is no longer the case
with crypto-assets and it raises specific challenges that are neithe r purely legal
nor purely technical. For once, crypto-assets are not going to re ly on a single
blockchain. Each blockchain may propose its version of inheritance.
In [WOB22] the case for a decentralized society is made. Among the m any
points raised, the idea that the key primitives are the accounts, or the wallets,
is central:
Note there is no requirement for a Soul to be linked to a legal name,
or for there to be any protocol-level attempt to ensure “one Sou l per
human.” A Soul could be a persistent pseudonym with a range of
SBTs that cannot easily be linked
The whole idea of inheritance is impacted by this. The questions raised are
not only of the technological realm.
Another non trivial evolution, that has both societal and technolo gical im-
plications, is that, by essence, a Blockchain is not an object that ca n be located
in a specific place. Therefore, it is not clear how the legal liabilities may b e
inferred. Typically something like ”which court has jurisdiction?” is no t an
easy question to settle. The same questions arise regarding the ta x code to be
considered.
Many pages of similar problems could be filled. They are not trivial issue s.
As mentioned earlier, the body of laws, practices and customs linked to legacy
is immense. It looks like that the useful abstractions in order to thin k about
those issues have yet to emerge. In that respect the inheritance problem is
very singular: the problem to solve is not yet clearly defined and cann ot be
possibly well defined. Solutions and concepts are going to emerge fr om practice
and adoption. Yet those are not issues that can be totally outsour ced, from a
blockchain point of view.
11
5 Conclusion
In this paper we have discussed many problems and ideas around the theme
of the inheritance of crypto-assets. There are several levels to consider. They
range from a broad societal point of view down to the technical det ails and the
specifics of protocols. Most of the challenges remain open at the tim e of writing.
It is not yet clear either what should be part of the blockchain infras tructure,
and what should be delegated to the outside world. This question is an integral
part of the discussion that has to be done.
Studies on the inheritance issue of crypto-assets are interesting in and of
themselves. They have the potential to lead to interesting results in the field of
blockchains in general.
•Theinheritanceprocessispartlyasocialissue. Assucheverytech nological
proposition should be made with that caveat in mind. In particular, it
means that solely technical solutions are not going to make it.
•Any proposition should be such that it includes new possibilities for the
blockchain to interact with the outside world. Those interactions sh ould
be more sophisticated than what the current implementation of Ora cles
offers. Typically, the fact that heirs may not necessarily be users o f a
blockchain implies that there are tools that have to be developed in or der
to allow interactions from the blockchain towards the real world. Wh ich is
a dual problem than the traditional problem that is addressed by Or acles.
•By its very nature the inheritance process is a very long time problem .
The time horizoncounts in decade. Very few issues havethis proper ty, but
as the move towards a digital society accelerate, more and more as pects
of our lives will be tackled using digital technologies. And so more and
more digital products will accompany us throughout our lives. How t o
manage such a kind of products? How can they be tested? Are some of
the questions that will have to be answered.
The intrinsic social dimension of many aspects of the inheritance pro cess
suggeststhat social media should be a useful tool to investigate. The interesting
feature of social media is that they are inherently distributed. Mor eover, they
could be used to developvirtual identities that do not relyon a numbe r recorded
in a database. Indeed, digital identity could be seen as the sum of int eractions
withinasocialnetworkratherthananumberinsideacentralizeddat abase. This
is of major importance because the whole point of having a blockchain based
mechanism for inheritance is to have a mechanism that is not relying on third
parties. How and with what level of warrantiesare the important qu estions that
future researches will have to address.
12
References
[ABRSS20] Hamda Al-Breiki, Muhammad Habib Ur Rehman, Khaled Salah,
and Davor Svetinovic. Trustworthy blockchain oracles: Review,
comparison, and open research challenges. IEEE Access , 8:85675–
85685, 2020.
[Bei11] Amos Beimel. Secret-sharing schemes: A survey. pages 11– 46, 05
2011.
[Bha22] Gita Bhatt. Reimagining money in the age of crypto and centr al
bank digital currency. International Monetary Fund Blog , Septem-
ber 2022.
[Bib] The Bible .
[But13] Vitalik Buterin. Ethereum white paper: A next generation sm art
contract & decentralized application platform. 2013.
[Cas22] Casa, https://keys.casa/bitcoin-inheritance-plan, 2022 .
[Ceb21] Daniel Cebo. Scientific relevance and future of digital immor tality
and virtual humans. CoRR, abs/2101.06105, 2021.
[DCB22] Graeme Dickerson-Southworth, Brian Chen, and James Br aman.
Securing the accounts of the deceased: Implications of compro-
mised profiles. In Constantine Stephanidis, Margherita Antona,
and Stavroula Ntoa, editors, HCI International 2022 Posters - 24th
International Conference on Human-Computer Interaction, HCII
2022, Virtual Event, June 26 - July 1, 2022, Proceedings, Par t IV,
volume 1583of Communications in Computer and Information Sci-
ence, pages 467–472. Springer, 2022.
[HHL+21] Tharaka Hewa, Yining Hu, Madhusanka Liyanage, Salil Kanhare,
and Mika Ylianttila. Survey on blockchain-based smart contracts:
Technical aspects and future research. IEEE Access , 03 2021.
[Hom] Homer. The Odyssey .
[Nak09] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic ca sh system,
http://bitcoin.org/bitcoin.pdf, 2009.
[Pro22] Fr´ ed´ eric Prost. On the heritage of crypto assets – tale s from the
crypt protocol, 2022.
[sar20] Sarcophagus - a decentralized dead man switch,
https://sarcophagus.io/, 2020.
[Sri22] Balaji Srinivasan. The Network State: How To Start a New Coun-
try. 2022.
13
[Ter22] Ternoa-whitepaper,https://github.com/capsule-corp- ternoa/white-paper/blob/main/white-paper-en.md,
2022.
[WJ17] Sam A. Williams and Will Jones. Archain: An open, irrevocable,
unforgeable and uncensorable archive for the internet. 2017.
[WOB22] E. Glen Weyl, Puja Ohlhaver, and Vitalik Buterin. Decentralize d
society: Finding web3’s soul. SSRN Electronic Journal , 2022.
14
|
{
"id": "2212.01194"
}
|
2303.17206
|
Innovative Countermeasures to Defeat Cyber Attacks Against Blockchain
Wallets: A Crypto Terminal Use Case
|
Blockchain transactions are signed by private keys. Secure key storage and
tamper-proof computers are essential requirements for deploying a trusted
infrastructure. In this paper, we identify some threats against blockchain
wallets and propose a set of physical and logical countermeasures to thwart
them. We present the crypto terminal device, operating with a removable secure
element, built on open software and hardware architectures, capable of
detecting a cloned device or corrupted software. These technologies are based
on tamper-resistant computing (javacard), smart card anti-cloning, smart card
content attestation, application firewall, bare-metal architecture, remote
attestation, dynamic Physical Unclonable Function (dPUF), and programming
tokens as a root of trust.This paper is an extended version of the paper
''Innovative Countermeasures to Defeat Cyber Attacks Against Blockchain
Wallets,'' 2021 5th Cyber Security in Networking Conference (CSNet), 2021, pp.
49-54, doi: 10.1109/CSNet52717.2021.9614649
|
http://arxiv.org/pdf/2303.17206v1
|
Pascal Urien
|
cs.CR
|
cs.CR
|
Innovative Countermeasures to Defeat Cyb er Attacks
A gainst Blockchain Wallets :
A Crypto Terminal Use Case
Pascal Urien
Telecom Paris
19 Place Marguerite Perey 91120 Palaiseau, France
Pascal.U rien@telecom - paris.fr
Ab s tract — Blockchain transactions are si gned by private keys.
Secure key storage and tamper - proof computers are essential
requirements for deploying a trusted infrastructure. In this
paper, we identify some threats against blockchain wallets and
propose a set of physical and logical countermeasu res to thwart
them. We present the crypto terminal device, operating with a
removable secure element, built on open software and hardware
architectures, capable of detecting a cloned device or corrupted
software. These technologies are based on tamper - resi stant
computing (javacard), smart card anti - cloning, smart card
content attestation, application firewall, bare - metal architecture,
remote attestation, dynamic Physical Unclonable Function
(dPUF), and programming tokens as a root of trust .
This paper is a n e xtended version of the paper "Innovative
Countermeasures to Defeat Cyber Attacks Against Blockchain
Wallets," 2021 5th Cyber Security in Networking Conference
(CSNet), 2021, pp. 49 - 54, doi: 10.1109/CSNet52717.2021.9614649.
Keywords: blockchain, securi ty, smartcard, PUF, remote
attestation
I. I NTRODUCTION
Blockchain transactions are signed by private keys, which
imply security requirements similar to those of electronic
signature processes or EMV payments made using bank cards
and payment terminals.
The m ain security requirements are secure key generation,
secure key storage, and tamper resistance. Some threats of key
theft are listed in [29].
A secret key is an integer value whose size is 32 bytes for a
256 - bit elliptic curve. The best practice should be to use a true
random number generator (TRNG). Nevertheless, many
wallets use passphrases as seeds for key generation; they are
based on a choice of words (e.g., 12 values chosen from 2048),
which leads to 2 132 possible combinations. The article [30]
sugges ts recovering the passphrase by a brute - force dictionary
attack. Another technique, called “brain wallet”, calculates the
private keys from the passphrases using a hash procedure; the
article [31] demonstrates brute - force attacks.
Many blockchains, for exa mple, Bitcoin or Ethereum, use
the ECDSA ( Elliptic Curve Digital Signature Algorithm )
signature. This procedure generates a random number k, and a
point on the elliptic curve (secp256k1) with the generator G, k.G= (x,y) from which is calculated an integer r= x mod n, n
being the order of the curve. The knowledge of k, the reuse of
k, or a known difference between two values of k, makes it
possible to recover the private key. The paper [32] studied
such a key leakage in Bitcoin. It extracted 647,110,920
sign atures and found 1,068 distinct r - values appearing at least
twice and used by 4,433 keys, or about 0.35% of the r - values.
The most frequently observed value is k = 1/2 mod n. It
should be mentioned that malicious random number
generators, such as kleptogra ms [33][34][35], can be used to
recover private keys after two signatures.
Even if the signatures are computed by a secure and trusted
device, they can be performed by malicious software, whose
purpose is to generate fraudulent transactions.
In EMV payment systems, point - of - sale (POS) terminals
are equipped with security stickers and battery - powered
electronics to detect attempted fraudulent use. The PCI
Security Standards Council lists approved companies and
vendors. Nevertheless, no standards are availabl e today for
assessing security in blockchain operations. In addition, low -
cost wallets are sold by e - commerce stores and delivered by
untrusted supply chains.
Secure elements, such as smart cards, are an effective
technology for providing secure storage an d trusted
cryptographic operations, with high levels of security (EAL
6+ according to Common Criteria - CC - standards) and
countermeasures to counter side - channel threats. However, as
with EMV cards, it is necessary to detect malicious clones.
Secure elemen ts require a terminal with additional
functionalities: user interface (touch screen, etc.) and
communication (USB, Bluetooth, etc.). Proof of integrity of
the software executed by the terminal is an essential
prerequisite. From an industrial property point of view,
detecting counterfeit electronic cards is an important feature.
In this paper, we describe a dedicated blockchain terminal
based on open hardware (i.e., Arduino platform) and software
technologies. It incorporates a set of countermeasures capable
of verifying the authenticity and integrity of the firmware and
detecting cloned devices. Thanks to the open hardware, the
terminal is realized with components supported by the
Arduino integrated development environment (IDE), but it can
also be integrate d into a dedicated electronic board.
The first prototype [9] used a two - line 16 - character LCD
and a 4x4 keyboard. These elements were replaced by a touch
screen in the second prototype [10], which also supports a
Bluetooth Low Energy (BLE) interface. An or iginal ISO7816
library has been developed to support smart cards by Arduino
environments. The firmware integrates a set of original
security features detailed in [16] [17] [19], whose goal is to
provide a bare - metal architecture with a root of trust, a
sof tware attestation procedure, and hardware fingerprints.
This paper is organized according to the following outline.
Section 2 briefly introduces the blockchain wallet state of the
art and identifies some threats; it also presents an original
algorithm to d etect duplicated code shards. Section 3
describes the crypto terminal, an open device that integrates a
set of countermeasures. Section 4 details the javacard security
features: tamper - resistant computing, anti - cloning, content
self - attestation. Section 5 presents the terminal security
features: applicative firewall and bare - metal architecture.
Section 6 introduces remote attestation using the BMAC
algorithm. Section 7 describes static and dynamic PUF.
Section 8 introduces programming tokens as a root of tr ust.
Finally, Section 9 concludes this paper.
II. . S TATE OF THE A RT
A. Blockchain Wallet
Fig1. Overview of blockchain wallets.
A blockchain wallet comprises two parts: a set of private
keys used to sign transactions and a set of parameters needed
to generate t ransactions. The secure storage and use of private
keys is a major security prerequisite.
While cryptographic keys can be used without
communication with the blockchain infrastructure, the
information needed to build a transaction must be retrieved,
which requires an Internet connection. Figure 1 illustrates two
broad categories of blockchain wallets: hot wallets (with
TCP/IP stack) and cold wallets (without TCP/IP stack).
The first generation of hot wallets was based on software
without tamper - resistant fe atures
Fig2. Private key storage in the wallet.dat file used by the bitcoin.exe
softwar e for windows
As an example, the Bitcoin.exe software for win32 was
written in 2009 by Satoshi Nakamoto [1]; it consists of about
16,000 lines of C++ code, and its bi nary image is 6 MB. It
included information managed by a non - SQL database, the Berkeley DB. In particular, the private keys were stored in the
file named wallet.dat. As shown in Figure 2, the private key
(colored in yellow) is not encrypted and can be iden tified by
its associated Bitcoin address :
" 16iLh 7ztLh2mwkhSvZeHxKWQVVMGGgfWGq "
In the Bitcoin system, the c oinbase transaction is the first
transaction in a block, used to transfer the potential reward to
an address identified by its Hash160 attribute:
ݏܽܪ ℎ 160 = ܦܯܧܲܫܴ 160 ൫ ܣܪܵ 256 ( ݕ݁ܭ݈ܾܿ݅ݑܲ ) ൯
The bitcoin.exe software generates a random address before
mining a new block. The loss or theft of the unprotected
wallet.dat file was a major risk, not always understood by
early miners.
Many blockchains use the Ellipti c Curve Digital Signature
Algorithm (ECDSA) signature. Given a message signature
made of two integers (r,s):
ݏ = ݇ − 1 ( ݁ + ݖ . ݎ ) ݀݉ ݊
with n being the group order, e the message hash, z the private
key, k a random number kG=(x,y), and r=x mod n.
An d given two signatures of two different messages, M 1
and M 2 , with the same r in (r,s 1 ) and (r,s 2 ), e 1 =hash(M 1 ),
e 2 =hash(M 2 ), the private key is computed as:
ݖ = ( ݁ 1 . ݏ 2 − ݁ 2 . ݏ 1 ) ݎ − 1 ( ݏ 1 − ݏ 2 ) − 1 ݀݉ ݊
Therefore, ECDSA generation requires a random number
generator (RN G) or a deterministic procedure based on the
message fingerprint (as detailed by RFC 6979). In 2010, EC
private keys were extracted from PS3 consoles by exploiting
the lack of a random number generator [21]. ECDSA,
therefore, relies on trusted software.
Fig3. Illustration of hierarchical deterministic wallets, according to the BIP32
specification
In Bitcoin, according to the BIP32 specification, keys are
computed in deterministic wallets (see Figure 3). A root secret
(512 bits), divided into two parts (I L0 and IH0), is calculated
from a seed. Then, child nodes, identified by a 32 - bit integer
index, compute 512 - bit secrets based on their parents’ IL and
IR attributes, using the CKD(m,i) procedure. Finally, the
private keys (256 bits) are computed, from whi ch the account
addresses are derived. Therefore, an address (and the
associated private key) is identified by a path in the BIP32 key
tree. The suggested hierarchy is as follows: master node,
wallet accounts, wallet chains and addresses.
The BIP32 seed can be computed from a passphrase
according to the BIP19 specification. The passphrase is a set
of words selected from a dedicated list of 2048 elements. The
computation procedure is based on the password - based key
derivation function (PBKDF2) defined by RFC 2898. This
mechanism allows the recovery of deterministic wallets, as the
key paths (i.e., the key identifiers) are public values.
Nevertheless, if the passphrase is stolen, the private keys can
be recovered by a brute - force attack on the key identifiers.
In 2014, a major Bitcoin company, Mt.Gox, went bankrupt
due to a data breach in untested software, leading to the
illegitimate use of private keys [29]. In addition, the privileges
of system administrators allow the hacking of private keys. As
an illustrat ion, Bitfi [2] is a UNIX smartphone without a
baseband chip, including an SD memory card, and offering
Wi - Fi connectivity. It was rooted, and private keys were
extracted.
To prevent these attacks, data centers use Hardware
Security Modules (HSMs), whose se curity requirements are
described in the FIPS 140 - 2 standard (“ Security Requirements
For Cryptographic Modules ”) [3]. Four security levels are
defined, ranging, according to the Common Criteria (CC)
terminology, from EAL1 to EAL4. Cryptographic keys are
ma naged by tamper - resistant systems whose access requires
multi - factor authentication (e.g., password, hardware key, and
PIN). For example, Coinbase recommends [4] storing the
PIN - protected hardware key in a vault so that its use involves
two human brains, o ne of which knows the smart card PIN
and the other the vault combination.
Some smartphone wallets are based on a hardware keystore,
which runs in a TEE ( Trusted Environment Execution )
processor [5]. Most Android mobiles are equipped with such
processors. T EE implements a secure computing environment;
however, according to [22], the hardware techniques and
processes used for smart cards do not apply to standard
System - on - Chip (SoC) technology.
The main idea of cold wallets is the offline storage of
private k eys. Communication with the outside world uses
USB, Bluetooth, Wi - Fi, or QR codes read by a camera and
displayed on screens (e.g., cobo vault).
There are two broad categories of cold wallets: those that
are fully software - based and those that include a tam per -
resistant device.
The Trezor One is an all - software device with a screen, two
buttons, and a USB interface. According to [6], the private
keys were recovered by a single - powered attack (SPA)
exploiting the unprotected implementation of the double and
a dd algorithm, in which a public key (zG, G being a generator)
is computed by parsing bit by bit, the private key z. ݖ = ܾ ݅ . 2 ݅ , ܾ ݅ ∈ { 0 , 1 } 255
݅ = 0
An elliptic curve addition is computed for every non - null bit,
which enables the bit recovery, for example, b y monitoring
the power consumption.
ܩݖ = ܾ ݅ . ܦ ݅ , ܦ 0 = ܩ , ܦ >݅ 0 = 2 . ܦ ݅ − 1 255
0
Some wallets use crypto memory (for example, Safe - T Archos )
or Trusted Execution Environment (for example, Archos Safe -
T Touch ). Crypto memory devices provide secure storage;
they work with mutual authentication procedures and
encrypted content.
The Ledger Nano S stores keys in a secure element. The
device comprises a microcontroller, a secure element, a screen,
two buttons, and a USB interface. Upon reset, a software
handler sends memory ch unks to the secure element, which
hashes their content, and finally checks a signature; upon
success, the secure element is unlocked. This mechanism was
broken [7] by using a duplicate area of memory. The reference
[8] demonstrated a successful replacement of the token
software.
Fig4. Illustration 64 bytes duplicate code shards
As illustrated by [7], software instruction blocks may be
duplicated. We call shards these blocks. We assume that
shards do not include instructions that explicitly modify the
pro gram counter (PC), such as JUMP or CALL. An example
of code shards is provided in Figure 4. We implemented
original procedures based on Ukkonen ’s algorithm for Suffix
Tree Construction [23] to detect and remove shards. This
algorithm finds the longest dupl icated binary string; replicas
are replaced by random numbers. Finally, it produces a list of
duplicate code fragments and their size.
Fig 5 . Illustration of code compression, using duplicated code shards; freed
memory space is colored in blue
As illustra ted by Figure 5 (using instruction mnemonics
from AVR processors), shards can be compressed by CALL,
JUMP, and RTS instructions. The shard instructions are
inserted in a subroutine, ended by RTS; the execution time is
increased, but some extra memory is av ailable for malicious
code. An example of the result is presented in Figure 6.
Fig6. Illustration of code compression, using duplicated code shards; which
frees 52 bytes
B. Threats
As mentioned above, hot wallets require online technology.
They are therefo re exposed to all the security threats cloud
providers face, the most critical being secure key storage and
their remote use. It should be noted that insider attacks are
predominant and require a specific physical and logical
security policy.
Cold wallets are obviously not exposed to such attacks;
however, they are subject to threats that are listed in the
following (non - exhaustive) list:
- T1) Lack of tamper - resistant storage and countermeasures
for side - channel attacks, which allows cryptographic keys to
be recovered at runtime;
- T2) Supply chain attacks aimed at malware injection or
malicious firmware modification;
- T3) Software integrity is not verified. A bootloader
checks the integrity of the updated firmware. It can be
corrupted, similar to rootkits ;
- T4) PIN or password hacking. For example, a key logger
captures the text entered on the keyboard;
- T5) Misuse of the device from a laptop or cell phone,
running a Trojan horse or worm;
- T6) Cloning of genuine devices. Clones, with equivalent
features , include hidden functions targeting the recovery of
cryptographic keys.
III. A BOUT THE C RYPTO T ERMINAL
The crypto terminal [10] is a keystore for blockchain
wallets, designed to prevent these threats, thanks to adapted
countermeasures. Its main services are th e generation of
signatures or transactions, with a high level of security and
trust. However, it requires an external software component
running on a PC or mobile device, which collects the
information necessary to generate the transaction under the
user’s control.
It is based on open software and hardware, namely Arduino
and javacard 3.0.4, which means that many form factors are
possible. The core of the security is a removable javacard [11],
which generates, computes, and stores the account keys and
perfo rms the transaction signatures.
Fig7. Crypto terminal hardware components
A. Open Hardware & Software
The crypto terminal (see Figure 7) comprises an 8 - bit
ATMEGA2560 microcontroller (16 MHz clock, 256KB
FLASH, 8KB SRAM, 4KB EEPROM), a USB chip (CH340),
a Bluetooth Low Energy (BLE) module [12] (CC2541,
256KB FLASH, 8KB SRAM), a 320x480 In - Plane Switching
(IPS) touch screen (ILI 9486), a USBASP programming token
[13] (ATMEGA8, 8KB FLASH, 1KB SRAM, 512B
EEPROM), and a removable smart card (EAL6).
The BLE chi p uses a five - wire interface for software
download, the specifications of which are public. It can be
flashed (up to 256KB) by open software, such as
CCLOADER [24].
The USB programmer for Atmel AVR microcontrollers
(USBASP) is an open project [13], includi ng both hardware
and software developments. It is supported by the open AVR
Downloader/UploaDEr (AVRDUDE) initiative. Therefore, a
complete platform is available for firmware download.
The ILI9486 is a device manufactured by ILI Technology
Corporation . It includes a 40 - pin interface with five control
lines, an 8 - bit data bus, and four wires dedicated to the
resistive touch screen
Fig8. Crypto terminal software components
The crypto terminal uses a set of five software packages
(shown in Figure 8):
- S1) th e main processor (AVR) firmware, up to 256KB;
- S2) the BLE module firmware, up to 256KB;
- S3) the firmware of the Java card;
- S4) USBASP bootloader;
- S5) the USBASP firmware, downloaded via the bootloader.
B. Countermeasures against Cyber Attacks
We believe that trust in signatures is a key feature for the
development of blockchain services. That is why we have
developed and published technologies and algorithms to
achieve integrity assurance for the software and hardware
involved in signing transactions.
Th e security model includes the following elements:
C1) The security core is an EAL5+/ EAL6 removable
security element (javacard), which stores the keys and
performs the transaction signatures. Access to the smart card
is protected by PINs.
C2) The authentic ity of the smart card is guaranteed by an
anti - cloning mechanism to detect “Evil Maid” - like attacks.
The idea of such attacks is to use a cloned card to collect the
user’s keys. The public key of the smart card is signed by a
certification authority (CA).
C3) The crypto terminal can duplicate the cryptographic
contents of the secure element. The content of the smart card
is self - attested (i.e., hashed and signed by the smart card).
C4) The crypto terminal acts as a firewall between the
security element and the “hot” environment connected to the
Internet. It also performs operations (key generation,
signatures, etc.) in “cold” mode (i.e., without being connected
to the Internet).
C5) Our security model is based on the “bare - metal”
concept (i.e., the terminal is delivered without firmware and
the Bluetooth module). Using a programming token, the user
may flash these devices at any time.
C6) The firmware is authenticated by a built - in “Remote
Attestation” algorithm. This algorithm [14] [15] [16] produces
a resul t (which we call the authentication code) that cannot be
predicted and requires a computation time depending on the
result. A server can generate these codes, but so can the
legitimate user, thus creating personal and unique
authentication codes.
C7) Physi cal authentications of the cryptographic terminal
processor and the programming token processor are
performed using dynamic Physical Unclonable Function (PUF)
techniques of SRAM (dPUF) [17]. A software memory probe
extracts fingerprints from the SRAM after power - up.
Innovative procedures have been developed to acquire such
fingerprints, including singular points called flipping bits.
Flipping bits [18] cannot be cloned by software. This
technique ensures that devices received by clients are
authentic.
C8) T he programming token is a root of trust [19]. It
manages the download of the processor firmware of the crypto
terminal. We insert bootloader software [20] inside the
programming token processor. The authenticity of this
software is proved by a remote attes tation algorithm
generating authentication codes displayed by flashing LEDs.
As mentioned earlier, the physical identity (dPUF) of this
token allows clone detection.
IV. J AVACARD S ECURITY E LEMENTS
The javacard runs a crypto currency (CC) application
written in the javacard programming language [11]. These
smart cards are available from multiple manufacturers, with
evaluation assurance levels (EAL) ranging from EAL5+ to
EAL6+, according to the terminology of the Common Criteria
standards. In a nutshell, the C C applet is a keystore, which
provides secure key storage and ECDSA signing on a
secp256k1 elliptic curve. The CC applet supports two - factor
authentication: the knowledge of two PINs enables two
working modes. The administrator mode gives access to all
sma rt card services; the user mode allows reading public
attributes and signatures.
According to Wikipedia, “ An evil maid attack is an attack
on an unattended device, in which an attacker with physical
access alters it in some undetectable way so that they ca n
later access the device, or the data on it . ” A smart card
software clone can include a backdoor. The principle of the
“evil maid” attack consists in recovering the cryptographic
keys of an unauthentic smart card.
To counter these threats, the CC applicat ion creates a
private (Priv k ) and public (Pub k ) key pair during its
instantiation. The public key is the identity of the device. The
hash of this value is signed by a certification authority (CA),
according to the ECDSA algorithm and the private key Priv CA .
So a tuple of two integer values (r,s) realizes the device
certificate:
ݐݎ݁ܥ ݇ = ( ݎ , ݏ ) = ܣܵܦܥܧ ݒ݅ݎܲ ܣܥ ൫ ܣܪܵ 256 ( ܾݑܲ ݇ ) ൯
In order to authenticate a genuine smart card, the crypto
terminal performs the following procedure:
- 1) Reading the public key, Pub k
- 2) Reading t he certificate of the public key, Cert k
- 3) Verifying Cert k with the CA’s public key, Pub CA
- 4) The knowledge of Priv k is proven through a
challenge/response procedure. The terminal generates a
random value (rnd) signed by the javacard private key:
ܣܵܦܥܧ ݒ݅ݎܲ ݇ ( ݀݊ݎ )
This signature is verified with Pub k .
The self - attestation of the smartcard contents is produced
according to the following procedure:
- 1) The crypto terminal generates a random value. The CC
application calculates a hash value of its contents,
concatenates this value with the random value, and generates a
signature with its private key. The hash is returned with the
signature; the response contains the following data:
ℎ ݏܽ ℎ | | ܣܵܦܥܧ ݒ݅ݎܲ ݇ ( ℎ ݏܽ ℎ | | ݀݊ݎ )
- 2) The crypto terminal veri fies the signature and, if
successful, displays the hash (i.e., the fingerprint of the smart
card’s contents).
V. T ERMINAL S ECURITY E LEMENTS
The firmware of the main processor controls the data
exchange with the secure element. PIN entry from the touch
scree n prevents key logger attacks on the laptop or
smartphone. All security - sensitive operations, such as signing,
setting or exporting keys, are confirmed by the user to prevent
unwanted actions by malware.
In the bare - metal approach, embedded firmware is era sed
and fully downloaded. Firmware flashing should prevent
supply chain attacks, as it provides implicit proof of software
integrity.
The programming sequence for the crypto terminal is as
follows:
- 1) The main processor code is downloaded via an ICSP
(In - Circuit Serial Programming) port. This firmware stores the
certification authority’s public key (used to authenticate the
smart card) and a loader for the BLE module.
- 2) The BLE module is flashed using the USB interface
and the built - in dedicated loader .
It should be noted that the touch screen device (ILI 9486)
also includes a microcontroller unit (MCU), whose internal
firmware cannot be updated. Nevertheless, the main processor
only transmits data to this device and controls communication,
thus reducin g the attack surface.
The main processor firmware incorporates a remote
attestation algorithm (detailed in Section 6) and is also
capable of dumping and hashing the BLE module code.
The programming token (USBASP) stores a 2KB
bootloader. Its integrity is p roven by a remote attestation
procedure detailed in Section 6.
VI. R EMOTE A TTESTATION
Integrity checking at runtime is a major security issue.
Bare - metal functionality allows firmware to be downloaded at
any time, so there is always a need to ensure the integr ity of
the software. To achieve this goal, we have designed a
dedicated remote attestation algorithm called bijective MAC
(bMAC).
Remote attestation is a process by which a trusted entity
(the verifier) remotely measures the internal state of an
untrusted and possibly compromised device (the verifier).
The bMAC verification is a self - verifying hash code (i.e.,
an instruction sequence that computes a fingerprint on itself
and the memory contents), such that the MAC checksum is
erroneous or the computation is slower if the instruction
sequence or the memory contents are changed.
bMAC computes a fingerprint (h) on a set (A) of memories
(FLASH, SRAM, EEPROM), whose size is m bytes,
according to a pseudo - random order, fixed by a permutation P,
such that:
ܥܣܯܾ ( ܲ ) = ℎ ( ܣ ( ܲ ( 0 ) | | … ܣ ( ܲ ( ݅ ) … | | ܣ ( ܲ ( ݉ − 1 ) )
The ICE algorithm presented in [14] computes a memory
checksum according to a particular permutation. The
permutation is an Invertible Mapping introduced in [25].
ܲ ( ݔ ) = ݔ + ݔ 2 ∨ ܥ ݀݉ 2 ݊
The least significant bit and the third bit of the constant C
are both set to 1. In [14], n=16 (16 - bit word) and C=5. The
ICE algorithm has been implemented in a 16 - bit von
Neumann architecture with a hardware multiplier, leading to
an optimized code size and execution time.
The paper [26 ] gives an exact characterization of the
permutation polynomials modulo n=2 w , with w ≥2
ܲ ( ݔ ) = ܽ 0 + ܽ 1 ݔ + ⋯ + ܽ ݀ ݔ ݀ ݀݉ 2 ݓ
P(x) is a permutation polynomial if and only if a 1 is odd, the
sum (a 2 +a 4 +a 6 +…) is even, and the sum (a 3 +a 5 +a 7 +…) is even.
The paper [27] demonstrates that:
ܲ ( ݔ ) = 1 + ݔ + ݔ 2 + ⋯ + ݔ ݀ ݀݉ ( ݁ )
is a polynomial permutation i n field F(q), with q=p e and p
prime, if and only if:
݀ = 1 ݀݉ ൫ ݍ ( − 1 ) ൯
We use the SHA256 or KECCAK - 256 procedures for the
MAC. The permutation P is based on exponential functions in
the group Z/pZ*, with p a Saint Germain prime (p=2q+1 , with
q prime, p>m), and p=7mod8, which allows us to
deterministically compute the generators (g k )
݃ ݇ = − ( 2 ݇ ݀݉ ) , ݇ ∈ [ 1 , ݍ − 1 ]
The P permutation is written as:
ܲ ( ݕ ) = ܨ ( 1 + ݕ ) − 1 , ݕ ∈ [ 0 , − 2 ]
With:
ܨ ( ݔ ) = ݃ 2 ݏ 1 ݃ 1 ݔ
݀݉ , ݔ , ݏ 1 ∈ [ 1 , − 1 ]
A pseudo - r andom generator, detailed in [28], produces the
parameters g 2 , g 1 , and s 1 from an integer value (31 bits) called
SEED. Then, the bMAC function produces a MAC value.
The term z=s 1 .g 1 x is computed by a simple recursive
procedure (i.e., s 1 g 1 x+1 = g 1 s 1. g 1 x mo d p). It is serialized as a
bit stream (b i ) of n bits. The term g 2 z is computed according to
a square and multiply algorithm:
݃ 2 ݖ = ෑ ݃ 2 2 ݅ ݊ − 1
݅ = 0 ܾ ݅ ݀݉ , ܾ ݅ ∈ { 0 , 1 }
Fig.9 Distribution of BMAC computing time, for ATMEGA2560, 256KB,
16MHz clock
We observe tha t the bMAC computation time (cT) follows,
roughly speaking, a normal distribution (see Figure 9) as a
function of the SEED value. We believe this distribution is
induced by the multiplication and modulus computations,
depending on specific integer values.
Fig.10 BMAC authentication code
The authentication code is a 16 - bit integer value
constructed with the two least significant bytes (see Figure 10)
obtained from:
ܥܣܯܾ ( ܦܧܧܵ ) ݎݔ݁ ܶܿ
An internal timer measures the computation time. The idea
o f the stop&start attack is to stop this timer to perform hidden
operations and then restart it. The timer uses a sub - clock
(N=64) of the processor (F clock /N), which protects against such
an attack because the stop operation creates a random error in
the ra nge 0 to T clock (N - 1).
We performed a stop&start attack by inserting instructions
into the BMAC code that stop the internal clock and then
restart it. This programming sequence has a prefix STS (short
for Store Direct to data Space), which costs 2 cycles, and a
suffix LDI+STS (LDI short for Load Immediate), which
requires 3 cycles (1+2). For a memory size m, we should
expect an increase in the measured time (i.e., number of
cycles) of T=5.m. Experimental results with about 1000
samples show a normal distri bution; on average, the increase
in computing time is about T= m x 2.07 cycles (i.e., T/m =
2.07 cycles). The timer is stopped after the first STS
instruction, which creates a (false) delay of 61, 62 or 63 cycles
with a probability of about 1/64. Therefore , an estimate of
T/m is:
ܶ
݉ # 5 ∗ 61
64 − 61
64 − 62
64 − 63
64 = 1 , 86 cycles
Fig.11 BMAC computing time average and standard deviation for three
devices (ATMEGA8, ATMEGA368, ATMEGA2560) with different memory
size.
We use AVR processors with a 16 MHz clock. Figure 11
shows the average computation time and standard deviation
for three devices: ATMEGA8 (8KB), ATMEGA368 (32KB),
and ATMEGA2560 (256 KB). The average is proportional to
the memory size, about 1s/KB. The logarithm of the standard
deviation seems to be proportional t o the memory size; in
other words, the BMAC entropy increases with the memory
size.
VII. D YNAMIC PUF
The sale of hardware and software clones, which are not
original devices, is a critical issue in complex supply chains.
Since microcontrollers incorporate stati c RAM, we have
developed authentication procedures based on the SRAM
Physical Unclonable Function (SRAM - PUF). The SRAM -
PUF is a physical identifier that can be used to authenticate
the main processor of the crypto terminal (ATMEGA2560)
and the programming token processor (ATMEGA8).
First, a memory probe firmware is downloaded into the
processor, which also includes a UART interface. Second, the
board, including this device, is powered via the ICSP port,
with a controlled rise time. Third, the content of the SRAM is
extracted via the serial interface.
Fig.12 SRAM memory cell (left part), output voltage versus input voltage
(right part).
On power - up, some SRAM cells take a fixed value. This
effect is induced by the physical and electrical asymmetry of
the P MOS and NMOS transistors (see Figure 12, left - hand
side).
During power - up (see Figure 12, right - hand side), the
output voltage of an SRAM cell remains close to VDD/2 until
the gain is sufficient to switch to VL (logic low) or VH (logic
high). For a highly mismatched cell, the output always takes a
fixed value. The voltage (Vs) at which the transition occurs
depends on the cell [17][18].
The paper [18] demonstrates the effect of voltage ramps on
the PUF SRAM. Given a ramp V(t)= t VDD/T (i.e.,
slope=VDD/T), f lipping bits are observed for slopes of high
values. Flipping bits are created by capacitance mismatch,
while most PUF - bits are due to voltage threshold differences
(VTH). In other words, voltage ramps reproductively switch
the value of some SRAM cells on power - up. A test RAM chip
was designed with 180nm technology and simulated. Bit
flipping was observed for T values below 15ms.
Thanks to the firmware memory probe, we read 1KB of
SRAM. We perform these operations N times to extract the
PUF cells. The stati c authentication of the processor requires
only one reading of the SRAM, which is compared to a
reference; the observed error is about 0.1%.
Fig13. SRAM PUF measurement results obtained with batches of 250
consecutive tries.
These values decrease slight ly, about 5% for N between
[250, 10000], and about 2% for N between [10000, 50000]. In
our experiment, we record a set of 250 measurements in about
10 minutes; these data are aggregated to define N results (i.e.,
N/250 records). These results suggest that errors (i.e., an
erroneous measurement of the state of a cell) occur randomly
and increase with the duration of the measurement, like a
Poisson distribution, according to a probability density
function:
ρ ( ݐ ) = λ e − λ t
t being the measureme nt duration. What leads to an error
probability :
p ( ݐ ) = 1 − ݁ − ߣ . ݐ # λ . t
So the number of cells always seen at zero or one decreases
like:
݊ ( ݐ ) = ݊ 0 ( 1 − ߣ . ݐ )
Some PUF cells (about 5%) are sensitive to the voltage rise
time and are cal led “flipping bits.” For a “high” slope
(>200mV/s), they have a content b k , which is switched to (1 -
b k ) for a “low” slope (<10mV/s). We define [17] Sy
waveforms constructed with two slopes (see Figure 14, right -
hand side), switching at voltage y, y=0 corre sponding to zero
and y=1 to VDD. Therefore, S1 is the first slope, and S0 is the
second. S0 (high slope) creates a flipping bit; S1 (low slope)
does not create a flipping bit. We perform 25 measurements
for each power - up with Sy, which allows us to estimat e the
threshold value y for each flipping bit. Figure 14 (left - hand
side) shows the results for some flipping bits; we observe a
low y threshold value.
Fig.14 Flipping - bits state according to y value for 25 measures (left part), Sy
powering - up signal (r ight part)
Dynamic processor authentication is based on flipping bits.
We use the Slope & Square power - up waveforms [17] to set
the state of the flipping bits. Rf is a square power - up
waveform that creates flipping bits. We define the Slope &
Square (Rs) w aveform with a small slope of 625/512 mV/mS
up to 512mS and then a fast rise time (see Figure 15, bottom),
which does not create flipping bits. Since the processor does
not operate at the threshold voltage of the flipping bits
(<500mV), it cannot predict t heir value at runtime.
Fig15. Dynamic PUF (dPUF) (see text for comments)
Figure 15 illustrates the static and dynamic authentication
for the main processor of the crypto terminal.
The upper left corner shows the result of 250 power - ups.
SRAM cells alway s read to one are colored in green, those
always read to zero are colored in yellow, and noisy bits (not
always one or zero) are colored in white. The flipping bits
observed for the Rf waveform versus the Rs waveform are
colored in red. This bitmap is a ki nd of fingerprint of the
processor.
The upper right corner shows the threshold voltage
distribution for the flipping bits, which switch below the
processor operating voltage at low voltage.
The lower left corner displays the comparison of the SRAM
content for a single Rs waveform, with the reference obtained
after 250 Rs. Only two errors (colored in red) are observed.
The lower right corner shows the comparison of the SRAM
content for a single Rf waveform, with the reference obtained
after 250 Rs. Many erro rs (colored in red) are observed.
VIII. P ROGRAMMING T OKEN AS R OOT OF T RUST
The USBASP programming token is built on an
ATMEGA8 microcontroller, with 1KB SRAM, 2KB FLASH
for the bootloader, and 6KB for the firmware. By default,
USBASP programmers do not have a bo otloader. We use the
bootloader for three reasons:
- To verify the bootloader’s integrity using an integrity
probe. This firmware runs the bMAC algorithm, which
calculates the authentication code displayed by blinking LEDs.
- To authenticate the microcontr oller, thanks to a memory
probe and dPUF measurements. First, two references are
collected with the power - up waveforms Rs and Rf. Then,
memory dumps are performed with the Rs and Rf waveforms.
- To download the firmware required by the USBASP
driver on the laptop side (e.g., Windows or Linux).
On the laptop side, the bootloader runs under the USBASP
protocol. Therefore, software such as AVRDUDE can be used
to download firmware to the MCU; the programming token is
able to flash itself.
The bootloader is not activated by default, so the loaded
firmware is executed. The bootloader is activated by a
shortcut with the ground, with a time slot of five seconds,
during which the internal flashing operation is activated.
It should be noted that programming tokens can program
each other, so a trusted USBASP card can flash untrusted
devices.
IX. C ONCLUSION
In this article, we described a crypto terminal based on
open hardware and software technologies. It is equipped with
a set of countermeasures to thwart cyber attacks aga inst
blockchain wallets. These innovative procedures, such as
remote attestation or dynamic PUF, could also be deployed for
other use cases, typically involving secure elements and an
associated terminal.
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|
{
"id": "2303.17206"
}
|
2006.05483
|
Blockchain in the management of science: conceptual models, promises and
challenges
|
Blockchain has received much attention recently, due to its promises of
verifiable, permanent, decentralized, and efficient data handling. In 2017-2019
blockchain and associated technologies such as smart contracts has progressed
beyond cryptocurrencies, and has been adopted in banking, retail, healthcare,
and other fields. This study critically examines recent applications of
blockchain in science, touching upon different stages of research cycle, from
data management to publishing, peer review, research evaluation and funding.
The paper is based upon a review of blockchain projects, relevant literature, a
set of interviews and focus groups with startup founders, scholars, librarians,
IT experts from the EU, USA, Russia, and Belarus. Proponents of blockchain for
science present this technology as a tool to make science free from bias, red
tape, data fraud, as well as provide innovative means to secure financial
backing for new ideas. However, these projects face a set of challenges. One
issue concerns introducing crypto economy, with its financial incentives, into
science, a field that emphasizes disinterested and non-pecuniary pursuit of
truth. Another source of concern relates to the ongoing conflict between the
principle of decentralization inherent to blockchain and the practice of
forcing it from above, by the state and other centralized entities.
|
http://arxiv.org/pdf/2006.05483v1
|
Artyom Kosmarski
|
cs.CY
|
cs.CY
|
Blockchain in the management of science:
conceptual models, promises and challenges
Blockchain has received much attention recently, due to its promises of verifiable, permanent,
decentralized, and efficient data handling. In 2017 -2019 blockchain (and a ssociated technologies
such as smart contracts) has progressed beyond cryptocurrencies, and has been adopted in
banking, retail, healthcare, and other fields. This study critically examines recent applications of
blockchain in science, touching upon differ ent stages of research cycle – from data management
to publishing, peer review, research evaluation and funding. The paper is based upon a review of
blockchain projects, relevant literature, a set of interviews and focus groups with startup
founders, schol ars, librarians, IT experts from the EU, USA, Russia, and Belarus. Proponents of
blockchain for science present this technology as a tool to make science free from bias, red tape,
data fraud, as well as provide innovative means to secure financial backing for new ideas.
However, these projects face a set of challenges. One issue concerns introducing crypto economy
(with its financial incentives) into science, a field that emphasizes disinterested and non -
pecuniary pursuit of truth. Another source of concern relates to the ongoing conflict between the
principle of decentralization inherent to blockchain and the practice of forcing it from above, by
the state and other centralized entities.
Keywords: blockchain, management of science, research data, decentr alization, incentive design
Highlights:
Blockchain technologies have provided a solution to challenges on all stages of the
research cycle, from data sharing to publishing and funding.
This study examines use cases of blockchain in science, drawing upon systematic review
of extant projects and a set of sociological interviews with stakeholders.
There is a tension between principles of decentralization and disintermediation inherent
to blockchain, and the practice of implementing DLT solutions in a central ized way, by
states and universities.
Introduction of token economy and crypto incentives into academia, while providing
scientists with new funding opportunities, runs the risk of corrupting the operation of
science as a collective disinterested search f or new knowledge.
Artyom Kosmarski ( Артём Космарский ), Laboratory for the Study of Blockchain in Education
and Science (LIBON), State Academic University for the Humanities (GAUGN), Moscow,
Russia.
kosmarski@gaugn.ru
Funding
This work was supported by the Russian Foundation for Basic Resear ch (RFBR), grant
number 18 -29-16184.
Artyom Kosmarski has done extensive research in the field of STS and anthropology of science.
Blockchain caught his attention as a hotbed of various and sometimes unpredictable solutions to
the grievances of academic community, as well as a pathway to a more flexible, grass -root and
reputation -based governance of science. Right now Artyom is studying the implementation of
smart contracts in universities (research project funded by the Russian Foundation for Basic
Resea rch) and investigating the potential of token -curated registries as a basis for reputation
metrics. He is also deputy head of the Laboratory for the Study of Blockchain in Education and
Science (LIBON) at the State Academic University for the Humanities (G AUGN), Moscow.
1. Introduction1
The rapid development of blockchain (distributed ledger technology, DLT), building
upon the success of Bitcoin, has made it a promising and potentially disrupting technology in a
multitude of fields. In 2017 -2018 blockchai n (and associated technologies such as smart
contracts) has been adopted in banking, retail, supply chain management, healthcare, even public
administration (Chung -Shan Yang 2019; Tönnissen & Teuteberg 2019; Angraal, Krumholz, &
Schulz 2017). The appeal of blockchain to industry and academia builds upon its promise to
make data stable, transparent, and decentralized.
A distributed ledger is a set of blocks connected by cryptographic tools in such a way as
to make it impossible to change the content of one block without interfering with all other
blocks. In this database, transactions are transparent, and the information about them is stored on
the computers of all the participants. This decentralization prevents altering or destroying the
data by infiltrat ing the core of the system.
However, it is not the data handling but the social appeal of blockchain (Atzori, 2015)
that has attracted the attention of academia. Principal advantages of blockchain in this
perspective, apart from stability and verifiabili ty of data, is the guarantee of trust in the trustless
environment and successful peer -to-peer interactions without the need for a central governing
body ("the third party"). These features dovetail with the logic of modern science: it is
international, de centralized – there is no governing body that decides everything (Polanyi, 1962)
– and develops thanks to networks of trust within the academic community (peer review system
1 This preprint was submitted to the “Technology in Society” journal in October 2019.
and invisible colleges ( Crane, 1972) . The analogy was not lost on a few early obse rvers:
“Scientific information in its essence is a large, dynamic body of information and data that is
collaboratively created, altered, used and shared, which lends itself perfectly to the blockchain
technology ” (van Rossum , 2017: 8 ).
Infrastructure and m anagement of science, on the contrary, is fraught with bias, red tape,
data manipulation, and non -transparent black boxes (Bunge , 1963 ) on every level, from research
data handling to funding, publishing, and research evaluation. How does the selection of
reviewers for a manuscript submitted to the journal go? Who assesses the quality of a scientist's
work? How and by whom are the recipients of a grant determined? The opacity of these
processes, their inertia, bureaucratization, corruption often cause discon tent of scientists
(Rockwell, 2009). Scientific research itself is also full of black boxes: p -hacking, ex post facto
hypothesizing, and outright scientific fraud (Head , Holman, Lanfear, Kahn & Jennions, 2015 ;
Fanelli , 2009 ). Thus, introducing blockchain i nto science might at least crack open some black
boxes and make the processes inside them more transparent, reliable, and efficient.
However, as science is not strictly business and still depends on public funding,
blockchain and crypto projects were slo w to emerge in this field. The first tentative projects and
academic publications appeared in 2015 -2016. Then, at the peak of the ICO boom in 2017,
several startups promised to solve all the problems of science with their tokenized economy,
some of them en ding up as scam (globex.sci, scientificcoin.com), others just petering out for the
lack of funds (scienceroot.com). Eventually, blockchain for science proved more workable as a
set of solutions for specific processes (peer review, data storage, funding, et c). At the same time,
there has emerged an extensive network of scholars, IT -experts and crypto -enthusiasts, many of
whom have joined Blockchain for Science association (blockchainforscience.com) and research
groups in Europe and the USA. Finally, corporat ions began to enter this field – IBM, for
instance, has obtained a patent for a platform for the collection and analysis of scientific data on
the blockchain (Suberg, 2018) . Therefore, early experience of blockchain adoption in science
management and organ ization makes it possible to review the challenges and barriers to its
implementation.
My paper is based upon a critical review of blockchain projects, relevant literature, a set
of interviews (N=22) and focus groups (N=3) with startup founders, scholars, librarians, IT
experts, and blockchain evangelists from EU, the USA, Russia, and Belarus. The interviews
were taken in November 2018 – September 2019 in the framework of an interdisciplinary
research project "Smart Contracts as an Instrument for Regulatio n and Administration of
Science", supported by the Russian Foundation for Basic Research (RFBR). Participants were
recruited through snowball sampling in social networks (the objective was to reach a varied
sample of experts that have had an experience of organizing or evaluating DLT projects for
science). The interviews were conducted in person or by Skype, and lasted on average 60
minutes. Focus groups (group discussions of a set of particular topics) were conducted in three
universities, with professors, staff, and post -graduate students who have demonstrated awareness
and interest in the issues of blockchain. The focus groups lasted 90 minutes.
The paper is organized as follows: the next two sections present an overview of
blockchain -based solutions fo r research data verification, academic publishing, and peer review.
Section four outlines the challenges of introducing crypto economy (with its financial incentives
and profit -seeking behavior) into science, a sphere that emphasizes disinterested and non -
pecuniary pursuit of truth. Section five discusses the ongoing conflict between the principle of
decentralization inherent to blockchain and the practice of forcing blockchain systems from
above, by the state and other centralized entities. Finally, I disc uss the strengths and weaknesses
of blockchain solutions in the context of digital economy and b2c IT products.
2. Blockchain and research data
The replication crisis is arguably the most wide -scale crisis afflicting contemporary
academia, particularly the social and life sciences. In many disciplines, the impossibility of
reproducing experiments has brought about retractions of seminal studies. In psychology, for
instance, researchers failed to reproduce the results of 59 of the 98 well -known works (Baker,
2015). A survey conducted by Nature in 2016 showed that more than 70% of the 1576 scientists
surveyed tried and failed to replicate their colleagues' experiments (Baker, 2016) . In the
biomedical sciences, the problem is no less acute (Ioannidis , 2005 ).
However, non -reproducibility is only the tip of the iceberg. Science suffers from errors
and distortions at all stages of the research cycle, from dubious data collection and protocol
procedures to the distorting of evidence to fit the hypothesis (Simmo ns, Nelson, Simonsohn,
2011), as well as p -hacking (Head, Holman, Lanfear, Kahn & Jennions, 2015 ). The pressure to
publish and journals' preference for positive rather than negative results (positive -results bias, see
(Sackett , 1979 ) have lead to the forma tion of the so -called false chain of research: new studies
are based on untested (and probably erroneous) old ones. However, these problems are not
caused exclusively by the malicious intent of fraudulent scientists. There are many reasons for
false data, from inevitable inaccuracies in large collaborations to salami -slicing (need to publish
even small results as soon as possible in order to extend funding (Fochler & Sigl, 2018).
Blockchain solutions could fix that by making the research cycle open and tran sparent,
and by facilitating data sharing. Discoveries may be rapidly recorded in the distributed registry,
indicating the authorship and date of discovery. Time -stamping on the blockchain acts as a novel
way to protect ideas without resorting to (slower) patents and publications (Benchoufi &
Ravaud, 2017 ; Moehrke, 2016 ). Furthermore, blockchain allows for tracking the entire scientific
project from hypothesis to data collection and further analysis. The stability of data on the
blockchain is essential here (all changes are trackable). By uploading the data into such database
and making it open to a broader academic community, researchers will no longer be able to
tamper with it, rigging the data to achieve necessary results, remove outliers, etc. ( Blockchai n for
Open Science and Knowledge Creation : 13-18)
However, regardless of the fact that virtually every text on blockchain in science praises
the potential of this technology to overcome the replication crisis and to foster open, fraud -free
science, little progress has been made in this direction. The first reason is technical: recording
vast amounts of research data on the blockchain requires considerable computation power, and
for most blockchains currently in use it would be a long and expensive process. Second,
transparency and availability of research data on DLT require mass support of scientists. If only
a handful of enthusiasts pursues this task, it w ill not become the norm for the academic
community, and this would compromise the whole project.
Next , making all research data transparent and verifiable takes researchers' time without
bringing apparent benefits in their careers. Therefore, open science on the blockchain could
probably be implemented only from above, forced upon the scientists by public agencies and
foundations. This fact is grudgingly acknowledged even by blockchain enthusiasts : “The state
should fund Blockchain and related digital infrastructure, there is no other option. It is a difficult
and complex task. You can't do these things th rough short -term grants or private money " (D.,
decentralized data startup founder). The big public agencies in Europe and other Western
countries are still too distrustful of DLT (at this early stage of their development). Also,
individual researchers lack resources and initiative to "blockchainify" their workflow. Therefore,
this trend of DLT for research data is rather slow to develop.
Another avenue for open science on the blockchain lies in encouraging transparency
through material incentives. In other words, a DLT platform with its tokens and rules that make
adherence to open science (e.g., open and valid data, negative results made public) lucrative for
scientists. The EUREKA platform (eurekatoken.io) has advanced in this direction, promising to
rewar d scholars with tokens for publishing both positive, negative, and uncertain results. Also, a
separate token fund is allocated to pay for replication studies and experiments aimed at
increasing reproducibility. The success of this initiative, however, depe nds on the price of
EUREKA tokens on the market (after a forthcoming ICO). Even still, the pegging of open
science principles to the vagaries of market speculation (the forces beyond scientists' control) is
risky at best.
3. Blockchain and academic publ ishing
Academic publishing is passing through turbulent times – the crisis of subscription
model, the rise of open access journals, and potentially disrupting Plan S with its forceful drive
of compulsory transition to open access journals (Else, 2018) . Regardless of the payment model,
the publishing cycle is extremely slow: writing an article, submitting it to a journal, searching for
reviewers, getting feedback, and finalizing takes months if not years (Smith, 2006). Researchers
are involved in the race f or priority, but the current system significantly slows down the
exchange of results, let alone ideas. Finally, the process itself is deeply flawed. Double -blind
review frequently turns out to be pseudo -anonymous, with little limits to reviewers' bias (Wan g,
Kong, et al., 2016). Also, reviewers are increasingly overworked and underpaid (Kovanis ,
Porcher , Ravaud & Trinquart , 2016) . The need for incentives in peer review, e.g., “academic
dollars” was voiced well before the advent of cryptocurrencies (Pruefer & Zetland, 2009).
The advantages of blockchain technology for solving these problems are quite obvious.
Minimally, it provides notarization. Recording a text or even a draft idea on the blockchain
(time -stamping) allows a scientist to assert priority and intellectual property rights, and then he
or she might freely share it as a preprint. Further, the decentralization and disintermediation
principles behind blockchain suggest an independent publishing platform where authors and
reviewers interact directly with each other in a p2p network, with no need for excessive
publishing and subscription costs. Not surprisingly, this idea has been so appealing that virtually
each blockchain startup in science promised an open access platform (scienceroot.com,
eurekatok en.io, pluto.network, orvium.io).
However, the success of these platforms has been so far limited, with less than a hundred
papers in each: scientists prefer to publish in established journals relevant to their research
communities. This trend is well -known in the sociology of innovation (Dahlin, 2014) : the
revolutionary benefits of new technology are insufficient to draw users away from customary
practices. Moreover, running an academic journal on an automated system of smart contracts is
an arduous task , and developing an efficient ecosystem for several journals with their own rules
and principles is a process beyond the scope of current blockchain projects (Janowicz , Regalis , et
al., 2018).
There is another way to integrate blockchain into academic pu blishing – by putting in
into the service of corporations . "Blockchain for Peer Review", a project implemented jointly by
the developer Katalysis (katalysis.io) and the Digital Science (a technology company), has
followed this path. Its main goal is to dev elop a protocol that would allow collecting information
about reviewers from publishers, storing it on the blockchain, and then making it possible to
evaluate the work of reviewers while maintaining their anonymity. In other words, the
distributed register allows recording the connection between the reviewer and the manuscript
without revealing the author's public identity. Players such as Springer Nature, Taylor & Francis,
Cambridge University Press, and ORCID are already involved in the project. However, this
approach to DLT is entirely devoid of critical disrupting qualities of blockchain –
decentralization, elimination of the third party (such as publishers). "Blockchain for Peer
Review" has met criticism from the community, precisely because it's a narr ow, technical and
corporate solution that does not bring about any disruption to the broader scientific ecosystem.
4. Blockchain, research funding, and incentivization
Research funding is fraught with bias, cumbersome, non -transpare nt, and ineffective
procedures – the "black boxes" mentioned above. Furthermore, scientists have to spend a great
deal of their time writing reports, grant applications, and doing paperwork (Link , Swann &
Bozemann, 2008 ). Finally, a pressing problem is the shrinking of fundi ng. Governments are
gradually moving away from large -scale research funding, hoping that business, industry, and
private foundations will replace it (Mervis, 2017).
How could DLT fix these issues? First of all, an automated system of disbursement of
funds with transactions on smart contracts will significantly reduce overhead costs and ease the
burden on accountants, auditors, and scientists themselves. It would save them from filling out a
lot of papers and make the whole process of allocation and distribu tion of funds more efficient.
Also, a funder might set a combination of conditions (e.g., citations, articles, datasets) and peg
the grant money to the fulfillment of these conditions (through smart contracts) – this approach is
implemented in the DEIP blo ckchain ecosystem (deip.world) .
However, the innovative drive of DLT lies in the fact that science funding and incentive
structures may be easily changed with blockchain. " We can experiment with new money
distribution schemes, grant schemes, and that woul d bring about cultural change. With
blockchain, things would change much quicker ” (S., blockchain evangelist) . Entering the
cryptocurrency sphere gives the scientist a chance to find money from investors whose interests
and outlook are very different from universities . In such cases, blockchain will provide investors
with a guarantee against scams and roguish projects: all the initial data and development of the
research can be traced, and the allocation of funds can be pegged to the achievement of certain
milestones.
Moreover, one could receive tokens if their research results are validated independently
by others, or used in their future work, as an economic equivalent of citation. Thus token
economy creates a potentially powerful channel for financing an d implementing breakthrough
ideas, even in basic science (Blockchain for Open Science and Knowledge Creation : 25)
In this sense, crypto -economic tools give more independence to scholars, opening an
alternative channel of recognition and funding, giving sci entists a clear economic interest to
engage in the crypto economy. In the end, science would get more independent economic agents,
apart from the state and big funding agencies and philanthropies. " There we have more
opportunities for more independent play ers, and for more intermediaries – and that is an
interesting contradiction, as they say that blockchain is all about disintermediation. And we will
probably have more players here, with less bureaucracy and more efficient, blockchain -based
ways to redirec t money in different directions ” (L., librarian ).
However, these innovations are not easily adopted. Most scientists are not entrepreneurs
working for themselves and pursue their goals within complex institutional structures, while the
latter are not alwa ys friendly towards new technologies.” Smart contracts might efficiently
indicate expenses of the lab – test animals, chemical agents, etc. – and allocate necessary funds.
But what if you have an accountants office working with these expenditures, for many years, 50
people in all, who would think of firing them? ”. (E., researcher). Furthermore, the dubious
reputation of crypto -economic tools (ICO scams, hacker attacks targeting crypto wallets),
sluggish transaction speeds of many blockchains makes university authorities doubt the
practicality of introducing DLT into their finances.
However, these are merely technical issues, easily to be overcome in the near future, as
the technology matures. There is a more fundamental challenge to merging the crypto -econo my
with science. At the heart of Bitcoin, the most successful blockchain project, as envisaged by its
architect Satoshi Nakamoto, lies an incentive system. Bitcoin arranges incentives and rewards
for all members of its ecosystem (miners, users, developers) in such a way that the output is
stable, secure, and at the same time decentralized digital currency. It is through incentives that
Satoshi Nakamoto has ensured that behavior beneficial to the common good of the system is
encouraged and that harmful behav ior is blocked (Nakamoto, 2008) .
Incentive design principles were conceptualized as one of the key benefits of blockchain.
They entered other cryptocurrencies and then got integrated into more complex and di verse
projects. The developers laid down what be havior will be encouraged by the participants. The
mechanism of encouragement itself is material: through tokens, which ultimately turn into
money. Finally, the third important feature of incentive design is voting and decision -making by
a simple majority of votes. For example, Gnosis, Augur, and other predictive markets on
blockchain encourage users to make accurate predictions and bet on the results of these
predictions. Steemit social network rewards popular posting with tokens (popularity is
determined by the number of votes cast, i.e., upvoting). Various token -curated registries (Goldin,
2017) encourage the creation of authoritative lists (cafes, universities, media), encouraging
responsible voting for or against the inclusion of a unit in the list. Th us, blockchain has made it
possible to create powerful incentive machines (McConaghy, 2018).
However, incentive design might go rogue as one moves the field where living creatures,
not machines, make decisions. In Bitcoin, incentives are automatic and embe dded within the
system so that people are not required to make decisions all the time (it is enough to just mine).
When this mechanism is replicated in science, for instance, it would hardly be realist ic to expect
rational behavior from the actors. Humans are subject to cognitive biases, herd instincts, often
prefer short -term gains to long -term benefits (Verbin, 2018)
Moreover, the "engine" of most blockchain startups is the system of tokens, which
motivate scientists, vote, connect individual system node s, and, ultimately, attract investors. The
“tokenization” of science is a dubious enterprise . For scientists, the desire for recognition and the
pursuit of truth – non-monetary incentives – are no les s important than material ones ( Jindal -
Snape & Snape, 2006). Introduction of quantitative metrics and market mechanisms might
corrupt science as a social institution: “ when you introduce monetary incentives into Wikipedia
or peer review, you destroy them ”(L., librarian) . In addition, market logic (when everyone strives
to maximize their profits) atomizes the scientific community, further undermining the logic of
science as the collective search for the truth, when the common goal is more important than
individual career success - the Mertonian norm of disinteres tedness (Merton 1973; Higginson et
al. 2016). We face a dilemma: blockchain for scienc e initiatives intend to build a self-regulating
system run by scientists themselves, stimulating scientific progress in a self -governing sphere,
but this new vision is ba sed on a race for rewards and monetary incentives. The advantages of
the crypto economy (new funding opportunities, more freedom) are partially offset by the
reluctance of scientists to take on the role of business people. Not every researcher is ready to act
as an investor or a startup manager who attract s investments for their project.
Blockchain – democracy or coercion?
Apart from a set of technical solutions, blockchain in science is turning into a movement
within academia, with its ideology and wide -ranging goals. The impetus of this movement is
mounting dissatisfaction with the oligopoly of large publishing houses, the "tyranny of metrics"
(Muller, 2018) and indicators, the alarming growth of biased and non -reproducible research, the
“prec ariatizatio n” of scientists . Blockchain addresses these concerns with its promise to radically
restructure the rules of the game in science: transparent transactions and decision -making, tokens
as flexible incentives, new communities based on common rules set in code (Berg, 2017) ,
decentralization, and an opportunity for scientists themselves to determine what is important (for
example, to encourage reproducible research).
However, the practical applications of blockchain in science often run afoul of this
ideology. Academics face vigorous time constraints and are naturally reluctant to engage in
community projects that demand active participation, discussion, voting, and other obligations of
participatory democracy (Pateman, 2012) . “The state is motivated to employ b lockchain to
investigate research fraud. The state, the university, not the scientists themselves. We planned to
structure the whole process of evaluating publications and dissertations in our university on the
blockchain. But the project failed because no body was motivated. Everybody cares only about
her data, and not about checking and evaluating her colleagues' input ” (A., vice -principal ).
In the meantime, corporate players are beginning to take an interest in blockchain in
science ( Suberg, 2018; Novot ny, Zhang et al. , 2018). This development reflects the general
dynamics of blockchain's fortunes: creators and enthusiasts of technology conceived it as a path
to digital democracy, life without banks, states, and corporations, whereas DLT are becoming a
tool to optimize management processes and strengthen the power of the same states and private
companies. "Corporations enjoy a significant head start in the race to program their logics into
mainstream blockchain applications, as well as the capacity to ena ct state policies that block new
applications threatening future disintermediation... the corporatization of blockchain toward the
ends of corporate sovereignty. ( Manski & Manski, 2018).
Corporate applications aside, DLT itself, as a technology, is not so decentralized and
libertarian as its creators and evangelists have portrayed it – blockchain coders and developers
enjoy an advantage over ordinary users, effectually acting as sovereign authority of their
platforms [Manski 156]. As one interviewee put it, “Buterin [co -founder of Ethereum] preaches
decentralization, but the meta -platform which sets the basic rules, the infrastructure on which a
whole lot of independent projects and tokens is based, belongs to him. Decentralized
autonomous organizations (DAO s) on Ethereum are not truly independent because they are
founded on his code. The more numerous and independent these projects become, the more his
monopoly is entrenched ” (O., researcher ). Finally, as blockchain technologies are implemented
from above, b y the states eager to make use of transparency and data permanence that DLT
offers, the scientists face the perspective of increased control and coercion – an extra obligation
to register all their data on the blockchain, for instance. Proponents of new te chnology hail this
as an essential step towards better science (van Rossum , 2017: 11 ), but from an end -user
perspective, this could look more like a burden than an advantage.
Conclusion
Taking into consideration ambitious goals and reasonable ideas put forward by
blockchain projects in science, it is slightly surprising that no platform or application has yet
become an unquestionable success story . This slow tempo of innovation may be explained by
legal uncertainties surrounding smart contracts and cryp tocurrencies ( thus making universities
and public foundations wary). Another barrier – to make use of DLT projects, users should be
minimally aware of the basic principles of DLT, which is rarely the case outside computer
science. Last but not least, certa in infrastructural conditions are to be met: fast and reliable
Internet, sufficient computing power to confirm transactions, solving the problem of user
identification, and access to cloud services (Rachovitsa 2018: 21) . Quite a few universities,
especiall y in developing countries, cannot ensure these conditions.
However, other trends are more favorable towards the implementation of blockchain in
the management of science. In recent years, scholars all over the world have been increasingly
mastering cloud applications aimed at automating different stages of the research cycle: note -
taking (Evernote), collaborative writing (Authorea, GoogleDocs, Overleaf), references and
citations management (Mendeley, Zotero), data exchange (Figshare, GitHub). Experimental
approaches have permeated the practice of science, be it new publishing models (open access),
new metrics, such as altmetrics, new forms and practices of reviewing (open peer review,
collaborative peer review). Blockchain fits into this trend quite natura lly. The problem is,
existing DLT projects for science lack a "killer app", simple and efficient, aimed at solving one
or maximum two problems evident to thousands of scientists – an approach modeling the success
of the applications mentioned above. Projec ts under development lean toward complex solutions,
ecosystems that reshape whole rules of the game. Such an ambitious approach is riskier and
would succeed only if enough stakeholders throw their weight behind a specific project.
"Introducing a blockchain for research and its successful adoption will depend on the
collaboration between all stakeholders: funders, government, institutions, publishers, and
researchers themselves, whether in their role as researcher, reviewer, editor or author" (van
Rossum, 20 17: 15) .
One could argue that blockchain has every chance of creating new opportunities and
opening new avenues in the management of science. But mere promises of a brighter digital
future are not enough. To make it to the top, DLT projects in science sho uld succeed in reaching
out to individual scientists, as well as to the scores of fragmented academic “tribes” (Becher &
Trowler, 2001) . Successful applications should integrate seamlessly into existing practices and
procedures into scientists' daily lives , should their work more comfortable rather than more
demanding. Finally, they should prove to key stakeholders, especially the universities and major
foundations, that blockchain provides a standard of accuracy, transparency, and reliability.
Conflict o f interest statement
The author declares no conflict of interest.
Funding
This work was supported by the Russian Foundation for Basic Research (RFBR), grant
number 18 -29-16184.
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|
{
"id": "2006.05483"
}
|
1905.08517
|
Blockchain in the Government Technology Fabric
|
Fuelled by the success (and hype) around cryptocurrencies, distributed ledger
technologies (DLT), particularly blockchains, have gained a lot of attention
from a wide spectrum of audience who perceive blockchains as a key to carry out
business processes that have hitherto been cumbersome in a cost and time
effective manner. Governments across the globe have responded to this promising
but nascent technology differently - from being apathetic or adopting a
wait-and-watch approach: letting the systems shape themselves, to creating
regulatory sandboxes and sponsoring capacity building, or in some instances
(arguably) over-regulating and attempting to put the blockchain genie back in
the bottle. Possible government role spans across a spectrum: regulating
crypto-currencies and initial coin offerings (ICO), formulating regulatory
frameworks for managing the adoption of blockchains, particularly in critical
infrastructure industries, facilitating capacity building, and finally,
embracing blockchain technology in conducting the activities of the government
itself - be it internally, or in using them to deliver public services. In this
paper we survey the last, namely, the use of blockchain and associated
distributed ledger technologies in the government technology (GovTech) stack,
and discuss the merits and concerns associated with the existing initiatives
and approaches.
|
http://arxiv.org/pdf/1905.08517v1
|
Anwitaman Datta
|
cs.CY, cs.CR
|
cs.CY
|
BlockchainintheGovernmentTechnologyFabric
AnwitamanDATTA1
1SchoolofComputerScienceand
Engineering,NTUSingapore
Correspondence
AnwitamanDATTA,SchoolofComputer
ScienceandEngineering,NTUSingapore
Email:anwitaman@ntu.edu.sgFuelledbythesuccess(andhype)aroundcryptocurrencies,
distributedledgertechnologies(DLT),particularlyblockchains,
havegainedalotofattentionfromawidespectrumofaudi-
encewhoperceiveblockchainsasakeytocarryoutbusiness
processesthathavehithertobeencumbersomeinacostand
timeeffectivemanner.Governmentsacrosstheglobehave
respondedtothispromisingbutnascenttechnologydiffer-
ently-frombeingapatheticoradoptingawait-and-watch
approach:lettingthesystemsshapethemselves,tocreating
regulatorysandboxesandsponsoringcapacitybuilding,or
insomeinstances(arguably)over-regulatingandattempt-
ingtoputtheblockchaingeniebackinthebottle.Possible
governmentrolespansacrossaspectrum:regulatingcrypto-
currenciesandinitialcoinofferings(ICO),formulatingregu-
latoryframeworksformanagingtheadoptionofblockchains,
particularlyincriticalinfrastructureindustries,facilitating
capacitybuilding,and finally,embracingblockchaintechnol-
ogyinconductingtheactivitiesofthegovernmentitself-be
itinternally,orinusingthemtodeliverpublicservices.In
thispaperwesurveythelast,namely,theuseofblockchain
andassociateddistributedledgertechnologiesinthegov-
ernmenttechnology(GovTech)stack,anddiscussthemerits
andconcernsassociatedwiththeexistinginitiativesand
approaches.
KEYWORDS
(Supra)nationalBlockchain,E-government(E-gov),Digital
Government,GovernmentTechnology(GovTech)
1arXiv:1905.08517v1 [cs.CY] 21 May 2019
2 ANWITAMANDATTA
1|INTRODUCTION
Sincearoundtheturnofthecentury,withtheriseofInternetpenetration(aroundthedot.comboomandbust),many
governmentsacrosstheglobeembracedtheuseofonlineservices(Heeks(2001)),bothasachannelfordelivering
servicestoitscustomers,aswellasformanagingthebackendandinternalprocesses.Fromthoseearlydaysofe-
government(e-gov),whichtheOECDde finesas‘theusebythegovernmentsofinformationandcommunicationtechnologies
(ICTs),andparticularlytheInternet,asatooltoachievebettergovernment ’(OECD(2014)),overthenextroughlytwo
decades,wehavearrivedatajuncture,whereriseindigitalliteracy,nearsaturationofinternetpenetrationinadvanced
societies,particularlyenabledbyubiquityofsmartphonesandmobileinternetinfrastructure,aswellasadvnaces
inbigdatamanagementandanalyticsenabledarti ficialintelligence(AI)techologies,putusinasweetspot,where
muchmorethantheoriginalobjectivesofe-governmentande-governance(forinstance,asoutlinedinOECD’se-
governmentimperativedocument(Fieldetal.(2003)))canbeachieved.TheOECDemphasizesthisbroaderscopewith
adistinctnomenclature‘digitalgovernance’,andde finesitasthe“useofdigitaltechnologies,asanintegratedpartof
governments’modernisationstrategies,tocreatepublicvalue.Itreliesonadigitalgovernmentecosystemcomprisedof
governmentactors,non-governmentalorganisations,businesses,citizens’associationsandindividualswhichsupports
theproductionofandaccesstodata,servicesandcontentthroughinteractionswiththegovernment”(OECD(2014)).
Whileoftenthetermse-government/governance1anddigitalgovernmentarealsousedinterchangably,thedistinct
OECDde finitionshelpcapturesubtleyetfundamentalchangesthathaveemergedovertime.Prominently,whilethe
coremissionofcateringtothepublicatlargeremainsthesame,themeanshasexpandedinitsscope,andtheemphasis
hasshiftedfromthegovernmentsdeliveringitontheirowntocreatinganenvironment,whereitcanbedoneusing
public-privatepartnerships,aswellasbyfacilitatingpurelyprivatelyfundedeffortsto flourish.Thisisbeingachievedby
creatinganecosystemcomprisingdigitalinfrastructureandregulatoryframeworksonwhichthediverseparticipants
canbuildupon.
Forinstance,manycountriesandcitieshaveembarkedonsmartcityinitiativesinthelastdecade.Theriseofapp
basedridesharingandhailingservicesisanexampleinstanceofhowprivatepartiesareaddressingurbantransportation
needs.Sharingeconomyingeneral,asaphenomenon(Heinrichsetal.(2013);Martin(2016)),forbetterorworse,
exempli fiesthismodelfurther.InFigure1weshowtheGoogletopicssearchtrend,(nottobetreatedasanirrefutable
evidenceoftheargumentwehaveputforward,butmore)asoneplausibleindicator.Wenoticethatsearchesforthe
topic‘Smartcity’haveincreasedovertime.Furthermore,thein-setmapindicatesthatthistermdominatesinmanyof
thedigitallyadvancedcountrieswhichembracede-governmentearlyon,andhencehavemovedupthedatavaluechain.
Whilethedrivetowardssmartcitiesisjustoneamongmanyaspectsofdigitalgovernment,itencapsulatesthegeneral
trendofmovingupinthedatavaluechain,thatisevolvingfromtheoriginaldigitizationdrivewhichwasfocussedon
creatinganelectronicchannelfordeliveringcitizenservicesandmanagingbackendgovernmentwork flows.
Thereareseveralintertwinedandcascadingfactorsatplayinthisevolution.Whatstartedasamovefrompaper
basedwork flowstodigitization,ledtocreationofahugevolumeofdatathatisreadilyavailableforautomated
processing.Theinfrastructuretostoreandprocesshumongousvolumeofdatastartedtomature,evenasthevolume
ofdatabeingacquiredalsokeepsrisingbyleapsandbounds.Thisdatacomesfromaplethoraofsources,andisvery
diverseinnature-socialmedia,sensorsdeployedformonitoringtheenvironment,cities,buildings, financialrecords,
healthrecords,tonamejustafew.Analysingthisdatayieldsintelligence,andcreatesopportunities,bothforsolving
(andidentifying)problems,aswellasthepositivesocietaland financialimpactsuchsolutionsyield.
1Thedistincttermse-governmentande-governancearesometimesusedtodistinguishtheuseoftechnologyformanagingthegovernment’sinternalactivities
versusservicedeliverytocitizens.Nevertheless,forbrevity,inthispaper,wewillusetheterme-goverment,andlikewise,digitalgovernment,tocaptureboth
meanings.
ANWITAMANDATTA 3
FIGURE 1 Datafromgoogletrendsforthetopicsof‘E-government’and‘Smartcity’worldwidefortheperiodfrom
January2004tillApril2019wereobtained(on2ndMay2019).Thenormalizedrelativefrequenciesareplottedhere.
Therelativedominanceoftheindividualtermsineachcountryisalsodepictedinthein-setmap(withthecolor
correspondingthedominantterm),obtainedfromtheGoogletrendswebsite https://trends.google.com/ .Forthe
creationofthemap,lowsearchvolumeregionswereincluded(greyregionsindicatelackofadequatedatastill).
Acommonunderlyingthemeinallthisistheavailabilityandsharingof(goodqualityof)data.However,thereare
severalchallengesthathamperthis,tonameafewprominentones:dataintegrationandportability(Doanetal.(2012)),
privacy,distributedcontrol,provenanceanddatausagetransparency.
Therearenumerousprivacyissues,andnotalltheissuesareevenwellunderstood.Thisincludesquestionsof
accesscontrol(whoshouldgetwhatdata),informationleakandsidechannels(evenifaspeci ficdatainitselfmaynot
primafacierevealsomething,inconjunctionwithsomeotherinformation,itmayrevealsomethingmorethanwhat
eachoftheindividualpiecesofinformationdisclosed).Evenwithindifferentgovernmentagencies,sharingcertain
datamaybeviolativeoftherightsofacitizenasperthelawsofthecountry.Sharingitwithnon-governmententities
compoundtheconcerns.Lackof(wellthoughtof)regulationsalsoleadtomanygreyareas.Furthermore,individual
dataaggregatorsneedtosatisfytheasscoiatedprivacyandsecurityrequirements,andtheymayalsowanttocontrol
thedatatheyowninamannerwheretheycanaccountforitsusage.
Forwhicheverreasons,thedatainthesystemmaybeofpoorquality,oroutrightwrong.Evenotherwise,itwould
bereasonabletoexpectcertainaccountabilityonthesourceoftheinformation.Thus,dataprovenanceandlineage,
alongwithabilitytotracewhoallhaveaccessedsaiddata,andforwhichpurposes,arealsoessential.
Technologicalsolutionsarethusneededtostore,process,andsharedatainasecuremanner,balancingtheneeds
(aspirational,aswellas,often,regulatory)ofutilityandprivacyinahighlydistributedenvironment,involvingmany
autonomousentities,whichmaynot(fully)trusteachother.Blockchaintechnologieshaveemergedasapotential
candidateprovidingaframeworktoaddressseveraloftheseconcerns.Atthisjuncture,itisworthemphasizingthat,
blockchains(i)maynotbetheonlyway,orthebestway,tosolvetheabovementionedproblems,(ii)maynotevenbe
solvingalltheproblemsenumeratedhere(letaloneotherissuesnotmentioned).Nevertheless,itisacandidatesolution
thatcannaturallyaddressissuessuchasdistributedcontrolamonguntrustedentities,andprovidescertainextentof
flexibilities,whichiswhytheyarebeingtriedoutinaplethoraofapplicationdomains.
Itisinthisbackground,promptedbothbyneedandpotentialbutalsohype,thatseveralgovernmentshavestarted
pilotprojectsofusingblockchainsaspartofthegovernmenttechnologystack.
4 ANWITAMANDATTA
Intherestofthispaper,we firstbrie flyreviewdistributedledgerandblockchaintechnologiesinSection2,thenin
Section3wereport(inanon-exhaustive,butrepresentativemanner)onseveralgovernmentblockchaininitiatives,and
finallyinSection4wedrawourconclusionswithacritiqueoftheseearlyefforts.
2|BACKGROUND:BLOCKCHAINS
DLTversusblockchains
NationalInstituteofStandardsandTechnology(NIST,U.S.DepartmentofCommerce)de finesblockchainsas
“tamperevidentandtamperresistantdigitalledgersimplementedinadistributedfashion(i.e.,withoutacentral
repository)andusuallywithoutacentralauthority(i.e.,abank,company,orgovernment)”(Yagaetal.(2018)).Thus,one
cancharacterizeablockchainwithfoursalientproperties:(i)immutability(append-only)datastructure,(ii)replication
ofthedataacrossmultipleparticipants,(iii)whetherandhowparticipationisrestricted,and(iv)howtheparticipants
establishanagreement(consensus)onhowtoaddnewdatatotheexistingdata.
Thisimmutabilityproperty-whereanychangestoexistingrecordscanbedetected(tamperevident),anditis
prohibitivelycostlyifnotimpossibleforone,oraminorityoferrantparticipantsmaintainingthedistributedledger
tointroduceillegitimatechanges(tamperresistant),makeblockchainsparticularlyappealingforawiderangeof
applicationsandscenarios.Storingrecordsof financialtransactionsisanaturalsuchapplication,andblockchainshave
beenpopularizedbytheirusageforemostinBitcoin,andsubsequentlybyaplethoraofotherdigital(crypto)currencies
whichleverageoncryptographyforbothguaranteeingintegrityoftherecords(asisachievedusinghashingandchaining
torealizeablockchain),aswellasformakingitprohibitivetoalterpastrecordsorinsertarbitrarynewrecords(e.g.,
Bitcoinusescryptographicpuzzlesforproof-of-worktoallowadditionofnewblocksintheblockchain)andforthe
purposeofauthenticationandauthorization(e.g.,publickeycryptography,speci ficallydigitalsignaturesareusedin
Bitcointodeterminethelegitimacyofspeci ficBitcoinsbeingspent).Butmanyotherapplications,particularlywhenever
anyformofauditisdesirable,wouldbene fitfromsuchanimmutabledatastructure.Evenforapplications,where
theinformationbeingstoredisinherentlymutable,forexample,propertyorlandownershiprecords,medicalhistory
records,etc.,theoverlyingapplicationcanleverageanimmutabledata-structureunderneathtorecordthehistoryof
changes,whileusingthelatestversionastheworkingdata.
Thereplicationofinformationacrossmultiplesitesisanecessityforenforcingtheimmutabilityandintegrityofthe
informationstoredintheblockchain,where,thereplicatedsitesactaswitnesses,sothatunilateralchangestopast
recordsorintroductionofnewrecordsarenotfeasible,andinsteadthedecisionsaremadebyestablishingaconsensus
onthesharedstate.
Sincethestateoftheblockchaincanbeupdatedonlythroughconsensusamongthemultiplesitesmaintaining
it,theblockchaincanalsobeusedtoestablishtrustamonginherentlyuntrustedentities.Userscanestablishasmart
contractusingsomeprogrammelogic(anddata):forexample,changethenameagainstwhichapropertyisregistered,
providedacertainamountofmoneyispaidtotheoriginalownerofsaidproperty,andrelyontheblockchaintocarry
outthepaymentwithtokenizedmoneyandenforcethenamechange.
Finally,sincetheinformationstoredintheblockchainisvisibletoeverypartipatingreplicationsiteintheblockchain
(andwhoeverelsehaveaccesstothedatetherein,moreonthisbelow),whichisdesirableforaudit,itmayappear
undesirablefromtheperspectiveofprivacyorcon fidentialityofinformation.However,creativeworkaroundtopreserve
privacyandcon fidentialitymaybepossibleforawiderangeofapplications,bystoringaproxyinformation(tokenization)
whichinitselfdoesnotrevealthecontent,butcarriesenoughinformationforthespeci ficpurposeoftheapplication.
Forinstance,ifonewantstoprovethatadigitialdocument(e.g.,educationalcerti ficate)wasgenuinelyissuedbycertain
ANWITAMANDATTA 5
organization,justacryptographichashsummaryofthedigitallysigneddocumentmaybestoredintheblockchain.
Whentheoriginaldocumentispresentedbytheownertoanyspeci ficpartyscrutinizingthedocument,thescrutinizer
canvalidatethatthedocumentwasindeedregisteredwiththeblockchainbythelegitimateentityauthorisedtoissue
saiddocument,while,thepresenceofthesaidsummaryintheblockchainrevealsnousableinformationinitself.Having
saidthat,theissueofinformationleakfromdatastoredinblockchain,particularlywhencoupledwithsideinformation
thatmightbeavailabletoanadversary,isstillnotathoroughlyunderstoodproblem,andisatopicthatwouldneed
substantialfurtherresearch.
2.1|Permissionless,PermissionedandPrivateBlockchains
Blockchainscanbebroadlyclassi fiedasbeingpermissionlessorpermissioned.Permissionlessblockchainsareopenfor
anyonetoreplicatetheblockchaindataandparticipateinthe(consensus)processtoupdatethesame.
Permissionedblockchainsincontrastrestrictthoseparticipatinginthemaintenanceoftheblockchain.Thisrestric-
tionmightbeimposedinacentralizedmannerorinadecentralizedmanner.Oneexampleofcentralizedmembership
controlwouldbethatagovernmentagencydecideswhoallcanparticipate.Anexampleofdecentralizedmember-
shipcontrolcouldbethatexistingmembersofanallianceoforganizationscollectivelydecidetoincludeorrevoke
memberships.
Furthermore,inapermissionedblockchain,thedataintheblockchainmaybeavailable(tobeaccessed/read)
exclusivelytoacertainsetofentities/individuals-makingitaprivateblockchain.
BlockchainissupposedtoprovideamechanismtoderivetrustTheEconomist(2015)amongagroupofentities
whichdonotinherentlytrusteachother.Inapermissionlessblockchain,wherethereisnoaprioritrustamongentities,
moreexpensivemechanisms(e.g.,solvingcyptographicpuzzlesasproof-of-workNakamotoetal.(2008))arerequiredin
ordertomitigatemalbehavingparticipants.Incontrast,inpermissionedorprivateblockchains,acertaindegreeoftrust
amongtheparticipantsisinherently(andimplicitly)assumed,andassuch,itallows flexibilityintheextentofstringency
requiredtoachieveconsensus.
2.2|Forks,Side-chainsandCross-chaining
Differentmembersmaintaningablockchainmaytrytoadddistinctnewblockssimultaneously,whichleadstowhatis
calledforking.Suchdivergenceiseventuallyreconciledthroughaconsensusmechanismtoarriveataconsistentshared
history.However,thisprovidesaninterestingopportunity,whereby,whiletheparticipantsagreeonthepasthistory
thathasbeenestablished,new/differentrulescanbeimposedforde finingwhatisavalidnewblock.Ifthenewblocks
arestillcompatibleandvalidaspertheoldersetofrules(backwardcompatible)itscalledasoftwork,while,otherwise,
itiscalledahardfork.Suchforkingalsoallowstheoriginalcommunityofmembersmainainingablockchaintosplit,and
createnewcommunities,withasharedhistoryuptillatimepoint,anddifferentonesfromthepointofforking.
Itmaybeundesirabletoputallinformationintheblockchain,eitherbecauseitoverloadsthesystem,orbecause
ofotherconcernssuchascompatibility,ortocarryoutexperimentationswithoutinterferingwiththecontentofthe
blockchain,orsimplytokeepcertaininformationoffthemainchain.Suchcasesareaccommodatedbyrunningparallel
ledger(s)maintainedindependently,whichhowevercross-reference(s)theprimaryblockchain,andmayalsousethe
primaryblockchainforrecordingcertain(butnotallthe)statesofsuchparallelledgers,whicharethencalledside-chains.
Finally,separateblockchainsmaywanttointerchangeamongthemselvesthestoreddata/informationorthe
associateddigitalassets,andthisleadstothenotionofanetworkofinter-operablebutindependentblockchains,i.e.,
cross-chainingButerin(2016).
6 ANWITAMANDATTA
3|BLOCKCHAINUSE-CASESFORDIGITALGOVERNMENT
Notaryandregistryservices: Themostnaturalapplicationofagovernmentserviceofblockchainisanotaryservice.
Drawingonthis,registryforanyrealworldassetscanalsobebuiltusingablockchain.Thisisindeedoneofthecanonical
usecasesofblockchainbeingpilotedinmanyplacesEder(2019);CastellanosandBenbunan-Fich(2018).Different
variationsexist,governmentledorbackedinitiatives,aswellasprivateentitiesmirroringthegovernment’slandregistry
recordonablockchain. Ingeneral,registryservicesofallsortscanberealizedusingblockchain,e.g.,aregistryof
companiesSmartDubai.ae(2016);TheMaltaIndependent(2019).
Digitalidentity: TheEuropeanUnionBlockchainObservatory&Forum( https://www.eublockchainforum.eu ),an
EuropeanCommissioninitiative,identi fiesdigitalidentityasafundamentalbuildingblockLyonsetal.(2018a)that
canberealizedusingablockchain,toactasadigitalequivalentofgovernmentissuedidentitydocuments. Sucha
mechanismideallyneedstohave flexibility,suchthatonlyrelevantaspectsofthedigitalidentityareexposed,without
revealingotheraspectsofidentityinformationnotrelevantforthegivencontext,thusaffordingindividualsadegree
ofsovereignityinmanagingtheiridentity.Itisenvisagedthatsuchanidentityservicecanbeleveragedtoprovidea
widerangeofotherservices.Somepilotcasesthathavetriedidentityservicesusingblockchainsincludee-voting2
andbikerental3triedinZug,Switzerland;workertrainingcerti ficationandcheck-in/outatworksitescarriedout
bySwissrailways4,debitcardsforrefugeeswithoutotherdocumentationsorbankaccountinFinland5. Allthe
threeusecasesfromSwitzerlandthatarementionedhereusedapublicblockchainbaseddecentralizedidentity
service( https://www.uport.me ),whiletheFinnishinitiativewasinliasionwithaprivateenterprise,Moni.Estonia,
consideredapioneerindigitalidentity,usesablockchainvariantcalledKSI(KeylessSignatureInfrastructure),created
byGuardtimeeestonia.com(2008).TheEuropeanUnionregulationeIDAS(electronicIDenti fication,Authentication
andtrustServices)mandatesadigitalidentitysystemthatinteroperatesacrossEU,howeveritsomewhatpredates
therecenthypearoundblockchaintechnologies,anditdoesnotutilizeblockchainasthecoreinfrastructuretorealize
it,butuseofandwithblockchainsisunderdeliberationServidaandMunoz(2018).TheID2020ID2020.org(2014)is
anotherpublic-privatepartnership,whileDecentralizedIdentityFoundationDIF(2017)isanindustryalliance,both
aimedatrealizingblockchainenableddigitalidentityinaglobalscale.Inthedigitalidentityspace,wethusseeuseof
bothpermissionlessaswellaspermissioned/privateblockchains,ledbygovernment,public-privatepartnerships,as
wellasprivateentities.
Digitalcerti ficatesandrecords: Theideasofaregistryservice,alongwithidentity,naturallyextendstoarepositoryof
certificatesandrecords.Thesecouldbeinherentattributes(intrinsictotheindividual,forexample:dataofbirth/death,
biometrics,health),attributesaccumulatedorcreatedovertime(healthrecords,educationalcredentials,wealthand
associatedpropertyand financialrecords,will&testament),orassigned(e.g.,governmentIDnumbers,creditriskscore).
TheserecordsfurthermorecanbegroupedasperthefrequencywithwhichtheychangeMorrisetal.(2018).Finally,a
thirddimensionrelatestoaccessrightsandprivacy/con fidentialityrequirementsoftheserecords,namelywhohave
therighttoaccesswhichpartsofsaidrecords,andwhohaverighttoupdatesaidrecords.Thesuitabilityofstoring
thespectrumofsuchrecordsoverblockchain,meetingtheaccesscontrol,con fidentiality,(system)accessandchange
logging,andcontentupdatedynamicsrequireexploration.Blockchainusetocurbdegreefraudincountriesasdiverse
asMalaysia6andMaltaPatel(2018)havebeenproposed.HashbasedintegritycheckandloggingisusedforEstonia’s
2https://medium.com/bitrates-news/swiss-city-of-zug-successfully-completes-blockchain-based-e-voting-trial-b becdc
3https://medium.com/uport/zug-residents-can-now-ride-e-bikes-using-their-uport-powered-zug-digital-ids- edacd
4https://medium.com/linum-labs/swiss-federal-railway-trials-first-digital-identity-pilot-on-ethereum-
acbc
5https://www.wired.com/story/refugees-but-on-the-blockchain/
6https://www.nst.com.my/news/nation/ //
/university-consortium-set-authenticate-degrees-using-blockchain
ANWITAMANDATTA 7
electronichealthrecords7,andmanyhealthcareindustryusecasesfromdrugtraceability8toinsuranceDeloitteare
expectedtoimprovethequalityofdata,serviceandcosttodeliverthesame.Again,mostoftheseareinexploratory
pilotstageatthemoment,andrangesfromgovernmentledeffortsliketheonefromEstonia,toalliancesofprivate
companiessuchastheSynapticHealthAlliance9.
Sovereign/ fiatcurrency:Giventhatblockchain’spopularityinthepastdecadeoriginatesfromthesuccessofcryptocur-
rencies,useofblockchaintosupportdigitalsovereign fiatcurrencies(asidethemanyprivatesector financialtechnology
innovationattempts)isnatural.Sinagpore’sprojectUbinTheMonetaryAuthorityofSingapore(2017),understew-
ardshipoftheMonetaryAuthorityofSingaporeandincollaborationwithmajor financialinstitutions(theAssociation
ofBanksinSingapore(ABS))carriedoutpilotstudiesofinter-banktransactionsusingdigitalledgertechnologies,and
developedthreemodelsfordecentralisedinter-bankpaymentandsettlements.WhiletheSingaporeprojectused
theblockchaintechnologysolelyasadistributedledgerforrecordingthetransactionsusingtokenizedcoins,“Crypto
Franc”wasproposedasabond10,withitsvaluepeggedtotheSwissfrancona1-to-1basis,wherepermissionednodes
federatingSwissCantonswereproposedtoenforcetheadherencetoregulatoryrequirements,andtomaintainthe
ledger.Thestatusofthisproposalisambiguousatthetimepointthisarticleisbeingwritten,andingeneral,thereseems
nogovernmentlevelsupportorintentforasovereignSwissdigitalcurrency11.Anotablebutdubious(becauseofthe
politicalbackground)andapparentlydefunctattemptwasVenezuela’sOilandMineralbackedPetrocurrency.Marshall
island,throughSovereignCurrencyActof2018RepublicoftheMarshallIslands(2018),introducedanewblockchain
basedcurrencycalledtheSovereign(‘SOV’),andironically,planstoissuephysicalnotesforthedigitalcurrency.AJuly
2018articlesurveysthestatusofstateissueddigitalcurrenciesonitsadoption,explorationandpossiblerejection11.
Aplatformfordatamanagementacrossmultiplestake-holders(anddigitaltwinning): Theterm‘digitaltwins’
hasrootsinthecyber-physicalsystemscommunity,butitcanbemorebroadlyinterpretedasSaddik(2018)“Adigital
replicaofalivingornon-livingphysicalentity. Bybridgingthephysicalandthevirtualworld,dataistransmitted
seamlesslyallowingthevirtualentitytoexistsimultaneouslywiththephysicalentity.”Inthatcontext,adiverserange
ofapplications,includinghealthmonitoringdevices;IoTenabled‘smart’solutionsacrossaspectrumofscale-smart
home,building,city;supplychains,canallbeseeasprojectingadigitaltwin.Blockchaincanserveasareal-timedata
managementframeworktoenabledigitaltwinningforthewholelifecycle(fromdataacquisitiontologgingitsaccess
providingprovenance,integrityaswellasaccountabilitywithaudittrailandtransparency).
Amulti-stakeholderdatasharingplatform,whichactsasanaturalaggregatorofdata,andallowssecuredata
management(including,facilitatingdataownerssovereignityoversaiddata)andthusenablesbothdigitalgovernment
aswellasnon-governmentalapplications(thisistermedas‘Blockchainplatformasaservice’inLyonsetal.(2018a))
canthusbeseenastheholygrailforablockchaininthegovernmenttechnolgystack.Severalgovernments,atcity,
state,countryaswellassupra-nationallevels,havedeployed,starteddevelopingorexpressedinterestinexploringthe
provisioningofsuchmulti/omni-purposeblockchains.ProminentexamplesincludeDubaiSmartDubai.ae(2016),State
ofIllinoisMorrisetal.(2018),Estoniaeestonia.com(2008),SwitzerlandSwisscom(2018),Australia12,andEUthrough
EuropeanBlockchainPartnership13.
7https://e-estonia.com/solutions/healthcare/e-health-record/
8https://www.ibm.com/blogs/blockchain/ //what-are-the-use-cases-for-blockchain-tech-in-healthcare/
9https://www.synaptichealthalliance.com
10https://www.swissinfo.ch/eng/stable-coin_crypto-bond-catapults-swiss-franc-onto-blockchain/
11https://cointelegraph.com/news/state-issued-digital-currencies-the-countries-which-adopted-rejected-or-researched-the-concept
12https://www.minister.industry.gov.au/ministers/karenandrews/media-releases/advancing-australias-blockchain-industry
13https://ec.europa.eu/digital-single-market/en/news/european-countries-join-blockchain-partnership
8 ANWITAMANDATTA
4|CRITIQUE:STATEANDTHECHAIN
Theembraceofblockchainsinthegovernmenttechnologystackisinitsnascence.Consequently,despitenumerous
newsarticles,pressreleasesandwhitepapers,actualtechnicaldetailsregardingmostoftheinitiativesareoftensparse,
sometimescontradictory,orjustabsentfromthepublicdomainintheseearlydays.Furthermore,designanddecisions
mayjustnotyetbe finalized,andsothingsnaturallychange.Whilewehavetriedourbestto filteroutthelatestrelevant
andcorrectinformation,itisapttonoteatthisjuncturethatsomeofthepointswemakeheremayinadvertentlybe
somewhatoffthemark,ormaybecomeobsoleteovertime.
Justlikeweseediverseformsofeconomicsystemsingeneral,wherecoreservicesforcitizens(suchashealth
care,transportation, financialservices,utilities)areprovidedinsomeinstancesbysolelygovernmentagencies,in
others,byonlyprivateentities,inyetothers,inprivate-publicpartnerships,and finally,alsoinformswhereprivateand
governmentrunentitiesbothoperateandcompeteinthemarket;fromtheexampleswehavediscussedabove,wesee
anechoofsimilardifferentformatsinthegovernmenttechnology(GovTech)spaceingeneral,andforblockchainsfor
GovTechinparticular.
Fortherestofourdiscussionshere,wefocusonthreeaspects:(i)blockchainsupportfordigitalsoverigncurrency,
(ii)blockchainasaplatformfordigitalidentity,and(iii)thenatureoftheunderlyingblockchaininfrastructure.The
otherspeci ficusecases,suchasregistryservicesorrepositoryfordigitalrecords,allinturnrelyontheunderlying
infrastructureandtheidentityservice.
Digitalsovereigncurrency: InLyonsetal.(2018a),thefollowingargumentisforwarded“Anotherimportant
buildingblock,inouropinion,ishavingdigitalversionsofnationalcurrenciesontheblockchain,forexamplethrough
blockchain-basedcentralbankdigitalcurrencies(CBDCs).Makingitpossibleforlegaltendertobecomeanintegralpart
ofblockchaintransactionswillmakeiteasiertoreapthebene fitsofnewtechnologieslikesmartcontracts.Onasystemic
level,CBDCscouldbringthebene fitsofdecentralisationtointer-bankpaymentsandreal-timegrosssettlementsystems,
amongotherthings”.
Thereareseveralcryptocurrency flavouredapproachestorealizeadigitalsoverigncurrency,Venezuela’sPetro
(nowapparentlydefunct)andMarshallIslands’‘theSovereign’(whichalsoisplannedtocomewithphysical‘banknotes’)
cometomind.Whilethereissomelevelofenthusiasmaboutsuchdigitalsoverigncurrencies(tobedistinguisedwiththe
non-state-backedcryptocurrencies),itisratherunnecessaryandatokenizationbasedapproachshowcasedinproject
Ubinisapragmaticsolutiontorealizecentralbankdigitalcurrencies(CBDCs).
ToquoteTheMonetaryAuthorityofSingapore(2017):“theSGD-on-ledgerisaspeci ficusecouponthatisissued
onaone-to-onebasisinexchangeformoney.Thecouponshaveaspeci ficusagedomain–inourcaseforthesettlement
ofinterbankdebts–butnovalueoutsideofthis.Oneisabletocashoutbyexchangingthecouponsbackintomoneylater
...SGD-on-ledgerhasthreeusefulpropertiesthatmakeitsuitedtoourprototype.First,unlikemoneyinbankaccounts,
wedonotreceiveinterestontheonledgerholdings.Theabsenceofinterestcalculationsreducesthecomplexityof
managingthepaymentsystem.Second,toensurefullredeem-abilityoftheSGD-on-ledgerformoney,eachtokenis
fullybackedbyanequivalentamountofSGDheldincustody.Thismeansthattheoverallmoneysupplyisunaffected
bytheissuanceoftheonledgerequivalentssincethereisnonetincreaseindollarclaimsonthecentralbank.Third,
SGD-on-ledgerarelimiteduseinstrumentsandcanbedesignedwithadditionalfeaturestosupporttheusecase–such
assecurityfeaturesagainstmisuse.”
ThethreehighlightedpropertiesfromtheprojectUbinreportemphasizetheimportanceofresponsiblyusing
blockchainwithoutcreatinginstabilitywhilesolvingactualpainpointsofdigitial financialactivitiesthatexistwithlegacy
infrastructure:particularlythattheprocessesareunnecessarilycomplexwithrespecttothefunctionalitiesprovided,
makingthesolutionsinef ficient(slowerand/orexpensive).Suchatokenizedapproachalsoallowsanaturalintegration
ANWITAMANDATTA 9
ofthecurrencywithotherwork flowsandfunctionalitiesthatmaybecarriedoutoveramulti/omni-purposeblockchain
platform.
DigitalIDinamulti-stakeholderenvironment: Digitalidentityhasbeenrepeatedlyemphasizedasoneofthe
‘killerapps’ofgovernmentblockchains.Forinstance,toquoteMorrisetal.(2018):“Acitizen-centricdigitalidentity
modelbasedondistributedledgertechnologiescouldbeusedtoconsolidatedisparatedatathatcurrentlyexists
acrossmultipleagenciesandlayersofgovernmentintoanetworkcenteredaroundacitizen’sorbusiness’credentials,
licensesandidentityattributes.Itwouldenablecitizenstoviewtheirpublicserviceidentityviaanidentityappon
theirsmartphoneandsharerelevantdatawithgovernmenttoaccesspublicservices.”AEuropeanUnionBlockchain
Observatory&ForumreportLyonsetal.(2018a)likewisestates“Oneofthemostimportantrequirementsinbuilding
adigitaleconomyandsocietyisviabledigitalidentitiesforallparticipants,whetherindividuals,companies,public
agenciesor,increasingly,machinesandotherautonomousagents.Theneedtobeabletoidentifyourselvesandothers
issoimportant,infact,thatitisconsideredtheessentialprerequisiteformostusecases.”Manyotherwhitepapers
WorldEconomicForum(2016);TheWorldBankGroup(2018);AustraliaPost(2016);Willars(2019)havelikewise
elaboratedtheimportanceofdigitalidentityintherecentyears.
TheEstonianblockchainatitscoredealswithdigitalidentityeestonia.com(2008),andseveraloftheproposed
governmentrunblockchainsaimtoprovideandutilizedigitalidentityinsomemanner.Yet,someoftheworld’slargest
digitalizedidentitysystemsareinfactnotblockchainbased.ThisincludesEU’selectronicIDenti fication,Authentication
andtrustServices(eIDAS14),India’sAadhaar15whichistheworld’slargestworld’slargestbiometricIDsystem
managedbyUniqueIdenti ficationAuthorityofIndia(UIDAI)andChina’ssocialcreditsystemTheEconomist(2016).
Sotheperformanceatscale,orthemulti-stakeholderusagescenariosinthemselvesdonotneccessitatetheuseofa
blockchain.Oneargumentforusingblockchainsisthenotionof‘self-sovereigndigitalidentity’Willars(2019).Inthis(as
wellasmanyothersecuritybene fitsthatareassumedand/orpromisedwithblockchain,suchasmoregenerallydata
sovereignty,portability,privacyandsecurity,integrityandaudittrail),thenuancesofhowtheunderlyinginfrastructure
isactuallydesigned,deployedanduseddetermineswhetherthesecurityguaranteesareactuallyrealized.Itistoo
earlytocommentonhowID2020.org(2014)orDIF(2017)technologystackevolves,butwewillthusdiscuss(below)
thespeci ficmodelusedineestonia.com(2008).Overall,whatisfactualisthatblockchainbaseddistributedledger
technologycanbeusedtosupportdigitalidentity.However,itisnotasingularoptiontodoso,norarealltheassumed
securityguaranteesinherentinvariants.
Theinfrastructurebehindablockchainasamulti/omni-purposeplatform: Asrecentlyasearlierthisyear,on9
February2019,theEuropeanMedicinesVeri ficationSystem(EMVS)16waslaunched17.Suchanapplicationperfectly
fitsablockchainusecase,giventhescaleandmulti-stakeholdernatureofthesystem.It(tothebestofourunderstanding)
howeverdoesnotuseblockchaintechnologies.Inanycase,manylarge-scalemulti-stakeholdersystemsingeneralexist
andoperatemeetingtheirdesignobjectives.Whiletechnologicallynotsingular,andmanyotheralternativerealizations
arepossible(asexempli fiedbydeployedsystems),oneargumentinfavourofablockchainistoexposeitasaplatform
orservice,wherenewapplicationscanbemodularlyintegrated,ratherthanhavingtodesignanddeploydifferent
systemsfromscratchforindividualapplications.Incidentally,sincesuchsystemsarebeingbuiltgroundup,theyareina
positiontoavoidsomeoftheproblemsfacedbymanylegacysystems,suchaspoorlystructured,non-standardizeddata,
interoperabilityacrosssystems(thoughblockchaininteroperabilityisstillanopenresearchissueLyonsetal.(2018b);
Buterin(2016);Rutter(2017))andtheabilitytomigratethedatato/fromanothersystem.Whilethesearenotinherent
14https://ec.europa.eu/digital-single-market/en/trust-services-and-eid
15https://uidai.gov.in/my-aadhaar/about-your-aadhaar.html
16https://emvo-medicines.eu/mission/emvs/
17https://emvo-medicines.eu/new/wp-content/uploads/EMVO-Press-Release-EMVS-Launch.pdf
10 ANWITAMANDATTA
propertiesofblockchain,thecreationofanewdigitalinfrastructureprovidesacoincidentalopportunity.
InLyonsetal.(2018b,a),somedesigndilemmasarediscussedatlength.Forinstance,atop-downapproachwhere
thegovernmentdeploys(andpossiblyenforces)theusageofasingleblockchainforeverygovernmentrelatedpurpose,
willhelpwiththeaforementionedstandardizationbydefault,andyet,itmayleadtoasinglevendorlock-in,whilelacking
theflexibilitytoaccommodateallpossibleusecases.ManyofthenationalblockchaininitiativesSmartDubai.ae(2016);
Swisscom(2018);eestonia.com(2008)appeartobefollowingthisapproachofasinglestandardizedblockchain.Incon-
trast,uncoordinatedexperimentationsofdifferenttechnologiesbydifferentagenciesmayleadtoduplicationofeffort,
aswellasfragmentationofplatforms.InLyonsetal.(2018a)amiddlegroundisadvocated:“ flexible,cloud-basedshared
infrastructurethathostsdifferentprotocolsaswellasdevelopertools,andanintegrateddevelopmentandoperations
environment”.Theauthorsfurtheradd“Ashared“sandbox”approach,evenonefeaturingmultipletechnologies,should
alsofosterknowledgesharingandmakeiteasierforagenciestoworktogethertoensureinteroperability”.Particularly
forasupra-nationalset-upsuchastheEU,thisapproachmaybeinevitable,sinceindividualmemberstateswouldlikely
embraceaspectrumofblockchainsolutions.
Whiletheabovedesigndilemmasarerelevant,inthispaper,wewanttohighlightafewother,arguablymorecritical
issues,thatneedscarefulattention.
ConsidertheKSIblockchain(usedineestonia.com(2008)),quotingGuardtime(2015)regardingdataprivacy
guarantees:“KSIdoesnotingestanycustomerdata;dataneverleavesthecustomerpremises.Insteadthesystem
isbasedonone-waycryptographichashfunctionsthatresultinhashvaluesuniquelyrepresentingthedata,butare
irreversiblesuchthatonecannotstartwiththehashvalueandreconstructthedata-dataprivacyisguaranteedatall
times.”.Sincetheblockchaindoesnotstoretheactualdata,primafaciedataprivacyisachievedusingtheblockchain,
whilealsovalidatingdataintegrity.However,dependingonthenatureofthedata/application-ifitissomethingthat
residesonanoff-chainstoragerepositoryandiscorrupted,theblockchainwouldbeabletodetectsuchcorruptionupon
usageofsaidoff-chaindata,butitdoesnotsupportpreventionorcorrection(forwhich,outofchainmechanismswould
berequiredinawelldesignedsystem).Likewise,thecon fidentialityofsuchdatamaystillbeviolatediftheoff-chain
repositoryisbreached.Fortheelectronicvotingsystemi-Voting,eestonia.com(2008)states:“theEstoniansolutionis
simple,elegantandsecure.Duringadesignatedpre-votingperiod,thevoterlogsontothesystemusinganID-cardor
Mobile-ID,andcastsaballot.Thevoter’sidentityisremovedfromtheballotbeforeitreachestheNationalElectoral
Commissionforcounting,therebyensuringanonymity.Withanymethodofremotevoting,includingtraditionalpostal
ballots,thepossibilityofvotesbeingforcedorboughtisaconcern.Estonia’ssolutionwastoallowvoterstologon
andvoteasmanytimesastheywantduringthepre-votingperiod.Sinceeachvotecancelsthelast,avoteralwayshas
theoptionofchanginghisorhervotelater.”Howeversuchbroadandstrongclaimofsecuritycallsforskepticism.For
instance,side-informationsuchastimeofauthentication/communicationmightrevealwhoaspeci ficpersonvoted
for,evenifthatinformationisnotexplicitlystored.WearenotassertingthattheEstonianblockchaindeploymentin
theire-Governmenttechnologystacknecessarilysuffersfromallthesevulnerabilities,andinfact,itisverylikelythat
sometheseconcernshavebeenlookedintoandmanyfurtherlayersofprotectionhavebeendeployed.Thepurpose
ofthisdiscussionusinghypotheticalsistoemphasizethattheblockchaindoesnotandcannotprovidearangeof
securityguaranteesinastand-alonemanner,yetweoftenseeamarketingpitchinthelinesof‘itssecurebecauseitisa
blockchain’.
Ineestonia.com(2008)itisfurtherstated:“WithKSIBlockchaindeployedinEstoniangovernmentnetworks,
historycannotberewrittenbyanybodyandtheauthenticityoftheelectronicdatacanbemathematicallyproven.It
meansthatno-one–nothackers,notsystemadministrators,andnotevengovernmentitself–canmanipulatethedata
andgetawaywiththat.”
However,generallyspeakingtheclaimthatdataisimmutablebecauseitisonablockchain(whichistheone
ANWITAMANDATTA 11
fundamentalfunctionalityablockchainissupposedtoprovide)mayoverlooksomefundamentalissues.Inthespeci fic
caseofeestonia.com(2008),theblockchainisfurtherpublishedinthephysicalmedia(newspapers),whichare
subscribedbymanylibrariesspreadworldwide,creatingagloballydispersedphysicalbackupwhichisnightoimpossible
totamper.
Publicblockchainsarehugelyinef ficientforthepurposesofe-Governmentusecases.Fromusabilityandcost
perspectives,thesolerationalchoiceistousepermissioned(andevenprivate)blockchains.Forinstance,Swisscom
(2018)states“SwissPostandSwisscomareconnectingtheirexistingprivateinfrastructuresforblockchainapplications.
Onthebasisofdistributedledgertechnology,thetwoinstancescheckeachotherandthushelptoestablishtrust.
Incontrastto"publicblockchains"(e.g.BitcoinandEthereum),thisprivateblockchaininfrastructurerequiresmuch
lessenergy,sinceitcanonlybeusedbyidenti fieduserswhohaveacontractualrelationshipwiththeprovidersofan
application.Thisenablesmoreef ficientagreementproceduresaswellassigni ficantlyhighersecurityandperformance.
Thisisanimportantprerequisiteformanycompaniestolaunchtheirownapplicationsbasedonblockchaintechnology.”
Theparticipatingentitiesinsuchpermissionedorprivateblockchainscancolludetogether,ormaybeforcedbya
(hypothetical,dystopian)government,tomanipulatethedata.Furthermore,inmanysuchdeployments,thesoftware
runningatallsitesaresourcedfromthesamevendor.Soasoftware(update)runbyallthesitesfromamaliciousor
compromisedvendorwouldbesuf ficienttosubvertthewholeblockchain’sintegrity.Thesearesomeverycriticalissues
thatneedmoreattention,particularlyinthecontextofblockchainuseinthegovernemnttechnologystack.Anapproach
likeeestonia.com(2008)utilizingoff-chaingloballydispersedphysicalback-upisaniftysafeguard.
Finally,towrapup,wewanttonotethatsmartcontractscanbeusedovera‘Blockchainplatformasaservice’to
automatemanytasks,including,nearrealtimemonitoringandactuationofactionplans,andinthelongerterm,to
enhancework flowsanddecisionprocessesfurtherdrivenbyanalytics(arti ficialintelligence).Suchautomationcan
significantlyimprovethecosteffectivenessandqualityofservicethatcanbedelivered.However,theopportunities
toleveragesuchdatausingtheblockchaininfrastrucutredirectlymayalsobeconstrained,dependingonthenature
oftheblockchaindeployment.Forexample,iftheactualdataisstoredoff-chain,andtokenizationisused,thenthe
natureoftokenizationwouldin fluencetheversatilityofapplicationsthatcanbebuiltontopoftheblockchain.This
initselfisnotnecessarilyabadthing,nordoesitaddfundamentallimitationsincreatingdecentralizedapplications
leveragingthetroikaofblockchains,smartcontractsandarti ficialintelligence.InLopezetal.(2019),anargumentfor(a
networkof)blockchainsbeingutilizedasagluetobindactualdataandservicesthatareoffthechain(torealizebetter
datasoverignity),andlikewisekeepingthelogicalsoattheedge(whichiswherethedataoriginatesand/orisutilized)is
forwarded.
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|
{
"id": "1905.08517"
}
|
2108.11818
|
Understanding Money Trails of Suspicious Activities in a
cryptocurrency-based Blockchain
|
The decentralization, redundancy, and pseudo-anonymity features have made
permission-less public blockchain platforms attractive for adoption as
technology platforms for cryptocurrencies. However, such adoption has enabled
cybercriminals to exploit vulnerabilities in blockchain platforms and target
the users through social engineering to carry out malicious activities. Most of
the state-of-the-art techniques for detecting malicious actors depend on the
transactional behavior of individual wallet addresses but do not analyze the
money trails. We propose a heuristics-based approach that adds new features
associated with money trails to analyze and find suspicious activities in
cryptocurrency blockchains. Here, we focus only on the cyclic behavior and
identify hidden patterns present in the temporal transactions graphs in a
blockchain. We demonstrate our methods on the transaction data of the Ethereum
blockchain. We find that malicious activities (such as Gambling, Phishing, and
Money Laundering) have different cyclic patterns in Ethereum. We also identify
two suspicious temporal cyclic path-based transfers in Ethereum. Our techniques
may apply to other cryptocurrency blockchains with appropriate modifications
adapted to the nature of the crypto-currency under investigation.
|
http://arxiv.org/pdf/2108.11818v1
|
Banwari Lal, Rachit Agarwal, Sandeep Kumar Shukla
|
cs.CR, cs.SI
|
cs.CR
|
Understanding Money Trails of Suspicious
Activities in a cryptocurrency-based Blockchain
Banwari Lal, Rachit Agarwal, Sandeep K. Shukla
CSE, IIT Kanpur, India,
Email:fbanwari, rachitag, sandeeps g@iitk.ac.in
August 27, 2021
Abstract
The decentralization, redundancy, and pseudo-anonymity features have
made permission-less public blockchain platforms attractive for adoption
as technology platforms for cryptocurrencies. However, such adoption has
enabled cybercriminals to exploit vulnerabilities in blockchain platforms
and target the users through social engineering to carry out malicious
activities. Most of the state-of-the-art techniques for detecting malicious
actors depend on the transactional behavior of individual wallet addresses
but do not analyze the money trails. We propose a heuristics-based ap-
proach that adds new features associated with money trails to analyze
and nd suspicious activities in cryptocurrency blockchains. Here, we fo-
cus only on the cyclic behavior and identify hidden patterns present in
the temporal transactions graphs in a blockchain. We demonstrate our
methods on the transaction data of the Ethereum blockchain. We nd
that malicious activities (such as Gambling, Phishing, and Money Laun-
dering) have di erent cyclic patterns in Ethereum. We also identify two
suspicious temporal cyclic path-based transfers in Ethereum. Our tech-
niques may apply to other cryptocurrency blockchains with appropriate
modi cations adapted to the nature of the crypto-currency under investi-
gation.
Keywords| Blockchain, Machine Learning, Temporal Graphs, Behavior Analy-
sis, Ethereum, Suspect Identi cation
1 Introduction
Blockchain technology works on a peer-to-peer (P2P) overlay network over the In-
ternet, employs cryptographic algorithms to secure the record of transactions, and
ensures transactions are valid and authorized. Permission-less blockchain-based appli-
cations are distributed, redundant, and provide their users with a sense of anonymity.
While this bene ts privacy, it also allows malicious actors to hide their true identities
and perform illegal transactions. The anonymity in most common blockchain-based
1arXiv:2108.11818v1 [cs.CR] 26 Aug 2021
cryptocurrency platforms is not full proof, and transaction data correlation has ef-
fectively apprehended some malicious actors in the past. However, such correlation
requires processing massive historic transaction datasets, creating graph databases,
and implementing search mechanisms speci c to the data. Therefore, Law Enforce-
ment Agencies (LEAs) are not always capable of identifying malicious actors. Knowing
this, malicious actors carry out illicit activities. Such misuse has, to date, resulted in
a considerable loss in cryptocurrencies such as Bitcoin and Ethereum [12, 4]. Cryp-
tocurrencies are among the most preferred forms of exchange for illicit transactions
pertaining to dark web markets, ransomware attacks, Phishing, Gambling, and laun-
dering funds by cyber-criminals. In [8], the authors show that Phishing generates
more than 50% of the cyber-crime revenue. Such cyber-attacks and activities exploit
the vulnerabilities present in the blockchain infrastructure or target its users through
social engineering. Thus, a critical issue that needs attention is to secure users from
cyber-attacks yet maintain their privacy.
Di erent state-of-the-art approaches such as [30, 2] exist that detect suspicious
accounts in cryptocurrency-based blockchains. However, these techniques either con-
sider only static features extracted from an aggregated graph of user interactions in
the blockchain [30] (thereby neglecting behavioral changes over time) or use temporal
features to identify behavioral changes and ignore di erent types of malicious activi-
ties [2] (i.e., consider all the malicious activities under one class). In [3], the authors
show that di erent malicious activities present in the Ethereum blockchain can be clus-
tered in 4 clusters. These 4 clusters contain accounts related to malicious activities
such as Phishing ,Gambling ,Money laundering , and others , respectively. However, the
authors consider only a few properties that were extracted after considering the \local
neighborhood" (a local neighborhood comprises of the accounts to which a particular
account has transacted with) to identify clusters. Further, they do not comment on
why they obtain such results. In our view, the results in [3] depend on how Phishing,
Gambling, Money laundering, and other malicious activities are performed and how
the money
ow happens between the accounts involved in these activities.
Thus, the question we ask is, Q: Does analyzing the money trails help distin-
guish the di erent malicious activities ? To answer the question, in [5, 35, 25], the
authors consider Motifs (basic building blocks that repeat themselves in a graph) and
two-hop cycles to understand motif applications in the temporal transaction graph of
a blockchain. However, they do not consider the temporal cycles of larger lengths.
In another work, in [19], the authors show that detecting money trails and under-
standing temporal user interactions help identify money laundering-based frauds that
exist in nance-based institutions. Considering such aspects and the advancements in
the state-of-the-art approaches, we aim to understand the di erences in the various
classes of malicious activities (those mentioned above) regarding how money
ow hap-
pens in cryptocurrency-based blockchains and how we can use these paths to identify
malicious activities. Thus, in this research, our goal is to understand the behavior
of di erent malicious activities (e.g., Phishing, Gambling, and Money Laundering)
in cryptocurrency-based blockchains such as Ethereum by understanding the money
trail (a path followed by a cryptocurrency) Note that our approach is generic and can
extend to any permission-less blockchain, with platform-speci c adaptations.
We propose a money trail-based approach and analyze accounts involved in di er-
ent malicious activities. Using the temporal cycles, we distinguish malicious activities
such as Phishing, Gambling, and Money laundering. We use money loss (amount
of cryptocurrency lost along a cyclic path) and the cyclic path's structure to under-
stand the behavior of malicious accounts in the blockchain. We nd that it is possible
2
to detect suspicious behavior using money trails, and all the accounts involved in
Phishing-based activities do not behave similarly. Further, some of the cyclic transfers
result in high money loss in a short time. In short, our contributions are:
1. We propose an approach based on money trails using temporal cycles:
(a) to characterize the behavior of malicious activities such as Phishing, Gam-
bling, and Money laundering in a cryptocurrency based blockchain,
(b) to detect suspicious temporal cycle-based money transfers using money
loss along the cyclic path.
2. As a result , we nd that most malicious activities in Ethereum have similarity
and can be clustered, while Phishing based accounts clusters themselves into
more than one cluster. Also, using money trails, we nd two suspicious temporal
cyclic path-based cryptocurrency transfers in the blockchain.
3. Current state-of-the-art approaches either use a machine learning or graph
embedding-based approach to detect and analyze suspicious behavior in the
blockchain. Our approach presents a new dimension to the existing approaches
to study malicious activities using money trails.
The structure of this article is organized as follows. Section 2 and Section 3 describe
relevant background material necessary for addressing the question Qand illustrate the
state-of-the-art approaches in search of answering this question, respectively. Section 4
details the graph construction, our assumptions, and the methodology used in tracking
the money trails. Section 5 presents the details of the data we use to validate our
approach, how we collect the data, statistics about the data, and our experimental
results. Section 6 nally conclude our paper and discuss future directions.
2 Background
Blockchain technology presents a decentralized (no central authority) data repository
of a digital ledger of transactions. The key di erence between a typical database
system and a blockchain ledger is how the data is structured. A ledger records trans-
actions in a way that makes it dicult or impossible to change the information present
in a transaction. Blockchain technology ensures that the ledger is almost impossible
to tamper with, and the integrity of the entries in the ledger is cryptographically en-
sured [32]. In a ledger, transactions are grouped to form \blocks" where each block
contains many transactions. These blocks are chained together to construct a ledger.
Each block in the ledger contains the details of the transactions included in the block,
its own hash value, and most importantly, the previous block's hash value and a times-
tamp. The decision on which block to include in the ledger among many competing
blocks created by multiple participants is taken through a \consensus algorithm" (it
veri es transactions, makes sure that the majority of members agree on the block to be
added to the blockchain, and decides the current state of the ledger). In a blockchain,
each new block (to be included in the ledger) is duplicated and distributed across the
entire \blockchain network" (the ledger is duplicated across a network of computers
instead of storing at a central location). Each member of a blockchain network has an
identical copy of the ledger.
There are primarily two types of blockchains:
3
1. A Private blockchain is a restricted blockchain where only a closed group of
users are allowed to use the blockchain. If the right to transact or create blocks
is restricted to permitted participants, then such a blockchain is also called a
\permission-ed blockchain." Such blockchains are used within a consortium of
organizations or enterprises that control accessibility and authorization. Some
example applications of a private blockchain are blockchains supporting voting
systems and digital identity.
2. A Public blockchain is a distributed ledger system which can be accessed by
everyone. Here, users are allowed to transact and add blocks without any per-
mission. Such blockchains are also called the permission-less blockchain. Here,
there is no central authority to control the access and authorization of partic-
ipants. A user can join or leave the permission-less blockchain whenever they
want. A user who is part of a permission-less blockchain can verify transac-
tions, access current and past records, or do \mining" (a process of transaction
veri cation and update of the records on the blockchain ledger).
Although there are two more types of blockchains: consortium-based and hybrid
blockchains, these blockchains are derived using the features of public and private
blockchains. We do not describe them here.
An activity that is undertaken to cause harm to someone or carry out activities
banned by law in certain jurisdictions is called a malicious activity . Some of the
most prominent malicious activities are Phishing, Gambling, and Money Laundering.
Although there are other activities such as Scamming and Heist, we only focus on
Phishing, Gambling, and Money Laundering in this paper. The reason for focusing on
these three is that the results in [3] show that most of the accounts involved in many
malicious activities in Ethereum show statistically signi cant behavioral similarities
to behaviors observed in accounts of these three activities. Note that these results are
based on the available ground truth about the marked malicious account. Accounts
related to many malicious activities (such as darknet marketplaces) are not marked
and thus are out of the scope.
Phishing is an activity where a cyber-criminal tries to steal digital cryptocurrency
or user's credentials by using social engineering methods (for example, creating a fake
website for wallets that look similar to the original website or send a fake email) [10]. In
Phishing activity, attackers do not exploit the vulnerabilities present in the system but
trick the human mind's inattention. Here, attackers aim to get sensitive information,
install the malware in the system, or steal digital currency. One of the widely known
Phishing attacks on blockchains was the \Bee Token ICO Scam" attack [15]. In the
Bee Token ICO scam, the attackers got hold of emails associated with the accounts
related to the Bee token and sent out emails to transfer Eth (a cryptocurrency used
by Ethereum).
Gambling [22] is a process of taking part in an activity in which a person risks
his money or a valuable object on the presumption of a speci c outcome of an event
that induces uncertainty of monetary loss or gain. Some Gambling activities are
a lottery, video lottery games, card games, and casino games. Due to anonymity
and transparency in the transactions, cryptocurrency blockchains are widely used in
Gambling-related activities. Some of the main advantages of using cryptocurrency for
Gambling are:
•Chances of fraud are less due to the immutability of the blockchain.
•Anonymity allows users to participate in Gambling from places where Gambling
is legally prohibited.
4
•The transaction fee is small.
Some online Gambling platforms such as Las Atlantis Casino and Wild Casino for
casino games also accept cryptocurrencies.
On the other hand, Money Laundering [14] is the process of obfuscating the true
source of an income by moving the money into various hands, through mixers, and
eventually to get it returned. At the end of such obfuscating transfer cycle, it seems
that the money is transferred from a legitimate source. Some of the money laundering-
based criminal activities are drug tracking, and terrorist funding, etc. Although it
is easier to do illicit activities in cryptocurrency due to user anonymity in blockchain,
the overall impact of cryptocurrencies on money laundering is less compared to cash
transactions. As of 2019, \only 0.5% of all Bitcoin transactions" [31] involved trading
Bitcoin on the dark web. In money laundering, criminals hide the true origin of illicit
funds using various methods (such as participating in Initial Coin O erings (ICO) and
converting one type of coin into another). In [14], the authors state three main stages
of money laundering in cryptocurrency. These stages are:
1.Placement: Cash or other types of crypto (altcoin) can be used to purchase
cryptocurrency from exchanges. A legal transaction requires identity veri ca-
tion, identi cation of the fund source, and following Anti Money Laundering
Practices (AML). Exchanges that cannot force AML practices with sub-par
tools and fails to check speci c identity, allow exploitation of vulnerabilities and
thereby enable money laundering. Certain exchanges have been found to be
involved in such practices.
2.Hiding: Once the digital currency is in play, criminals use the anonymizing
aspect of cryptocurrency blockchains to hide the source of the money.
3.Integration: In this phase, the criminals declare the money as a result of a
pro table venture or other cryptocurrency appreciation to legitimize the illicit
money.
3 Related Work
Several state-of-the-art approaches detect malicious accounts associated with such il-
licit activities in cryptocurrency blockchains, especially in Bitcoin and Ethereum [29,
12, 1, 2, 3, 18]. These state-of-the-art approaches either detect malicious activities
using machine learning (ML) algorithms or by analyzing graphs using various met-
rics. Here, we rst discuss state-of-the-art approaches using ML and then the graph
analysis-based approaches. We nally demonstrate the state-of-the-art approaches for
nding temporal cycles in a temporal graph.
ML-based approaches use the features extracted from the underlying social in-
teraction network. These features include degree (both inDegree and outDegree),
transaction fee, balance, and clustering coecient. Some of these approaches use
the aggregated blockchain graph [29], while some use temporal graphs [2]. In [29],
the authors use two aggregated graphs to detect suspicious users and transactions
in Bitcoin. One graph has users as nodes for detecting suspicious users, and the
other has transactions as nodes for detecting suspicious transactions. The approach
uses features based on degrees (inDegree, outDegree, uniqueInDegree, uniqueOutDe-
gree, and clustering coecient), average (in-transaction, out-transaction), and balance.
The authors used three unsupervised ML techniques ( a) K-means clustering, ( b) Ma-
halanobis Distance-Based Method, and ( c) Unsupervised SVM to detect suspicious
5
user/transaction. In [29], the authors detect two users and one transaction out of the
known suspicious user/transactions in the bitcoin. However, in [29], the authors do
not use temporal aspects of blockchain's underlying social interaction network.
In [2], the authors propose an ML-based approach that uses graph-based temporal
features (such as burst and attractiveness) inspired by past attacks on a blockchain.
They show that indegree and outdegree present in the social interaction network of
blockchain transactions follow power-law. The authors achieve balanced accuracy
87:2% using ExtraTreeClassi er towards detecting malicious accounts. Nonethe-
less, when using the unsupervised learning approach, they detect 814 new malicious
accounts that have a high probability of being malicious over time. However, in [2],
the authors consider all the malicious activities under one class. Thus, their results
are biased towards a malicious activity with the most number of tagged accounts [3].
In [3], the authors show that the Neural Network model is resistant to such bias and
detects any adversarial data. Further, they also show that most malicious activities
can be clustered into four clusters. However, they do not specify the reason behind
their results and why four clusters are obtained.
There are a few state-of-the-art approaches that focus on speci c malicious ac-
tivities [6, 11]. In [6], the authors focus on the \Ponzi scheme" [13] to detect illicit
behavior using supervised ML techniques such as Random Forest Classi er, Bayes
Network Classi er, and RIPPER Classi er. They handle the class imbalance prob-
lem using the oversampling (minority class's data is replicated) and the cost-sensitive
(misclassi cation of minority classes is penalized more) approaches. They nd the
combination of cost-sensitive approach and Random Forest classi er provides the best
accuracy [6]. In [11], the authors use ML to identify the smart contracts involved in
the Ponzi scheme in Ethereum. Their approach uses features based on user account
and those from op-codes of the smart contracts. However, being dedicated to a speci c
malicious activity, these approaches do not apply to other attacks due to di erences
between di erent malicious activities.
In [9], the authors perform a graph-based analysis of the transactions present in
the Ethereum blockchain. They use three graphs ( a) Money Flow Graph (MFG): a
directed graph for money transfer based transactions, ( b) Smart Contract Creation
Graph (CCG): a directed graph where an edge ( u,v) represents the node ucreating
contract node v. Here, node type is either Smart Contract (SC) or Externally Owned
Account (EOA), and ( c) Contract Invocation Graph (CIG): a directed graph where
an edge ( u,v) indicates node uinvokes the contract node v. After analyzing the 3
graphs using di erent metrics (such as degree distribution, clustering, node connec-
tivity, strongly/weakly connected component), the authors nd that only a few SCs
are dominant. The authors also propose an approach to address two security issues
(\attack forensics" - for a given malicious smart contract, nding all accounts con-
trolled by the attacker, and \anomaly detection" - detection of abnormal contract
creation) on Ethereum blockchain using cross graph analysis. However, the authors
do not comment on any speci c malicious activity.
In [26], the authors propose a Temporal Weighted Multidigraph Embedding (T-
EDGE) approach based on graph embedding to classify Phishing/non-Phishing ac-
counts. The approach uses the temporal weighted multi-edged directed graph where
nodes represent unique accounts in Ethereum and the edges are temporal and con-
tain details related to the transactions. The role of the graph embedding is to de-
tect implicit features of the accounts in the transaction network of the Ethereum
blockchain. The authors use two baseline embedding methods for the classi cation of
Phishing/non-Phishing accounts. As one baseline method, they use a random tem-
6
poral walk on the dynamic transaction graph to nd the comprehensive properties
(structural relationship) between accounts. This type of random walk contains money
ow information in the blockchain. As a second baseline method, the authors use the
skip-gram [27] model to update the process parameters in the Neural Network using
the Stochastic Gradient Descent algorithm. Using T-EDGE, the authors show that
time-dependent walks and edge information are essential in a time-dependent trans-
action network. Further, they show that their approach achieves accuracy 82%.
However, their approach is dedicated to a particular malicious activity, and no in-
sights are provided on the applicability of the approach to other malicious activities.
In [35], the authors propose an approach based on motifs (a subgraph that re-
peats themselves in a network or group of networks) to nd \mixing services" [7]
(services that allow users to mix their coins with those of other users to enhance the
anonymity of the transactions) in the Bitcoin blockchain. One of the mixing services
prevalent in the nancial sector and cryptocurrency-based blockchains is Money laun-
dering. In [35], the authors used two types of temporal directed transactions graphs
(a) homogeneous Address-Address Interaction Network (AAIN) and ( b) heterogeneous
Transaction-Address Interaction Network (TAIN). The approach uses hybrid motifs
composed of temporal homogeneous motifs in AAIN and attributed temporal hetero-
geneous (ATH: local subgraphs of heterogeneous information network) motifs in TAIN.
In [35], the authors detect 6:4% of total addresses as mixing addresses using hybrid
motifs as a key feature. Their detection model is based on the ground-truth informa-
tion, thus unknown complex mixing strategies that may exist might not be detected.
In [19], the authors propose an approach for detecting frauds using money trails in the
temporal transactions graph. They integrate their approach with a real-time fraud
detection system in a private bank. This approach was able to detect 2-4 new il-
licit activities every month since 2017. The approach in [19] also allowed banks to
set new preferences (for example, the limited time between transactions or maximum
percentage of money loss is allowed).
The state-of-the-art approaches related to general cycle detection [21, 23, 24] are
based on a depth- rst search (DFS) algorithm [33]. In [21], the authors propose an
approach to nd all the elementary cycles present in a graph using the DFS. However,
the approach does not consider temporal aspects for nding elementary cycles. On
the other hand, in [23, 24], the authors provide methods to nd all the elementary
temporal cycles in a temporal graph. Nonetheless, these approaches do not apply
other preferences (for example, the maximum percentage of money loss allowed). Such
aspects inhibit us from using these approaches for our problem as, besides timestamps,
transaction amount-based attributes are also important.
In summary, state-of-the-art approaches related to illicit activity detection in the
blockchain using ML and graph embedding are well studied. However, most ML-
based approaches are either biased towards a malicious activity with a large number
of tagged accounts/transactions or do not consider temporal aspects. Also, most ML-
based approaches do not use hidden patterns produced (such as cycle-based transfers)
by the transactions network of blockchain. Thus, in our view, an approach to detect
money trails for analysis of di erent malicious activities needs to be formalized. This
paper is our attempt to do precisely that.
7
4 Methodology
This section provides a detailed description of our methodology towards identifying
time-respecting cycles to detect money
ow.
4.1 Cycle Detection
The standard cycle detection methods do not consider the temporal aspects. Thus, to
nd time-respecting cycle to detect money
ow, there is a need to modify the cycle
detection method. In a temporal cycle the order of the edges in a temporal path
is restricted by the time in which they occur and can have only one starting node.
However, in standard cycle detection methods an edge can be involved in more than
one cycle. Thus, it is essential to identify all such cases in which two or more cycles
contain common edge/edges. Figure 1 shows an example of such cases and depicts
four di erent temporal graphs where an edge Ati:x ! Bin the graph represents that
a user Ahas transferred xcontains the other details such as amount transferred, gas
price incurred. For now for simplicity, consider for xto be amount of cryptocurrency
transferred to user Bat time ti. Our method is based on following special cases.
1. Figure 1a shows two temporal cycles ( a)At1:15 ! Bt3:25 ! Ct4:20 ! Aand ( b)
At2:10 ! Bt3:25 ! Ct4:20 ! Ain the graph where the edges Bt3:25 ! Cand
Ct4:20 ! Aare common in both the temporal cycles. All the incoming edges
from node A to node B have less timestamp than outgoing edges from B in
the temporal cycle. So instead of considering two di erent edges from node A
to node B, we merge both the edges from node A to node B into one edge, in
this case, At1;t2:25 ! Bt3:25 ! Ct4:20 ! A. Here, we add other information like
amount of cryptocurrency transferred via both the edges. Figure 1b shows the
graph after our modi cation.
2. Figure 1c, although di erent, again shows two temporal cycles ( a)At1:20 !
Bt3:20 ! Dt4:20 ! Aand ( b)Ct2:20 ! Bt3:20 ! Dt4:20 ! Cin the graph. Here,
one temporal cycle starts from node A and the other from node C. But the
timestamp of the edge from node A is less than the edge from node C. We
give the edge from node with a lower timestamp a higher priority because that
edge (transaction) has happened earlier in the blockchain. If the timestamp of
two edges is equal, then the edge that transfers higher cryptocurrency or pays
higher gas price gets higher priority. Thus, in the example, the temporal cycle
originating from node A gets priority.
3. Further, Figure 1d shows two temporal cycles ( a)At1:50 ! Bt2:20 ! Ct4:20 ! A
and ( b)At1:50 ! Bt3:30 ! Dt5:30 ! Ain the graph. Edge At1:50 ! Bis common in
both the cycles. If we consider up to timestamp t4, money recovered at account
A (incoming money in the temporal cyclic path with respect to outgoing money)
is only 20 cryptocurrency. However, when considering t5, the remaining money
is recovered by account A. Thus, we consider both the temporal cycles. Note
that this also depends on the data we have.
Figure 2 shows the contact sequence of the graph present in Figure 1e. Table 1
shows all the 6 temporal cycles present in this graph that meet our criteria and the
remaining 3 cycles after the application of our modi cations mentioned above. The 3
8
(a) Example Graph
(b) Graph after applying rst modi cation
(c) Example Graph considered for second mod-
i cation
(d) Example Graph considered for third mod-
i cation
(e) Example Graph towards all modi cations
Figure 1: Cases considered for modi cations
9
Figure 2: Contact Sequence of graph represented in Figure 1e.
#All Temporal Cycles Temporal Cycles After Modi cations
1At1:100 !Ct4:50 !Dt5:40 !A
At1:100 !Ct4;t6:110 !Dt5;t7:70 !A 2At1:100 !Ct4:50 !Dt7:30 !A
3At1:100 !Ct6:60 !Dt7:30 !A
4At1:100 !Ct7:60 !Bt9:40 !AAt6:50 !Ct7:60 !Bt9:40 !A5At6:50 !Ct7:60 !Bt9:40 !A
6Dt5:50 !Et6:60 !D Dt5:50 !Et6:60 !D
Table 1: List of Cycles before and after applying our modi cations
temporal cycles start at 3 distinct timestamps ( a)At1:100 ! C, (b)At6:50 ! C, and ( c)
Dt5:50 ! E. From special case 2, the temporal cycles starting from edge At1:100 ! C
gets highest priority while the temporal cycles starting from edge At6:50 ! Cgets the
least priority. Next at node C, there are three outgoing edges but considering case 3
only the edge Ct4:50 ! Dgets priority. Similarly, now at node D, edge from node D
to node A gets priority. The reason for not considering the other edge Dt5:50 ! Ein
the temporal cyclic path starting from A is due to our assumption that no other node
except starting node is allowed to repeat more than once in a cycle. Using special
case 1, edges from node C to node D and from node D to node A get merged. So
the rst three temporal cycles results in only one temporal cycle (cf. Table 1). After
applying the di erent cases, the fourth cycle becomes invalid because we do not allow
the repetition of edges in any temporal cycles.
As another example, in Figure 3a, without using our modi cations, there are, in
total, 27 cycles, but after applying the rst modi cation, the total number of temporal
cycles reduce to only 1. Total money transferred along the temporal cycle after ap-
plying the modi cations is equal to the total money (30 cryptocurrencies) transferred
before applying our modi cations. Thus, using our modi cations, there is no loss in
the money. Similarly, in Figure 3b, node A and node B transact with each other.
Here, the total number of temporal cycles is 9 (5 starting from node A and 4 starting
from node B). After applying the second and the third modi cation, only 3 temporal
cycles remain.
10
Algorithm 1 Temporal Cycle Detection Method
1:Input
2:Blockchain's Transaction Graph ( G (V;E))
3:visVertex fTwo states: visited and unvisited g
4:visEdge fThree states: nally visited, partially visited and unvisited g
5:
6:orderedEdges
7:foraccount2startingAccounts do
8: foredge2G[account ]do
9: orderedEdges.append( <edge> )
10:orderedEdges.sort() #based on <blocknumber;valueTransferred;gasPaid>
11:foredge2orderedEdges do
12:previousPath fg
13: AllCycles(previousPath.append( <edge> ))
14:
15:procedure AllCycles (previousPath= St0:x0 !v0t1:x1 !v1t2:x2 !
v2tk:xk !vk)
16:currAcc vk
17:currBlockNo tk
18:flag 0
19: ifS== currAcc then
20: OUTPUT previousPath.append <(currAcc;S;t )>
21: Mark all the edges involved in this cycle as nally visited
22: return True
23:outGoingEdges f(currAcc;v;t )2EandcurrBlockNo<t g
24: outValue 0
25: fortemp2outGoingEdges do
26:
ag1 0
27: tmin temp.blockNumber
28: if tmin>currBlockNo and visEdge[temp] and visVer-
tex[temp.to()] are unvisited then
29: ifoutValue<inValue then
30: visEdge[temp] Mark partially visited
31: visVertex[currAcc] Mark Visited
32:
ag1 AllCycles(previousPath. <(currAcc;temp;t min)>)
33: if
ag1==True then
34:
ag True
35: outValue+=temp.value
36: visVertex[currAcc] Mark unvisited
37: returnflag ==True
11
(a) Example 1
(b) Example 2
Figure 3: Examples to show that how modi cations can be applied for reducing
the number of temporal cycles
4.2 Our Algorithm for nding Temporal Cycles
A depth- rst search (DFS) algorithm is a well-known algorithm used to detect cycles
in a static graph. For each connected component, DFS produces a DFS tree. A cycle
is present in the graph if there is a back edge (an edge that joins a node to itself or one
of its ancestors in the DFS tree). We use a modi ed DFS algorithm based on the cases
described above to nd temporal cycles in the graph (cf. Algorithm 1). Following are
the inputs to our algorithm:
(a) The transactions graph G(V; E) represents graph generated using the trans-
actions between nodes over time. Here, Vis the set of user accounts in the
blockchain. Eis the set of directed transactions between 2 accounts in V. Note
that these transactions are temporal. We use the block number to represent the
time at which the transaction occur. An edge (or a transaction) in E, besides
timestamp, also contains information related to the transaction such as value
(money) transferred, gas paid for the transaction, and whether it is an internal
transaction or not.
(b) Consider startingAccounts to be a set of all malicious accounts. From S2
startingAccounts our temporal cycles starts because we aim to analyze di erent
tagged malicious activities.
(c) We use a depth- rst search (DFS) based recursive approach starting from node
S2startingAccounts to nd cycles. A path to a node vkfrom Sis called
thepreviousPath . The previousPath could be understood as a fragment of
a potential cycle. Note that vkrepresents current account from which next
transactions is to be explored for next edge in the temporal path. An example
ofpreviousPath isSt0:x0 ! v0t1:x1 ! v1t2:x2 ! v2tk:xk ! vk.
(e) There are three types of edges, ( i) visited edges (edges that belong to a cycle),
(ii) contenders (edges that are in a temporal path which may evolve to a cycle),
and ( iii) not visited edges (edges that are not yet considered). visEdge mark the
set of edges that already belong to all the temporal cycles that were previously
explored. Edges that are involved in the previousPath are the contenders.
visEdge is used to know if an edge has already occurred in any of the detected
temporal cycles.
12
(f) Similar to visEdge ,visV ertex is used to know whether a vertex is already visited
or not for a particular cycle. Note that a vertex can be in multiple cycles. Thus,
visV ertex is local to each cycle.
(e) We sort all the transactions from all S2startingAccounts and store them in
theorderedEdges . First sorting is based on the timestamps (at which the trans-
actions occur), the lower timestamp of a transaction higher priority it gets. If
timestamps are same for two transactions, then we sort based on higher cryp-
tocurrency transferred. If the cryptocurrency transferred is also same for the
two transactions, then we prioritize the transaction based on other attributes in
xsuch as gas price (transaction that pays more gas).
Our recursive approach returns all the temporal cycles. We initialize the previousPath
with the edge from which temporal cycles start (an edge from orderedEdges ) and node
S2startingAccount . During the recursive exploration of the temporal graph in the
depth- rst search manner, starting from the last visited account in the previous path,
there is a temporal cycle if the path ends at S. At the current node, we explore
all outgoing edges in sorted order (cf. Algorithm 1 line 25), which respect temporal
aspects of the temporal path de ned in previousPath . At each step, if the sum of
transacted money (value transferred in a transaction) in the outgoing edges involved
in the temporal cycle is greater than incoming money at that node from the incom-
ing edge (cf. Algorithm 1 line 29), then further exploration of the outgoing edges at
that node is stopped and the node is marked visited (cf. Algorithm 1 lines 31-32).
Not allowing further exploration is based on special case 3 because once we trail the
complete money for an incoming transaction at any node, we stop to trail that money
further in the graph. When a cycle is detected (cf. Algorithm 1 lines 20-22), all the
edges involved in the temporal cycle are marked as visited (one edge is allowed to
appear in only one temporal cycle). A visited edge during exploration of a particular
temporal cycle is not revisited. The procedure AllCycles returns true if there is a
temporal cycle, else it returns false. Using the procedure and steps de ned above, we
list all the valid temporal cycles based on our modi cations.
Our modi cations does not change the functional approach of the DFS algorithm.
As shown in [24, 23], the DFS-based approach is valid for nding temporal cycles
of n-hop length where n2N. The time complexity of our algorithm is dependent
on the number of temporal cycles in the temporal graph. The time complexity of
the DFS approach is O(jVj+jEj). Thus, the time complexity of our approach is
O(C(jVj+jEj)), where Cis the number of valid temporal cycles present in the
transaction graph.
5 Evaluation
This section rst presents the details of the data we collect and the pre-processing we
perform. We then provide a detailed analysis of our results based on our methodology.
5.1 DataSet
There are two most prominent cryptocurrency-based permissionless blockchains: Bit-
coin [28] and Ethereum [16]. We choose Ethereum blockchain transaction data because
it is more rich and diverse. Etherscan [17] makes available the information about ac-
counts and transactions categorised into di erent illicit activities.
13
Figure 4: Sample Internal Transaction in Ethereum
Ethereum has two types of accounts Externally Owned Accounts (EOA)s and
Smart Contracts (SC)s. SCs get executed on Ethereum Virtual Machine (EVM). The
transactions between two SCs (also called internal transactions) are not stored on
the ledger, but they can be inferred using EVM. Transactions between two EOAs
and EOAs and SCs are stored on the ledger of the Ethereum blockchain and are
called external transactions. These transactions are publicly available using Etherscan
APIs [17]. We, thus, use the Etherscan APIs to download the transactions. Both
the types of transactions have a di erent internal JSON structure. For our analysis,
we consider all the internal and external transactions of the chosen accounts from
the genesis block (block number 0) to block number 10747845 (generated on date:
28/08/2020).
5.1.1 Transaction Structure
As described before, the two types of transactions have di erent structures. A sample
internal transaction shown in Figure 4 while a sample external transaction is shown
in Figure 5. We now describe the terminology used in these types of transactions and
only those that help us understand the money trails.
•from: hash of the sender address of the transaction.
•to:hash of the receiver address of the transaction.
•hash: transaction hash.
•blockHash: hash of the block in which transaction appears.
•blockNumber: it is the block to which transactions appears.
•timeStamp: milliseconds after epoch when the block was generated.
•value: It is the amount of cryptocurrency transferred in Wie. For Ethereum,
1018Wei equals 1 Ether.
•contractAddress: hash of a contract address. It is not null only if the trans-
action is a contract create transaction.
14
Figure 5: Sample External Transaction in Ethereum
•gas: The maximum amount of gas units that the transaction can consume.
Units of gas represent computational steps.
•gasUsed: The amount of gas used for a transaction.
•gasPrice: the price of Gas in Gwei. For Ethereum, 109Gwei equals 1 Ether.
•isError/txreceipt status: True if transaction is unsuccessful else false.
5.1.2 Dataset Statistics
Some of the most common malicious activities involved in the blockchain are Gambling,
Phishing, and Money Laundering. These activities have resulted in a sizable losses
of cryptocurrency over time in the blockchain. With the increasing popularity of
blockchain, adding new dimensions to the analysis of these activities in the blockchain
is necessary. Also, in Ethereum, these activities have enough tagged accounts and
transactions to understand their behavior in terms of money trails.
Table 2 details the statistics about the transaction dataset we extract from Ethereum
using Etherscan APIs [17] for our methodology validation. In the Table 2 we present
two types of statistics. One, representing that which shows the statistics where we
present the EOAs, SCs, and transactions between accounts of speci ed malicious ac-
tivity. In the second, we present statistics where we present the EOAs, SCs, and
transactions between the accounts of speci ed malicious activities and their one-hop
neighbors. Note that here the one-hop neighbors include benign accounts as well. Our
results presented next consider these two types. For simplicity, from here, we refer
to these two types as type-A and type-B and demonstrate the results obtained using
transactions involved in these two types.
In [3], the authors used more (such as Heist, Scamming) malicious activities other
than those we consider. Other than Scamming malicious activities, all other malicious
activities have fewer tagged accounts and transactions. We categorize these activities
as one and under the class \other" malicious activities. Most of the scamming-related
accounts are also tagged as Phishing in Ethereum. Thus, we consider these accounts
15
Table 2: Statistics
including one-hop Total
Type of Activity benign neighbors EOAs SCs Transactions
Gambling7 4 38 930
3 33158 67289 5942672
Phishing7 4076 693 1422
3 24527 5893 1108836
Money Laundering7 813 2 2157
3 11728 3021 256113
only once under Phishing. For \other" malicious activities (total 315 accounts), we
have used data only to analyze whether di erent types of malicious activities transfer
money in the temporal cyclic path with each other or not. Thus, we have not included
the information for other malicious activities in Table 2.
5.2 Data Pre-processing
In our experiments, we use only successful internal and external transactions. From
the internal and external transaction data, we construct a graph that contains the
details such as ( a) from, ( b) to, ( c) block number, ( d) isInternalTransaction: true
if the transaction is internal else false, ( e) value, ( f) gas, and ( g) gas used. We use
block numbers to represent timestamp because we do not have the exact time when
the transaction happened. We remove the transactions which transfer 0 Eth (Cryp-
tocurrency used by Ethereum). Transactions that have 0 Eth transferred can give
some important insights. But in our work, we have not used such transactions be-
cause these transactions result in 100% money loss, but we aim to nd the suspicious
cycles with very little loss along the path. In the future, these transactions also can be
included. Each transaction represents a directed temporal edge in the graph, except
if account A sends money to an account B multiple times in the same block, then we
merge these transactions into a single transaction. As shown in Figure 6a, account A
transacts with account B two times in the same block represented by block number
9272415. Thus, we merge both of these transactions into one transaction (cf. Fig-
ure 6b). With the computational resources currently available to us, it is impossible
to perform experiments on the whole Ethereum blockchain for path-based analysis of
the graph due to the large size of the Ethereum blockchain. We perform our experi-
ments on the tagged malicious accounts (particularly Phishing, Gambling and Money
laundering). One type of Money laundering activity in the Ethereum blockchain is the
activity (commonly called as UpBit hack) that involved the UpBit exchange. There
are speculations that some insider performed malicious activity during the movement
of money from a hot to a cold wallet [20]). In this work, we use accounts tagged as
\Upbit Hack" in the Ethereum blockchain as Money laundering accounts.
5.3 Results
This section presents results obtained. The presented results pertain to when accounts
involved in di erent types of malicious activities: Gambling, Phishing, Money Laun-
dering, other malicious activities, as well as when considering all malicious activities
under one class.
16
(a) More than one transaction between
two accounts in the same block
(b) Merged Transactions of Same Block
Figure 6: Processing of transactions happening between two accounts in the
same block
5.3.1 Gambling
We rst apply our methodology to accounts labeled under the Gambling category by
Etherscan [17]. As a rst step, we generate the graph from these accounts and their
transactions. Here, we consider only those transactions which happen between only
Gambling accounts. As it is usually done, for cryptocurrency also, we assume that
accounts associated with Gambling activity put their money in a depository (or an
exchange in terms of blockchain) for betting/lottery. Upon win, the account gets back
the funds assured (for example, people Gambling in casinos [34]). Using our approach,
we nd that out of the 42 marked Gambling accounts (4 EOAs and 38 SCs), only
2 SCs have temporal cycles, that too cycles involving only themselves. We do not
disclose the addresses of these accounts due to privacy reasons. However, one of these
accounts is an exchange. We nd that the total number of temporal cycles is 136958,
if we do not apply our modi cations de ned in Section 4. After the application of
our modi cations, the total number of temporal cycles reduces to 346. All of these
temporal cycles are of two-hop length. Note that two-hop length does not mean that
the transactions happen in two consecutive time instances. The inter-event time (time
between the two transactions) can be >1. The two accounts involved in the temporal
cycles transact with each other regularly. All the transactions involved in the temporal
cycles are internal transactions as the transactions happen between two SCs. From
these results, it is clear that most Gambling accounts are not transacting with each
other for Gambling activities. However, the accounts involved in the temporal cycle
follow a traditional gambling approach.
Next, we add more accounts to extract more insights and know whether our as-
sumptions hold when accounts not marked as Gambling are added. Here, we add those
accounts to the graph that are not marked as Gambling but have received funds from
the marked Gambling accounts, i.e., Gambling accounts have an out edge to these
accounts. The added accounts have high cosine similarity (calculated using features
de ned in [3]), more than 0.99, with the tagged Gambling accounts. The number of
temporal cycles does not change after adding these accounts and their transactions
(limited to considered accounts). Next, we add the neighbors (neighbors of neighbors
of Gambling accounts that we added in the last step). We repeat adding neighbor
accounts to the graph four times. But, in all the steps, the number of temporal cycles
does not change. It means that most Gambling accounts do not show temporal cyclic
17
(a) Example 1
(b) Example 2
Figure 7: Suspicious Temporal Cycles in the Gambling
behavior with other Gambling accounts or accounts transacting directly to marked
Gambling accounts.
To identify the largest cycle, we add all the accounts (and their transactions) that
have a path from the Gambling accounts into our graph. We nd that the number of
temporal cycles increases many-fold ( >1014). We nd that 6 accounts contribute the
most number of temporal cycles in Gambling. With the limited computing resources
we have had at our disposal, we were not able to extract the exact number of cycles
from these 6 Gambling accounts. For the remaining 36 Gambling accounts, we nd that
the total number of cycles is 1313104, and the maximum hop length of the temporal
cyclic path is 23. We nd that these 36 accounts are involved in temporal cycles that
do not include exchange accounts. These results show that the Gambling accounts do
not transfer money in a temporal cyclic path to other Gambling accounts while the
Gambling accounts have money trails with other non-gambling accounts. We nd few
suspicious temporal cyclic path-based money transfers (cf. Figure 7). In Figure 7,
we represent a node by the last four characters of the account address hash and an
edge by the key-value pair where the key is the block number in which the transaction
appears. The value represents the amount of Ether transferred via the transaction.
Money loss along the temporal cycles present in Figure 7 is negative. This type of
behavior indicates that there is a possibility that the account won the bet/lottery. Our
approach gives all of the temporal cycles for a given threshold of maximum money loss
allowed along the cyclic path. For money loss >10%, we nd that the number of
temporal cycles does not increase signi cantly, while the number of cycles with money
loss<10% is signi cantly high in the Gambling.
5.3.2 Phishing
We now apply our methodology to accounts labeled under Phishing. There are a
total of 4769 Phishing tagged accounts (4076 EOAs and 693 SCs). We nd that a
total of 103 accounts are involved in the time respecting cyclic transfers. There are
55 accounts from which a minimum of one cycle starts. Most Phishing accounts have
very few transactions. This limits the chances of them being present in a temporal
cycle. There are a total of 1682 cycles in the graph before applying our modi cations.
After the application of our modi cations, the total number of temporal cycles reduce
to 164. Here, most of the cycles are of two-hop. The maximum hop-length of a cycle
obtained is 3. We nd that some of these transfers show suspicious behavior. As
shown in Figure 8, money loss along the cyclic path is less than 10%.
Then, similar to the analysis of Gambling accounts, we add out-neighbors (accounts
to which Phishing accounts send funds) of Phishing accounts and have high similarities
(>0:99) with them. We observe that the number of cycles does not change. After
that, we add more accounts to get more insights and know whether the results are
18
(a) Example 1
(b) Example 2
Figure 8: Some of the Suspicious Temporal Cycles in the Phishing
Table 3: Results
Activity Gambling Phishing Money Laundering
Considering type-A type-B type-A type-B type-A type-B
No. of cycles 346 131304 164 4315 40 90
Max. Hop Length 2 23 3 20 6 6
No of cycles with337 50108 128 1945 8 42( 10%) loss
No. of unique accounts2 4305 103 3196 69 150involved in all cycles
consistent when the graph size increases. Irrespective of the similarity, we add all
the out-neighbors of the tagged Phishing accounts. We nd >108temporal cycles, a
signi cantly high number when our modi cations are not applied. However, only 4315
cycles remain when we apply our modi cations. In these 4315 cycles, the maximum
hop-length is 20. Figure 8a shows one suspicious cycle detected in the analysis of
Phishing accounts. Here, the money loss along the cyclic path is negative, meaning
Phishing accounts have received funds. Also, 1945 temporal cycles have less than 10%
money loss.
5.3.3 Money Laundering
Next, we study money laundering-based accounts. As it traditionally happens, for
cryptocurrency-based blockchains, we assume that accounts involved in money laun-
dering will produce more cycles than those involved in Gambling or Phishing. For
a special case of Money laundering, depicted by accounts related to Upbit exchange,
there are 815 tagged accounts. Here, out of these 815 accounts, only two accounts are
SCs. We extract both internal and external transactions for these accounts. However,
all the internal transactions have an error
ag `=True' (meaning the transaction is un-
successful). Thus, we do not consider these transactions. Most of these accounts have
very few transactions (3 to 5 transactions only). Thus, the chances of these accounts
appearing in a temporal path-based cyclic path is less.
As before, we rst analyze the transactions between only the tagged accounts. We
nd that there are a total of 83 temporal cycles when we do not apply our modi -
cations. After the application of our modi cations, only 40 temporal cycles remain.
19
(a) Example 1
(b) Example 2
(c) Example 3
Figure 9: Some of the Suspicious Temporal Cycles in the Money Laundering
Here, 69 unique tagged accounts are involved in these temporal cycles. The maxi-
mum hop length in these temporal cyclic paths is 6. It means money laundering-based
accounts are more actively involved in the cyclic temporal path-based money trans-
fers. Thereby validating that money laundering based accounts transact in cycles in
cryptocurrency-based blockchain as well. Figure 9 shows a suspicious temporal cyclic
path-based transfer. As before, the node name represents the last four characters of
the tagged malicious accounts in the Ethereum blockchain. Loss of the money along
the temporal cyclic paths in Figure 9a is signi cantly less. In Figure 9a, the completion
time for cyclic transfer is 15 (starting block number is 9066502 while the ending block
number is 9066487) blocks. This is equivalent to 3min in the Ethereum blockchain.
Money transfer along the temporal cyclic path in a short span indicates illicit activity.
But the money lost in Figure9b is high. Note that our graph is only limited to Money
laundering accounts. It is quite possible that adding all of the neighbors and their
neighbors continuously until no new neighbor is found will result in the recovery of
the whole amount in the cyclic path-based transfer. In Figure 9b, despite high money
loss except for the rst transaction, all other transactions have very little loss of money
along the path. One of the reasons for these types of cycles is the limited data set. We
add all the out-neighbors of the money laundering accounts into our graph and their
corresponding transactions to get more consistent results. If we consider our modi ca-
tions, the number of cycles we get is only 90. The maximum number of hops present
in these cycles is 6. The number of cycles has not increased signi cantly, but the cy-
cles with very less loss (less than 10% money loss) during cyclic temporal path-based
transfer increased. The temporal cycles in which high loss occurs indicate suspicious
activity. Figure 9c shows one of the detected suspicious temporal cyclic path-based
transfer in money laundering after adding one-hop neighbors.
In short, our results are summarized in Tables 3 and 4. Table 3 provides results for
20
Table 4: Distribution of cycles concerning Hop-length obtained using transac-
tions involved in the two types de ned previously.
Gambling Phishing Money Laundering
Hop-Length type-A type-B type-A type-B type-A type-B
2 346 113136 156 3812 29 54
3 0 4718 8 246 6 14
4 0 12087 0 135 2 5
5 0 826 0 40 2 16
6 0 334 0 23 1 1
7 0 81 0 27 0 0
8 0 63 0 11 0 0
9 0 8 0 4 0 0
10 0 17 0 2 0 0
11 0 8 0 5 0 0
12 0 6 0 5 0 0
13 0 9 0 1 0 0
14 0 0 0 0 0 0
15 0 0 0 2 0 0
16 0 0 0 1 0 0
17 0 8 0 0 0 0
18 0 1 0 0 0 0
19 0 0 0 0 0 0
20 0 0 0 1 0 0
23 0 2 0 0 0 0
the number of valid temporal cycles, maximum hop-lengths in the cycles found using
our approach for a particular malicious activity, and the number of temporal cycles
in which money loss along the temporal cyclic path is less than 10%. While Table 3
lists the number of accounts that are involved in any valid temporal cycles. Table 4
presents an exact number of cycles for valid temporal cycles of di erent hop-lengths
as found using our approach for the di erent malicious activities.
5.3.4 Other Activities
We also apply our approach to other malicious activities. We nd that 113 out of 214
scamming accounts are tagged as Phishing accounts. We nd that these scamming
accounts are not involved in any cyclic path-based money transfer. Even after adding
one-hop neighbor accounts, which are tagged both as scamming and Phishing, we do
not nd any temporal cycles originating except from two EOAs. Thus, it is clear that
the accounts that are tagged both as Phishing and scamming behave di erently from
the other Phishing accounts and that the Phishing accounts do cluster themselves into
more than one cluster.
5.3.5 Combined Graph
Next, we combine accounts of all the malicious activities (including \other activities")
to know whether malicious activities in the blockchain do cyclic path-based money
21
transfers with other types of malicious activities. We nd that only one Gambling
account is involved in a cyclic transfer with two Phishing accounts. But the Gambling
account involved in the cyclic transfer is an exchange account. Accounts involved in
malicious activities in the Ethereum blockchain do not transfer cryptocurrency in a
cyclic path to accounts involved in other malicious activities. Also, we found that
\other" malicious activities have no cyclic path-based money transfer with themselves
except for 3 accounts. Thus these "other" activities also cluster themselves into a
di erent cluster.
From our results, it is clear that money laundering-based malicious activities are
di erent from other malicious activities. Phishing-based accounts do not cluster into
one cluster. Phishing accounts cluster themselves into a minimum of two di erent
clusters (accounts also involved in scamming do cluster into di erent clusters than the
accounts that are only involved in Phishing activity). Also, most of the Gambling
accounts do not transfer money in a cyclic path with themselves. After adding more
transactions into validation data, we nd that Gambling accounts are transacting with
neighbors regularly. Also, Gambling and Phishing-based accounts that are not involved
in scamming show a similar behavior to Phishing accounts after adding neighbor ac-
counts. Both Gambling and Phishing activities transfer money using temporal cycles
having larger hop counts, and the number of temporal cycles also increases. With the
availability of limited computing resources, we could not validate our methodology on
a larger dataset. Nonetheless, our approach can be extended to a larger dataset where
more computation power is available.
From our experiments, it is clear that most malicious activities can be clustered
into four clusters. One cluster has money laundering-based activity, another cluster
has Phishing and Gambling-based activities, the third cluster contains scamming and
Phishing-based activity, and the fourth cluster contains "other" malicious activities.
Using Neural Networks, in [3], the authors showed that most of the marked malicious
activities in the Ethereum blockchain clusters into four di erent clusters, but they
have not given in-depth insights on why such type of similarities and di erences occur.
This work also obtains similar clustering indications by exploring a hidden aspects in
the blockchains transaction network, i.e., money-trails.
6 Conclusion
With the evolution of blockchain 2.0 and beyond, interests in blockchain technology
is on a rapid rise. New use cases (such as healthcare for making a secure system for
health records) of blockchain have created many applications worldwide that neither
support any cryptocurrency nor crypto-tokens. As more and more solutions based on
blockchain technology are introduced, we nd more cases of exploitation of the tech-
nology and its users. Most of these exploit previously unknown vulnerabilities in the
technology stack or gullibility of the users to fall for social engineering attacks. Such
attacks result in substantial disruptions and losses of digital assets. In permission-less
public blockchains such as cryptocurrency blockchains, the losses amount to monetary
loss. Over the last few years, various state-of-the-art approaches have been proposed
to counter attacks and detect suspicious behavior in the blockchain. Most of these
approaches are ML-based and do not distinguish between multiple classes of malicious
activities and club them together under one class. While others focus only on speci c
kind of malicious activity. Further, these techniques do not track the cryptocurrency
ow.
22
In this work, we use a 'track the money trail' approach for collecting additional
behavioral information regarding di erent malicious activities. We use temporal trans-
actions network for nding temporal cycles to track the money
ow along these tem-
poral cyclic paths. Based on the temporal cyclic path and money loss results along the
path, we found that the considered malicious activities are clustered into four clusters.
Also, we found that accounts involved in the Phishing activities cluster themselves
into more than one cluster. We found that accounts involved in both Phishing and
scamming cluster into di erent clusters than other accounts involved in Phishing only.
Thus, it bears out that suspicious cyclic path-based money transfers in cryptocurrency
blockchains can distinguish suspicious accounts of various types of suspicious activities.
Illicit activity detection in a blockchain is challenging yet much necessary. There
is a demand for forensics that involves nding suspicious activities and attribution
from law enforcement agencies, regulators, and regular participants in cryptocurrency
blockchains. In future, we aim to integrate our approach with the machine learning
approaches and use the properties of these money-trail cycles as features. This may
help to detect suspicious activities with high accuracy. Also, parallel computing can
be used to reduce computation time for the cycle identi cation algorithms. We have
used only EOAs and SCs based transactions; in the future, the token transfers paths
and cycles may also be used to get more insights through our approach.
List of Abbreviations
Acronym Meaning
EOA Externally Owned Accounts
SC Smart Contracts
Blockchain Permission-less Blockchain
ICO Initial Coin O erings
DFS Depth First Search
FH First Hop Outgoing Neighbours of an Account in the Transaction Graph
ML Machine Learning
NN Neural Network
SVM Support Vector Machine
CCG Contract Creation Graph
CIG Contract Invocation Graph
MFG Money Flow Graph
ATH Attributed temporal Heterogeneous Motif
AAIN Homogeneous Address-Address Interaction Network
TAIN Heterogeneous Transaction-Address Interaction Network
Table 5: List of Acronyms.
Acknowledgements
This work is partially funded by the National Blockchain Project at IIT Kanpur spon-
sored by the National Cyber Security Coordinator's oce of the Government of India
23
and partially by the C3i Center funding from the Science and Engineering Research
Board of the Government of India.
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26
|
{
"id": "2108.11818"
}
|
2501.10378
|
The Societal Implications of Blockchain Technology in the Evolution of
Humanity as a "Superorganism"
|
This article examines the broader societal implications of blockchain
technology and crypto-assets, emphasizing their role in the evolution of
humanity as a "superorganism" with decentralized, self-regulating systems.
Drawing on interdisciplinary concepts such as Nate Hagens' "superorganism" idea
and Francis Heylighen's "global brain" theory, the paper contextualizes
blockchain technology within the ongoing evolution of governance systems and
global systems such as the financial system. Blockchain's decentralized nature,
in conjunction with advancements like artificial intelligence and decentralized
autonomous organizations (DAOs), could transform traditional financial,
economic, and governance structures by enabling the emergence of collective
distributed decision-making and global coordination. In parallel, the article
aligns blockchain's impact with developmental theories such as Spiral Dynamics.
This framework is used to illustrate blockchain's potential to foster societal
growth beyond hierarchical models, promoting a shift from centralized authority
to collaborative and self-governed communities. The analysis provides a
holistic view of blockchain as more than an economic tool, positioning it as a
catalyst for the evolution of society into a mature, interconnected global
planetary organism.
|
http://arxiv.org/pdf/2501.10378v1
|
Martin Schmalzried
|
cs.CY, cs.CR
|
cs.CY
|
1
The Societal Implications of Blockchain Technology in
the Evolution of Humanity as a “Superorganism”
Martin Schmalzried
martin.schmalzried@ucdconnect.ie
Abstract
This article examines the broader societal implications of blockchain technology and
crypto -assets, emphasizing their role in the evolution of humanity as a "superorganism"
with decentralized, self -regulating systems. Drawing on interdisciplinary concepts such
as Nate Hagens' "superorganism" idea and Francis Heylighen's "global brain" theory, the
paper contextualizes blockchain technology within the ongoing evolution of governance
systems and global systems such as the financial system. Blockchain's decentr alized
nature, in conjunction with advancements like artificial intelligence and decentralized
autonomous organizations (DAOs), could transform traditional financial, economic, and
governance structures by enabling the emergence of collective distributed decision -
making and global coordination.
In parallel, the article aligns blockchain’s impact with developmental theories such as
Spiral Dynamics. This framework is used to illustrate blockchain’s potential to foster
societal growth beyond hierarchical models, promoting a shift from centralized
authority to collaborative and self -governed communities. The analysis provides a
holistic view of blockchain as more than an economic tool, positioning it as a catalyst for the evolution of society into a mature, interconnected global planetary organism.
2
1. Introduction
The emergence of blockchain technology and crypto -assets, particularly Bitcoin, has
sparked significant interest and debate regarding their potential impact on society,
economics, finance and governance. Since the introduction of Bitcoin by the
pseudonymous Satoshi Nakamoto (Nakamoto, 2008) , blockchain has been utilized for
its ability to enable decentralized, transparent, and secure transactions without the
need for intermediaries. This technological innovation carries the promise of
revolutioniz ing various sectors, including finance, supply chain management, and even
voting systems (Iansiti & Lakhani, 2017) all the while sparking many controversies
including security risks, fraud and scams (Kerr et al., 2023) .
Despite the growing body of research on blockchain and crypto -assets, there is a
notable gap in framing these technologies within a broader narrative that encompasses their societal implications and evolutionary significance. The innovative approach of
this paper lies in contextualizing crypto -assets, blockchain, and Bitco in within the larger
narrative of humanity's development, drawing ontheories such as Nate Hagens'
concept of the human superorganism and Francis Heylighen's "global brain" hypothesis. By d oing so, the aim of this paper is to provide deeper meaning and
understanding of how these technolog ical developments can be understood in the
broader context of the development of humanity as a whole.
Nate Hagens (Hagens, 2020) describes humanity as a superorganism driven by
collective behaviors that lead to exponential growth and resource consumption. This
perspective highlights the challenges posed by the current trajectory of human development, which is characterized by a purs uit of economic growth often at the
expense of environmental sustainability and social equity. Heylighen's "global brain" concept (Heylighen & Lenartowicz, 2017) envisions the internet and associated
technologies as forming a collective intelligence that can process information and solve
problems at a global scale. In both cases, the prevalent idea is that of humanity forming
a global collective organism with its own sets of characteristics and features.
This paper explores how blockchain technology, in conjunction with other technological
advances such as artificial intelligence (AI) and the metaverse, can play a key role in
transitioning humanity from an unsustainable superorganism undergoing exponential
and uncontrollable growth into a mature global entity with balanced growth and
development (a balanced “global brain”) . This paper posit s that blockchain's
decentralized nature aligns with the evolutionary shift toward more distributed and autonomous syst ems of governance and economic exchange. This shift mirrors the
stages of human development outlined in Spiral Dynamics theory (Beck & Cowan, 1996), which describes the evolution of human consciousness and societal structures through distinct levels, each with its own value systems and worldviews.
3
Spiral Dynamics provides a framework for understanding the parallels between
individual human development stages —such as infancy, childhood, adolescence, and
adulthood —and the evolution of human societies. For instance, the transition from
egocentric (adol escent) stages to more collaborative and integrative (adult) stages
reflects a shift from self -centered behaviors to a greater emphasis on community and
systemic thinking. Applying this model, this paper examine s how current societal
structures are evolvin g and how technologies like blockchain fit within this broader
evolution .
Furthermore, this paper delves into the symbolic significance of Bitcoin and other crypto -assets in the context of financial systems and monetary policy , explor ing the
idea that Bitcoin represents a form of " fail safe mechanism " or “savings” , analogous to a
young adult earning their first income and seeking autonomy from parental control —in
this case, autonomy from centralized financial institutions and governmental bodies .
This analogy extends to the challenges and opportunities inherent in this transitio n,
including regulatory concerns, potential mismanagement, and the need for guidance
and support to ensure a smooth shift toward decentralized systems.
The paper also addresses the critical question of how existing monetary policies and
financial systems can adapt to the rise of crypto -assets by discuss ing the
incompatibility between a debt -based monetary system reliant on continuous
economic growth and a future where crypto -assets and stablecoins become
mainstream. In this context, this paper introduce s Stéphane Laborde's (2010) Relative
Theory of Money, which proposes a monetary system based on a universal dividend distributed equally among all participants, thereby eliminating the need for money
creation through debt and interest , which is part of the natural inflection points within a
developing organism in nature , transitioning out of exponential growth towards a
“plateau” or a stabilization in its development .
In short , this paper aims to provide a comprehensive analysis that frames blockchain
technology and crypto -assets within a broader narrative of human development and
societal evolution. By integrating interdisciplinary theories and drawing symbolic
parallels, this paper seek s to offer new insights into the potential pathways for a smooth
transition between existing systems and novel and emerging systems . This holistic
approach underlines the importance of viewing technological advancements not
merely as too ls for economic gain but as catalysts for deep societal transformation.
4
2. Theoretical f ramework
2.1 Nate Hagens' concept of the h uman superorganism
In his paper entitled “ Economics for the Future - Beyond the Superorganism ”, Nate
Hagens presents a narrative that frames humanity as a "superorganism, " a collective
entity driven by shared behaviors, values, and an insatiable appetite for growth and
resource consumption. This concept draws parallels between biological organisms and
human societies, sugg esting that just as individual cells operate within a larger
organism, individuals function within the broader context of society, contributing to collective outcomes that transce nd individual intentions.
Hagens argues that at this stage, the human superorganism is characterized by
unconscious patterns of exponential growth fueled by cultural narratives, technological
advancements, and economic systems that prioritize immediate gains over long -term
sustainability. This growth is often depicted as be ing out of control, leading to
environmental degradation, resource depletion, and social inequities. The
superorganism operates under the illusion of perpetual expansion, disregarding the
finite nature of Earth's resources. The recognition of humanity as a superorganism
emphasizes the need to understand the systemic drivers of our collective behavior.
Hagens suggests that addressing global challenges requires a shift from individualistic
perspectives to a holistic understandi ng of interconnected systems. This shift involves
re-evaluating economic models, governance structures, and societal values to foster a
sustainable balance between human activities and the Earth's ecological limits. In this
paper, blockchain and crypto -assets are part of this process of re -evaluation and shift.
2.2 Parallel concepts in collective intelligence and societal evolution
2.2. 1 Francis Heylighen's "Global Brain" h ypothesis
Francis Heylighen introduce d the concept of the "global brain, " a metaphor for the
collective intelligence emerging from the interconnectedness of individuals through technology, particularly the internet. The global brain represents a self -organizing
network where information and knowledge are shared and processed collectively, enhancing the problem -solving capabilities of humanity as a whole. Heylighen posits
that as communication technologies advance, they facilitate the development of a distributed cognitive system that mirrors th e neural networks of a biological brain. This
system allows for rapid dissemination of information, collaborative innovation, and
adaptive responses to global challenges. The global brain is seen as an evolutionary
step toward a more integrated and intelli gent society. The global brain hypothesis aligns
with the superorganism concept by emphasizing the collective aspects of human cognition and behavior. It suggests that technologies enabling decentralized communication and collaboration such as blockchain and crypto -assets can enhance
5
the efficiency and adaptability of the global superorganism , and are a part of this global
brain’s natural development .
2.2. 2 Ben Goertzel's v iews on a rtificial general intelligence and d ecentralization
Ben Goertzel explores the intersection of artificial intelligence (AI), collective
intelligence, and societal evolution (Goertzel, 2015) . He envisions the development of
Artificial General Intelligence (AGI) that can understand, learn, and apply knowledge in
a generalized way, much like a human being. Goertzel argues that AGI, integrated within
decentralized networks, can significantly enhance humanity's cognitive capabilities.
Goertzel emphasizes the importance of decentralization in AI development, advocating
for open, collaborative platforms that democratize access to AI technologies. He
suggests that decentralized AI systems can prevent the concentration of power and
promote a more equitable distribution of technological benefits. By integrating AGI into
a global brain framework, humanity can achieve higher levels of consciousness and
problem -solving abilities. Furthermore, Goertzel highlights the potential of blockchain
techn ology to support decentralized AI networks. Blockchain's secure, transparent, and
distributed ledger systems can facilitate the coordination and incentivization of
contributions to AI development. This synergy between AI and blockchain could
accelerate the evolution of the global brain, leading to more sophisticated forms of
collective intelligence.
2.3 Spiral dynamics theory applied to s ocietal and i ndividual development
To understand the parallels between individual human development and societal
evolution, this paper leverages Spiral Dynamics theory, developed by Don Edward Beck
and Christopher Cowan (Beck & Cowan, 2014) . Spiral Dynamics is a psychological and
sociological model that describes the evolution of human consciousness and cultural values through a series of stages, each characterized by distinct worldviews,
motivations, and social structures.
The stages, often represented by colors for ease of reference, include:
1. Beige (Survival/Safety): This foundational stage focuses on basic survival instincts
and physiological needs. Societies at this level are concerned with immediate
necessities, and individuals operate primarily on instincts.
2. Purple (Tribal/Animistic): Marked by a sense of community and mysticism, this stage
values tradition, rituals, and the guidance of ancestral spirits. Social structures revolve
around kinship and collective safety.
3. Red (Egocentric/Power Gods): Individuals assert themselves, seeking power,
dominance, and immediate gratification. Societies may be ruled by strong leaders or
warlords, emphasizing personal freedom without regard for others.
6
4. Blue (Absolutist/Mythic Order): Characterized by adherence to absolute truths, order,
and stability. Societies develop structured institutions, laws, and moral codes, often
underpinned by religious or ideological doctrines.
5. Orange (Achievist/Scientific Achievement): This stage values rationality,
individualism, and progress. Societies encourage competition, innovation, and the
pursuit of success through scientific and economic advancements.
6. Green (Communitarian/Egalitarian): Emphasizes community, relationships, and
social responsibility. Societies prioritize equality, environmentalism, and consensus -
driven decision -making.
7. Yellow (Integrative/Systemic): Individuals recognize the complexity of systems and
seek to integrate knowledge from various disciplines. Societies focus on flexibility,
sustainability, and holistic approaches to problem -solving.
8. Turquoise (Holistic/Globalist): Represents a global consciousness that transcends
individual and collective interests. There is an emphasis on unity, interconnectedness,
and the synergy of life systems.
Spiral Dynamics argues that individuals and societies can evolve through these stages,
though not necessarily in a linear fashion. Each stage builds upon the previous ones,
incorporating earlier values while expanding to encompass more complex perspectives.
2.4 Parallels between individual development, societal evolution, and spiral dynamics
To illustrate the fractal patterns in human and societal development, this paper
present s a comparative framework that aligns stages of individual human growth with
societal evolution, perceptions of authority, and the corresponding Spiral Dynamics
stages . This framework tends to show how individual psychological development
mirrors societal transformations, particularly in the context of governance and the
perception of authority.
7
Table 1. Comparative f ramework of individual and s ocietal development stages
Individual
development
stage Perception of
parents Societal
evolution
stage Perception of
authority Spiral Dynamics
stage Description
Infancy (0 –2
years) Parents perceived
as mysterious,
omnipotent beings;
the world is
magical and
incomprehensible Early Tribal
Societies Nature filled with
spirits and gods; reliance on
shamans and
mystical figures. Purple (Magical
Animistic) Focus on safety,
tradition, and
mysticism; strong
family bonds; reality
explained through
myths and magic.
Early Childhood
(3–6 years) Parents seen as
kings; admiration
and acceptance of
authority without
question. Formation of
Monarchies Kings and priests
revered as divine or
chosen by gods;
authority is
absolute. Blue
(Authoritarian
Absolutist) Emphasis on order,
rules, and obedience;
belief in one right way;
institutions maintain
stability.
Late Childhood
to Adolescence
(7–12 years) Beginning to
recognize parents' imperfections;
questioning authority; desire for
autonomy
increases. Rise of
Democracies ,
census suffrage Leaders elected
but no longer
revered; citizens
critique and demand
accountability. Orange
(Achievist
Strategic) Focus on individualism,
achievement, and
rationality; questioning
traditional authority; pursuit of success and
autonomy.
Adolescence
(13– 19 years) Rebellion against
parents; critical of
authority; seeking
independence
while still reliant on
support. Demand for
Rights and
Social
Movements ,
equal suffrage Citizens advocate
for civil rights and
increased
freedoms;
challenge existing
systems. Green
(Communitarian
Egalitarian) Emphasis on equality,
community, and social
responsibility;
challenging existing
systems for inclusivity.
Young
Adulthood (20 –
29 years) Establishing
independence; navigating
responsibilities;
redefining
relationship with
parents. Emergence of
Decentralized
Governance Development of
self -governing
communities; use
of blockchain and
DAOs. Yellow
(Integrative
Systemic) Systems thinking
emerges; recognition of complexity; seeking
sustainable solutions.
Mature
Adulthood (30+
years) Fully independent;
parents become advisors; individual
contributes
meaningfully to
society. Self -Governed
Societies Centralized
governments make
way for
autonomous
communities;
collaborative
problem -solving. Turquoise
(Holistic) Global consciousness
develops; holistic
understanding and
interconnectedness
emphasized.
2.5 Explanation of the f ramework
1. Infancy and e arly tribal societies
- Human level perspective: Infants perceive their parents as omnipotent and
mysterious entities who control their environment in incomprehensible ways
(Piaget, 1952) . Their world is filled with wonder and magic, with no clear
distinction between self and others.
- Societal parallel: Early human societies interact with nature through animism
and shamanism, believing in spirits and gods that influence their reality (Eliade,
8
2024) . Shamans serve as intermediaries between the physical world and the
spiritual realm.
- Spiral Dynamics s tage: Purple —Characterized by a magical -animistic worldview,
strong family bonds, and reliance on traditions and rituals.
2. Early Childhood and monarchies
- Human level perspective: Children view their parents as authoritative figures or
kings, accepting their decisions without question but recognizing some
similarities (Erikson, 1968) . They admire their parents and internalize rules.
- Societal parallel: Societies evolve into monarchies with kings perceived as
divinely appointed rulers. Authority is absolute, and obedience is expected (Tilly,
2017) .
- Spiral Dynamics s tage: Blue —Emphasizes order, conformity, and a strict
hierarchical structure. Laws and institutions are established to maintain stability.
3. Late c hildhood to a dolescence and d emocratic movements
- Human level perspective: As children grow, they begin to notice their parents'
imperfections and question authority. They seek greater autonomy and start
forming their own opinions (Vygotsky, 1978) .
- Societal parallel: The rise of representative democracy sees citizens no longer
viewing leaders as infallible. There is increased scrutiny, demand for accountability, and a push for individual right (Dahl, 2020) .
- Spiral Dynamics s tage: Orange —Focuses on individualism, rationality, and the
pursuit of personal success. Traditional authorities are questioned in favor of
meritocracy.
4. Adolescence and s ocial movements
- Human level perspective: Teenagers often rebel against parental control, seeking
independence while still relying on family support. They critique inconsistencies and advocate for personal freedoms (Steinberg, 2005) .
- Societal parallel: Social movements emerge, demanding civil rights, equality,
and greater participation in governance. Citizens protest against perceived
injustices and advocate for systemic change (Inglehart, 2020) .
- Spiral Dynamics s tage: Green —Values community, equality, and environmental
concerns. There is a focus on consensus -building and social responsibility.
5. Young a dulthood and decentralization
- Human lev el perspective: Young adults establish independence, manage
responsibilities, and redefine relationships with their parents as peers or
advisors (Arnett, 2000) .
9
- Societal parallel: The emergence of decentralized governance models, such as
blockchain technology and Decentralized Autonomous Organizations (DAOs),
reflects a shift toward self -governance and reduced reliance on centralized
authorities (Klaus, 2017) .
- Spiral Dynamics s tage: Yellow —Characterized by integrative thinking, flexibility,
and an appreciation for systemic interconnectedness. Solutions are sought that
are sustainable and adaptive.
6. Mature a dulthood and s elf-governed societies
- Human level perspective: Mature adults are fully independent and contribute
meaningfully to society. They often guide younger generations and collaborate
with others for common goals (Levinson, 1986) .
- Societal parallel: Societies evolve into self -governed communities where
whatever is left of centralized institutions such as governments facilitate rather
than control. There is collaboration between authorities and citizens for
problem -solving and a progressive shift to self -governing systems without any
centralized intermediary .
- Spiral Dynamics s tage: Turquoise —Embodies holistic thinking, global
consciousness, and an emphasis on the well -being of all life forms. Recognizes
the interconnectedness of systems.
10
3. Humanity's d evelopment as a f ractal pattern
The concept of fractal patterns suggests that structures and behaviors observed at one
scale can be mirrored at other scales, exhibiting self -similarity across different levels of
complexity (Cannon, 1984) . In the context of human development and societal
evolution, this implies that the stages an individual undergoes from conception to
maturity may be thought of as an unconscious basic template or pattern for the broader
developmental trajectory of human societies.
3.1 Symbolic parallels in finance and t echnology
The evolution of financial systems evolves in parallel to the developmental stages of
human growth and societal transformation. Symbolic parallels can be drawn between
individual financial independence, the decentralization of economic systems, and the maturation of societal structures facilitated by technology such as blockchain. Other
symbolic parallels can be drawn between the financial system and bodily systems within the human body, shedding an original perspective on the role of finance within
humanit y’s superorganism.
3.1. 1 Financial independence and d ecentralization
The transition from adolescence to adulthood is marked by achieving financial independence, which involves earning income, managing expenses, and making financial decisions autonomously (Shim et al., 2010) . This period is often challenging,
as young adults may lack experience in financial management, leading to potential
issues such as overspending or getting into debt. Other typical behaviours found in
young adults that move in together in a shared apartment or dorm include: mismanagement of food, unwashed dishes, overflowing garbage cans, dirty clothes piling up, and so on.
Similarly, the emergence of blockchain technology and cryptocurrencies represents a societal shift toward financial independence from traditional centralized institutions like banks and governments (Nakamoto, 2008) . Early adopters of crypto -assets have
encountered challenges including market volatility (Gandal et al., 2018) , regulatory
uncertainty, and security vulnerabilities (Castonguay & Stein Smith, 2020). These
obstacles reflect the problems associated with adopting new financial paradigms at a
societal level , as a fractal projection at a higher level of complexity, of the problems
associated to transitioning from adolescence to adulthood and gaining financial
independence . Within the blockchain space, one can clearly see the “unwashed dishes
and overflowing garbage cans”, which prompts reactions typical of parents coming to visit their young adults’ apartment: “he/she is clearly not ready to live on his/he r o w n ”,
which takes the form of numerous regulatory initiatives meant to “clean up” the wild space of experimentation within the crypto -sphere. Yet these same experimentations as
young adults are formative and lead to the emergence of responsible adults who can
11
hold their own, who are no longer dependent on their parents, and who can “innovate”
and improve upon the parental “template” pushing society to evolve forward, with new
ideas and values which eventually, are considered commonplace. Democracy, for
instance , as an ideal, was far from an acceptable “norm” a few centuries ago, and the
beginnings of democracy implemented in the early 19th century were n’t always
successful. Yet nowadays, very few citizens would advocate to return to monarchies of
divine right.
3.1.2 Money as humanity’s circulatory system: Bernard Lietar’s perspective
Bernard Lietaer, a renowned monetary theorist, compares the financial system to the circulatory system in the human body, with money acting as the blood that flows through economic "veins" to nourish various parts of society (Lietaer, 2013) . He argues
that just as a healthy circulatory system is vital for physical well -being, a robust and
diverse monetary system is essential for economic stability and resilience. Lietaer
emphasizes the need for complementary currencies and decentralized fina ncial
structures to promote sustainability and adaptability in the face of economic crises.
3.1. 3 Money as a n ervous system: Brett Scott's p erspective
Brett Scott offers a nother metaphor by likening money to the nervous system of a
societal superorganism, where finance acts as the motor cortex that coordinates collective action (Scott, 2024) . In his article "Money is a Nervous System, " Scott
critiques traditional metaphors that compare money to blood flowing through an economic body. He argues that such metaphors are misleading because they suggest
that money carries intrinsic value like nutrients in blood plasma, obscuring the true nature of finance.
Scott posits that money does not contain value itself but serves as an impulse that
activates economic agents, much like nerve signals prompt muscles to move. He
explains that value resides in people and their labor, not within the money. Monetary
transact ions are, therefore, not transfers of value but signals or information that
mobilize resources and labor within the economy.
This perspective aligns with the concept of humanity as a superorganism, where individuals function as cells within a larger entity. The financial system, acting as the
nervous system, coordinates activities across the superorganism, enabling complex inter actions and collaborations at scale. The central nervous system, represented by
major financial centers and institutions, orchestrates large -scale economic actions
through the issuance and management of money , where and how money should
circulate in the ec onomy , etc.
12
3.1. 4 Combining both perspectives
A more comprehensive understanding of the financial system may emerge by viewing it
as a combination of both the circulatory and nervous systems. In this dual metaphor,
money serves both as the signaling mechanism and the carrier of value. Just as the
circ ulatory system transports oxygen and nutrients necessary for cells to perform their
functions, money provides the means for individuals and businesses to engage in
economic activities. Oxygen enables the execution of the nervous system's commands,
sustaini ng cellular functions; similarly, money enables economic agents to access vital
goods and services such as food, shelter, heat and so on.
Moreover, while the nervous system transmits information and coordinates actions, it
requires the circulatory system to deliver the energy and nutrients that make such
actions possible. In the context of the economy, the Internet could be likened to the
neural network facilitating communication, while money acts as both the impulse
(signal) and the medium (energy) that mobilizes resources.
This integrated perspective addresses the complexity of the financial system and its
multidimensional role within humanity’s superorganism. It acknowledges that money is
not merely a carrier of value or a signaling mechanism but functions as both, enabling
the "orders" of the economic system to be carried out while sustaining the activities of
humans .
3.1.5 From centralization to decentralization
While inside the womb, a baby’s vital or biological resources depend solely on a
centralized external organ: the placenta. This organ serves as the centralized
intermediary between the mothers’ resources and the baby (developing organism).
While in gestation, the baby develops certain organs which will enable it to gain a
certain degree of autonomy without maintaining the same degree of total dependency
on his mother’s resources and the placenta, which takes the form of breathing oxygen
with his own lungs. The transition from one system to the other takes the form of cutting
the umbilical cord, at which point, the baby takes his first breath. In order to ensure a
rather smooth transition from one system to another, the baby stocks up on some fat,
as the lungs and stomach are not yet fully mature and operational.
While living in his/her parents’ home, a child’s access to societal resources depend
solely on centralized external agents: the parents. Parents serve as the centralized
intermediary between society’s resources and the child (developing human individual).
A child cannot walk into a store and help him or herself to goods/services without the
intermediation of parents. While living in the parental home, the child develops certain
skills and undergoes certain developmental stages (toddler, child, teenager,
ado lescent, young adult) which enable the child to gain a certain degree of autonomy
without maintaining the same degree of total dependency on his parents’ resources and
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finances. In order to ensure a smooth transition from living at his parents’ home to living
on his own, a young adult will get a first student job and set some money aside. The
transition from one system to the other takes the form of cutting the umbilical cord a
second time, when the young adult moves out of the parental nest and gains full
financial autonomy.
While living under the rule of governments, a society’s access to the planetary
resources depends solely on the affordances granted by centralized institutions which
play the role of the symbolic “parent” to society. Governments serve as a centralized
inte rmediary between the planet’s resources and society (developing human
collective). In order to access planetary resources, one must pay taxes, respect the law,
hold property titles, ask for permits, and so forth. While living under the rule of
governments, society matures in its ability to self -govern and act responsibly without
needing centralized governments to enforce “civil” behaviours or collective decisions .
Gradually, a society gains a certain degree of autonomy from governments which takes the form of adopting their own currencies, coordinating their actions via DAOs
(decentralized autonomous organisations) . In order to ensure a smooth transition from
living under the rule of centralized governments to self -governed communities, and stop
relying on c entralized financial institutions (fiat money, or the debt-based monetary
system), a society will start to set some money aside (Bitcoin ) to weather the transition
from one financial system to another. In this instance, there are a number of parallels
between Bitcoin and “fat” that the baby stocks up on before birth (as in the first fractal
level above). In order for a baby (global organism) to store fat, it needs to spend ex tra
“energy” in order to convert food into a molecule directly assimilable by the b ody (the
individual cells). In order for society to store “value” , it needs to spend extra energy in order to convert electricity into an energy which is directly assimilable by humanity
(individual humans). As seen above, money is a form of “energy” and i nformation which
puts humans to “work”. In case of a global financial collapse, arguably , the only
“system” which is relatively neutral (not under the control of a centralized authority or
government) and globally accessible is Bitcoin. Any citizen with an internet connection
can create a Bitcoin wallet and start transacting (receiving/sending Bitcoin), which is exemplified in cases where currencies and the financial system have failed, such as
Lebanon (Finianos, 2023) . During times of economic uncertainty or systemic transitions
between financial systems, Bitcoin can serve as a stabilizer. Just as fat reserves provide energy during periods of scarcity, Bitcoin offers a decentralized asset that can preserve
wealth and ensure that major necessary transactions can still take place when
traditional financial systems are under stress (Baur & Dimpfl, 2021) . As with any
organism, exchanges must continue. The same holds true for humanity at the global scale. If exchanges were to suddenly freeze due to a collapse of the financial system,
humanity would no longer behave like a collective global organism, but would revert
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back to fragmented, isolated communities operating independently, much like cells
that lose their cohesive functioning when an organism's circulatory system fails.
Furthermore, as humanity progresses towards a more interconnected and
technologically advanced society, the role of artificial intelligence (AI) becomes
significant. AI could be envisioned as the " cognitive facilitator " of the global
superorganism, processing vast amounts of data (the sensory input generated by
humans ) and generating coherent strategies (the motor output) that guide collective
actions (Goertzel, 2015) . This analogy emphasizes that while money (as both nervous
and circulatory systems) facilitates the functioning of the economic body, AI represents
the cognitive processes that can potentially bring higher levels of decentralized
coordination and create a meta- coherence that cannot be achieved with traditional
centralized top -down institutions.
By integrating these analogies , we can better understand the ethical and practical
implications of evolving financial systems to accommodate blockchain and crypto -
assets. For instance, the concept of a universal basic income (UBI) aligns with the
circulatory system metaphor, where each "cell" (individual) receives the necessary
"oxygen" (financial resources) to survive and function without stringent controls or
conditions (Van Parijs, 2017) . While transitioning to such a system (see the section
below on the Relative Theory of Money), Bitcoin and other crypto -assets can serve as a
“temporary bridge” much like the example of “fat” discussed above.
The recent move by various governments around the world to set up a “Bitcoin strategic reserve” is directly tied to the symbolic role of Bitcoin as a "store of value" during
transitional periods, symbolically reflecting the way organisms store fat to prepa re for
significant changes
1. By accumulating Bitcoin reserves, these governments aim to
safeguard their economies against potential instabilities in the traditional financial
system, much like how a baby relies on fat reserves during the shift from placental nourishment to independe nt feeding.
This strategic move underscores the recognition of Bitcoin's potential to act as a
financial stabilizer in the face of economic uncertainty. It aligns with the broader
transition from centralized to decentralized systems, where reliance on traditional fiat
currencies and centralized financial institutions is gradually diminishing. By embracing
Bitcoin, governments are acknowledging the evolving financial landscape and the need to adapt to new forms of value exchange that operate independently of centralized
control.
1 https://crypto.news/bitcoin -reserve -bill- introduced -in-brazils -congress/
https://www.washingtonpost.com/business/2024/11/27/trump -strategic -bitcoin -reserve -plan/
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3.2 Parental reactions and r egulatory responses
In the context of individual development, parents may react to their child's pursuit of
financial independence with concern or attempts to maintain control, fearing that the
child is unprepared for the responsibilities (Aquilino, 2006) .
Analogously, governments and regulatory bodies often respond to the rise of
decentralized finance with restrictive measures, citing risks such as fraud, money
laundering, and threats to financial stability (European Central Bank, 2019) . These
apprehensions are not unfounded. The decentralized and permissionless nature of
some crypto -assets presents challenges, including susceptibility to fraud, market
manipulation, and security breaches. Early adopters may suffer losses due to
inexperience or insufficient safeguards, much like young adults who might make
imprudent financial choices without proper guidance (Shim et al., 2010) . However,
adopting a punitive approach may be self -defeating, in the same way as a parent
seeking to forcefully regain control over his/her young adult, which may inadvertently
precipitate their desire for independence and autonomy, albeit experienced in a chaoti c
way.
3.3 Navigating the Transition
A supportive approach in both parenting and societal governance can facilitate
smoother transitions to independence. Parents who provide guidance without exerting
overbearing control help young adults develop competence, confidence, and
responsibility (Schoeni & Ross, 2005) . They recognize the importance of allowing their
children to make mistakes and learn from them, fostering growth and maturity.
Similarly, governments can adopt a collaborative stance toward the evolving financial
landscape by:
• Creating regulatory sandboxes: Allowing innovators to test new financial
products and services in a controlled environment, enabling regulators to
understand emerging technologies and develop appropriate frameworks
(Zetzsche et al., 2017) .
• Promoting best practices: Encouraging the development of industry standards
and self -regulatory organizations that can establish guidelines for ethical
behavior and risk management within the DeFi space .
• Engaging with s takeholders: Involving technologists, entrepreneurs, consumers,
and other stakeholders in the policymaking process to ensure that regulations are informed, balanced, and supportive of the transition from centralized to
decentralized systems (Finck, 2018) .
• Providing free auditing services for new blockchain initiatives: Establishing government -funded programs that offer security audits, code reviews, and smart
16
contract verification services to early -stage blockchain projects, reducing
barriers to entry while ensuring baseline security standards and protecting users
from preventable vulnerabilities. These services can help prevent technical
failures and security breaches that could harm users and damage confidence in
the broader DeFi ecosystem, while also creating a knowledge base of common
vulnerabilities and best practices that can benefit the entire industry.
• Revise existing systems and regulations : Initiating comprehensive reforms of
fundamental financial and regulatory frameworks to accommodate the paradigm
shift introduced by permissionless blockchain technology. This includes
reimagining core mechanisms like money creation (moving from debt -base d
money creation to alternative monetary systems such as the Relative Theory of Money), taxation frameworks, and regulatory approaches. Rather than forcing blockchain innovation to conform to legacy systems, this appr oach recognizes
the need to fundamentally redesign these systems to leverage the unique properties of blockchain technology while ensuring public benefit and stability.
By adopting such measures, governments can help bridge the gap between traditional financial systems and emerging decentralized models. This approach aligns with the maturation process described in Spiral Dynamics, where
societies transition from authorita tive structures (Blue) to more participatory and
integrative systems (Yellow and Turquo ise) .
The shift toward decentralized governance can occur through various pathways, each presenting unique challenges. An abrupt transition involves a rapid move away from
centralized authority, which can lead to instability and resistance from existing institut ions unprepared for sudden change. Alternatively, a gradual independence
allows for a smoother transition, where governments and citizens collaborate to delegate responsibilities progressively. This approach can mitigate potential disruptions
and foster mu tual adaptation to new governance models. The same pattern applies to a
young adult seeking independence from his/her parents: a smooth transition is
preferable to an abrupt transition, whereby the young adult has to manage on his own
with no parental support.
To ensure a stable and beneficial transition, governments can adopt strategies that
support citizen -led initiatives. One such strategy is the delegation of competencies,
gradually transferring responsibilities like infrastructure maintenance, environmental
management, and social services to DAOs or community organizations , all the while
maintaining a presence to monitor the outcomes in order to step in should these experiments turn sour (Atzori, 2015) .
17
4. Implications for m onetary policy
4.1 Incompatibility with existing financial systems
The rise of crypto -assets and decentralized finance (DeFi) presents significant
challenges to traditional monetary policy and existing financial systems. The
coexistence of fiat currency and widespread crypto -assets could lead to an artificial
multiplication of the monetary supply, potentially causing economic instability. This
concern arises because crypto -assets like Bitcoin may serve as alternative means of
payment and stores of value, operating parallel to traditional currencies without following the same logic in terms of issuance/creation. .
In a debt -based monetary system, money is primarily created through the issuance of
loans by banks, which requires continuous economic growth to service the interest on
that debt (Keen, 2011) . The introduction of crypto -assets disrupts this model by
providing alternative channels for transactions and value storage that are independent of central banks and traditional financial institutions. If crypto -assets become widely
accepted as means of payment, the effective monetary mass increases without corresponding controls, potentially leading to inflationary pressures and undermining the effectiveness of monetary policy. In essence, money can now circulate twice. For
instance, if someone sells 1000 tokens of a given crypto -asset which has been mined or
airdropped (created out of thin air) for 1000€, and those same tokens are accepted widely as payment for goods and services, then the person who sold the tokens is now
in possession of 1000€ to spend, while the person in possession of the 1000 tokens can
also spend those into the economy. In this sense, Bitcoin cannot be viewed as “digital
gold” , as one cannot walk into a bakery and scratch of a few miligrams of a gold bar to
pay for bread. In order to use gold as a “means of payment”, one must first sell it against
an accepted currency. Bitcoin, on the other hand, could theoretically be used as a means of payment as its value can be broken down into subunits, and also thanks to innovations such as the lightning network allowing for near -free and fast transactions
(Divakaruni & Zimmerman, 2023) . However, using Bitcoin as means of payment can only
be a temporary fix, much like relying on bodily fat, for obvious reasons: Bitcoin’s
deflationary characteristics. For payments, only a currency which manages to maintain
its purchasing power over time c an qualify as money or as a medium of exchange,
otherwise the incentive to either hold it (in case of deflation) or get rid of it (in case of
hyperinflation) distorts economic activities.
Moreover, the volatility inherent in many crypto -assets introduces additional risks.
Fluctuations in the perceived value of crypto -assets can distort the balance between
the monetary mass and the goods and services available in the economy. This
instability could complicate the central bank's ability to manage inflation and maintain
economic stability. While these effects have not been seen yet given the marginal
adoption and weight of crypto -assets within the larger financial system, this could
18
become a problem if the rate of adoption continues and crypto -assets become more
and more accepted and recognized as means of payments.
Furthermore, the rise of stablecoins —crypto -assets pegged to traditional currencies or
assets —adds another layer of complexity to the monetary system (Bullmann et al.,
2019). Stablecoins can circulate alongside fiat currency, effectively doubling the money
in circulation if not properly accounted for. The way stablecoin issuers manage
underlying assets, such as investing customer deposits in bonds, could lead to the
same mone y being counted twice in the financial system, exacerbating the tensions
identified above .
The incompatibility of a debt -based monetary system with the growing use of crypto -
assets points to the necessity to undergo a similar transition to the cutting of a baby’s
umbilical cord at birth, transitioning from a centralized “system” which enables growth
of the organism (the debt -based monetary system, which is perfectly designed to
incentivize growth) to another system where the logic is no longer based on growth but
on adequacy between the organisms’ needs , based on the organisms’ actions. While a
baby inside the womb grows steadily, with energy consumption going up, a baby outside
of the womb is equipped with mechanisms allowing to modulate energy consumption based on the need (whether the baby is sleeping, crawling, lying down etc), via its lungs
(taking in more or less oxygen) or stomach (eating more or less food) . In this sense, the
Relative Theory of Money may be a solution.
4.2 The Relative Theory of Money: An alternative monetary system
The Relative Theory of Money (RTM), developed by French mathematician and
economist Stéphane Laborde, presents a novel approach to monetary systems that
mirrors the decentralized yet coordinated functioning of biological organisms (Laborde,
2017) . By placing the human being at the center of monetary creation, the RTM seeks to
emulate the way individual cells in a body contribute to and are influenced by the
organism's overall state. This perspective aligns with the concept of humanity as a
superorganism, where collective intelligence and coordinated actions emerge from the
interactions of individual agents (Heylighen & Lenartowicz, 2017) .
The RTM addresses fundamental challenges in monetary policy, particularly the
difficulty of maintaining money as a stable measure of value while achieving certain
social and economic objectives. Traditional monetary systems struggle with this
balance, often leading to inflation, deflation, or unequal wealth distribution. The RTM
offers a solution by introducing a monetary framework grounded in principles of
relativi ty and individual participation.
At the core of the RTM is the recognition that the economy cannot exist without human
beings; they are the fundamental element upon which all economic activity is built. In
essence, a billion euro is worth nothing for a person stranded alone on a desert island.
19
Money and the economy is only relevant in the context of a human society, facilitating
human transactions and relations. This idea parallels the way an organism relies on the
existence and health of its individual cells. Just as each cell contributes to the vitality of
the body, each person contributes to the economy through their consumption,
production, and decision -makin g.
Laborde emphasizes that every individual should have an equal opportunity to
influence the economy, based on the idea that value of goods and services is subjective
and determined by personal assessments. This principle aligns with the concept of
money as a voting mechanism, where spending decisions signal preferences and
influence the direction of economic development, which Lindblom would call the
“do llar-vote” (Lindblom, 2002) .
Drawing inspiration from Einstein's theory of relativity, the RTM proposes that money, as
a universal medium of exchange, must function identically in any frame of reference. In
monetary terms, this means establishing a stable referential system that allow s for
consistent measurement of value over time and space. The RTM achieves this by
introducing two units of account: the traditional currency unit (e.g., euros or dollars)
and the Universal Dividend (UD).
The UD is a relative unit that represents a proportion of the total monetary mass,
recalculated periodically to account for changes in the economy and population. By
expressing values in UDs, the system maintains price stability and avoids the
distortions caused by inflation or deflation in the traditional currency unit. In the RTM,
monetary creation occurs through the regular distribution of the Universal Dividend to
every individual in the monetary zone. Each person receives an equal amount of
currency un its daily, which can be spent in the economy. The value of the UD increases
over time at a consistent rate (for instance, 10% annually).
By counting in UDs, the system naturally regulates wealth distribution. Individuals who accumulate more currency units see their relative wealth decrease over time in UD
terms unless they continue to participate actively in the economy. Conversely, those
with fewer currency units experience an increase in their relative wealth in UDs,
promoting a convergence toward a “mean” .
To understand the principle of relativity introduced by counting and displaying
wealth/prices in UD, here are two tables. In a standard currency framework, as
illustrated in the first table, three individuals start with initial balances ( 1000, 2000,
3000 units) and accrue wealth through compounded growth (receiving the UD, valued at
200 units and increased 10% per year) . This system results in an exponential increase in
the total money supply, potentially leading to hyperinflation, where money’s purchasing
power erodes as its quantity in circulation rises.
20
By contrast, using the UD as reference, the amount of UD that each individual holds is
gradually pulled towards a mean, as the value of the UD is periodically re -evaluated. In
this representation (second table), individuals begin with UDs proportional to t heir
initial balances (5 UD, 10 UD, 15 UD), and the total money supply remains stable at 30
UD, while their relative share of those 30 UD tends to equalize (assuming no
exchanges) . If the UD, on year one, is equal to 200, and increased by 10% each year,
then the relative share of UD that the most wealthy person controls (P3 = 15UD) shrinks
over time naturally, given the mathematical properties of the Relative Theory of Money.
21
With such a system, one could do away with nearly all policies aiming at redistributing
wealth via taxation. Such a system is also growth agnostic. It does not require growth or
degrowth. It simply creates a monetary system which enables each human to
periodically “vote” on what the economy should look like on a daily basis.
22
The RTM mirrors the functioning of biological systems, particularly in how resources are
allocated and energy is distributed within an organism. In the human body, each cell
requires a baseline amount of energy to function and can receive more resources as
needed based on the body's activities (Alberts et al., 1994) . Similarly, the RTM provides
each individual with a baseline amount of currency through the UD, allowing them to participate in the economy and influence its direction. Wherever money accumulates,
this prompts the human “superorganism” to “move” in a specific direction, enrolling
humans via the accumulated money to carry out the tasks or creating the goods that
were “voted” for upon the initial spending of the UD.
This approach creates a form of economic homeostasis, where wealth distribution
adjusts naturally over time, preventing extreme disparities and promoting overall
stability. The system facilitates the emergence of collective intelligence, as individual
spending decisions aggregate to reflect societal values and needs . Again, this mirrors
the functioning of a biological organism, whereby energy concentrates in organs
depending on the need (for instance, if a human starts to run, energy flows to the legs),
and energy normalizes once the need disappears (for instance, once a human stops
running and sits down).
4.4 Implementation and practical considerations
Implementing the RTM would involve significant changes to the current financial
infrastructure. All citizens would receive the UD directly into designated accounts, and
monetary creation would no longer depend on bank -issued loans. Existing loans could
be repaid using the newly generated money from the UD, and new lending could occur
through mechanisms like covered bonds, where banks serve as intermediaries between
investors and borrowers without creating new money (Gundersen et al., 2011) .
Eventually, such lending could occur directly via DeFi protocols, bypassing the need for
centralized institutions such as banks.
The amount each citizen receives as a UD would be calculated based on the total
monetary mass, population, and desired growth rate. For instance, in the Eurozone, with
a monetary mass of roughly €10 trillion and 330 million citizens, the UD could range
from €138 to €232 per month per person, depending on the growth rate. While this may
seem modest, it provides a baseline income that, when combined with other earnings
and the velocity of money, su pports economic activity. One must also factor in that
under the RTM, the creation/destruction cycle of money is completely different from
that of a debt -based monetary system, thus the initial baseline UD injection cannot be
considered as a “salary” but as the initial “impulse” that sets the economy and humans
within it into motion .
23
By decoupling money creation from debt and interest, the RTM promotes an economy
that can adjust organically without the artificial requirement for continuous growth.
Economic activity can fluctuate based on actual needs and resources, aligning with
susta inable development goal s (Jackson, 2009) .
The RTM has been implemented experimentally through the cryptocurrency Ğ1
(pronounced "June"), which operates on a blockchain and incorporates the Universal
Dividend concept (Malafosse et al., 2022) . Ğ1 uses a "web of trust" system for user
authentication, requiring new members to be certified by existing ones through face -to-
face meetings. While the growth of Ğ1 has been modest, it provides valuable insights into the practicalities and challenges of adopting the RTM.
Incidentally, the roll -out of CBDCs could enable quickly switching to a system based on
the Relative Theory of Money in case of a global financial crisis or collapse of the debt-
based monetary system. For instance, each European citizen could instantly rec eive
their UD on their digital euro account, while in the Eurozone, governments coordinate to
transition from displaying prices in individual currency units (the euro) to UD.
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5. Discussion
In conclusion, the fractal patterns of humanity’s development identified in this paper
suggest that the transition toward self -governance via decentralized systems and away
from a debt -based monetary system is not optional but an inevitable stage in our
collective evolution. Just as individuals progress from dependence to independence,
huma nity is poised to shift from centralized, authority -driven systems to more
distributed, autonomous frameworks. This transformation is guided by the same
underlying developmental principles observed in personal growth: the push for
independence, the desire for self -governance, and the need for sustainable practices
that align with our interconnected global reality. Blockchain technology and crypto -
assets are signs of this incoming shift, offering practical mechanisms through which
humanity can establish decentralized, transparent, and fairer systems that resonate with the values of autonomy and equity now emerging on a societal scale. This
transition will take some time. While a baby’s transition can take minutes (abruptly
cutting the umbilical cord) to a few days (in case one leaves the placenta attached as in
Lotus birth) (SONAR, 2023) , and while a young adult’s transition can take a few days to a
few months or even years , humanity’s transition from centralized systems to
decentralized ones can take a few years or decades.
The real choice before us, then, is not whether to undertake this transition but how we approach it. Various stakeholders, actors and institutions can resist, clinging to
centralized structures in a way that mirrors the fractal pattern of a parent struggling to
control a rebellious young adult —a path that will likely lead to chaos, conflict, and
reactive measures that further entrench current issues of inequity and environmental
strain. Alternatively, by recognizing these fractal patterns, society has the o pportunity to
engage consciously and responsibly in this transformation. Embracing decentralized
systems with awareness enables a smoother transition, applying the developmental
trajectories identified in Spiral Dynamics and aligning with frameworks like the Relative
Theory of Money, which advocates for an adaptive, resilient financial system that distributes value sustainably and equitably. In this approach, governments and
institutions act as facilitators rather than control lers, supporting the shift thro ugh
collaborative regulation, innovation, and open engagement. By choosing to navigate this evolution with foresight, we can foster a stable and inclusive decentralized future that
respects both our collective interdependence and individual autonomy, enabl ing
humanity to grow into a mature, cohesive superorganism aligned with the needs and
values of a global society , and steering clear of the self -destructive path that the
superorganism is currently on, as argued by Nate Hagens . A healthy parent takes pride
in raising his/her child to the point where they become fully autonomous and self -
governing adults. Governments and current centralized institutions of power should
take pride in raising a society to the point where it is capable of self -governance.
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|
{
"id": "2501.10378"
}
|
2307.01599
|
A Scalable Reinforcement Learning-based System Using On-Chain Data for
Cryptocurrency Portfolio Management
|
On-chain data (metrics) of blockchain networks, akin to company fundamentals,
provide crucial and comprehensive insights into the networks. Despite their
informative nature, on-chain data have not been utilized in reinforcement
learning (RL)-based systems for cryptocurrency (crypto) portfolio management
(PM). An intriguing subject is the extent to which the utilization of on-chain
data can enhance an RL-based system's return performance compared to baselines.
Therefore, in this study, we propose CryptoRLPM, a novel RL-based system
incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of
five units, spanning from information comprehension to trading order execution.
In CryptoRLPM, the on-chain data are tested and specified for each crypto to
solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of
CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting
results on three portfolios indicate that CryptoRLPM outperforms all the
baselines in terms of accumulated rate of return (ARR), daily rate of return
(DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin,
CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and
2.1767 respectively.
|
http://arxiv.org/pdf/2307.01599v1
|
Zhenhan Huang, Fumihide Tanaka
|
q-fin.PM, cs.LG
|
q-fin.PM
|
A Scalable Reinforcement Learning-based System Using On-Chain Data for
Cryptocurrency Portfolio Management
ZHENHAN HUANG and FUMIHIDE TANAKA, University of Tsukuba, Japan
On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the
networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for
cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data
can enhance an RL-based system’s return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a
novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from
information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to
solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios’ cryptos at
any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate
of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the
ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively.
CCS Concepts: •Computing methodologies →Machine learning ;•Information systems →Expert systems .
Additional Key Words and Phrases: reinforcement learning, quantitative finance, cryptocurrency
ACM Reference Format:
Zhenhan Huang and Fumihide Tanaka. 2023. A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency
Portfolio Management. 1, 1 (July 2023), 12 pages. https://doi.org/XXXXXXX.XXXXXXX
1 INTRODUCTION
Blockchain networks or platforms, each with its native cryptocurrency (cryptos), are numerous today. Analogously, the
blockchain can be compared to a company, while cryptocurrency is akin to its publicly traded shares. On-chain data, or
on-chain metrics of a blockchain network, are like a company’s fundamentals.
Just as fundamentals disclose significant information about a company, on-chain data provide precise, comprehensive
records of a blockchain network. Cryptocurrency valuations are influenced by factors including typical on-chain metrics
such as circulating supply, exchange flows, and balance on exchanges. Most on-chain data are real-time, sequentially
recorded, capturing operational details and metrics of a specific blockchain network and its native cryptocurrency.
Due to the aforementioned nature of on-chain data, people aspire to utilize and incorporate on-chain data into their
systems for price prediction and quantitative trading [ 2,7–9,14], since the price of crypto can be determined by multiple
factors, e.g., hash rate, a typical on-chain metric. Therefore, the incorporation of on-chain data into quantitative trading
systems is naturally expected.
Authors’ address: Zhenhan Huang, huang@ftl.iit.tsukuba.ac.jp; Fumihide Tanaka, fumihide.tanaka@gmail.com, University of Tsukuba, 1-1-1 Tennodai,
Tsukuba, Ibaraki, Japan, 305-8577.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not
made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components
of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to
redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
©2023 Association for Computing Machinery.
Manuscript submitted to ACM
Manuscript submitted to ACM 1arXiv:2307.01599v1 [q-fin.PM] 4 Jul 2023
2 Huang and Tanaka
However, such utilization of on-chain metrics in an RL-based system for PM has not been implemented so far [ 10,12,
13]. The extent to which this utilization could help the systems outperform the baselines in terms of return performance
is an intriguing question that remains unanswered.
Hence, we propose CryptoRLPM, a novel and scalable end-to-end RL-based system incorporating on-chain data for
cryptocurrency PM. CryptoRLPM, a mid-frequency (10-to-30-minute) PM system, consists of five units covering the
process from information comprehension to trading order execution. On-chain metrics are tested and specified for
each cryptocurrency, overcoming the issue of metric ineffectiveness. Additionally, we introduce the Crypto Module
(CM), based on MSPM [ 6], to ensure scalability and reusability. Each CM reallocates a single-asset portfolio, including
a risk-free asset (cash), necessitating the use of 𝑛CMs for an 𝑛-asset portfolio. This setup enables trained CMs to be
reusable for the reallocation of any given portfolios. Furthermore, this setup facilitates CryptoRLPM to allow scalable
portfolios, with the underlying cryptocurrencies of the portfolios able to be changed at any time as desired. Backtesting
with three portfolios constructed for this study, CryptoRLPM demonstrates positive accumulated rate of return (ARR),
daily rate of return (DRR), and Sortino ratio (SR), outperforming all the baseline. Specifically, CryptoRLPM shows at
least a 83.14% improvement in ARR, at least a 0.5603% improvement in DRR, and at least a 2.1767 improvement in SR,
compared to the baseline Bitcoin.
To the best of our knowledge, CryptoRLPM is the first RL-based system adopting on-chain metrics comprehensively
for cryptocurrency PM. The benchmarking results indicate that CryptoRLPM robustly outperforms the baselines.
2 METHODOLOGY
CryptoRLPM is structured into five primary units, which collectively cover the entire process from information
comprehension to trading order execution: (i) Data Feed Unit (DFU), (ii) Data Refinement Unit (DRU), (iii) Portfolio
Agent Unit (PAU), (iv) Live Trading Unit (LTU), and (v) Agent Updating Unit (AUU). The architecture of CryptoRLPM
is illustrated in Figure 1.
The five units are interrelated, with each one responsible for at least one distinct task. From a holistic perspective,
the Data Feed Unit (DFU) and Data Refinement Unit (DRU) function as the base units related to data generation .
The Portfolio Agent Unit (PAU) is responsible for the initial training of RL agents for one or more portfolios. The Live
Trading Unit (LTU) and the Agent Updating Unit (AUU) handle the live trading functionality, as well as the maintenance
of the agent and the reallocation of portfolios. In the subsequent sections, we will break down and explain the technical
details and tasks of each unit. However, the introductions to the LTU and AUU will be rather conceptual, as the purpose
of this study is to validate the viability and outperformance of CryptoRLPM. While we do not intend to conduct live
trading using CryptoRLPM in this study, we plan to present the implementation of its live trading functionality in
future studies.
2.1 Data Feed Unit (DFU)
The Data Feed Unit (DFU) is the most fundamental unit of CryptoRLPM, controlling the acquisition of data both for
initial model training and for subsequent ongoing data feed requirements during live trading and model retraining. The
system design of DFU is displayed in Figure 2.
2.1.1 Data Retrieval. After confirming the portfolio’s underlying cryptocurrencies, the DFU retrieves historical price
data and on-chain metrics using Binance REST API and Santiment’s SanAPI (SanAPI Basic Subscription) respectively [ 1,
16]. The retrieved data are stored in two separate SQLite databases. The Data Refinement Unit (DRU) will fetch the
Manuscript submitted to ACM
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management 3
Fig. 1. The architecture of CryptoRLPM, illustrating the abstract compositions of each of the five units.
Fig. 2. The system design of DFU, with the data flow indicated.
stored data, and then feed them into the Portfolio Agent Unit (PAU) for model training, Live Trading Unit (LTU) for live
trading, and Agent Updating Unit (AUU) for model retraining.
2.1.2 On-chain Metrics. On-chain metrics refer to the information generated from the decentralized ledgers of
blockchains. For instance, Daily Active Addresses, which represent the number of distinct addresses participating
in a transfer of a given crypto on a specific day, indicate the daily level of crowd interaction (or speculation) with
that crypto [ 15]. Since most blockchains have their own native cryptocurrencies, the on-chain metrics of a specific
blockchain provide insights into its real-time status and ongoing activities.
If we liken a blockchain to a public company, the blockchain’s crypto resembles the company’s stock, while on-chain
metrics mirror its fundamentals. On-chain metrics, due to blockchain’s decentralized nature, offer more accurate and
Manuscript submitted to ACM
4 Huang and Tanaka
transparent measurements than traditional company fundamentals, and are continually public and recorded in real
time.
As per the Efficient Market Hypothesis (EMH) [ 4], a blockchain’s crypto valuation presumably reflects all accessible
information, including on-chain metrics. Therefore, it is hypothetically to anticipate the incorporation of on-chain data
into quantitative trading systems. Nevertheless, to the best of our knowledge, such an integration into an RL-based PM
system remains unexplored so far.
Available Metrics: The on-chain metrics employed in this study are those available under the SanAPI Basic Subscription
Plan, and vary depending on different crypto. Given that on-chain and social metrics are often intertwined on API
platforms and in practical applications, we do not distinguish between them in this study; both are considered as
on-chain metrics.
2.2 Data Refinement Unit (DRU)
For any given crypto (e.g., Bitcoin), we conduct correlation tests between the on-chain metrics and three-period returns.
Figure 3 illustrates the system design of the DRU, as indicated by the dashed line. The term "three-period returns" refers
to the percentage change (returns) in a crypto’s price over periods of 12, 24, and 48 days. For instance, if we employ
Bitcoin’s daily OHLCV data, then the three-period returns correspond to the percentage changes in Bitcoin’s daily
closing prices every 12, 24, and 48 days, respectively.
Fig. 3. The system design of the DRU, illustrating the data flow and component structure.
2.2.1 Correlation Test for Feature Selection. However, the effective metrics in predicting a particular crypto’s price may
not be applicable to other cryptos, especially considering that not every crypto has the same set of available metrics.
Thisineffectiveness of metrics has been barely considered in existing studies. Thus, in this study, we design a scheme
in the DRU to sort the metrics so that they are specified for each crypto in order to mitigate the issue of ineffective
metrics. Our objective is to select valid on-chain metrics from a large pool to construct the environment with which the
RL agents interact. To accomplish this, we examine the linear relationship between each of the three-period returns and
the on-chain metrics for a specific crypto. This involves determining the Pearson’s correlation coefficients between the
Manuscript submitted to ACM
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management 5
returns and metrics. The coefficients are divided into three groups, corresponding to the three-period returns. Within
each group, the metrics are sorted according to their correlation coefficients, and the highest and lowest five from each
group are selected. The selected metrics from all three groups are then ranked by their appearance frequency, and the
top-10 metrics are used as valid features to construct the agents’ environment in the PAU.
Dimension Reduction: To further enhance agent learning efficiency, we apply rolling normalization and rolling PCA to
the selected metrics for dimension reduction before feeding them into the subsequent units. The principal components
that explain at least 80% of the variance are extracted as the representation of the top-10 metrics and are subsequently
fed into the PAU, LTU, and AUU.
2.3 Portfolio Agent Unit (PAU)
PAU incorporates the key modules from MSPM[ 6]. MSPM is a multi-agent RL-based system designed to address
scalability and reusability challenges in RL-based PM. MSPM consists of two key modules, the Evolving Agent Module
(EAM) and the Strategic Agent Module (SAM). The EAM leverages a DQN agent to generate asset-specific signal-
comprised information. Conversely, the SAM utilizes a PPO agent to optimize the portfolio by connecting with multiple
EAMs and reallocating the corresponding assets. As described in [ 5], the Strategic Agent Modules (SAMs) of MSPM can
be built separately rather than jointly. Namely, each SAM reallocates a single-asset portfolio that includes a risk-free
asset (i.e., cash). In a similar vein, within CryptoRLPM, we define a Crypto Module (CM) as a composite module,
consisting of an Evolving Agent Module (EAM) and a SAM, dedicated to trading a single crypto. Thus, for example, 𝑛
CMs will be necessary for the reallocation of an 𝑛-asset portfolio. With this setup, a trained CM can be integrated into
any given portfolio’s weighted reallocation alongside other CMs. Furthermore, for efficient training, the EAM within
a CM can be optional in certain circumstances, such as when sentiment-included on-chain metrics are fed directly
from the DRU to the SAM within the CM. This configuration allows the PAU to be scalable, accommodating variable
underlying cryptos in any given portfolio at any time.
2.3.1 Settings of PAU. Figure 4 illustrates the system design of the PAU, as framed by the dashed line. For the agent
training of crypto 𝑥, on-chain metrics are fed into the DRU from the DFU for selection and dimension reduction.
Subsequently, these refined metrics, along with the OHLCV data, are transferred from the DRU to the dedicated EAM
of crypto𝑥within the PAU. Alternatively, for the sake of efficient training, the refined metrics can be directly fed into
the SAM, as shown by the orange dashed line. In this case, the EAM becomes optional, but the high-quality trading
signals from the EAM will not be utilized [ 6]. The signals generated by the EAM, in conjunction with the new OHLCV
data, constitute the signal-comprised information that is fed into the SAM of crypto 𝑥for decision-making. The trained
models are stored separately in the Model Storage. The PAU continues to interact with the AUU for model updates and
the LTU for live trading. The EAM and SAM settings are adopted from [ 6] and [ 5], albeit with modifications. Detailed
descriptions and discussions of these modifications follow:
Environment: Each crypto-specific CM is composed of a pair: an EAM (optional) and an SAM. The EAM’s RL-based
agent interacts with an environment formalized by the historical OHLCV and refined on-chain metrics of the designated
crypto. The environment for the SAM’s RL-based agent is the combination of signals produced by the trained EAM and
new OHLCV, or signal-comprised information. Each CM is reusable and periodically retrained by the AUU.
State: Within the dedicated CM, the SAM collaborates with the EAM to establish the weight of a specific crypto.
The state𝑣𝑡that the EAM observes at every time step 𝑡includes the recent 𝑛-interval (e.g., 30-minute) OHLCV and
Manuscript submitted to ACM
6 Huang and Tanaka
Fig. 4. The system design of the PAU, detailing the data flow and illustrating the components.
refined on-chain metrics of the designated crypto, where 𝑣𝑡=(𝑠𝑡,𝜌𝑡),𝑠𝑡is the𝑛-interval OHLCV, and 𝜌𝑡represents
the refined on-chain metrics from the DRU. In line with the original SAM setting in MSPM, the state 𝑣+
𝑡observed by
the SAM at time step 𝑡involves the new historical OHLCV stack 𝑠𝑡and the trading signals 𝑎𝑠𝑖𝑔𝑡. Since the SAM in
CryptoRLPM is assigned to one crypto, for 𝑣+𝑡∈R𝑓×𝑚×𝑛,𝑓denotes the number of features (OHLCV and on-chain
metrics),𝑚=2signifies the designated crypto and cash, and 𝑛represents the recent 𝑛intervals.
Deep Q-network Agent: As introduced previously, both the EAM and SAM use a Deep Q-network (DQN) agent
to interact with their environments. Additionally, for the estimates of action-value functions of the EAM and SAM,
𝑄𝜃𝐸𝐴𝑀(𝑠𝑡,𝑎𝑡)and𝑄𝜃𝑆𝐴𝑀(𝑠𝑡,𝑎𝑡), we continue adopting the settings in [ 5], using a 1-D convolutional neural network
(CNN) and a simple 4-layer CNN architecture for representation, respectively.
Action Space of EAM:. At every time step 𝑡, the DQN agent in the EAM selects an action 𝑎𝑡—either buy, sell, or
hold—for the designated crypto. The actions taken by the EAM establish the crypto’s trading signal . These actions,
stacked with the new OHLCV, are later fed into the SAM within the same CM.
Action Space of SAM:. In CryptoRLPM, each CM represents a portfolio consisting of the designated crypto and the
risk-free asset (cash), which is reallocated by the SAM within it. The SAM of CryptoRLPM assigns full weight to either
the risk-free asset or the crypto. In simple terms, at every time step 𝑡, the action𝑎𝑡taken by the SAM of CryptoRLPM is
a choice from [0., 1.] or [1., 0.], indicating the reallocation weight of the portfolio of the designated crypto and cash.
With this setting, once an SAM is trained, it can be combined with other CMs and integrated into the voted-weight
reallocation of any given multi-crypto portfolio.
Reward Function: The reward functions for both the EAM and SAM of CryptoRLPM follow the settings in the original
MSPM [5, 6].
2.4 Live Trading Unit (LTU)
As CryptoRLPM aims to be an end-to-end system for cryptocurrency portfolio management, it naturally incorporates a
live trading functionality. In this section, we introduce the Live Trading Unit (LTU) of CryptoRLPM, which manages the
Manuscript submitted to ACM
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management 7
live reallocation of the portfolio at 10-to-30-minute intervals. The realization of LTU depends on the APIs of specific
exchanges; further implementation details will not be discussed here. The system design of LTU, framed by a dashed
line, is shown in Figure 5.
Fig. 5. The system design of LTU, with data flow and components illustrated.
At each𝑛interval, new data are fetched and refined as per the schemes of the first two units. The newly fetched
and refined data are fed into PAU for weight inference of the CMs (each corresponding to a designated crypto) in the
portfolio. The set 𝑃𝑡comprises the reallocation weights obtained from all 𝑚CMs (cryptos) of the portfolio at time step 𝑡:
𝑃𝑡=n
𝑝1
𝑡,...,𝑝𝑚
𝑡
|𝑝𝑖
𝑡∈R2for every𝑖∈{1,...,𝑚}o
, (1)
and the voted weight 𝑤𝑡will be formalized as the reallocation weight of the portfolio at time step 𝑡
𝑤𝑡=Í𝑑𝑖
𝑡
𝑚,for𝑖∈{1,...,𝑚} (2)
The formalized reallocation weight 𝑤𝑡of the portfolio is transformed into the format required by the designated
exchange’s API (e.g., Binance). Whenever the portfolio’s weight is updated and formatted, a reallocation request is sent
to the exchange via their APIs.
2.5 Agent Updating Unit (AUU)
The Agent Updating Unit is responsible for scheduled model re-training and unscheduled updates of CMs. After each
fixed interval, set in days, the agent models are re-trained, and the portfolio is updated if there are changes to the
underlying cryptos, such as scaling or replacing.
3 EXPERIMENTS
3.1 Preliminaries
3.1.1 Portfolios. We propose three portfolios for our experiments:
(1) Portfolio(a) includes two cryptos:
Manuscript submitted to ACM
8 Huang and Tanaka
•Names: Bitcoin (BTC) and Storj (STORJ)
(2) Portfolio(b) includes three cryptos, and shares two cryptos with Portfolio(a):
•Names: Bitcoin (BTC), Storj (STORJ), and Bluzelle (BLZ)
(3) Portfolio(c) includes four cryptos, and shares three cryptos with Portfolio(b):
•Names: Bitcoin (BTC), Storj (STORJ), Bluzelle (BLZ) and Chainlink (LINK)
There are four distinct cryptos denominated by USDT [ 18] (a U.S. dollar equivalent stablecoin) included in the three
portfolios. The reusability of CM and scalability of PAU allow the application of the trained crypto-designated CMs to
different portfolio-designated PAUs, enhancing efficiency in model training. Consequently, we only need to train four
CMs for the four cryptos, and organize these CMs in PAUs to represent and reallocate the three portfolios.
3.1.2 Data Ranges. In our experiments, the DFU retrieves historical 6-hour OHLCV data ( 𝑠𝑡) from [ 1] and on-chain
metrics (𝜌𝑡) from [ 16], which are later refined by the DRU. In this study, the refined metrics are directly fed into the
CMs’ SAMs from the DRU, leveraging the modularized design of the CM and ensuring efficient training. After that, data
is split into three subsets: (i) CM(training) from October 2020 to December 2021; (ii) CM(validation) from January 2022
to February 2022; and (iii) CM(backtesting) from March 2022 to September 2022. Notably, the data ranges for different
portfolios vary slightly in practice, based on the varying underlying cryptos. Table 1 lists the ranges of the datasets.
Table 1. Description of Data Ranges
Purpose Range
CM(training) 2020 Oct ∼2021 Dec
CM(validation) 2022 Jan ∼2022 Feb
CM(backtesting) 2022 Mar ∼2022 Sept
3.1.3 Performance Metrics. To measure the performance of the CryptoRLPM system and its baselines, we employ
three performance metrics: (i) Daily rate of return (DRR), (ii) Accumulated rate of return (ARR), and (iii) Sortino ratio
(SR) [17]. Higher values for these metrics often indicate higher performance.
3.2 Results and Discussion
3.2.1 Backtesting Performance. This study primarily aims to validate the feasibility and effectiveness of the proposed
system design, and thus, the baselines used for comparison are the historical performances of the underlying cryptos
of each portfolio. We conduct backtesting on our CryptoRLPM system and compare its performance against these
baselines.
As depicted in Figure 6, Figure 7, and Figure 8, CryptoRLPM consistently outperforms the baselines across all
three portfolios, achieving positive values in terms of ARR, DRR, and SR, while the baselines yield negative values
on ARR. Specifically, when compared to Bitcoin, CryptoRLPM achieves at least a 83.14% improvement on ARR, a
0.5603% improvement on DRR, and a 2.1767 improvement on SR. These results demonstrate the strong performance
of CryptoRLPM in generating capital returns, and validate the system’s potential applicability in crypto PM. Table 2
details CryptoRLPM’s outperformance over the baselines in terms of the ARR, DRR, and SR.
It is worth noting that CryptoRLPM achieves promising SR for all portfolios, which indicates CryptoRLPM’s robust
ability at profit-making and adaptability to the ever-changing market.
Manuscript submitted to ACM
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management 9
Fig. 6. CryptoRLPM outperforms all baselines on Portfolio(a) in terms of the cumulative returns in backtesting.
Fig. 7. CryptoRLPM outperforms all baselines on Portfolio(b) in terms of the cumulative returns in backtesting.
Fig. 8. CryptoRLPM outperforms all baselines on Portfolio(c) in terms of the cumulative returns in backtesting.
Manuscript submitted to ACM
10 Huang and Tanaka
Table 2. Comparison of backtesting performance of the baselines and CryptoRLPM.
Baselines
CryptoRLPM BTC STORJ BLZ LINK
Portfolio (a) ARR (%) 31.26 -51.88 -55.95 - -
DRR (%) 0.2485 -0.3118 -0.1585 - -
SR (%) 0.8709 -1.3082 -0.3879 - -
Portfolio (b) ARR (%) 79.87 -51.88 -55.95 -37.72 -
DRR (%) 0.4386 -0.3118 -0.1585 0.1001 -
SR (%) 1.3877 -1.3082 -0.3879 0.2654 -
Portfolio (c) ARR (%) 43.71 -51.88 -55.95 -37.72 -47.60
DRR (%) 0.3140 -0.3118 -0.1585 0.1001 -0.1785
SR (%) 0.9311 -1.3082 -0.3879 0.2654 -0.4347
3.2.2 Scalability of CryptoRLPM and PAU. In CryptoRLPM, each crypto is reallocated by a dedicated, decentralized
Crypto Module (CM), rendering it a scalable PM system. The scalability means that trained CMs of the underlying
cryptos are reusable and changeable for any portfolio.
As an example, for a portfolio 𝑃𝑒𝑥𝑎𝑚𝑝𝑙𝑒 with three trained CMs/cryptos: [𝑎, 𝑏, 𝑐], to replace crypto 𝑐with a new
crypto𝑥, we train a new CM( 𝑥), unplug the CM( 𝑐), and plug in the trained CM( 𝑥). Scaling up or down a portfolio is even
easier. To exclude a crypto, say 𝑏, we simply unplug CM( 𝑏). To add a new crypto 𝑦to𝑃𝑒𝑥𝑎𝑚𝑝𝑙𝑒 , we plug in a trained
CM(𝑦).
Figure 9 illustrates the scalability of the architecture. Trained CMs for any cryptos are reusable for different portfolios
and can be added or removed at will.
Fig. 9. An intuitive illustration featuring the scalability of PAU’s architecture. Trained CMs of any cryptos are reusable for different
PAUs/portfolios. Trained CMs can be added/plugged to, or removed/unplugged from, any PAUs at will.
4 LIMITATIONS AND FUTURE WORK
In this study, for the efficient training and backtesting of CryptoRLPM, we directly feed refined metrics into PAU from
DRU, bypassing EAMs trading signals of CMs. We defer the use of EAMs trading signals to future research, and we
anticipate this usage may further enhance CryptoRLPM’s performance (ARR, DRR and SR). We also intend to include
additional baselines for benchmarking, such as conventional PM strategies, e.g., CRP [ 3], or RL-based methods, e.g.
Manuscript submitted to ACM
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management11
ARL [ 11]. Our focus lies in validating CryptoRLPM’s outperformance through backtesting and benchmarking in this
study. We plan to present the live trading functionality of CryptoRLPM in future studies.
5 CONCLUSION
We propose CryptoRLPM, a reinforcement learning (RL)-based system incorporating on-chain data for end-to-end
cryptocurrency (crypto) portfolio management (PM). CryptoRLPM’s scalability, embodied in its five units, with the
reusability of the Crypto Module (CM), enable changes in portfolios’ cryptos at any time, demonstrating the system’s
adaptability to dynamic market conditions. Additionally, we demonstrate CryptoRLPM’s ability to efficiently incorporate
on-chain metrics for each crypto, overcoming the challenge of metric ineffectiveness. In backtesting with three portfolios,
CryptoRLPM consistently delivered positive accumulated rate of return (ARR), daily rate of return (DRR), and Sortino
ratio (SR), outperforming all baselines. In comparison to Bitcoin, a prevalent baseline, CryptoRLPM registers at least a
83.14% improvement in ARR, at least a 0.5603% enhancement in DRR, and at least a 2.1767 improvement in SR. Our
study with its findings highlight the substantial potential of integrating on-chain data into RL-based crypto PM systems
to enhance return performance.
ACKNOWLEDGEMENT
This work was supported by JST SPRING, Grant Number JPMJSP2124.
REFERENCES
[1] Binance. 2022. Market Data Endpoints . https://binance-docs.github.io/apidocs/spot/en/
[2]Bruno Casella, Lorenzo Paletto, et al .2023. Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting. In Proceedings
of the IEEE International Conference on Blockchain and Cryptocurrency 2023 . 1–4.
[3]Victor DeMiguel, Lorenzo Garlappi, and Raman Uppal. 2007. Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strat-
egy? The Review of Financial Studies 22, 5 (12 2007), 1915–1953. https://doi.org/10.1093/rfs/hhm075 arXiv:https://academic.oup.com/rfs/article-
pdf/22/5/1915/24429471/hhm075.pdf
[4]Eugene F. Fama. 1970. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance 25, 2 (1970), 383–417.
http://www.jstor.org/stable/2325486
[5]Zhenhan Huang and Fumihide Tanaka. 2022. Investment Biases in Reinforcement Learning-based Financial Portfolio Management. In 2022 61st
Annual Conference of the Society of Instrument and Control Engineers (SICE) . 494–501. https://doi.org/10.23919/SICE56594.2022.9905789
[6]Zhenhan Huang and Fumihide Tanaka. 2022. MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial
portfolio management. PLOS ONE 17, 2 (02 2022), 1–24. https://doi.org/10.1371/journal.pone.0263689
[7]Nishant Jagannath, Tudor Barbulescu, Karam M. Sallam, Ibrahim Elgendi, Braden Mcgrath, Abbas Jamalipour, Mohamed Abdel-Basset, and
Kumudu Munasinghe. 2021. An On-Chain Analysis-Based Approach to Predict Ethereum Prices. IEEE Access 9 (2021), 167972–167989. https:
//doi.org/10.1109/ACCESS.2021.3135620
[8]Huisu Jang and Jaewook Lee. 2017. An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on
blockchain information. Ieee Access 6 (2017), 5427–5437.
[9]Patel Jay, Vasu Kalariya, Pushpendra Parmar, Sudeep Tanwar, Neeraj Kumar, and Mamoun Alazab. 2020. Stochastic neural networks for cryptocurrency
price prediction. Ieee access 8 (2020), 82804–82818.
[10] Zhengyao Jiang, Dixing Xu, and Jinjun Liang. 2017. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.
arXiv:1706.10059 [q-fin.CP] https://arxiv.org/abs/1706.10059 arXiv:1706.10059.
[11] Zhipeng Liang, Hao Chen, Junhao Zhu, Kangkang Jiang, and Yanran Li. 2018. Adversarial Deep Reinforcement Learning in Portfolio Management.
arXiv:1808.09940 [q-fin.PM] https://arxiv.org/abs/1808.09940 arXiv:1808.09940.
[12] Giorgio Lucarelli and Matteo Borrotti. 2019. A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading. In Artificial
Intelligence Applications and Innovations , John MacIntyre, Ilias Maglogiannis, Lazaros Iliadis, and Elias Pimenidis (Eds.). Springer International
Publishing, Cham, 247–258.
[13] Dimitri Mahayana, Elbert Shan, and Muhammad Fadhl’Abbas. 2022. Deep Reinforcement Learning to Automate Cryptocurrency Trading. In 2022
12th International Conference on System Engineering and Technology (ICSET) . 36–41. https://doi.org/10.1109/ICSET57543.2022.10010940
[14] Muhammad Saad, Jinchun Choi, DaeHun Nyang, Joongheon Kim, and Aziz Mohaisen. 2019. Toward characterizing blockchain-based cryptocurrencies
for highly accurate predictions. IEEE Systems Journal 14, 1 (2019), 321–332.
Manuscript submitted to ACM
12 Huang and Tanaka
[15] Santiment. 2022. daily-active-addresses . https://academy.santiment.net/metrics/daily-active-addresses/
[16] Santiment. 2022. On-chain, Social and Financial API . https://api.santiment.net
[17] Frank A. Sortino and Lee N. Price. 1994. Performance Measurement in a Downside Risk Framework. The Journal of Investing 3, 3 (1994), 59–64.
https://doi.org/10.3905/joi.3.3.59
[18] Tether. 2022. What are Tether tokens and how do they work? https://tether.to/en/how-it-works
Manuscript submitted to ACM
|
{
"id": "2307.01599"
}
|
2102.00659
|
Quantum crypto-economics: Blockchain prediction markets for the
evolution of quantum technology
|
Two of the most important technological advancements currently underway are
the advent of quantum technologies, and the transitioning of global financial
systems towards cryptographic assets, notably blockchain-based cryptocurrencies
and smart contracts. There is, however, an important interplay between the two,
given that, in due course, quantum technology will have the ability to directly
compromise the cryptographic foundations of blockchain. We explore this complex
interplay by building financial models for quantum failure in various
scenarios, including pricing quantum risk premiums. We call this quantum
crypto-economics.
|
http://arxiv.org/pdf/2102.00659v1
|
Peter P. Rohde, Vijay Mohan, Sinclair Davidson, Chris Berg, Darcy Allen, Gavin K. Brennen, Jason Potts
|
q-fin.PR, quant-ph
|
q-fin.PR
|
Quantum crypto-economics: Blockchain prediction markets for the evolution
of quantum technology
Peter P. Rohde,1,∗Vijay Mohan,2Sinclair Davidson,2Chris Berg,2Darcy Allen,2Gavin Brennen,3and Jason Potts2
1Centre for Quantum Software & Information (QSI),
University of Technology Sydney, NSW,
Australia
2RMIT Blockchain Innovation Hub,
RMIT University, VIC,
Australia
3Center for Engineered Quantum Systems,
Dept. of Physics & Astronomy,
Macquarie University, 2109 NSW,
Australia
(Dated: February 2, 2021)
Two of the most important technological advancements currently underway are the
advent of quantum technologies, and the transitioning of global financial systems towards
cryptographic assets, notably blockchain-based cryptocurrencies and smart contracts.
There is, however, an important interplay between the two, given that, in due course,
quantum technology will have the ability to directly compromise the cryptographic
foundations of blockchain. We explore this complex interplay by building financial models
for quantum failure in various scenarios, including pricing quantum risk premiums. We
call this ‘quantum crypto-economics’.
CONTENTS
I. Introduction 1
II. Blockchain & cryptography 3
A. Blockchain 3
B. Hash functions 3
C. Public-key cryptography 4
D. Digital signatures 4
III. The quantum challenge to blockchain 5
IV. A financial market indicator of quantum failure 7
A. Crypto-bonds without quantum failure 7
B. Quantum failure with a risk-free asset in same unit of
account 7
C. A Shor-attack on blockchain X 7
D. The impact of expansion 8
E. Quantum failure & heterogenous blockchains 10
V. Conclusion & future research directions 11
Acknowledgements 11
References 11
I. INTRODUCTION
Quantum computing (Nielsen and Chuang, 2000) has
become widely recognised as having the potential to com-
promise essential elements of present-day cryptographic
techniques, especially public-key cryptography and digital
∗dr.rohde@gmail.com; www.peterrohde.org;
www.keybase.io/peter_rohdesignatures. This has profound implications for emerging
technologies such as blockchain. There are many mis-
conceptions, however, on how this could occur. We spell
out the margins where quantum computing could impact
blockchain. We also present a financial market predictor
of the likelihood of a successful quantum computer at-
tack on blockchain-based assets. We define a successful
quantum attack on a blockchain as ‘quantum failure’.
As a note on terminology, we refer to the origi-
nal blockchain described by (Nakamoto, 2008) as the
‘Blockchain’, and its associated cryptocurrency is ‘Bit-
coin’. When we use the term ‘blockchain’ we are referring
to any generic blockchain. We discuss the Blockchain and
blockchains in general for our purposes in the next section.
For a discussion of blockchains in general see (Malekan,
2018) for an introductory coverage, (Werbach, 2018) for
a more advanced overview, and (Berg et al., 2019) for a
discussion of the economic implications of the blockchain.
We argue that quantum failure can manifest in two
important and distinct ways: first, as a purely monetary
phenomenon that reduces the value of the native cryp-
tocurrency, but keeps the integrity of the ledger intact,
and second, as an accounting/technological phenomenon
that undermines the integrity of the ledger itself, making
the blockchain and its native cryptocurrency worthless.
We treat (and model) these as two distinct problems
associated with quantum failure.
Consider the monetary aspect of a quantum attack
first. Quantum failure can allow an attacker to solve a
computational problem faster than other miners (on aver-
age), thereby earning the majority of the block rewards
over the length of time the attack persists. For a sys-arXiv:2102.00659v1 [q-fin.PR] 1 Feb 2021
2
tem such as the bitcoin Blockchain, this implies that
mining can produce coins faster than the current 6.25
coins every 10 minutes (potentially until all 21 million
feasible Bitcoins have been produced). We refer to this
phenomenon as Grover-expansion , because it increases the
rate of monetary expansion (in this instance, of the native
currency of a blockchain), a phenomenon well-known and
well-understood in economics.
A couple of points are worth noting here. First, as
defined here, Grover-expansion does not necessarily de-
stroy the blockchain because if done on a small scale, it
simply adds legitimate entries at a faster pace. Intuition
would suggest that this monetary expansion will reduce
the value of the native cryptocurrency, but need not nec-
essarily reduce the value to zero. Second, many blockchain
systems have a difficulty parameter built into the algo-
rithms that oversee the rate at which these computations
can be solved. In the case of Bitcoin, for example, the
algorithm attempts to ensure that, on average, a block is
added every 10 minutes. One could argue, therefore, that
the difficulty parameter will adjust to negate the faster
rate at which the quantum attacker solves the mining
problem. The reality, however, is that the difficulty pa-
rameter is adjusted at discrete points in time, based on
average computation rates in the past. For Bitcoin, the
difficulty parameter is adjusted every 2016 blocks; at the
current reward rate of 6.25 Bitcoin for every block, the
attacker could earn a maximum of 12,600 Bitcoin before
the parameter is adjusted. Moreover, once adjusted, the
attacker would still be the fastest to solve the now more
difficult computational problem.
If Grover attacks are done on a large scale, i.e by a
large mining pool equiped with quantum computers, then
even more dangerous attacks are possible like the 51%
attack. This occurs when the pool has over half the com-
putational power of the network and allows dominating
the blockchain. For example, such a dominant pool can
perform a ‘double spend attack’ by performing a spend
transaction on one branch of the blockchain while grow-
ing a parallel branch where that spend record is missing.
Given the computational dominance, this parallel chain
will likely grow larger than the original and trusted nodes
will adopt it, hence allowing for a second spend at no
additional cost.
The second manner in which a quantum attacker can
exploit the blockchain is by falsifying digital signatures
and stealing existing tokens. This quickly erodes trust in
the ledger and could elicit panic selling.
We refer to this type of attack as a Shor-attack ; by
rendering the blockchain entries unreliable, a Shor-attack
would reduce the value of the native cryptocurrency, and
indeed all assets denominated in that cryptocurrency,
to zero. While this problem is distinct from Grover-
expansion, it is quite possible that in some instances,
depending on the nature of the blockchain, both attacks
are launched simultaneously (or sequentially, with Grover-expansion preceding a Shor-attack), resulting in a Grover-
Shor attack.
There are two major threat vectors enabled by a Shor-
attack. The first is a fast steal. After a legitimate transac-
tion has been added to the network but before it has been
verified (usually within 10 minutes), the attacker can learn
the private key of the sender from the publicly announced
key. Then the attacker can broadcast a new transaction
from the same sender’s address to themselves. If the at-
tacker offers a higher transaction fee, that transaction
will take priority in the queue and will be verified first,
meaning successful and unstoppable theft. The second is
the recovery of lost Bitcoin. It is estimated up to 33%
of Bitcoin allocated so far on the network are associated
with dormant public addresses from owners who presum-
ably have lost their private keys and cannot access the
coins (Stewart et al., 2018). A Shor-attack would allow
the attacker to learn the private key and take those coins.
This clearly would increase available supply and devalue
the currency especially if released quickly.
To understand the impact these attacks can have on
financial markets, we construct a model that utilises sim-
ple bond pricing analytics, along with well-known parity
conditions from exchange rate economics, to derive a
financial indicator associated with the possibility of quan-
tum failure. This model is useful for a number of reasons.
First, it provides us with a method to infer the belief
the market places on the probability of a quantum attack
on a specific blockchain based on the risk premium of
a bond denominated in the native cryptocurrency (or
crypto-bond ) of the blockchain. Second, the simplicity of
the model structure allows for a careful delineation of how
different types of quantum attacks — Grover-expansion
and a Shor-attack — impact the risk premium for a
crypto-bond threatened with a quantum attack. Third,
the use of familiar parity conditions yields intuitive in-
sights on relationships between the crypto and fiat (say
USD) economy. For example, it is readily shown that
under certain simplifying assumptions, the percentage
change in the exchange rate between the cryptocurrency
and USD equals (approximately) the difference between
the rate of Grover induced monetary expansion in the
blockchain and the rate of inflation in the US economy;
this is, of course, similar to purchasing power parity, and
its derivation and intuition does indeed follow from that
well-known exchange rate parity condition. Fourth, the
model facilitates some simple comparative static exercises
to gauge how parametric changes affect bond pricing ana-
lytics in this situation. Finally, the model sets the stage
for a preliminary consideration of strategic issues relevant
in this complex environment where quantum attacks that
must be protected from ex anteand defended against ex
post.
To motivate the final point regarding strategic interac-
tion in this environment, we note that not all blockchains
are equal — some are more valuable than others. It is
3
also unclear what the motivations of a quantum hacker
may be. A hacker motivated by financial gain may attack
a blockchain based on their perception of success and
amount of financial gain to be had. An ideological hacker,
however, may choose to attack a very different blockchain
for very different reasons. Whatever their motivation, it
is unlikely that a quantum hacker would command the
resources to attack all blockchains at the same time. This
implies the existence of a malicious quantum hacker would
become known before all blockchains could or would be
attacked. This in turn raises questions as to what the
optimal response would be to this information? It is very
clear that a research agenda should exist that explores all
these issues and likely responses to quantum failure.
In Sec. II, we set out the features of blockchain that
are important for our argument. We then explain the
quantum computing challenge to blockchain in Sec. III,
and our financial model in Sec. IV. A conclusion and
suggestions for further research follows in Sec. V.
II. BLOCKCHAIN & CRYPTOGRAPHY
A. Blockchain
The Blockchain, which forms the basis for modern
cryptocurrencies including Bitcoin, is a distributed data-
structure which maintains a cryptographically irrevocable,
chronologically-ordered ledger of transactions between
agents.
The original blockchain (the Blockchain) was designed
by Satoshi Nakamoto (Nakamoto, 2008) to facilitate Bit-
coin. This native internet currency has two important
features of interest. First it avoids a double-spending
problem and it solves the Byzantine generals’ problem,
which briefly put is the following: “How do you assure
that multiple distant parties agree on the same plan of
action even in the presence of a small number of malicious
traitors?”
It performs both these functions by keeping an im-
mutable record of the history of each Bitcoin. Economic
agents — known as miners — are incentivised to maintain
the Blockchain through the issuance of Bitcoin tokens as
they add blocks containing transactions to the Blockchain.
In the Blockchain protocol, miners spend computational
effort to solve a puzzle (known as ‘proof of work’) and
the first one to do it is rewarded with a Bitcoin issuance
as well as a transaction fee.
These Bitcoins are issued at a known, but diminish-
ing, rate that was algorithmically established by Satoshi
Nakamoto. In total there will be 21 million Bitcoin is-
sued over time, with the issuance amount halved roughly
every 4 years. It is useful to differentiate between the
Bitcoin issuance rate and a monetary inflation rate. The
issuance rate is the rate at which the number of tokens
in a blockchain increase over time. An inflation rate isthe rate at which a monetary unit loses value. High is-
suance of a monetary unit (popularly known as ‘printing
money’) is usually associated with high inflation rates. It
is important, however, to maintain a distinction between
these two concepts. A high rate of issuance of a blockchain
token (Bitcoin being the original token) is not guaranteed
to result in price inflation (devaluation of that token).
Importantly for our purposes, blockchain is based on
several cryptographic primitives:
1.Digital signatures are used to certify transactions
under a consensus algorithm, whereby a sufficient
number of independent parties must collectively
agree to sign off on the legitimacy of a transaction
for it to be transcribed to the ledger.
2.Hashing is used to provide chronological links be-
tween connected transaction blocks, thus making
the blockchain immutable, and Merkle tree data
structures allow for fast and efficient verification of
transactions by all parties (Merkle, 1988).
3.The computational difficulty of inverse hashing is
used in some proof-of-work protocols, such as Bit-
coin, for mining new coins.
These cryptographic primitives, however, have suscep-
tibilities to the deployment of quantum computing, dis-
cussed in Sec. III.
B. Hash functions
Hash functions, also an essential cryptographic primi-
tive with widespread applications, that take an arbitrary
input bit-string and reduce it to a short, fixed-length hash
that acts as a fingerprint of the original data in highly
compact form,
h(x)→y. (2.1)
While the hash doesn’t contain the original data, good
(cryptographic) hash functions are designed to make it
incredibly difficult to infer what the (or a) likely input
was that it corresponds to, on the basis that they are one-
way functions (i.e computationally easy to evaluate in the
forward direction, but extremely computationally difficult
to invert) and exhibit highly quasi-random behaviour.
The problem of inverting hash functions — finding an
input that maps to a given hash,
x=h−1(y), (2.2)
is, from a computational complexity perspective, concep-
tually similar to the problem of brute-forcing a symmetric-
key crypto-system, and can be described mathematically
in the same manner as a satisfiability problem. Note that,
unlike the similarly-structured case of symmetric-key en-
cryption, there is no key involved, and because the length
4
of the output is fixed, there are necessarily collisions ,
whereby multiple unique inputs map to the same output
hash.
The Bitcoin Blockchain implements proof-of-work via
inverse hashing , whereby a legitimate coin is defined as
a bit-string whose hash lies within a particular range.
Specifically,
SHA256 (SHA256 (c)) =y (2.3)
where SHA256 is a standard 256-bit hash function (256-
bit Secure Hash Algorithm), and crepresents a legitimate
coin when the output bit-string ysatisfies the constraint
of having a fixed number of leading zeros. Since hash
functions are one-way functions by definition, the mining
process requires repeatedly hashing random bit-strings via
brute-force until an input is found satisfying the output
constraint.
In economic terms, this implies that monetary expan-
sion is directly related to the hash rate of computers
allocated towards the mining process, modulated by a
difficulty function , which algorithmically ensures that min-
ing becomes progressively harder by adjusting the num-
ber of necessary leading zeros in the output bit-string
yfrom Eq. (2.3). This imposes an asymptotic cap on
the token supply. Thus, the token issuance rate is effec-
tively determined by the collective hash rate, and the
algorithmically-imposed difficulty function.
C. Public-key cryptography
The most important cryptographic primitive we have
today is public-key cryptography. Here the encryption-
decryption process relies on two distinct keys, a public-key
which can onlybe used to encrypt messages, and a private-
keywhich can onlybe used to decrypt them. For this
reason, such protocols are also referred to as asymmetric
cryptography . Both keys combined are jointly referred to
as akey-pair.
The encryption and decryption operations can be de-
fined mathematically as,
f(m,k pub)→c,
f−1(c,kpriv)→m, (2.4)
wherefandf−1are the encryption and decryption oper-
ations,mis the plain-text message, cis the cipher-text,
andk={kpub,kpriv}is the key-pair comprising the public
and private keys.
The reason asymmetric cryptography is so useful, is
that in most real-world scenarios in a globalised economy,
we cannot go and meet people in advance of communi-
cating with them to securely exchange secret keys. In
fact, we may not even know them. By making everyone’s
public-keys openly available, we are able to securely sendmessages to them, without first having to perform any
secret key-exchange with them a priori in a dark alleyway.
Importantly, one of the main criteria for such protocols
is that the public and private keys cannot be computed
from one another. Obviously, if one were able to look
up a public-key on a key-server, and infer the associated
private-key from it, the whole thing would be useless.
The original asymmetric cryptographic protocol was
RSA, named after its inventors (Rivest et al., 1978). More
recently, more efficient elliptic-curve cryptography (ECC)
has become the norm (Koblitz, 1987; Miller, 1985).
The mathematical basis of RSA is that a private-key
is defined by two prime numbers, and the corresponding
public-key is given by their product. The computational
security arises from the unproven, but widely held belief
that integer factorisation is an extremely computationally
complex problem — believed to be NP-intermediate —
that classical computers cannot efficiently solve. This
directly equates to their inability to infer private-keys
from public-keys — a so-called trapdoor function — and
hence the cryptographic integrity of RSA. ECC is based
on a different, but closely related mathematical problem,
with the same essential cryptographic properties.
D. Digital signatures
While public-key cryptography can be utilised for se-
curing messages, by reversing the roles of the public and
private keys it can also be used to authenticate messages
via the provision of digital signatures . When you receive
anemailfromsomeone,youwanttobesureitwasactually
they who sent it, and that the email you received hasn’t
been modified or forged by someone else. Similarly, when
logging into an online service like your email, users want
to be sure they’re interacting with whom they actually
think it is.
Usingthesamepublic-keysystemasbefore,leteveryone
who wishes to be able to authenticate their messages
create an additional key-pair. This time, they make the
key that can only be used for decryption public, keeping
the one that can only be used for encryption private.
Now when they send a message they want to digitally
sign, they encrypt it (or just a hash of it), that they
send along with the message. This encrypted hash acts as
a digital signature, which others cannot falsify without
knowing the privately-held encryption key. However, the
publiclyavailabledecryptionkeycanbeused,uponreceipt
of a message along with its digital signature, to decrypt
the signature and compare it with the message itself,
thereby establishing its integrity.
5
III. THE QUANTUM CHALLENGE TO BLOCKCHAIN
Large-scale quantum computers directly compromise
both RSA and ECC, via a quantum algorithm known
as Shor’s integer factorisation algorithm (Shor, 1994)
and closely related discrete logarithm algorithm, allowing
them to efficiently calculate private-keys directly from
their associated public-keys. If and when quantum com-
puting eventuates, this implies that the entire crypto-
graphic backbone of our contemporary internet infrastruc-
ture would be compromised.
In addition to compromising encrypted messages, Shor-
armed quantum computers could by the same logic also
compromise digital signatures.
The analysis changes slightly, however, for symmetric
cryptography (and similarly for inverse hashing). Here
two parties share a secret-key in common, which is used
for both encryption and decryption. Because the same key
facilitates both roles, this is also referred to as symmetric
cryptography. The foremost current standard symmetric-
key algorithm is AES256 (Advanced Encryption Standard,
with 256-bit key-length) (Daemen and Rijmen, 2002).
This standard is a widely-used cryptographic primitive in
today’s internet software infrastructure.
In general, good symmetric cryptographic techniques
are regarded as being very strong, in the sense that they
are considered highly robust against conventional cryptan-
alytic techniques, such as differential cryptanalysis, which
aren’t known to provide significant shortcuts over brute-
force attacks. It’s therefore largely reasonable to associate
their security with their vulnerability to brute-forcing,
whereby we systematically try out all possible keys, at-
tempting to decrypt an intercepted message using each
one, and then running some simple tests (e.g statistical
or language tests) to flag whether one such decryption
attempt is likely to correspond to the unencrypted plain-
text message (it’s effectively certain that an incorrect key
will not pass this test, providing a false-positive).
Using an encryption algorithm with key-length n, there
are2npossible choices to work through, of which on
average we’d have to try half until we find the right one.
This exponential scaling grows extremely rapidly, and
already 2256combinations (as for AES256) far exceeds
what any classical computer, present or future, could
realistically iterate through systematically, from which
the security of the algorithm arises.
This brute-force approach to cracking a code can be
interpreted as what computer scientists refer to as a sat-
isfiability problem — for some function that maps a plain-
text input message and a key to an output cipher-text,
f(m,k)→c, which input value mevaluates to a partic-
ular output c, assuming I don’t know the key k? In this
case, the input is a given choice of key, and the binary
output answers the question ‘is this a legitimate decryp-
tion?’. In general, satisfiability problems are extremely
computationally hard to solve. From the field of computa-tional complexity theory (Arora and Barak, 2009), they
are known to be NP-complete in general. Although it’s
unproven whether NP-complete problems can or cannot
be efficiently solved on classical computers (proving this is
one of the biggest open questions in the field of theoretical
computer science), it is very strongly believed that they
cannot be.
Formally, for a good cryptographic code, evaluating the
function,
f(m,k)→c, (3.1)
should be computationally easy, whereas evaluating its
inverse,
f−1(c,k)→m, (3.2)
should also be computationally easy if kis known, but
extremely computationally hard (or impossible) if it is
not.
A quantum algorithm known as Grover’s search algo-
rithmprovides a relatively modest quantum advantage in
solving this class of problems (Grover, 1996). The com-
putational advantage it provides is to effectively quadrat-
ically reduce the input search-space over the equivalent
classical brute-force approach. That is, whereas previously
we had to search over 2npossible input configurations,
now we effectively have to search over only√
2n= 2n/2of
them. The right hand side of this equation provides the
direct interpretation that it compromises security to the
extent of effectively halving the respective key-length: n
becomesn/2. That is, the security of AES256 is effectively
reduced to that of AES128. This is not an insurmount-
able problem, however, as a security response would be
to simply switch to an AES512 standard.
Note that this enhanced scaling provided by Grover’s
algorithm is in the ideal context of error-free quantum
computation. In reality, large-scale quantum computers
necessarily require error-correction (Shor, 1995), which
incurssignificantoverheadsofitsown(thescalingofwhich
is highly architecture-dependent). For this reason, any
practical future quantum advantage will be significantly
less than the already modest advantage described above.
Indeed, if the error-correction overhead is too great, there
may be no advantage at all.
While symmetric cryptography is far more robust
against quantum attacks than asymmetric cryptography,
it cannot be used for digital signatures by virtue of its
symmetry.
Although it appears that symmetric cryptography is
robust to the advent of quantum computing, this is not
proven. Security remains at the level of computational
security, i.e the assumption that computers, classical or
otherwise, are unable to provide sufficient computational
resources to implement a systematic brute-force attack,
or that future cryptanalytic techniques will not provide
shortcuts around it.
6
A far stronger claim to security is via information-
theoretic security , whereby no such assumptions about
adversarial computational capabilities are made. There is
one (and only one) such symmetric-key algorithm which
provides this information-theoretic level of security — the
one-time-pad (OTP) algorithm, also known as the Vernam
cipher. This algorithm, however, is absurdly impractical
for use. For any given encrypted message, a key of exactly
the same length must be employed, which cannot then be
reused; hence the name.
Encryptionisperformedviaabit-wiseXOR(ormodulo-
2 addition) operation between the plaintext message and
key,
c=m⊕k. (3.3)
Decryption is performed by repeating the same procedure
on the encrypted cipher-text,
m=c⊕k. (3.4)
Thus the protocol is symmetric.
This ‘solution’, however is trivial: If agents had the
ability to share a key of the same length as the message
itself, which couldn’t subsequently be reused, they could
use that opportunity to directly communicate the mes-
sage.1In short, one-time-pad encryption has very limited
applicability.
Quantum cryptography provides a potential avenue to
resolving the problems associated with the OTP protocol.
By exploiting the randomness inherent in the measure-
ment of quantum states, something imposed by the laws
of quantum mechanics, it is possible to construct proto-
cols for securely sharing long random bit-strings between
remote parties. This is known as Quantum Key Distribu-
tion (QKD) (Bennett and Brassard, 1984; Ekert, 1991).
It is impossible for the shared random strings to be com-
promised via intercept-resend attacks. This provides only
the ability to securely share random data not messages
themselves. But by utilising this secure shared random-
ness as a source for OTP keys, it is possible, in principle,
to resolve its impracticalities.
Of course, QKD need not only be used for OTP keys,
but could be used for sharing secrets keys for any other
symmetric-key algorithm, such as AES256. This creates
a hybrid protocol, whereby the distribution of secret-keys
is facilitated by QKD, but any unknown vulnerabilities in
the underlying conventional crypto-system go unchanged.
1The reason the key can only be safely employed once is because
if two cipher-texts encrypted with the same key are XORed
together we obtain the same as the XOR of the plain-texts,
c1⊕c2= (m1⊕k)⊕(m2⊕k) = m1⊕m2, upon which we
can directly apply a conventional two-letter frequency attack to
statistically predict m1andm2. As soon as either of these are
known we trivially extract the key via k=m⊗c.In contemporary encrypted communications systems it
is common to employ similar hybrid schemes combining
symmetric and asymmetric elements, where a public-key
system is used to share a shorter session-key , that is
subsequently employed in a symmetric cipher2. Typically
this is performed using the Diffie-Hellman key-exchange
protocol (Diffie and Hellman, 1976; Merkle, 1978). Such
a hybrid QKD system effectively substitutes only the
component associated with the exchange of session-keys.
While quantum algorithms do not entirely compro-
mise hashing, they very much compromise digital sig-
natures based on conventional public-key cryptographic
techniques. This implies the ability for future quantum
computers to transcribe fraudulent transactions to the
Blockchain via falsifying the consensus, thereby undermin-
ing the integrity of the transaction ledger it maintains.
In the worst-case scenario, this could effectively invali-
date the future value of transcribed contracts from the
point in time at which such a Shor-enabled compromise
of the ledger becomes viable.
This highlights the importance of ‘post-quantum cryp-
tography’ for future quantum-proof blockchain imple-
mentations. Already efforts are underway towards this
goal. For example, the Quantum Resistant Ledger em-
ploys hash-based digital signatures for this purpose (note
that hash-based signatures, despite being presumably ro-
bust against quantum attack vectors, have caveats of
their own). And NIST has launched a project to identify
and standardise post-quantum cryptographic protocols
(csrc.nist.gov/projects/post-quantum-cryptography).
In summary, Grover’s algorithm can enhance hash-
inversion as an instance of a satisfiability problem. This
implies that quantum computation has the potential to
distort the algorithmic token supply policy. We define
the ability to artificially enhance monetary expansion
as ‘Grover expansion’. This combined with Shor’s algo-
rithm ability to compromise digital signatures suggests
that blockchain technology would be severely compro-
mised by the advent of quantum computing. This ability
of quantum computing to compromise the public key
cryptography that underpins blockchain, either through
Grover-expansion or a Shor-attack, or both, can be de-
scribed as ‘quantum failure’. An analysis of quantum
attack vectors on cryptocurrencies is presented in (Aggar-
walet al., 2017). One of the notable results there, is that
when accounting for fault tolerance overheads, the time-
line for a successful Shor-attack is much sooner, perhaps
10-15 years, than for a substantial Grover-attack.
2Themotivationforexchangingasession-keyforuseinasymmetric
cipher,asopposedtodirectlycommunicatingusinganasymmetric
cipher, is that the former are block-ciphers that map plain-text
messages to cipher-text messages of the same length, |c|=|m|,
whereas the latter induces significant space overheads in the
cipher-text,|c|/greatermuch| m|, thereby making it most space efficient to
employ the latter once-off and the former thereafter.
7
IV. A FINANCIAL MARKET INDICATOR OF
QUANTUM FAILURE
In this section, we construct a simple model based
on (Bierman and Hass, 1975; Yawitz, 1977) to predict
the impact of quantum failure on the assets in a crypto-
economy.
A. Crypto-bonds without quantum failure
To keep the analysis intuitive, we consider a zero-
coupon bond, B, denominated in some cryptocurrency,
X. The bond has face value A, and time to maturity
of one-year. We do not allow other maturities, although
these could readily be incorporated to derive yield curves.
Risk in this model centres around the risk of quantum
failure. While there is no equivalent of risk-free treasury
bonds in the crypto-bond market, future bond face value
payments can be guaranteed through smart contracts.
Consequently, default risk can be eliminated through
the requirement that 100% of the borrowed funds be
kept as collateral (possibly in assets denominated in a
different currency, or even physical assets) in escrow via
a smart contract. Thus, while expensive, smart contracts
can eliminate the possibility of the idiosyncratic risk of
default by the bond issuer. We assume that all agents are
risk-neutral.
The first scenario we consider is one where either due
to technological advancements in blockchain technology
or lack of advancement in quantum ones, or both, a bond
has no risk of quantum failure. Thus, in addition to zero
idiosyncratic default risk, we impose zero systemic quan-
tum risk. Given a yield to maturity (YTM) of iand price
P, we have,
P=A
1 +i. (4.1)
B. Quantum failure with a risk-free asset in same unit of
account
Now consider a hypothetical intermediate case where
there are two bonds existing simultaneously denominated
in the same cryptocurrency X:
•B, which is risk-free as before.
•ˆB, which is susceptible to quantum attack.
This cannot happen in general, because either the entire
Xnetwork is susceptible to quantum failure in the form
of a Shor-attack (in which case all Xdenominated assets
are at risk) or the entire network if free from risk of
quantum failure. Since quantum risk is systemic, this case
is unrealistic and hypothetical because it assumes that
quantum risk is idiosyncratic to some bonds. However, it
is a useful intermediate stage in the thought process.Let0<ρ< 1be the probability that quantum failure
does not occur in the one-year period until maturity. We
assume this belief is held by all market participants. The
extremities of ρ= 0andρ= 1are trivial. In the case
ofρ= 0quantum failure never occurs and the problem
reduces trivially to the standard analysis. In the second
instance,ρ= 1, quantum failure occurs with certainty, in
which case no rational agent will hold risky crypto-bonds
at any positive price. We assume that if quantum failure
occurs for ˆB, it will pay zero to the holder of the bond.
For the risky asset we have, similar to before,
ˆP=A
1 +ˆi. (4.2)
Arbitrage between the risky and risk-free bonds yields,
ˆi=1 +i
ρ−1. (4.3)
From this we can determine the bond risk premium
that arises from the possibility of quantum failure,
R=ˆi−i
=(1 +i)(1−ρ)
ρ. (4.4)
This result essentially translates risk of quantum failure
to its respective bond risk premium. So, if quantum risk
were to be idiosyncratic, by looking at the risk-premium
R=ˆi−i, we can infer the expected probability 1−ρthe
crypto-market is placing on quantum failure occurring,
providing a mechanism (for even non-market-participants)
to infer quantum risk from a market-based indicator.
C. A Shor-attack on blockchain X
Now consider the existence of two separate systems, one
of which is free from quantum risk and one that is exposed
to quantum failure. It is not necessary that the former
exists in the crypto world — it could well be the fiat
economy. The two requirements needed to conceptualise
‘risk-free’ in this context are:
1.There must be no idiosyncratic risk of default for
the bond.
2.The system must not be susceptible to quantum
failure.
Fiat can satisfy the first requirement through the is-
suance of government securities (treasury bills and bonds)
and, even if public key cryptography fails, can satisfy
the second requirement through the exclusive use of cash
(notes and coins). A crypto-system can satisfy the first
requirement through smart contracts, and the second only
if the required technological advancements occur.
8
For the purposes of our analysis, there is one system (ei-
ther fiator crypto) using currency X∗, andone blockchain,
X, that is susceptible to quantum risk in the form of a
Shor-attack that will render its cryptocurrency worthless.
The former has a risk-free bond, B∗; the latter’s crypto-
bond, which is susceptible to quantum failure, is ˆBfrom
Sec. IV.B, and will pay out zero if there is a Shor-attack.
In the following we assume X∗to be fiat, specifically
USD.
Since these two systems rely on different units of ac-
count to price assets, there is a current spot exchange
rate,S, that measures the price of 1 unit of X∗in terms
ofX. Thus, a bond priced at Punits ofXis worthP/S
units ofX∗, and so on.
Further, suppose the expected spot rate at the end of
the one-year period when the bond matures is Se. There
are two possibilities:
1.The exchange rate is fixed: Xis a stablecoin pegged
toX∗(USD), in which case Se=S.
2. The exchange rate is flexible: Se/negationslash=S, in general.
Suppose exchange rates are flexible. To link the yield to
maturity of the crypto-bond with risk of quantum failure
to the yield to maturity of the risk-free treasury bond, we
proceed via two steps. First, we compare two assets with
identical risk attributes — the (hypothetical) risk-free
crypto-bond, B, denominated in X(from Sec. IV.B) with
the risk-free bond denominated in X∗. Second, we value
the risky crypto-bond, ˆB(denominated in X).
For the first step, if i∗is the interest rate on B∗, as-
suming uncovered interest parity (UIP) holds,
1 +i=Se(1 +i∗)
S. (4.5)
For an equilibrium where both ˆBandB∗are held, from
Eqs. (4.1) and (4.5), we have,
ˆi=Se(1 +i∗)
ρS−1, (4.6)
Se=ρS/bracketleftBigg
1 +ˆi
1 +i∗/bracketrightBigg
, (4.7)
or in terms of percentages,
˙Se=ρ/bracketleftBigg
1 +ˆi
1 +i∗/bracketrightBigg
−1, (4.8)
where,
˙Se≡Se−S
S. (4.9)
Thus, the equilibrium expected appreciation or depreci-
ation ofX∗in terms ofXdepends not only on the interest
rate differential, but also on the probability of quantumfailure. For any other spot rate expectation, given the
yields to maturity, arbitrage possibilities will exist.
The risk premium on the risky crypto-bond becomes,
R=ˆi−i∗
=(1 +i∗)(1 + ˙Se−ρ)
ρ. (4.10)
Compared to the previous result from Eq. (4.4), there is
anaddedelementofforeignexchangerisk,thatiscaptured
by the presence of ˙Se.
In the case where Xis a stablecoin, ˙Se= 0, and if we
knowi∗(say the interest rate on USD 1-year treasury
bond) and ˆi(the yield to maturity on the risky bond), we
can inferρ, the market expectation for quantum failure. In
the case of a flexible exchange rate, however, the market’s
beliefs about ˙Sewould have to be estimated before such
an inference about market perceptions of quantum failure
can be made.
D. The impact of expansion
To investigate the impact of increases in the rate of coin
issuance through Grover-expansion, we begin by noting
that typically no cryptocurrency, including Bitcoin, acts
as a unit of account for the purchase and sale of goods and
services. Rather, as pointed out by (Bolt and Van Oordt,
2019), vendors who accept payments in cryptocurrencies
often simply convert a fiat price to the cryptocurrency
price using an exchange rate S.
Assuming that the currency free from quantum risk,
X∗(USD), is the unit of account, this essentially implies
that the Law of One Price holds. If so, for any good or
serviceφ, the price denominated in cryptocurrency Xis
pφ=p∗
φS, wherep∗
φis the price of φdenominated in X∗.
Since this is true for every good, it follows that Purchasing
Power Parity (PPP) holds for any aggregate measure of
price levels (such as the Consumer Price Index) in any
periodt. Denoting the price levels in period tasCtand
C∗
t, PPP implies,
Ct=C∗
tSt. (4.11)
Consider now the transactions version of the quantity
equation (Fisher, 1911), used in (Bolt and Van Oordt,
2019) in the context of pricing cryptocurrencies, which
we now suppose holds in cryptocurrency system X,
CtTt=MtVt. (4.12)
Here,
•Vtdenotes the velocity of cryptocurrency Xduring
periodt.
•Ttdenotes the quantity of goods and services trans-
acted on blockchain Xduring period t.
9
•Ctis the aggregate price level in blockchain X.
•Mtis the quantity of tokens (money) issued in cryp-
tocurrency X.
Eq. (4.12) can now be expressed as,
Ct
C∗
t(C∗
tTt) =MtVt, (4.13)
whereC∗
tTtdenotes the value of transactions on
blockchain Xbut denominated in currency X∗. Given
that PPP holds, it follows from Eq. (4.11) that,
St=MtVt
C∗
tTt. (4.14)
If PPP holds in every period, we have,
St−1=Mt−1Vt−1
C∗
t−1Tt−1. (4.15)
To integrate this with the bond market analysis, it is
worthwhile outlining the timeline more precisely, as shown
in Fig. 1. Specifically, we assume that each period lasts
for 1 year and we consider period t−1, as the past year.
The bond is issued at the end of period t−1(the current
time) when the exchange rate is known to be St−1. The
bond matures at the end of the following one-year period
t.
With probability the crypto-economy survives period ρXtPeriod tBonds mature at end of period tBonds issued at end of period t−1Period t−1
Figure 1 Timeline for the asset pricing model.
As is evident from the timeline in Fig. 1, all the vari-
ables realised during period t−1are pre-determined and
known at the time of bond issuance (the end of period
t−1). Consequently, St−1,Mt−1,Vt−1,C∗
t−1andTt−1are
predetermined variables at the time of bond issuance and
when investment decisions are made. At this time, more-
over, investors must form expectations over the realisation
of periodtvariables. In the absence of a quantum attack,
the issuance of money is algorithmically determined for
cryptocurrencies, and Mtis known to investors at the end
of periodt−1. Investors must form expectations over the
remaining variables, which include: Se
t,Ce∗
t,Ve
tandTe
t.
With this timeline, it follows that,
Se
t=MtVe
t
Ce∗
tTe
t. (4.16)Given the relatively short timeline we are focusing on
(two periods, tandt−1), let us assume that the velocity of
the cryptocurrency is both stable andcommon knowledge,
such thatVt−1=Vt=V. Then, from Eqs. (4.15) & (4.16),
we have, in the absence of quantum attack,
Se
t
St−1=Mt
Mt−1C∗
t−1
Ce∗
tTt−1
Te
t. (4.17)
Converting this to percentage changes gives,
1 +˙Se=1 +µ
(1 +π∗e)(1 + ˙Te), (4.18)
where:
•Mt
Mt−1= 1+µ, whereµis the rate of change of money
supply, or in other words, the rate at which new
tokens are issued in X, which is an algorithmically
determined constant.
•Ce∗
t
C∗
t−1= 1 +π∗e, whereπ∗eis the expected inflation
of goods and services in the fiat system X∗.
•Te
t
Tt−1= 1+ ˙Te, where ˙Teis the rate at which transac-
tions are expected to change over the given period.
•Se
t
St−1= 1 + ˙Se, where ˙Seis the expected apprecia-
tion/depreciation of X∗in terms of X.
In order to focus on the role of token issuance on ex-
change rate expectations, let us assume that the volume
of transactions using cryptocurrency is expected to be
stable, i.e ˙Te= 0. This assumption is readily dropped if
the focus shifts from token issuance to gauging the impact
of increasing or decreasing popularity of Xmeasured in
terms of the volume of transactions, or if a more general
approach is required. Under this assumption we obtain,
1 +˙Se=1 +µ
1 +π∗e. (4.19)
The following approximation then holds (ignoring ˙Seπ∗e
for small percentage changes),
˙Se∼=µ−π∗e. (4.20)
Assuming that transaction volumes are stable ( ˙Te= 0)
and that the velocity of cryptocurrency Xis stable, the
percentage change in the exchange rate is approximately
equal to the rate of token issuance for cryptocurrency
Xminus inflation of goods and services in the risk-free
systemX∗. So, for example, if BTC (blockchain X) token
supply increases by 5% every year and inflation of goods
and services in the US (risk-free system X∗) is 2%, we
would expect the USD to appreciate by approximately
3% in terms of BTC. As an aside, it is evident that the
expectation of inflation in the US may itself be driven by
the expected rate of change of the USD money supply by
the Federal Reserve.
10
Now suppose we allow for the possibility of a quantum
attack and Grover-expansion in period t. LettingµGrep-
resent the maximum possible monetary expansion that
can be achieved by the quantum attacker by virtue of
speeding up the mining process through enhanced com-
putational (quantum) capabilities, Eq. (4.16) transforms
to,
SG
t=MG
tVe
t
Ce∗
tTe
t, (4.21)
where,
MG
t
Mt−1= 1 +µG. (4.22)
Thus, allowing for the possibility of Grover-expansion
the expected exchange rate is now,
Se
t=ρMtVe
t
Ce∗
tTe
t+ (1−ρ)MG
tVe
t
Ce∗
tTe
t
=Ve
t
Ce∗
tTe
t[ρMt+ (1−ρ)MG
t].(4.23)
This yields the parallels to Eqs. (4.19) & (4.20),
1 +˙Se=1 +ρµ+ (1−ρ)µG
1 +π∗e,
˙Se∼=ρµ+ (1−ρ)µG−π∗e.(4.24)
Eq. (4.24) suggests that the expected exchange will
incorporate the possibility of Grover-expansion in a quan-
tumattack.Moreover,thehigherthemagnitudeofGrover-
expansion the market expects, the more the USD is ex-
pected to appreciate (and the cryptocurrency depreciate).
Apart from being interesting in its own right as a way
to predict exchange rate changes based on algorithmi-
cally determined token increases and (fiat) inflation, this
exchange rate forecast impacts the risk-premium of the
risky bond if we combine combine Grover-expansion with
a Shor-attack,
R=ˆi−i∗
=1 +i∗
ρ/bracketleftbigg1 +ρµ+ (1−ρ)µG
1 +π∗e−ρ/bracketrightbigg
.(4.25)
This implies that if ˆiandi∗are known along with
the algorithmically determined µ, the market expectation
of the impact of Grover-expansion ( µG), and a publicly
available forecast of expected inflation in system X∗, we
can infer the market expectation of the probability of
quantum failure, 1−ρ.
Finally, we can state some simple comparative static
results. All else being equal, the risk premium on crypto-
bond ˆBincreases when, from Eq. (4.25):
1.The rate of token issuance is higher (since
∂R
∂µ=1+i∗
1+π∗e>0), unless deflation on goods and ser-
vices inX∗(say fiat) is more than 100% ( π∗e<0
and1 +π∗e<0).2.The rate of money supply increases due to Grover-
expansion is higher (since∂R
∂µG=(1−ρ)(1+i∗)
ρ(1+π∗e)>0),
unless deflation on goods and services in X∗(say
fiat) is more than 100% ( π∗e<0and1 +π∗e<0).
3.Inflationofgoodsandservicesin X∗decreases(since
∂R
∂π∗e=−(1+i∗)(1+ρµ+(1−ρ)µG)
ρ(1+π∗e)2<0).
4.And, of course, as the probability of Xsurviving
decreases (since∂R
∂ρ=−(1+i∗)(1+µG)
ρ2(1+π∗e)<0).
While we do not investigate further generalisations of
the model, a number of extensions are possible, including:
increasing the periods of analysis to generate a yield curve;
increasing the maturity of the bond to allow for coupon
payments; allowing for changes in the survival rate when
there are more periods; investigating the role of demand
through changes in transaction volumes, and so on. While
these add greater sophistication to the model, they do not
change the fundamental intuition behind the assessment
of quantum risk when crypto-bonds are available.
E. Quantum failure & heterogenous blockchains
Now suppose that there is one risk-free system (crypto
or fiat),X∗, andnblockchain systems {X1,X2,...,Xn}
that are at risk of quantum failure with associated sur-
vival rates of{ρ1,ρ2,...,ρn}. Without loss of generality,
supposeρ1> ρ 2> ... > ρ n, such that the market be-
lievesX1has the greatest chance of surviving a quantum
attack, and Xnthe lowest.
One question is why this belief structure exists. Given
that quantum attacks are costly, the answer may depend
on the attacker’s objective. There are two separate issues
here: first the security of blockchains vary; second, the
value of blockchains vary. Moreover, ‘value’ itself may
depend on the attacker: a mercenary attacker may place
the highest value on the blockchain with largest capitali-
sation; a political attacker may target a blockchain that
causes the greatest harm to a certain group. Irrespective
of their motive, the expected benefit (to the attacker) of
an attack on Xiisσivi, whereσidenotes the probability
that an attack will succeed, and vithe (subjective) value
to the attacker upon success.
For the market to form a clear assessment of {ρi},
agents may therefore first need to establish who the at-
tacker is and their motives. That is, it is quite possible
that the probabilities of quantum failure, {1−ρi}, do not
depend on technological security {σi}alone.
Once such an assessment of {ρi}is formed, however,
the set of risk premia {Ri}reflect the market assessment
of quantum failure in each system.
It is likely that in this situation, there being a single
dominant blockchain — for example, the state of affairs
conceived by Bitcoin maximalists — is not necessarily a
11
good thing, because all the resources of the attacker could
be devoted to compromising the security of the dominant
system.
Indeed, this opens up a number of strategic possibilities
open to attackers and market participants.
For example, suppose the attacker commences an attack
onXi. Assuming they do not have the resources to attack
allnsystems, how should participants in other systems
respond? Should they immediately abandon blockchains
X\Xiand flee to the safety of X∗? But if that were
the case, they could can destroy all nsystems by simply
attacking the system with the greatest σi. The flight from
all these blockchains will: (a) reduce their security to zero
as miners depart; and, (b) drive the exchange rate value
of all cryptocurrencies in terms of X∗,1/Si, to crash
(presumably to zero), making tokens worthless during the
process of exchanging them to X∗.
A better market response might be to diversify portfo-
lios across blockchains (possibly stochastically) such that
mercenary attackers are less likely to find highly concen-
trated capitalisation worth attacking. This suggests some
degree of randomisation (mixed strategies) both by agents
and attackers. There are some interesting game-theoretic
considerations here that may be worth exploring in future
research.
V. CONCLUSION & FUTURE RESEARCH DIRECTIONS
We have identified two vulnerabilities that blockchains
have to the advent of quantum computing. Quantum hack-
ers could falsify blocks being added to a blockchain and/or
double spend tokens on any given blockchain depending
on the features of the blockchain. This behaviour would
manifest itself as monetary inflation — a well-known and
well-understood problem in the fiat economy. We are able
to deploy standard economic analysis to develop a finan-
cial indicator that would reveal whether quantum failure
had occurred or not. Our indicator relies on the existence
of financially motivated individuals and pricing relation-
ships that depend on the existence of efficient markets. As
such we are confident that the indicator will be reasonably
reliable in detecting quantum failure.
Our indicator, however, is only the beginning of an
understanding of quantum failure. Scaleable quantum
computing will not arrive spontaneously or immediately
be deployed to looting blockchains or stealing wealth.
Widespread adoption is likely to be gradual and, initially,
can be expected to be expensive and possessed by few.
In short, the initial applications are unlikely to be ma-
licious. If the cost of dedicating quantum infrastructure
to compromising a given cryptographic asset outweighs
the realisable profit from doing so — assuming financially
motivated hackers — a rational quantum-capable player
would not be expected to make this investment. Thus,
one would reasonably expect cheap cryptographic assetsto retain their integrity via the lack of incentive to com-
promise them. On the other hand, cryptographic assets
of tremendous value would logically stand higher in the
list of priorities to quantum hackers.
These insights raise all manner of questions as to how
best to respond to the generalised problem of quantum
computers hacking blockchains. Which blockchains are
likely to be hacked and by whom? How should observers
react? Would a successful quantum attack result in a
‘flight to quality’? What does ‘flight to quality’ mean? Is it
a flight to fiat? Should individuals diversify their exposure
to quantum risk across blockchains? If so, should the risk
of quantum attack be thought of as being systematic risk
as in Markowitz portfolio risk (Markowitz, 1952)?
Another avenue of study is the effect on crypto-
economics when many adversarial agents are equiped
with quantum computers. For example, in Grover’s al-
gorithm there is a non-negligible probability to solve a
problem by measurement before the algorithm finishes.
This gives rise to a mixed Nash equilibrium strategy for
time to measure among many players trying to solve a
problem like inverse hashing for mining first (Lee et al.,
2018). This would have implications on the fluctuations
in transaction speeds, presenting new opportunities for
market manipulation.
Then there are geo-political risks. In addition to pri-
vate agents developing quantum computing capability for
their own purposes, nation states are also interested in
quantum technology. How will this technology be regu-
lated and controlled? The existence of rogue nation states
developing capacity in this space is an immediate chal-
lenge to digital economic infrastructure over and above
blockchain.
We propose that the analysis of the interplay between
quantum computing and blockchain as economic infras-
tructure be labelled quantum crypto-economics .
ACKNOWLEDGEMENTS
Peter Rohde is funded by an ARC Future Fellow-
ship (project FT160100397). Chris Berg, Sinclair David-
son, and Jason Potts are funded by an ARC Discov-
ery (DP200101808). This research was fundedin part
by the Australian Research Council Centre of Ex-
cellence for Engineered Quantum Systems (Project num-
ber CE170100009)
REFERENCES
Aggarwal, Divesh, Gavin K. Brennen, Troy Lee, Miklos San-
tha, and Marco Tomamichel (2017), “Quantum attacks
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plexity: a modern approach (Cambridge University Press).
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Bennett, C H, and G. Brassard (1984), “Quantum cryptogra-
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of the IEEE 175, 8.
Berg, Chris, Sinclair Davidson, and Jason Potts (2019), Un-
derstanding the Blockchain Economy: An Introduction to
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of bond risk differentials,” Journal of Financial and Quanti-
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virtual currencies,” Journal of Money, Credit and Banking
52, 835.
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jndael: AES – the Advanced Encryption Standard (Springer-
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The Economic Journal 21, 393.
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database search,” in Proceedings of the 28th annual ACM
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ics of Computation 48, 203.
Lee, Troy, Maharshi Ray, and Miklos Santha (2018), “Strate-
gies for quantum races,” arXiv:1809.03671.
Malekan, Omid (2018), The story of the Blockchain (Triple
Smoke Stack, New York).Markowitz, Harry (1952), “Portfolio selection,” The Journal
of Finance 7, 77.
Merkle, R C (1988), “A digital signature based on a con-
ventional encryption function,” Advances in Cryptology -
CRYPTO ’87 293, 369.
Merkle, Ralph C (1978), “Secure communications over insecure
channels,” Communications of the ACM 21, 294.
Miller, V (1985), “Use of elliptic curves in cryptography,”
CRYPTO: Lecture Notes in Computer Science 85, 417.
Nakamoto, Satoshi (2008), “Bitcoin: A peer-to-peer electronic
cash system,” https://www.bitcoin.org/bitcoin.pdf.
Nielsen, M A, and I. L. Chuang (2000), Quantum Computation
and Quantum Information (Cambridge University Press,
Cambridge).
Rivest, R, A. Shamir, and L. Adleman (1978), “A method for
obtaining digital signatures and public-key cryptosystems.”
Communications of the ACM 21, 120.
Shor, Peter W (1994), “Algorithms for quantum computation:
discrete logarithms and factoring,” in Symposium on the
Foundations of Computer Science , Vol. 35, p. 124.
Shor, Peter W (1995), “Scheme for reducing decoherence in
quantum computer memory,” Physical Review A 52, R2493.
Stewart, I, D. Ilie, A. Zamyatin, S. Werner, M. F. Torshizi,
and W. J. Knottenbelt (2018), “Committing to quantum
resistance: a slow defence for bitcoin against a fast quantum
computing attack,” R. Soc. Open Sci. 5, 180410.
Werbach, Kevin (2018), The Blockchain and the New Archi-
tecture of Trust (The MIT Press, Cambridge).
Yawitz, J B (1977), “An analytical model of interest rate
differentials and different default recoveries,” Journal of
Financial and Quantitative Analysis 12, 481.
|
{
"id": "2102.00659"
}
|
2106.10012
|
XRP Network and Proposal of Flow Index
|
XRP is a modern crypto-asset (crypto-currency) developed by Ripple Labs,
which has been increasing its financial presence. We study its transaction
history available as ledger data. An analysis of its basic statistics,
correlations, and network properties are presented. Motivated by the behavior
of some nodes with histories of large transactions, we propose a new index: the
``Flow Index.'' The Flow Index is a pair of indices suitable for characterizing
transaction frequencies as a source and destination of a node. Using this Flow
Index, we study the global structure of the XRP network and construct
bow-tie/walnut structure.
|
http://arxiv.org/pdf/2106.10012v1
|
Hideaki Aoyama
|
q-fin.GN
|
q-fin.GN
|
arXiv:2106.10012v1 [q-fin.GN] 18 Jun 2021XRP Network and Proposal of Flow Index
Hideaki Aoyama
Graduate School of Advanced Integrated Studies in Human Sur vivability, Kyoto University, Kyoto
606-8306, Japan
RIKEN iTHEMS, Wako, Saitama 351-0198, Japan
Research Institute of Economy, Trade and Industry (RIETI), Tokyo 100-0013, Japan
E-mail: hideaki.aoyama@gmail.com
(Received May 17, 2021)
XRP is a modern crypto-asset (crypto-currency) developed b y Ripple Labs, which has been increas-
ing its financial presence. We study its transaction history available as ledger data. An analysis of
its basic statistics, correlations, and network propertie s are presented. Motivated by the behavior of
some nodes with histories of large transactions, we propose a new index: the “Flow Index.” The
Flow Index is a pair of indices suitable for characterizing t ransaction frequencies as a source and
destination of a node. Using this Flow Index, we study the glo bal structure of the XRP network and
construct bow-tie/walnut structure.
Presented at the“Blockchain in Kyoto” (February 17–18, 202 1), and will appear in the JPS Confer-
ence Proceedings.
KEYWORDS: Power distribution, Ripple Labs., Ledger data, bow-tie/wa lnut structure
1. Introduction
The world of crypto-assets is dynamic and complex (we use the term “crypto-asset” instead of
“crypto-currency” throughout this paper. See [1]). Its pre sence in the financial market has steadily
increased since its inception in early 2013. Understanding the nature of this world is important.
Ever since its inception, all transaction data (except for a few, which we will elaborate in the next
section) are stored and available through various media, pr oviding researchers with ample opportunity
to study the intriguing properties, similar to Bitcoin. [2– 4].
Traditional monetary transactions through financial insti tutions, such as banks, are crucial for
analyzing and understanding inter-firm and firm-household r elationships. However, the availability
of such data is quite limited because of privacy concerns, ex cept for a few rare cases [5].
In this study, we present a basic analysis of the XRP world obs erved through ledger data, which
is the transaction record comprising the amount of XRP, sour ce account, destination account, and
the day and time (in coordinated universal time (UTC)) of tra nsactions. (For a reasonable and read-
able introduction to XRP, see [6].) Accounts are just 33 lett er-long codes such as “rfceigRxmgA-
jWR6LH1L7YsooWKMqM5Pr6,” and no other information on the o wner (name, address, etc.) are
available. Although this makes interpreting the results of analysis rather difficult, because of its im-
portance and data availability, it remains a worthwhile end eavor.
In Section 2, we describe our ledger data and its basic proper ties, including the distribution of
the number of transactions, properties of the time series wi th the day-of-the-week analysis, and cor-
relations. Section 3 is devoted to analyzing the XRP network of the transactions, wherein nodes are
accounts and edges are transactions. Section 4 describes th e new proposal of the Modified Inverse
Herfindahl-Hirschman Index andFlow Index . Section 5 is devoted to studying the global structure of
the XRP network, similar to bow-tie /walnut decomposition, using the Flow Index. Section 6 o ffers
1
Fig. 1. Annual number of transactions. The points connected with da shed lines denote all transactions,
whereas those with solid lines denote XRP–XRP.
discussion and conclusion.
2. Data and its Basic Statistics
The ledger data we analyze are for 2,463 days of 1 /2/2013–9/30/2019. The first 32,570 ledgers
were lost because of “a mishap in 2012” [7].
2.1 Data Selection
From this data set, we extract data with the following criter ia.
(1) The ledger contains transactions between 1,525 currenc ies and crypto-assets. Most of these are
from XRP to XRP; however, some of them are from XRP to others, o thers to XRP, and others
to others. We provide a yearly breakdown for those with XRP–X RP, and the rest is provided in
Fig.1. We use XRP–XRP transactions only.
(2) The data set contains “partial payments” [8], wherein th e actual transferred amount is di fferent
from the transaction amount because of the payment of transf er fees. In reality, they are rare:
0.018% of all of the XRP–XRP transactions, with the minimum o f 0% in 2013 and a maximum
of 0.041% in 2019. The distribution of “Amount” and “Deliver ed amount” and the delivered
amount is provided in Fig. 2, wherein we observe some pattern s of quantization of the delivered
amount and the proportionality between the amount and the de livered amount. We drop these
“partial payment” transactions from the following analysi s.
After filtering, we arrive at the data of the sizes listed in Ta ble II.
2.2 Data Distributions
The cumulative distribution function (CDF) of the annual XR P transaction is plotted in Fig. 3,
wherein the dashed straight line has a gradient of =−1. The data for XRP >105fit are appropriate to
this line, except for the first year of 2013. This means that th e XRP distribution has a power-decaying
fat tail, XRP−1in CDF and XRP−2in the Probability Distribution Function (eps). The absolu te value
of this exponent of CDF tail is called the “Pareto index.”. Th e current Pareto index 1 is known to be a
phase transition point between an “Oligopoly” phase and a “P seudo Equality” phase [9, 10]: In case
the fat tail is thinner and the Pareto index is larger than 1, t he share of those with higher ranks (top,
largest, second largest, and so forth) have zero shares when an infinite number of entries are present.
2
Source Destination No. of Transactions
XRP XRP 43,624,956
CCK CCK 3,836,798
CNY CNY 933,208
EUR EUR 836,061
USD USD 667,504
SFO SFO 487,965
BTC BTC 473,921
JPY JPY 207,385
ETH ETH 168,908
GWD GWD 146,054
Table I. Top 10 transaction currencies.
Fig. 2. Distribution of “Amount” and “Delivered amount” of the XRP– XRP transactions whose “Amount”
is not equal to “Delivered Amount.” The dashed diagonal has a gradient equal to one wherein the amount is
equal to the delivered amount.
In contrast, the top-ranking ones have finite shares, even wh en an infinite number of entries exists.
The top 10 transactions (in amount) are listed in Table III. A s shown in Table II, the total traded
amount is∼1.1651×1012XRP. Therefore, the top two transactions in this table occup y 17% of all
∼45 million transactions, which is indeed a large share.
This criticality of the Pareto index being equal to one is mos t easily understood when referring to
the case of the size of firms in a country. Empirical analysis o f firms in developed countries (such as
Japan, France, Germany, and the United Kingdom) showed that the firm size (number of employees,
amount of sales, or income) distribution has a Pareto index v ery close to one through many years. This
is in contrast to the distribution of personal income whose P areto index varies around 2 (say, 1.5–2.5),
depending on the economic situation of the year. For firms, bu siness competition drives the Pareto
index downward (fatter tail), as big firms attempt to dominat e the market. In contrast, various political
pressures and measures by the central bank and the ministrie s against monopoly and oligopoly are
active. The current author argued that the balancing critic al point is at a Pareto index equal to one [9].
However, a big difference between this argument on firm size and the current XRP t ransaction exists.
3
Year # Transactions # Source # Destination # All nodes XRP (/1012)
2013 1,104,589 22,410 548,38 54864 0.2031
2014 2,897,049 35,477 130,658 131,525 0.1368
2015 6,147,511 28,362 56,724 64,310 0.0625
2016 7,652,661 31,370 68,167 75,230 0.2939
2017 7,883,617 297,558 666,749 693,787 0.2109
2018 7,483,668 424,695 714,184 826,622 0.1310
2019 10,379,060 378,700 434,675 535,724 0.1266
All years 43,548,155 1,287,516 1,810,387 1,810,676 1.1651
Table II. Number of transactions, nodes (source, destination, and ei ther of them) and the amount of traded
XRP.
Fig. 3. Annual Cumulative Distribution Function (CDF) of the amoun t of XRP transactions.
The former is “stock,” while the latter (the transaction amo unt we analyze) is “flow.” Moreover, the
XRP world is free from central governing organization and th ere is no measure against monopoly and
oligopoly. Thus, the reason behind the current finding remai ns a mystery for the moment.
2.3 Time Series
Fig. 4 shows the daily amount of transactions. Fig. 5 shows th e daily number of users (blue,
orange, and green lines show number of sources, destination s, and either sources or destinations,
respectively). We observe that both the amount and number of users are highly volatile and that most
users trade both as a source and destination.
The left panel of Fig. 6 shows the details of the daily number o f transactions in 2018, wherein
Sundays (in UTC) are shown with a vertical dashed line. The re duction of transactions on Saturdays
and Sundays is visible to the naked eye. This indicates that m ost of the nodes are operated by humans.
(Although the time is in UTC and the data cover the entire worl d, the Saturdays and Sundays in UTC
4
Fig. 4. Daily amount of transactions in linear (left) and log scale ( right).
Fig. 5. Daily number of users in linear scale (left) and log scale (ri ght).
are approximately weekends in most economically active reg ions, as the Pacific Standard time (PST)
in the United States is UTC-7, and Japan Standard Time (JST) i s UTC+9.) It is clear that the absolute
values of the Fourier components in the right panel present a clear peak at a period of seven days.
The daily number of users also shows reduction on weekends. H owever, the daily total amount of
transactions does not show a clear periodicity. This may be b ecause of its high volatility. This weekly
periodicity is weaker in other years. A similar behavior was found in the analysis of the volume and
No. XRP Destination Data & Time Source
1 1.0×1011bn0864 2016-11-07T07:50:20Z bn0347
2 1.0×1011bn0864 2016-11-07T07:51:10Z bn0347
3 1.1×1010bn0530 2014-06-10T21:59:40Z bn0598
4 1.0×109bn0166 2014-06-10T22:01:50Z bn0915
5 9.1×109bn0151 2014-06-10T22:07:40Z bn0781
6 9.0×109bn0545 2013-01-26T22:35:20Z bn0103
7 9.0×109bn0760 2013-01-26T22:36:00Z bn0103
8 9.0×109bn1010 2013-01-26T22:39:00Z bn0103
9 9.0×109bn0298 2013-01-26T22:39:30Z bn0103
10 8.0×109bn0135 2013-01-26T22:36:40Z bn0103
Table III. Top 10 transactions. “bnxxx” is a encoded node name defined by the author.
5
number of Bitcoin transactions [2].
Fig. 6. Detail of the daily transaction numbers in 2018. The left pan el is the daily series, and the right panel
is its Fourier decomposition.
2.4 Correlation
There is a correlation that obeys an interesting phenomenol ogical law. Fig. 7 shows the correlation
between daily number of users and daily total amount of trans actions, in which di fferent colors show
different years. The dashed line has gradient =1.5, which means that
[Amount]∝[Number of users]1.5. (1)
We observe yearly development toward large numbers of users and a larger amount of transactions
roughly along this line. A close examination of the annual da ta reveals that the distribution split into
a group above and another below this line in 2013 and 2014, res pectively; however, this converges in
later years. This power law is curious, calling for the model ing of agents in this XRP world: Since
Eq.(1) means
[Amount per user] ∝[Number of users]0.5, (2)
This means an interesting characteristic of the herding beh avior in XRP trade: in a day of high activity
with a number of users larger than usual, the amount of averag e transactions increases. For example,
if the number of users becomes 10 times as much, the amount of a verage transactions becomes√
10≃
3.2 times as much.
Other correlations between the number of destinations, num ber of sources, and number of trans-
actions show no such behavior. All three correlations follo w linear proportionality (power exponents
are close to one).
3. XRP Network
Let us examine the network(s) they form, where nodes are acco unts, and edges are transactions.
The CDFs of in-degree and out-degree are plotted in Fig. 8. We observe that, except for 2015–
2017, it also has a fat tail with Pareto index 1. Again, this is an interesting finding, awaiting deeper
insight and/or modelling.
3.1 Nodes with Large Transaction History
As noted above, the transactions cover a vast range of 10−6(minimum unit) XRP to ∼1011
XRP (Fig. 3). As dealing with them all is unproductive, we int roduce a threshold for their biggest
6
Fig. 7. Scatter plot of daily number of users and daily total amount o f transactions.
Fig. 8. Annual CDF of the in-degree (left) and out-degree (right) of XRP transaction network.
7
Fig. 9. Networks of the nodes selected with three thresholds listed in Table IV.
transaction. Let us first examine nodes with transactions eq ual to or greater than 107,8,9XRP at least
once during the entire period (2013–2019). The network size s they form are listed in Table IV, and
the corresponding networks are visualized in Fig. 9. In deno ting the node, we use the set of 1,136
nodes in the≥107category and name the nodes with “bn” plus its number (0001–1 136) in the set.
(Hereafter, we shall call those 1,136 nodes as “big nodes,” a nd the 94 nodes with threshold =109
XRP as “huge nodes.”) For example, “bn0001” is the first node i n a set of big nodes.
3.2 Some Notable Big Nodes
Some of the nodes that made these huge transactions have a rat her notable transaction history,
some of which are listed below.
Pair Nodes This is a pair of nodes, among which a large amount of XRP was tr ansferred, with
no other notable activity. An example of this type of node is ( bn0347, bn0864) at the top two in
Table III. Within a minute, 2 ×1011XRP was transferred from the former to the latter. The former
had no other activity, while the latter had numerous transac tions considered to be negligible
amounts compared to these transactions. Their transaction histories are provided in Fig. 10,
where blue dots present the day and amount of transactions as destination (receival of XRP),
red dots show those of transactions as source, and green line s show balance, assuming it is zero
initially.
Bridge Nodes This node receives a large amount of XRP and sends it to anothe r node, with no
other notable activity. The node bn530, the third in Table II I, is an example of this case, whose
transaction history is plotted in Fig. 11.
In characterizing these nodes considering the amount and fr equency of transactions, noting that
some nodes make these huge transactions and many transactio ns of small amounts is important. A
good example is the node bn0846 in Fig. 10: This node made seve ral transactions of small amounts
as both a destination and source. However, they are negligib le compared to the two large transactions
on the same day, totaling 2.0 ×1011XRP, which are the only significant transactions in characte rizing
the transaction behavior of this node. As made clear from thi s example, a simple count of the number
of transactions (as a source or destination) or total number of transactions cannot be considered a
good measure of its activity. What counts is the number of “si gnificant” (in the amount) transactions
Threshold # Nodes # edges
1071,136 5,187
108262 685
10994 170
Table IV . The sizes of the networks formed by nodes with threshold 107,8,9XRP.
8
Fig. 10. An example of Pair Nodes.
Fig. 11. A bridge node.
it made. For this purpose, we propose a new index called the “F low Index,” which gives e ffective
number of transactions in each of the directions.
4. Flow Index
4.1 Herfindahl-Hirschman Index
The Herfindahl-Hirschman Index [11] (hereafter abbreviate d to “HH Index,””) is used in several
data analysis areas to quantify how numbers are distributed to components in a list. Consider a list ℓ
ofNnon-negative numbers, whose total number is equal to 1:
ℓ=/parenleftbigℓ1,ℓ2,···,ℓN/parenrightbig, ℓ m≥0,N/summationdisplay
m=1ℓm=1. (3)
(One might think of this list as a list of shares: For example, the first entryℓ1is the share of the 1st
firm in the sales of a certain good, ℓ2share of the second firm, and so on.) Its HH Index H(ℓ) is
9
defined as follows:
H(ℓ)=N/summationdisplay
k=1ℓk2, (4)
and satisfies 0<H(ℓ)≤1.
Here are some examples:
ℓ=/parenleftbig1,0,···,0/parenrightbig;H(ℓ)=1, (5)
ℓ=1
m/parenleftbig1,1,···,ℓm=1,0,···,0/parenrightbig;H(ℓ)=1/m. (6)
As presented here, the HH Index H(ℓ) is a measure of the concentration of the values in ℓ: If it is
concentrated to just one component, H(ℓ)=1. If it is less concentrated, the smaller H(ℓ) is.
The inverse HH Index, 1 /H(ℓ), may be used as a measure of the e ffective number of entries, as
1/H(ℓ)=min the latter case (6). However, it has one undesirable prope rty. In the next subsection,
we describe the method and propose a modification for overcom ing it.
4.2 Modified Inverse Herfindahl-Hirschman Index
Let us examine the behavior of the inverse HH Index for a gener alization of (6).
ℓ(r)=1
m+r/parenleftbig1,1,···,ℓm=1,ℓm+1=r,0,···,0/parenrightbig, (7)
with 0≤r≤1, which has
H(ℓ(r))=m+r2
(m+r)2. (8)
The inverse is plotted in Fig. 12 as a function of m+r(≡x). As shown here,1/H(ℓ(r))=mat integer
values of x=m(r=0), as noted above. However, it flattens as r→1, and the derivative with
respect to xis discontinuous at x=m. Essentially, the inverse HH Index is much less sensitive to
the reduction in the distribution of numbers ( rdecreases starting at r=1 for fixed m−1) than its
expansion ( rincreases starting at r=0 for a fixed m). It also deviates in certain ways from the dashed
diagonal line f(x)=x, while having a measure closer to this line is desirable.
Fig. 12. Behavior of the inverse HH Index 1 /H(ℓ(r)) as a function of x=m+r.
10
To overcome this di fficulty, we define the following measure, modified Inverse Herfi ndahl-Hirschman
Index of the n-th order:
Mn(ℓ)=¯Hn−1(ℓ)
¯Hn(ℓ), (9)
where ¯Hn(ℓ) is a modified HH Index:
¯Hn(ℓ)=N/summationdisplay
k=1ℓn
k, (10)
identical to HH Index for n=2,¯H2(ℓ)=H(ℓ). As ¯H1(ℓ)=1,M2(ℓ) is the inverse HH Index,
M2(ℓ)=1/H(ℓ).
For the case of the list ℓ(r) in (7),
Mn(ℓ(r))=(m+r)(m+rn−1)
m+rn, (11)
and it satisfies Mn(ℓ(0))=m. Its behavior for n=2,3,10 is plotted in Fig. 13. The higher n, the
closer Mn(ℓ(r)) to m+r, because rn→0 for n→∞ (0≤r<1).
lim
n→∞(m+r)(m+rn−1)
m+rn→m+r. (12)
Because of this property, one may choose nto be a large number for analysis. In the following, we
usen=20 because the difference observed in the right panel of Fig. 13 is at most ∼1.4%.
Fig. 13. Left: Modified inverse HH Index Mn(ℓ(r)) as a function of x=m+rforn=2,3,20 from top (blue)
to bottom (green). Right: Mn(ℓ(r))/x.
4.3 Flow Index
The modified inverse HH Index defined above is useful for quant ifying a node’s transaction his-
tory. Let us denote the time series of the daily outflow by foutand the daily inflow by finfor this
discussion. All the components of foutandfinare positive. Their “normalized” (in the sense that the
total of all components is equal to 1, as in Eq.(3)) versions a re denoted ¯foutand ¯fin. In a case with no
flow, for example, fout={}(an empty set), we define ¯fout={}andMn(¯fout)=0, and so on.
Here we are dealing with the flows aggregated daily. Alternat ively, one may deal with tick data
of the flows. The di fference is that if a node makes several large transactions wit hin a short period
of time, treating them as one transaction is most appropriat e. Daily aggregation takes care of them
11
unless several transactions are made in a time window that in cludes 0:00 UTC. For this reason, the
daily aggregation is chosen in this study.
Take the node bn0864 shown in the right panel of Fig. 10. This n ode fits the case discussed above:
two transactions of 1 .0×1011XRP each within 50 s of time were made. Daily aggregation trea ts them
as one 2.0×1011XRP transaction. It also included lots of small income over 1 7 days. Therefore, its
effective inflow history is best summarized to be on “(very close to) just one occasion.” Its modified
inverse HH Index is, in fact, M20(¯fin)=1.00005, quantifying this fact.
This is not the end of the story. This node had payment over two days (two red dots in the right
panel of Fig. 10) and its modified inverse HH Index for the outfl ow is M20(¯fout)=1.20427. However,
the amount of outflow was negligible compared to the inflow. We need to discount the outflow relative
to the inflow for this node.
To do so for all nodes, we introduce the following quantity, t heFlow Index :
A=/parenleftBigg
M20(¯fin)Max( fin)
Max( fin,fout),M20(¯fout)Max( fout)
Max( fin,fout)/parenrightBigg
. (13)
Max( fin) is the maximum of the components of the outflow, and Max( fin,fout) denotes the maximum
of the components of the joined set of inflows and outflows.
For node bn0864, we obtain
A=(1.0005,5.8955×10−10), (14)
a very satisfactory result.
One may think of modifying the above by using the total volume of flows instead of maximum
values. However, that does not work. This can be further expl ored by our readers.
5. Global Structure of the XRP Network
The left panel of Fig. 14 is a scatter plot of 1,136 big nodes (g reen, threshold=107XRP) and
1,176 huge nodes (blue, above threshold =109XRP) (see Table IV) on the Flow Index plane. We
observe that the nodes are distributed somewhat widely on th e lower-left part of the Flow Index
plane. This implies a tendency of nodes with a small number of effective transactions (as counted
by the Flow Index) tend to trade as destination and as origin u nevenly. In contrast, those with a
higher number of transactions are located close to the diago nal, meaning that they tend to trade as
destinations and origins evenly. This tendency is true for b oth big nodes and huge nodes.
The right panel of Fig. 14 shows the details of the left panel. We observe the existence of nodes
that mostly participate in one mode. Motivated by this type o f distribution, we classify the nodes
with A1≤0.5 (red rectangle) as “OUT” components, as they are mostly on t he destination side. This
means they are at the final goal of XRP when viewed as part of the whole network. We found 193
nodes. Similarly, we classify the nodes with A2≤0.5 (purple rectangle) as “IN” components; there
are 52 of them.
Using this criteria, we can draw bow-tie /walnut-like structures [12], as shown in Fig. 15.
This characterizes the global structure of the XRP network, which forms the basis for understand-
ing the dynamics and development of this complex structure.
12
Fig. 14. Scatter plot of all big nodes (green) and huge nodes (blue) on the Flow Index plane. The left panel
shows all of the big nodes, while the right panel shows detail s close to the origin.
Fig. 15. The bow-tie/walnut-like structure of big nodes obtained by the use of Flo w Index.
13
6. Concluding Remarks
In this study, we presented an analysis of XRP transaction re cords from ledger data.
These data are huge and complex: In addition to the number of t ransactions, The distribution
of traded amount, frequency of transactions, and so on cover huge ranges, some of which cover 18
orders of magnitude (10−6to 1011XRP). A notable empirical finding includes the power distrib ution
of several quantities with a Pareto exponent close to one, an d a power-law correlation between the
daily number of transactions and the daily amount of transac tions. The former remains a puzzle:
While Pareto index equal to one is known to be the beginning of the monopoly/oligopoly phase, we
have a reasonable explanation behind it only for stock quant ities like number of employees at firms.
The current transaction amount between nodes is a flow.. The l atter, the power-law correlation, may
be explained in terms of “herding behavior.” Proper modelli ng with the use of deeper analysis of
the current data should lead to the explanation of this corre lation. These subjects are worth more
extensive exploration in the future.
The XRP network, a directive network with nodes as transacti on accounts and edges as transac-
tions, is another main subject of this research. To concentr ate on the central structure of this network,
we placed a threshold for the maximum amount of each node. We s elected nodes that made trans-
actions of more than ≥107XRP at least once and called them big nodes. To examine transa ction
frequency while considering the huge range of transaction a mount each nodes make, we defined a
new index called “Flow Index,” borrowing and extending the i dea of the Herfindahl-Hirschman In-
dex. We further introduced classification of nodes using the Flow Index and arrived at a view of the
entire network as a bowtie /walnut-like.
We believe this work establishes a foundation for not only th e XRP network but also other dy-
namic networks of transactions. Further research on this ne twork should reveal details of the activities
and clarification of each node’s characteristics on the stru cture of the bow-tie /walnut-like decompo-
sitions.
Note added in proof:
Toward the end of writing this manuscript, the author learne d of a new paper by Fujiwara and
Islam [13], where they examined the Bitcoin network formed b y “regular users.” This approach is
complementary to our current analysis. While the latter cho oses to select users based on the number
of transactions, the former analyzes the frequency of trans actions. The current author believes that a
new approach based on both ways of thinking and picking up goo d features from both is waiting for
us in the near future.
Acknowledgments
The author would like to acknowledge Ripple, which is provid ing financial and technical sup-
port through its University Blockchain Research Initiativ e. He would also like to thank Yuichi Ikeda
for providing him with the ledger data, Yoshi Fujiwara and Hi ro Inoue for technical support on data-
handling, Yuzuki Nomura for helping him with text entry and e diting, and Editage (www.editage.com)
for English language editing.
References
[1] Library of Congress, USA. Regulatory approaches to cryp toassets: Japan.https://www.loc.gov/
law/help/cryptoassets/japan.php .
[2] Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, and Hiwo n Yoon. Analyzing outliers activity from the
time-series transaction pattern of bitcoin blockchain. Evolutionary and Institutional Economics Review ,
16(1):239–257, 2019.
[3] Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, and Hiwo n Yoon. Unfolding identity of financial institu-
tions in bitcoin blockchain by weekly pattern of network flow s.Evolutionary and Institutional Economics
14
Review , pages 1–27, 2020.
[4] Yoshi Fujiwara and Rubaiyat Islam. Hodge decomposition of bitcoin money flow. In Advanced Studies
of Financial Technologies and Cryptocurrency Markets , pages 117–137. Springer, 2020.
[5] Fujiwara Yoshi, Inoue Hiroyasu, Yamaguchi Takayuki, Ao yama Hideaki, Tanaka Takuma, and Kikuchi
Kentaro. Money flow network among firms’ accounts in a regiona l bank of japan. EPJ Data Science ,
10(1), 2021.
[6] Introduction to XRP. https://xrpcommunity.blog/introduction-to-xrp-2019- edition/ .
[7] Bithomp page. https://bithomp.com/genesis .
[8] XRP ledger page. https://xrpl.org/partial-payments.html .
[9] Hideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Hiroshi I yetomi, and Wataru Souma. Econophysics and
companies: statistical life and death in complex business n etworks . Cambridge University Press, 2010.
[10] Hideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Hiroshi Iyetomi, Wataru Souma, and Hiroshi Yoshikawa.
Macro-econophysics: New studies on economic networks and s ynchronization . Cambridge University
Press, 2017.
[11] Stephen A Rhoades. The Herfindahl–Hirschman index. Fed. Res. Bull. , 79:188, 1993.
[12] Abhijit Chakraborty, Yuichi Kichikawa, Takashi Iino, Hiroshi Iyetomi, Hiroyasu Inoue, Yoshi Fujiwara,
and Hideaki Aoyama. Hierarchical communities in the walnut structure of the japanese production net-
work. PloS one , 13(8):e0202739, 2018.
[13] Yoshi Fujiwara and Rubaiyat Islam. Bitcoin’s crypto flo w newtork. 2021. in this volume.
15
|
{
"id": "2106.10012"
}
|
2307.07066
|
Proof of Training (PoT): Harnessing Crypto Mining Power for Distributed
AI Training
|
In the midst of the emerging trend of integrating artificial intelligence
(AI) with crypto mining, we identify three major challenges that create a gap
between these two fields. To bridge this gap, we introduce the
proof-of-training (PoT) protocol, an approach that combines the strengths of
both AI and blockchain technology. The PoT protocol utilizes the practical
Byzantine fault tolerance (PBFT) consensus mechanism to synchronize global
states. To evaluate the performance of the protocol design, we present an
implementation of a decentralized training network (DTN) that adopts the PoT
protocol. Our results indicate that the protocol exhibits considerable
potential in terms of task throughput, system robustness, and network security.
|
http://arxiv.org/pdf/2307.07066v1
|
Peihao Li
|
cs.CR, cs.CE, cs.DC, cs.LG
|
cs.CR
|
O R I G IN A L A RT IC L E
Ne t w o r k s
Proof of Training (PoT): Harnessing Crypto Mining
Power for Distributed AI Training
Peihao Li1*
1Co-founder at Intellichain Solutions KFT,
1054 Budapest, Hungary
Correspondence
Peihao Li, Intellichain Solutions KFT,
Honvéd utca 8. 1. em. 2. ajtó, 1054
Budapest, Hungary
Email: peihao.li@kaust.edu.sa
Funding informationIn the midst of the emerging trend of integrating artificial
intelligence (AI) with crypto mining, we identify three ma-
jor challenges that create a gap between these two fields.
To bridge this gap, we introduce the proof-of-training (PoT)
protocol, an approach that combines the strengths of both
AI and blockchain technology. The PoT protocol utilizes the
practical Byzantine fault tolerance (PBFT) consensus mech-
anism to synchronize global states. To evaluate the perfor-
mance of the protocol design, we present an implementa-
tion of a decentralized training network (DTN) that adopts
the PoT protocol. Our results indicate that the protocol ex-
hibits considerable potential in terms of task throughput,
system robustness, and network security.
K E Y W O R D S
proof of training, AI, blockchain, hash power, distributed network,
consensus mechanism
1|INTRODUCTION
1.1 |Motivations
Crypto mining is the process of creating and adding new blocks to a blockchain network through the use of various
consensus mechanisms based on different resources (mining rigs, staked tokens etc..), with Proof of Work (PoW) being
the most commonly used [21, 24]. In a blockchain network built on the PoW consensus mechanism, miners compete to
create the subsequent valid block by being the first to solve a cryptographic puzzle, earning a reward for their efforts.
The consensus algorithm, which integrates an appropriate rewards distribution system, is the core of a blockchain
*Equally contributing authors.
1arXiv:2307.07066v1 [cs.CR] 13 Jul 2023
2
network. The most prominent blockchain projects in the crypto industry, such as Bitcoin (BTC) and Ethereum (ETH),
uses the PoW consensus mechanism, with the latter recently shifted to Proof of State (PoS) [17]. According to bitcoin
energy consumption analysis [5, 8], the yearly electricity consumption of Bitcoin mining exceeds that of United Arab
Emirates (119.45 TWh) in 2021 and Sweden (131.79 Twh) in 2022. The majority of the energy consumed is dedicated
to solving cryptographic puzzles. While this process enables trustless consensus, it does not offer any additional
practical benefits. In fact, the apparent lack of a theoretical upper bound on the energy consumption of the PoW
mechanism has raised global concerns, leading to the development of alternative consensus mechanisms, such as
PoS, and changes in institutional policies. For instance, Tesla announced in 2021 that it would no longer accept BTC
due to climate concerns [23].
Crypto mining is a rapidly changing industry. In 2022, Ethereum transitioned from the energy-intensive Proof of
Work (PoW) consensus mechanism to an alternative called Proof of Stake (PoS), in response to growing environmental
and energy concerns. Consequently, this change led to a substantial reduction in power demand, ranging from 99.84%
to 99.9996% [14]. Ethereum’s reduction in energy consumption could be comparable to the electrical power needs
of a nation like Ireland or even Austria, whose advancement has a significantly positive impact on environmental
sustainability. However, it has also resulted in a substantial amount of unused hashrate, equivalent to 1,126,674 GH/s
[3], which now lacks a specific application. This brings the potential for miners to shift their computational resources
from crypto mining to other areas like internet of things (IOT) and data services [2, 1]. This transition can remain fully
within the blockchain space, by using these resources to run processes hosted on decentralized blockchain-based
networks.
Meanwhile, with the integration of artificial intelligence (AI) into various sectors of the economy, the demand
for computational resources to fuel this machine intelligence is experiencing rapid growth. Training a model like
ChatGPT incurs expenses exceeding $5 million, and operating the initial ChatGPT demo costs OpenAI approximately
$100,000 per day prior to the surge in its current usage [13]. Due to the extensive number of neural parameters and
significant GPU hours required, the high computational demands of model optimization present substantial challenges
for academic researchers and small-scale enterprises, limiting the widespread use of artificial intelligence technologies.
It is therefore unsurprising that an increasing number of crypto miners are exploring ways to utilize their existing
computation infrastructures to contribute to the advancement of AI, redirecting its previously mining-focused compu-
tational resources for machine learning and other high-performance computing (HPC) applications, as demonstrated
by Hive Blockchain. The company’s long-term HPC strategy involves shifting from Ethereum mining to HPC appli-
cations, including artificial intelligence, rendering, and video transcoding, with an anticipated revenue generation of
approximately $30 million per month.
Considering the developments mentioned, we believe that the emerging trend of combining and integrating these
resources has the potential to significantly enhance the development process of AI tools in both technical and financial
aspects. This would provide AI tool developers with a more affordable plan to monetize their innovations, including
simplified training and marketplace access. Instead of exclusively commercializing their creations through major tech-
nology corporations, developers have the opportunity to contribute to the decentralization of technology by shifting
their assets from centralized entities to a global commons. In the long run, this new direction is anticipated to yield
significant societal benefits by optimizing resource allocation and minimizing costs.
1.2 |Challenges
Despite the considerable potential, the decentralization of software and hardware underlying AI remains in its early
stages, due to the absence of well-developed consensus frameworks. Several pioneering studies have innovatively
3
proposed new consensus schemes based on training machine learning models [4, 2, 6, 19, 10]. However, a notable gap
exists between the theoretical foundations of these frameworks and their practical implementations. SingularityNET
and FetchAI [4, 2] present a general high level framework but without technical details clearly shown. Coin.AI [6]
further addressed this issue by proposing Proof of Useful Work (PoUW). However, they do not have customized AI
training task, which can greatly reduce their network efficiency in serving clients, restricting their applicability to a
limited range of business models. Authors in [19] further addressed this issue by incorporating features of customized
clients. The design’s limitation is mainly the inherent flaw in its blockchain structure, where the inclusion of test data
within a block’s body can rapidly consume the storage capacity of consensus nodes.
While Proof of Work (PoW) has been proven to be quite secure and effective since the launch of Bitcoin and
Ethereum, an industry-level consensus mechanism explicitly designed for decentralized AI training remains absent. In
general, we identify the following major challenges currently hindering the progress and realization of a decentralized
AI utility network:
1. Reliable validation mechanism. Although resource consuming, PoW exhibits favorable time complexity for vali-
dation, ensuring efficient processing within the system. Upon mining a block, the network can efficiently verify
its validity and append it to the local chain with ease. Another benefit of PoW is its determinacy in the global
state, which guarantees that if a node is honest and abides by the complete set of rules within the system, it will
consistently achieve the same state at a specific timestamp, consequently validating the system with confidence.
However, in the context of decentralized machine learning, it is inherently challenging to ascertain whether a
miner has genuinely performed its task as required. This is because when using different GPUs to perform the
same AI training task with the same optimizer and dataset, it is still possible to obtain completely different results.
Factors such as parallelism, random seeds, and rounding errors can all lead to differences in results, thus posing
significant challenges for implementing validators within the network. Consequently, it is impossible for an en-
tity to provide verifiable evidence that they have executed the necessary tasks to train a model by following the
PoW-like consensus mechanism.
2. Ownership protection from model-stealing attacks. In decentralized AI training, once a trained model is released
publicly in the network by a miner to claim network rewards, it will be broadcasted by other nodes either unaltered
or manipulated (i.e., the model is stolen and the attacker claims ownership) until it fully propagates throughout
the network. The model’s actual owner may need to prove that they trained the model as a means to claim
ownership. Proof of Learning (PoLe) [19] introduced an anti-theft scheme utilizing inner product-based functional
encryption (IPFE) and IPFE with function-hiding (IPFE-FH). However, the problem of guaranteeing that the data
node receives the complete model information remains unaddressed and requires further exploration. Ideally, it
would be preferrable that the network receives full model info before applying the validation process.
3. Absense of efficient consensus protocols for delivering services. Upon successfully developing a consensus
mechanism for decentralized AI training, it is of great interest to subsequently integrate it within a practical
blockchain framework. According to the FLP impossibility theorem, which states that in an asynchronous dis-
tributed system where at least one process can fail, it is impossible to create a consensus algorithm that guaran-
tees both safety and liveness at the same time [16], which is why most blockchain systems adopt synchronous
consensus mechanisms. However, storage and bandwidth can be quite expensive in such systems since the sys-
tem always store nreplicas of the global states. Therefore, we require the protocol to store only the necessary
states, minimizing storage requirements. Given the rapid evolution of AI models, integrating the entire system into
a layer-1 (L1) blockchain solution1may not be the most optimal approach [19, 6]. Such systems typically maintain
1A Layer-1 (L1) blockchain refers to the foundational layer of a blockchain network, which consists of the base protocol that governs the consensus mechanism,
4
a consistent block production rate2, thus ensuring a stable transaction throughput capacity. However, in a decen-
tralized AI training system, the workload dynamically fluctuates in response to market supply and demand. There
may be periods when the system experiences inactivity due to a lack of incoming training jobs, during which the
majority of nodes become stale without a flow of rewards. In such a system, the primary objective is to generate
valuable AI models, with transaction validation serving as a secondary function. A well-constructed framework
should address these aspects by dynamically adjusting system workload according to the influx of training jobs,
enabling seamless system upgrades over time, and ensuring the ease of use and security for users’ assets. Such
a protocol is currently lacking in the industry.
2|PROOF OF TRAINING
Our primary objective is to establish a robust consensus protocol called proof of training (PoT) that lays the foundation
for harnessing the power of crypto mining for distributed artificial intelligence (AI) training. The development of this
protocol is crucial for enabling the efficient and secure utilization of computational resources across a decentralized
network, with the ultimate goal of advancing AI model training. In this section, we will concentrate on the functions
and utilities of the protocol, abstracting away from specific network designs. A comprehensive discussion of network
realization can be found in Section 3.
2.1 |Notations
We denote the set of naggregator nodes running the global ledger LbyA={Ai}n
i=1whereAirepresents each
aggregator node, coordinating the client C, the service provider Pand the protocol validator V. We denote all
participants in the network by N={A,C,P,V}, with each individual denoted as Ni. We letSNdenote participant-
specific security variables, including components for asymmetric encryption. We let SN[pk]denote the public key of
nodeNi, andSN[sk]denote the corresponding private key.
We use the notation Mto denote an AI model and Mto denote the full set of nsmodels generated in a given
specification where Ms=(M1,M2,···,Mns). Specifically, we use the notation MCto denote the model supplied
by a client for the network to train, typically with a certain initialization. Additionally, we introduce Dtrainto represent
the training data and Dtestfor the test data.
We denote VRF_Model (M,Dtest)→( score)as the validation function, the purpose of which is to validate the
model. Generally the model validation function VRF_Model3is specified by the client C.
We letσN(m)=SigSN(m)denote a signature on message mwith respect toSN, i.e., using corresponding private
keySN[sk]. Let VRF_Sig(SN[pk],σ,m)→{0,1}denote a corresponding signature verification algorithm. Specifi-
cally we define σM
P=SigSP(M) as the model signature message of service provider Pon its generated model M.
transaction processing, and data storage. This layer is responsible for the core functionality of the blockchain system and provides the infrastructure for
building additional layers or applications on top of it.
2The block production rate in a blockchain refers to the frequency at which new blocks are generated and added to the blockchain. For example, TRON (TRX)
network has a fast block production rate, with a new block being produced every 3 seconds.
3Two crucial properties are required here: simplicity and certainty . The computation complexity of VRF_Model should beO(1), and the output score of the
computation should be identical across different nodes, as long as Nhonestly perform the function.
5
2.2 |Consensus Assumptions
In this paper, we employ the term "global ledger" (in uppercase), denoted by L, to refer to the fundamental data struc-
ture maintained by PoT protocol in order to support the specific services it offers. While blockchains are one method
for implementing a reliable ledger, there are alternative approaches as well. We anticipate that PoT protocol imple-
mentations will utilize Byzantine Fault Tolerant (BFT) systems for their underlying ledgers, which significantly predate
blockchains like EOS.io [15]. For the sake of convenience, we utilize BFT-type notation and properties throughout
this paper, though we stress that PoT implementations can be realized using permissionless consensus protocols as
well. We view a ledger generally as having a few key properties:
•Append-Remove: Data, once added, can be removed but cannot be modified.
•Public: The contents are accessible to everyone, which are consistent across time.
•Available: The ledger can always be written to by authorized writers and read by anyone in a timely way.
A wide variety of modern BFT protocols are supported in the PoT protocol. The exact choice will depend on trust
assumptions and characteristics among the network nodes. The PoT protocol could in principle be implemented in a
highly performant permissionless blockchain or in an adaptive and scalable layer-2 or blockchain system4.
2.3 |Protocol Overview
(proof-of-training) A PoT scheme enables an efficient service provider Pto convince the network of aggregator nodes
AthatPhas trained the model MC, given by a client C, with validations from V. It also enables the selection of
the winner who generated the best model Moptimum . A PoT protocol is characterized by a tuple of polynomial-time
algorithms:
(Claim ,Validate ,Verify ,Finalize)
•PoT.Claim generates the claim message for a trained model trained via the initial model MCand dataDtraingiven
by a clientC. The service provider trains the model and saves the outputs for further processing. PoT .Claim is
employed to generate model ownership claim messages and broadcast models, which are subsequently used for
claiming rewards. Furthermore, it supplies information necessary for executing PoT .Validate and PoT .Verify. This
process might rely on third-party services, such as model storage and parameter setup.
•PoT.Validate evaluates the models claimed by service providers and subsequently broadcasts a validation message
to the network. This message includes the model’s performance score and the identity of the service provider,
thereby providing an evaluation of their contribution.
•PoT.Verify checks whether a validation from Vis correct. PoT .Verify can be run by any node N(either a participant
or validator) in the network to determine whether a certain validator has correctly validated a model, thereby
convincing the global ledger Lthat the global states are correct. It’s important to note that any incorrect states
that are successfully challenged will be corrected, with significant economic incentives awarded to the challengers,
which further ensures the safety of the protocol.
4A Layer 2 (L2) in blockchain refers to a secondary protocol or framework built on top of an existing blockchain, primarily aiming to enhance the network’s
scalability, efficiency, and transaction throughput. Layer 2 solutions leverage the security and decentralization of the underlying blockchain (Layer 1), while
offloading a portion of the computational workload to a separate network or system. This enables faster and cheaper transactions, as well as more complex
operations, without burdening the base layer. Examples of Layer 2 solutions include state channels, sidechains, and rollups.
6
•PoT.Finalize is run by the aggregators based on global ledger Lto finalize rewards distribution. It summarizes
all the validated models and corresponding validators which validated them. The optimum model’s owner shall
receive the majority of the rewards while the validators which validated the model receive the rest of the rewards
to incentivize active participation and honest validation.
2.4 |Practical PoT Construction
In a practical PoT scenario where a client Caims to train a model with data D, the protocol requires that Cmakes
the initial model (potentially with initialized model parameters) MCand training dataDtrainpublicly accessible at time
t05. The protocol also requires Cto specify the duration of training time ∆Ttrain, after which the test data Dtestshall
be released by client Cfor validation and verification purposes. Once the current timestamp tsatisfies the condition
t>t0+∆Ttrain, the network rejects new incoming model signatures σP(Moutput). Meanwhile, a service provider P
will broadcast the generated model Moutput corresponding to σP(Moutput)broadcasted earlier. Mσ
outputaggregates
model signatures generated by all service providers which validators execute validation function with.
Generate a Claim
PoT.Claim(MC,Dtrain,SP) → (σP(Moutput)t1, Moutput
t2), whereSPdenotes participant-specific security vari-
ables forP.Moutput is the latest generated model6byPbased onMCandDtrainwithin∆Ttrainspecified byC. We
uset1andt2to denote two separate timestamps in the process, which indicate the broadcasting times of the content,
with the following condition:
t0<t1<t0+∆Ttrain <t2 (1)
Once the current timestamp tof the global ledger Lsatisfiest>t0+∆T, the network rejects further model signature
messages andPstarts to broadcast Moutput corresponding to the previous model signature message σM
P.
•INPUTS:
– initial modelMC
– dataDtrain
– key parameterSP
•OUTPUTS: model signature message σP(Moutput)t1, generated model Moutput
t2
Validating the Models
PoT.Validate(σM
P,M,Dtest,SP[pk],SV) → (σV(πD
V)t4,
πD
V
t5), whereMis a model the validator Vnewly re-
ceives.SVdenotes participant-specific security variables for V.Dtestdenotes the testing data released by the client
att3, and we use t4andt5to denote two separate timestamps in the process, with ∆Tvalidate specified by the client or
default values in the protocol:
t0+∆Ttrain≤t3<t4≤t3+∆Tvalidate <t5 (2)
5t0will be set by the primary of aggregator nodes following the PBFT synchronization protocol.
6As long ast1<t0+∆Ttrain satisfies, the service provider Pwill keep optimizing the model Moutput and once better model M∗
outputis generated,Pwill
sendσP(M∗
output)t1to replace the previous model signature message σM
P.
7
Utilizing the VRF_Model function, validators can effectively evaluate the performance metrics of each model. The
outputπD
Vrefers to the validation message provided by the validator, which contains meta data such as the key and
score ofMand identity ofP.
•INPUTS:
– model signature message σM
P
– generated model M
– dataDtest
– public key of service provider SP[pk]
– key parameterSV
•OUTPUTS: validation signature message σV(πD
V)t4, validation message
πD
V
t5
Verifying the Validations
PoT.Verify(πD
V,Dtest)→{0,1}, which checks whether a validation from Vis correct. PoT .Verify can be run by any
nodeNi(either a participant or validator) in the network and convince the global ledger Lwhether a certain validator
has correctly validated a model. If not, the node will send Sig SN(cVπ)to the network along with a challenge message
cVπ, which other participants can verify (σN(cVπ)t6,
cVπ
t7). We denote t6andt7as two seperate timestamps and we
use∆TChallenge to denote the client specified or protocol default challenge period, which satisfies:
t5<t6≤t3+∆Tvalidate+∆TChallenge <t7 (3)
If the challenge is successful, the challenged validator Vwill be penalized, and the challenger Niwill be rewarded by
receiving part of the penalization.
•INPUTS:
– validation message πD
V
– dataDtest
•OUTPUTS: verification boolean value b:{0,1}, challenge message (σN(cVπ)t6,
cVπ
t7)&&(¬b)
Distributing the Rewards
After the challenge period of a client’s order, PoT.Finalize (M,π)→(M optimum ,SPoptimum[pk],V)is run by the global
ledgerLto finalize reward distribution. Mis the vector containing all the validated models (M1,M2,···).πis the
vector of global validation messages indicating the performance of different models in M.Moptimum andPoptimum
are the optimum model (with the highest score) and its corresponding owner after sorting operations performed by
L.Vis the vector containing addresses of all the corresponding validators (SV1[pk],SV2[pk],···) which validated
Moptimum . The ownerPoptimum shall receive the majority of the rewards while the validators Vreceive the rest of
the rewards to incentivize active participation and honest validation.
•INPUTS:
– validated models M
– global validation messages π
•OUTPUTS: optimum modelMoptimum , owner’s public key SPoptimum[pk], corresponding validators V
Fig. 1 presents a brief overview of the data flow between different participants within the protocol. ‘StorageM‘,
8
F I G U R E 1 Brief overview of data flow between protocol participants. Data flows are represented by arrows,
each labeled with the corresponding operation and color-coded based on the participant interaction. Access to
different storage components by various participants is omitted for simplicity, whereas all participants can access
any storage through a query link.
‘StorageC‘, ‘StorageVe‘, and ‘StorageVa‘ represent storage resources provided by miners, clients, verifiers, and validators,
respectively. These can be either centralized storage services or IPFS [7]. It’s the participants’ responsibility to ensure
that this stored data remains accessible to other network participants, while also meeting a certain bandwidth limit
required by the protocol implementation. If this is not guaranteed, the system will invoke a voting process that may
render the participant invalid. Fig. 2 provides an illustration of the protocol by showcasing a sequential diagram that
describes the protocol logic and data flow across different phases of a complete task cycle.
2.5 |Miscellaneous Notes
•Network Securities and Cryptoeconomic Aspects: The decentralization of AI model generation across various nodes
requires robust security measures, particularly when some nodes may be compromised or corrupted. Ensuring
that nodes have a financial incentive to act honestly is crucial in maintaining the integrity of the system. One such
method is staking, which requires nodes to place deposits of utility tokens, with the potential for confiscation
in cases of misbehavior. This incentive design has already been successfully employed in numerous blockchain
implementations, as evidenced by the literature [20]. We require the aggregator nodes , which maintain the global
ledgerL, to stake a significant amount of utility tokens in order to become an aggregator node. Misbehavior will
result in the loss of their staked tokens. In this way, we can ensure the security of the L1 layer of the protocol.
In addition to the aggregators, the appropriate number of tokens that different roles in the network should stake
depends on various factors, such as the value of the tokens, the expected rewards, the risk of penalties, and
9
the overall economic model of the network. Here are some suggestions to help determine the staking amounts
for different roles: Service providers should stake an amount that reflects their commitment to providing quality
services and generating accurate models. The staking amount should be high enough to discourage fraudulent
behavior with slows down the validation process of the network but not too high to create a barrier to entry for
genuine providers. Validators should stake an amount that demonstrates their commitment to performing honest
and accurate validations. The staking amount should be substantial enough to prevent validators from approving
fraudulent claims or models, yet not so high as to create transaction friction that prevents honest validators from
participating. Verifiers should stake a relatively smaller amount compared to service providers and validators, as
their primary role is to verify the validators’ work, which is generally expected to be accurate.
•Concurrent Roles : It is possible for various nodes to assume different roles and responsibilities simultaneously in
order to maintain the integrity and efficiency of the system, as the network can make better use of available
resources. For example, nodes with high GPU power can act as both validators and service providers. In general,
it is expected that any node within the network would be capable of performing verification, as this process can
be efficiently optimized. It is absolutely essential to apply the verification algorithm to a model associated with
two or more clusters of validations, as at least one or more clusters of validations are guaranteed to be incorrect.
Meanwhile, a model linked to a single cluster of validations is highly likely to have been correctly validated.
•Validation Definiteness : The validation process must yield consistent results, ensuring that for a given model and
test data, the output remains constant across honest nodes with varying settings. This requirement eliminates
any potential confusion in both validation and verification procedures. Consequently, it is recommended that the
PoT implementation itself always supply the validation function, ensuring adaptability and upgradability within
the system. Clients should not be allowed to provide their own validation functions for their models to avoid
inconsistencies. Instead, they should be given options to select from available validation functions. To accom-
modate a wide range of use cases, it is crucial for the network to be compatible with most mainstream models,
such convolutional neural networks (CNN) and Long Short-Term Memory Network (LSTM). This can be achieved
by incorporating the validation functions for these models into the system’s foundational layer, thus ensuring a
consistent and reliable validation process across all nodes.
•Commitment Scheme : A commitment scheme7in the context of decentralized training systems enables partici-
pants to commit to a generated model while keeping it hidden from others, with the ability to reveal the commit-
ted model later. Such commitment schemes are designed so that a participant cannot claim the model without
committing to it at an earlier timestamp than that of the real owner (in the global ledger L). This approach has im-
portant applications in PoT protocol implementations, including model ownership claim/verification, and rewards
distribution. Recall PoT .Claim algorithm, interactions in the commitment scheme take place in two phases:
1.Thecommit phase : During this phase, a participant trains a model and commits to it by broadcasting its
signature to the network.
2.Thereveal phase : In this phase, the participant reveals the trained model by sharing it with the network,
allowing other participants to validate its performance and verify the ownership claim.
Given the commitment scheme, malicious service providers are theoretically unable to steal any models, as they
do not possess the model’s signature in the global ledger during the model revealing phase, when the network
stops accepting new model signatures.
7The concept of commitment schemes was formalized by Gilles Brassard, David Chaum, and Claude Crépeau [9] as part of numerous zero-knowledge protocols
for NP.
10
F I G U R E 2 Sequential diagram describing protocol logic and data flow in different phases of a task cycle. It
contains the training phase [t0,t0+∆Ttrain], the validation phase [t0+∆Ttrain,t3+∆Tvalidate], the challenge phase
[t3+∆Tvalidate ,t3+∆Tvalidate+∆TChallenge], and the finalization phase. The time instances are defined in Eqs. 1, 2,
and 3.
11
3|PROTOCOL IMPLEMENTATION WITHIN NETWORK ARCHITECTURE
We present an in-depth exploration of the proof-of-training (PoT) protocol within a peer-to-peer network architec-
ture through the design and implementation of a decentralized training network (DTN). The DTN aggregates models
offered by multiple independent service providers, and the network participants self-coordinate to provide the best
models to clients. This coordination is decentralized and does not require trusted parties. The secure operation of the
system is ensured by the PoT protocol, which verifies that operations are correctly carried out by network participants.
3.1 |The DTN Construction
In decentralized service networks, blockchains fulfill two roles: they serve both as registers of cryptocurrency owner-
ship and as foundations for decentralized services. In our system, the registration of participants and distribution of
rewards happen on-chain, whereas the actual execution of training, validation, and other model-related computations
occur off-chain due to the inherent costs and limitations of on-chain operations. On-chain operations are not only
slow and expensive, but also restricted, unable to benefit from real-world data and various functionalities that simply
can’t be accomplished on-chain. These functionalities include diverse forms of computation, fast data distribution
between miners and clients, and flexible infrastructure upgrades, among other features.
To effectively leverage the potential of this decentralized network for AI training, a two-layer architecture is im-
plemented: the on-chain component (SC), which records the value flow in the network, and the off-chain component
(exec), consisting of a set of protocols running on the DTN where utilities are performed. By securely integrating
the on-chain functionality with the vast array of off-chain services offered by the DTN, it can exhibit the robustness
and upgradability that traditional Layer 1 solutions often lack. In the L1-L2 design, the protocols and infrastructures
primarily operate off-chain in the decentralized network, whereas token utilities such as transfer and withdrawal op-
erate on Layer 2 of any mainstream blockchain. This setup allows the system to continuously update with additional
features and utilities, while keeping the network’s assets and user experience unaffected. Further details are depicted
in Fig. 3.
3.1.1 |Network
The DTN is a decentralized training network that is publicly verifiable and designed on incentives . Clients pay a network
of miners8for model generation and retrieval. Miners compete to train the best models in exchange of payments.
Miners receive their payments only if the network has verified that their service was correctly provided.
Definition Our DTN scheme is a tuple of algorithms run by clients and network participants:
(Put,Get,Manage)
•Put(order)→OID: Clients execute Putto submit a training order under a unique identifier OID(order ID). The
training order includes all information necessary for service providers to execute the training task.
•Get(OID)→model : Clients execute Getto retrieve trained models that are stored using OID, upon task comple-
tion.
8The terms ’miners’, ’service providers’ and ’validators’ can be used interchangeably in this section.
12
F I G U R E 3 Conceptual figure depicting on-chain / off-chain components in the DTN, which consists of two
major components: an on-chain component SC , resident on a mainstream blockchain, and an off-chain component
exec that executes on a DTN. The DTN serves as a bridge between the two components as well as connecting the
system-level contract with off-chain resources such as service providers, validators, decentralized storage, etc.
•Manage: Manage() : The network of participants coordinates via Manage to: control the available computational
resources, validate the service offered by providers and repair possible faults. The Manage algorithm is mostly run
by a network of aggregator nodes.
3.1.2 |The Global Ledger and Data Storage
In our decentralized training network, the Global Ledger Lplays a key role as a system record, logging all essential
network interactions. The ledger contains three key components: the orders record , the task cycle data , and the node
info. The orders record logs all orders placed by clients within the network, each containing the specific task details
requested by a client; including the required model, data, and associated rewards. The task cycle data records the
metadata of tasks that have undergone the full cycle of model generation and validation within the network; including
the generated model signatures, related validation outcomes, and potential challenges. The node info section saves the
details of all registered nodes (miners and validators) within the network, including their reputation and performance
history. Collectively, these components of the ledger boost the network’s performance by ensuring all operations
are traceable and accessible in a timely manner. The aggregator node , tasked with the responsibility of publishing
multi-signature transactions on the blockchain and updating contract states, plays a central role in managing ledger
data and global states. Through the application of the Practical Byzantine Fault Tolerance (PBFT) algorithm [11], it
effectively maintains, updates, and synchronizes the Global Ledger L. Besides storing and managing a synchronized
copy of the global ledger, the aggregator nodes also act as data access points for other network participants. They
13
provide on-demand access to the global ledger, ensuring its data is always available for different network operations.
Clients in the network are responsible for providing training and test data links, while miners must supply model in-
stances. These data must be consistently accessible throughout the task cycle. Failure to comply with this requirement
can lead to order or model claim invalidation through a community voting process. It is the participants’ responsibility
to download the necessary data to their local storage for efficient training and validation processes.
3.1.3 |Economics and Cryptoeconomics
To encourage correct behavior in the DTN, the system implements a cryptoeconomic incentive model. Each node is
required to deposit a certain amount of tokens into a smart contract as a stake during registration. This staked amount
acts as a financial commitment and failure to comply the rules may result in the lost of staked tokens.
The staking system also provides protection against Sybil attacks. By introducing a cost for network participation,
the system discourages entities from creating multiple nodes with the intention of disrupting network operations. This
cryptoeconomic model incentivizes nodes to act in the best interests of the DTN, thereby enhancing its security and
overall efficiency.
Client Economics
The design of our network’s economic system ensures that clients’ tasks are handled with precedence, proportional
to the average rewards offered over time. This approach prevents an overload of low-reward tasks that could strain
the network’s computational resources. Moreover, it incentivizes miners to prioritize tasks that yield higher returns,
thereby optimizing the network’s efficiency.
3.2 |Data Structure
The Decentralized Training Network (DTN) utilizes several primary data structures for operation purposes.
Orders
Anorder in the context of our DTN is a declaration of intent to request a service. Clients issue orders to the network
to request services, and miners compete to provide the best services.
Claims
Aclaim in our DTN is a commitment made by a miner to deliver a trained model. Miners broadcast their claims to the
ledger, which allows them to start competing rewards. A claim consists of the signature of the trained model and the
model itself after t2, following the PoT protocol’s requirements in Eq. 1
Models
The models is a mapping between a model identifier (MID) and its corresponding model instances, which is built by
using information extracted from claims . This data structure increase the system’s efficiency by directly associating a
model’s link with its identifier, enabling quick look-ups and access.
Validations
Avalidation is the result of an evaluation process carried out by a validator to compute the performance of a trained
model in the network. The validator uses the validation function and testing data as input parameters to this process.
Upon completing the evaluation, the validator broadcasts the validation message to the network. This message com-
prises a validation signature at t3which serves as a seal of the validation, and a validation instance that details the
14
TA B L E 1 Core Data Structures in our DTN scheme
Data Structures
Order Network Record Table
Oi:=⟨reward, type, time, link ⟩
•reward: the economic incentive provided to the miners
for training a model.
•type: the kind of model that is to be trained.
•time: the Unix time instances including the training time
t0and the validation time t2.
•link: the link specific to model’s training/testing data
and metadata necessary for the task (such as initial model
parameters).
Orders
O1..On
•Oi, current orders from txPool.
Validation
validation message :=⟨MID, score, vStake ⟩Mi
•MID: the hash of the model instance generated for the
order’s request.
•score: the model’s performance metrics.
•vStake: the amount of stakes a validator is willing to
commit to support a particular validation message.
Validations(V1..Vn)
•Vi, current validations from txPool.
Challenge
challenge message :=⟨VID, cStake⟩Vi
•VID: the hash of the original validation for a model.
•cStake: the amount of stakes a verifier is willing to com-
mit to support a particular challenge message.
Challenges(c1..cn)
•ci, current challenges from txPool.table:
Oi→Oi’s cycle data, ...}
order’s cycle data :=⟨order , phase, mList, vList, cList ⟩order
•order : the original order of the model.
•phase: An enum indicating the current phase of the
task. It can be either ’model generation phase’ or ’valida-
tion phase’.
•mList: list of generated models and its corresponding
validations and challenges (if any) {G1..Gm}.
Gi:=⟨MID, vList, cList⟩Oi
•MID: the hash of the generated model instance.
•vList: the list of validations corresponding to the gener-
ated model instance {V1,V2..}.
–Vi:=⟨validation message , sig⟩Oi
– sig: the signature of the validation message .
•cList: the objections raised against a existing validation
identified by VID {c1,c2..}.
–ci:=⟨MID, VID, sig⟩Vi
– VID: the hash of the validation being challenged.
– sig: the signature of the challenge message .
Global Ledger
L:=⟨models, txPool, table, nInfo ⟩
•models: the maps between a MID and its model in-
stances, whereMMID=models[MID]
•txPool :=⟨orders ,claims ,validations ,challenges⟩
•table: the network record table.
•nInfo: the node info structure containing a node’s regis-
tration metadata and reputation.
model’s performance metrics at t5, following the PoT protocol’s requirements in Eq. 2.
Challenges
Achallenge includes a digital signature at t6and a challenge message at t7, following the PoT protocol’s requirements
in Eq. 3. The digital signature is generated by the challenger signing the hash of the challenge message. The challenge
message itself holds the specific validation being challenged and the amount of staked tokens backing the challenge.
Network Record Table
The Network Record Table functions as a key-value database. The table’s structure is designed to map the hash of
each order to a list of data structures which contains the following components: the original order issued by the client,
thephase indicating the current phase of the task (as defined in Fig. 2), the ModelList comprising generated models
related to the order with each model containing the ValidationList detailing evaluations carried out on the model and
15
theChallengeList capturing any objections raised against existing validations.
3.3 |Protocol Implementations
In this section, we focus on the operations carried out by various participants - clients, the network, and the miners.
We illustrate the process flow of of different algorithms.
3.3.1 |Client Cycle
We give an overview of the client cycle.
1. Put :The client orders model training service .
Clients can train their models by paying service providers with DTN utility tokens. A client initiates Putby sub-
mitting an order to the network. Subsequently, service providers have the freedom to decide whether they wish
to compete for this order, which they can do by submitting claims, along with generated models, to the network.
Clients have the flexibility to determine the amount of training time by modifying the ’time’ variable in their orders.
A longer training time may potentially yield higher accuracy in the resulting models.
2. Get :Client retrieves model from the network .
Clients can retrieve any model stored in the DTN by fetching model links from the network. A client initiates
Getby submitting an API request to one of the aggregator nodes. This node then retrieves the link from its local
database. When the best model generated by the miners is found, the client receives a notification (with the
model link) from the network. It is the miners’ responsibility to ensure that their model links are always live to
avoid penalties from the network.
3.3.2 |Mining Cycle (for service providers)
We give an overview of the mining cycle of service providers. Service providers earn rewards by competing to generate
the model with highest score in the validation evaluation.
1. Register : Service Providers pledge their computational resources to the network. This is done by depositing
collateral, via a transaction in the network, using Manage.RegisterResource . This collateral is locked in for the
time intended to provide the service, and is returned upon request of the service provider if the provider decides
to stop committing to the network, using Manage.UnRegisterResource . Once the service provider is registered,
they can start generating model claims which will be added to the global ledger.
Manage.RegisterResource/UnRegisterResource
•INPUTS:
– current global ledger Lt
– registration request register
•OUTPUTS: current global ledger Lt′
2. Fetch Orders : Service providers can fetch training orders from the network. Once registered, service providers
specify how many orders they would like to fetch using the Manage.FetchOrders function. Upon executing this
function, the corresponding number of training orders are fetched from the global ledger, sorted by the average
16
reward per unit of training time, and sent to the service provider. These orders contain details about the model
training tasks, including the necessary data, the model to be used, and the amount of training time required.
Once fetched, service providers can freely decide which orders they want to handle based on their available
computational resources and other preferences.
Manage.FetchOrders
•INPUTS:
– current global ledger Lt
– number of orders to fetch nOrders
•OUTPUTS: fetched orders fetchedOrders (sorted by average reward per unit of training time)
3. Compete Rewards : Service providers compete for the client’s order by executing the training with the provided
training data. They aim to generate a model with higher training accuracy. Once a new, more accurate model
is generated, it is broadcasted to the network. This new model replaces any previous models with lower perfor-
mance for that specific order. This process continues until the specified training time has elapsed. Subsequently,
the validation process begins for that order9.
Manage.Claim
•INPUTS:
– current global ledger Lt
– the generated POT.claim
•OUTPUTS: updated global ledger Lt′
4. Sending Models : Service Providers are responsible for ensuring the availability of links to generated model in-
stances throughout the full mining cycle. This is done through the Get.SendModel function. If a service provider
fails to maintain the availability of these model links, the network may invalidate the model, which will result in
the service provider not receiving the rewards.
Get.SendModel
•INPUTS:
– model ID MID
– model link mLink
•OUTPUTS: success status sStatus
3.3.3 |Mining Cycle (for verifiers)
We give an overview of the mining cycle of verifiers10. Verifiers earn rewards by challenging the wrong validations
in the validation phase of an order.
1. Register : Verifiers pledge their computational resources to the network. This is done by depositing collateral, via
a transaction in the network, using Manage.RegisterVerifier . This collateral is locked in for the time intended
to provide the service, and is returned upon request of the verifier if the verifier decides to stop participating in
9While the Manage.Claim function describes the process of competing for a single order, it’s important to note that service providers can execute this
function in parallel to compete for multiple orders simultaneously. This parallelism allows service providers to optimally utilize their computational resources
and maximize their potential rewards.
10It’s worth noting that both service providers and validators can take on the role of a verifier, as long as they have sufficient computational resources. This
overlap of roles allows for increased flexibility and efficiency in the network, as entities with more resources can contribute more significantly to the network’s
operations.
17
the network, using Manage.UnRegisterVerifier . Once the verifier is registered, they can start challenging the
validations in the validation phase of an order.
Manage.RegisterVerifier/UnRegisterVerifier
•INPUTS:
– current global ledger Lt
– registration request register
•OUTPUTS: current global ledger Lt′
2. Fetch Validations : Verifiers can fetch validations for a specific order from the network. Once registered, ver-
ifiers specify which order they would like to fetch validations for by providing the OIDof the order using the
Manage.FetchValidations function. Upon executing this function, the corresponding validations for that order
are fetched from the global ledger and sent to the verifier. Once fetched, verifiers can determine whether any of
these validations should be challenged by executing the POT.verify function on them.
Manage.FetchValidations
•INPUTS:
– current global ledger Lt
– order ID OID
•OUTPUTS: fetched validations fetchedValidations for the specific order
3. Challenge Validations : Verifiers challenge the validations in the validation phase of an order by executing the
Manage.Challenge function. If a validation is found to be incorrect, the verifier earns the reward associated with
the successful challenge.
Manage.Challenge
•INPUTS:
– current global ledger Lt
– the incorrect validation V
•OUTPUTS: updated global ledger Lt′
3.3.4 |Network Cycle
We give an overview of the network cycle.
1. Refresh : The global ledger will repeatedly refresh orders in the transaction pool and corresponding data structures.
For instance, it will check if an order has transitioned from the training phase to the validation phase. If so,
the global ledger updates the state variable and starts accepting validations and challenges, while miners begin
sending models. This refresh uses the Unix timestamp as a reference and updates the system variables according
to the PoT protocol.
Manage.Refresh
•INPUTS:
– current global ledger Lt
•OUTPUTS: updated global ledger Lt′
2. Update : The global ledger periodically commits the system states to smart contracts on the main chain, updating
the global ledger accordingly. When orders pass both the validation and challenge phases, they are marked as
18
’sealed’ and include the information of the winning miner, before being placed into a pending rewards queue. At
each update cycle, aggregator nodes coordinates to multisign a transaction to the main chain, which updates
the smart contracts, enabling miners to receive their rewards. Simultaneously, the global ledger removes the
orders, along with their corresponding models and validations, once rewards have been distributed. This process
is designed to save space in local storage. It’s worth noting that the length of the update cycle is determined by
a voting process among DTN nodes.
Manage.Update
•INPUTS:
– current global ledger Lt
•OUTPUTS: updated global ledger Lt′
TA B L E 2 Example execution of the DTN, grouped by network participants and sorted chronologically by row
Client Network Miner
PutOrders (..,Oi) CompeteOrders (..,Oselected )Put Validate (..,Mi)
AllocRewards (..) Challenge (..,Vi)
AddOrders (..,Oseal)
GetModels (..,oID)Get GetModels (..,oID)
SendModels (..,mID)
TrackDeliver (..)
Register (..)ManageRefresh ()
Update ()
UnRegister ()
3.4 |Guarantees and Requirements
The DTN is designed to ensure integrity ,retrievability ,incentive compatibility ,public verifiability and flexibility in the
network, as detailed below:
•Achieving Integrity: Models, orders, and validations are identified by their respective cryptographic hashes (IDs).
Clients only need to keep the hashes of the order and model to retrieve the model and verify the integrity of
the content received. Network participants can utilize these hashes to reference specific data structures, thus
simplifying the input requirements for function calls.
•Achieving Retrievability: All participants are required to stake tokens to register in the network to join mining,
creating a strong incentive for them to consistently keep data available. If a client is unable to fetch the model
they paid for, they may initiate an irretrievable report. The network will then call upon other participants to verify
the report. Once a significant number of participants (with the total number of registered tokens supporting their
consensus) verify and agree, the service provider will be penalized for failing to provide the model instance.
•Achieving Incentive Compatibility: Miners and validators are rewarded for the computation resources they pro-
19
vide. Those who fail to fulfill their commitments or submit incorrect proofs are penalized, incentivizing honest
participation in the network.
•Achieving Public Verifiability and Auditability: All network participants, including miners and validators, have the
ability to verify the validity of validations stored in the global ledger. They are economically incentivized to audit
all work on the network, as successful challenges can earn rewards through utility tokens taken from dishonest or
malicious validators. Unsuccessful challenges, however, result in a loss of collateral for the challenger. This system
encourages positive behavior while simultaneously preventing any wrongdoing in the network, thus enhancing
the autonomous feature of the system.
•Flexibility: The network utilizes a community Decentralized Autonomous Organization (DAO)[12] to decide on
critical system parameters, such as the length of the challenge period, among others. This mechanism allows the
system to adapt and evolve over time in response to the needs of the community and the growth of the business.
This flexibility, combined with the network’s robust design, creates a strong foundation for a secure, efficient, and
user-responsive DTN.
4|SIMUILATIONS
4.1 |Global Ledger Synchronizations
In the first part, we primarily focus on the synchronization of the transaction pool (txPool) within the global ledger.
The txPool holds the most recent transactions and provides all necessary information for the global ledger to reach
global states. The network of aggregator nodes maintains this global ledger, assembling incoming transactions from
various network participants and synchronizing them with the global txPool.
The synchronization mechanism we implement is the Practical Byzantine Fault Tolerance (PBFT) algorithm, which
enforces consensus among nodes regarding the pool of transactions, thus ensuring consistent data synchronization
across the network. The efficacy of this synchronization mechanism, especially in real-world scenarios, is crucial to our
system’s performance and throughput. Therefore, we will conduct a series of simulations to evaluate the effectiveness
of our PBFT-based synchronization within the global ledger.
4.1.1 |A Localhost Network Analysis
During our simulation, we used crypto/x509 andencoding/pem , for facilitating the digital signatures and SHA-256 for
hashing algorithm. The source code implementing PBFT algorithm can be accessed on the author’s GitHub page, for
accommodating future improvements and extensions.
•SHA-256 Hash: For any hashing needs, the SHA-256 algorithm is used which produces a 256-bit (32-byte) hash.
•RSA-2048 Signature: RSA-2048 is used for signatures, meaning the size of a signature would be equal to the size
of the key, i.e., 2048 bits or 256 bytes.
•String Fields: Assuming a UTF-8 encoding which is common in Go, a string uses 1 byte per character for most
common characters, although some characters can use more.
As shown in Table 3, we analyze the approximate size of orders, validations, and challenges, depending on their
respective fields. The order structure, consisting of reward, type, time, link, and a signature fields, costs approximately
312 bytes plus the size of varying fields. The Validation structure is made up of MID, score, vStake, and signature fields,
20
Algorithm 1 Practical Byzantine Fault Tolerance (PBFT)
1:Phase 1 [Request]: Client, Miners, Validators, Verifiers sends a request to the Primary aggregator node.
2:Phase 2 [Pre-Prepare]: The Primary node broadcasts a PRE-PREPARE message to all the Aggregator nodes.
3:Phase 3 [Prepare]: The Aggregator nodes validate the PRE-PREPARE message, and upon validation, they broad-
cast a PREPARE message to all the Aggregator nodes.
4:Phase 4 [Commit]: After receiving 2fPREPARE messages from different nodes, the Aggregator nodes broadcast
a COMMIT message.
5:Phase 5 [Reply]: After receiving 2f+1COMMIT messages from different nodes, the Aggregator nodes apply the
operation and send a REPLY message to the Client, Miners, Validators, Verifiers .
6:Phase 6 [Result Acceptance]: The Client, Miners, Validators, Verifiers accept the operation result after receiving
f+1identical REPLY messages from different Aggregator nodes.
costing approximately 304 bytes. The Challenge structure includes VID, cStake, and a signature fields, costing around
296 bytes. These sizes are necessary considerations when simulating the system’s throughput, as they affect aspects
such as ledger synchronization speeds and bandwidth requirements.
TA B L E 3 Size in bytes of orders, validations and challenges
Data Structure Field Size (bytes) Notes
Order reward 8 Size of float64
type varies Assuming 10 bytes for a string of length 10
time 8 Size of int64
link varies Assuming 30 bytes for a string of length 30
sig 256 Size of RSA-2048 signature
Total∼312 Plus the size of varying fields
Validation MID 32 Size of SHA-256 hash
score 8 Size of float64
vStake 8 Size of float64
sig 256 Size of RSA-2048 signature
Total∼304
Challenge VID 32 Size of SHA-256 hash
cStake 8 Size of float64
sig 256 Size of RSA-2048 signature
Total∼296
We categorize networks into three sizes: small, medium, and large. Small networks consist of up to 10 nodes,
used in cases such as sample or demo networks. Medium-sized networks, with 10 to 30 nodes, represent moderately
distributed systems that could span across several geographical regions or countries. Large networks, with more
than 30 nodes, represent global L1-L2 systems such as Chainlink [22]. For our analysis, we focus on the PBFT’s
21
synchronization time within the designed DTN structure, excluding considerations of network connection and data
transfer latencies.
In Table. 4, we analyze how variations in network size and message size affect synchronization time. By adjusting
these parameters, we can measure the capacity of our system to handle varying client request loads. For instance,
simulations might involve synchronizing a single order with 10 validations (totaling 3432 bytes), or ten orders each with
10 validations (totaling 6160 bytes), or one order with 100 validations (totaling 30512 bytes), among other scenarios.
TA B L E 4 Synchronization times for different message sizes and network sizes (in seconds).
Network Size
Scenario (Message Size)
Small (10 nodes) Medium (30 nodes) Large (100 nodes)
1 order, 10 validations (3432 bytes) 0.0387 0.132 0.463
1 order, 50 validations (15412 bytes) 0.0374 0.106 0.418
1 order, 100 validations (30512 bytes) 0.0466 0.149 0.373
10 orders, 50 validations (15640 bytes) 0.0452 0.113 0.407
10 orders, 100 validations (30740 bytes) 0.0338 0.125 0.392
The experiments were conducted on a 64-bit Ubuntu 22.04.2 LTS system powered by the 12th Generation
Intel®Core™i7-12700T processor with 20 cores, and equipped with 32GB memory.
As seen in Table 4, the network size significantly affects the synchronization time across all scenarios. As the
number of nodes in the network increases, so does the synchronization time, requiring more time to update nodes in
larger networks.
Despite differences in message size, the impact on synchronization time appears relatively complex. In fact, the
system shows considerable efficiency in handling large packages, regardless of the number of orders and validations.
This suggests that, without considering network latency, the system is designed to efficiently manage substantial
volumes of transactions simultaneously. Thus, given our PBFT-based design, we can conclude that the network size
plays a more substantial role in influencing synchronization time than the message size. Meanwhile, the network
handles different message sizes effectively and robustly.
4.1.2 |A Real Network Analysis
Apaft from the theoretical txPool synchronization time analyzed in the previous section, we introduce a more compre-
hensive simulation of the PBFT synchronization algorithm. This simulation is designed to emulate real-world network
conditions in distributed consensus scenarios. It considers the importance of variable network conditions, particularly
network latency and bandwidth limitations, as these significantly impact the performance of a distributed system.
In real-world scenarios, nodes within a distributed network are typically spread across different geographical
regions, each subject to unique network conditions. These variations in network latency and bandwidth can greatly
influence the performance of the consensus algorithm. Consequently, it’s crucial to incorporate these parameters into
the network simulation, providing a more realistic analysis of the consensus algorithm’s performance.
Analyzing recent data trends, global latency times and packet delivery rates can serve as reliable reference points
for our simulation inputs. Data from May 2023 reveals average latency times of around 29ms for regional round
22
trips within North America, 15ms for those within Europe, and 71ms for transatlantic round trips. These are general
trends and the actual times can fluctuate based on a number of factors, including the specific locations within the
regions, the network conditions, and time of day11. For transpacific and other international round trips, latency values
are typically slightly higher than 300ms, but still within acceptable ranges for efficient network performance. These
average latency and packet delivery figures provide us with a solid basis to input realistic and relevant values into
our simulations. Regarding the bandwidth, slow networks are classified as those with bandwidths less than 1 Mbps,
medium networks range from 1 Mbps to 100 Mbps, and fast networks are those with bandwidths greater than 100
Mbps.
We emulate a global network with varying network sizes, ranging from 10 to 50 nodes. The network latency varies
between 30ms and 300ms, reflecting typical delay times both within a country and for transpacific connections. We
further adjust the actual bandwidth limit, testing slow, medium, and fast speeds, although the theoretical bandwidth
could be significantly higher. Additionally, we alter the size of the synchronized message (number of orders) from 100
orders to 10,000 orders to examine the performance metrics.
TA B L E 5 Synchronization times for different message sizes, network sizes, and bandwidth limits (in seconds).
Bandwidth Limit
Scenario (Message Size) Network Size (nodes)
Slow (0.1 Mbps) Medium (30 Mbps) Fast (125 Mbps)
100 transactions Small (10 nodes) 8.609 1.494 1.497
100 transactions Medium (30 nodes) 8.707 1.685 1.755
1000 transactions Medium (30 nodes) 73.536 1.682 1.833
100 transactions Large (50 nodes) 8.697 1.842 1.752
200 transactions Large (50 nodes) 15.984 1.908 1.893
5000 transactions Large (50 nodes) 37.532 1.767 1.678
10000 transactions Large (50 nodes) - 7.215 2.074
The experiments were conducted on a 64-bit Ubuntu 22.04.2 LTS system powered by the 12th Generation Intel ®
Core™i7-12700T processor with 20 cores, and equipped with 32GB memory.
The results in Table 5 illustrate the impact of different message sizes, network sizes, and bandwidth limits on
synchronization times. The message contains a bundle of transactions where each transaction can be identified as
either an order, a validation, or a challenge, all of which have approximately similar sizes. As the message size increases,
particularly under bandwidth-constrained conditions, synchronization times increase significantly. This effect is less
apparent under high bandwidth conditions, indicating that adequate network bandwidth can guarantee high network
throughput and robustness. Furthermore, the network size does not dramatically affect the synchronization times for
small message sizes. Given the results, we emphasize the importance of sufficient bandwidth in the implementations
of a Decentralized Training Network (DTN). Also considering the multi-sig process of the aggregator nodes, we don’t
suggest the number of nodes be large enough because that will complicate the DAO election process. As a result, a
network size of 30-50 nodes and an average bandwidth requirement of 30 Mbps for aggregator nodes are suggested
in the DTN implementation.
11https:/ /www.verizon.com/business/terms/latency/
23
4.2 |Mining Rewards Distribution
In this section, we analyze the process of mining reward distribution on the Binance Smart Chain (BSC), known for its
affordability with lower transaction costs compared to other blockchains. Rewards, generated by sealing orders, are
distributed to accounts that could be owned by miners, verifiers or aggregators.
As the multi-signature setup, adopting the (k,n)configuration ensures robustness in our operations, even in the
face of up to ffaulty nodes. We maintain the capacity to approve and execute transactions as the choice of kensures
a consensus requirement.
Aggregator nodes, using the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, collate the rewards
for each account and update this information within the smart contracts. To safeguard the integrity of the distribution
process, a multi-signature system is employed during the transactions. The associated cost of updating an account is
computed as: Cost =Gas Price×Gas Cost×Token Price.
Function Gas Cost Executions Tokens Cost
proposeReward 86,875 Once 0.000396 $0.098
confirmReward 45,371 ktimes 0.000207 $0.051
executeReward 161,888 Once 0.000739 $0.18
TA B L E 6 Summary of function executions on BSC Mainnet. Note that the ‘Cost‘ and ‘Tokens‘ values are
calculated based on the current token price and gas price. As of June 20 2023, the gas price on BSC is 4.562 Gwei,
and the token price is $246.2.
In Table 6, we present a detailed cost analysis associated with executing key functions for reward distribution on
the Binance Smart Chain (BSC) mainnet. Each function’s cost is calculated in tokens and their corresponding USD value.
By summing the costs of proposing, confirming (with 30 confirmations), and executing a reward, we can estimate the
total cost our network spends to distribute rewards to a single account. Given the gas price on BSC (4.562 Gwei as of
June 20) and the token price of $ 246.2, this aggregate cost is approximately 0.007582 tokens or $ 1.87per account. The
estimation can help us in understanding the scalability and economic feasibility of implementing reward distribution
mechanisms on Layer 2 networks on the BSC. Moreover, it’s necessary to note that these costs may fluctuate due to
variations in gas and token prices.
5|DISCUSSIONS
5.1 |Protocol Capacity and Scalability
The previous sections have described the proof-of-training (PoT) protocol along with its implementations and simula-
tions in a decentralized training network (DTN). While implementations in DTN can vary, the network’s performance
can approximately represent the protocol’s performance, since the underlying structure and program logic remain con-
sistent. The aggregator nodes serve as a platform in the system, coordinating clients, miners, and validators, enabling
self-governance to initiate, process, and finalize services. While the actual influx of transactions (including orders,
claims, validations, and challenges) will largely depend on the customer base and total hash power of the network,
the processing power of the global states maintained by the aggregator nodes can be analyzed.
According to the simulation, the network exhibits favorable results in synchronizing system tasks and operations.
24
By considering internet geographical latency and setting specific bandwidth limits and a certain number of aggregator
nodes, the network can synchronize thousands of transactions every second. Given the approximate sizes of the
order, claim, validation, and challenge respectively, we can infer that the protocol can manage at least 10-100 models
every second.
A significant advantage of the design is the allocation of computation-heavy tasks and storage to network partic-
ipants. This strategy prevents the overconsumption of global storage, which could potentially be expensive, consid-
ering that updating global states is a synchronous process. The global ledger only stores order, model, and validation
information, the sizes of which are in the unit of kilobytes. Meanwhile, processing them requires a computational
complexity ofO(1)orO(n). This approach enables the system to handle an empirically unlimited number of task
requests and model finalizations simultaneously.
5.2 |Protocol Security
In most blockchain protocols, the security of a protocol is guaranteed by cryptoeconomics, i.e., attacking the system
is more costly than complying with it. Similarly, in the proof-of-training (PoT) protocol, one would need to obtain
more tokens than the counterparties to initiate attacks, which can often prove quite expensive. Unless the potential
rewards are substantial, there is little incentive for someone to attack the protocol. Even in high reward instances,
they attract more attention from miners and validators in the network. Consequently, the tokens committed to the
task increase significantly, raising the cost of any potential cheating attempts.
Another possible attack scenario involves tampering with the Manage.Update() process in the aggregator nodes,
allowing hackers to withdraw all tokens from the rewards contract. To compromise the multi-sig design of the PoT
protocol, the miners would need a ( k/n) portion of the total staked tokens by the aggregator nodes. We call this
Linear staking impact , meaning that to be successful, an attacker must have a budget Bgreater than a ( k/n) portion
the combined staked tokens of all aggregator nodes. More precisely, we mean that as a function of k,B(k)=dkin a
network ofnaggregator nodes, each with a fixed staked amount d. Given our requirement for aggregator nodes to
stake a significant amount of tokens to act as network coordinators, a hacker would need at least 10% of the total
circulation if 20% of tokens are held by the aggregator nodes (assuming k= 18 andn= 30). Therefore, the cost of
such an attack is generally much higher than the tokens in the reward contract.
As shown in Table 6, it costs an aggregator an average of $1.87 to finalize an order and update it on the blockchain
mainnet. So, how do we incentivize them to cover this cost? Regarding the economic incentives for aggregator
nodes, the PoT protocol suggests two possible approaches. The network can periodically issue new tokens to reward
aggregators. However, this method would introduce an annual inflation in token value based on the reward rate over
time. An alternative approach is to tax each sealed order by a certain percentage ( r). As long as the cumulative taxes
exceed the cost of updating transactions, the aggregators will make a profit. This profit provides a strong incentive
for the aggregators to perform diligently and honestly in their role as an aggregator node.
5.3 |Protocol Advantages
We believe the protocol’s major advantage lies in its consensus mechanism design and optimized data structure, which
provide significant capacity and scalability benefits compared to other solutions in this field. With this protocol design,
the network coordinator, which maintains the global ledger and global states, is relieved from handling large data
storage or heavy computation tasks inherently in most AI training processes. These tasks are delegated to participant
nodes with sufficient resources. Participants are given strong cryptoeconomic incentives to act honestly and diligently,
25
resulting in a system that can largely self-govern, thus enhancing the protocol’s capacity and scalability. Participants
are regulated by a voting mechanism. If, for example, any participant fails to provide storage and bandwidth for an
instance download, they may be penalized by other nodes on the network and potentially lose their staked tokens
during the voting process. The protocol can therefore ensure that participants remain committed to their orders and
services, guaranteeing system liveliness.
Another major advantage of the protocol over others lies in the design of its L1-L2 system structure, which ensures
the easy upgradability of the system. AI is a rapidly shifting industry with new types of models being developed
on a daily basis. The protocol uses Layer-2 (on-chain) applications to deposit, withdraw, and transfer users’ assets,
while most operations are carried out on Layer-1 (off-chain) for upgradability purposes. For any new models, we
can integrate them into the system by asking miners and aggregators to upgrade to the latest version of the exec .
Then, clients will be able to specify new model types in their orders. Theoretically, the system can include any type
of AI model into the L1 infrastructure, given that there is a valid validation function for that model which meets the
protocol’s requirements mentioned in section 2.
Question: Can the protocol handle training task of Large Language Models (LLM) such as chatGPT?
In the PoT protocol, although a ’miner’ denotes a single node, there can actually be many GPU cards behind that node,
as seen in the case of mining pools. Hundreds or even thousands of miners can join a mining pool to receive rewards.
Given the significant computing power of a mining pool, it is much more likely to receive rewards in a competitive
process. These rewards are then evenly distributed among mining pool participants based on their contributed com-
puting power. A significant advantage of a mining pool is its reliability: unlike a single mining entity, which can become
faulty at any time, the mining rigs gathered in a pool are typically more reliable. Consequently, they can handle more
complex training algorithms like those described in [18], particularly when dealing with large models. It’s clear that
mining pools are capable of handling large language models (LLMs) with billions of parameters. We believe that, with
this component taken into consideration, the protocol can certainly handle LLM training tasks. This can be achieved
by specifying detailed parameters on the client side and offering proportionate rewards to miners based on their
contributions.
6|CONCLUSIONS AND FUTURE WORKS
In conclusion, our work successfully bridges the emerging gap between artificial intelligence (AI) and crypto mining
by addressing three major challenges that are currently keeping these two fields apart. The proof-of-training (PoT)
protocol combines the strengths of both AI and blockchain resources, thereby enhancing the potential of both.
The capacity, scalability, upgradability, and security attributes of the protocol have been rigorously evaluated and
discussed throughout this study. By innovatively integrating a delicate system design and robust economic incentives,
our solution circumvents common drawbacks of blockchain technology such as high storage and computation costs
and limited network data access, while bolstering its strengths, such as security and user accessibility. We believe the
protocol can be a game changer in the industry, providing individuals with affordable and straightforward access to
resources which were previously exclusive to large companies and enterprises.
One aspect not covered in this paper is the execution of experiments involving the interaction between clients and
miners with actual tasks being resolved. This is mainly because any simulation in this aspect would merely represent a
specific case of the system’s capacity and throughput. However, to analyze the protocol from a financial perspective,
we set it as part of our future works: To engage the current hash power in the market by introducing network utility
26
tokens and implementing a complete version of the DTN, which would enable a detailed analysis of the system’s
performance on real-world tasks, leading to further developments and understanding of the PoT protocol.
Code Availability
The source code used in this study for the implementation of the proof-of-training (PoT) protocol and the decentralized
training network (DTN) is available for review, use, and modification under the terms of the MIT License. You can
access the repository at: https://github.com/P-HOW/proof-of-training .
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|
{
"id": "2307.07066"
}
|
2001.03937
|
Simulated Blockchains for Machine Learning Traceability and Transaction
Values in the Monero Network
|
Monero is a popular crypto-currency which focuses on privacy. The blockchain
uses cryptographic techniques to obscure transaction values as well as a `ring
confidential transaction' which seeks to hide a real transaction among a
variable number of spoofed transactions. We have developed training sets of
simulated blockchains of 10 and 50 agents, for which we have control over the
ground truth and keys, in order to test these claims. We featurize Monero
transactions by characterizing the local structure of the public-facing
blockchains and use labels obtained from the simulations to perform machine
learning. Machine Learning of our features on the simulated blockchain shows
that the technique can be used to aide in identifying individuals and groups,
although it did not successfully reveal the hidden transaction values. We apply
the technique on the real Monero blockchain to identify ShapeShift
transactions, a cryptocurrency exchange that has leaked information through
their API providing labels for themselves and their users.
|
http://arxiv.org/pdf/2001.03937v1
|
Nathan Borggren, Hyoung-yoon Kim, Lihan Yao, Gary Koplik
|
cs.CR
|
cs.CR
|
Simulated Blockchains for Machine Learning Traceability and
Transaction Values in the Monero Network
Nathan Borggren, Hyoung-yoon Kim, Lihan Yao and Gary Koplik
Geometric Data Analytics, Inc., Durham, NC, 27701
Abstract
Monero is a popular crypto-currency which focuses on privacy. The blockchain uses cryptographic
techniques to obscure transaction values as well as a `ring con dential transaction' which seeks to hide a
real transaction among a variable number of spoofed transactions. We have developed training sets of
simulated blockchains of 10 and 50 agents, for which we have control over the ground truth and keys,
in order to test these claims. We featurize Monero transactions by characterizing the local structure of
the public-facing blockchains and use labels obtained from the simulations to perform machine learning.
Machine Learning of our features on the simulated blockchain shows that the technique can be used to
aide in identifying individuals and groups, although it did not successfully reveal the hidden transaction
values. We apply the technique on the real Monero blockchain to identify ShapeShift transactions, a
cryptocurrency exchange that has leaked information through their API providing labels for themselves
and their users.
1 Introduction
We have successfully applied machine learning (ML) in the past by combining features derived directly from
blockchains with labels aggregated from o -chain sources [ 1,2]. Other researchers have used ML techniques
as well to deanonymize exchanges [ 3], identify malware [ 4], and other institutions [ 5]. With the exception of
[2], these analyses have largely focused on Bitcoin as the labels are easily sourced, albeit dicult to verify
[6]. However, other crypto-currencies have also greatly progressed and provide sucient liquidity to be
incorporated into cross-currency mixing schemes [2, 7].
Although many crypto-currencies are derivative of Bitcoin, originating in a fork of the code repository
followed by some cosmetic changes, some coins have adopted di erent basic assumptions and introduced
substantially new codebases and cryptographic techniques to overcome some of the privacy limitations inherent
in Bitcoin. One such coin is Monero [ 8]. Monero has introduced some structural di erences to Bitcoin that
were sucient to render our Bitcoin-like ML machinery inapplicable.
In particular, the following features of the Monero blockchain obstruct our previous analysis.
Addresses are not included in the blockchain, obfuscating the recipient of a transaction.
Transaction values are obfuscated
Inputs to a transaction are combined with spoofed transactions (the RingCT), obfuscating the origin of
a transaction.
Despite these improvements, Monero is still susceptible to some level of tracking using heuristics [ 9] and
understanding residual e ects of hard-forks [10].
Can we regain an ML approach to the deanonymization of Monero? A few hurdles need to be overcome.
First, featurizations of the Monero blockchain will be heavily topological in their nature; timestamps, number
of inputs, size of RingCTs and the connectivity among transactions are the raw materials from which we can
make features. Second, the monerod wallet client, in comparison with the bitcoind wallet client, has removed
a number of features that are of use to blockchain analysts but not necessary for the day to day use of the
coin. Lastly, large repositories of labels of transactions are either unavailable, unreliable, or classi ed.
1arXiv:2001.03937v1 [cs.CR] 12 Jan 2020
To overcome the rst hurdle, in Sec. 3, we will quantify some topological aspects in the blockchain
neighborhood of a given transaction. We have utilized [ 11], the open source repository behind xmrchain.net ,
to overcome the second hurdle. To address the third hurdle, we have collected and veri ed labels aggregated
from the ShapeShift API. These labels allows us to deanonymize only one entity, ShapeShift, and while we
do so in Sec. 5 to demonstrate the use of our features on the real monero blockchain, but for the sake of
generality and additional con dence we have chosen another course which we hope will inspire other privacy
analysis on Monero and other blockchains.
In cybersecurity studies, it is commonplace to generate synthetic datasets to develop and assess threat
detection techniques [ 12]. We use this analogy to provide a framework to understood the inner workings of
Monero in action; test networks are created including ten and fty person networks to provide a dataset for
an ML analysis. For the ML assessment, the blockchain, that is the public-facing aspect of the network, is
used to develop featurizations while the wallet contents are used to supply the labels as depicted in g 1.
Figure 1: A test-network is used to generate a blockchain and provide labels.
Monero also provides new opportunities for machine learning that were not present or applicable in the
Bitcoin-like case. In Section 4, in addition to exploring the agent identi cation task we will explore these
other use cases. For example, we can use our features in a regression analysis seeking to nd the missing
transaction value. Although this is shown to be unsuccessful in this case, we did achieve success in recovering
the real input out of the spoofed transactions.
2 Simulating Blockchains
To simulate a Monero economy where multiple agents transact with each other and mine simultaneously,
we use tmux, an application for creating and managing multiple terminal sessions on a single machine. We
run a shell script to create a tmux-based network of Monero agents based on hard-coded wallet information.
On this network, each agent runs a mining operation and a wallet remote procedure call (RPC) attached to
an allocated set of ports. All agents on the network are connected to each other and keep a synchronized
blockchain.
The economy les specify how the economy will operate. Each economy le contains a list of transaction
amounts, destination addresses, and wait times between the transactions that the particular agent will adhere
to. We generate these les with a stochastic model. We use a Python program to process these les and
make HTTP-based transaction requests to the Monero wallet RPCs accordingly.
Other than the constraint that the wait times between transactions must be sucient to keep the delays
in transaction processing relatively small, the content of the economy les is up to imagination. We consider
several economic scenarios with varying assumptions and create the economy les accordingly.
Three of the ve scenarios we consider are ten-agent economies, and the rest are fty-agent economies. The
wait intervals between transactions and the transaction amounts are generated by the Poisson distributions.
For scenario s03, the agents wait intervals come from varying Poisson parameters ranging from 45 to 90,000,
while the transaction amounts come from the same Poisson parameter for all agents. Scenario s04 uses
varying Poisson parameters for generating transaction amounts as well. Scenario s05 is the same as s03, but
2
(a) graph for s03 and s04
(b) graph for s05
(c) graph for s06
(d) Graph and s07
Figure 2: Graphs of economy les, showing connectivity of trading partners with edges, and color shows
transaction size.
the economy is divided into two pools. That is, the agents only transact within the pool they belong to.
Telling these pools apart based on public transaction data is one of the machine learning questions we pursue.
For one of the two fty-agent scenarios (s06), we create two pools of equal number of agents. Additionally,
we create two di erent cycles of transactions for the two pools, simulating a time zone di erence in the real
world. Finally, in our nal fty-agent scenario (s07), we devise a transaction network with ve ten-agent
pools. Fig. 2 shows the experimental setup.
To examine the blockchain data, we link the Onion Monero Blockchain Explorer to the blockchain of one
of the agents. Table 1 shows a summary of the datasets generated, while g 3 shows the dramatic sparsity
that can be achieved by removing the spoofed transactions from the blockchain.
Simulation Number of Blocks Number of Transactions
s03 23812 4898
s04 25509 4923
s05 41583 4923
s06 37281 24807
s07 58551 7070
Table 1: Summary of Simulation Datasets
(a) For s06, all edges between transactions are shown
(b) For s06, only true edges between transactions are shown
Figure 3: Removing the spoofed transactions shows a much clearer image of the blockchain activity.
3
3 Featurizing Monero Blockchains
Real Monero Blockchain For most machine learning tasks on real cryptocurrency blockchains, the
diculty in procuring labels for supervised learning remains a central challenge, despite the accessibility of
blockchain activities themselves. In Monero, this is especially so.
In certain situations, labels and unseen blockchain information may be collected by law enforcement,
hedge funds and hobbyists. We utilize transaction data from Shapeshift service. The dataset contains around
1,700,000 transaction entries of which 20,000 are ShapeShift transactions being converted into Monero.
For each transaction entry, the data contains 7 0-hop features - features intrinsic to the transaction as it
appears on the Monero explorer interface, e.g. transaction timestamp, ring size, day of week, hour of day, etc.
From the 0-hop features, 175 1-hop features, aggregate statistics including mean, max, standard deviation,
regarding the transaction neighbors' 0-hop features are also collected. The 182 features are Z-normalized
prior to the predictive task.
Figure 4: A neighborhood around a transaction is featurized by collecting statistics of the 0-hop and 1-hop
transactions.
This approach considers the neighborhood information of a particular transaction, while not requiring
the extraction of the entire network. We nd that the 1-hop features have been informative in Shapeshift
transaction predictions. On test nets, group membership prediction and transaction value regression tasks
have also bene ted from neighborhood information.
In a related work [ 13], we propose a correlation statistic for mitigating the noise introduced by the mixin
mechanism. This feature follows from the hypothesis regarding multiple wallet usage: when a transaction
contains multiple ring CTs, the real inputs within each ring are contributed by the same user or users
exhibiting similar behavior. For example, a receiver may expect a token amount exceeding the value stored
within a single wallet, so the sender provides inputs from multiple wallets that were previously traded at a
similar frequency themselves . We quantify this intuition by tabulating a population statistic over transactions
containing exactly two rings. The ( i;j)-th entry of the correlation matrix is the correlation in time between
i-th oldest entry of the rst ring with j-th oldest entry of the second. The binning of the matrix can be
presented in hours of the day, or by partitioning the relative timestamp di erence between inputs i,j. While
our features do not explicitly reference the measured correlation matrix, our features have been chosen to be
sensitive to these e ects.
Test Nets Of the ve test nets s03, s04, s05, s06, s07, all were inputs to the transaction value regression
task, while group membership prediction was studied in the last three test nets. The preprocessing of Monero
explorer information and featurization follows the featurization process of the real Monero blockchain.
4
4 Machine Learning
4(a) Machine Learning Spoofed/Real Transactions
An interesting new ML task that Monero uniquely o ers is in identifying which of a given set of RingCT
elements was the real previous transaction. Our correlation analysis suggests that pattern-of-life behaviors,
such as a users' typical transaction time of day can reveal information about the true input. The features we
have computed are sensitive to these di erences and give reason to suggest that ML can recover real signal
despite the mixins.
Fig. 5 shows a depiction of the task at hand. In black is a screenshot of the blockchain with an arrow in
red showing the true input. The true input is known by analyzing the wallets, shown in white, of the users
after the simulation terminates.
Figure 5: In black background is the public facing blockchain, in white are the known transactions of the
given user. The ML exercise is to recover the index of the real transaction.
4(b) Machine Learning User Identity and Group Membership
In the task of identifying users and groups from transaction logs, we applied neural networks and random forest
for classi cation. An observation in this case is a transaction - we classify the group that the transaction's
receiver belongs to. The groups across scenarios trade strictly within-group, and only during designated time
intervals. These time intervals repeat in a cyclic pattern, akin to timezones.
For both models, a randomized search over hyperparameters was conducted. The neural network is
composed of 2 dense layers with 182 and nneurons respectively, where the second layer ranges from 10 to 30.
The random search over random forest hyperparameters include: number of decision trees, maximum
depth, maximum features, minimum samples per split, and the splitting criterion. Because random forest
has an interpretable feature importance weighting, the top 3 informative features for group membership
classi cation are: S05 The informative S05 features involve the input minute among a transaction's inputs.
They are listed in decreasing order of importance: Sum of average input minutes, average of max input
minutes, and average of minimum input minutes. S06 The informative features are: sum of input seconds,
average of maximum input hours, and sum of max input hours S07 The informative features are: median of
input minute sums, maximum of input minutes, and average of maximum input minutes.
4(c) Machine Learning Transaction Value
Model performance for the value regression task is the R2coecient.
R2= 1 P
i(yi ~yi)2
P
i(yi y)2
5
Figure 6: The membership in a group is known from the wallet les and economy instructions.
whereyis the transaction value in expectation. We set the baseline predictor of this task to be predicting
shapeshift transaction values in expectation, and its R2performance is 0. A perfect score is 1, and models
that improve on our baseline performance lies in the range (0 ;1].
To predict the transaction values of the i-th entry, we tted two models: Epsilon-Support Vector Regression
(SVR) and neural network.
For both models, a randomized search was completed over hyperparameters. The SVR hyperparameters
include its kernel, regularization parameter C, and tolerance parameter . The neural network is composed
of 2 dense layers, with 182 and nneurons respectively, nranging within [10, 30]. The learning rate
was
another hyperparameter searched over. Given a hyperparameter set, model performances are computed over
5-folds of the data then averaged.
We nd that more information regarding transactions is required to predict their values. The SVR model
has aR2value of 0:16, and the neural network has a value of 0:1, both below the baseline performance of
0. Future directions of value regression include
1. In the case of multiple rings, incorporating input-level correlations between rings.
2. Integrating known user-level information to value prediction.
4(d) ML results
Fig. 8 shows the results of our machine learning e orts. We expect that uniformity between the users
and a severe lag in time transactions posted compared to the design speci cations contributed to the poor
performance in Scenario 6 and Scenario 7.
5 Machine Learning on the real Monero network; Identifying ShapeShift
Previously in [ 2] we had collected and validated ShapeShift transactions using their API. This collection of
data provided labels for Bitcoin, ZCash, Litecoin, and Dash transactions. Our featurizations of Bitcoin-like
blockchains allowed us to successfully recall 73% of ShapeShift transactions while analyzing only 20% of the
Bitcoin blockchain.
In Table 2, we have extended that analysis to identify ShapeShift transactions whose target currency was
Monero. Surprisingly, the classi er for Monero outperformed that for Bitcoin. We attribute this to the fact
that during its peak in 2018, ShapeShift was responsible for upwards of 4% of the entire Monero blockchain,
where only one in a thousand transactions in Bitcoin were attributed to ShapeShift. Although our dataset
was still greatly imbalanced, the extent of the imbalance was not nearly as severe.
6
Figure 7: The transaction value on the blockchain is missing in the black. We attempt to recover the value
from the wallet in white through regression on the features.
Metric Summary Statistic ShapeShift Not ShapeShift
Precision Mean 0.050062 0.999161
SD 0.000129 0.000030
Recall Mean 0.941982 0.794504
SD 0.002058 0.000548
Table 2: Our features enable a high recall of ShapeShift transactions, identifying 94% of the transactions
looking at 20% of the blockchain.
The most important feature was by far the number of rings used in the tx, with seven of the other nine
top-ten-features also involving summary statistics of the number of rings. These are shown in Fig. 9.
6 Conclusions
We have found that despite the complexities created by the enhanced privacy features of Monero, the
blockchain is still susceptible to deanonymization and information gathering by machine learning techniques.
To establish this fact, test networks were developed to generate simulated blockchains. These blockchains
were featurized while the wallets were parsed to provide labels. Monero proved to be robust against our
e orts in recovery of the obfuscated transaction values, while classi ers for spoofed transaction identi cation
and faction/group/user identi cation proved informative.
Additionally we have shown that ML on the real Monero blockchain can be used to identify a given party
provided a sucient collection of labels. We used as our example the cryptocurrency exchange ShapeShift to
demonstrate this, but expect this to hold true if other large collections of labels were recovered, for example
from wallets recovered in criminal investigations. Despite having values for ShapeShift transactions, our
regression attempts to recover these values proved unsuccessful. We hypothesize that future e orts could use
much deeper features such as tracing all the way back to coinbase transactions, noting which version of Monero
was being used for previous transactions, and incorporate features that look forward in time rather than the
exclusively backwards features we have used. Such features may perhaps allow value information to leak
into the features and provide success to a regression analysis, but the task would indeed be computationally
expensive.
It is noted that recent versions of Monero now enforce the RingCT size to be eleven; as ring number
7
Figure 8: ML results for the various tasks are shown. In transaction value predictions, our regressors did not
surpass baseline of guessing average transaction values.
derived features had been the most informative features in the ShapeShift analysis, we expect this change to
greatly enhance privacy going forward. However, as previous studies have shown, the residue from history
of the past transactions will likely linger for some time as these new characteristics reshape the underlying
distributions of features.
We would like to thank the Machine Learning and Emerging Technologies exemplars at the LAS at
North Carolina State University for funding and continuous feedback. The irony does not escape us that the
generosity of code and data from Monero, Onion explorer and the ShapeShift API, all participants devoted
to privacy and security in the blockchain realm, were essential to this anti-privacy investigation and we are
grateful for their contribution.
8
(a) the features were ranked in accordance to their importance
(b) Descriptors for the important
features
Figure 9: Characteristics of the RingCT topology were largely informative.
References
[1]Nathan Borggren. Deep learning of entity behavior in the bitcoin economy. https://ncsu-las.org/
wp-content/uploads/2017/12/borggren-gda_bitcoin.pdf , 2017.
[2]Nathan Borggren, Gary Koplik, Paul Bendich, and John Harer. Deanonymizing shapeshift: Linking
transactions across multiple blockchains. https://ncsu-las.org/wp-content/uploads/2019/01/LAS_
Shapeshift_Poster_1543186217.pdf , 2017.
[3]Stephen Ranshous, Cli A. Joslyn, Sean Kreyling, Kathleen Nowak, Nagiza F. Samatova, Curtis L. West,
and Samuel Winters. Exchange pattern mining in the bitcoin transaction directed hypergraph. Lecture
Notes in Computer Science (including subseries Lecture Notes in Arti cial Intelligence and Lecture Notes
in Bioinformatics) , 10323 LNCS:248{263, 2017.
[4]Cuneyt Gurcan Akcora, Yitao Li, Yulia R. Gel, and Murat Kantarcioglu. Bitcoinheist: Topological data
analysis for ransomware detection on the bitcoin blockchain. CoRR , abs/1906.07852, 2019.
[5]Francesco Zola, Maria Eguimendia, Jan Lukas Bruse, and Raul Orduna Urrutia. Cascading machine
learning to attack bitcoin anonymity, 2019.
[6] Ales Janda. Wallet explorer. https://www.walletexplorer.com , 2013-2017.
[7]Haaroon Yousaf, George Kappos, and Sarah Meiklejohn. Tracing transactions across cryptocurrency
ledgers. CoRR , abs/1810.12786, 2018.
[8]
u ypony et al. Monero project. https://github.com/monero-project/monero/blob/master/src/
wallet/wallet2.cpp , 2015.
[9]Andrew Miller, Malte M oser, Kevin Lee, and Arvind Narayanan. An empirical analysis of linkability in
the monero blockchain. CoRR , abs/1704.04299, 2017.
[10]Abraham Hinteregger and Bernhard Haslhofer. An empirical analysis of monero cross-chain traceability.
CoRR , abs/1812.02808, 2018.
[11]moneroexamples et al. onion-monero-blockchain-explorer. https://github.com/moneroexamples/
onion-monero-blockchain-explorer , 2016.
9
[12]J. Glasser and B. Lindauer. Bridging the gap: A pragmatic approach to generating insider threat data.
In2013 IEEE Security and Privacy Workshops , pages 98{104, May 2013.
[13] Nathan Borggren and Lihan Yao. Correlations of multi-input monero transactions, 2019.
10
|
{
"id": "2001.03937"
}
|
1810.12596
|
VAPOR: a Value-Centric Blockchain that is Scale-out, Decentralized, and
Flexible by Design
|
Blockchains is a special type of distributed systems that operates in unsafe
networks. In most blockchains, all nodes should reach consensus on all state
transitions with Byzantine fault tolerant algorithms, which creates bottlenecks
in performance. In this paper, we propose a new type of blockchains, namely
Value-Centric Blockchains (VCBs), in which the states are specified as values
(or more comprehensively, coins) with owners and the state transition records
are then specified as proofs of the ownerships of individual values. We then
formalize the "rational" assumptions that have been used in most blockchains.
We further propose a VCB, VAPOR, that guarantees secure value transfers if all
nodes are rational and keep the proofs of the values they owned, which is
merely parts of the whole state transition record. As a result, we show that
VAPOR enjoys significant benefits in throughput, decentralization, and
flexibility without compromising security.
|
http://arxiv.org/pdf/1810.12596v2
|
Zhijie Ren, Zekeriya Erkin
|
cs.DC
|
cs.DC
|
arXiv:1810.12596v2 [cs.DC] 14 Dec 2018VAPOR: a Value-Centric Blockchain that is
Scale-out, Decentralized, and Flexible by Design
Zhijie Ren and Zekeriya Erkin
Department of Intelligent Systems
Delft University of Technology, The Netherlands
{z.ren, z.erkin}@tudelft.nl
Abstract. Blockchains is a special type of distributed systems that op -
erates in unsafe networks. In most blockchains, all nodes sh ould reach
consensus on all state transitions with Byzantine fault tol erant algo-
rithms, which creates bottlenecks in performance. In this p aper, we
propose a new type of blockchains, namely Value-Centric Blo ckchains
(VCBs), in which the states are specified as values (or more co mprehen-
sively, coins) with owners and the state transition records are then spec-
ified as proofs of the ownerships of individual values. We the n formalize
the “rational” assumptions that have been used in most block chains. We
further propose aVCB, VAPOR, thatguarantees secure valuet ransfers if
all nodes are rational andkeep theproofs of thevalues theyo wned, which
is merely parts of the whole state transition record. As a res ult, we show
that VAPOR enjoys significant benefits in throughput, decent ralization,
and flexibility without compromising security.
Keywords: Blockchain,DistributedLedgers,ConsensusAlgorithm,Sc al-
ability, Decentralization
1 Introduction
Blockchain technology, also referred as distributed ledger techno logy, considers a
distributed system operating in a network with untrusted nodes. I n blockchains,
all nodes of the system apply the same rules to process consistent data, which
mainly takes form of data blocks chained with unbreakable hash func tions.
We can categorize all existing blockchains into two categories by the ir data
structures: one follows the idea of Bitcoin [22] and we call Transact ion-Centric
Blockchains (TCBs), and the other follows from Ethereum [32] and t he classical
state machine replication model, we call Account-Centric Blockchain s (ACBs).
The former is commonly referred as ledgers, since all data are tran sactions, i.e.,
value transfer records. The concepts of account and balance ar e not explicitly
emphasized. The latter, on the other hand, the states of nodes lik e their balances
and other variables are defined and the state transition records, e.g., the trans-
actions, are put to the back-end of the system. In either case, a ll nodes in the
blockchain system should essentially always keep a consistent state regardless
of whether the concept of state is explicitly emphasized. Then, in blo ckchains,
2 Z. Ren and Z. Erkin
nodes should only pre-agree with the initial state, i.e., the genesis blo ck, and
then be able to use a consistent rule to independently validate each in put and
then perform their state transitions. As a result, both TCBs and A CBs require
the complete state transition records to be acquired reliably and co nsistently by
all nodes in the network, which causes a critical bottleneck in the pe rformance of
blockchain. In this paper, we use the term “traditional blockchains ” to refer to
all blockchains that all nodes need to acquire the whole state trans ition records.
A straightforwardconsequence of the bottleneck is the scalability issue which
has been addressed in several other works [9,31]. The throughpu t of blockchains
does not grow with the number of nodes as the requirement of comm unication,
computation, and storage grow at least proportionally to the numb er of nodes
in the network. Hence, the throughput is limited to the capacity of t he least
capable node in the network and will not increase as the network gro ws.
Then, we also observe that centralization is an indirect consequenc e of the
requirement for the whole state transition record. As novel block chain systems
are pursuing high throughput in terms of transaction per second ( TPS), the
requirement for communication, computation, and storage becom es a threshold
too high for normal users to participate. Then, the participation threshold is
a crucial factor in evaluating the decentralization of the blockchain , since a
blockchain with a high participation threshold will be consequentially un friendly
to normal users and more centralized, regardless of whether a fu lly decentralized
consensus algorithm is used.
The third problem we address in traditional blockchains is inflexibility. A s
blockchainsaredecentralizedbytheirnature,anupgradeorchan getothesystem
is much more difficult than centralized systems as inconsistency might happen if
nodes follow different rules. Some examples of such inconsistency ar e “forks”like
Bitcoin Cash/Bitcoin and Ethereum Classic/Ethereum, which cause t he system
to split and degrade in security.
In this paper, we address the problem of “all nodes need to acquire and agree
with allstatetransitions”whichessentiallycausesallabovemention edproblems.
To solvethis problem, we proposea new type ofblockchainscalled Valu e-Centric
Blockchains (VCBs) that are equally secure as traditional blockcha ins but re-
quires each node to only acquire partial state transitions. More pr ecisely:
–We formalize the rationality of nodes in value transfer system, we ca ll Ratio-
nality of Value Owner (RVO), which has already been explicitly or implicitly
used in almost all blockchains without specification.
–We propose a novel type of blockchains, called VCBs, which differ fro m
traditional blockchains as the states are specified as the distribut ion for
all values. A value can have an arbitrary amount and can be concept ually
interpreted as a banknote. Then, all state transitions are sorte d into proofs
for the ownership of individual values.1
1Similar ideas can be found in many classical digital cash sys tems, i.e., Ecash [7,8].
The relationship and difference between VCBs and early digit al cash systems will
be discussed in Subsection 7.1.
VAPOR 3
–We propose a VCB called VAPOR in which nodes only needs to hold the
proof of their own values. We further prove that it guarantees se cure and
fullydecentralizedvalue-transferundertheRVOassumption.Mor eover,with
examples, we show that VAPOR can be easily extended with extra fun ction-
alities like fast payment channels.
–WeshowthatVAPORhassignificantadvantagesovertraditionalblo ckchains
in throughput, decentralization, and flexibility.
This paper is organized as follows. In Section 2, we formally introduce the
rationality of value owners in blockchains. Then, in Section 3, we intro duce
VCBs, their features, and the conditions required for a valid VCB. I n Section 4,
we introduce a VCB, called VAPOR, and prove that it guarantees relia ble value
transfer. We show some examples of extension of VAPOR in Section 5 and show
the advantages of VAPOR over traditional blockchains in Section 6. At last, we
compareoursystemtosomerelatedworksinSection7andconclude inSection8.
2 Rationality of Value Owner
Blockchain technology is no stranger to the notion of rationality as it was in-
troduced as one of the fundamentals of Bitcoin. However, the rat ional behaviors
of nodes in blockchains, especially regarding the values they owned, are seldom
formalized. A commonly utilized rationality assumption is that rational trans-
action issuers are motivated to prove to the receivers that the tr ansactions are
successful. It is mostly in the form of transaction fees, i.e., rationa l nodes would
like to pay reasonable transaction fees so that their transactions could be added
to the chains by the “miners”, which is shown as the evidences that t he transac-
tions are successful. It has also been utilized in other forms, e.g., in t he Tangle
[27], rational nodes will do a POW and validate two previous transactio ns to
make a transaction and in Omniledger [17], rational nodes will take init iative
in issuing their inter-shard transactions to all related shards and t ake effort in
completing the transactions.
There is another type of rationality, the rationality of receiving valu es, which
is mostly ignored in literature. In Bitcoin for instance, once a transa ction is
issued, a rational receiver should observe the chain for the trans action and a
number of consecutive blocks to confirm the transaction. Howeve r, this is not
emphasized since in most blockchains, the receiver do not need to va lidate extra
information besides the blockchain itself. However, some off-chain s olutions like
Lightning Network (LN) and Plasma [25,26] introduce new requireme nts for the
rationalreceiversto validatesomeoff-chaininformationto confirm atransaction.
Finally, we also specify a rationality, the rationality of holding values, w hich
is usually considered trivial. In the basic Bitcoin system, it is simply holdin g
the private key and keeping it secret. However, in current Bitcoin s ystem, there
are some special transactions called Pay-to-Script-Hash (P2SH) transactions, in
which the values are locked by scripts that the value owners should b e able
to provide. Then, in LN, rational nodes also need to keep certain “c ommitment
transactions”secretly. Moreover,they should activelymonitort he chain to check
4 Z. Ren and Z. Erkin
if some specific transactions appear and take certain responses. Otherwise, their
received transactions could be canceled.
In this paper, we formally introduce the Rationality of the Value Owne rs
(RVO),whichisthecombinationofallthreerationalitiesmentionedab ove.These
rules are in fact no stronger than the common rationality assumptio ns made in
existing blockchains. We say that if a rational node follow the RVO rule s, then
he (we use the pronouns “he” for a node throughout this paper) w ould use his
communication,computation,andthestorageresourcestoperf ormthefollowing:
– Rationality in Holding Value: If he owns a value, he will make sure that
he could prove the ownership.
– Rationality in Sending Value: If he sends a value, then he will take
responsibility of proving to the receiver that 1), he owned this value ; 2), the
value is successfully transferred to the receiver.
– Rationality in Receiving Value: If he receives a value, then he will take
responsibility of validating 1), the authenticity of that value; 2), th e value
transfer is successful.
3 Value Centric Blockchains
The data structure of VCBs is similar to many “off-chain” schemes like [19].
Each node individually puts its own transactions in off-chain transact ion blocks
and periodically sends an abstract of those blocks to a globally agree d main
chain. Then, the key elements in VCBs are values and their ownership . A value
can be conceptually interpreted as a banknote with arbitrary deno mination.
Virtually, there exists a list of all values in the system, their amount, and their
owners which updates with the system states. Moreover, for eac h ownership,
there is aproofandan verificationalgorithmthat couldbe used to de termine the
ownership, which consists of a subset of all transaction blocks. In this section, we
introducethe basicconceptsin VCBs:the mainchain,the values, th everification
algorithm, and the conditions required for a valid VCB, i.e., a valid VCB sh ould
be able to guarantee secure value transfers between nodes.
3.1 Main Chain
For a VCB, we define the main chain as a sequence of data blocks chained
with unbreakable hash function, denoted by B={B1,B2,...}. The main chain
shouldhavethefollowingproperty,whichisessentiallyachievedbyall traditional
blockchains.
Property 1 (Consensus on the Main Chain).
– Asynchronous Consistency: In the situation where the message delay in
the network is arbitrary, if an honest node agrees with a block Bias thei-th
block of the chain, then another honest node will not agree with B′
i/ne}ationslash=Bias
thei-th block of the chain.
VAPOR 5
– Synchronous Liveness: In the situation where the message delay in the
networkcouldbe bounded by aconstant τ, ifanhonest node proposesames-
sagem, then eventually an honest node will agree with a block Bcontaining
m.
The main chain has two functions. First, it serves as a global clock. T hroughout
this paper, we use the term “the system is at state Bi” to represent a state that
thesystemhasjustreachedconsensuson Bi.Second,itisusedtoreachconsensus
on data that needs global agreements, e.g., the initial value distribu tion, the
verification algorithm, and digital signatures of the transaction blo cks of nodes,
which we will specify later.
3.2 Value, Ownership, and Proof
We assume that there are Nnodes in the network, denoted by 1 ,2,...,N. We
assume that there is a unique public key attached to each node and w e can
match the node and its public key when both are shown. In VCB, at ea ch
state of the system Bi, associated with a value vj,j= 1,2,..., we have the
amount of the value Q(B1) ={Q(v1),Q(v2),...,}and the owner of the value
O(vj,Bi)∈ {NA,1,2,...,N}. Here,O(vj,Bi) =NAsuggests that this value
is not owned by anyone at state Bi. We define value distribution of state Bias
V(Bi) ={[vj,O(vj,Bi)] :∀vj}. The initial value distribution and the amount of
each value, i.e., V(B1) andQ(B1), are contained in the first block of the main
chainB1. Then, for a transaction, or more specifically a transfer of the va lue
vjfrom owner xtoy, denoted by txm(vj,x→y), we will have O(vj,Bi) =x
andO(vj,Bi+1) =yfor a certain state Bi. Furthermore, we define a verifica-
tion scheme, consists of an verification algorithm GetOwner (vj,Bi,p) and proofs
P(vj,Bi) for alli,j, that satisfies that 1), GetOwner (vj,Bi,p) returns O(vj,Bi)
ifp=P(vj,Bi); 2),GetOwner (vj,Bi,p) returns “Fail” if p/ne}ationslash=P(vj,Bi). The
algorithm GetOwner (vj,Bi,p) should also be agreed in B1.
Now, we have all fundamental elements of VCBs: for a state Bi, there exists
a set of values vj,∀j, their corresponding owners O(vj,Bi), their proofs of the
ownership of the values P(vj,Bi), and an algorithm GetOwner (vj,Bi,p) that
could determine the owner of a value when the proof is given.
Creating, Demolishing, Merging, and Dividing Values The creation and
demolition of values are crucial in many blockchains with Nakamoto-like con-
sensus algorithms, since usually part of the incentives is given by cre ating new
values. On the other hand, merging and dividing values are optional s ince the
value exchange does not require the values to be divisible or mergeab le, e.g.,
fiat currencies with banknotes and coins. Hence, we introduce how values could
be created or demolished here, and the merging and dividing of values will be
introduced in Section 5.1 as an additional functionality.
The creation and demolition of value should be agreed by all nodes, th us
will be contained in the main chain. More precisely, to create a new valu evj:
[vj,O(vj,Bi)]/∈ V(Bi), a statement [ Add:vj,Q(vj),O(vj,Bi+1)] should be in
6 Z. Ren and Z. Erkin
blockBi+1. Similarly, to demolish value vj, we put a statement [ Delete:vj] in
blockBi+1.
3.3 Validity of VCB
As far as we know, a rigorous definition of a valid value transfer syst em is still
lacking, which remains a non-trivial and interesting topic for future research. In
this work, we aim to propose a system that provides an equivalent va lue transfer
functionality as other traditional blockchain systems, e.g., Bitcoin. Hence, we
have the following definition for a valid VCB.
Definition 1 (Valid VCB). Firstly, we give the following properties.
– Ownership: The owner of a value vjis able to validate the value and prove
it to others, i.e., if O(vj,Bi) =x, then node xwill eventually have P(vj,Bi).
Moreover, the ownership can only be transferred by the owner .
– Liquidity: The owner of a value can transfer it to any other node within
a certain period of time, i.e., if O(vj,Bi) =x, then node xcan make
O(vj,Bi+k) =yfor some k,k≥1.
– Authenticity: All values have at most one owner at each state, i.e., for all
vj,Bi, we have O(vj,Bi)∈ {NA,1,2,...,N}.
A VCB is valid if and only if Ownership and Authenticity are gu aranteed un-
der asynchronous network settings and Liquidity is guarant eed in synchronous
network settings.
3.4 RVO Rules in VCBs
In a VCB, the RVO rules becomes:
– Rationality in Holding Value: At a state Bi, if node xis the owner
of value vj, he will always make sure that he has a proof psuch that
GetOwner (vj,Bi,p) =xunless he sends vjatBi.
– Rationalityin SendingValue: Atastate Bk,foravalue vjthatO(vj,Bk) =
x, if node xwould like to send this value, he will take responsibility of pro-
viding to the receiver y: 1), the time of the transaction Bi,i > k; 2), a
proofpsuch that GetOwner (vj,Bi−1,p) =xand; 3), a proof psuch that
GetOwner (vj,Bi,p′) =y.
– Rationality in Receiving Value: For node yto receive this transaction,
it will check 1), GetOwner (vj,Bi−1,p) =x; and 2), GetOwner (vj,Bi,p′) =y.
4 VAPOR
In this section, we propose a VCB, namely VAPOR, which stands for t he five
basic elements of our system, Value, Agreement, Proof, Ownersh ip, and Ratio-
nality. As introduced in Section 3, a valid VCB should have the following.
VAPOR 7
–A main chain that guarantees Property 1.
–Theownerandproofofvalue O(vj,Bi),andavalidauthenticatingschemein-
cludingP(vj,Bi) for alli,jand a verification algorithm GetOwner (vj,Bi,p)
as described in Subsection 3.2.
Now we describe these two parts in VAPOR. Then, we prove its validity and
state its features.
4.1 Main Chain and Its Consensus Algorithm
There are two major types of algorithms that could achieve Proper ty 1: BFT al-
gorithms and Nakamoto-like algorithms. The former includes [6,13,18,2 1] which
explicitly requires the identity/public keys and the number of nodes t o be prede-
termined and known by all nodes. The latter is inspired by Bitcoin and h as been
greatly developed in recent years. It contains a large number of alg orithms such
as Proof-of-Work based algorithms [10,16,24], Proof-of-Stake ba sed algorithms
[4,12,15], Directed Acyclic Graph based algorithms [27,29,30], etc. This type of
algorithms do not require nodes to be predetermined. However, ec onomical and
game theoretical aspects have to be introduced to prevent Sybil attack as well as
to encourage honest behaviors, and Property 1 is achieved with ov erwhelmingly
high probability rather than absolute.
In VAPOR, any of the existing consensus algorithms that guarante e Prop-
erty1 (with ahigh probability)can be used forthe main chain B={B1,B2,...}.
Then,VAPORhasthesamerequirementsastheconsensusalgorith mandachieve
the same level of security. For instance, if PBFT [6] is chosen, the n VAPOR al-
lows less than 1/3 of the predetermined nodes to be malicious. Then, if Bitcoin
POW is chosen, then VAPOR tolerates less than 1/4 of the total minin g power
to be malicious [11] and the confirmation of the transactions is proba bilistic.
4.2 Proofs and the Verification Algorithm
The main content of VAPOR is transactions. The proofs of the owne rship of
values are just different subsets of the whole transaction set. He re, we first in-
troduce the data structure of the transactions, then introduc e how the proof is
chosen for each value.
Transaction Blocks In VAPOR, each node independently makes transaction
blocks with the transacitons sent by itself. A transaction txm(vj,x→y) is
defined as
txm(vj,x→y) = [vj,y,sn],
in which snis an internal serial number generated by node xto identify his
transactions. Since transactions are then put in blocks with index o fx,xis
omitted in individual transactions. Note that here mis a virtual global trans-
action identifier we used in this paper and it does not actually acknowle dged
8 Z. Ren and Z. Erkin
by any node. Periodically, a node puts transactions in a transaction blockband
send an abstract,
a(x) = [x,H(pkx),Sigx(x|H(pkx)|MR(b))],
to reach consensus on the main chain, where H(pkx) is the hash of the public
key ofxandSigx(H(pkx)|MR(b)) is a digital signature made with H(pkx)
concatenated with the Merkle root of bencrypted by the private key of x. In
each round, at most one abstract from a node can be included in the main chain.
Ifmultiple differentabstractsfromthesamenodearereceivedin th esameround,
then only one of them is considered valid. By the property of digital s ignature,
the content of bis immutable once the abstract a(x) is confirmed on the main
chain. Hence, we denote the abstract a(x) contained in block Bibyai(x) and
the block bbybi(x) and call it a confirmed block. Then, as Bis agreed by all
nodes, blocks bi(x),∀xwill also form a chain that as immutable as B. Then, we
defineCB={bi(x),∀i,x}.
Transaction Fee for Abstracts In our system, instead of individual transac-
tions, the consensus is only reached on the abstracts. Then, for many consensus
algorithms, a transaction fee should be provided to the block propo sers, namely
the miners, for them to include the abstract. The amount of the tr ansaction
fee should not be fixed so that a market can be created between th e nodes and
the miners. It can be achieved by introducing a new type of transac tions in
which the receiver is the miner, i.e., in a transaction block bi(x), nodexcould
create transactions in form of txm(vj,x→[miner]) = [vj,x,[miner],sn], where
[miner] is a variable that equals to the proposer of the block Bi. A non-trivial
problem for the transaction fee is that the sender of this transac tion does not
know the receiver in advance, which hinders him from sending the pro of to the
receiver. Hence, in the scope of this paper, the transaction fees are only feasible
if the main chain uses BFT algorithms or algorithms that the block prop oser
is determined before the block, e.g., [10,12,15]. Then, the sender will give the
proof of this transaction to the corresponding node so that the a bstract would
be included.
Value Ownership and Proof Firstly, we define the ownership of values as
the following.
Definition 2 (Value Ownership).
–The initial value ownership is agreed on the main chain, eith er by the initial
value distribution in B1or value creation in Bk,k≥1.
–We assume that node xstarted owning a value vjatBi′. Then, he will transit
the ownership of this value to node yif he makes a transaction in a confirmed
blockbi(x)and has not make any transaction of this value in any confirmed
blocksbk(x),k∈[i′+1,i−1].
VAPOR 9
–If there are more than one transaction of the same value in one transaction
block, it is a clear sign of an attempt of double spending. Hen ce, we forbid
this by stating that if a value is transacted more than once by its owner in a
confirmed block, then the owner of that value is NA.
Then, we define the proof P(vj,Bi) as a subset of CB, which is essentially
all confirmed transaction blocks that are considered in the second item of Def-
inition 2, as well as all necessary public keys to verify them. The algor ithm
Proof(vj,Bi,CB) can be used to get the proof P(vj,Bi), which is given in Ap-
pendix A.
Verification Algorithm Further, as defined in Subsection 3.2, a verification
algorithm in a VCB should be able to determine the ownership when the p roof
is given and output “Fail” if any input other than the correct proof is given.
In Algorithm 1, we propose GetOwner (vj,Bi,p) that outputs O(vj,Bi) ifp=
P(vj,Bi) and outputs ‘Fail’ for p/ne}ationslash=P(vj,Bi).
Algorithm 1 Verification Algorithm GetOwner (vj,Bi,p)
Get the block of initial distribution (creation) of value vjin the main chain: Bindex
Setowneraccording to the initial distribution from the main chain.
index++;
whileaindex(owner) exists in Bindexdo
ifbindex(owner) or the public key of ownerdoes not exist in pthen return Fail;
ifMerkle root and signature do not match then return Fail;
count←number of transactions of vjinbindex(owner);
ifcount= 0then
index++;
else ifcount= 1then
index++;
owner←the receiver of the transaction of vj;
else return Fail;
ifindex> ithen
ifAll data in pare blocks and all blocks have been checked then return
owner;
else return Fail;
Thevalidityof GetOwner (vj,Bi,p)asanverificationalgorithmcouldbeeasily
shown. First, it uses the same method as the second item in Definition 1 to
check whether pconsists of the exact transaction blocks as P(vj,Bi) and any
mismatch returns ‘Fail’. Then, since the algorithm use exactly the sam e rules
as the definition of ownership to determined the owner, it returns O(vj,Bi) if
p=P(vj,Bi).
10 Z. Ren and Z. Erkin
4.3 Validity of VAPOR
Here,weprovethat VAPORisavalidVCB underRVOrulesandthe cons istency
of the system is uncompromised even if RVO rules do not hold.
Theorem 1. In VAPOR,the properties of a valid VCB will hold in the follow ing
conditions.
Properties Ownership Liquidity Authenticity
Conditions RVO rules Synchrony —
Due to space limitation, we only give an outline of the proof and provide the
full proof in Appendix B. The Ownership could be proved by induction: for each
owner of the value, he is always able to receive the proof of the value from a
rational previous owner. Moreover, only the owner can transfer the value since
the transaction only happens when the block is confirmed. The Liquid ity follows
from the Synchronous Liveness property of the main chain. Then, the Authen-
ticity follows from the Asynchronous Consistency of the main chain, which also
guarantees the consistency of all confirmed transaction blocks. Then, Authen-
ticity is proved as at each state, the values, owners, and proofs a re based on the
confirmed transactions blocks in a deterministic and one-to-one ma pped fashion.
The holding condition of each property in Theorem 1 provides a good in -
sight on VAPOR and its differences from traditional blockchains. Firs t, even if
RVO rules do not hold, e.g., a sender refuses to send the proof to th e receiver, it
only causes a fail to prove the ownership of this exact value. The Liq uidity and
Authenticity of the system are not violated and other values are no t corrupted.
Second, the Ownership does not depend on synchrony. Hence, if a value is trans-
ferred and the network lose synchrony for Liquidity, the proof of the value could
still be delivered to the receiver if the sender is rational.
4.4 Features of VAPOR
The most distinctive feature of VAPOR is that each node only needs t o acquire
and keep the proofs of the values that it owns, i.e., at a state Bi, nodexonly
needs to have P(vj,Bi),∀O(vj,Bi) =x. To efficiently record the proofs, we
propose the following implementation:
–The main chain is stored and updated according to the consensus alg orithm.
–A node keeps a transaction block database of for all confirmed tra nsaction
blocks that he has.
–A node keeps a value ownership table that updates with the main chain and
keeps track of the values, their owners, and the proofs that he k nows, which
includes his own values. The proofs are simply pointers to the transa ction
block database.
VAPOR 11
Comparing to TCBs and ACBs, a transaction of multiple values need to be
recorded as multiple transactions in VAPOR. However, for all these transactions
plus all transactions included in the same transaction block, only one signature
is required in VAPOR, which is in fact more efficient in storage. The comm u-
nication is also efficient as transaction blocks are acquired directly fr om the
sender of the value with point-to-point communication and guarant eed security
under the RVO rules. Moreover, the receivers could inform the sen der about the
transaction blocks that it already has to avoid overhead. Then, as a trade-off
between storage and communication, a node can choose to not dele te the proofs
of the already spent values. This means that they do not need to re -acquire some
transaction blocks for future received values.
5 Extending VAPOR by Modifying the Verification
Algorithm
In Section 4, we introduced how transactions could be verified with t he verifica-
tion algorithm GetOwner with the proof P(vj,Bi). In this section, we show the
flexibility of this framework by providing examples of extended funct ionalities.
More precisely, we will show that the functionalities of value division, f ast off-
chain transactions, and value-related smart contracts can be ea sily achieved by
simple modifications to the verification algorithms.
5.1 Value Division
The functionality of value division can be achieved with a new type of tr ansac-
tions called value division that has the form:
[Divide:vsource→(vsource,1,Q(vsource,1)),...,(vsource,n,Q(vsource,n)).
The index sourceforms a chain that can be traced back to the origin. Then, to
validate a value divided from another value, we simply call GetOwner to check
the owner of each value on the chain recursively from the origin. This new type
of transactions can either be added by making modifications to GetOwner or
defining another algorithm GetOwnerDV on the main chain that recursively calls
GetOwner . We describe GetOwnerDV in Appendix C.
5.2 Fast Off-chain Payment
In VAPOR, the confirmation of the transaction is dependent on the main chain,
thus it essentially has the same latency as traditional blockchains. H owever, a
fast off-chain payment solution like LN or Plasma [25,26] can also be de ployed in
VAPOR. Brieflyspeaking,an off-chainpaymentscheme worksasfollo ws.Firstly,
somevalue is lockedon the main chainasthe deposit forthe “fast pay mentchan-
nel” to a particular receiver. Then, transactions can be made to th at receiver
without confirmations on the main chain. The safety of the transac tions are
12 Z. Ren and Z. Erkin
guaranteed by a mechanism for the receiver to take all deposit whe n the sender
tries to cancel a transaction. However, this mechanism requires s ynchrony be-
tween the receiver and the main chain. Then, there is a mechanism allo wing the
sender to safely shut the off-chain payment channel at any time.
In VAPOR, similar ideas can be implemented under the same synchrony
assumption. A node can independently lock its values for a receiver a nd then
makes off-chain transactions by signing them and sending signed tra nsactions to
the receivers as proofs. Then, the verification scheme should be m odified to be
ableto verifythese proofs.Thedetail ofthis schemewill be givenin A ppendix D.
5.3 Smart Contracts
In the previous subsections, it is revealed that additional function alities can be
easily achieved by changing the rules for verification, which is merely a mod-
ification to GetOwner , or agreeing on new verification algorithms on the main
chain. In fact, as long as values are transferred and there are int erested parties
following RVO rules, smart contracts can be written in VAPOR as new v erifica-
tion algorithms with one principle: only data that is against the value ow ners’
interest is required to be put on the main chain and other data can be safely
moved off-chain to the corresponding value owners. We give an exam ple of such
smart contracts, a betting game, in Appendix E.
6 Advantages of VAPOR
It has been shown that in VAPOR, nodes do not necessarily need to r ecord
the whole transaction set to allow secure value transfer. This fund amental dif-
ference from traditional blockchains leads to the advantageous in throughput,
decentralization, and flexibility.
6.1 Throughput
The most straightforwardadvantageofVAPOR is the throughput becausenodes
only need to acquire the proofs of their own values instead of the wh ole trans-
action set, as stated in Subsection 4.4. However, this improvement is not trivial
to quantify as it depends heavily on the networks and the transact ion patterns.
Here, we theoretically analyze the throughput in terms of the tran saction cost
C, defined as a combination of the expected bandwidth, computation , and stor-
age resources required to communicate, validate, and store a tra nsaction in the
whole network.
Unlike traditional blockchains, the cost of an individual transaction in VA-
POR is determined by the proof size, which is situational. Hence, we ca lculate
Cby looking at the expected transaction blocks in a round that a node even-
tually needs to acquire, which we denote by b. Then, we have C=O(b) since a
transaction will be eventually acquired by bnodes on average. Let us consider a
transaction block bi(x). It will eventually be acquired by node yif nodexholds
VAPOR 13
a value at state Biand at a state Bj,j > inodeyreceives that value. In other
words, for the set of values Vi(x) holding by node xat stateBi, if all other nodes
will receive a value from Vi(x) sometime in the future, then VAPOR have no
throughput gain over traditional blockchains. In all other cases, as long as there
exists some nodes that will never acquire any value in Vi(x), then we have b < N
and VAPOR has a throughput benefit.
In [28], a concept of spontaneous sharding is proposed, which roug hly works
as the following. When performing a transaction, a rational node will choose the
value with the least transaction blocks to transmit among all values t hat he has.
In other words, they tends to use the values for which the most pa rt of the proof
is already known and validated by the receiver, e.g., the value that on ce owned
by the receiver. As a result, some values will only cycle in a part of the network,
namely a shard, instead of the whole network. Then, a node holding gvalues is
equivalent to participating in gshards and bwill then equal to the expected size
of the union of these shards. Then, it is shown in [28] that in many sce narios,
we have C=O(b) =o(N), i.e., the throughput will scale out. Note that any
group of frequent transacting nodes can decide to perform this o ptimization at
any time to gain the throughput benefit, regardless of the rest of the network.
Hence, since spontaneous sharding gives direct benefit to individua ls even if
other nodes refuse to cooperate, the “the tragedy of the comm ons” [14] problem
will not occur. We refer the readers to [28, Remark 2] for more disc ussion.
6.2 Decentralization
In Section 1, we address the centralization problem due to the high p articipation
threshold. In VAPOR, this problem is significantly mitigated due to the value
centric principle: nodes only transmit and store the data needed fo r validation of
their own values, which is mostly not the whole transaction set. For e xample, in
traditional blockchains,for nodes who only own a few coins in a blockc hain, they
still have to acquire and validate the whole chain to validate their own v alues
and make transactions. In VAPOR, their cost of validating their own values and
making transactions is O(1).
6.3 Flexibility
As shown in Section 5, VAPOR enjoys benefits of easy modification, e xtension,
and upgrading by simply agreeing on new verification algorithms on the main
chain. However, this can be pushed one step further by allowing nod es to in-
dividually choose the algorithms that they like to use. Then, hard for ks like
Bitcoin/Bitcoin Cash or Ethereum/Ethereum Classic can be avoided. Instead,
the forks will be “hidden” as some values might not be validated by som e users
as they disagree with a certain rules. However, they could still agre e with the
main chain and contribute to the security of the entire system. We c onsider this
as an advantage of flexibility, as nodes are more freely to agree/dis agree with
each other, without destroying the consistency of the whole syst em as long as
they have the basic agreement.
14 Z. Ren and Z. Erkin
7 Related Works
This work is mainly inspired and developed from [28]. However, it does ha s
similarities to other studies if we view VAPOR in different perspectives. We
explain the similarities and relationsof this work and other worksin this section.
7.1 Value Centric Principle
The origin of describing value transfer systems by values (or altern atively called
coins, notes, bills) can be dated back to some pioneering digital cash works like
[7,8,23]. However, in these schemes, the notions of value and transa ction are
interchangeable as a central authority is required to validate each transaction.
Hence, Bitcoin, as well as most of its successors known as alt-coins , use TCBs
that focus on the validity of individual transactions rather than th e value. The
main difference from TCBs and VCBs can be clarified using the example o f the
Simple-Payment-Verification (SPV) nodes in Bitcoin. SPV nodes could verify
whether all related transactions of a value are validated by the mine rs and are
on-chain, but they could not validate the authenticity of this value, i.e., could
not detect double-spending.
Chainspace [2] is a blockchain with sharding that uses a similar value-ce ntric
idea for inter-shard transactions, i.e., each transaction should inc lude a “Trace”
pointing back to the source of the value, so that the validators fro m the value-
receiving shard only need to check the shards of the sources to pr event double
spending. However, it has more redundancy as the value-centric id ea is used in a
shard level instead of the node level, and thus has less throughput improvement
comparing to VAPOR.
7.2 Off-Chain and DAG Techniques
In the perspective of data structure, VAPOR has its similarities to m any off-
chain systems like RSK [19] as data is stored off-chain and a main chain is
used for the hash of the data. However, most off-chain systems c ompromise in
decentralization as some trusted nodes are required to validate th e contents of
the off-chain data. Also, comparing to the off-chain payment schem es like LN
and Plasma [25,26], VAPOR essentially moves all proofs for values off-c hain. As
a result, it is no longer necessary to use deposits to enforcing the c onsistency
of the off-chain and on-chain values. Then, it is also similar to Hashgra ph [3]
in the sense that node individually create their own transactions. Ho wever, in
Hashgraph, all nodes eventually need the whole transaction set.
7.3 Sharding
Recently, many sharding schemes have been proposed to divide the network into
small shards. Then, the transactions in a shard do not need to be c ommunicated
outsidetheshard.However,akeyproblemisthatthedoublespend ingprevention
VAPOR 15
of inter-shard transactions relies on the security of shards inste ad of the whole
network, which is a degradation in the security. Shards can be eithe r determined
artificiallyby the networktopology[5] or at random[17,20], ordeter mined based
on applications or users [1,2], to reduce the number of inter-shard transactions
as well as the probability of malicious shards. However, our system g uarantees
no degradationon securitysince essentially,the shardsarespont aneouslyformed
by the value transfer patterns. In other words, all shards are s ecure for their own
intra-shard transactions and there will be no inter-shard transa ctions.
7.4 Performance Comparison
It is difficult to make fair throughput comparison between VAPOR and other
systemsusingauniformstandard,e.g.,transactionpersecond(T PS),asschemes
have different security assumptions and the throughput also depe nds on the net-
work settings. Therefore, we use a theoretical approach to ana lyze and compare
the throughput and security of VAPOR with a typical system of eac h kind, i.e.,
LN for off-chain schemes, PHANTOM for DAG, and Omniledger for sha rding
schemes. We consider the transaction cost C(defined in Subsection 6.1) and
the security Sof a transaction, which is defined as the amount of compromised
nodes (corresponding resources for POW or POS) required to per form a double-
spending attack. We present the results in Table 1.
Schemes VAPOR LN PHANTOM Omniledger
C O(b) O(1) O(N) O(d)
S O(N)O(1) orO(N)O(N) o(N)
Table 1. The cost and security of a transaction in VAPOR, LN, PHANTOM, and
Omniledger for the whole network. Here bis the average transaction blocks of each
state acquired by a node and dis size of the shard.
The cost and security of VAPOR are given in Subsection 6.1 and Subse c-
tion 4.3, respectively. For LN, note that this transaction is differen t from classi-
cal notion of transactions as it relies on a deposit and the value would be locked
until the channel is shut down. The security relies on the synchron y between the
receiver and the system (explained in Appendix D), thus would be com promised
if either one is compromised. PHANTOM uses a block DAG structure to remove
the dependency of security on the throughput of a chain-struct ure blockchain.
However, all nodes still need to eventually acquire all transactions and the sys-
tem will not scale out. Omniledger reduces the cost to O(d) wheredis the shard
size and promises a throughput benefit that is proportional to N/d. However, as
Omniledger yields a random approach to keep the malicious nodes within each
shard to be below 1/3, the security of the system becomes a non-t rivial function
ofdandN, which is dominated by Nbut not explicitly stated in [17].
16 Z. Ren and Z. Erkin
8 Conclusion
In this paper, we address and formalize the fundamentals of a value -transfer sys-
tem and the rationalityassumptions. The highlight of this workis that we clarify
the redundancy in traditional blockchains for value-transfer and how this redun-
dancy can be removed by using the rationality assumptions and VCBs . We hope
that this work would set a theoretical framework for future block chain designs
and inspire many theoretical studies on other basic concepts in bloc kchains, e.g.,
the rational assumptions in non-value-transfer blockchains.
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A Algorithm Proof(vj,Bi,CB)
We define the proof of the ownership P(vj,Bi) as a subset of CBthat output by
an algorithm Proof(vj,Bi,CB) shown in Algorithm 2.
18 Z. Ren and Z. Erkin
Algorithm 2 Proof(vj,Bi,CB)
Get the block of initial distribution (creation) of value vjin the main chain: Bindex
Setowneraccording to the initial distribution from the main chain.
index++
Proof={}
whileaindex(owner) exists in Bindexdo
ifMerkle root and signature do not match then return Proof
Addbindex(owner) and the public key of ownertoProof
count←number of transactions of vjinbindex(owner)
ifcount= 0then
index++
else ifcount= 1then
index++
owner←the receiver of the transaction of vj.
else return Proof
ifindex> ithen return Proof
B Proof for Theorem 1
Proof.Firstly, we prove Ownership by induction. It is clear that the first ow ner
of any value vjwill have the proof of this value, which are basically all of his
public key and his own confirmed transaction blocks until the block be fore the
one that spends it. Then, assume that the t-th owner of vj, denoted by ot,
has the proof P(vj,Bk) proving the ownership O(vj,Bk) =otat state Bk.
Then, assume that the t+1-th owner, ot+1starts to own the value at state Bi,
i.e.,O(vj,Bi−1) =ot,O(vj,Bi) =ot+1. Then, by the definition of proof, there
exists a transaction in bi(ot) that send the value to ot+1. By the Rationality of
Holding Value in RVO, otwould not make this transaction unless he would like
to send this value. Then, by the Rationality of Sending Value in RVO, otwill
take responsibility of giving proof P(vj,Bi) toot+1. Again, by the definition of
proof,P(vj,Bi) is merely P(vj,Bk)∪{bl(ot) :k < l≤i}∪{public key of ot},
which can be independently provided by ot. Hence, we prove that in this case
ot+1will eventually has the proof P(vj,Bi). Furthermore, it is clear that only
the owner of a value could transfer it as a transaction must be includ ed in a
block confirmed with the private key of the owner.
Then, we prove Liquidity. To transact a value, the owner simply need s to
put a transaction in a confirmed transaction block. Then the prope rty (Partial)
Synchronous Liveness in Property 1 guarantees that the transa ction block can
be confirmed as the abstract will be included in the main chain.
At last, we prove Authenticity. This is actually guaranteed by the de sign of
VAPOR. Firstly, the initial ownership of a value is unambiguous becaus e it is
on the main chain which has Asynchronous Consistency in Property 1 . Then,
the ownership transition is always determined by a confirmed block wh ich is
immutable. Then, there are three possibilities for the number of tra nsactions of
the same value in a confirmed block: 1) if there is no transactions of t hat value,
VAPOR 19
then the ownership remains unchanged; 2) if there is one transact ion of that
value, then the ownership is changed to the receiver; 3) if there ar e more than
one transactions of that value, then the ownership becomes NA. Since all three
possibilities result in unambiguous ownership, we proved Authenticity ./squaresolid
C Verification Algorithm for Value Division GetOwnerDV
Here we introduce GetOwnerDV in Algorithm 3. Note that in here, a minor mod-
ification should be made on GetOwner so that the result will not be ‘Fail’ if
redundant elements are detected in p.
Algorithm 3 Verification Algorithmfor Divided Value GetOwnerDV (v[seq],Bi,p)
Find all value division transactions and their correspondi ng states in p. Order the
states by [ s1,s2,...];
j←the first entry of [ seq];
t←1;
whilet≤the length of seq.do
owner=GetOwner (vj,Bs1,p);
Check if the corresponding value division transaction is in bst(owner) and the
sum of the amount of the divided value equals to the amount of t he source value.
Return ‘Fail’ if the check fails.
t++,j= [j,next element in seq];
ifAll blocks in pare checked then return owner
else return Fail
D Off-chain Payment Scheme
Our fast payment scheme contains two new type of transactions, two new types
of message to the main chain, and a new verification algorithm GetOwnerFP . If
nodexwants to make fast payment to node y, he simply performs the following:
–Nodexmakes deposit transactions to lock up a number of values with in-
dications that they could only be send to y, confirm the blocks, and send
them to node yto initialize the fast payment.
–When a fast payment of value vjis issued, node xsends a signed transaction
ofvjto nodey, denoted by tx. Then, node ycan include this transaction in
his own blocks at any time and confirm them to receive the value.
–When node xwants to end the fast payment and unlock a value vk, he sends
an unlock message to the main chain.
–The unlock will succeed in Trounds if no objection message shows in the
main chain. An objection message can be made by any node by sending tx
to the main chain.
20 Z. Ren and Z. Erkin
Then, in GetOwnerFP we define three new rules on checking the proofs for own-
ership:
1. A value vjlocked by node xis no longer considered as owned by x, butNA
indicating no owner. It will be reconsidered as owned by xif there is only
one unlock message is on the main chain, assume that it is included in Bi,
and there is no objection message included in Bk,i+1≤k≤i+T.
2. A value vjis transacted from node xto node yin state Biif it is locked
by node xto send to node yat a state Bi′,i′< i, and there is a signed
transaction by xincluded in block bi(y). There should not be a unlocking
message for this value on the main chain that is not responded for mo re than
Tblocks.
Note that although a fast transaction is only confirmed when the blo ck is con-
firmed, the transaction itself is completed as soon as the signed tra nsaction is
received by node y, since node ycan then independently make the proof of him
owning this value.
Some drawbacks in existing off-chain payment schemes, e.g., LN, are : 1),
the values in the transactions and deposit will be locked until the cha nnel is
closed. Hence, it is a different type of transaction and can only be co nsidered as
a supplement to the value transfer system. 2), the receiver shou ld have a certain
synchrony, i.e., the receiver should be able to issue a transaction to the chain to
take the deposit before it is refunded to the sender when he catch es the sender
cheating. 3), the security of this scheme is not formally proved. A b ig advantage
of the off-chain payment scheme in VAPOR is that node ycan spend vjas soon
ashe ownsit, without requiringshutting downthe wholechannel, i.e., a ll deposit
values been spend or unlocked. Moreover, we could use similar argum ents as the
proofin Subsection 4.3 to provethe Ownershipproperty holds when the network
is synchronous and the RVO rules apply.
E Betting Game
Here, we give a smart contract for on-chain betting. Node xand node ywould
like to bet even or odd on the hash of block Bi. Then, we simply add a new
type of transaction which is Bet: [vj,x,y,B i,sn]. The bet transaction will lock
the value vjuntilBiwith one unlocking condition: another value with the same
amount is bet by ybeforeBiwithxand the ownership will depend on the hash
ofBi. Then, the verification algorithm is simply checking the lock transact ion,
the ownership for both values, and the hash of Bi, i.e., if node xbet on even,
then the ownership of both locked values will be node xat stateBiif the hash
ofBiis even.
However,the difficulty is to make sure that both node xand node ycould get
the proofs of ownership and the locking message for both values. T his is a prob-
lem since there is always one node in the betting would benefit from not sharing
the proof and/or the locking message, which will cause a scenario sim ilar to Two
Generals Problem. As a result, the verification algorithm must also ch eck for a
VAPOR 21
confirmation send by one node on the main chain, which shows the agr eement
for both nodes that both proofs are acquired. Without such confi rmation, the
value will be unlocked at state Bito its original owner.
|
{
"id": "1810.12596"
}
|
2105.10464
|
Pravuil: Global Consensus for a United World
|
Pravuil is a robust, secure, and scalable consensus protocol for a
permissionless blockchain suitable for deployment in an adversarial environment
such as the Internet. Pravuil circumvents previous shortcomings of other
blockchains:
- Bitcoin's limited adoption problem: as transaction demand grows, payment
confirmation times grow much lower than other PoW blockchains
- higher transaction security at a lower cost
- more decentralisation than other permissionless blockchains
- impossibility of full decentralisation and the blockchain scalability
trilemma: decentralisation, scalability, and security can be achieved
simultaneously
- Sybil-resistance for free implementing the social optimum
- Pravuil goes beyond the economic limits of Bitcoin or other PoW/PoS
blockchains, leading to a more valuable and stable crypto-currency
|
http://arxiv.org/pdf/2105.10464v1
|
David Cerezo Sánchez
|
cs.CR, cs.DC, econ.GN, q-fin.EC
|
cs.CR
|
Pravuil: Global Consensus for a United World
David Cerezo Sánchez
david@calctopia.com
24th May 2021
Abstract
Pravuil1is a robust, secure, and scalable consensus protocol for a
permissionless blockchain suitable for deployment in an adversarial envi-
ronment such as the Internet. Pravuil circumvents previous shortcomings
of other blockchains:
- Bitcoin’s limited adoption problem: as transaction demand grows,
payment confirmation times grow much lower than other PoW blockchains
- higher transaction security at a lower cost
- more decentralisation than other permissionless blockchains
- impossibility of full decentralisation and the blockchain scalability
trilemma: decentralisation, scalability, and security can be achieved simul-
taneously
- Sybil-resistance for free implementing the social optimum
- Pravuil goes beyond the economic limits of Bitcoin or other PoW/PoS
blockchains, leading to a more valuable and stable crypto-currency
Keywords : consensus, permissionless, permissioned, scalability, zero-
knowledge, mutual attestation, zk-PoI
1 Introduction
Athirdgenerationofblockchainshasbeendevelopedfeaturingthelatestadvances
in cryptography and sharding to reach maximum performance and security in
Internet settings: they usually make use of advances in BFT-like consensus
protocols [ GK18,LLS+21] and collective signatures[ RGK19] to obtain 1000s of
transactions per second.
In this work, we introduce Pravuil 1, a robust, secure, and scalable consensus
protocol for real-world deployments on open, permissionless environments that,
unlike other proposals, remains robust to high adversarial power and adaptation
while considering rational participants and providing strong consistency (i.e.,
no forks, forward-security, and instant transactions). Our protocol is also the
1In the Book of the Secrets of Enoch[ Pla26], an archangel “swifter in wisdom than the other
archangels”, scribe and recordkeeper.
1arXiv:2105.10464v1 [cs.CR] 21 May 2021
first to integrate real-world identity on layer 1 as required by current financial
regulations, obtaining Sybil-resistance for free: a very useful property considering
the electrical waste produced by Bitcoin, its Achilles’ heel that this blockchain
circumvents for the first time by obviating to pay the Price of Crypto-Anarchy
[Cer19].
To achieve the desired goals, we introduce a new consensus protocol in
which we prioritise robustness against attackers and censorship-resistance. We
then incorporate zero-knowledge Proof-of-Identity [ Cer19] while maintaining
an open, permissionless node membership mechanism enabling high levels of
decentralisation. Finally, we will show a working system of the proposed design
in an open-sourced Testnet at https://github.com/Calctopia-OpenSource.
1.1 Contributions
In summary, we make the following contributions:
•we propose a consensus protocol that remains robust, secure, and scalable
among rational participants in an Internet setting
•we prove liveness, safety, and censorship-resistance of our new consensus
protocol
•we discuss the underlying rationale of our design and prove all the advan-
tages that it provides over previous blockchain designs
•we provide an open-source implementation running on a Testnet
2 Related Literature
Previous blockchain designs [ GK18,RGK19,BMC+15,LLS+21] deal with the
different trade-offs of the scalability trilemma (security vs. scalability vs. decen-
tralisation) and they don’t usually concern with the economic consequences of
their design (e.g., the Price of Crypto-Anarchy) or the legal consequences of the
lack of real-world identity as required by recent legislation (FATF’s Travel Rule).
Previous designs of ByzCoin/OmniLedger/MOTOR ([ KKJG+16,KKJG+17,
KK19]) proposed Proof-of-Work(PoW) as a Sybil-resistance mechanism: al-
though their consensus protocol is more advanced and performant than Bitcoin,
they would still pay for the Price of Crypto-Anarchy [ Cer19]. And although
other blockchains (e.g., [ DGK+20]) provide methods to anonymise real-world
identities, they fail to incorporate these privacy techniques on their consensus
protocol as they keep on using Proof-of-Stake as a Sybil-resistance mechanism,
thus they still pay the Price of Crypto-Anarchy [ Cer19], suffer from Bitcoin’s
limited adoption problem [ HJS19] and exist within the same economic limits
[Bud18].
2
Bitcoin ByzCoin/MOTOR Pravuil
Secure
Decentralised
Scalability 4
Real-world Identity 4 4
Free Sybil-resistance 4 4
Lawfulness 4 4
Unlimited adoption 4 4
No economic limitations 4 4
3 Background and Model
3.1 Prior Work
Pravuil builds over ByzCoin[ KKJG+16], OmniLedger[ KKJG+17], and MOTOR
[KK19]: in the next section 4, we extend these protocols to address issues that
prevent their deployment in an adversarial environment such as the Internet.
3.2 Assumptions
In this work, we assume the following model and definitions:
Definition 1. (Strongly-consistent broadcast [RC06]). A protocol for
strong consistent broadcast satisfies the following conditions except with negligi-
ble probability:
•Termination : If a correct party strongly-consistent broadcasts mwith
tagID, then all correct parties eventually strongly-consistent deliver m
with tagID.
•Agreement : If two correct parties PiandPjstrongly-consistent deliver
mandm0with tagID, respectively, then m=m0.
•Integrity : Every correct party strongly-consistent delivers at most one
payloadmwith tagID. Moreover, if the sender Psis correct, then mwas
previously strongly-consistent broadcast byPswith tagID.
•Transferability : After a correct party has strongly-consistent delivered
mwith tagID, it can generate a string MIDsuch that any correct party
that has not strongly-consistent delivered message with tag IDis able
tostrongly-consistent deliver some message immediately upon processing
MID.
•Strong unforgeability : For anyID, it is computationally infeasible to
generate a value Mthat is accepted as valid by the validation algorithm
for completing IDunlessn 2tcorrect parties have initialised instance
IDand actively participated in the protocol.
3
Definition 2. (Partial synchronous model [DDS83, DLS88]). In a par-
tially synchronous network, there is a known bound and an unknown Global
Stabilisation Time (GST), such that after GST, all transmissions between honest
nodes arrive within time .
Definition 3. (n=3f+1 [FLM86]). The proportion of malicious nodes that
an adversary controls accounts for no more than 1=3of the whole shard. The
rest of the nodes are rational, that is, maximisers of their transaction rewards.
Definition 4. (Round-adaptive adversary [PS16]). We assume a mildly-
adaptive, computationally bounded adversary that chooses which nodes to
corrupt at the end of every consensus round and has control over them at the
end of the next round.
Definition 5. (Strong Consistency [KKJG+16]).The generation of each
block is deterministic and instant, with the following features:
•There is no fork in a blockchain. By running a distributed consensus
algorithm, state machine replication is achieved.
•Transactions are confirmed almost instantly. Whenever a transaction is
written into a block, the transaction is regarded as valid.
•Transactions are tamper-proof ( forward security ). Whenever a transaction
is written to a blockchain, the transaction and block cannot be tampered
with and the block will remain on the chain at all times.
Definition 6. (BLS [BLS01] and BDN [BDN18] signatures). Boneh-
Lynn-Sacham and Boneh-Drijvers-Neven signatures are assumed secure.
Definition 7. (Global PKI [ICA21]). Our blockchain design assumes a
global PKI, not directly for consensus purposes, but as a node-admission and
Sybil-resistance mechanism[Cer19].
Definition 8. (Permissionless network [SJS+21]).In a permissionless
network:
•Anyone can join a node without requiring permission from any party.
•Any node can join or leave at any time.
•The number of participating nodes varies at any time and is unpredictable.
4 Detailed Design
Pravuil builds over ByzCoin[ KKJG+16], OmniLedger[ KKJG+17], and MOTOR
[KK19]: ByzCoin[ KKJG+16] envisions a Bitcoin[ Nak09] protocol that uses
strongly consistent consensus, scaling with multi-cast trees and aggregate Schnorr
signatures. OmniLedger[ KKJG+17]addsshardingoverByzCoin[ KKJG+16], and
4
MOTOR[ KK19] strengthens the robustness of ByzCoin[ KKJG+16] for an open,
adversarial network such as the Internet.
Pravuil improves over previous works by using another source of randomness,
drand[DRA21a], and by incorporating zero-knowledge Proof-of-Identity[ Cer19]
as a Sybil-resistance mechanism into the first layer of the consensus protocol.
4.1 Goals
To sum up, Pravuil has the following goals:
•Robustness : the consensus round can only be disrupted by controlling
the leader node.
•Scalability : the protocol performs well among hundreds of nodes ( n=
600).
•Fairness : the malicious leader can only be elected with a probability equal
to the percentage of malicious nodes in the system (i.e., the adversary
cannot always control the leader).
We detail the extensions over a previous BFT protocol such as ByzCoin/MOTOR
in order to obtain an improved blockchain-consensus algorithm.
4.2 Rotating Leader
View-change protocols assume a predetermined schedule of leaders, making them
susceptible to adversaries that compromise the next fleaders.
To prevent this attack, our blockchain uses drand[ DRA21a ]: an efficient ran-
domness beacon daemon that utilises bilinear pairing-based cryptography, t-of-n
distributed key generation, and threshold BLS[ BLS01] signatures to generate
publicly-verifiable, unbiasable, unpredictable, highly-available, distributed ran-
domness at fixed time intervals. As described in its online specification[ DRA21c],
drand uses the BLS12-381 curve, the Feldman[ Fel87] Verifiable Secret Sharing
protocol and the Joint Feldman protocol[ GJKR99 ] for DKG generation; using
threshold BLS signatures as a source of randomness is proven secure[ GLOW20 ]
according to its security model[DRA21b].
Remark 9.In this work, we inherit all the previous security theorems from
ByzCoin[KKJG+16], OmniLedger[KKJG+17], and MOTOR [KK19].
Theorem 10. (Robustness / Liveness). The adversary cannot predict nor bias
the leader election.
Proof.The unpredictability property follows from the unforgeability of the
BLS[BLS01] signing algorithm, and the unbiasability property follows from the
deterministic nature of the BLS[ BLS01] signing algorithm. The leader of view v
is determined by the outcomes of drand’s public service, and all the nodes can
publicly-verify its election when needed. Thus, the adversary cannot predict nor
bias the leader election, preventing the adversary from breaking liveness.
5
Theorem 11. (Safety / Censorship-resistance). A round-adaptive adversary
cannot always control the consensus decision.
Proof.As the leader election is unpredictable (theorem 10), the adversary can
only hope that one of its randomly compromised nodes gets chosen. Given that
1
3d
is the probability that the adversary controls dconsecutive leaders, the adversary
cannot control the leader forever since
lim
d!11
3d= 0
thus the adversary always controls the consensus decision.
4.3 Zero-Knowledge Proof-of-Identity
In a previous work, we introduced zero-knowledge Proof-of-Identity[ Cer19] for
biometric passports [ ICA21] and electronic identity cards to permissionless
blockchains in order to remove the inefficiencies of Sybil-resistant mechanisms
such as Proof-of-Work [ Nak09] and Proof-of-Stake [ KN12]. Additionally, attacks
[RMD+20,AAM21] on PoW sharded permissionless blockchains are prevented
with zk-PoI: an identity will be the same on all the shards, and the attacker can’t
mine new identities for different shards as it’s possible on PoW blockchains.
Although some could consider the latest zero-knowledge implementations fast
enough, their implementations are still too experimental for production. For the
first release, we will use the SGX implementation based on mutual attestation,
which works as follows (more details on the original paper [Cer19]):
Figure 4.1: Simplified overview of mutual attestation protocol.
6
An approximate picture of the worldwide coverage follows:
Figure 4.2: Legend: 1) National identity card is a mandatory smartcard; (2)
National identity card is a voluntary smartcard; (3) No national identity card,
but cryptographic identification is possible using an ePassport, driving license
and/or health card; (4) Non-digital identity card.
5 Discussion
In this section, we discuss the economic rationale underpinning the unique
features of this blockchain design that helps it to overcome previous shortcomings
and achieve an improved blockchain tailored to real-world settings according to
the experiences from the last decade (e.g., Bitcoin[Nak09]).
5.1 Overcoming Bitcoin’s Limited Adoption Problem
In a recent paper[ HJS19], it is shown that a PoW payments blockchain (i.e.,
Bitcoin) cannot simultaneously sustain a large volume of transactions and a
non-negligible market share:
Proposition 12. (Adoption Problem [ HJS19]). Adoption decreases as demand
rises (i.e., the adoption rate of a network, c, decreases in N). Moreover, the
blockchain faces limited adoption,
lim
N!1c= 0:
Even allowing dynamic PoW supply (i.e., by relaxing PoW’s artificial supply
constraint) achieves widespread adoption only at the expense of decentralisation:
7
Proposition 13. (Decentralisation implies Limited Adoption [ HJS19]). PoW
blockchains necessarily face either centralisation,
lim
N!1supV1;
or limited adoption,
lim
N!1c= 0:
The previous propositions expose that the lack of widespread adoption
constitutes an intrinsic property of PoW payments blockchains: as transaction
demands grow, fees increase endogenously. Attracted by this growth, more nodes
join the validation process, expanding the network size and thus protracting
the consensus process and generating increased payment confirmation times:
only users insensitive to wait times would transact in equilibrium, and limited
adoption arises. Moreover, this limitation cannot be overcome as it’s rooted in
physics (i.e., network delay).
As pointed out by the previous proposition, centralised blockchains overcome
the limited adoption problem: for example, permissioned blockchains that remain
secure on an open, adversarial network such as the blockchain proposed in this
paper, enabling lower payment confirmation times when omitting PoW’s artificial
supply constraint ,
Proposition 14. (Lower Payment Confirmation Times [ HJS19]). For any
PoW protocol, there exists a permissioned blockchain that remains secure on an
open, adversarial network (i.e., Pravuil), which induces (weakly) lower payment
confirmation times.
Additionally, omitting PoW’s artificial supply constraint facilitates timely
service even for high transaction volumes:
Proposition 15. (No Limited Adoption Problem [ HJS19]). In any permissioned
equilibrium, widespread adoption can be obtained,
lim
N!1c
P= minRP
(VP);1
:
5.2 Obtaining Higher Transaction Security At A Lower
Cost
In another recent paper[ BH21], it is shown that permissioned blockchains have a
higher level of transaction safety than a permissionless blockchain, independent
of the block reward and the current exchange rate of the crypto-currency.
For a PoW permissionless blockchain, let Rbe the block reward in the
corresponding crypto-currency, xthe associated exchange rate to fiat currency,
wthe block maturation rate (e.g., for Bitcoin, R= 6;25;x= $60:000;w= 100),
fbe the probability of detecting that blocks have been replaced, and plbe the
value above which transactions are not safe,
pl=fwRx:
8
Note that 51% attacks are becoming more common, specially for purely finan-
cial reasons [ SSVK20]. For a permissioned blockchain, let Pibe the punishment
applied to each node iif it participates in an attack, 2[0;1]be the probability
that nodes that participated in an attack will be punished, and Pbe the value
above which transactions are not safe,
P=f X
i2BPi;
withBbeing the set of Nnodes with the lowest Pi. Typical punishments include
confiscating all the funds deposited on the blockchain and banning them from
the blockchain, among others.
Proposition 16. ([BH21]). A permissioned blockchain that is safe in an open,
adversarial environment (i.e., Pravuil) has a higher level of maximum value for
transaction safety than a PoW permissionless blockchain if
X
i2BPi>wRx:
Even with small values of will result in higher safety for larger transactions
than PoW permissionless blockchains:
Proposition 17. ([BH21]). For >0and high enough Pi’s, a permissioned
blockchain that is safe in an open, adversarial environment (i.e., Pravuil) is
more resilient than PoW permissionless blockchains whenever
X
i2BPi>wRx
:
Ultimately, the cost of providing incentives to the validating nodes not to
participate in potential attacks (i.e., validating incentives such as block rewards)
will be lower for permissioned blockchains.
Proposition 18. ([BH21]). Suppose that pl>0and p>0, then at equi-
librium the validator incentives in the permissioned blockchain that is safe in
an open, adversarial environment (i.e., Pravuil) are lower than for the PoW
permissionless.
According to the model of this paper, in order to increase the transaction
safety, we only need to need increase:
• , a probability that reflects user’s trust in the system
•Pi, a penalty that could also include legal action
In general, the mere existence of credible penalties Piwith positive probability
is enough for the system to remain secure, without needing to exert punishments
in the case of rational attackers. Additionally, note that these parameters are not
economic parameters of the system, unlike the parameters for PoW permissionless
blockchains.
9
5.3 An Empirical Approach to Blockchain Design
Motivated by the abstract analysis from the previous sub-section 5.2, we use the
numerical comparisons between crypto-currencies from the paper [ GAR18] to
compare permissionless and permissioned blockchains in practice:
BTCETHBCHLTCADAUSDT Mean
Popularity 12478 9
Cost 1.33 21.662.664.33 52.83(*)
Consistency 1.332.331.33 23.66 1
Functionality 25224.33 2
Performance 1.331.66 22.33 3 11.88(*)
Security 444443.33 3.88
Decentralisation 53.334.333.663.33 1.33
Total 14.9918.3215.3216.722.65 13.66
Performance/Cost 0.280.410.460.71.79 10.77(*)
(Perf*Sec)/Cost 1.131.661.842.797.18 3.332.99(*)
Security/Cost 0.85 10.921.22.39 3.331.61(*)
Table 1: Permissionless blockchains. (*): p < 0.05
XRPEOSXLMTRXMIOTA Mean
Popularity 35611 10
Cost 4.66 54.66 5 54.84(*)
Consistency 4.33 5444.66
Functionality 1.33 51.33 53.66
Performance 4.334.66 44.66 54.53(*)
Security 2.333.33 43.33 3.66 3.33
Decentralisation 12.662.333.33 2.33
Total 17.9825.6520.3225.32 24.31
Performance/Cost 3.234.662.984.66 54.10(*)
(Perf*Sec)/Cost 7.5215.5111.9415.51 18.3 13.76(*)
Security/Cost 1.733.332.983.33 3.66 3(*)
Table 2: Permissioned blockchains. (*): p < 0.05
Using two-samples t-tests assuming unequal variances, we compare the fol-
lowing means between permissionless and permissioned blockchains, remarking
that they are statistically significant:
•Cost: permissionless blockchains are costlier (2.83) than permissioned
blockchains (4.84). Please note that a higher cost score means that the
blockchain is considered to have better costs (i.e., lower costs), and the
10
ranking obtained from this cost score must be reversed to be useful in the
next rankings.
•Performance : permissionless blockchains are less performant (1.88) than
permissioned blockchains (4.53).
•Performance/Cost : permissionless blockchains show worse performance
regarding cost (0.77) than permissioned blockchains (4.10).
•(Performance*Security)/Cost : permissionless blockchains show worse
performanceandsecurityregardingcost(2.99)thanpermissionedblockchains
(13.76).
•Security/Cost : permissionless blockchains show worse security regarding
cost (1.61) than permissioned blockchains (3).
It’s clear from the empirical data that permissionless blockchains are considered
worse than permissioned blockchains when considering cost, performance and
security.
5.4 Achieving More Decentralisation Than Other Permis-
sionless Blockchains
In yet another recent publication [ BHMB21 ], it is noticed that permissioned
blockchains could achieve more decentralisation than permissionless blockchains:
real-world permissionless blockchains are quite centralised [ GBE+18], as there
aren’t formal checks for the underlying centralisation.
In order to obtain a more decentralised permissioned blockchain that is safe
in an open, adversarial network (i.e., Pravuil), the node admission/gatekeeping
function must be decentralised and opened: precisely, this ideal state is achieved
with our zero-knowledge Proof-of-Identity [ Cer19], as previously explained in
sub-section 4.3.
11
Figure 5.1: Comparing decentralisation (from [BHMB21]).
5.5 Overcoming The Scalability Trilemma
The scalability trilemma postulates that a blockchain system can only at most
have two of the following three properties: decentralisation, scalability, and
security.
Figure 5.2: Pravuil overcomes the Scalability Trilemma.
12
In Pravuil, decentralisation, scalability, and security can be achieved simulta-
neously:
•Decentralisation : as discussed in the previous sub-section 5.4, Pravuil
can be more decentralised than other permissionless blockchains by using
zero-knowledge Proof-of-Identity, as previously explained in sub-section
4.3. It also circumvents the impossibility of full decentralisation [Cer19].
•Scalability : Pravuil inherits the scalable Rotating-Subleader (RS) com-
munication pattern from MOTOR [ KK19], specifically created to avoid
the communication bottleneck experienced by classic BFT protocol when
run over limited bandwidth networks.
•Security : Pravuil is secure as previously proved in theorem 10 and theo-
rem 11.
5.6 Obviating the Price Of Crypto-Anarchy of PoW/PoS
Crypto-currencies
In a previous paper [ Cer19], it was pointed out that the most cost-efficient Sybil-
resistant mechanism is the one provided by a trusted national PKI infrastructure
[Dou02] and a centralised social planner would prefer the use of National Identity
Cards and/or ePassports in order to minimise costs: instead, permissionless
blockchains are paying very high costs by using PoW/PoS as Sybil-resistant
mechanisms. The Price of Crypto-Anarchy compares the ratio between the worst
Nash equilibrium of the congestion game defined by PoW blockchains and the
optimal centralised solution, quantifying the costs of the selfish behaviour of
miners.
Definition. (#26 from [Cer19]). LetNashCongestedEquil Sbe the set
of strategies given as the solution of the optimisation problem of Theorem 25
from [Cer19], then the Price of Crypto-Anarchy is given by the following ratio:
Price of Crypto-Anarchy =max s2NashCongestedEquil Cost (s)
Cost (zk-PoI )
In practice, the real-world costs of Zero-Knowledge Proof-of-Identity are almost
zero as the identity infrastructure is subsidised by governments. However, the
situation for PoW/PoS blockchains is quite the opposite:
•PoW blockchains: in 2018, Bitcoin, Ethereum, Litecoin and Monero con-
sumed an average of 17, 7, 7 and 14 MJ to generate one US$ [ KT18], and
in 2021 Bitcoin may be consuming as much energy as all data centers glob-
ally [Dig21,dV21] at 100-130 TWh per year. Holders of crypto-currency
ultimately experience the Price of Crypto-Anarchy as inflation from mining
rewards, see next Table 3:
13
Name Reward
per
blockBlock time Blocks
per
dayPrice Yearly
mining
rewardYearly
Inflation
BTC 6,25 10 m 144 $50000 $18,061 B 4,12%
ETH 2 13,2 s 6545 $3780 $16,425 B 1,76%
DOGE 10,000 1 m 1440 $0,49 $2,575 B 4,06%
LTC 12,5 2,5 m 576 $320 $840 MM 3,94%
BCH 6,25 10 m 144 $1275 $418 MM 1,75%
ZEC 3,125 75 s 1152 $301 $395 MM 11,84%
XMR 1,02 2 m 720 $407 $109 MM 1,5%
Table 3: Empirical Price of Crypto-Anarchy.
•PoS blockchains: in theory, the costs are identical to the cost of PoW
schemes, except that instead of electrical resources and mining chips,
it takes the form of illiquid financial resources[ GG19] and in practice,
Proof-of-Stake is not strictly better than Proof-of-Work as the distribution
of the market shares between both technologies has been shown to be
indistinguishable (Appendix 3, [EAK+17]).
Bitcoin miners have earned a total of $26.75B as of April 2021: it’s not necessary
to pay so much for Sybil resistance, instead, miners could be paid for other
tasks (e.g., transaction fees). As previously discussed, obtaining Sybil-resistance
for free is not only the key to overcome Bitcoin’s limited adoption problem
(section 5.1) and to achieve more decentralisation than other permissionless
blockchains (section 5.4), but also to go beyond the economic limits of Bitcoin
as discussed in the next section 5.7.
5.7 Beyond the Economic Limits of Bitcoin
In a paper about the economic limits of Bitcoin [ Bud18], it is pointed out that
Bitcoin is prohibitively expensive to run because the recurring, “flow”, payments
to miners for running the blockchain (particularly, the cost of PoW mining) must
be large relative to the one-off, “stock”, benefits of attacking it. Let Vattackbe
the expected payoff to the attacker, Pblockbe the block reward to the miner and
representing the duration of the attack net of block rewards, then
Pblock>vattack
;
placing serious economic constraints to the practicality and scalability of the
Bitcoin blockchain, a problem that seems intrinsic to any anonymous, decen-
tralised blockchain protocol. Consequently, the author poses the open question
of finding another approach to generating anonymous, decentralised trust in a
14
public ledger that is less economically expensive: indeed, the technical solution
hereby presented4.3 that incorporates zero-knowledge Proof-of-Identity[ Cer19]
is the technology that is both “scarce and non-repurposable”, affordable and not
susceptible to sabotage attacks that could cause a collapse in the economic value
of the blockchain that the author of [ Bud18] would seem meritorious to close
said open question.
Amorerecentpaper[ GG19]continuesthepreviouseconomicanalysis[ Bud18],
extending it to PoS and permissioned settings. For the permissionless PoS setting,
it finds that the costs are identical to the cost of PoW schemes, except that
instead of electrical resources and mining chips, it takes the form of illiquid
financial resources; however, zk-PoI[ Cer19] is free. For the permissioned case
concerning this paper, if the block reward is set exogenously, it finds that a
permissioned blockchain would have lower costs than permissionless PoW or PoS
blockchains in the economic model of [Bud18].
5.8 More Valuable and Stable Crypto-currencies
A review of previous literature in economic research reveals the following in-
teresting facts regarding the intricate relationship between PoW mining (i.e.,
hashrate, electricity and/or equipment costs) and crypto-currency prices:
•Thereisapositiverelationshipbetweenmininghashrateandprice[ GPB+15,
Hay16]: the causality is primarily unidirectional going from the price to
the hashrate [ FK20], although mining incidents and political shocks that
affect mining also negatively impact prices.
•Bitcoin’s security is sensitive (elastic) to mining rewards and costs, al-
though temporary mining cost and price shocks do not affect the long-run
blockchain security [ CdKR21 ]: a 1% permanent increase in the mining
reward increases the underlying blockchain security by 1.38% to 1.85% in
the long-run; positive shocks to electricity prices in China have a negative
impact on the hashrate in the short-run; a 1% increase in the efficiency of
mining equipment increases the computing capacity between 0.23% and
0.83% in the long-run; in the short-run mining competition intensity has a
statistically positive impact leading to expansion of mining capacity, but
in the long-run, the relationship is reversed.
•High fixed mining rewards are the source of the instability to reach an
equilibrium between miners and users [ Iyi18]; instead, mining rewards
should be adjusted dynamically.
•The production of crypto-currency by miners is jointly determined with
the price used by consumers [ PB18]: the equilibrium price depends on
both consumer preferences (i.e., price increases with the average value of
censorship aversion, and current and future size of the network) and the
industrial organisation of the mining market (i.e., price increases with the
number of miners and decreases with the marginal cost of mining). Price-
security spirals amplify demand and supply shocks: for example, a sudden
15
demand shock provoked by a government banning the crypto-currency in
a country would lead to price drops, itself leading to miners decreasing
hashrate, further decreasing prices and the feedback loop continuing until
a new equilibrium is reached in multiple rounds. In other words, Bitcoin’s
security model embeds price volatility amplification.
•In a PoW blockchain, it’s impossible to simultaneously achieve all the
three following goals [ Pag20]: maximise crypto-currency price, blockchain’s
security, and social welfare.
Similar results can be found for PoS blockchains because they are substituting
electricity and mining costs for illiquid and volatile financial resources [ GG19].
In general, the interdependencies can be described graphically as the following
cycles and spirals:
Figure 5.3: Interdependencies [CdKR21], with broken negative feedback loops.
16
Figure 5.4: Spirals [PB18], with broken negative feedback loops.
However, we break most of the previous interdependencies and spirals with our
strongly-consistent blockchain with free Sybil-resistance:
•blockchain and transaction security are independent of blockchain mining
capacity, mining costs and rewards, and price: once a transaction is
instantly committed, it’s committed forever.
•there aren’t price-security spirals for demand and supply shocks: changes
in prices do not lead to changes in security.
•as blockchain’s security is independent of price, it’s possible to maximise
crypto-currency price and social welfare.
Ultimately, our blockchain design leads to more valuable and stable crypto-
currencies.
6 Implementation
Pravuil has a Testnet deployed with a working implementation consisting of:
•a blockchain layer in Go and Java, invoking drand [ DRA21a ] as described
in this paper 4.2.
•zero-knowledge Proof-of-Identity [Cer19] in Python and C.
•mobile apps for Android (Typescript, Java) and iOS (Typescript, Objective-
C, Swift).
•secure smart contracts in Obliv-Java [Cer17].
All the code will be open-sourced at https://github.com/Calctopia-OpenSource,
including future developments.
17
7 Conclusion
In this work, we presented Pravuil, an improvement over previous blockchains
that is suitable for real-world deployment in adversarial networks such as the
Internet. Pravuil achieves this feat by:
•unpredictably rotating leaders using drand [ DRA21a] to defend against
adversaries and censorship attacks: drand is an Internet service that
generates publicly-verifiable, unbiasable, unpredictable, highly-available,
distributed randomness at fixed time intervals.
•using for the first time zero-knowledge Proof-of-Identity [ Cer19] as a Sybil-
resistancemechanismtoovercomeBitcoin’slimitedadoptionproblem[ HJS19]
and to go beyond the economic limits of Bitcoin[ Bud18], delivering more
decentralisation than other permissionless blockchains [BHMB21].
•based on the design of a blockchain layer that scales-out with strong
consistency prioritising robustness over scalability.
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22
|
{
"id": "2105.10464"
}
|
1711.03028
|
Simplicity: A New Language for Blockchains
|
Simplicity is a typed, combinator-based, functional language without loops
and recursion, designed to be used for crypto-currencies and blockchain
applications. It aims to improve upon existing crypto-currency languages, such
as Bitcoin Script and Ethereum's EVM, while avoiding some of the problems they
face. Simplicity comes with formal denotational semantics defined in Coq, a
popular, general purpose software proof assistant. Simplicity also includes
operational semantics that are defined with an abstract machine that we call
the Bit Machine. The Bit Machine is used as a tool for measuring the
computational space and time resources needed to evaluate Simplicity programs.
Owing to its Turing incompleteness, Simplicity is amenable to static analysis
that can be used to derive upper bounds on the computational resources needed,
prior to execution. While Turing incomplete, Simplicity can express any
finitary function, which we believe is enough to build useful "smart contracts"
for blockchain applications.
|
http://arxiv.org/pdf/1711.03028v2
|
Russell O'Connor
|
cs.PL
|
cs.PL
|
Simplicity: A New Language for Blockchains
Russell O'Connor
roconnor@blockstream.com
2017-12-13
Abstract
Simplicity is a typed, combinator-based, functional language without
loops and recursion, designed to be used for crypto-currencies and block-
chain applications. It aims to improve upon existing crypto-currency lan-
guages, such as Bitcoin Script and Ethereum's EVM, while avoiding some
of the problems they face. Simplicity comes with formal denotational
semantics de ned in Coq, a popular, general purpose software proof as-
sistant. Simplicity also includes operational semantics that are de ned
with an abstract machine that we call the Bit Machine. The Bit Ma-
chine is used as a tool for measuring the computational space and time
resources needed to evaluate Simplicity programs. Owing to its Turing
incompleteness, Simplicity is amenable to static analysis that can be used
to derive upper bounds on the computational resources needed, prior to
execution. While Turing incomplete, Simplicity can express any nitary
function, which we believe is enough to build useful \smart contracts" for
blockchain applications.
0 License
This work is licensed under a Creative Com-
mons \Attribution 4.0 International" license.
〈https://creativecommons.org/licenses/by/4.0/deed.en 〉
1 Introduction
Blockchain and distributed ledger technologies de ne protocols that allow large,
ad-hoc, groups of users to transfer value between themselves without needing
to trust each other or any central authority. Using public-key cryptography,
users can sign transactions that transfer ownership of funds to other users. To
prevent transactions from con
icting with each other, for example when one
user attempts to transfer the same funds to multiple di erent users at the same
time, a consistent sequence of blocks of transactions is committed using a proof
of work scheme. This proof of work is created by participants called miners.
Each user veri es every block of transactions; among multiple sequences of valid
blocks, the sequence with the most proof of work is declared to be authoritative.
1arXiv:1711.03028v2 [cs.PL] 13 Dec 2017
Bitcoin [20] was the rst protocol to use this blockchain technology to create
a secure and permissionless crypto-currency. It comes with a built-in program-
ming language, called Bitcoin Script [6, 22], which determines if a transaction
is authorized to transfer funds. Rather than sending funds to a speci c party,
users send funds to a speci c Bitcoin Script program. This program guards the
funds and only allows them to be redeemed by a transaction input that causes
the program to return successfully. Every peer in the Bitcoin network must exe-
cute each of these programs with the input provided by the transaction data and
all these programs must return successfully in order for the given transaction to
be considered valid.
A typical program speci es a public key and simply requires that a valid
digital signature of the transaction for that key be provided. Only someone
with knowledge of the corresponding private key can create such a signature.
Funds can be guarded by more complex programs. Examples range from
simpler programs that require signatures from multiple parties, permitting es-
crow services [14], to more complex programs that allow for zero-knowledge
contingent payments [19], allowing for trustless and atomic purchases of digital
data. This last example illustrates how Bitcoin Script can be used to build
smart contracts, which allow parties to create conditional transactions that are
enforced by the protocol itself.
Because of its Script language, Bitcoin is sometimes described as pro-
grammable money.
1.1 Bitcoin Script
Bitcoin Script is a stack-based language similar to Forth. A program in Bitcoin
Script is a sequence of operations for its stack machine. Bitcoin Script has
conditionals but no loops, thus all programs halt and the language is not Turing
complete.
Originally, these programs were committed as part of the output eld of a
Bitcoin transaction. The program from the output being redeemed is executed
starting with the initial stack provided by the input redeeming it. The program
is successful if it completes without crashing and leaves a non-zero value on the
top of stack.
Later, a new option called pay to script hash (P2SH) [1] was added to Bitcoin.
Using this option, programs are committed by specifying a hash of the source
code as part of the output eld of a Bitcoin transaction. The input eld of a
redeeming Bitcoin transaction then provides both the program and the initial
stack for the program. The transaction is valid if the hash of the provided
program matches the hash speci ed in the output being redeemed, and if that
program executes successfully with the provided input, as before. The main
e ect here is moving the cost of large programs from the person sending funds
to the person redeeming funds.
Bitcoin Script has some drawbacks. Many operations were disabled by Bit-
coin's creator, Satoshi Nakamoto [21]. This has left Bitcoin Script with a few
arithmetic (multiplication was disabled), conditional, stack manipulation, hash-
2
ing, and digital-signature veri cation operations. In practice, almost all pro-
grams found in Bitcoin follow a small set of simple templates to perform digital
signature veri cation.
However, Bitcoin Script also has some desirable properties. All Bitcoin
Script operations are pure functions of the machine state except for the
signature-veri cation operations. These signature-veri cation operations re-
quire a hash of some of the transaction data. Together, this means the pro-
gram's success or failure is purely a function of the transaction data. Therefore,
the person creating a transaction can know whether the transaction they have
created is valid or not.
Bitcoin Script is also amenable to static analysis, which is another desirable
property. The digital-signature veri cation operations are the most expensive
operations. Prior to execution, Bitcoin counts the number of occurrences of
these operations to compute an upper bound on the number of expensive calls
that may occur. Programs whose count exceeds a certain threshold are invalid.
This is a form of static analysis that ensures the amount of computation that
will be done isn't excessive. Moreover, before committing a program to an
output, the creator of the program can ensure that the amount of computation
that will be done will not be excessive.
1.2 Ethereum and the EVM
Ethereum [31] is another crypto-currency with programmable transactions. It
de nes a language called EVM for its transactions. While more expressive and
exible than Bitcoin Script, the design of Ethereum and the EVM has several
issues.
The EVM is a Turing-complete programming language with a stack, random
access memory, and persistent storage. To prevent in nite loops, execution is
limited by a counter called gas, which is paid for in Ethereum's unit of account,
Ether, to the miner of the block containing the transaction. Static analysis isn't
practical with a Turing-complete language. Therefore, when a program runs
out of gas, the transaction is nulli ed but the gas is still paid to the miner to
ensure they are compensated for their computation e orts.
Because of direct access to persistent storage, EVM programs are a function
of both the transaction and the state of the blockchain at the point the transac-
tion is inserted into the blockchain. This means users cannot necessarily know
what the result of their transaction will be when they create it, nor can they
necessarily know how much gas will be consumed. Users must provide enough
gas to cover the worst-case use scenario and there is no general purpose algo-
rithm that can compute that bound. In particular, there is no practical general
purpose way to tell if any given program will run out of gas in some context
either at redemption time or creation time. There have been some e orts to
perform static analysis on a subset of the EVM language [5], but these tools are
limited (e.g. they do not support programs with loops).
Unlike Bitcoin, there are many instances of ad-hoc, one-o , special purpose
programs in Ethereum. These programs are usually written in a language called
3
Solidity [12] and compiled to the EVM. These ad-hoc programs are regularly
broken owing to the complex semantics of both Solidity and the EVM; the
most famous of these failures were the DAO [8] and Parity's multiple signature
validation program [26].
1.3 A New Language
In this paper, we propose a new language that maintains or enhances the desir-
able properties that Bitcoin Script has while adding expressiveness. Our design
goals are:
•Create an expressive language that provides users with the tools needed
to build novel programs and smart contracts.
•Enable static analysis that provides useful upper bounds on the amount
of computation required.
•Minimize bandwidth and storage requirements and enhance privacy by
removing unused code at redemption time.
•Maintain Bitcoin's design of self-contained transactions whereby programs
do not have access to any information outside the transaction.
•Provide formal semantics that facilitate easy reasoning about programs
using existing o -the-shelf proof-assistant software.
A few of these goals deserve additional explanation:
Static analysis allows the protocol to place limits on the amount of compu-
tation a transaction can have, so that nodes running the protocol are not overly
burdened. Furthermore, the static analysis can provide program creators with
a general purpose tool for verifying that the programs they build will always
t within these limits. Additionally, it is easy for the other participants in a
contract to check the bounds on the smart contract's programs themselves.
Self-contained transactions ensure that execution does not depend on the
global state of the blockchain. Once a transaction's programs have been vali-
dated, that fact can be cached. This is particularly useful when transactions
are being passed around the network before inclusion in the blockchain; once
included in the blockchain, the programs do not need to be executed again.
Finally, formal semantics that work with proof-assistant software provide
the opportunity for contract developers to reason about their programs to rule
out logical errors and to help avoid scenarios like the DAO and Parity's multi-
signature program failure.
Like Bitcoin Script and the EVM, our language is designed as low-level
language for executing smart contracts, not as a language for coding in directly.
As such, we expect it to be a target for other, higher-level, languages to be
compiled to.
We call our new language Simplicity .
4
iden:A`As:A`B t :B`C
compst:A`C
unit:A`1
t:A`B
injlt:A`B+Ct:A`C
injrt:A`B+C
s:AC`D t :BC`D
casest: (A+B)C`Ds:A`B t :A`C
pairst:A`BC
t:A`C
taket:AB`Ct:B`C
dropt:AB`C
Figure 1: Typing rules for the terms of core Simplicity.
2 Core Simplicity
Simplicity is a typed combinator language. Each well-typed Simplicity expres-
sion is associated with two types, an input type and an output type. Every
expression denotes a function from its input type to its output type.
2.1 Types in Simplicity
Types in Simplicity come in three
avors.
•The unit type, written as 1, is the type with one element.
•A sum type, written as A+B, contains the tagged union of values from
either the left type Aor the right type B.
•A product type, written as AB, contains pairs of elements with the rst
one from the type Aand the second one from the type B.
There are no recursive types in Simplicity. Every type in Simplicity only
contains a nite number of values; the number of possible values a type contains
can be computed by interpreting the type as an arithmetic expression.
2.2 Terms in Simplicity
The term language for Simplicity is based on Gentzen's sequent calculus [13]. We
write a type-annotated Simplicity expression as t:A`Bwhereta Simplicity
expression, Ais the expression's input type, and Bis the expression's output
type.
5
The core of Simplicity consists of nine combinators for building expressions.
They capture the fundamental operations for the three
avors of types in Sim-
plicity. The typing rules for these nine combinators is given in Figure 1.
We can classify the combinators based on the
avor of types they support.
•The unitterm returns the singular value of the unit type and ignores its
argument.
•The injland injrcombinators create tagged values, while the case com-
binator, Simplicity's branching operation, evaluates one of its two sub-
expressions based on the tag of the rst component of its input.
•The paircombinator creates pairs, while the take and drop combinators
access rst and second components of a pair respectively.
•The iden and comp combinators are not speci c to any
avor of type.
The identerm represents the identity function for any type and the comp
combinator provides function composition.
Simplicity expressions form an abstract syntax tree. The leaves of this tree are
either iden orunitterms. The nodes are one of the other seven combinators.
Each node has one or two children depending on which combinator the node
represents.
Precise semantics are given in the next section. Extensions to this core set
of combinators is given in Section 4.
2.3 Denotational Semantics of Simplicity
Before giving the semantics of Simplicity terms, we need notations for the values
of Simplicity types.
•We writehi:1for the singular value of the unit type.
•We writeL(a) :A+Bwhena:Afor left-tagged values of the sum type.
•We writeR(b) :A+Bwhenb:Bfor right-tagged values of the sum
type.
•We writeha;bi:ABwhena:Aandb:Bfor values of the pair type.
We emphasize that these values are not directly representable in Simplicity
because Simplicity expressions can only denote functions; we use these values
only to de ne the functional semantics of Simplicity.
Simplicity's denotational semantics are recursively de ned. For an expres-
siont:A`B, we de ne its semantics JtK:A!Bas follows.
6
Jiden K(a):=a
JcompstK(a):=JtK(JsK(a))
Junit K(a):=hi
JinjltK(a):=L(JtK(a))
JinjrtK(a):=R(JtK(a))
JcasestKhL(a);ci:=JsKha;ci
JcasestKhR(b);ci:=JtKhb;ci
JpairstK(a):=hJsK(a);JtK(a)i
JtaketKha;bi:=JtK(a)
JdroptKha;bi:=JtK(b)
We have formalized the language and semantics of core Simplicity in the Coq
proof assistant [29]. The formal semantics in Coq are the ocial semantics of
Simplicity, and for core Simplicity, it is short enough that it ts in Appendix A
of this paper. Having semantics in a general purpose proof assistant allows
us to formally reason both about programs written in Simplicity and about
algorithms that analyze Simplicity programs.
2.4 Completeness
Simplicity cannot express general computation. It can only express nitary func-
tions, because each Simplicity type contains only nitely many values. However,
within this domain, Simplicity's set of combinators is complete: any function
between Simplicity's types can be expressed.
Theorem 2.1 (Finitary Completeness) LetAandBbe Simplicity types.
Letf:A!Bbe any function between these types. Then there exists some core
Simplicity expression t:A`Bsuch that JtK=f.
We have veri ed this theorem with Coq.
This theorem holds because functions between nite types can be described,
in principle, by a lookup table that maps inputs to outputs. In principle, such a
lookup table can be encoded as a Simplicity expression that does repeated case
analysis on its input and returns a xed-output value for each possible case.
However, using this theorem to construct Simplicity expressions is not prac-
tical. For example, a lookup table for the compression function for SHA-256 [24],
which maps 768 bits to 256 bits, would be astronomical in size, requiring 2776
bits. To create practical programs in Simplicity, we need to take advantage of
structured computation in order to succinctly express functions.
7
2.5 Example Programs
The combinators of core Simplicity may seem paltry, so in this section we il-
lustrate how we can construct programs in Simplicity. We begin by de ning a
type for a bit, 2, as the sum of two unit types.
2:=1+1
We choose an interpretation of bits as numbers where we de ne the left-
tagged value as denoting zero and the right tagged value as denoting one.
dLhie2:= 0
dRhie2:= 1
We can write Simplicity programs to manipulate bits. For example, we can
de ne the notfunction to
ip a bit.
not:2`2
not:=comp (pair iden unit ) (case(injr unit ) (injl unit ))
By recursively taking products, we can de ne types for multi-bit words.
21:=2
22n:=2n2n
For example, the 232type can represent 32-bit words.
We recursively de ne the interpretation of pairs of words as numbers, choos-
ing to use big-endian order.
dha;bie22n:=dae2n2n+dbe2n
We can write a half-adder of two bits in Simplicity.
half-adder :22`22
half-adder :=case (drop(pair(injl unit )iden))
(drop(pair iden not ))
We can prove the half-adder expression correct.
Theorem 2.2 (Half Adder Correct) Leta;b:2be two bits. Then
dJhalf-adder Kha;bie22=dae2+dbe2
We have proven this theorem in Coq. This particular theorem can be proven
by exhaustive case analysis (there are only four cases) and equational reasoning
using Simplicity's denotational semantics.
We continue and de ne a full adder by combining two half adders. From
there we can build ripple-carry adders for larger word sizes by combining full
8
adders from smaller word sizes. The interested reader can nd the Simplicity
expressions for these full adders in Appendix B.
Continuing, we can build multipliers and other bit-manipulation functions.
These can be combined to implement more sophisticated functions.
We have written the SHA-256 block compression function in Simplicity. Us-
ing 256-bit arithmetic, we have also constructed expressions for elliptic curve
operations over the Secp256k1 curve [9] that Bitcoin uses, and we have built a
form of ECDSA signature validation [23] in Simplicity.
To gain an understanding of the size of Simplicity expressions, let us examine
our implementation of the SHA-256 block compression function, sha-256-block :
22562512`2256. Our Simplicity expression consists of 3 274 442 combinators.
However, this counts the total number of nodes in the abstract syntax tree that
makes up the expression. Several sub-expressions of this expression are dupli-
cated and can be shared. Imagine taking the abstract syntax tree and sharing
identical sub-expressions to create a directed acyclic graph (DAG) representing
the same expression. Counting this way, the same expression contains only 1 130
unique typed sub-expressions, which is the number of nodes that would occur
in the DAG representing the expression.
Taking advantage of shared sub-expressions is critical because it makes the
representation of typical expressions exponentially smaller in size. We choose
to leave sub-expression sharing implicit in Simplicity's term language. This
ensures that Simplicity's semantics are not a ected by how sub-expressions are
shared. However, we do transmit and store Simplicity expressions in a DAG
format. This DAG representation of expressions is also important when we
consider static analysis in Section 3.3.
Using our formal semantics of Simplicity in Coq, we have proven our imple-
mentation of SHA-256 is correct.
Theorem 2.3 (SHA-256 Correct) Leta:2256andb:2512. Let
sha-256-block :22562512`2256be our Simplicity implementation of the
SHA-256 block compression function. Let SHA256Block :22562512!2256be
the SHA-256 block compression function. Then
Jsha-256-block Kha;bi=SHA256Blockha;bi
Our reference implementation of the SHA-256 compression function in Coq1
is taken from the Veri ed Software Toolchain (VST) project [3] where they use
it as their reference implementation for proving OpenSSL's C implementation
of SHA-256 correct [2]. If we combine our Coq proof with the VST project's
Coq proof, we would get a formal proof that our Simplicity implementation of
SHA-256 matches OpenSSL's C implementation of SHA-256.
1The type of the reference implementation for the SHA-256 compression function in Coq
actually uses lists of 32-bit words. To simplify the presentation here, we have omitted the
translation between representations of the compression function's inputs and outputs.
9
3 The Bit Machine
One of the design goals for Simplicity is to be able to compute a bound on the
computation costs of evaluating a Simplicity program. While the denotational
semantics of Simplicity are adequate for determining the functional behavior
of Simplicity programs, we need operational semantics to provide a measure of
computational resources used. To do this, we create an abstract machine, called
the Bit Machine, tailored for evaluating Simplicity programs.
The Bit Machine is an abstract imperative machine whose state consists of
two non-empty stacks of data frames. One stack holds read-only frames, and
the other stack holds write-only frames. A data frame consists of an array of
cells and a cursor pointing either at one of the cells or pointing just beyond the
end of the array. Each cell contains one of three values: a zero bit, a one bit, or
an unde ned value. The Bit Machine has a set of instructions that manipulate
the two stacks and their data frames.
Notionally, we will write a frame like in the following example.
[010?1?1]
This frame contains 7 cells. The ? character denotes a cell with an unde ned
value. The 0and 1characters denotes cells with the corresponding bit value.
The frame's cursor is pointing to the underscored cell.
When a frame's cursor is pointing past the end of the array, we will write
an underscore after the array's cells like the following.
[010?1?1]
Table 1 illustrates an example of a possible state of the Bit Machine. This
example contains a read-frame stack that has four data frames and a write-frame
stack that has two data frames. The top frame of the read-frame stack is called
theactive read frame , and the top frame of the write-frame stack is called the
active write frame .
read frame stack write frame stack
[1001 1??110101000 ] [11??1101 ]
[0000] [ 111??]
[]
[10]
Table 1: Example state for the Bit Machine
The Bit Machine has ten instructions to manipulate its state, which we
describe below.
•nop: This instruction doesn't change the state of the machine.
10
•write (b): This instruction writes bit value bto the cell under the active
write frame's cursor and advances the cursor by one cell.
{The valuebmust be either 0or1or else the machine crashes.
{The active write frame's cursor must not be beyond the end of the
array or else the machine crashes.
{The cell written to must have an unde ned value before writing to it
or else the machine crashes.
•copy (n): This instruction copies ncells from the active read frame, start-
ing at its cursor, to the active write frame, starting at its cursor, and
advances the active write frame's cursor by ncells.
{There must be at least ncells between the active read frame's cursor
and the end of its array or else the machine crashes.
{There must be at least ncells between the active write frame's cursor
and the end of its array and they all must have an unde ned value
before writing to them or else the machine crashes.
{Note that unde ned values can be copied.
•skip(n): This instruction advances the active write frame's cursor by n
cells.
{This instruction is allowed to advance the cursor to the point just
past the end of the array; however if nis large enough that it would
advance it past this point, the machine crashes instead.
•fwd(n): This instruction moves the active read frame's cursor forward by
ncells.
{This instruction is allowed to advance the cursor to the point just
past the end of the array; however if nis large enough that it would
advance it past this point, the machine crashes instead.
•bwd(n): This instruction moves the active read frame's cursor backwards
byncells.
{If this instruction would move the cursor before the beginning of the
array, the machine crashes instead.
•newFrame (n): This instruction allocates a new frame with ncells and
pushes it onto the write-frame stack, making it the new active write frame.
{The cursor for this new frame starts at the beginning of the array.
{The cells of the new frame are initialized with unde ned values.
•moveFrame : This instruction pops a frame o the write-frame stack and
pushes it onto the read-frame stack.
11
{The moved frame has its cursor reinitialized to the beginning of the
array.
{If this would leave the machine with an empty write frame stack, the
machine crashes instead.
•dropFrame : This instruction pops a frame o the read-frame stack and
deallocates it.
{If this would leave the machine with an empty read-frame stack, the
machine crashes instead.
•read : This instruction doesn't modify the state of the machine. Instead
it returns the value of the bit under the active read frame's cursor to the
machine's operator.
{if the value under the active read frame's cursor is unde ned, or if it
is past the end of its array, the machine crashes instead.
The Bit Machine is deliberately designed to crash at anything resembling
unde ned behavior.
3.1 Bit Machine Values
We can use the Bit Machine to evaluate Simplicity programs on their inputs.
First, we de ne how many cells are needed to hold the values of a given type.
bitSize (1):= 0
bitSize (A+B):= 1 + max(bitSize (A);bitSize (B))
bitSize (AB):=bitSize (A) +bitSize (B)
The unit type doesn't need any bits to represent its one value. A value of
sum type needs one bit for its tag and then enough bits to represent either of
its left or right values. A value of product type needs space to hold both its
values.
We precisely de ne a representation of values for Simplicity types as array
of cells below:
phiq1:= []
pL(a)qA+B:= [0][?]padl(A;B )paqA
pR(b)qA+B:= [1][?]padr(A;B )pbqB
pha;biqAB:=paqApbqB
For the notation above, denotes the concatenation of arrays of cells and
[b]ndenotes an array of ncells all containing b.
12
The padding for left and right values lls the array with sucient unde ned
values so that the array ends up with the required length.
padl(A;B):=max(bitSize (A);bitSize (B)) bitSize (A)
padr(A;B):=max(bitSize (A);bitSize (B)) bitSize (B)
Below are some examples of values represented as arrays of cells for the Bit
Machine. We de ne the inverse of de2nto bebc2nthat maps numbers to the
values that represent them.
pL(b3c22)q22+2= [011]
pR(b0c2)q22+2= [1?0]
The array of cells contains a list of the tags from the sum types and that is
about it. Notice that values of a multi-bit word type, 2n, have exactly ncells
and the representation is identical to its big-endian binary representation.
3.2 Operational Semantics
The operational semantics for Simplicity are given by recursively translating
a Simplicity expression into a sequence of instructions for the Bit Machine.
Figure 2 de nes /uni27EAt:A`B/uni27EB, which results in a sequence of instructions for the
Bit Machine that evaluates the function JtK.
We have formalized the Bit Machine in Coq and Figure 2's translation of
Simplicity expressions into Bit Machine instructions. We have veri ed, with
Coq, that our Bit Machine implementation of Simplicity respects Simplicity's
denotational semantics.
Theorem 3.1 (Correctness of Operational Semantics) Lett:A`Bbe
any Simplicity expression. Let v:Abe any value of type A. Initialize a Bit
Machine with
•the value pvqAas the only frame on the read-frame stack with its cursor
at the beginning of the frame, and
•the value [?]bitSize (B)as the only frame on the write-frame stack with its
cursor at the beginning of the frame.
After executing the instructions /uni27EAt:A`B/uni27EB, the nal state of the Bit Machine
has
•the value pvqAas the only frame on the read-frame stack, with its cursor
at the beginning of the frame, and
•the value pJtK(v)qBas the only frame on the write-frame stack, with its
cursor at the end of the frame.
13
/uni27EAiden:A`A/uni27EB:=copy (bitSize (A))
/uni27EAcompst:A`C/uni27EB:=newFrame (bitSize (B));/uni27EAs:A`B/uni27EB;
moveFrame ;/uni27EAt:B`C/uni27EB;
dropFrame
/uni27EAunit:A`1/uni27EB:=nop
/uni27EAinjlt:A`B+C/uni27EB:=write (0);skip(padl(B;C));/uni27EAt:A`B/uni27EB
/uni27EAinjrt:A`B+C/uni27EB:=write (1);skip(padr(B;C));/uni27EAt:A`C/uni27EB
/uni27EAcasest: (A+B)C`D/uni27EB:= match read with
8
>>>>>><
>>>>>>:0!fwd(1 + padl(A;B));
/uni27EAs:AC`D/uni27EB;
bwd(1 + padl(A;B))
1!fwd(1 + padr(A;B));
/uni27EAt:BC`D/uni27EB;
bwd(1 + padr(A;B))
/uni27EApairst:A`BC/uni27EB:=/uni27EAs:A`B/uni27EB;/uni27EAt:A`C/uni27EB
/uni27EAtaket:AB`C/uni27EB:=/uni27EAt:A`C/uni27EB
/uni27EAdropt:AB`C/uni27EB:=fwd(bitSize (A));/uni27EAt:B`C/uni27EB;
bwd(bitSize (A))
Figure 2: Operational Semantics for Simplicity using the Bit Machine.
In particular, the Bit Machine never crashes during this execution.
The fact that the Bit Machine never crashes means that during execution it
never reads from an unde ned cell, nor does it ever write to a de ned cell. This
means that, if one implements the Bit Machine on a real computer, one can
use an ordinary array of bits to represent the array of cells and cells that are
supposed to hold unde ned values can be safely set to any bit value. As long as
this implementation only executes instructions translated from Simplicity and
started from a proper initial state, the resulting computation will be the same
as if one had used an explicit representation for unde ned values.
3.3 Static Analysis
Using the Bit Machine, we can measure computational resources needed by a
Simplicity program. For instance we can:
•count the number of instructions executed by the Bit Machine.
•count the number of cells copied by the Bit Machine.
14
extraCellBnd (iden:A`A):= 0
extraCellBnd (compst:A`C):=bitSize (B)+
max(extraCellBnd (s:A`B);
extraCellBnd (t:B`C))
extraCellBnd (unit:A`1):= 0
extraCellBnd (injlt:A`B+C):=extraCellBnd (t:A`B)
extraCellBnd (injrt:A`B+C):=extraCellBnd (t:A`C)
extraCellBnd (casest: (A+B)C`D):=max(extraCellBnd (s:AC`D);
extraCellBnd (t:BC`D))
extraCellBnd (pairst:A`BC):=max(extraCellBnd (s:A`B);
extraCellBnd (t:A`C))
extraCellBnd (taket:AB`C):=extraCellBnd (t:A`C)
extraCellBnd (dropt:AB`C):=extraCellBnd (t:B`C)
cellBnd (t:A`B):=bitSize (A) +bitSize (B) +extraCellBnd (t:A`B)
Figure 3: De nition of cellBnd .
•count the maximum number of cells in both stacks at any point during
execution.
•count the maximum number of frames in both stacks at any point during
execution.
The rst two are measurements of time used by the Bit Machine, and the
last two are measurements of space used by the Bit Machine.
Using simple static analysis, we can quickly compute an upper bound on
these sorts of resource costs. In this paper, we will focus on the example of
counting the maximum number of cells in both stacks at any point during ex-
ecution. Figure 3 de nes a function cellBnd that computes an upper bound on
the maximum number of cells that are needed during execution.
Theorem 3.2 (Static Analysis of Cell Usage) Lett:A`Bbe any Sim-
plicity expression. Let v:Abe any value of type A. Initialize a Bit Machine as
speci ed in Theorem 3.1. The maximum number of cells in both stacks of the
Bit Machine at any point during the execution of /uni27EAt:A`B/uni27EBfrom this initial
state never exceeds cellBnd (t:A`B).
We have formalized the above static analysis and proven the above theorem
in Coq.
15
These kinds of static analyses are simple recursive functions of Simplicity
expressions, and the intermediate results for sub-expressions can be shared. By
using a DAG for the in-memory representation of Simplicity expressions, we
can transparently cache these intermediate results. This means the time needed
to compute static analysis is proportional to the size of the DAG representing
the Simplicity expression, as opposed to the time needed for dynamic analysis
such as evaluation, which may take time proportional to the size of the tree
representing the Simplicity expression.
The Bit Machine is an abstract machine, so we can think of these sorts of
static analyses as bounding abstract resource costs. As long as the abstract
resource costs are limited, then the resource costs of any implementation of
Simplicity will also be limited. These sorts of precise static analyses of resource
costs are more sophisticated than what is currently available for Bitcoin Script.
Notice that the de nition of cellBnd does not directly reference the Bit Ma-
chine, so limits on the bounds computed by these static analyses can be imposed
on protocols that use Simplicity without necessarily requiring that applications
use the Bit Machine for evaluation. While we do use an implementation of the
Bit Machine in our prototype Simplicity evaluator written in C, others are free
to explore other models of evaluation. For example, the Bit Machine copies
data to implement the idencombinator, but another model of evaluation might
create shared references to data instead.
3.3.1 Tail Composition Optimization
Tail call optimization is an optimization used in many languages where a proce-
dure's stack frame is freed prior to a call to another procedure when that call is
the last command of the procedure. The comp combinator in Simplicity behaves
much like a procedure call, and we can implement an analogous optimization for
the translation of Simplicity to the Bit Machine. Interested readers can nd this
tail composition optimized translation, /uni27EAt:A`B/uni27EBtco
o , de ned in Appendix C,
along with a static analysis of its memory use.
3.4 Jets
Evaluation of a Simplicity expression with the Bit Machine recursively traverses
the expression. Before evaluating some sub-expression t:A`B, the Bit Ma-
chine is always in a state where the active read frame is of the form
r1pvqAr2
for some value v:Aand some arrays r1andr2, and where the cursor is placed
at the beginning of the pvqAarray slice.
Furthermore the active write frame is of the form
w1[?]bitSize (B)w2
for some arrays w1andw2, and where the cursor is placed at the beginning of
the [?]bitSize (B)array slice.
16
After the evaluation of the sub-expression t:A`B, the active write frame
is of the form
w1pJtK(v)qBw2
and where the cursor is placed at the beginning of the w2array slice.
Taking an idea found in Urbit [32], we notice that if we recognize a familiar
sub-expression t:A`B, the interpreter may bypass the BitMachine's execution
of/uni27EAt:A`B/uni27EBand instead directly compute and write pJtK(v)qBto the active
write frame. Following Urbit, we call such a familiar expression and the code
that replaces it a jet.
Jets are essential for making Simplicity a practical language. For our Block-
chain application we expect to have jets for at least the following expressions:
•arithmetic operations (addition, subtraction, and multiplication) from
8-bit to 256-bit word size,
•comparison operations (less than, less than or equal to, equality) from
8-bit to 256-bit word size,
•logical bitwise operation for 8-bit to 256-bit word sizes,
•constant functions for every possible 8-bit word,
•compression functions from hash functions such as SHA-256's compression
function,
•elliptic curve point operations for the Secp256k1 curve [9], and
•digital signature validation for ECDSA [23] or Schnorr [28] signatures.
We take advantage of the fact that the representation of the values used for
arithmetic expressions in the Bit Machine match the binary memory format for
real hardware. This lets us write these jets by directly reading from and writing
to data frames.
Jets have several nice properties:
•Jets provide a formal speci cation of their behavior. The implementation
of a jet must produce output identical to the output that would be pro-
duced by the Simplicity expression being replaced. There is no possibility
for an ambiguous interpretation of what a jet computes.
•Jets cannot accidentally introduce new behavior or new side e ects be-
cause they can only replicate the behavior of Simplicity expressions. To
add new behavior to Simplicity we must explicitly extend Simplicity (see
Section 4).
•Jets are transparent when it comes to reasoning about Simplicity expres-
sions. Jets are logically equal to the code they replace. Therefore, when
proving properties of one's Simplicity code, jets can safely be ignored.
17
Naturally, we expect jetted expressions to have properties already proven and
available; this will aid reasoning about Simplicity programs that make use of
jets.
Because jets are transparent, the static analyses of resource costs are not
a ected by their existence. To encourage the use of jets, we anticipate discounts
to be applied to the resource costs of programs that use jets based on the
estimated savings of using jets.
When a suitably rich set of jets is available, we expect the bulk of the
computation speci ed by a Simplicity program to be made up of these jets,
with only a few combinators used to combine the various jets. This should
bring the computational requirements needed for Simplicity programs in line
with existing blockchain languages. In light of this, one could consider Simplicity
to be a family of languages, where each language is de ned by a set of jets that
provide computational elements tailored for its particular application.
4 Integration with Blockchains
Core Simplicity is language that does pure computation. In order to interact
with blockchains we extend Simplicity with new combinators.
4.1 Transaction Digests
The main operation used in Bitcoin Script is OPCHECKSIG which, given a
public key, a digital signature, and a SigHash type [15], generates a digest of the
transaction in accordance with the SigHash type and validates that the digital
signature for the digest is correct for the given public key. This operation allows
users to create programs that require their signatures for their transactions to
be authorized.
In core Simplicity we can implement, and jet, the digital signature validation
algorithm. However, it is not possible to generate the transaction digest because
core Simplicity doesn't have access to the transaction data. To remedy this, we
add a new primitive combinator to Simplicity
sighash :SigHashType`2256
where SigHashType := (1+2)2is a Simplicity type suitable for encoding all
possible SigHash types.
This combinator returns the digest of the transaction for the given SigHash
type. Together with the Simplicity implementation of digital signature veri ca-
tion, we can implement the equivalent of Bitcoin's OPCHECKSIG .
We can add other primitives to Simplicity to access speci c elds of trans-
action data in order to implement features such as Bitcoin's timelock oper-
ations [30, 7]. While Simplicity avoids providing direct access to persistent
storage, one could imagine an application where transactions contain data for
transactional updates to a persistent store. Simplicity could support primitives
18
to read the details of these transactional updates and thereby enforce any set
of programmable rules on them.
4.2 Merklized Abstract Syntax Tree
Recall that when using P2SH, users commit to their program by hashing it and
placing that hash in the outputs of transactions. Only when redeeming their
funds does the user reveal their program, whose hash must match the committed
hash.
Pay to Merklized Abstract Syntax Tree , orP2MAST [17], enhances the P2SH
idea. Instead of hashing a linear encoding of a Simplicity expression, we use
the SHA-256's block compression function, SHA256Block :22562512!2256,
to recursively hash Simplicity sub-expressions. This computes a Merkle root of
Simplicity's abstract syntax tree that we denote by #( ) and de ne as
#(iden):=SHA256Blockhtagiden;b0c2512i
#(compst):=SHA256Blockhtagcomp;h#(s);#(t)ii
#(unit):=SHA256Blockhtagunit;b0c2512i
#(injlt):=SHA256Blockhtaginjl;h#(t);b0c2256ii
#(injrt):=SHA256Blockhtaginjr;h#(t);b0c2256ii
#(casest):=SHA256Blockhtagcase;h#(s);#(t)ii
#(pairst):=SHA256Blockhtagpair;h#(s);#(t)ii
#(taket):=SHA256Blockhtagtake;h#(t);b0c2256ii
#(dropt):=SHA256Blockhtagdrop;h#(t);b0c2256ii
#(sighash ):=SHA256Blockhtagsighash;b0c2512i
where tagc:2256is an appropriate unique set of initial vectors per combinator.
We use the SHA-256 hash of the combinator name for its tagvalue.
When computing Merkle roots, like other kinds of static analysis, the inter-
mediate results of sub-expressions can be shared. Bitcoin Script cannot share
sub-expressions in this manner due to the linear encoding of Bitcoin Script
programs.
Another advantage of Merkle roots is that unused branches of case expres-
sions can be pruned at redemption time. Each unused branch can be replaced
with the value of its Merkle root. This saves on bandwidth, storage costs, and
enhances privacy by not revealing more of a Simplicity program than necessary.
Again, this is something not possible in Bitcoin Script.
To enable redemption of pruned Simplicity expressions, we add two new
combinators to replace the case combinator when pruning.
s:AC`D h :2256
assertlsh: (A+B)C`Dh:2256t:BC`D
assertrht: (A+B)C`D
19
These assertion combinators follow the same form as the case combinator,
except one branch is replaced by a hash. The semantics of these combinators
require that the rst component of their input be LorRas appropriate and
then they evaluate the available branch. The h:2256values do not a ect the
semantics; they instead in
uence the Merkle root computation.
#(assertlsh):=SHA256Blockhtagcase;h#(s);hii
#(assertrht):=SHA256Blockhtagcase;hh;#(t)ii
Notice that we use tagcasefor the tags of the assertions. During redemption,
one can replace casestatements with appropriate assertions while still matching
the committed Merkle root of the whole expression.
#(casest) = #( assertls#(t)) = #( assertr #(s)t)
As long as the assertions hold, the computation remains unchanged. If an
assertion fails, because you pruned o a branch that was actually necessary,
then the computation fails and a transaction trying to redeem funds in that
manner is invalid.
Because failure is now a possible result during evaluation, we add a combi-
nator failto allow one to develop programs that use assertions.
fail:A`B
#(fail):=SHA256Blockhtagfail;b0c2512i
When combined with the signature veri cation expression, one can build
the equivalent of Bitcoin's OPCHECKSIGVERIFY operation to assert that a
signature is valid.
Degenerate assertions can also be used to enhance privacy by mixing entropy
into Merkle roots of sub-expressions. Given t:A`Bthe expression
comp (pair(injl iden )unit) (assertl (taket)h) :A`B
is semantically identical to t, but mixes hinto its Merkle root computation.
When used inside branches that are likely to be pruned, this prevents adversaries
from e ectively grinding out Simplicity expressions to see if they match the hash
of the missing branch.
The Merkle root of an expression does not commit to its types. We use
rst-order uni cation [27] to perform type inference on Simplicity expressions,
replacing any remaining type variables with the unit type. Because the types of
pruned branches are discarded, the inferred types may end up smaller than in the
originally committed program. When this happens, the memory requirements
for evaluation with the Bit Machine may also decrease.
20
4.3 Witness Values
During redemption, the user must provide inputs, such as digital signatures and
other witness data, in order to authorize a transaction. Rather than passing this
input as an argument to the Simplicity program, we embed such witness values
directly into the Simplicity expressions using the witness combinator.
b:B
witnessb:A`B
Semantically, the witness combinator just denotes a constant function, which
is something we can already write in core Simplicity. The di erence lies in its
Merkle root computation.
#(witnessb):=SHA256Blockhtagwitness;b0c2512i
The valuebis not committed by the Merkle root. This allows the user to
setwitness value at redemption time, without a ecting the expression's Merkle
root. Users can use witness combinators at places where digital signatures are
needed and at places where choices are made at redemption time.
Using witness combinators enhances privacy because they are pruned away
when they occur in unused branches. This leaves little evidence that there was
an optional input at all.
The amount of witness data allowed at redemption time is limited at com-
mitment time because each witness data type is nite and there can only be
a nite number of witness combinators committed. This limits the computa-
tional power of Simplicity. For example, it is not possible to create a Simplicity
program that allows hashing of an unbounded amount of witness data in order
to validate a digital signature. However, it is possible to create a Simplicity
program that allows a large, but bounded, amount of witness data to be hashed
that is more than will ever be needed in practice. Thanks to pruning, only
the code used to hash the amount of witness data that actually occurs need be
provided at redemption time.
Type inference determines the minimal type for witness values. This pre-
vents them from being lled with unnecessary data.
The witness combinator is not allowed to be used in jets.
4.4 Extended Simplicity Semantics
In order to accommodate these extensions to core Simplicity, we need to broaden
Simplicity's semantics. We use a monad, M, to capture the new e ects of these
extensions.
M(A):=Trans!A?
This monad is a combination of an environment monad (also called a reader
monad) with an exception monad.
21
This monad provides implicit access to a value of Trans type, which we
de ne to be the type of transaction data for the particular blockchain. During
evaluation, it is lled with the transaction containing the input redeeming the
Simplicity program being evaluated. This is used to give semantics to the sighash
and other similar primitives.
The monad also adds a failure value to the result type, denoted by ?:A?
This allows us to give semantics to assertions and fail.
Given an expression t:A`B, we denote this extended semantics by JtKM:
A!M(B). The core Simplicity semantics are lifted in the natural way so that
JtKM(a) =e:Trans:JtK(a)
holds whenever tis composed of only core Simplicity combinators. The extended
set of combinators have the following semantics
Jsighash KM(a):=e:MakeSigHash (a;e)
JassertlshKMhL(a);ci:=e:JsKMha;ci(e)
JassertlshKMhR(b);ci:=e:?
JassertrhtKMhL(a);ci:=e:?
JassertrhtKMhR(b);ci:=e:JtKMhb;ci(e)
JfailKM(a):=e:?
JwitnessbKM(a):=e:b
where MakeSigHash (a;e) is a function that returns a hash of a given transaction
ein accordance with the given SigHash type a.
The operational semantics are also extended to support our new combinators
by providing the Bit Machine with access to the transaction data and by adding
an explicit crash instruction to support assertions and fail.
We note that the e ects captured by our monad are commutative and idem-
potent.2While Simplicity's operational semantics implicitly specify an order of
evaluation, the extended denotational semantics are independent of evaluation
order. This simpli es formal reasoning about Simplicity programs that use the
extended semantics. That said, we expect the majority of Simplicity program's
sub-expressions to be written in core Simplicity whose denotational semantics
contain no e ects at all, making reasoning about them simpler still.
4.5 Using Simplicity Programs in Blockchains
Simplicity programs are Simplicity expressions of type p:1`1, that may use
any of our extended combinators. Users transact by constructing their Simplic-
2We use the terms `commutative' and `idempotent' in the sense of King and Wadler [16]
as opposed to the traditional category theoretic de nition of an idempotent monad.
22
ity program and computing its Merkle root #( p). They have their counterparty
create a transaction that sends funds to that hash.
Later, when a user wants to redeem their received funds, they create a trans-
action whose input contains a Simplicity program whose Merkle root matches
the previous hash. At this point they have the ability to set the witness values
and prune any unneeded branches from case expressions. The witness combi-
nators handle the program's e ective inputs, so the program pis evaluated at
the valuehi:1. No output is needed because the program can use assertions to
fail in case of invalid witnesses. If the execution completes successfully, without
failing, the transaction is considered valid.
The basic signature program that mimics Bitcoin's basic signature program
is composed of the following core Simplicity expressions
checkSig :Signature(PubKey2256)`2
pubKey :A`PubKey
where checkSig checks whether a given signature validates for a given public key
and message hash, and pubKey returns a user's speci c public key.
These expressions can be combined into a basic signature veri cation pro-
gram.
basicSigVerify bc:=comp (pair(witnessb)
(pair pubKey (comp (witnessc)sighash )))
(comp (pair checkSig unit ) (case fail unit ))
Other, more complex programs can be built to perform multi-signature
checks, to implement sophisticated smart contracts such as zero-knowledge con-
tingent payments, or to create novel smart contracts.
5 Results and Future Work
In many ways, Simplicity is best characterized by what features it leaves out
rather than what features it contains.
•Simplicity has no state. Purely functional, expression-based languages
facilitate equational reasoning about the semantics of expressions. For
example, there are no concerns about aliased references to a global heap,
so there is no need to work with separation logic or Hoare logic.
•Simplicity has no named variables. Using combinators lets us avoid deal-
ing with binders and environments for bound variables. This helps keep
our interpreter and static analyses simple and further eases equational
reasoning about Simplicity expressions.
23
•Simplicity has no function types and therefore no higher-order functions.
While it is possible to compute upper bounds of computation resources of
expressions in the presence of function types, it likely that those bounds
would be so far above their actual needs that such analysis would not be
useful.
•Simplicity has no unbounded loops or recursion. It is possible to build
smart contracts with state carried through loops using covenants [25],
without requiring unbounded loops within Simplicity itself. Bounded
loops, such as the 64 rounds needed by our SHA-256 implementation,
can be achieved by unrolling the loop. Because of sub-expression sharing,
this doesn't unreasonably impact program size. We do not directly write
Simplicity, rather we use functions written in Coq or Haskell to generate
Simplicity. These languages do support recursion and we use loops in
these meta-languages to generate unrolled loops in Simplicity.
Throughout this paper we have noted which theorems we have veri ed with
Coq. Other proofs are under development. In particular, we plan to formally
verify the denotational and operational semantics of the full Simplicity language,
including assertions and blockchain primitives.
To validate the suitability of Simplicity, we will be building example smart
contracts in a test blockchain or sidechain [4] application using Simplicity.
We also plan to use the VST project [3] to prove the correctness of our
C implementation of the Bit Machine. This would let us formally verify that
the assembly code generated by the CompCert compiler [18] from our C imple-
mentation respects Simplicity's formal semantics. In particular, we would be
able to prove that substituting jets, such as SHA-256, with fast C or assembly
implementations preserves the semantics of Simplicity.
Simplicity is designed as a low-level language interpreted by blockchain soft-
ware. We anticipate higher-level languages will be used for development and
compiled to Simplicity. Ivy [10] and the -State Authentication Language [11],
are existing e orts that may be suitable for being compiled to Simplicity. If
these higher-level languages come with formal semantics of their own, we will
have the opportunity to prove correct the compiler to Simplicity for these lan-
guages. For the time being, generating Simplicity with our Haskell and Coq
libraries is possible.
6 Conclusion
We have de ned a new language designed to be used for crypto-currencies and
blockchains. It could be deployed in new blockchain applications, including
sidechains [4], or possibly in Bitcoin itself. Simplicity has the potential to be
used in any application where nitary programs need to be transported and
executed under potentially adversarial conditions.
Our language is bounded, without loops, but is expressive enough to repre-
sent any nitary function. These constraints allow for general purpose static
24
analysis that can e ectively bound the amount of computational resources
needed by a Simplicity program prior to execution.
Simplicity has simple, functional semantics, which make formal reasoning
with software proof assistants relatively easy. This provides the means for people
who develop smart contract to formally verify the correctness of their programs.
We have written several low-level functions in Simplicity such as addition,
subtraction, and multiplication for various nite-bit words and formally veri ed
their correctness. With these we have built mid-level functions such as elliptic
curve addition and multiplication, ECDSA signature validation, and a SHA-
256 compression function. This is already sucient to create simple single and
multi-signature veri cation programs in Simplicity. We have formally veri ed
our SHA-256 compression function is correct and have plans to formally verify
the remaining functions.
25
A Simplicity Semantics in Coq
Below is the formal de nition of core Simplicity as expressed in Coq [29]. The
Term type is the type of well-typed Simplicity expressions. The eval function
provides the denotational semantics of well-typed Simplicity expressions.
B Full Word Adders in Simplicity
Below we recursively de ne Simplicity programs for ripple-carry full-adders for
any 2n-bit word size. A full-adder takes two n-bit words and a carry-in bit as
inputs and returns a carry-out bit and an n-bit word.
full-adder n: (2n2n)2`22n
full-adder 1:=comp (pair(take half -adder ) (drop iden ))
(comp (pair(take(take iden ))
(comp (pair(take(drop iden )) (drop iden ))
half-adder ))
(pair(case(drop(take iden )) (injr unit ))
(drop(drop iden ))))
full-adder 2n:=comp (pair(take(pair(take(take iden ))
(drop(take iden ))))
(comp (pair(take(pair(take(drop iden ))
(drop(drop iden ))))
(drop iden ))
full-adder n))
(comp (pair(drop(drop iden ))
(comp (pair(take iden )
(drop(take iden )))
full-adder n))
(pair(drop(take iden ))
(pair(drop(drop iden )) (take iden ))))
Theorem B.1 (Full Adder Correct) Letnbe a power of two. Let a;b:2n
be twon-bit words. Let c:2be a bit. Then
dxe22n+dye2n=dae2n+dbe2n+dce2
where
Jfull-adder Khha;bi;ci=hx;yi
26
We have veri ed the above theorem in Coq.
We have similarly de ned multiplication for 2n-bit words and proven its
correctness theorem in Coq.
C Tail Composition Optimization
We de ne a variant of the translation of Simplicity to the Bit Machine, /uni27EA/uni27EB,
to add a tail composition optimization (TCO). This optimization moves the
dropFrame instruction earlier, potentially reducing the memory requirements
for execution by the Bit Machine. This is analogous to the tail call optimization
found in other languages. Our new de nition will be a pair of mutually recursive
functions, /uni27EA/uni27EBtco
o and/uni27EA/uni27EBtco
on.
The de nition of /uni27EA/uni27EBtco
o is identical to that of /uni27EA/uni27EB, replacing /uni27EA/uni27EBby/uni27EA/uni27EBtco
o ,
except for the /uni27EAcompst:A`C/uni27EBtco
o clause which is given below.
/uni27EAcompst:A`C/uni27EBtco
o :=newFrame (bitSize (B));
/uni27EAs:A`B/uni27EBtco
o ;moveFrame ;/uni27EAt:B`C/uni27EBtco
on
In the tail position of the comp combinator, we removed the dropFrame
instruction and call /uni27EA/uni27EBtco
oninstead. The de nition of /uni27EA/uni27EBtco
onis given in Figure 4.
We have formally veri ed in Coq the following correctness theorem for /uni27EA/uni27EBtco
o .
Theorem C.1 (Correctness of TCO Operational Semantics) Lett:
A`Bbe any Simplicity expression. Let v:Abe any value of type A.
Initialize a Bit Machine with
•the value pvqAas the only frame on the read-frame stack with its cursor
at the beginning of the frame, and
•the value [?]bitSize (B)as the only frame on the write-frame stack with its
cursor at the beginning of the frame.
After executing the instructions /uni27EAt:A`B/uni27EBtco
o , the nal state of the Bit Machine
has
•the value pvqAas the only frame on the read-frame stack with its cursor
at the beginning of the frame, and
•the value pJtK(v)qBas the only frame on the write-frame stack with its
cursor at the end of the frame.
In particular, the Bit Machine never crashes during this execution.
The proof proceeds by establishing that the machine state transformation
induced by executing the instructions
/uni27EAt:A`B/uni27EBtco
o ;dropFrame
27
/uni27EAiden:A`A/uni27EBtco
on:=copy (bitSize (A));dropFrame
/uni27EAcompst:A`C/uni27EBtco
on:=newFrame (bitSize (B));/uni27EAs:A`B/uni27EBtco
on;
moveFrame ;/uni27EAt:B`C/uni27EBtco
on
/uni27EAunit:A`1/uni27EBtco
on:=dropFrame
/uni27EAinjlt:A`B+C/uni27EBtco
on:=write (0);skip(padl(B;C));/uni27EAt:A`B/uni27EBtco
on
/uni27EAinjrt:A`B+C/uni27EBtco
on:=write (1);skip(padr(B;C));/uni27EAt:A`C/uni27EBtco
on
/uni27EAcasest: (A+B)C`D/uni27EBtco
on:= match read with
8
>><
>>:0!fwd(1 + padl(A;B));
/uni27EAs:AC`D/uni27EBtco
on
1!fwd(1 + padr(A;B));
/uni27EAt:BC`D/uni27EBtco
on
/uni27EApairst:A`BC/uni27EBtco
on:=/uni27EAs:A`B/uni27EBtco
o ;/uni27EAt:A`C/uni27EBtco
on
/uni27EAtaket:AB`C/uni27EBtco
on:=/uni27EAt:A`C/uni27EBtco
on
/uni27EAdropt:AB`C/uni27EBtco
on:=fwd(bitSize (A));/uni27EAt:B`C/uni27EBtco
on
Figure 4: Operational semantics for Simplicity using the Bit Machine with TCO.
and the instructions
/uni27EAt:A`B/uni27EBtco
on
are identical.
We would like an improved static analysis of the memory use of this TCO ex-
ecution. Figure 5 de nes the static analysis cellBndtco(t:A`B) which bounds
the memory use of the TCO execution. The static analysis becomes somewhat
more intricate with the more complex translation. But we may proceed with
con dence because we have a formal veri cation in Coq of the following theorem.
Theorem C.2 (Static Analysis of Cell Usage with TCO) Lett:A`B
be any Simplicity expression. Let v:Abe any value of type A. Initialize a Bit
Machine as speci ed in Theorem C.1. The maximum number of cells in both
stacks of the Bit Machine at any point during the execution of /uni27EAt:A`B/uni27EBtco
o
from this initial state never exceeds cellBndtco(t:A`B).
The proof proceeds by establishing that the additional memory used
by/uni27EAt:A`B/uni27EBtco
o and/uni27EAt:A`B/uni27EBtco
onin any machine state is no more than
max(n1;n2) and max(n1 m;n 2) respectively where hn1;n2i=extraCellBndtco(t:
A`B) andmis the number of cells in the active read frame before executing
/uni27EAt:A`B/uni27EBtco
on.
There is a possibility for a head composition optimization where the
newFrame (n) instruction is delayed in order to potentially save memory.
28
It is unclear to us if this is worth the added complexity, so we have not pursued
this yet.
Acknowledgements
Thank you to Shannon Appelcline for his help editing this paper.
29
extraCellBndtco(iden:A`A):=h0;0i
extraCellBndtco(compst:A`C):= lethn1;n2i=
extraCellBndtco(s:A`B) in
lethm1;m2i=
extraCellBndtco(t:B`C) in
letb=bitSize (B) in
hmax(b+n1;m1;b+m2);b+n2i
extraCellBndtco(unit:A`1):=h0;0i
extraCellBndtco(injlt:A`B+C):=extraCellBndtco(t:A`B)
extraCellBndtco(injrt:A`B+C):=extraCellBndtco(t:A`C)
extraCellBndtco(casest: (A+B)C`D):= lethn1;n2i=
extraCellBndtco(s:AC`D)
in
lethm1;m2i=
extraCellBndtco(t:BC`D)
in
hmax(n1;m1);max(n2;m2)i
extraCellBndtco(pairst:A`BC):= lethn1;n2i=
extraCellBndtco(s:A`B) in
lethm1;m2i=
extraCellBndtco(t:A`C) in
hm1;max(n1;n2;m2)i
extraCellBndtco(taket:AB`C):=extraCellBndtco(t:A`C)
extraCellBndtco(dropt:AB`C):=extraCellBndtco(t:B`C)
cellBndtco(t:A`B):= lethn1;n2i=extraCellBndtco(t:A`B) in
bitSize (A) +bitSize (B) +max(n1;n2)
Figure 5: De nition of cellBndtco.
30
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commit/4bd188c4383d6e614e18f79dc337fbabe8464c82 , August 2010.
https://bitcoin.svn.sourceforge.net/svnroot/bitcoin/trunk@131 .
[22] S. Nakamoto. Re: Transactions and Scripts: DUP HASH160 ...
EQUALVERIFY CHECKSIG. https://bitcointalk.org/index.php?
topic=195.msg1611#msg1611 , June 2010.
[23] National institute of standards and technology. FIPS pub 186-4 federal
information processing standards publication digital signature standard
(dss). 2013.
[24] National institute of standards and technology. FIPS 180-4, secure hash
standard, federal information processing standard (FIPS), publication 180-
4. Technical report, DEPARTMENT OF COMMERCE, August 2015.
[25] R. O'Connor. Covenants in elements alpha, 2016. Blog post, https://
blockstream.com/2016/11/02/covenants-in-elements-alpha.html .
[26] Parity. The multi-sig hack: A postmortem, July 2017. https://
paritytech.io/blog/the-multi-sig-hack-a-postmortem.html .
32
[27] M. S. Paterson and M. N. Wegman. Linear uni cation. Journal of Com-
puter and System Sciences , 16(2):158 { 167, 1978.
[28] C. P. Schnorr. Ecient identi cation and signatures for smart cards. In
Proceedings of CRYPTO '89 , 1989.
[29] The Coq Development Team. The Coq Proof Assistant Reference Manual:
Version 8.6. https://coq.inria.fr/refman/ , 2016.
[30] P. Todd. BIP65: Op checklocktimeverify. Bitcoin Improvement
Proposal, 2014. https://github.com/bitcoin/bips/blob/master/
bip-0065.mediawiki .
[31] G. Wood. Ethereum: A secure decentralised generalised transaction ledger,
2014. http://gavwood.com/paper.pdf .
[32] C. Yarvin, P. Monk, A. Dyudin, and R. Pasco. Urbit: A solid-state inter-
preter, May 2016. http://media.urbit.org/whitepaper.pdf .
33
|
{
"id": "1711.03028"
}
|
2206.05081
|
The Evolution Of Centralisation on Cryptocurrency Platforms
|
More than ten years ago the blockchain was acclaimed as the solution to
overcome centralised trusted third parties for online payments. Through the
years the crypto-movement changed and evolved, although decentralisation
remained the core ideology and the necessary feature every new crypto-project
should provide. In this paper we study the concept of centralisation in
cryptocurrencies using a wide array of methodologies from the complex systems
literature, on a comparative collection of blockchains, in order to define the
many different levels a blockchain system may display (de-)centralisation and
to question whether the present state of cryptocurrencies is, in a
technological and economical sense, actually decentralised.
|
http://arxiv.org/pdf/2206.05081v2
|
Carlo Campajola, Raffaele Cristodaro, Francesco Maria De Collibus, Tao Yan, Nicolo' Vallarano, Claudio J. Tessone
|
physics.soc-ph, cs.CR, econ.GN, q-fin.EC
|
physics.soc-ph
|
The Evolution Of Centralisation on Cryptocurrency
Platforms
Carlo Campajola1,2,3, Raffaele Cristodaro2, Francesco Maria De Collibus2, Tao Yan2,
Nicol `o Vallarano2,3, and Claudio J. Tessone2,3,*
1DLT Science Foundation, London, United Kingdom
2University of Zurich, Blockchain & Distributed Ledger Technologies, Department of Informatics, Z ¨urich, Switzerland
3UZH Blockchain Center, Z ¨urich, Switzerland
*claudio.tessone@uzh.ch
ABSTRACT
More than ten years ago the blockchain was acclaimed as the solution to overcome centralised trusted third parties for online
payments. Through the years the crypto-movement changed and evolved, although decentralisation remained the core ideology
and the necessary feature every new crypto-project should provide. In this paper we study the concept of centralisation in
cryptocurrencies using a wide array of methodologies from the complex systems literature, on a comparative collection of
blockchains, in order to define the many different levels a blockchain system may display (de-)centralisation and to question
whether the present state of cryptocurrencies is, in a technological and economical sense, actually decentralised.
Introduction
In the wake of the 2008 Great Financial Crisis, the whitepaper “Bitcoin: A Peer-to-Peer Electronic Cash System”1was published
by the pseudonymous Satoshi Nakamoto. The paper began with a direct attack to the state of electronic payments on the
internet: a critique on the central role of third parties to provide trust among users and the complete absence of non-reversible
payments online. After describing the problem the paper proceeds to present Nakamoto’s solution, a relatively old2and up
to then fringe technology: the blockchain, a distributed and decentralised ledger where information is maintained consistent
across the network by a peer-to-peer consensus protocol and secured via cryptography. Bitcoin was the first and thus far most
successful platform of what have become commonly known as “cryptocurrencies”, digital assets that rely on cryptographic
security to solve the double-spending problem, typically through a decentralised ledger rather than a centralised authoritative
server.
Following Bitcoin’s success, many more cryptocurrency projects started, each one with its own specific quirk yet charac-
terised by the same common goal: to build a decentralised payment system, available to anyone without the need of reciprocal
trust (or a third party providing it). For this reason the proponents of Bitcoin and other cryptocurrencies typically call it a “truly
democratic” form of money3, as opposed to the “tyrannical money” issued by governments.
The main purpose of our study is to question whether the initial aspirations behind the creation of Bitcoin have been fulfilled.
As pointed out by Taleb4, Bitcoin and the likes suffer from design problems that lead to intrinsic fragilities, which in turn can
compromise their utility. In our work we investigate these fragilities: we perform a large scale comparative analysis of multiple
cryptocurrency platforms, sourcing the data directly from the respective blockchains, and adopt methods from network and
complex systems science to define and investigate the concept of decentralisation in cryptocurrencies systems.
The blockchains we analyse are Bitcoin, Ethereum, Bitcoin Cash, Litecoin, Dogecoin, Monacoin and Feathercoin. We
analyse data ranging from the systems inceptions until December 2020, adopting state of the art methodologies5to transform
the raw blockchain data into a meaningful dataset of economic transactions between “entities”. Entities are intended to be the
closest possible representation of distinct economic agents on the blockchain: they can correspond to individuals, groups of
individuals or businesses that operate in the system, coping with the pseudonymous nature of cryptocurrencies.
The entities on the blockchain interact through transactions, i.e. by sending tokens to one another to exchange value. This
interaction mechanism is naturally suitable for a network representation, which has been widely adopted in the literature6,
where entities are mapped to nodes and directed links are established following the transactions flow. This provides a new
perspective to study a cryptocurrency as an interconnected system of agents7of which we are able to quantitatively define and
measure the centralisation. As an example, the emergence of a giant connected component8in the transaction networks of all
the cryptocurrencies under study signals how all the users participate to the same economic community. Another interesting
point is the detection of a core component9of the network, a subset of highly inter-linked nodes which keep the networkarXiv:2206.05081v2 [physics.soc-ph] 3 May 2023
Figure 1. (a) Total number of addresses appeared on blockchain as function of total number of identified entities. Each plot
point corresponds to the totals measured on the same block, with weekly frequency. Axes are logarithmic and the dashed line is
reference for y=x. (b) Average number of addresses per entity over time.
together, taking a middle-men positioning in the networks. Moreover we are able to measure the wealth of entities and relate it
to their position in the transaction networks. Similar analyses have been performed in the past for the “cumulative” network, i.e.
the network containing all transactions that happened up to a given point in history6: we go beyond by splitting the time-frame
in weekly time-windows, thus reducing the reciprocal impact of transactions spread far apart in time. It is indeed not particularly
reasonable to consider in the same static network transactions which happened over 10 years apart. A similar approach in
this respect has been recently taken for Bitcoin10, 11, and we generalise their analysis to six more platforms, providing an
unprecedented quantitative overview of the crypto market.
In the following section we describe the impact that the identification of entities has on the structure of the blockchain data
and of transaction networks. We then focus on the networks and their global properties, such as the distribution of degrees
and the identification of the network core, and then tackle the problem of mining power concentration and how it affects the
distribution of wealths in the system. We conclude by discussing the results. For readability purposes the technical details
about the data and methods are reported in the Materials and Methods section, following the discussion.
Results
Clustering matters. We begin by analysing the effect of applying address clustering on UTXO-based blockchain data. Figure
1a shows the relation between the number of entities, identified by adopting the heuristic address clustering logic described in
the Materials and Methods section, and the number of addresses that have appeared on the blockchain. Each data point refers
to the totals observed up to a given block, with weekly frequency, and time “flows” from the bottom left to the top right of
the panel. The results show strong similarities across the six platforms considered: this consistency is expected, as all these
platforms share the same architecture in the address management protocols. New addresses are continuously generated with
each transaction, and more active entities will accumulate more addresses on average. It is interesting to notice that there are
several “regimes” in Figure 1a, with the slope of the curve changing over time. We plot this slope, i.e. the average number of
addresses per entity, as a function of time in Figure 1b. Three regimes can be identified in the history of these cryptocurrencies:
an early stage where the average grows, a relatively stable period between 2014 and 2017, followed by a decline after 2018.
Interestingly, the transition from one regime to the other is marked by two of the well-known “crypto bubbles”, in 2013 and
2017, that were possibly the drivers of these shifts12. The first saw the creation of multiple so-called “alt-coins”, alternative
cryptocurrencies running on their own blockchains which aimed to capitalise on the large influx of funds in the ecosystem, a
class which includes all non-BTC assets in our analysis. In the period between the bubbles the average number of addresses
owned by entities stayed at a relatively high value: relatively few new participants were entering the system since public
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Figure 2. Size of the largest Strongly Connected Component in UTXO-based cryptocurrencies.
interest had declined after the bubble burst, which means that transactions (and thus address creation) were primarily involving
pre-existing entities. The picture changed after the 2017-18 bubble, when cryptocurrencies entered the mainstream of financial
assets, receiving serious consideration by markets and institutions and leading to the consolidation of major service providers
like leading exchanges Coinbase, Binance, etc. Since then public attention for the crypto ecosystem has stayed high and a large
number of new entities entered the system, most of the times controlling just one or two addresses since they wouldn’t use the
blockchain to operate many transactions anymore. Indeed after 2018 blockchains started to experience congestion problems13,
i.e. blocks reached capacity and thus adding new transactions to the blockchain became more costly. This created incentives
to operate off-chain, mostly through service providers or so-called “layer 2” technologies like the Lightning Network14, thus
reducing the average number of addresses per entity.
We can then consider the effect that address clustering has on the structure of transaction networks. We measure the size of
the largest Strongly Connected Component (SCC) in the address networks and compare it to the one in the entity networks,
shown in Fig. 2 for the six cryptocurrencies of our analysis. Such a metric is essential to measure the fragmentation of the
economy: if many nodes don’t belong to the largest SCC it is a significant sign of decentralisation, as it means that there are no
bridges across different communities; on the other hand if the vast majority of nodes belongs to the largest SCC it signals that
such bridges exist and that a token can travel from one side of the network to the other. It is clear the address networks do not
show the so-called “giant component” (i.e. a SCC that spans the whole network), which would suggest that these economies
are highly fragmented and decentralised; however the illusion becomes apparent as soon as one looks at entity networks, where
the largest SCC spans almost the entire network for most of the time. We should stress out the emergence of a Giant Connected
Component is a necessary yet not sufficient condition for the economy to be centralised: a disconnected economy composed of
isolated communities which keep money from flowing around cannot be centralised as a whole. A notable outlier in this is
Dogecoin, where the largest SCC seems to be of about the same size in both representations, spanning approximately half the
network. This observation then provides evidence that address clustering is a necessary step to take to get meaningful results
about the economic structure of cryptocurrencies, as the obfuscation of identities through multiple addresses significantly
affects the observations. In the following we will only present results on the entity networks for UTXO-based cryptocurrencies,
unless specified otherwise.
Degree distributions. The degree of a node represents the number of counterparts that entity exchanges tokens with, and is
typically considered a measure of importance within the network. What is particularly relevant is to consider the distribution
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BTC LTC DOGE BCH FTC MONA ETH
aMinimum 1.74 1.31 1.45 1.47 1.60 1.49 1.50
Maximum 10.99 5.84 3.83 9.89 4.81 7.69 3.42
1. Quartile 2.42 2.43 2.45 2.28 2.33 2.46 1.68
3. Quartile 2.93 3.08 3.09 2.94 3.18 3.21 1.81
Median 2.69 2.71 2.88 2.65 2.68 2.72 1.76
DPar Median 0.03 0.05 0.05 0.04 0.08 0.10 0.03
DBin Median 0.35 0.41 0.42 0.37 0.37 0.41 0.43
Table 1. Summary statistics on exponents of power-law fit of degree distribution on the weekly transaction networks.
Figure 3. Assortativity coefficient for weekly transaction networks.
of degrees, as its shape is a direct consequence of the way in which the networks form. The degree distribution has been
shown to be strongly skewed, akin to a power-law Pareto type distribution, for many traditional economic and financial systems
such as interbank markets15, corporate payments16and in experiments17. This has also been observed in several analyses of
cryptocurrency networks of transactions that represent the whole history6, 18, and our results broadly agree with the literature on
the much shorter weekly timescale we select. We estimate the maximum likelihood parameters of a Pareto distribution and a
binomial distribution for the degrees, which would be consistent with the null hypothesis of a Barabasi-Albert-type network or
an Erdos-Renyi random network respectively, and find that the former is a much better description of the data than the latter in
all of our samples. While we do not find that either distribution properly fits the data - indeed a Kolmogorov-Smirnov test
rejects both null hypotheses at the 20% level in the vast majority of weeks - we still find that the Kolmogorov-Smirnov distance
is much lower for a Pareto-type distribution ( DParin Table 1) rather than a binomial ( DBin), hinting to the presence of fat tails.
In Table 1 we report the statistics for the exponent aof a Pareto fit of the degree distribution, averaged over the whole sample.
We find that in general the power-law exponent is between 2.5 and 3 for the UTXO-based cryptocurrencies - for which heuristic
address clustering is available - and around 1.76 for Ether. This stark similarity across platforms points towards a scale-free
structure of these transaction networks, with few hubs and large amounts of lowly connected nodes. This is further confirmed
by computing the assortativity coefficient, which we report in Fig. 3. This is typically negative, with a tendency for smaller
platforms like Monacoin and Feathercoin to show stronger disassortativity. This simple analysis is itself consistent with a
strong centralisation in the flow of tokens, where the vast majority of transactions happens between low-degree nodes and large
intermediaries like exchanges, custodians and service providers. Our observations largely agree with the evidence presented
by11on the Bitcoin transaction network, where the authors find that most of on-chain transactions (excluding self-transactions,
which we also exclude) involve exchanges.
Core-periphery structure. Economic and financial networks often exhibit the so-called “core-periphery structure”9, 19, 20.
This is a macroscopic property of the network, that presents a split between a minority of nodes (the “core”) with a strong
connectivity between themselves and the remaining nodes of the network (the “periphery”) that are mostly connected to core
nodes and have relatively few links to other peripheral nodes. We apply the core-periphery classification algorithm from21on
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Figure 4. Fraction of nodes in the network core as a function of time.
the weekly transaction networks time-series and then consider the size of the core group as a fraction of the total size of the
network. We report this quantity for each weekly transaction network in Figure 4. We find that the relative size of the core has
consistently decreased over time in all the analysed cryptocurrencies before 2018, and since then it has stabilised, excluding the
particular cases of Monacoin and Feathercoin. The fact that the relative size of the core of these networks has been shrinking is
further evidence that the cryptocurrency economy has been shifting towards a centralised model, becoming much closer to the
traditional financial system that it criticised heavily in its early days. A marked core-periphery structure points towards the
transformation of the blockchain ledger into a kind of “interbank market”, where few large intermediaries move funds on behalf
of their many clients much like bank transfers and brokerage happen in traditional banking. Indeed a shrinking core corresponds
to a bigger periphery, i.e. the vast majority of entities (up to 99.99% in the case of Bitcoin and Ethereum) is loosely connected
to the rest of the network and relies on a small minority to intermediate transactions. This structure has been consolidating since
2018, consistent with our observations about the SCC and the amount of addresses per entity that we previously discussed.
The exception here, represented by Monacoin and Feathercoin, is mostly due to the drop in popularity these two platforms
have experienced after the burst of the 2018 crypto bubble. Monacoin has also suffered from attacks on its mining protocol22,
and the market capitalisation of both coins has not recovered to its 2018 peak. The size of the core of both cryptocurrencies
is extremely small (less than 5 nodes from 2018 onwards) but the total size of the network is also relatively small, with few
hundreds of active entities, thus leading to the observed results.
Mining concentration. An analysis of centralisation on cryptocurrency platforms would not be complete without reporting
measures of mining power concentration. All blockchains in this study are based on the Proof-of-Work (PoW) consensus
mechanism, where the right to add a new block to the blockchain is granted to whoever finds the solution to a cryptographic
puzzle. These entities are typically called “miners”, and have an incentive to operate given by a coinbase transaction that mints
new tokens attributed directly to the miner. The consistency of blockchain data relies on the assumption that mining power
is distributed and decentralised: if at any point in time a single miner (or a pool of miners) holds more than 50% of the total
computational power devoted to solving the PoW puzzle, the blockchain can be subjected to a so-called “51% attack” where
the majority miner writes false information on the blocks (e.g. double spending transactions) without anybody being able to
counteract, since the majority miner will always produce the longest chain with no opposition. A short-term “51% attack“ may
lead to a huge decrease of confidence in a blockchain’s content; the persistence of such an attack could mean the end of a
blockchain project.
The Nakamoto index provides us with a measure of the system’s distance from a 51% attack: named after the pseudonymous
author of the Bitcoin whitepaper, it is defined as the minimum number of miners that need to coordinate at any point in time to
run a 51% attack. There have been several reports of hashing power centralisation on Bitcoin11, 23: based on blockchain data
alone, we are able to estimate this index for all the analysed cryptocurrencies, and we show the result in Figure 5. We estimate
the hashing power of miners by measuring the fraction of blocks they mine in a week, and then calculate the Nakamoto index
over that same week. Miners are identified as the entities receiving the coinbase transaction: differently from other parts of
this work, in Figure 5 we show results that are obtained using only the multiple inputs heuristic method for address clustering,
in order to avoid merging too many pools and miners together. We report similar results for the combined heuristics in the
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Figure 5. Nakamoto index estimated weekly for the analysed cryptocurrencies.
Supplementary Information.
The majority of analysed platforms shows worrying results. The most striking case is Ethereum, with a Nakamoto index
that is almost never above 3 and in some weeks drops to 2: this is particularly concerning since Ethereum is used as the base
protocol for many decentralised applications, including decentralised exchanges and other decentralised finance instruments
which could introduce additional incentives for miners to coordinate in a 51% attack24, 25. The picture looks marginally better
for Bitcoin and Litecoin, where the mining power appears relatively less centralised (although it still is very concentrated
at times), while on Dogecoin, Feathercoin, Monacoin and Bitcoin Cash (after it forked from Bitcoin in August 2017) the
Nakamoto index is always low, and at times even goes to 1, meaning a single miner could have (and maybe has) successfully
run a 51% attack on some weeks. Our evidence qualitatively agrees with previously presented models23and evidence for
Bitcoin11, where in the latter the authors had additional information available from deanonymisation services specific to Bitcoin,
which we don’t have access to.
Wealth inequality and spatial distribution. Our final analysis concerns the distribution of tokens among entities. We define
as wealth the balance held by an entity (address or cluster of addresses) at a given point in time, representing the funds that they
are entitled to spend according to the blockchain ledger. To avoid considering inactive wallets, our statistics only consider
addresses or entities with non-zero balance. Clearly, if cryptocurrencies were used simply as a peer-to-peer payment system as
implied by the Bitcoin whitepaper1(and many other whitepapers), the level of centralisation and inequality of wealths should
not be very different from what is measured in traditional economic systems26. This is very far from what can be measured
on the system we analyse. In Figure 6a we show the Gini coefficient of wealths, measured weekly on each cryptocurrency
throughout our sample. This coefficient is a standard measure of economic inequality and is related to the area between the
Lorenz curve - which plots the cumulative share of wealth as a function of the fraction of population owning that share - and the
equality line - i.e. the straight 45 degrees Lorenz curve. A Gini coefficient of 0 means perfect equality, i.e. each individual has
the same wealth, whereas a Gini coefficient of 1 means total inequality, with one individual owning everything. Remarkably,
only Bitcoin in its early years has a Gini coefficient that is lower than the world average wealth inequality Gini coefficient
(which is around 0.9, according to26). After 2012, for all the cryptocurrencies in our analysis the Gini index is basically
indistinguishable from 1, meaning that the distribution of wealth is extremely unequal across active wallets. We want to stress
that this is not due to having many zero-wealth entities in the sample, as we remove these before calculating the Gini coefficient.
Finally, we characterise the spatial distribution of wealth on our transaction networks. To this end we calculate, for
each weekly transaction network, the minimum distance that each node has from a miner. Miners are special entities in
cryptocurrencies because they are the ones that generate new tokens, as every time they successfully solve the Proof of Work
validation puzzle they are entitled to a reward that is created by the protocol. This means that all tokens start their “life” in a
6/14
Figure 6. Wealth inequality and spatial distribution. (a) Evolution of the Gini index of wealths in analysed cryptocurrencies.
(b) Distribution of tokens as a function of distance from the miner. Plot points identify the mean fraction of wealth, averaged
over time, that is held by nodes at a given distance from the nearest miner. Error bars give the 90% confidence band, i.e. in 90%
of weeks the fraction of tokens held at a given distance is within the error bars.
miner’s wallet and then diffuse from there into the rest of the economy, which is why they are a meaningful reference node to
consider in transaction networks. We then proceed to calculate the amount of wealth that is held by nodes at the same distance
from a miner, normalised by the total amount of cryptocurrency circulating, and then consider this quantity across all weeks in
our sample. We plot the result in Figure 6b. The plot points represent the average wealth share owned by entities at a given
distance from a miner, while the error bars show the 90% confidence band, i.e. the range of values within which the wealth
share lies in 90% of weeks in our sample. We find remarkable similarities between the analysed cryptocurrencies: the average
wealth share is a decreasing function of distance from miners, with a behaviour that is close to an exponential, and miners
(at distance 0) are almost always the wealthiest nodes. The only platform where this doesn’t happen is Ethereum, where the
largest wealth share is at a distance of 2 from miners. This is likely due to the difference in the accounting protocol between
UTXO-based systems and Ethereum’s account-based system, but we lack conclusive evidence about this. What appears clear
from our analysis though is that the largest fraction of wealth is held by relatively few entities - as summarised by the Gini
coefficient - that are in the immediate vicinity of miners, with a tail that is quickly vanishing as distance grows.
Discussion
In this paper we have presented multiple evidences of the centralised nature of major cryptocurrencies, despite their statutory
technological decentralisation. We described how the protocol of Bitcoin and similar UTXO-based blockchains works to
preserve anonymity and how this can be partially reverse-engineered to cluster addresses that are likely belonging to the
same entity, and how switching from a raw address-based description to an entity-based description radically changes the
observations. In particular we found interesting dynamics in the evolution of the average number of addresses per entity,
which is remarkably similar across platforms and experiences breakpoints in conjunction with large price movements. A first
circumstantial evidence of the centralisation process comes from these dynamics, namely the decrease of the average number
of addresses per entity after 2018, together with the consolidation of cryptocurrencies as popular speculative assets and the
growth of large intermediaries. The result is consistent with a shift in the platforms usage, with new entities entering the system
through centralised access points rather than independently.
The introduction of weekly transaction networks allowed us to analyse the structural properties of cryptocurrency economies.
We found further evidence of the importance of address clustering by measuring the size of the largest Strongly Connected
Component (LSCC) of the networks: while on address networks the LSCC contains less than half of active addresses, when
considering entities the LSCC includes almost all the nodes of the networks. We take this as a further sign that there are entities
which control multiple addresses, acting as “bridges” between different sub-components of the economy. The networks we
studied exhibit a fat-tailed degree distribution, with few entities participating in a large number of transactions each week
while the vast majority only performs very few, and a negative assortativity coefficient, which constitutes further evidence
7/14
Blockchain BTC ETH BCH LTC DOGE MONA FTC
Height 661386 12950000 664161 1969743 3927743 2212711 3491853
Date of collection 14.12.2020 03.08.2021 02.12.2020 23.12.2020 07.10.2021 07.01.2021 07.01.2021
Table 2. Summary information on data collection from public blockchains.
of the existence of large intermediaries that centralise the flow of tokens27. Furthermore these networks exhibit a marked
core-periphery structure, much like other trading networks from traditional economic and financial systems, and as time goes
on the cores become increasingly smaller compared to the total size of the network.
Finally we focused our analysis on the role of miners, which are particularly important actors in these systems as they hold
the responsibility of keeping the ledger consistent. We find that in all the studied platforms the mining power, namely the
estimated share of hashing rate that each miner holds, is worryingly concentrated in the hands of few entities to the point that
the Nakamoto index often drops to few units, implying a high risk of a successful 51% attack on the blockchain. We also found
that miners are typically the wealthiest entities in the system, and that the stark inequality in wealths is incompatible with the
typical narrative of a decentralised, peer-to-peer payments system.
Overall we argue that these economies have grown into something that is very different from what they were originally
designed to be. The popular narratives, also seen in some mainstream media, that cryptocurrencies are a decentralised solution
for payments where financial institutions hold no power over money creation and distribution, are largely false. Similar
processes have been reported in second-layer solutions, such as the Lightning Network14, 28. As a matter of fact, we see that
miners and intermediaries hold a huge power in these systems, both technically and economically speaking, which turns them
into an unregulated financial sector, a new “shadow banking” system29. This unchecked concentration of power is potentially
dangerous for the many retail investors that have holdings in cryptocurrency, and proper regulation regarding the activities and
products that service providers can offer is overdue.
Materials and Methods
Data. Blockchain data for the seven cryptocurrencies of our analysis is sourced directly from the respective networks by
running consensus nodes, collecting data at block heights reported in Table 2. The figures presented in the paper show results
until the last date that is available for all blockchains, December 2, 2020.
The blockchain protocols analysed in this study belong to two different families when it comes to their accounting standard:
the Unspent Transaction Output (UTXO) standard and the account-based standard, the latter being specific of Ethereum alone
in our sample, while all others belong to the former. In UTXO blockchains transactions are recorded as transfers of access
rights to a certain amount of tokens, stored on an address: this is done by referencing one or more addresses currently owned by
the transaction sender, and directing the related tokens to one or more addresses owned by the transaction receiver. Typically
the first set of addresses is called the “input” of the transaction, while the second is its “output”. Outputs can only be used
(“spent”) once as inputs to a transaction, hence the name of Unspent Transaction Output accounting. Ownership of addresses
is proven by public-private key cryptography: in order to access funds from an address, the private key is needed to sign the
transaction in a way that any other user can verify via the public key. The same entity can own any number of addresses, thus
making tracing of transactions between entities more challenging, as there is no guarantee that a transaction involves addresses
from one, two or many different entities. Multiple heuristic algorithms have been developed over the years to exploit common
patterns in transactions input-output structures to cluster together addresses that are likely to belong to the same entity. We
adopt multiple combinations of these algorithms in our analysis, and report the details below.
In contrast to the UTXO model described above, the account-based blockchains use a different and perhaps more intuitive
mechanism for exchanging amounts of cryptocurrencies and other cryptoassets. Instead of holding the private key that can
unlock the unspent output for a new transaction where it will be used as input, account-based blockchains have their public key
directly transformed as an address, and that address has a specific balance associated. There is then a reduced need to apply
address clustering to account-based blockchains, since new addresses are not often created within the same wallet. The first and
up to the time of writing most successful example of account-based blockchains is Ethereum, with its native cryptocurrency
Ether30.
Address clustering in UTXO blockchains. The entities, i.e. clusters of addresses, have been generated using the C++ library
BlockSci, which is an open-source software package for blockchain analysis31. We combined multiple heuristic algorithms that
are provided within the library to produce our own heuristic methods; we briefly summarise them below and direct the reader to
BlockSci’s documentation1for further details. Most of the methods rely on the identification of change addresses , i.e. outputs
1https://citp.github.io/BlockSci/
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Ox48 0.1
Ox62 3
Ox22 5
Ox61 2.1
Ox11 0.5
Ox22 5
Ox89 9.5
Ox21 5
# block 25/03/14 01:36
Ox49 3
Ox21 5
Ox48 2
Ox32 4.5
Ox88 3
Ox33 0.2
Ox89 9.5
Ox31 9
Ox87 1.2x48
x49x21x62
x61
x22x89
x11x88x87
x33
x31
x32Blockchain AddressNetwork EntityNetworkParsing lutseeringFigure 7. Schematic representation of transaction networks construction. On the left, an archetypal UTXO blockchain block
is represented, with coloured transaction input/outputs identifying clusters according to common heuristics. This is parsed
through BlockSci into the middle representation, the transaction network at the addresses level, which is then further reduced to
the entity network on the right when multiple addresses/nodes belonging to the same entity are merged.
of a transaction that belong to the sender, to which excess input is directed. Indeed the protocol design imposes that the input
value of a transaction exactly matches the output value plus the fee, which makes change addresses very common.
The heuristic methods we adopted are the following:
•Multiple inputs - if multiple addresses appear as inputs to the same transaction, they are likely to belong to the same
entity that had to sign them all to create the transaction;
• Reuse of an address - if an address appears as an input but also as an output, the address has been reused as change;
•Optimal change: if the transaction has multiple inputs and the value of exactly one output is lower than any of the inputs,
then it is likely the change. This is based on the argument that if a bigger output was the change, then one or more inputs
would have not been needed to transfer the same amount to another wallet;
•Creation of a new address - in case an output address is appearing for the first time on the blockchain, that address has
likely been generated to be the change address;
•Peeling chains - a transaction is considered a potential peeling chain if it includes one input and two outputs, and the
chain is identified if one of the outputs is used as input to a future transaction with the same structure. In that case, the
output that continues the chain is the change address. These chains are commonly seen when addresses with large UTXO
values are spent in many smaller value transactions.
Our main clustering methodology combines the above heuristics through logical operators that aim to reduce the chance
that a false positive occurs. In particular we identify a change address only if all heuristics identify the same address as potential
change, except for peeling chains which can never be compatible with other patterns by design. Hence a change address is
identified if it is the only one that respects one of the following conditions: i) it is a reused input address; ii) it is a new address
with a value smaller than any of the inputs; iii) it is the continuation of a peeling chain. If a change is identified, it is added to
the same cluster to which the input addresses belong. A robustness analysis adopting a more conservative choice of clustering
strategy is presented in the Supplementary Information.
Transaction networks. We construct transaction networks to obtain a general representation of different blockchains. We
choose a weekly aggregation to remove intraweek seasonalities, as we notice that the number of active entities and transactions
is lower during weekends. We then consider all transactions between entities - i.e. removing self-transactions like change
outputs - happening in a given week, adding the involved entities as nodes and connecting them through directed links from
inputs to outputs. This process is summarised by Figure 7. Differently from previous literature on cryptocurrency transaction
networks6, 18, we consider non-cumulative network representations, meaning that links represent only the transactions happening
on that specific week, similarly to what is done in11at monthly aggregation. This allows us to obtain a more representative
structure for the state of the economy, avoiding considering in the same way connections that may have happened years apart
from each other.
9/14
Acknowledgments
C.C. acknowledges support from the Swiss National Science Foundation grant #200021 182659. N.V . acknowledges support
from the IOTA foundation.
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Supplementary Information: Robustness to address clustering
In this document we present additional results that complement the main text, changing the address clustering algorithm to an
alternative strategy that tends to aggregate less. These results provide a robustness check against the relatively arbitrary choice
of heuristic algorithms that are used to identify entities, and in particular to the potential critical point that our results in the
main text are tainted by excessive aggregation of addresses under the Combined Heuristics (CH) strategy.
For this reason we choose to present here the results adopting only the “Multiple Inputs” (MI) heuristic described in the
main text, which follows the logic that if multiple addresses appear as inputs to the same transaction, they are likely to belong
to the same entity that had to sign them all to create the transaction.
In summary, our results are mostly qualitatively unchanged when changing the address clustering strategy. Here we
highlight the key similarities and differences by referencing the tables and results from the main text in comparison to the ones
presented in this Supplementary Information.
•in Figure S1 we show the comparison between the size of the largest Strongly Connected Component of the Addresses
network and the Entities network under the MI heuristic. We see that it is qualitatively similar to Figure 2 of the main
text for all coins with the notable exceptions of Bitcoin and Bitcoin Cash. Here the SCC of the MI Entities network does
not span the entire set of nodes, whereas the CH Entities did;
•in Table S1 we report the equivalent of Table 1 in the main text for the MI Entities networks (except for ETH which
is the same). We find that the fitted Pareto exponents are typically smaller and the fitted Pareto distributions are closer
to the empirical ones according to the Kolmogorov-Smirnov distance, while the fitted binomials are typically worse
fits, suggesting that degree distributions are even more fat-tailed in the case of MI Entities. Figure S2 shows the degree
assortativity coefficient which we find to be slightly closer to 0 than what we see for the CH Entities, although it still is
always negative;
• The core size shown in Figure S3 is remarkably similar to the one shown in Figure 4 of the main text;
•The Nakamoto index, which in the main text is shown for Entities identified through the MI heuristic, is much smaller in
the case of CH Entities as shown in Figure S4. We chose to report the “safer” figure in the main text as the CH Entities
might be subject to over-aggregation, which in this particular case would depict a more troublesome situation than the
already worrying picture of Figure 5 in the main text.
12/14
Figure S1. Size of the largest Strongly Connected Component in UTXO-based cryptocurrencies.
BTC LTC DOGE BCH FTC MONA ETH
aMinimum 1.64 1.31 1.64 1.58 1.52 1.66 1.50
Maximum 4.93 3.66 3.36 4.93 5.67 3.97 3.42
1. Quartile 2.04 1.87 1.86 1.96 1.93 1.97 1.68
3. Quartile 2.46 2.52 2.33 2.43 2.76 2.21 1.81
Median 2.25 2.07 1.97 2.19 2.26 2.09 1.76
DPar Median 0.03 0.05 0.04 0.03 0.07 0.06 0.03
DBin Median 0.35 0.41 0.49 0.37 0.44 0.41 0.43
Table S1. Summary statistics on exponents of power-law fit of degree distribution on the weekly transaction networks.
Figure S2. Assortativity coefficient for weekly transaction networks.
13/14
Figure S3. Fraction of nodes in the network core as a function of time.
Figure S4. Nakamoto index estimated weekly for the analysed cryptocurrencies.
14/14
|
{
"id": "2206.05081"
}
|
2007.14423
|
JugglingSwap: Scriptless Atomic Cross-Chain Swaps
|
The blockchain space is changing constantly. New chains are being implemented
frequently with different use cases in mind. As more and more types of crypto
assets are getting real world value there is an increasing need for blockchain
interoperability. Exchange services today are still dominated by central
parties which require custody of funds. This trust imposes costs and security
risks as frequent breaches testify. Atomic cross-chain swaps (ACCS) allow
mutual distrusting parties to securely exchange crypto assets in a peer-to-peer
manner while preserving self-custody. Fundamental ACCS protocols leveraged the
scripting capabilities of blockchains to conditionalize the transfer of funds
between trading parties. Recent work showed that such protocols can be realized
in a scriptless setting. This has many benefits to blockchains throughput,
efficiency of swap protocols and also to fungibility and privacy. The proposed
protocols are limited to assets transferable by either Schnorr signatures or
ECDSA that are assuming the same elliptic curve parameters. In this work we
present JugglingSwap, a scriptless atomic cross-chain swap protocol with a
higher degree of interoperability. We weaken the assumptions about blockchains
that can be included in the ACCS protocol, and only require that (1) a
threshold variant exists to the underlying digital signature scheme and (2) it
is based on the elliptic curve discrete logarithm problem (ECDLP). The fair
exchange is achieved by a gradual release of secrets. To achieve this we use a
new building block we call Juggling: a public key verifiable encryption scheme
to transfer segments of secret shares between parties, which can also be of
separate interest. Juggling is then tailored to a specific private key
management system design with threshold signatures security.
|
http://arxiv.org/pdf/2007.14423v1
|
Omer Shlomovits, Oded Leiba
|
cs.CR
|
cs.CR
|
JugglingSwap: Scriptless Atomic Cross-Chain
Swaps
Omer Shlomovits, Oded Leiba
KZen Research
Abstract. The blockchain space is changing constantly. New chains are
being implemented frequently with di erent use cases in mind. As more
and more types of crypto assets are getting real world value there is an
increasing need for blockchain interoperability. Exchange services today
are still dominated by central parties which require custody of funds.
This trust imposes costs and security risks as frequent breaches testify.
Atomic cross-chain swaps (ACCS) allow mutual distrusting parties to se-
curely exchange crypto assets in a peer-to-peer manner while preserving
self-custody. Fundamental ACCS protocols [40,29] leveraged the script-
ing capabilities of blockchains to conditionalize the transfer of funds
between trading parties. Recent work [42,36] showed that such proto-
cols can be realized in a scriptless setting. This has many bene ts to
blockchains throughput, eciency of swap protocols and also to fungi-
bility and privacy. The proposed protocols are limited to blockchains
supporting either Schnorr or ECDSA signatures that are sharing the
same elliptic curve parameters.
In this work we present JugglingSwap , a scriptless atomic cross-chain
swap protocol with a higher degree of interoperability. We weaken the
assumptions about blockchains that can be included in the ACCS proto-
col, and only require that (1) a threshold variant exists to the underlying
digital signature scheme and (2) it is based on the elliptic curve discrete
logarithm problem (ECDLP). The fair exchange is achieved by a grad-
ual release of secrets. To achieve this we use a new building block we
callJuggling : a public key veri able encryption [15] scheme to transfer
segments of secret shares between parties, which can also be of separate
interest. Juggling is then tailored to a speci c private key management
system design with threshold signatures security.
1 Introduction
1.1 Background
The problem of fair exchange is how two mutually distrusting parties can jointly
exchange digital assets such that each party receives the other party's input or
neither does. There exist a variety of fair exchange protocols in the literature,
all with their own speci cations and system model. We divide fair exchange
protocols to three categories:arXiv:2007.14423v1 [cs.CR] 28 Jul 2020
2 Omer Shlomovits, Oded Leiba
{Trusted Third Party: fair exchange becomes trivial if both parties can agree
on a mutual trusted third party. The parties will send the trusted party
the digital assets. The trusted party will verify correctness and redistribute
to the counterparty. To make it a more scalable solution there is a line of
research to design optimistically fair protocols, such that the trusted third
party gets involved only in case of disputes which are assumed to be rare
[6,30,38].
{Gradual Release of Secrets: This method achieves partial fairness such that
one party can get advantage over the other party but this advantage is
polynomially bounded. The idea is to enable each party to release their
secret bit by bit in alternation [14].
{Monetary Penalties: parties are incentivized to complete the protocol fairly,
and if one party receives its output but aborts before the other party does,
the cheating party will have to pay a penalty [25,32,33].
Focusing on blockchains, fair exchange is a precursor for escrow protocols with
cryptocurrencies [25], markets, auctions, games [32] and atomic cross-chain swaps
(ACCS). An ACCS is a task in which multiple parties wish to exchange digital
assets from possibly distinct blockchains, in such a manner where all parties
receive their desired output from another party, or neither does. In this paper
we consider speci cally the two-party setting.
Blockchains are decentralized by nature, therefore a core challenge is to design
protocols without a single point of failure in the form of a trusted third party
which takes full custody of users' funds. Early ACCS protocols leveraged the
blockchain itself as a decentralized trusted party that can lock and release funds
according to pre-programmed rules. The rst successful atomic cross-chain swap
construction is credited to TierNolan [40]. It leverages Bitcoin-like script for cre-
ating conditional transactions by the two exchanging parties: each party locks
its funds such that the counterparty can spend them by using the preimage
of an agreed hash value. When one party spends a transaction, it reveals the
secret to the other party which can use it to spend the other conditional trans-
action on the other blockchain. There is also a timeout after which the funds
can be refunded if not redeemed by the counterparty. Such conditional spending
is termed Hashed Timelock Contract (HTLC) and has been speci ed and ex-
tended in [43] and [46]. Herlihy in [29] has formalized the theory of such atomic
cross-chain swaps. Poelstra [42] has introduced the notion of scriptless scripts
and described how to achieve atomic swaps without the need of explicit scripting
within transactions. Instead, Schnorr signatures [44] are used: the spending of a
transaction reveals a secret merely by the unlocking signature itself, and only to
the counterparty. This has many bene ts to blockchains throughput, eciency
of swap protocols and also to fungibility and privacy. Malavolta et al. [36] has
extended this technique to ECDSA. The latter two studies have opened the
door for scriptless atomic cross-chain swaps for a variety of existing blockchains.
However, they are still limited to blockchains which support either Schnorr or
ECDSA signatures that share the same elliptic curve parameters, namely - the
same group generated from the same xed generator.
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 3
1.2 Our Contribution
Our solution, the JugglingSwap ACCS protocol, is based on gradual release of
secrets. We observe that there are two ways to transfer value in a blockchain:
{A transaction backed by a digital signature.
{A transfer of a secret key.
To the best of our knowledge, all existing exchange methods use the rst
method. The rst method naturally has more constraints to it: either it depends
on the scripting capabilities of the underlying blockchain [29,40,46] or on speci c
mechanics of the underlying signature scheme [42,36]. The second method is
purely cryptography-dependent with small variance between blockchains and
thus can t and be reused for many chains architectures.
The vast majority of blockchains tie funds to key pairs of an elliptic curve group
where the elliptic curve discrete logarithm problem (ECDLP) is considered hard.
Exchanging between two chains in that case comes down to encrypting the secret
key with the other party's public key. The two problems that arise:
1. When one party transfers a secret key to a second party it does not lose
knowledge of this secret key. The transfer is like a "copy" whereas in a
transaction based swap the transfer is like a "cut": the sending party loses
access to the funds. The undesired result is that both sending and receiving
parties can use the secret key at the end of the transfer.
2. The exchange is not fair. The rst to send the encryption will risk the other
party to not complete the protocol.
We address these issues in the following way: rst, we add a layer of threshold
cryptography for secret key management. The cross-chain interoperability re-
mains and we get better security. We start our protocol assuming that each of
the two trading parties, the owners , has a joint two-out-of-two (f2;2g) address
with the same centralized provider .
Second, we introduce and use a new building block we call Juggling : a novel
veri able encryption [15] construction for transferring segments of a discrete
logarithm. It allows a prover to encrypt a secret in parts and prove that each
resulting ciphertext is indeed an encryption of a segment of a secret key of some
known public key, under some known encryption key.
Combining these two building blocks, the ACCS protocol goes as follows:
1. The two owners and the provider generate two f3;3gaddresses: one on each
of the two blockchains. Each party keeps a local secret key share.
2. Each owner deposits funds into the f3;3gaddress of its source blockchain.
3. The two owners start executing two interleaving Juggling protocols, in order
to achieve a partial fair exchange of their respective secret shares of the
twof3;3gaddresses. In other words - they gradually release their secret
shares, segment by segment. Eventually both owners hold 2 shares of their
counterparty'sf3;3gaddress and only 1 share of their own f3;3gaddress.
4. Each owner co-signs with the provider in order to withdraw the funds from
the counterparty's f3;3gaddress to its own wallet.
4 Omer Shlomovits, Oded Leiba
During the execution of the Juggling protocol (step 3), in case one party
decides to cheat, the maximal advantage is to be one segment ahead. For small
enough segment bit size this is acceptable. The provider's role can theoretically
be played by a trusted execution environment (TEE).
The resulting ACCS protocol is scriptless. The blockchain footprint of our method
is between one to two regular transactions on each blockchain. Therefore, it o ers
similar bene ts as the ACCS protocols in [42,36] in terms of improved blockchain
throughput and more ecient swaps (cheaper than their script-dependant alter-
natives), as well as in fungibility and privacy. On top of that, because Juggling
is applicable on arbitrary groups, JugglingSwap has weak assumptions on the
participating blockchains: it only requires that (1) a threshold variant exists for
the underlying signature algorithm and (2) it is based on ECDLP. Therefore,
the set of pairs of blockchains supported by our protocol strictly contains the
set of possible pairs by prior art, so it o ers a higher degree of interoperability.
Finally, We believe that the Juggling protocol can be of separate interest and
used as a building block for other constructions as well.
1.3 Related Work
Gradual Release of Secrets: The notion of partial fairness, originated in
[9,21], was proposed as a way to overcome the impossibility result of perfect
fairness in a setting without a trusted third party [17]. Several protocols, such
as [12,19], were built on top of this security notion. Previous works are mainly
focused on bit by bit release of either the secret itself or a digital signature from
the secret and are not generalizable to elliptic curves DLog. Our protocol is
aimed for gradually releasing a ciphertext encrypting an EC-DLog, where the
encryption correctness is publicly veri ed for every segment and segment size is
con gurable.
Crypto Asset Exchange: Several types of exchange methods exist, most of
them are used in practice today. In centralized-custodial exchanges (e.g. [2]), the
exchange is a trusted third party. Funds are deposited to the exchange platform,
trading is done within the platform and nally the funds can be withdrawn to
a private wallet. From the moment of the deposit and until the withdrawal the
funds are owned by the exchange which at any moment can be hacked, stop
working or become malicious and steal all the funds currently held by the plat-
form.
Centralized but non-custodial trading systems (e.g. [4]) are platforms that guar-
antee security before and after the trade but not in the middle: after funds have
been sent to the system by the user and before they are returned, the user must
trust the trading platform, which again can be hacked, su er denial of service
(DoS) or become malicious and steal the funds. The presence of such a single
point of trust clearly does not match well the decentralized nature of blockchain
technology.
Another form of exchange is decentralized exchanges (or "DEXes", e.g. [1,5]).
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 5
These are trustless platforms that settle trades on the blockchain. They can suf-
fer from security issues such as frontrunning and transaction reordering [7,18].
The on-chain trading makes scalability another issue. Lastly, as they typically
utilize smart contracts, they are limited to support only the assets of the speci c
blockchain the contracts are deployed on.
Tesseract [8] is using Intel SGX enclave as a trusted execution engine to provide
a better trusted centralized custodial exchange. The problem with this method
is that SGX can su er from security vulnerabilities as shown recently [47]. Ar-
wen's [28] protocol o ers centralized non-custodial approach based on building
blocks that are used in the Lightning network protocol [43], namely an escrow
service with o -chain trading and on-chain settlement. The problems with this
protocol are the same as the problems with the Lightning network, for exam-
ple: (1) limited capacity, (2) an exchange needs to have many coins tied up to
escrow channels, (3) ts only blockchains with speci c features such as expiry
time transactions and (4) requires a new implementation for every blockchain
added.
2 Preliminaries
Here we provide high level informal de nitions for Threshold Signatures, Veri-
able Encryption and Bulletproofs range proofs. For a more detailed treatment
we refer to the original papers.
2.1 Threshold Signatures
LetS=(Key-Gen, Sign, Ver) be a signature scheme. Following [24] A ft;ng-
threshold signature scheme TSforSis a pair of protocols (Thresh-Key-Gen,
Thresh-Sign) for the set of parties P1;:::;Pn. Thresh-Key-Gen is a distributed
key generation protocol used by the players to jointly generate a pair ( Q;x) of
public/private keys on input a security parameter 1. At the end of the protocol,
the private output of party Piis a valuexisuch that the values ( x1;:::;xn) form
aft;ng-threshold secret sharing of x. The public output of the protocol contains
the public key Q. Public/private key pairs ( Q;x) are produced by Thresh-Key-
Gen with the same probability distribution as if they were generated by the Key-
Gen protocol of the regular signature scheme S. Thresh-Sign is the distributed
signature protocol. The private input of Piis the value xi. The public inputs
consist of a message mand the public key Q. The output of the protocol is a value
sig2Sign(m;x). The veri cation algorithm for a threshold signature scheme is,
therefore, the same as in the regular centralized signature scheme S
De nition 1. We say that a (t;n)-threshold signature scheme TS=(Thresh-
Key-Gen,Thresh-Sign) is unforgeable, if no malicious adversary who corrupts
at mostt 1players can produce, with non-negligible (in ) probability, the
signature on any new (i.e., previously unsigned) message m, given the view of
6 Omer Shlomovits, Oded Leiba
the protocol Thresh-Key-Gen and of the protocol Thresh-Sign on input messages
m1;:::;mkwhich the adversary adaptively chose.
This is analogous to the notion of existential unforgeability under chosen message
attack as de ned by [26]. Notice that now the adversary does not just see the
signatures of kmessages adaptively chosen, but also the internal state of the
corrupted players and the public communication of the protocols.
We call the special case of t=n, namely a protocol which allows a group
of signers to produce a short, joint signature on a common message where all
participants are required to be honest, a multi-signature .
2.2 Veri able Encryption
Loosely speaking, veri able encryption [15] for a relation Ris a protocol that
allows a prover to convince a veri er that a given ciphertext is an encryption
under a given public key of a value !such that (;!)2R for a given .
Let (Gen, Enc, Dec) be a public key encryption scheme, and let (pk ;sk) be a key
pair. A veri able encryption scheme proves that a ciphertext encrypts a plaintext
satisfying a certain relation R. The relationRis de ned by a generator algorithm
G0which on input 1outputs a description = [R;W,] of a binary relation
RonW. We require that the sets R;Wandare easy to recognize (given
). For2, an element !2W such that ( ;!)2R is called a witness for
. The idea is that the encryptor will be given a value , a witness !for, and
then encrypts !yielding a ciphertext . After this, the encryptor may prove to
another party that decrypts to a witness for . In carrying out the proof, the
encryptor will of course need to make use of the random coins that were used by
the encryption algorithm: we denote by Enc'(pk, m) the pair ( ,coins ), where
is the output of Enc(pk ;m) andcoins are the random coins used by Enc to
compute . In such a proof system, the (honest) veri er will output 0 or 1, with 1
signifying accept. We require that the proof system is sound, in the sense that if a
veri er accepts a proof, then with overwhelming probability, indeed decrypts to
a witness for . However, it is convenient, and adequate for many applications, to
take a more relaxed approach: instead of requiring that decrypts to a witness,
we only require that a witness can be easily reconstructed from the plaintext
using some ecient reconstruction algorithm. Such an algorithm recon takes as
input a public key pk, a relation description = [R;W,], an element 2,
and a message m 2Mpk[frejectg, and outputs !2W[frejectg.
De nition 2. A proof system (P;V), together with mutually compatible encryp-
tion scheme (Gen;Enc;Dec ), relation generator G0, and reconstruction algo-
rithmrecon , form a veri able encryption scheme, if Correctness, Soundness
and Special honest-veri er zero knowledge properties hold as de ned in [15].
2.3 Bulletproofs Range Proofs
Range proofs are proofs that a secret value, which has been encrypted or commit-
ted to, lies in a certain interval. Range proofs do not leak any information about
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 7
the secret value, other than the fact that they lie in the interval. Bulletproofs [13]
can be used as an ecient instantiation of range proofs and work on Pedersen
commitments [41]. Formally, let group element Vbe a Pedersen commitment
to valuevusing randomness r. The proof system will convince the veri er that
v2[0;::;2n 1]. Bulletproofs are based on Inner Product Argument and main-
tain perfect completeness, perfect special honest veri er zero-knowledge, and
computational witness extended emulation, de ned in [13].
3 The Juggling Protocol
3.1 Speci cation and De nitions
LetGbe an elliptic curve group of prime order qwith base point (generator) G.
PartyPiknows discrete log (DLog) xisuch thatQi=xiG.
De nition 3. m-Segmentation of xis the division of xtomsegments [x]kjm
k=1
such thatx=P
kfk[x]kwherefk= 2(k 1)lfor segment length in bits l.
De nition 4. We say that a proof is a proof of correct encrypted k-segment
if it is a publicly veri able zero knowledge proof for encryption scheme fGen;Enc;
Decg, public key Y, value, relationR, ciphertext ckand parameters k;m such
thatck=Enc(Y;[x]k)is the encryption of [x]kusing public key Y, where [x]kis
thek'th segment of m-Segmentation of a witness !=xsuch that (;!)2R.
De nition 5. A proof system (P;V), together with mutually compatible encryp-
tion scheme (Gen;Enc;Dec )and parameter mis aSegmented-Veri able En-
cryption (S-VE) if (a) it is a VE for witness !and (b) the witness can be
m-Segmented such that for each segment kthe prover can prove correct encrypted
k-segment for the same witness !.
De nition 6. Juggling protocol is S-VE implementation where the witness is
an elliptic curve DLog and segment encryptions and proofs are being released
serially.
It is assumed that there exists a PKI such that all public keys are registered. If
it is not the case, all participants will add a setup phase for key pairs generation
and registration.
3.2 Auxiliary Proofs
All proofs are for public EC parameters G;q;G .
Proof of EC-DDH membership: This is a proof of membership that a tuple
is an Elliptic Curve Decisional Die Hellman (DDH) tuple: ( G;xG;yG;xyG ).
This proof is an adaptation of the proof in [16] for elliptic curves. Proof for the
following relation: the witness is !=x, the statement is = (G1;H1;G2;H2).
The relationRoutputs 1 if H1=xG1andH2=xG2.
The protocol works as follows:
8 Omer Shlomovits, Oded Leiba
1. The prover sends a1= G1anda2= G2to the veri er with 2RZq
2. The veri er sends a random challenge e2RZq
3. The prover responds with r= xe
4. The veri er checks that a1=rG1+eH1anda2=rG2+eH2
See [16] for the formal proof.
Proof of correct encryption: This is a proof of knowledge that a pair of
group elementsfD;Egform a valid homomorphic ElGamal encryption ("in the
exponent") [45] using public key Y. Speci cally, the witness is != (x;r), the
statement is = (G;Y;D;E ). The relationRoutputs 1 if D=xG+rY,E=rG.
1. The prover chooses s1;s2, computes A1=s1G;A 2=s2Y;A 3=s2Gand
sends to the veri er T=A1+A2;A3.
2. The veri er picks a challenge e.
3. The prover computes z1=s1+exandz2=s2+er.
4. The veri er accepts if z1G+z2Y=T+eDandz2G=A3+eE.
Proof of correct encryption of DLog: This is a version of the previous
proof where we also know public key Qand want to prove that fD;Egis ho-
momorphic ElGamal encryption that encrypts the DLog of Q, speci cally. This
is a proof for the following relation: the witness is != (x;r), the statement is
= (G;Y;Q;D;E ). The relationRoutputs 1 if Q=xG,D=xG+rY,E=rG.
To prove this the prover needs (a) to prove knowledge of the DLog of Qin base
Gand (b) to prove that ( G;E;Y;D Q) is a DDH tuple. The proof can be
optimized by using the same challenge as follows:
1. The prover chooses s1;s2, computes A1=s1G;A 2=s2Y;A 3=s2Gand
sendsA1;A2;A3to the veri er.
2. The veri er picks a challenge e.
3. The prover computes z1=s1+exandz2=s2+er.
4. The veri er accepts if z1G=A1+eQandz2G=A3+eEandz2Y=
A2+e(D Q).
The protocols can become non-interactive using Fiat-Shamir transform. Both
Constructions: proof of correct encryption and proof of correct Encryption of
DLog are Sigma protocols [27] with straight forward security proofs. We provide
the proofs in Appendix A.
3.3 Construction
We observe that S-VE can be implemented using an encryption scheme with
homomorphic properties. We choose homomorphic ElGamal encryption as the
base encryption scheme [45]. Figure 1 shows a sequence diagram of the protocol.
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 9
1.Key Generation:
For EC parameters G;q;G and security parameter every party chooses
randomy2Zqfor secret key and computes Y=yGfor public key. All
public keys are registered.
2.Encryption:
Upon input ( x;Q;Y ) whereQ;Y are public keys and xis a secret key
such thatQ=xGthe encryptor (interchangeably will be called the
prover, depends on operation) divides xtomequal segments (last seg-
ment can be padded with zeros). The segments [ x]kjm
k=1should be small
enough to allow extraction of [ x]kfrom [x]kGin polynomial time in the
security parameter.
(a) For every segment k: the encryptor computes the homomorphic El-
Gamal encryption : fDk;Ekg=f[x]kG+rkY; rkGgfor random
rk.
(b) The encryptor publishes Dkjm
k=1together with mBulletproofs ([13],
non-interactive zero knowledge range proofs) proving that every Dk
is a Pedersen commitment [41] with value smaller than 2l(*).
(c) The encryptor publishes E=P
kfkEkand plays the prover in proof
of correct encryption of DLog (3.2) with witness ( x;P
kfkrk) and
statement ( G;Y;Q;D;E ), whereD=P
kfkDk.
(d) An encryption for a segment [ x]kwill be full once the encryptor pub-
lishesEkof this segment together with a proof of correct encryption
(3.2).
3.Decryption:
Given a secret key y, for every pairfDk;Ekg, [x]kcan be decrypted
by extracting [ x]kfromDk yEk= [x]kGusing algorithm for break-
ing DLog. After all msegments are decrypted x=P
kfk[x]kcan be
reconstructed.
(*) Except to the MSB segment which is tightly bounded to the lmost
signi cant bits of q.
Juggling Scheme
3.4 Security Analysis
In this section we show that the construction is following the speci cations.
Lemma 1. If DDH is hard relative to G, then the scheme fKey Generation,
Encryption, Decryption gis CPA-secure encryption scheme.
Proof sketch: ElGamal encryption in the exponent, like standard ElGamal en-
cryption is CPA-secure under DDH [31,45]. fDk;Ekgis ElGamal encryption in
10 Omer Shlomovits, Oded Leiba
for in rp RangeProof.Prove( )
ProofOfCorrectEncryptionOfDLog.Prove(( ), ( ))
, , , rp
assert RangeProof.V erify(rp ,, )
assert ProofOfCorrectEncryptionOfDLog.V erify( , ( ))
for in
assert ProofOfCorrectEncryption.V erify( , ( ))
DLog( )for in
ProofOfCorrectEncryption.Prove(( ), ( ))
,
Fig. 1. The Juggling protocol sketch . Here Pis the encryptor/prover, and Vis
the decryptor/veri er. Proofs and encryptions are being released serially by P, where
after each iteration they are being veri ed and decrypted by V.
Note: when both parties ful ll both roles (each encrypting its own secret), these itera-
tions can interleave: the rst party receiving the k'th segment will then send back its
own k'th segment.
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 11
the exponent with randomness rk. Because the randomness rkis changed for
every encryption k2f1;::;mgthe di erent encryptions are unrelated.
Lemma 2. The proof system fP;VginEncryption protocol together with ho-
momorphic ElGamal encryption scheme is a proof of correct encrypted k-segment
of DLog of Q, with parameters k, m, Y and security parameter .
Proof sketch: The proof system consists of 2 m+ 1 proofs given in a speci c
order and correspond to gradual release of the ciphertext:
1.mBulletproofs.
2. Proof of correct encryption of DLog.
3. Proof of correct encryption for rst segment
4. ...
5. Proof of correct encryption for the m'th segment
the order of the last mproofs can be di erent and it is easy to show that it
does not e ect security.
The security proof shows that only m+ 2 proofs are needed to proof correct
encrypted k-segment. Speci cally, for segment kthere is no use for the m 1
proofs of correct encryption for the other segments.
Correctness: This part of the proof is where we need range proofs, which are ef-
ciently instantiated by Bulletproofs. To intuitively understand why, let us rst
describe a possible attack and why bulletproofs can mitigate it. Proof of correct
encryption for the k'th segment proves to the veri er that fDk;Ekgis homo-
morphic ElGamal encryption using public key Y. The general proof of correct
encryption proves to the veri er a statement about the shifted sum (P
kfk) of
theDk's andEk's. Namely, thatfD;Egis Homomorphic ElGamal encryption
ofxunder public key Y. A simple attack would be to encrypt biased segments
such that the shifted sum will remain the same but each segment encryption will
be decrypted wrong. W.l.o.g let us change only two segments: for segment kto
encrypt the point vk=ukG= [x]kG+bkG= [x]kG+f 1
kGand for segment k+1
to encrypt the point vk+1=uk+1G= [x]k+1G+bk+1G= [x]k+1G f 1
k+1G. After
decryption, value extraction and segment summation the result would remain
the same and equal to xbut both segment encryptions fDk;Ekg;fDk+1;Ek+1g
would have extracted to wrong values. In particular the k'th segment will be
extracted to [ x]k+f 1
k6= [x]k. The implication of such attack is that if a prover
aborts in the middle of the gradual release, then the decryptor will remain with
nothing while it was expected that it will have all the already published segments
of the secret. We now show how assuming Bulletproof security prevents this spe-
ci c attack and then generalize it to all possible changes to encrypted segments
[x]kjm
k=1. In our attack there are four cases: one case where both uk;uk+1are in
range [0;:::;2l 1] and three cases where at least one of uk;uk+1are not in the
range [0;:::;2l 1]. For the last three cases it is easy to see that at least one
Bulletproof range proof will not verify with high probability. For the rst case
in order for the proof of correct Encryption of DLog to pass we require the sum
bkfk+bk+1fk+1=nq, to equal a multiple of q, the order of the curve. Every
12 Omer Shlomovits, Oded Leiba
option other than n= 0 andbk=bk+1= 0 will result in at least one of bk;bk+1
out of range [0 ;:::;2l 1] with high probability.
We are now ready to generalize this argument:
Claim 1. For anyksegment ciphertext not in the form [ x]k+bkwithbkinde-
pendent of [ x]kveri cation will pass with negl() probability.
Proof: Assuming correctness of mbulletproofs, proof of correct encryption of
DLog and proof of correct encryption of ksegment, if the k'th ciphertext is of
di erent form it means that at least one other segment must encrypt an ad-
ditional compensating element in the k'th segment to cancel the change. This
additional element would have cause the range proof for this segment to fail with
probability 1 negl().
Claim 2. For anyksegment ciphertext in the form [ x]k+bk, ifbk6= 0 veri -
cation will pass with negl() probability.
Proof: The following must hold:P
kbkfk=nqIfbk6= 0 for some kat least
one of the bk's will lead to uk= [x]k+bk>2l. Assume to the contrary that
for allk:bk<2l [x]kandq=P
kbkfk. Combining this equations we get
x<P
k2lfk q. We assume for simplicity that the most signi cant segment of
qis all-ones. Usually this will restrict the secret key to a very small number such
that for random secret key there is negligible probability that the equation will
hold. If the secret key is not chosen randomly or if we want to make the prob-
ability lower we can simply use secret keys with most signi cant bit xed and
equal to 0 (This will not e ect security, see discussion in [34] on using secret key
x<q= 3). This can be also proven in Encryption time by providing a range proof
onfD;Eg. Now the most signi cant segment can be range proofed to be in the
range [0;:::;2l 1 1] such that 2ml 1+Pm 1
k=12lfk qis negative and therefore
assuming bulletproof security, this is possible with only negl() probability.
Soundness: Soundness comes from the soundness of proof of correct encryp-
tion of DLog. There exist algorithm Athat givenQand two transcripts of this
proof with two di erent challenges e6=e0outputs!=xsuch thatQ=xG. This
is enough because algorithm A0for soundness of the proof of correct encrypted
k-segment of DLog Qcan useAand output the k'th segment of the result.
Special honest veri er ZK: All three types of proofs: bulletproof, proof of cor-
rect encryption of DLog and proof of correct encryption have the special HVZK
security property, meaning there exist a simulator Sthat can output computa-
tional indistinguishable distribution by calling the special HVZK simulators of
the underlying relevant proofs.
Theorem 1. Assuming DDH is hard in Gconstruction 3.3 is a Juggling protocol
according to de nition 6 in the random oracle model.
Proof sketch: The proofs of correct encryption combined form a VE of witness x
and DLog relation ( Q;x)2RifQ=xG. The prover cannot change witness from
the range proof to the proof of correct encryption of DLog since otherwise the
prover would have broken the binding property of Dkas a commitment, which
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 13
is equivalent to nding the DLog of Ywhich assume to be hard. Same is true
for changing witness from proof of correct encryption of DLog to the proofs of
correct encryption . Property 2 of S-VE de nition holds because Lemma 2 is true
for allkin the m-Segmentation of x. Assuming DDH is hard: The encryption
scheme is CPA secure (lemma 1) and bulletproof is zero knowledge range proof
([13] De nition 13).
4 Atomic Cross-Chain Swap Protocol
4.1 Background
Without many exceptions, all blockchains today are using a digital signature
algorithm that has an existing variant for threshold signing. Most dominant are
ECDSA and Schnorr based signatures (including EdDSA) [11,34,20,23,35,37,22].
In addition BLS signatures show great promise for the blockchain use case [10].
A wallet is a user interfacing software that generates and manages private keys,
constructs and signs messages (transactions) for the relevant cryptocurrencies.
Our wallet design is based on ft;ng-Thresh-Key-Gen and ft;ng-Thresh-Sign
with minimal amount of participants. We aim to t it to a simple client-server
communication model and therefore take t=n= 2. For the fair exchange pro-
tocol we will need a f3;3gthreshold signature scheme. We assume that multiple
cryptocurrencies are supported by this wallet.
4.2 Roles and Network
We de ne two roles: owner andprovider . The owner is the end user who owns the
funds in the account and holds one secret share of the secret key. The provider
is another share holder of the secret key but does not provide funds to the
shared account. Its role is to provide the additional security in the system aiding
and enabling the owner to generate keys and transact in distributed fashion.
From network perspective, one provider is connected to many owners which to-
gether maintain the provider, for example paying its cost in transaction fees. The
provider can run on any machine: from a Trusted Execution Environment (TEE)
to a machine operated by an incentivized human operator. Multiple providers
can compete for owners. Access to a full node can be done either by both parties
locally or outsourced to a number of other parties.
4.3 Setup
The provider and owner will run f2;2g-Thresh-Key-Gen. Public keys are used to
generate blockchain addresses. It is assumed that two parties, P1andP2, that are
sharing the same provider (both run f2;2g-Thresh-Key-Gen with the provider)
wish to conduct an exchange between two assets on two di erent chains. In de-
tails, each party Piholds funds (i.e. tokens) on the blockchain they wish to swap
14 Omer Shlomovits, Oded Leiba
from,bi, in an inputf2;2g-address we denote Api
in. The amount of funds held by
each party should be at least the amount they wish to swap, denoted as ci.
Each party Piwould also have a withdraw address we denote Api
out, on the
blockchain they wish to swap to, b3 i. This address is not necessarily generated
in advance and can be generated later as the protocol progresses, but for sake of
simplicity we de ne it in the setup phase. Note: we provide a description in terms
of addresses but an exact description holds with respect to unspent transaction
outputs (UTXOs).
The amounts and maker/taker roles are matched by a public matching engine.
4.4 The Protocol
1. PartyP1(P2) runs Juggling.KeyGen to create a decryption/encryption
key pairy1andY1(y2andY2). The parties then exchange each other's
public (encryption) keys Y1andY2.
2.P1;P2;Srunf3;3g-Thresh-Key-Gen: The output is public key pk1and
secret shares xp1
1;xp2
1;xs
1to each party. We call Qi
1thelocal public key
of partyiifxi
1is the elliptic curve DLog of Qi
1.pk1is used to derive
addressa1in blockchain b1.
3.P1;P2;Srunf3;3g-Thresh-Key-Gen again with outputs pk2;xp1
2;xp2
2;xs
2
andQi
2for the local public keys. pk2is used to derive address a2in
blockchain b2.
4.P1(P2) runsf2;2g-Thresh-Sign with the provider to transfer from Ap1
in(Ap2
in)
an amount of c1(c2) tokens to a1(a2). The provider Swill broadcast the
two signed transactions to the two blockchains simultaneously.
5.P1andP2runJuggling.Encryption with inputsfxp1
1;Qp1
1;Y2gandfxp2
2;
Qp2
2;Y1grespectively. Encryption is done with segmentation enabled: P1
andP2runJuggling.Encryption up to step (d). If one of the previous
steps failed - abort.
Repeat for k2f1;:::;mg:
(a)P1sendsEk(encryption of [ xp1
1]k) and corresponding proof of correct
encryption.
(b)P2andSverify proof of correct encryption (aborts if false) and
decrypt the segment encryption. P2andSsend backE0
k(encryption
of [xp2
2]k) and corresponding proof of correct encryption.
(c)P1andSverify proof of correct encryption (abort if false) and de-
crypt the segment
6. After decryption is done P1(P2) andSrunf3,3g-Thresh-Sign to sign
the withdraw transaction which empties a2(a1) intoAp1
out(Ap2
out).
The transaction can now be broadcast by either SorP1(P2).
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 15
{3,3}-Thresh-Key-Gen
{3,3}-Thresh-Key-Genpublic key to derive
address in
blockchain
public key to derive
address in
blockchain
depositTx buildT ransaction(blockchain = , from = , to = )Each :
broadcast(signedDepositTx1 , blockchain = )
broadcast(signedDepositTx2 , blockchain = )Each :
Juggling.KeyGen()
{2,2}-Thresh-Sign(depositTx ) {2,2}-Thresh-Sign(depositTx2)
signedDepositTx1 signedDepositTx 2
Fig. 2. The Atomic Swaps protocol sketch - part 1
16 Omer Shlomovits, Oded Leiba
Juggling.Enc( )Each :
assert Juggling.V erify( )
Juggling.Dec( )
assert Juggling.V erify( )
Juggling.Dec( )
Each :
{3,3}-Thresh-Sign(withdrawTx1)withdrawTx buildT ransaction(blockchain = , from = , to = )
{3,3}-Thresh-Sign(withdrawTx2)for
Each :
injectSignatureAndBroadcast(withdrawTx , signature = , blockchain = )
Fig. 3. The Atomic Swaps protocol sketch - part 2
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 17
5 Analysis
In this section we give informal discussion on security aspects of the protocol.
First, we show correctness of the scheme. Second, we show that the protocol
guarantees robustness against malicious adversaries and in particular we show
that users of the underlying f2;2g-key management infrastructure do not need
to make any additional security assumptions when conducting a swap. Finally,
we discuss practical aspects such as cost and privacy.
5.1 Use of Juggling
The security of the scheme relies on the security of Juggling scheme: The protocol
consists basically of two interleaving Juggling protocols, where the two trading
parties are switching the roles of the encryptor and the decryptor. Key generation
is done as part of the creation of f3;3g-addresses. Encryptor Piencrypts its
secret share of the f3;3g-public key with Y3 i. The segmentation property of
the encryption together with the publicly veri able proofs are taking care of
the partial fairness of the exchange: if segment kis false both parties P1;P2
will either have the same amount of already decrypted segments or one of the
parties will have one less decrypted segment. Assuming small segments and that
the protocol stopped at a stage where extraction of the rest of the secret share
is possible, both parties will need the same resources to get the full secret share.
We discuss later the case where one party aborts very early in the protocol such
that full extraction is not possible.
After all encrypted segments were transferred to the other party and decrypted,
thef3;3g-addresses are e ectively degenerated into f2;2g-addresses between
the provider Sand the counterparty which is the new owner. The owner can
eventually spend from these addresses using the f3;3g-Thresh-Sign algorithm
by contributing its own secret share created at the key generation phase and
the counterparty's secret share it has successfully decrypted, together with the
provider's secret share.
5.2 The Role of the Provider
The provider Scan serve as a PKI because it is a centralized authority used
by all owners to derive public keys. This can be strengthened in several ways.
For example: assuming Spublic key is known to all, Scan generate a certi cate
for everyf2;2g-address that it takes part in generating. This certi cate can be
checked by the participating parties in a swap as well as by an external matching
engine.
Furthermore, The provider can be decentralized (i.e. broken into multiple signing
parties) or run on a TEE. Upon detection of a
awed segment encryption by one
of the parties, Scan revoke the party from using the swap service or even from
the wallet service.
A provider may be nancially incentivized and charge fees for providing the
18 Omer Shlomovits, Oded Leiba
additional security and enabling the fair exchange protocol.
The provider is trusted for:
1. Availability.
2. Fair submission of deposit transactions - in step 4 in the protocol the provider
is required to submit both deposit transactions which fund a1anda2, or none
in case one of the owners did not send its own transaction.
If assumption (1) is not satis ed, the protocol would not be able to progress
and in most scenarios both parties' funds would be kept locked. In particular, if
the provider would interact in a f3;3g-Thresh-Sign with one party but not with
the other, then the latter would have its funds locked but the one which was
able to get the signature has actually succeeded to complete the trade and got
the counterparty's deposit. If assumption (2) is not satis ed, one party might
lock funds by having its deposit transaction submitted by the provider and con-
rmed, while the other party has aborted and did not stake anything. We should
also wrap within this same assumption the requirement that the provider will
not collude with a party to revert an uncon rmed deposit transaction, which
would lead to the same described situation. Since the input addresses created by
f2;2g-Thresh-Key-Gen with the provider, it is able to prevent speci cally such
attempt.
However, it is important to highlight that in terms of potential outcomes for the
dishonest provider: the provider can freeze funds but it cannot steal funds. The
latter means that it cannot extract secret keys nor trick participants to move
funds to an arbitrary address of its choice. Also note that violations of the afore-
mentioned trust assumptions can be publicly veri able - an owner may accuse
a provider by presenting a transcript of the threshold key generation protocols
and optionally the Juggling gradual release protocol, so that an external ob-
server of the relevant blockchains can witness that only one deposit or withdraw
transaction has been submitted. That way the provider can be held accountable
for denying service from the owner. Combined with the economical incentive
potential for an honest behavior, these points may give further justi cation for
the trust assumptions.
5.3 Adversary Model
Our goal is to not add additional assumptions on adversarial behavior on top of
what we already assume for a f2;2g-wallet design with an owner and a provider,
namely: (1) the provider can stop the service, (2) the provider cannot steal secret
keys. The rst assumption is dealt outside the scope of this paper: we assume
that each party has a recovery method for f2;2g-addresses, independent of the
provider, to reclaim the full secret key. In our protocol, the provider is assumed
to be trusted for the speci c points detailed in Section 5.2, namely, availability
and fair submission of deposit transactions which preserve the aforementioned
adversarial behavior limitations (and is not trusted for anything else e.g., for
integrity). The owners P1;P2can possibly turn malicious at any point. In case
JugglingSwap: Scriptless Atomic Cross-Chain Swaps 19
one of the owners P1;P2become malicious, the publicly veri able nature of the
encryption is used to catch the bad actor. The provider Scan play the role
of one of the parties. Without loss of generality, say that Splays the role of
(or colludes with) P2. In such a case, note that P1will not start the Juggling
protocol (i.e. the gradual release secret exchange protocol) until it veri es a
con rmed transaction transferring the expected amount ( c2) into the expected
addressa2on the destination blockchain b2.P1has participated in the creation
of this speci c address through the f3;3g-Thresh-Key-Gen algorithm. Therefore,
the provider Swill be able to get P1's secret share only if it has funded a2with
sucient funds which are redeemable by P1. This is equivalent to the honest
case where a counterparty P2doesn't collude with the server and deposits the
needed funds.
Lastly, a malicious party may try to withdraw both of the deposits from a1and
a2. W.l.o.g. say it is P2. Here,P1has a secret share xp1
2it has contributed to the
secondf3;3g-Thresh-Key-Gen, which is required for a successful f3;3g-Thresh-
Sign and is not revealed to any other party (even not through the Juggling
protocol's gradual release). So even when the provider colludes with P2, they
will not be able to withdraw funds from a2withoutP1's cooperation.
5.4 Practical Aspects
It is important to note that unlike other ACCS protocols, in this protocol we do
not make any assumptions on the blockchains in the trade. This means that in
the general case either both parties completed the trade or that both parties did
not complete the trade: there is no going back and the funds will stay locked.
If the blockchains used have a time lock mechanism it can smoothly be plugged
in the protocol: Sand the parties will sign funding transactions with time locks
such that once they are published if one party decides to go back both parties
will get the refund after the time locks are over.
In terms of interoperability, the protocol enables swaps between popular ECDSA
blockchains, e.g. Bitcoin and Ethereum, and emerging EdDSA blockchains such
as Facebook's Libra, Algorand and Tezos1. This is not possible with existing
scriptless ACCS protocols [42,36] that require both blockchains to use one of
Schnorr or ECDSA signature schemes that share the same elliptic curve group
generated by the same xed generator. The footprint of the protocol on the
blockchain is two standard transactions on b1and two standard transactions on
b2. The cost of the protocol is two standard transactions for every party. Using
the scheme to swap assets on the same blockchain or using it twice to complete
the pathb1!b2!b1will result in a mixing solution.
1Tezos does have support for ECDSA under the curve Secp256k1 as in Bitcoin and
Ethereum, but exchanging into an address of a di erent signing algorithm may not
be ideal in terms of wallets and tools support.
20 Omer Shlomovits, Oded Leiba
6 Implementation
As a proof of concept we show an atomic swap between Bitcoin and Ethereum.
For key management system we implemented Lindell's [34] f2;2gECDSA key
generation and signing protocols (protocols 3 :1 and 3:2 in [34]) for Elliptic curve
Secp256k1. This is the digital signature scheme and elliptic curve used by both
Bitcoin and Ethereum. Our code abstracts both the elliptic curve layer and the
digital signature layer such that other types of elliptic curves and digital sig-
natures can be plugged in easily. We tested it with curve25519 and library we
wrote for multi party Schnorr signatures based on [39].
We usedm= 32 segments of l= 8 bits for the 256 bit keys. We used brute-force
to nd the DLogs which is not optimal and in practice one of known protocols
that can achieve 2l=2time should be used.
We implemented the fair exchange protocol for the case where the provider is not
allowed to play P1orP2. This allows to relax the requirement on the of f3;3g
addresses and use only f2;2g-Thresh-key-generation and Signing. The cryptog-
raphy layer is written in Rust. The wrapping communication layer between the
parties and their blockchain nodes are written in JavaScript (Node.js). It is open
source and can be found in [3].
6.1 Future Work
We plan to add support for more blockchains: connecting other types of elliptic
curves and threshold signature algorithms to the system. For threshold-ECDSA
we will switch to a newer protocol [23] with support for f3;3gaddresses. We
wish to run experiments to set up optimal system parameters (i.e. optimal bit
lengthl) and to measure performance.
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A Security for Auxiliary Proofs
Here we provide security proofs that the auxiliary proofs from section 3.2 are
secure.
A.1 Proof of Correct Encryption:
{ Completeness: z1G+z2Y=s1G+exG+s2Y+erY=A1+A2+e(xG+
rY) =T+eD.z2G=s2G+erG=A3+eE
{ Special Soundness: Given two transcripts fT;A 3;e;z 1;z2gand
fT;A 3;e0;z0
1;z0
2git is easy to extract rfrome;e0;z2;z0
2and thexfrom
e;e0;z1;z0
1.
{ Special HVZK : Given the statement G;Y;D;E ande, choose random z22
Zqand compute A3=z2G eE. Choose random z12Zq, and compute
T=z1G+z2Y eD.
A.2 Proof of Correct Encryption of DLog:
{ Completeness: z1G=s1G+exG =A1+eQ,z2G=s2G+erG=A3+eE,
z2Y=s2Y+erY=A2+eQ+erY eQ=A2+e(D Q)
{ Special Soundness: Given two transcripts fA1;A2;A3;e;z 1;z2gand
fA1;A2;A3;e0;z0
1;z0
2git is easy to extract rfrome;e0;z2;z0
2and thexfrom
e;e0;z1;z0
1.
{ Special HVZK : Given the statement G;Y;Q;D;E ande, choose random
z22Zqand compute A3=z2G eE,A2=z2Y e(D Q). Choose random
z12Zq, and compute A1=z1G eQ.
|
{
"id": "2007.14423"
}
|
2007.06201
|
The Blockchain Based Auditor on Secret key Life Cycle in Reconfigurable
Platform
|
The growing sophistication of cyber attacks, vulnerabilities in high
computing systems and increasing dependency on cryptography to protect our
digital data make it more important to keep secret keys safe and secure. Few
major issues on secret keys like incorrect use of keys, inappropriate storage
of keys, inadequate protection of keys, insecure movement of keys, lack of
audit logging, insider threats and non-destruction of keys can compromise the
whole security system dangerously. In this article, we have proposed and
implemented an isolated secret key memory which can log life cycle of secret
keys cryptographically using blockchain (BC) technology. We have also
implemented a special custom bus interconnect which receives custom crypto
instruction from Processing Element (PE). During the execution of crypto
instructions, the architecture assures that secret key will never come in the
processor area and the movement of secret keys to various crypto core is
recorded cryptographically after the proper authentication process controlled
by proposed hardware based BC. To the best of our knowledge, this is the first
work which uses blockchain based solution to address the issues of the life
cycle of the secret keys in hardware platform. The additional cost of resource
usage and timing complexity we spent to implement the proposed idea is very
nominal. We have used Xilinx Vivado EDA tool and Artix 7 FPGA board.
|
http://arxiv.org/pdf/2007.06201v1
|
Rourab Paul, Nimisha Ghosh, Amlan Chakrabarti, Prasant Mahapatra
|
cs.CR, cs.AR
|
cs.CR
|
The Blockchain Based Auditor on Secret key Life
Cycle in Reconfigurable Platform
Rourab Paul
Computer Science & Engineering,
Siksha ’O’ Anusandhan (Deemed
to be University), Odisha, India
rourabpaul@soa.ac.inNimisha Ghosh
Computer Science & IT,
Siksha ’O’ Anusandhan (Deemed
to be University), Odisha, India
nimishaghosh@soa.ac.inAmlan Chakrabarti
School of IT,
University of Calcutta
Kolkata, India
achakra12@yahoo.comPrasant Mahapatra
Dept. of Computer Science
University of California, USA
pmohapatra@ucdavis.edu
Abstract —The growing sophistication of cyber attacks, vulner-
abilities in high computing systems and increasing dependency on
cryptography to protect our digital data make it more important
to keep secret keys safe and secure. Few major issues on secret
keys like incorrect use of keys, inappropriate storage of keys,
inadequate protection of keys, insecure movement of keys, lack
of audit logging, insider threats and non-destruction of keys
can compromise the whole security system dangerously. In this
article, we have proposed and implemented an isolated secret key
memory which can log life cycle of secret keys cryptographically
using blockchain (BC) technology. We have also implemented a
special custom bus interconnect which receives custom crypto
instruction from Processing Element (PE). During the execution
of crypto instructions, the architecture assures that secret key
will never come in the processor area and the movement of
secret keys to various crypto core is recored cryptographically
after the proper authentication process controlled by proposed
hardware based BC. To the best of our knowledge, this is the
first work which uses blockchain based solution to address the
issues of the life cycle of the secret keys in hardware platform.
The additional cost of resource usage and timing complexity we
spent to implement the proposed idea is very nominal. We have
used Xilinx Vivado EDA tool and Artix 7 FPGA board.
Index Terms —Blockchain, FPGA, Key Memory, Secret Key
Life Cycle
I. I NTRODUCTION
The security of a crypto system depends on three primary
keys. Symmetric Key : is used to encrypt bulk data in symmet-
ric key algorithms like AES, TDES, DES etc. Private Keys : of
public-private key pair used in Asymmetric Key Cryptography
such as RSA, Diffie Helmen etc. Private key is used for
signature generation and key exchange process. Hash Key :
is used to check integrity and authenticity of transactions and
data with algorithms like SHA-3. Increasing volume of secret
key and data protected by those keys makes the cryptographic
key management relevant in current research trend [1]. There
are several threats causes compromised key. In this article we
have discussed about few major threats.
A. Storage
In many sense, processor based architectures are flexible
but it can be exposed to software threats like cache attack[2],
bus snooping [3], memory disclosure attack [4] etc. hence the
storage of secret keys should be separated form software area
and it should be isolated from any physical connection of
processor.B. Insecure Movement
A security processor allows key movements between several
cores like Key Memory, RNG, Hash, Symmetric Key and
Asymmetric Key core. All these key movement to the key
memory and from the key memory should be authenticated
and secured. These transactions of key mainly suffers two
issues. AThe buses used for these movements can be snooped
by software attacks. BThe request of key can be issued
from some compromised IP placed inside the architecture.
To overcome attack-A the most effective solutions are bus
encryption [3], or partition of software area from these buses
[5]. Attack-B can be prevented by verifying the signature of
the requestee IP. To the best of our knowledge we did not find
any similar solution in hardware platform.
C. Non-destruction
Once the signature of requestee IP is verified, the key should
be released to the proper destination. Except the pre-master
key, other keys should be destroyed securely. This removal
of key should be logged cryptographically and should non-
traceable.
D. Audit Logging
The secret keys of a security processor is the most sensitive
data. The creation, deletion and the movement of secret key
should be logged and audited cryptographically otherwise
it will be difficult to identify a compromise for forensic
investigation.
E. Incorrect use of keys
The generated master keys are for specific purposes. Hash
core can not use encryption key and vice versa. If the keys are
used for something else, the proper protection action should be
taken. The main contribution of this article are stated below:
This article proposes a security processor architecture
which is partitioned in three separated areas such as
processor area, crypto area, and confidential area. This
architecture assures that secret key memory is isolated
form processor area.
The architecture proposes a blockchain based auditor
to monitor secret key lifecycles. The architecture pre-
vents insecure movement, unauthorized key request, non-
destruction issued and incorrect use of keys.arXiv:2007.06201v1 [cs.CR] 13 Jul 2020
The proposed security processor can execute 21 custom
crypto instructions as shown in Table II.
Custome Bus
InterconnectInput Address(3:0)Output Address(3:0)
RNG_op
Buff
Hash_opBuff
Hash_key
En_keyRNG_done
Buff_rd
Hash_doneBuff_wr
Hash_key_rdy
En_key_rdy
Hash_in
Hash_in_rdy
pub_en_in
pub_en_in_rdypub_op
pub_op_rdy0 0
1 1
2 2
3
3
4
Fig. 1: Custom Bus Interconnect
input
Addroutput
AddrBus RSA RNGHASH ENC MKM Buff
Operation (5 down to 0) Source IP Addr
(3 down to 0)Dest IPs
(3 down to 0)Bus
SelBuf
cap
Bus
cap
Fig. 2: Control Word Register
II. A RCHITECTURE
In this architecture we have partitioned three areas such
as(i)processor area, (II) crypto area and (iii)confidential
area. The whole architectural details is stated in [6]. The
partitioned and isolated confidential area to store secret keys
assures that the secret key never come in the processor
area because the processor area is very much vulnerable
for various software threats. The main contribution of the
proposed article is to add a special private blockchain to
audit the movement of secret keys. Each secret key movement
is authenticated and registered cryptographically. To adopt
blockchain, we have designed the data path controller
(DPC), custom bus interconnect (CBI), buffer and a
Signature Checker (SC) core. The architecture can exe-
cute 21 crypto instructions divided in 5 categories as shown
in Table II. All these proposed instructions are executed
byDPC andCBI . As instructed by the TLS white pa-
per [7], the architecture processes instructions for pre
master key generation ,master key generation ,hash and
encryption process. For the proposed blockchain process we
have created a new category named as common purpose . The
details of other instructions are not described because of page
restriction. It is to be noted that blockchain data is written in
main memory which can be accessed by the PE.
A. Custom Bus Interconnect
The proposed custom bus interconnect (CBI) is a junction
through which various crypto cores and the buffer which is
created as gateway of Master Key Memory (MKM) can
communicate. Various paths of crypto cores andbuffer are
controlled by 4 bits input and 4 bit output addresses. Input-
output address pins are controlled by data path controller
(DPC) IP. In our current version of design, we have 4 inputs
coming from buffer , output of hash , output of RNG and
output of RSA . The 5 outputs of CBI are connected with
PE
DMA Bus
Inter
ConnectLocal
MemoryPE Bus
Inter
connectIP2
IPn
DMA
AES
BlockSHA 3
BlockBus
StreamerRNG
Master
Key MemoryProcessor Area
Confidential
AreaCrypto Area
Data
Cipher
HashShared
MemoryJunctionPath
Controller
MKM
3
RSA
En-Pre
master
Key2
B
u
FTimer
Signature
Checker
BC
dataFig. 3: System Architecture
buffer , key of AES , key of hash and key of RSA block.
The synchronization signals of input cores and output cores
are also taken care of by CBI . The fig. 1 shows the synchro-
nization signals such as RNG _done ,Buff _rd,Hash _done ,
Buff _rdy,Hash _key_rdy andEn_key_rdy. The CBI is
combination of MUX and DE-MUX where MUX’s output is
connected with DEMUX’s input. The input address and output
address pins are the selecting inputs of MUX and DE-MUX
respectively.
B. Data Path Controller
The proposed data path controller (DPC) is a slave IP
ofPE. The PE can send 21instructions by Application
Peripheral Interface (API) call for various crypto operations
of TLS protocol. The data path controller maintains a
control word register to execute the 21 instructions as shown
in Table II. The PE can communicate DPC by AXI bus
through PE bus interconnect . In the current version of
hardware, DPC has 16 bits control word to control various
crypto cores placed in the crypto area. The data path and
binaries for all proposed instructions are shown in II. As
shown in fig. 2, the 11th bit is set to logic 1to enable the
custom bus interconnect , otherwise it will be logic 0to
disable the said interconnect. The 16th to 13th bits are used
to select the input address. If the input address is0000 ,
RNG will be selected. It will be 0001 ,0010 and0011 to select
Buff ,Hash andPubEn core respectively. The 12th and 9th
bit is to address outputs. The output address will be 0000 ,
0001 ,0010 ,0011 and0100 to select the Buff ,Hash _Key ,
En_key,Hash _InandPub_en_incore. The 6th to 1st bit
ofcontrol word register (CWR) are used to enable different
hardware blocks such as RSA ,RNG ,Hash ,Enc,MKM
andBuff . The PE directly can write to CWR to control the
data path of the hardwares placed in the crypto area .
C. Buffer
The proposed buffer is a gateway to write or read keys to the
MKM . This buffer can accommodate data ,timestamp from
timer IP,pre hash from Signature Checker , read/write
operation, signature of data and the system status. The
TABLE I: Comparison Results
#key
storageIncorrect
useInsecure
movement DestructionAudit
log
Propo
sedkey
memory X
[5]
2011key
memory X X X
[8]
2010key
memory X X X
[6]
2018key
memory X X X
[9]
2019main
memory X X X
[3]
2009main
memory X X
[2]
2015cache
memory X X
[10]
2007main
memory X X X
X=possible, =not possible
system status comes from the enable ports and ready ports
of all the existing IPs including crypto cores. Instruction-1
writes 384bit random number from RNG to the data portion
ofBuffer . Similarly instruction 9, 17 and 18 can write 512
bit data from KECCAK and 1024 bits from RSA respectively.
D. Signature Checker
TheSignature Checker (SC) consists two main parts such
as KECCAK hash and RSA block to verify signature of the
IPs requested to read or write to MKM . This IP receives the
signature from buffer . The signature is the encrypted hash
of data. The SCfirst decrypt it by the RSA block with the
public key of requestee IP. The requestee IP is addressed by
the source IP address available in CWR . After that SC gets
the hash of data. The data is already available in the buffer .
If the hash of buffer data is matched with the decrypted data
by RSA then the requested transaction will be granted by SC.
III. I MPLEMENTATION & R ESULTS
The proposed architecture is implemented in Artix-7
(csg324-100t)FPGA using Vivado Tool. The additional hard-
wares added to the original architecture to protect the keys
form major threats as stated in Table I are SC,buffer and
theCBI . The signature checker consist of a KECCAK and
RSA-1024 which cost 4188 and 31008 slices respectively.
The base architecture without blockchain consumes 50k logic
cells. The architecture with blockchain to protect severe key
threats consumes 95k logic cells which is around 45% of
the total logic cells available in Artix-7 FPGA. As shown
in Table I articles [5], [8] and [6] proposed dedicated secret
key memories which are completely isolated from processor
area to prevent software threats but this architecture does not
prevent incorrect use, insecure movement and non-destructions
issues of keys. If any spoofing IPs or dishonest probes already
exist inside the architectures of [5], [8] and [6] and try to read
secret key stored in dedictaed key memory, the system will
allow the key transaction to those malicious nodes. Though
the dedicated key memories proposed in these article are
physically isolated but can not prevent said incorrect and
insecure movement of secret keys. Articles [5], [8] and [6]
do not have any facility to investigate the key movement andTABLE II: Instructions Implemented in Proposed Architecture
# Instruction Flow FunctionCWR
(hex)Bus
Inter
Connect
1PE Send
Re-Seed PE to RNGSend new seed to RNG from PE.R-
-NG needs seed to generate rand. no. 0010 AXIRNG2 Gen RND RNG to BuffEnable RNG to Generate Random
Number, and write RND in data
portion of Buffer 0050 CustomMaster by3RNG Write
Block Gen to BuffBlock Generation for Write Operation
on MKM from RNG. Buff reads, time
stamp fromtimer IP,prehash from
Signature Checker , write operation
the system status, source & dest. IP ID 0091 CustomPre4Re-Key
RSA PE to RSA PE writes pub key of server to RSA 0020 AXI
5PE Get
En-RNG RSA to PE Encrypt RND using server pub key XXXX AXI
6PE Send Rands
in HashPE to Hash
by DMAPE sends server random number
and client random number to Hash XXXX AXIMaster Key7Hash Read
Block Gen to BuffBlock Generation for Read Operation
from MKM by Hash. Buff reads , time
stamp fromtimer IP,prehash from
Signature Checker , read operation,
the system status, source & dest. IP ID 11C1 Custom
8Get M-Key
Hash Buff to Hash read master key to generate four keys 1149 Custom
9 Gen Keys Hash to Buff Write keys to Buff 2049 Custom
10Hash Write
Block Gen Hash to BuffBlock Generation for write Operation
to MKM 20C9 CustomEncryption11En Read
Block Gen to BuffBlock Generation for Read Operation
from MKM by En. Buff reads , time
stamp fromtimer IP,prehash from
Signature Checker , read operation,
the system status, source & dest. IP ID 12C1 Custom
12Get EN Key
from Buff Buff to En Read En. key from buffer 1245 DMA
13 GEN EN Shared to SharedAES block reads plaintext form SM
& write cypher to SM using DMA XXXX Custom
14Hash Read
Block Gen to Buff Same as Instruction 7 11C1 Custom
15Get Key
Hash Buff to Hash Same as Instruction 8 1149 CustomHash
16 GEN Hash Shared to SharedKECCAK block reads plaintext form SM
& write digest to SM using DMA XXX DMA
17Hash of
Buff load Buff to Hash1st step of signature: load
Buff data to Hash input 1341 Custom
18Hash
to Buff Hash Op to Buff2nd step of signature: load hash
output to buffer 2049 Custome19PubEn
of Buff Buff to PubEn3rd step of signature: load buffer
in PubEn input 1461 CustomSignatur20PubEn
to BuffOutput of public
key encrypted
data to buffer4thstep of signature: load encrypted da-
ta of PubEn to buffer which is the signa-
ture of buffer data in 1st step 3061 Custom
21 Verify Sig Buff to MKMif signature matched
buffer data sent to MKM 1XX3 Custom
to prevent non-destruction issue. Amazon Web Server [9] is
one of the most recent software based key management archi-
tecture which can audit and investigate tracking of secret keys,
but it cannot prevent the insecure movement and incorrect
use of key. It does not have any signature checking facility
on the key request. In our architecture we have introduced
dedicated secret key memory which is physically isolated
from processor area. Any read write operation on this secret
key memory named as Master Key Memory (MKM) is
blockchain based. We have observed the usual operations on
MKM are related with RNG ,HASH , and AES block. The
Public andPrivate keys of all these crypto IPs are already
generated in offline by an automatic script during the RTL
development. The pre master key is generated by RNG
which needs to be written in MKM through buffer as shown
in Table II instructions 2, 3, and 17 to 21. Instruction 2 writes
random number generated by RNG into buffer. The buffer
also stores the public keys of RNG (Source IP) and MKM
(Destination). The buffer includes timestamp form TIMER
IP, and stores all the current status of Done andEnable pin of
available IPs in the proposed design. The instructions 17 to 21
generate signature of the write operation on MKM byRNG .
TABLE III: Summery of Trade-off
Partition Path SC Base
Memory Controller RSA KECCAK Design
Slice 0 76 31008 4188 50223
BRAM 7.5 0 0 0 32
Clock 2 2 3048 24 NA
latency 20 ns 10 ns 86us 67.2 ns NA
Feature
/ roleprevent
SW attackscontrol crypto
instrcutionsprevent insecure movment &
incorrect use of keys; audit logimplements basic hw
for secuirty processor
NA=Base desgn consists of diferent hardwares having different latency & clock
This signature will also be stored in buffer . This signature
will be verified by the Signature Checker IP placed inside
Confidential Area as stated in fig.3. If the signature is
matched then the pre master key generated by RNG will be
written in MKM , otherwise the transaction will be discarded
form the buffer . The other read write requests on MKM are
(ii)read pre master key byHASH t to generate master key
(Instruction 7 and 8)(ii)write masterkeys inMKM (Instruc-
tion 9 and 10). (iii)read master key form MKM byhash
(Instruction 14 and 15)and (iv) read master key formMKM
byAES (Instruction 11 and 13). All these read write opera-
tions follows Instruction 17 and 21 for authentic checking of
the requestee. The verification process of signature checker
IP using the buffer data with system status, timestamp,
signature and the pre hash prevents incorrect use and insecure
movement of keys. The MKM also delete the keys which are
already read to address non-destruction issue. The partitioned
memory for secret keys prevents software attacks. The timing
diagram of instruction 17, 18 and 19, 20 is shown in fig. 4
and fig. 5 respectively. Table III shows the trade off of latency
and resource usage with security features.
Fig. 4: Timing Overhead of Instruction 17 & 18
Fig. 5: Timing Overhead of Instruction 19 & 20IV. C ONCLUSIONS
This article proposes a hardware blockchain with a parti-
tioned and dedicated secret key memory which prevents most
of the software attacks, insecure key movement, incorrect use,
non-destruction use secret key. Apart from this, if breach
of keys occurs in the system, the hardware blockchain can
investigate previous key transactions. To the best of our
knowledge, the FPGA based blockchain to prevent threats
stated in Table I is never explored. The additional hardware
adopted for blockchain is very nominal in-terms of resource
usage and throughput.
REFERENCES
[1] Cryptomathic White Paper Rob Stubbs. Selecting the
right key management system. Workshop COSADE, ,
pages –, Feb 2019.
[2] Patrick Simmons. Security through amnesia: A software-
based solution to the cold boot attack on disk encryption.
InProceedings of the 27th Annual Computer Security
Applications Conference , ACSAC ’11, pages 73–82, New
York, NY , USA, 2011. ACM.
[3] X. Chen, R. P. Dick, and A. Choudhary. Operating
system controlled processor-memory bus encryption. In
2008 Design, Automation and Test in Europe , pages
1154–1159, March 2008.
[4] L. Guan, J. Lin, B. Luo, J. Jing, and J. Wang. Pro-
tecting private keys against memory disclosure attacks
using hardware transactional memory. In 2015 IEEE
Symposium on Security and Privacy , pages 3–19, May
2015.
[5] Michael Grand, Lilian Bossuet, Bertrand Le Gal, Guy
Gogniat, and Dominique Dallet. Design and implementa-
tion of a multi-core crypto-processor for software defined
radios. In Reconfigurable Computing: Architectures,
Tools and Applications - 7th International Symposium,
ARC 2011, Belfast, UK, March 23-25, 2011. Proceed-
ings, pages 29–40, 2011.
[6] Rourab Paul. Partitioned security processor architecture
on fpga platform. IET Computers and Digital Techniques ,
12:216–226(10), September 2018.
[7] E. Rescorla T. Dierks. The transport layer security (tls)
protocol version 1.2. 2008.
[8] L. Gaspar, V . Fischer, F. Bernard, L. Bossuet, and
P. Cotret. Hcrypt: A novel concept of crypto-processor
with secured key management. In 2010 International
Conference on Reconfigurable Computing and FPGAs ,
pages 280–285, Dec 2010.
[9] Assaf Namer Maitreya Ranganath. How to use aws
secrets manager to securely store and rotate ssh key pairs.
Advanced (300), AWS Secrets Manager, Security, Identity,
and Compliance , 18 Sept, 2019.
[10] K. Harrison and S. Xu. Protecting cryptographic keys
from memory disclosure attacks. In 37th Annual
IEEE/IFIP International Conference on Dependable Sys-
tems and Networks (DSN’07) , pages 137–143, 2007.
|
{
"id": "2007.06201"
}
|
2310.13900
|
Private Proof of Solvency
|
The Private Proof of Solvency is a groundbreaking solution in the realm of
Proof of Solvency, offering a secure, efficient, and privacy-preserving method
for crypto custody providers such as centralized cryptocurrency exchanges or
enterprise custody providers. By leveraging the inherent state concept of every
blockchain and pioneering cryptographic techniques like zkp, our approach
ensures businesses can prove their reserves without revealing their
transactions, addresses, or the total amount of liabilities.
|
http://arxiv.org/pdf/2310.13900v1
|
Hamid Bateni, Keyvan Kambakhsh
|
cs.CR, cs.CE
|
cs.CR
|
Private Proof of Solvency
Hamid Bateni (hamid@europe.com), Keyvan Kambakhsh (keyvankambakhsh@gmail.com)
Nobitex Labs
Abstract
The ”Private Proof of Solvency” project is a groundbreaking solution in the realm of Proof of
Solvency, offering a secure, efficient, and privacy-preserving method for crypto custody providers
such as centralized cryptocurrency exchanges or enterprise custody providers. By leveraging
the inherent state concept of every blockchain and pioneering cryptographic techniques, our ap-
proach ensures businesses can prove their reserves without revealing their transactions,
addresses, or the total amount of liabilities .
Contents
1 Introduction 1
2 Proof of Liability 2
2.1 Commitment . . . . . . . . . . 2
2.2 Merkle Tree . . . . . . . . . . . 2
2.3 Leaves Structure . . . . . . . . 3
2.4 Proof Statement . . . . . . . . . 4
3 Proof of Reserve 4
3.1 Ethereum . . . . . . . . . . . . 5
3.1.1 World State and MPT . 6
3.1.2 GetProof Method . . . . 6
3.1.3 Eth Balance Proof . . . 7
3.1.4 ERC20 Balance Proof . 8
3.2 Bitcoin . . . . . . . . . . . . . . 8
3.2.1 UTXO and ChainStat . 8
3.2.2 Bitcoin Proof Statement 9
4 Proof of Solvency 9
5 Future Works 10
5.1 Contract . . . . . . . . . . . . . 10
5.2 Custodian Scoring Platform . . 10
5.3 User Credit . . . . . . . . . . . 11
1 Introduction
Crypto custody providers currently face the
challenge of maintaining numerous addresses
for user assets. Conventional methods to cre-
ate a proof of reserve require the consolidationof these assets into single or multiple known
wallet addresses. Our innovative approach
eliminates this process by utilizing the inher-
ent state concept of every blockchain.
The state, achieved by processing
blockchain transactions on the blockchain pro-
tocol nodes, holds data such as the balance
associated with an address. For instance,
Ethereum maintains this state in the Merkle
Patricia data structure, while Bitcoin employs
a LevelDB database with a key-value struc-
ture that keeps the active Unspent Transac-
tion Outputs (UTXOs). In Bitcoin terms, the
balance represents the total active UTXOs an
address holds.
Our project introduces a novel process for
businesses to provide proof of reserve:
1. Create a proof of liabilities tree based
on user data on the business database.
2. Sign a message with the private key of
the addresses they want to prove reserve
with.
3. Provide these messages as private input
for our Zero-Knowledge Proof (ZKP)
circuit.
4. Submit the output to a contract and an-
nounce their new submission for check-
ing.arXiv:2310.13900v1 [cs.CR] 21 Oct 2023
Private Proof of Solvency
By leveraging ZKP, businesses can prove their
reserves without the need to reveal their trans-
actions, addresses, or the total amount of lia-
bilities, thereby maintaining privacy while en-
suring the integrity of the process. In essence,
the ”Private Proof of Solvency” project offersa robust, privacy-preserving solution that sig-
nificantly enhances the Proof of Solvency pro-
cess for crypto custody providers, paving the
way for a more secure financial ecosys-
tem.
2 Proof of Liability
The first step in the proof of solvency process involves the proof of liabilities. This step aims to
demonstrate the total amount of liabilities, or obligations, that exist. Liabilities in this context refer
to the balances that the custody provider owes to its customers[2]
2.1 Commitment
A commitment in the field of cryptography
refers to a binding agreement to a chosen piece
of information. Once this agreement is made,
it becomes irreversible and unalterable. Es-
sentially, it’s akin to sealing a message in an
envelope - the message cannot be changed
once it is sealed.
In cryptographic terms, a commitment
scheme allows an entity to commit to a cho-
sen value, while keeping it hidden from others.
It’s designed to be both binding and hiding.
Binding ensures that once the commitment is
made, it cannot be changed. Hiding ensures
that until the reveal, no information about the
committed value is leaked.
For the Proof of Solvency process, we use
a cryptographic commitment to demonstrate
the existence and integrity of the liabilities.
This is where we introduce the Merkle Tree
as our commitment scheme. The root of the
Merkle Tree serves as the commitment to all
liabilities, and each leaf node of the tree rep-
resents an individual liability.
The Merkle Tree is an efficient and se-
cure method for verifying liabilities. Us-
ing this structure, we can provide proof of
the existence and integrity of liabilities with-
out revealing the actual liabilities until neces-
sary. This approach strikes a balance between
transparency and privacy, which is crucial for
crypto custody providers. In the following sec-
tions, we will discuss the structure and prop-erties of the Merkle Tree in more detail.
2.2 Merkle Tree
A Merkle Tree is a tree in computer science in
which every leaf node is labeled with the cryp-
tographic hash of a data block, and each non-
leaf node is labeled with the cryptographic
hash of the labels of its child nodes. The value
of a non-leaf node is determined by the hash
of its children nodes, and this continues recur-
sively until the tree’s root is achieved.
This structure is particularly effective be-
cause it allows for efficient and secure verifica-
tion of the contents of large data structures.
The Merkle Tree allows us to verify data with
a significantly smaller subset of the total in-
formation.[7]
In the scenario of Proof of Solvency, we use
the Merkle Tree as our cryptographic commit-
ment. The root of the Merkle Tree acts as a
commitment to all liabilities, and each leaf in
the tree corresponds to a liability. This allows
2
Private Proof of Solvency
for an efficient and secure way to prove the
existence and integrity of liabilities.
By using a Merkle Tree, we can provide a
proof path for any given leaf node (liability)
up to the root. This path, also known as the
Merkle path, allows anyone to verify that a
specific liability is part of the tree. They can
do this by recomputing the hashes from the
leaf up to the root and comparing it with the
root hash. This approach is particularly use-
ful because it allows for verification without
needing access to all data points, providing a
balance between transparency and data effi-
ciency.
In the following sections, we will delve
deeper into the leaf structure of the Merkle
Tree and how we can use it to create proof
statements.
2.3 Leaves Structure
Until now, we have discussed how we can com-
mit to a list of data by using a hash tree,
specifically, a Merkle tree. Now let’s delve into
the structure of the data we are committing
to.
In our commitment to data via a Merkle
tree, each leaf (except the last right leaf) con-
tains key-value pairs of data:
User 1 Leaf
•User Identifier
•Network Identifire:
–Asset 1: amount
–Asset 2: amount
•Network Identifier:
–Asset 3: amount
–Asset 4: amount
The ’User Identifier’ is a unique identifier
for each user. The ’Asset Identifier’ refers
to the identifier for each asset. For exam-
ple, in the Ethereum network, we consider thezero address as the identifier for ETH, and for
each token, we consider their token address
as the identifier. The ’amount’ is the balance
the user holds of that specific asset according
to the business database that wants to prove
their liabilities amount.
This approach to committing to our liabil-
ities is similar to other proof of solvency ap-
proaches. However, traditionally, committing
to our liabilities in this way would require us
to make our total liabilities public. To prove
the correctness of the sum, we would need to
enlist the help of an auditor firm or use Zero-
Knowledge Proofs (ZKP). But in doing so, we
would have to reveal our total liabilities.
The approach we’re introducing allows the
total liabilities to remain private. Let’s ex-
plain how this is achieved by discussing the
last right leaf of our Merkle tree:
Last Right Leaf:
•Network Identifier:
–Asset 1 ID: Total Balance
user1 + ... + user10000
–Asset 2 ID:
user1 + ... + user10000
•Network Identifier:
–Asset 3 ID:
user1 + ... + user10000
–Asset 4 ID:
user1 + ... + user10000
The Total Balance is the sum of the bal-
ances of all users for a specific asset (Asset1:
user1 balance + user2 balance + ...).
We then use a ZKP protocol to create a
circuit and leverage the characteristic of veri-
fiable computation to prove that the total sum
in the last right leaf is calculated correctly by
adding previous leaves related attributes and
that there is no negative balance in any leaf.
So now, we commit to a liabilities tree that
includes all of our user liabilities. We also
hold the total amount we commit to and have
3
Private Proof of Solvency
a proof for the correctness of the entire tree
and calculation, without revealing the total
amount of our liabilities. This allows us to
maintain privacy while still proving our sol-
vency.
2.4 Proof Statement
In this section, we will discuss the entire proof
statement of our proof of liabilities process.
In simple terms, we’ve developed a com-
mitment tree with a specific leaf structure that
allows us to make the following proof state-
ment: ”I know a Merkle tree with a public
root and nodes, the leaves of which contain
data about my user balances. The last right
leaf in this tree contains data about the total
sum of my liabilities per asset.”
There are two important aspects of this
proof statement:
•I have a Zero-Knowledge Proof (ZKP)
for this tree that demonstrates that the
data in the last leaf is calculated cor-
rectly and that there is no negative bal-
ance in any leaf. This ZKP proof allows
me to verify the integrity and correct-
ness of the total liabilities without hav-
ing to reveal the actual amounts.[6]•My users can download the snapshot re-
lated to their balances from the website
and also get the path to verify that their
balances are included in the total liabili-
ties. This allows users to independently
confirm that their balances have been
taken into account in the proof of liabil-
ities.
Based on this approach, as a business, I only
need to publish the Merkle root and nodes,
not the leaf data. With the ZKP, I prove that
my total obligations are included in the last
right leaf without having to make this data
public. This maintains the privacy of the to-
tal liabilities while still allowing users and au-
ditors to verify the correctness of the proof of
liabilities.
In the Proof of Solvency section, we will
revisit the last right leaf and discuss how it
aids us in maintaining the privacy of the total
liabilities while enabling efficient and correct
verification.
Note: In implementation, it is advisable
to use a Merkle Sparse Tree. This reduces
some complexity and makes the circuit pro-
cess more efficient.
3 Proof of Reserve
In the context of a cryptocurrency business, Proof of Reserve involves demonstrating that the
business holds enough cryptocurrency assets to cover the balances it owes to its customers.
Proof of Reserve is a method by which a
business can demonstrate that it holds the
necessary reserves to meet its obligations.
Currently, the most common approach in-
volves revealing an address and moving funds
to that address, and then proving ownership
of the address by signing a message or pre-
announcing the address before transferring
funds.
However, there are several problems with
this approach:
As a business dealing with a large num-ber of users, maintaining numerous crypto
addresses can be cumbersome. Aggregat-
ing all funds into one or a few known ad-
dresses for the proof of reserve incurs signifi-
cant costs. All of the business’s addresses be-
come publicly known, affecting security and
privacy. Third parties could potentially track
all transactions and discover important data
that could impact the business’s security. To
achieve a private proof of reserve, we have
identified two potential approaches, each with
their own pros and cons:
4
Private Proof of Solvency
With the introduction of EIP-7503[1], we
realized that the basic idea of the EIP could
be used to achieve our goal. Instead of trans-
ferring funds to a burnt address and privately
proving a burn, we could transfer funds to a
multiplication of a point on the elliptic curve
for which we know the private key of the ref-
erence point. After the transfer, we can cre-
ate a proof that we’re transferring funds to
a multiplication of the reference point (desti-
nation point = reference-point ∗gs). Since no
one knows ’s’, our destination address remains
private, and we can create a proof using the
transaction root in a block that we’ve done
that. However, there are some issues with this
approach:
•a. The business still needs to transfer
their funds to prove their reserve.
•b. In Solidity, we only have access to
the last 256 block hashes, which makes
proof generation difficult.
We then came up with the idea of using the
state of each blockchain instead of privately
proving a specific transaction. This approach
solves the problems of the previous approach
but it’s tricky and differs from one blockchain
to another. In the following sections, we will
discuss how we can use the state to create
such a proof in Ethereum and then in Bitcoin.
Proof of Reserve involves two key steps:
1. Demonstrating ownership of an address:
This step involves proving that a partic-
ular address used in the Proof of Reserve
process is indeed owned by the business.
This is important to ensure that the as-
sets being accounted for in the Proof of
Reserve are actually controlled by the
business and not by a third party.
2. Proving a specific balance in the owned
address: Once ownership of the address
is established, the next step is to prove
that this address holds a specific balance
of a particular asset. This confirms thatthe business holds the necessary reserves
to meet its obligations.
We will discuss the proof of address ownership
in the Proof of Solvency section. In the follow-
ing section, we will explore how we can prove
that a specific address has at least a minimum
balance.
3.1 Ethereum
Ethereum is a public blockchain protocol that
uses an account-based model for its account-
ing system. This model is a key aspect of
Ethereum’s architecture and allows for a wide
range of financial transactions and applica-
tions.
The protocol distinguishes between two
types of accounts: externally owned accounts
(EOAs) and contract accounts. EOAs are
controlled by private keys and are used for
simple transactions. Contract accounts, on
the other hand, are governed by their internal
code and are used to create smart contracts.
Ethereum also introduced the concept of
a Virtual Machine (VM), specifically the
Ethereum Virtual Machine (EVM). The EVM
is a runtime environment that executes smart
contracts on the Ethereum network. These
smart contracts are self-executing contracts
with the terms of the agreement directly writ-
ten into lines of code.
These features make Ethereum an incred-
ibly powerful platform for a wide range of
decentralized applications, including but not
limited to financial applications.
In addition to its account-based model
and the Ethereum Virtual Machine (EVM),
Ethereum employs a data structure called
the Merkle Patricia Tree (MPT) to manage
the state of the entire blockchain network.
The World State, a crucial component of
Ethereum’s architecture, is represented by the
MPT, enabling the efficient storage and re-
trieval of account information, balances, and
smart contract data. The MPT is a key data
structure that enhances the integrity and se-
curity of the Ethereum blockchain by provid-
5
Private Proof of Solvency
ing a tamper-evident way to organize and up-
date the state of the network, allowing for
rapid verification and validation of transac-
tions and contracts. This combination of the
MPT and the EVM forms the backbone of
Ethereum’s decentralized ecosystem, facilitat-
ing the creation and execution of complex, se-
cure, and transparent applications.[8]
3.1.1 World State and MPT
The World State in Ethereum is the global
state of the Ethereum system, which is com-
posed of many smaller objects known as ac-
counts. Each account is a data structure that
contains four fields: the nonce, balance, stor-
ageRoot, and codeHash. The nonce is a scalar
value equal to the number of transactions sent
from this address, the balance is a scalar value
equal to the number of Wei owned by this ad-
dress, the storageRoot is a 256-bit hash of the
root node of a Merkle Patricia tree, and the
codeHash is the hash of the EVM code of this
account.
The Merkle Patricia Trie (MPT) is a cryp-
tographic data structure that maps keys to
values. In the context of Ethereum, the MPT
is used to map addresses to account states. It
is a modified version of the Patricia Trie and
the Merkle tree, hence the name. The MPT
has three types of nodes: leaf nodes, extension
nodes, and branch nodes.
Leaf nodes and extension nodes are sim-
ilar in that they both contain a path and a
value. The difference lies in what the value
represents. In a leaf node, the value is the
account state, whereas in an extension node,the value is the hash of the next node. Branch
nodes contain 17 items. The first 16 items
point to other nodes in the trie, and the 17th
item contains the value of the account state
if a key ends at this node. The root hash of
the MPT, which is a cryptographic hash of all
the data in the trie, is stored in the header of
each block. This allows for quick and efficient
verification of data.
By using the MPT, Ethereum can effi-
ciently store the entire state of the system and
quickly retrieve, update, or verify any part of
it. This is crucial for maintaining the integrity
and performance of the Ethereum network.
3.1.2 GetProof Method
The Ethereum JSON-RPC API provides an
eth getProof method, a function employed by
Ethereum execution nodes. This method re-
quires an address, an array of storage keys,
and a block identifier as arguments, subse-
quently returning an object encompassing in-
formation about the account and its storage.
The returned data includes the account’s
balance, nonce, storage hash, and code hash,
complemented by a list of nodes (in Recur-
sive Length Prefix, or RLP, form) that form
the proof of the specified account and its stor-
age.[4]
This proof essentially comprises a subset
of the Merkle Patricia Trie (MPT) necessary
for verifying the account’s data. It encom-
passes the MPT nodes along the path from
the root to the account node, and to the stor-
age nodes if storage keys were specified. Us-
ing this proof, the correctness of the account’s
data and its inclusion in the state of the spec-
ified block can be independently verified.
6
Private Proof of Solvency
The path within the MPT is determined
by the account’s address, which is employed
as the key in the trie. The path to the key
is a sequence of nibbles (half-bytes) derived
from the key.
Proof verification involves commencing at
the root node of the MPT and following the
path specified by the key. Each step involves
comparing the node’s hash against the ex-
pected hash in the proof. If all hashes cor-
respond and the path leads to the expected
account data, the proof is validated.
This mechanism allows for the verification
of an account’s state at a specific block with-
out requiring access to the entire Ethereum
state, a feature that significantly enhances the
scalability and efficiency of the Ethereum net-
work.
3.1.3 Eth Balance Proof
Building upon the information from the previ-
ous sections, we understand that an Ethereum
address is mapped to specific data in the
Ethereum World State via the Merkle Patri-
cia Trie (MPT). The balance of an account is
one such data point that is mapped.
To prove that a specific address holds at
least a minimum amount of Ether, we can de-
sign a Zero-Knowledge Proof (ZKP) circuit in
the following manner:
Our circuit will have two public inputs:
•The minimum amount
•The block rootAnd several private inputs:
•MPT Proof path
•Account Data
•Block Header Data
The circuit for proving the eth minimum
balance should follows this flow:
1- Hash the account data
2- Insert the hash into the MPT path
3- Verify the MPT path
4- Insert the calculated root as the state root
alongside the other block header data
5- Calculate the block hash based on the block
header data
6- Compare the calculated block hash with
the public block root. If they match, it means
we have correctly proved the data of an ac-
count in a specific block
7- In the final step, check if the balance in the
account data is greater than or equal to the
public minimum amount.
After executing all steps in our ZKP cir-
cuit, we can prove that a we know a specific
address holds at least a minimum Ether bal-
ance without revealing the address itself to
other people.
To implement the described flow in the
ZKP circuit, certain specific functions in our
zkp circuit need to be implemented. These
include the Keccak hash function, Recursive
Length Prefix (RLP) encoding, and MPT ver-
ification.
The Keccak hash function is a cryp-
tographic hash function that is used in
Ethereum for various purposes, including cal-
culating the block hash and the hashes of ac-
count data and MPT nodes. Implementing
this function in the circuit is crucial for veri-
fying the MPT path and the block hash.
Recursive Length Prefix (RLP) encoding
is a space-efficient object serialization scheme
used in Ethereum. It’s used to encode the
block header data and the account data. Im-
plementing RLP encoding in the circuit allows
us to handle these data structures properly.
7
Private Proof of Solvency
Lastly, MPT verification is necessary to
check the validity of the proof path in the
MPT. This involves following the path spec-
ified by the account’s address and checking
the hashes at each node against the expected
hashes in the proof.
3.1.4 ERC20 Balance Proof
While the Ether balance proof involves veri-
fying the state MPT for a specific address we
want to prove, the ERC20 balance proof in-
volves an additional step: verifying the con-
tract storage for the key related to our ad-
dress.
ERC20 tokens are implemented as smart
contracts on the Ethereum platform. The
balance of ERC20 tokens for each address is
stored in the contract’s storage, not in the ac-
count state as in the case of Ether. Therefore,
to prove an ERC20 balance, we must access
and verify the contract’s storage.
The contract’s storage is a separate MPT
where each key-value pair is a mapping of an
address to its balance. To prove a specific
ERC20 token balance, we first need to gen-
erate a proof for the contract storage. This
can be done using the eth getProof method
by providing the key related to the specific
address we want to prove. This will return
a proof that can commit to the value of the
token balance for that address.
Once we have this contract storage proof,
we then verify the state MPT for the con-
tract address, not the address we want to
prove. This involves hashing the contract ac-
count data, inserting the hash into the MPT
path, verifying the MPT path, and checking if
the calculated balance in the contract storage
proof matches the public minimum amount.In summary, the key difference for ERC20
balance proof is the addition of verifying the
contract storage via its own MPT, and using
the contract address to verify the state MPT.
This allows us to prove that a specific address
holds at least a minimum ERC20 token bal-
ance without revealing the address itself, pro-
viding a privacy-preserving proof of reserve for
ERC20 tokens.
3.2 Bitcoin
Bitcoin, the first decentralized cryptocur-
rency, was introduced in 2009 by an individual
or group known as Satoshi Nakamoto. It op-
erates on a peer-to-peer network where trans-
actions are verified by network nodes through
cryptography and recorded on a public ledger
known as blockchain.
Unlike Ethereum’s account-based model,
Bitcoin uses an Unspent Transaction Out-
put (UTXO) model for its accounting system.
In this model, transactions output a certain
number of bitcoins, which can be spent by
future transactions. Each UTXO represents
a chain of ownership encoded in the Bitcoin
blockchain, and the sum of these UTXOs rep-
resents a user’s total bitcoin balance.
Bitcoin nodes maintain a database of ac-
tive UTXOs, also known as the chain state.
This database is crucial for verifying new
transactions, as it allows nodes to check
whether the UTXOs a transaction wants to
spend are indeed valid and have not been
spent yet. By only keeping track of unspent
bitcoins, the UTXO model simplifies transac-
tion verification and improves the scalability
of the Bitcoin network.[5]
3.2.1 UTXO and ChainStat
At the heart of Bitcoin’s operation is the Un-
spent Transaction Output (UTXO) model. In
this model, each transaction begins with in-
puts that are references to previous transac-
tion outputs and ends with outputs that spec-
ify a new Bitcoin amount and a new owner.
These outputs then become the inputs of fu-
8
Private Proof of Solvency
ture transactions, forming a chain of owner-
ship. Transaction types in Bitcoin, such as
Pay-to-Public-Key-Hash (P2PKH) and Pay-
to-Script-Hash (P2SH), govern the conditions
under which these UTXOs can be spent.
The Chain State in Bitcoin is a key-value
database that maintains a record of all active
UTXOs. With the arrival of every new block,
spent UTXOs are removed, and new UTXOs
are added to the Chain State. This dynamic
update of the Chain State ensures that it ac-
curately reflects the current state of all trans-
actions.
The balance of a Bitcoin address is deter-
mined by the total amount of bitcoins in the
UTXOs that the address owns.
Each full node in the Bitcoin network inde-
pendently maintains the Chain State. Every
node computes its own Chain State by apply-
ing all transactions from the blockchain start-
ing from the genesis block. This decentralized
approach ensures that every node has a com-
plete and independent verification of the state
of all transactions, contributing to the security
and robustness of the Bitcoin network.
However, this also means that the Chain
State is not directly included in the block
header and varies from node to node, making
it challenging to create a proof of reserve that
commits to a specific number of active UTXOs
owned by an address. In the next section, we
will discuss a solution to this challenge.3.2.2 Bitcoin Proof Statement
The first approach to proving a Bitcoin
balance might be to adopt the idea from
EIP7503. This would involve transferring a
specific amount of Bitcoin in a transaction,
and then privately proving the transaction
and the amount. This approach is feasible
but involves operational costs, making it less
than ideal.
A more efficient approach might involve
the following steps:
1. Announce that the platform intends to
commit to its Bitcoin balances up to a
specific block.
2. Download the entire Chain State for
that block from a Bitcoin node. This
Chain State includes every active
UTXO at the time of the block.
3. Insert every UTXO from the Chain
State as a leaf in a Merkle tree. This
process converts the Chain State into a
structure that allows for efficient proofs.
4. Publish the root of the Merkle tree.
Anyone can then verify the correctness
of the published root with the help of a
Bitcoin node and some additional code.
With this approach, we now have a Merkle
tree representing all active UTXOs at a spe-
cific block. As a business, we know which
UTXOs belong to our addresses. We can then
create a Zero-Knowledge Proof (ZKP) circuit
to prove that we know some leaves (UTXOs)
that belong to a specific address and prove
their paths in the Merkle tree.
This approach allows us to commit to a
specific number of active UTXOs owned by
an address without revealing the address it-
self, providing a privacy-preserving proof of
reserve for Bitcoin addresses.
4 Proof of Solvency
Proof of Solvency is a method by which a cryptocurrency platform can demonstrate that it has
9
Private Proof of Solvency
sufficient funds (assets) to cover its obligations (liabilities), without revealing sensitive details about
its customers or its own operations. This provides a level of transparency and trust for the
platform’s users, while maintaining privacy.[3]
Proof of solvency constitutes a crucial as-
pect of trust in digital asset platforms. It con-
sists of two main components: Proof of Liabil-
ities and Proof of Reserves. Proof of Liabili-
ties asserts the total obligations a business has
to its customers, while Proof of Reserves con-
firms the total assets the business holds. We
have discussed various approaches to achieve
private proofs for both components, and now,
we aim to combine these to achieve a Private
Proof of Solvency.
Let’s explore the whole picture for achiev-
ing private Proof of Solvency:
Initially, a business commits to its liabil-
ities. This involves creating a Merkle tree
of all customer balances and publishing the
root of this tree. the business creates a Zero-
Knowledge Proof (ZKP) that attests to the
correctness of the liabilities tree. The busi-
ness also provides an infrastructure that al-
lows users to verify that their liabilities have
been correctly included in the tree.
In the next step the business proves its re-serves using one of the methods we introduced
earlier. This involves creating a proof for each
of the business’s addresses.
Lastly, the business integrates these proofs
in a ZKP circuit to show that the total re-
serves are at least equal to the total liabilities.
This involves comparing the total balance of
specific addresses with the balance of the last
right leaf in the liabilities tree. These inputs
are kept private in the circuit, with the only
public data being the block root and the root
of the liabilities tree.
By successfully completing this process,
the business achieves a private Proof of Sol-
vency. This demonstrates that the business
has sufficient reserves to cover all its liabilities
without revealing any sensitive information,
such as individual customer balances or busi-
ness addresses. This approach not only en-
hances the privacy of customers and the busi-
ness but also bolsters trust in the platform by
providing a verifiable proof of solvency.
5 Future Works
In the Future Works section, we will discuss potential enhancements and applications of the methods
presented in this paper. We aim to inspire further exploration in the field of privacy and trust in
digital asset platforms.
5.1 Contract
One potential area for future work involves the
creation of a smart contract for storing the
historical data of a business’s Proof of Sol-
vency. This contract would allow businesses
to announce new rounds of Proof of Solvency,
set the root of the Proof of Liabilities tree and
its associated proof, and also submit proofs
related to the Proof of Reserves. It would
also perform a final check of solvency, ensur-
ing that the total reserves are at least equalto the total liabilities. This could provide a
transparent and verifiable record of a busi-
ness’s solvency over time, enhancing trust in
the platform.
5.2 Custodian Scoring Platform
Based on the protocol introduced in this pa-
per, we could also develop a platform for scor-
ing custodian service providers. This plat-
form would evaluate custodians based on their
Proof of Reserves, the frequency of their sol-
10
Private Proof of Solvency
vency periods, their historical record of sol-
vency, and other relevant factors. Such a plat-
form could provide users with a transparent
and objective way to evaluate the trustwor-
thiness of different custodians, aiding in their
decision-making process.
5.3 User Credit
Currently, the Proof of Solvency protocol is fo-
cused on businesses, but it could be extended
to users as well. With some modificationsto the protocol and collaboration from busi-
nesses, users could create private proofs that
they hold at least a minimum balance in the
custody of a specific business during their last
submitted Proof of Solvency round. Users
could also create proofs for historical balances,
such as proving that they had at least a mini-
mum balance for the last five submitted Proof
of Solvency rounds by a specific business. This
could provide users with a privacy-preserving
way to demonstrate their financial stability
and creditworthiness.
References
[1] Hamid Bateni , Keyvan Kambakhsh. Eip7503. 2023.
[2] Vitalik Buterin. Having a safe cex: proof of solvency and beyond. 2022.
[3] Nic Carter. Nic’s por, wall of fame. 2023.
[4] Kamil Jezek. Ethereum data structures. 2021.
[5] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system. 2008.
[6] Maksym Petkus. Why and how zk-snark works: Definitive explanation. 2019.
[7] WikiPedia. Merkle tree.
[8] DR. GAVIN WOOD. Ethereum: A secure decentralised generalised transaction ledger.
11
|
{
"id": "2310.13900"
}
|
1811.06667
|
Evolutionary Game for Consensus Provision in Permissionless Blockchain
Networks with Shard
|
With the development of decentralized consensus protocols, permissionless
blockchains have been envisioned as a promising enabler for the general-purpose
transaction-driven, autonomous systems. However, most of the prevalent
blockchain networks are built upon the consensus protocols under the
crypto-puzzle framework known as proof-of-work. Such protocols face the
inherent problem of transaction-processing bottleneck, as the networks achieve
the decentralized consensus for transaction confirmation at the cost of very
high latency. In this paper, we study the problem of consensus formation in a
system of multiple throughput-scalable blockchains with sharded consensus.
Specifically, the protocol design of sharded consensus not only enables
parallelizing the process of transaction validation with sub-groups of
processors, but also introduces the Byzantine consensus protocols for
accelerating the consensus processes. By allowing different blockchains to
impose different levels of processing fees and to have different
transaction-generating rate, we aim to simulate the multi-service provision
eco-systems based on blockchains in real world. We focus on the dynamics of
blockchain-selection in the condition of a large population of consensus
processors. Hence, we model the evolution of blockchain selection by the
individual processors as an evolutionary game. Both the theoretical and the
numerical analysis are provided regarding the evolutionary equilibria and the
stability of the processors' strategies in a general case.
|
http://arxiv.org/pdf/1811.06667v1
|
Zhengwei Ni, Wenbo Wang, Dong In Kim, Ping Wang, Dusit Niyato
|
cs.GT, cs.CR, cs.DC
|
cs.GT
|
arXiv:1811.06667v1 [cs.GT] 16 Nov 2018Evolutionary Game for Consensus Provision in
Permissionless Blockchain Networks with Shards
Zhengwei Ni∗, Wenbo Wang∗, Dong In Kim†, Ping Wang‡and Dusit Niyato∗
∗School of Computer Engineering, Nanyang Technological Uni versity, Singapore 639798
†School of Information and Communication Engineering, Sung kyunkwan University (SKKU), Suwon, Korea 16419
‡Department of Electrical Engineering & Computer Science, Y ork University, Toronto, Canada ON M3J 1P3
Abstract —With the development of decentralized consensus
protocols, permissionless blockchains have been envision ed as
a promising enabler for the general-purpose transaction-d riven,
autonomous systems. However, most of the prevalent blockch ain
networks are built upon the consensus protocols under the
crypto-puzzle framework known as proof-of-work. Such prot o-
cols face the inherent problem of transaction-processing b ottle-
neck, as the networks achieve the decentralized consensus f or
transaction confirmation at the cost of very high latency. In
this paper, we study the problem of consensus formation in a
system of multiple throughput-scalable blockchains with s harded
consensus. Specifically, the protocol design of sharded con sensus
not only enables parallelizing the process of transaction v alidation
with sub-groups of processors, but also introduces the Byza ntine
consensus protocols for accelerating the consensus proces ses.
By allowing different blockchains to impose different leve ls of
processing fees and to have different transaction-generat ing rate,
we aim to simulate the multi-service provision eco-systems based
on blockchains in real world. We focus on the dynamics of
blockchain-selection in the condition of a large populatio n of con-
sensus processors. Hence, we model the evolution of blockch ain
selection by the individual processors as an evolutionary g ame.
Both the theoretical and the numerical analysis are provide d
regarding the evolutionary equilibria and the stability of the
processors’ strategies in a general case.
Index Terms —Permissionless blockchains, sharding, ELAS-
TICO, evolutionary game
I. I NTRODUCTION
The past decade has witnessed the fast development of
the blockchain technologies, especially as the decentrali zed
immutable ledger database (i.e., cryptocurrencies) in the
FinTech sector. Most of the studies on blockchains have
been focused on the development in cryptocurrencies and
the related domains [1]. However, in recent years, more
focus about blockchain applications is also put upon the
domain of self-organization in general-purpose decentral ized
systems [2]. Given the decentralized consensus achieved by
the blockchain network, smart contracts [3] are deployed
in the form of general-purpose scripts/functions and store d
on each consensus node (i.e., processor)1in the network.
Following the order prescribed by the consensus about the
blockchain states, each smart contract is executed in a repl i-
cated manner and thus guarantees to produce a uniform output
across the network. Thanks to the technical maturation of
smart contracts, blockchains are now envisioned as an enabl er
for self-organization in wireless networks and decentrali zed
1We use the two terms, i.e., node and processor, interchangea bly.Service
Providers
Service
Hosting
Peers(Mobile )
ClientsBlockchainRegistration and
Service Negotiation
(1)Data/Computation
Offloading(2)
Service Delivery(3)
(4)
(6) (5)
Operations in the form of smart contract
Interactions between entities (including data flow)
Fig. 1. A generic framework of blockchain-based self-organ ization in a
service system of three parties. All the deals are settled in a sequence of
smart contracts: (1) service requesting by the clients, (2) access granting
by the providers, (3) requesting service hosting (e.g., auc tion for computa-
tion/storage/utility offloading) by the providers, (4) set tlement of the hosting
requests, (5) delivery negotiation between hosting peers a nd clients and (6)
service completion and payment settlement upon proofs of de livery.
cyber-physical systems. More specifically, existing studi es,
e.g., autonomous access control [4] and service provision [ 5],
employ blockchains as an integrator to channel the services
upon demands as well as audit the operations of different
parties in the system.
A generic paradigm for blockchain-based self-organizatio n
in networking applications is described by Figure 1 from the
perspective of blockchain users. With the embedded cryp-
tographic functionalities (e.g., asymmetric keys [2]) and the
automated transactions based on smart contracts, blockcha ins
are ready to provide the overlaid/virtual channels of secur ed
data/service/payment delivery among trustless parties in the
system [2]. As illustrated in Figure 1, this is achieved by
encapsulating the controlling rules into smart contracts a nd
the data (e.g., control signals) into blockchain transacti ons. In
particular, when blockchain networks are implemented with
permissionless consensus protocols, it is possible to real ize
the scheme of network management in a purely decentralized
manner. Furthermore, when the blockchain maintenance is
delegated to groups of nodes with dedicated storage and
computing power, we have the blockchain as a Platform as
a Service (PaaS) in a similar way to that in the context of
cloud computing.
Nevertheless, although permissionless blockchains provi de
a promising approach to transaction-driven automation for
network control problems, most of the existing permis-
sionless consensus protocols are based on Proof-of-Work
(PoW) [1] and sacrifice the efficiency, i.e., transaction-
processing throughput, for a higher level of consensus se-
curity [2]. For example, the popular Ethereum network [6]
with a framework of Tuning-complete smart contracts can
only support less than 20 Transactions Per Second (TPS).
As a result, these blockchains cannot satisfy the low-laten cy
requirements in most of the networking applications and
services such as access handing-off between groups of road
side units in vehicular-to-infrastructure communication . To
guarantee controller response in milliseconds, throughpu t-
scalable protocols such as ELASTICO [7] are proposed to
support both open access as in permissionless networks and
low latency as in consortium distributed systems [8]. In bri ef,
the throughput-scalable blockchain networks adopt the PoW -
based crypto-puzzle design for node-identity verification and
the classical Byzantine Fault-Tolerant (BFT) protocols (e .g.,
practical BFT [9]) for distributed transaction ordering. F urther-
more, the concept of sharding is adopted from the distribute d
database [10] to enable the parallelization of transaction pro-
cessing. Thus, the blockchain network is able to increase th e
TPS as the number of consensus processors increases.
In this paper, we investigate the scenario of general-purpo se
PaaS based on permissionless blockchains using sharding-
based consensus protocols. In particular, we study the prob -
lem of consensus provision at the node level for multiple
blockchains. Noting that the decentralized processors in a
blockchain network are trustless, we assume that the pro-
cessors are rational (i.e., profit-driven) and non-malicio us.
Namely, the independent and homogeneous processors partic -
ipate in the consensus processes of parallel blockchains an d
dedicate their resources in exchange for the optimal consen sus
rewards, i.e., the transaction fees collected from the clie nts.
For ease of exposition, we use the ELASTICO protocol [7]
to exemplify the approach of system analysis for consensus
participation. Without limiting the blockchains to any spe cific
service provision system, we essentially study a general ca se of
eco-system formation for self-organization with blockcha ins.
Then, by formulating the behaviors of consensus nodes as an
evolutionary game, we provide a series of analytical result s
regarding the equilibrium states and their stability in the
evolution of the eco-system.
II. P RELIMINARIES OF SHARDED BLOCKCHAINS
A. Protocol Fundamentals of ELASTICO
Blockchain networks with sharding protocol partition the
processing processors inside into smaller BFT committees.
Each committee processes a disjoint set of transactions, wh ich
is called shard here. Thus, the tasks of transaction process-
ing are divided into multiple groups and done in parallel.
Blockchains with shards overcome the fundamental scalabil ity
limits of many popular blockchain systems, e.g., Bitcoin [1 0].
The transaction processing rate is able to scale almost line arly
with the number of processors in the network, which ensures
that the requirements of real-time resource-access manage ment
systems can be met.
Now, we briefly introduce how ELASTICO [7] works.
ELASTICO proceeds in loosely-synchronized epochs, each ofwhich processes a set of transactions. According to [7], in e ach
epoch, one processor mainly executes 5 procedures:
1) The processor is first required to solve PoW puzzles
based on the concatenation of a public random seed, its
own public key and Internet protocol (IP) address. This
procedure allows other processors to verify the identity
of the processor. In addition, the processor is randomly
assigned to a committee based on the last few bits of its
PoW solution. For example, assuming a total number of
22= 4committees, if the last 2 bits of the PoW solution
is “01”, the processor will be assigned to committee 2
if this committee is not full. However, since the target
committee to assign to may be full, the processor may
need to solve more than one PoW puzzle.
2) Once a processor is accepted by the network and as-
signed to a committee, it will discover and establish
point-to-point connections with other committee peers
following an algorithm of decentralized randomness
generation described in [7, Section 3.3].
3) Then, an authenticated Byzantine agreement protocol,
e.g., practical BFT (PBFT) [9], is run within a committee
to agree on the set of transactions (i.e. shard) allocated to
it. Since different committees work in parallel, the net-
work latency only depends on the number of processors
in one committee rather than the entire network.
4) Once an agreement is reached in each committee, all the
results will be merged. Then, the final result is broadcast
to the network.
5) Finally, a scheme described in [7, Section 3.6] is exe-
cuted by a global committee to randomly generate a new
random seed for the next epoch.
B. Average Epoch Time for ELASTICO
As per the experimental results given by [7], the epoch time,
i.e., the duration of one epoch, is mainly dominated by two
parts, committee formation time and consensus time.
1) Average committee formation time: Committee forma-
tion time is the time used for randomly dividing processors
into different committees. This time is mainly due to the cos t
of solving PoW puzzles. We assume that there are totally
nprocessors in the blockchain network. They are divided
into2scommittees, with a fixed number of cprocessors in
each committee. Thus, n= 2sc. As we mentioned previously,
one processor needs to solve more than one PoW puzzle if
the originally assigned committee is full. The problem of
calculating the total number of the required PoW solutions
is equivalent to the extended coupon collector problem [11] .
The expected number of PoW solutions is given in [7, Section
10.1]. When cis fixed, it has a superlinear relationship with
n, which means that the expected number of PoW puzzles
solved by one processor is increasing with n. In other words,
when the number of processors per committee is fixed, if there
are more processors in the blockchain network, one processo r
is expected to solve more PoW puzzles. In this paper, the
expected number of PoW puzzles solved per processor is
defined as a continuously differentiable and monotonically
increasing function of n,f(n), and we have f(0) = 0 . Assume
that given a fixed puzzle difficulty, the average time for solv ing
one PoW puzzle is T. Then, the average committee formation
time can be expressed as Tf(n).
2) Average consensus time: The consensus time is deter-
mined by the intra-committee agreement for the given shard
and the inter-committee agreement for the final result. It is
mainly due to the network latency, which is usually caused
by the propagation delay of physical links, the forwarding
latency of gateways, and the queueing and processing delays
of intermediate nodes. In ELASTICO, we can observe that
most of communications among processors are limited within
the individual committees, so for a given committee size, th e
time to reach consensus remains almost constant for differe nt
network sizes. As [7, Figure 1] shows, the consensus require s
103 seconds for 400 processors and 110 seconds for 800
processors. Hence, we can think the consensus time is only
dependent on the committee size c, which is denoted by g(c).
C. Average Reward and Cost in ELASTICO
In this subsection, we quantitatively model the average
reward and cost of one processor per epoch in ELASTICO.
1) Average reward: The processor receives a payment
by adding new transaction records into the blockchain. We
assume that the transaction records are generated by the
users with a rate µ, and the price per transaction is set as
r. Thus, the average reward of one processor per epoch is
µr(Tf(n)+g(c))/n.
2) Average cost: The cost of one processor is dominated
by the energy used for solving PoW puzzles. We assume that
the cost of getting one PoW solution is ςon average. Thus,
the average cost of one processor per epoch is ςf(n).
III. S YSTEM MODEL AND PROBLEM FORMULATION
A. Payoff Functions
We consider Nindividual processors organizing themselves
intoMblockchain networks built upon ELASTICO. That is,
the processors choose to participate in one of the blockchai n
networks to receive their revenue. We assume that the pro-
cessors have identical computing power and the average time
for solving one PoW puzzle of fixed difficulty is T. We
also assume that all blockchain networks adopt the same
parameter of committee size c. We use the subscript ito denote
other parameters for the ith blockchain network. Without
loss of generality, the index of one blockchain network is
determined by µiriin a descending order. That is, µ1r1≥
··· ≥µMrM>0. The vector of population fractions of the
blockchain networks is denoted by x= [x1,...,x M]⊤, where
[·]⊤is the notation of transpose. Thus, xis in an(M−1)-
simplex, i.e., X={x∈RM
+:/summationtextM
i=ixi= 1}. We call xstate
vector (orstate ) andXstate space .
According to Section II, the expected payoff per unit time
(i.e., second) of a processor in the ith blockchain network can
be expressed as
ui(x) =µiri(Tf(Nxi)+g(c))
Nxi+˜τ−ςf(Nxi)
Tf(Nxi)+g(c),0≤x≤1, (1)where˜τ >0can be regarded as the share taken by network
operators (e.g., the boosting nodes). By defining αi=µiri/N,
τ= ˜τ/N , andh(xi) =ςf(Nxi)/(Tf(Nxi) +g(c)),ui(x)
can be simplified as
ui(x) =αi
xi+τ−h(xi),0≤x≤1. (2)
We can easily obtain that h(xi)is monotonically increasing
withxiandh(0) = 0 .
B. Dynamical System Formulation
In this process, some processors may switch from one
blockchain network to another, causing a change of x. Since
the payoff of the processor is dependent on x, other proces-
sors may also adjust their choice of consensus participatio n
accordingly to choose new blockchain networks. Hence, in
this paper, we study population fractions of the blockchain
networks as a dynamical system. The state vector at time t
is denoted by x(t) = [x1(t),...,x M(t)]⊤, and we define
x0=x(0)as the initial state. At time t, the rate at which
the population fraction of ith blockchain network grows is
dxi(t)
dt, and we define ˙x(t) =/bracketleftBig
dx1(t)
dt,...,dxM(t)
dt/bracketrightBig⊤
. We
assume that all the processors are bounded rational and self -
interested, so the forces regulating the state vector are fr om
the difference of payoffs. That is, the processors always sw itch
from a blockchain network with low payoff to one with high
payoff. Since the payoffs at time tare determined by x(t),
˙x(t)can be described by a function of x(t), here defined as
ϕ(·) :X →RM. Thus, this dynamical system is described by
the following ordinary differential equations (ODEs):
˙x(t) =ϕ(x(t)),∀t∈R, i= 1,...,M. (3)
Specially, we adopt the following replicator equations [12 ]:
ϕi(x) =xi(ui(x)−¯u(x)), (4)
where¯u(x) =/summationtextM
i=1xiui(x), which can be regarded as the
average payoff. Notice that here we ignore time tsinceϕ(·)
is autonomous, that is, does not depend explicitly on time. W e
can easily find/summationtextM
i=1ϕi(x) = 0 , so that if x0∈ X, we always
havex(t)∈ X for anyt∈R.
We are interested in how the vector of population fractions,
i.e., state vector, changes with time for different initial states.
Usually, it is described by a function ξ(·,x0) :T→ X , where
Tis an open interval containing t= 0, such that ξ(0,x0) =
x0, and∀t∈T,
d
dtξ(t,x0) =ϕ(ξ(t,x0)). (5)
The function ξ(·,x0)is called a solution of (3).
IV. A NALYSIS OF THE GAME
A. Uniqueness of Solutions for Different Initial Points
In this subsection, we show that ∀x0∈ X , we have a
unique solution ξ(·,x0). It means that once the initial state
is determined, how the population fractions evolve over tim e
is totally determined. It is stated in the following theorem .
Theorem 1. Forϕ(·) :X →RMdescribed in (4)and∀x0∈
X, the system (3)has a unique solution.
Proof. We can obtain that
∂ϕi(x)
∂xi= (1−2xi)/parenleftbiggαi
xi+τ−h(xi)/parenrightbigg
−(xi−x2
i)/parenleftBigg
αi
(xi+τ)2+dh(x)
dx/vextendsingle/vextendsingle/vextendsingle/vextendsingle
x=xi/parenrightBigg
−M/summationdisplay
j=1,j/negationslash=ixj/parenleftbiggαj
xj+τ−h(xj)/parenrightbigg
, (6)
∂ϕi(x)
∂xj=−xi/parenleftbiggαj
xj+τ−h(xj)/parenrightbigg
+xixj/parenleftBigg
αj
(xj+τ)2+dh(x)
dx/vextendsingle/vextendsingle/vextendsingle/vextendsingle
x=xj/parenrightBigg
. (7)
(6) and (7) indicate that∂ϕi(x)
∂xiand∂ϕi(x)
∂xjexist and are
continuous in X. Hence, ϕ(x)is Lipschitz continuous in X.
By the Picard-Lindel¨ of theorem [12, Theorem 6.1], we obtai n
Theorem 1.
B. Existence of Equilibria
Mathematically, an equilibrium (a.k.a., rest point orcritical
point ) under a solution mapping ξis a state vector x∈ X
such that ξ(t,x) =xfor allt∈R[12, Definition 6.4]. In our
model, it means that if the vector of population fractions is
at an equilibrium, this population distribution will remai n the
same, which implies that there are no “job-hoppings” in the
blockchain networks. In addition, according to [12, Propos ition
6.3], if the vector of population fractions finally converge s over
time, it will converge to an equilibrium.
Now, we are ready to give all the possible equilibria.
Based on whether there are any processor, we can divide the
considered blockchain networks into two specific sets, i.e. ,
theworking blockchain set W={i:xi>0}and the
resting blockchain set ¯W={i:xi= 0}. A state is an
equilibrium if and only if ϕ(x)vanishes at this state. For
a givenW={i1,...,i|W|}, it means that
ui1(x) =···=ui|W|(x). (8)
Theoretically, there are totally 2M−1possibleW. However,
for some values of W, there may not be any equilibria.
Now we give the conditions that a given Whas at least one
equilibrium.
Consider a field K={(a,b) :a,b >0,a
1+τ−h(1)≤b≤
a
τ} ⊂R2, and a function K(·) :K →[0,1]such that K(ˆa,ˆb)
is the solution for the equation
ˆa
x+τ−h(x) =ˆb. (9)
Notice that when K(ˆa,ˆb)is continuous and monotonically
increasing with ˆaand decreasing with ˆb. Then, we give the
following theorem.Theorem 2. For a given set of working blockchains
W={i1,...,i|W|}, ifαi1
1+τ−h(1)<αi|W|
τand
/summationtext|W|−1
j=1K/parenleftBig
αij,αi|W|
τ/parenrightBig
<1, a unique equilibrium exists.
Otherwise, there is no equilibrium.
Proof. First, we show ifαi1
1+τ−h(1)<αi|W|
τand
/summationtext|W|−1
j=1K/parenleftBig
αij,αi|W|
τ/parenrightBig
<1, a unique equilibrium exists.
Sinceαi1≥ ··· ≥ αi|W|, ifαi1
1+τ−h(1)<αi|W|
τ, we must
have/parenleftBig
αij,αi|W|
τ/parenrightBig
∈ K,j= 1,...,|W|−1, and
0≤K/parenleftBig
αij,αi|W|
τ/parenrightBig
<1, j= 1,...,|W|−1. (10)
Notice that K/parenleftBig
αi|W|,αi|W|
τ/parenrightBig
= 0. In addition, since for j=
2,...,|W|,
αij
1+τ−h(1)≤αi1
1+τ−h(1)<αi|W|
τ≤αij
τ, (11)
we have
0< K/parenleftbigg
αij,αi1
1+τ−h(1)/parenrightbigg
≤1, j= 2,...,|W|.(12)
Obviously the following holds
K/parenleftbigg
αi1,αi1
1+τ−h(1)/parenrightbigg
= 1. (13)
Thus,
|W|/summationdisplay
j=1K/parenleftbigg
αij,αi1
1+τ−h(1)/parenrightbigg
>1. (14)
Since/summationtext|W|
j=1K/parenleftBig
αij,αi|W|
τ/parenrightBig
<1andK(ˆa,ˆb)is continuous
and monotonically decreasing with ˆb, there must exist a unique
¯b∈/parenleftBigαi1
1+τ−h(1),αi|W|
τ/parenrightBig
such that
|W|/summationdisplay
j=1K/parenleftbig
αij,¯b/parenrightbig
= 1, (15)
and the population fraction of the ijth blockchain network is
indeedK/parenleftbig
αij,¯b/parenrightbig
.
Whenαi1
1+τ−h(1)≥αi|W|
τ.∀xi1,xi|W|∈(0,1], we can
easily obtain that
αi|W|
xi|W|+τ−h/parenleftbig
xi|W|/parenrightbig
<αi|W|
τ
≤αi1
1+τ−h(1)≤αi1
xi1+τ−h(xi1). (16)
Hence, there are no equilibria in this case.
Finally, it is obvious that whenαi1
1+τ−h(1)<αi|W|
τand
/summationtext|W|−1
j=1K/parenleftBig
αij,αi|W|
τ/parenrightBig
≥1,ui1(x) =···=ui|W|(x)>0
will lead to/summationtext|W|
j=1xij>1. Hence, there are no equilibria in
this case.
C. Asymptotic Stability of the Equilibria
Among all the equilibria, we are especially interested in
those which are “robust”. That means, all sufficiently small
perturbations of the equilibrium induce a backward movemen t.
The mathematical definition of asymptotic stability in a dy-
namical system can be found in [12, Definitionn 6.5].
We assume that x∗is an equilibrium and let Jϕ
x∗be the
M×MJacobian matrix of ϕ(·)at the state x∗. Then,Jϕ
x∗
can be expressed as
Jϕ
x∗=
∂ϕ1(x)
∂x1∂ϕ1(x)
∂x2...∂ϕ1(x)
∂xM∂ϕ2(x)
∂x1∂ϕ2(x)
∂x2...∂ϕ2(x)
∂xM............
∂ϕM(x)
∂x1∂ϕM(x)
∂x2...∂ϕM(x)
∂xM
/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle
x=x∗,(17)
where∂ϕi(x)
∂xiand∂ϕi(x)
∂xj,j/ne}ationslash=iare given in (6) and
(7), respectively. According to [13, Theorem 8.4.3], x∗is
asymptotically stable if the real part of every eigenvalue o f
Jϕ
x∗is negative, and it is unstable if any eigenvalue of Jϕ
x∗
has a positive real part. Based on our observations of Jϕ
x∗, we
can obtain the following lemma.
Lemma 1. For a given set of working blockchains W=
{i1,...,i|W|}and its corresponding equilibrium x∗
W,∀k∈
¯W,λk=αk
τ−αi|W|
xi|W|+τ+h/parenleftbig
xi|W|/parenrightbig
is one eigenvalue of Jϕ
x∗.
Proof. From (6) and (7), we can obtain that ∀k∈¯W,
∂ϕk(x)
∂xk/vextendsingle/vextendsingle/vextendsingle/vextendsingle
xk=0
=αk
τ−M/summationdisplay
j=1,j/negationslash=kxj/parenleftbiggαj
xj+τ−h(xj)/parenrightbigg
=αk
τ−/parenleftBigg
αi|W|
xi|W|+τ−h/parenleftbig
xi|W|/parenrightbig/parenrightBigg
=λk.(18)
In addition, when xk= 0,∀˜k/ne}ationslash=k, we have
∂ϕk(x)
∂x˜k= 0. (19)
We can observe that the matrix Jϕ
x∗
W−λkIM×Mhas its
elements of kth row all equal to 0, where IM×Mis the
M×Midentity matrix. Thus, det/parenleftBig
Jϕ
x∗
W−λkIM×M/parenrightBig
= 0,
wheredet(·)is the determinant. Hence, λkis an eigenvalue
ofJϕ
x∗
W.
Based on the Lemma 1, we can further obtain Theorems 3.
Theorem 3. For a given set of working blockchains W=
{i1,...,i|W|}and its corresponding equilibrium x∗
W, if∃k∈
¯W, andλk≥0, this equilibrium is not asymptotically stable.
Proof. From Lemma 1, we know that λkis an eigenvalue of
Jϕ
x∗
W. According to [13, Theorem 8.4.3], Jϕ
x∗
Whas at least one
non-negative eigenvalue, so x∗
Wis not asymptotically stable.
Then, we can obtain the following corollaries.Corollary 1. A set of working blockchains W=
{i1,...,i|W|}with|W|=whas a corresponding equilibrium.
IfW /ne}ationslash= [1,w]∩N, this equilibrium is not asymptotically stable.
Proof. SinceW /ne}ationslash= [1,w]∩N, there must exist a positive
integerlsuch that l≤wandl∈¯W. Clearly, λl≥0.
According to Theorem 3, we have this corollary.
Corollary 2. If the set of working blockchains [1,w]∩Nhas a
corresponding equilibrium, for any positive integer rsuch that
r < w , the set of working blockchains [1,r]∩Nmust have a
corresponding equilibrium, which is not asymptotically st able.
Proof. It is easy to obtain
α1
1+τ−h(1)<αw
τ≤αr
τ, (20)
and
r−1/summationdisplay
j=1K/parenleftBig
αj,αr
τ/parenrightBig
≤r−1/summationdisplay
j=1K/parenleftBig
αj,αw
τ/parenrightBig
≤w−1/summationdisplay
j=1K/parenleftBig
αj,αw
τ/parenrightBig
<1. (21)
Hence, according to Theorem 2, the set of working
blockchains [1,r]∩Nmust have an equilibrium.
We denote this equilibrium by x∗
[1,r]∩N, with Jaco-
bian matrix Jϕ
x∗
[1,r]∩N. Let/summationtextr
j=1K(αj,¯br) = 1 and/summationtextw
j=1K(αj,¯bw) = 1 . Clearly, ¯br<¯bw. Sincew /∈[1,r]∩N,
according to Lemma 1,αw
τ−¯bris an eigenvalue of Jϕ
x∗
[1,r]∩N.
Sinceαw
τ>¯bw>¯br, according to Theorem 3, x∗
[1,r]∩Nis not
asymptotically stable.
Theorem 4. Letw∗= max{w:α1
1+τ−h(1)<
αw
τ,/summationtextw−1
j=1K/parenleftbig
αj,αw
τ/parenrightbig
<1}. Then, only the corresponding
equilibrium of the set of working blockchains [1,w∗]∩Nhas
probability to be asymptotically stable.
Proof. According to Corollaries 1 and 2, all the other equilib-
ria are not asymptotically stable. Then, we must have Theore m
4.
V. P ERFORMANCE EVALUATION
In this section, we provide the numerical analysis of the pop -
ulation dynamics of ELASTICO. We assume four blockchain
networks, for which α1= 0.7,α2= 0.5,α3= 0.3, and
α4= 0.1. In addition, we set τ= 0.01andh(x) = ln(1+ x).
In this condition, from our analysis in Section IV-B, we
can easily obtain xe1= [0.4225,0.3148,0.1975,0.0652]⊤
andxe2= [0.4499,0.3369,0.2132,0]⊤are two equilibria.
By calculating the eigenvalues of Jacobian matrix of ϕ(·)at
the state xe1, we can find that xe1is asymptotically stable.
Meanwhile, according to Corollary 2, we can see that xe2is
not asymptotically stable.
We show the population dynamics from an initial point
x0= [0.4498,0.3369,0.2132,0.001]⊤, which is a very small
deviation from xe2. From Figure 2, we can observe that, in-
stead of moving backward to its neighboring equilibrium xe2,
0 5 10 15 20 25 30 35 4000.050.10.150.20.250.30.350.40.45
Fig. 2. The population dynamics for four blockchain network s, from the
initial point x0= [0.4498,0.3369,0.2132,0.001]⊤.
0 1 2 3 4 5 612345678910
Fig. 3. The payoff dynamics for four blockchain networks, fr om the initial
pointx0= [0.4498,0.3369,0.2132,0.001]⊤.
the state moves away from this area and settles down over time
toward the new equilibrium xe1. This observation coincides
with our analysis given in the previous paragraph. Then, in
Figure 3, we show the dynamics of payoff for each blockchain
network. We can observe that at the beginning, since there ar e
only a few processors in the fourth blockchain, the payoff
per second of one processor is much higher than other three
blockchains. However, as more and more processors move to
the fourth blockchain, the payoff for the fourth blockchain
decreases while those for the other three blockchains incre ase.
Finally, they reach an equilibrium and all the blockchains h ave
the same expected payoff.
Finally, we show how the asymptotically stable equilibrium
changes with the prices. We set α1= 0.7κ,α2= 0.5κ,α3=
0.3κ, andα4= 0.1κ, whereκvaries from 0.5to1.5. Figure 4
shows that, as the prices increase, the population fraction s of
blockchain networks with higher prices increase while thos e
of blockchain networks with lower prices decrease.
VI. C ONCLUSION
In this paper, we have investigated the process of eco-syste m
formation for a large population of consensus nodes to join
one consensus process from multiple permissionless, shard ed
blockchains. In particular, we have considered the scenari o of
multiple blockchains adopting the ELASTICO protocol, whic h
combines the proof-of-work puzzle and the Byzantine fault-
tolerant agreement protocol to achieve both open access and
low transaction-processing latency. We have considered a g en-0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.50.050.10.150.20.250.30.350.40.45
Fig. 4. The asymptotically stable equilibrium for α1= 0.7κ,α2= 0.5κ,
α3= 0.3κ, andα4= 0.1κ, whereκchanges from 0.5to1.5.
eral scenario where different blockchains may provide diff er-
ent transaction fees and have different transaction-gener ating
rates. We have studied the blockchain-selection behaviors
of the independent, bounded-rational consensus nodes with
identical computational power. The behaviors of blockchai n
selection by the consensus nodes have been formulated as
an evolutionary game based on replicator dynamics. We have
provided a series of analytical and numerical results, whic h
reveal the consensus-formation mechanism in a permissionl ess
network of blockchains for multiple service provision.
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Y . Wen, “A survey on consensus mechanisms and mining managem ent
in blockchain networks,” arXiv preprint arXiv:1805.02707 , 2018.
[3] K. Christidis and M. Devetsikiotis, “Blockchains and sm art contracts for
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[4] K. Kotobi and S. G. Bilen, “Secure blockchains for dynami c spectrum
access: A decentralized database in moving cognitive radio networks en-
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[8] M. Vukoli´ c, “The quest for scalable blockchain fabric: Proof-of-work vs.
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[9] M. Castro and B. Liskov, “Practical byzantine fault tole rance and
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[10] K. Croman, C. Decker, I. Eyal, A. E. Gencer, A. Juels, A. K osba,
A. Miller, P. Saxena, E. Shi, E. G¨ un Sirer, D. Song, and R. Wat tenhofer,
“On scaling decentralized blockchains,” in Financial Cryptography and
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WAHC , Christ Church, Barbados, Feb. 2016, pp. 106–125.
[11] D. J. Newman, “The double dixie cup problem,” The American Mathe-
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[12] J. W. Weibull, Evolutionary game theory . MIT press, 1997.
[13] N. Lebovitz, Ordinary Differential Equations , ser. Mathematics Series.
Brooks/Cole, 1999.
|
{
"id": "1811.06667"
}
|
1806.11399
|
A Rolling Blockchain for a Dynamic WSNs in a Smart City
|
Blockchain is one of the most popular topics for discussion now. However,
most experts still see this technology as only part of Bitcoin, other
crypto-currencies or money transfer systems. Often, new solutions, proposed by
young researchers, are blocked by reviewers, only because these solutions can
not be used for Bitcoins. However, Blockchain technology is more universal and
can be used also in other areas, for example, in IoT, WSN and mobile devices.
This paper considers the implementation of Blockchain technology in sensor
networks as an element of IoT. The concept of "Rolling Blockchain" was
proposed, which can be used to build WSN with the participation of Smart Cars,
as nodes of the network. The order of block formation and structure in the
chain is proposed and a mathematical model is created for it. We estimate the
optimal number of WSN nodes, the number of connections between nodes, for
specified network reliability values, was performed.
|
http://arxiv.org/pdf/1806.11399v1
|
Sergii Kushch, Francisco Prieto-Castrillo
|
cs.CR
|
cs.CR
|
IEEE INTERNET OF THI NGS JOURNAL (J -IOT), MANUSCRIPT ID 1
A Rolling Blockchain
for a Dynamic WSNs in a Smart City
Sergii Kushch, Senior Member, IEEE, Francisco Prieto -Castrillo
Abstract —Blockchain is one of the most popular topics for discussion now. However, most experts still see this technology as
only part of Bitcoin, other crypto -currencies or money transfer systems. Often, new solutions, proposed by young researchers,
are blocked by reviewers, only because these solutions can not be used for Bitcoins. However, Blockchain technology is more
universal and can be used also in other areas, for example, in IoT, WSN and mobile devices.
This paper considers the implementation of Blockchain technology in sensor networks as an element of Io T. The concept of
"Rolling Blockchain" was proposed, which can be used to build WSN with the participation of Smart Cars, as nodes of the
network.
The order of block formation and structure in the chain is proposed and a mathematical model is created for i t. We estimate the
optimal number of WSN nodes, the number of connections between nodes, for specified network reliability values, was
performed.
Index Terms —Blockchain; Wireless Sensor Network, Distributed network, Rolling Blockchain; Internet of Things, Smart City
—————————— ——————————
1 INTRODUCTION
HE essence of Blockchain (BC) technology is the secure,
distributed storage of any kind of information. BC can store
data on transactions; on who, to whom and what amount of
money has been transferred (cryptocurrencies, bank transac-
tions). Currently, this is the main area in which Blockchain is
used.
However, attempts are being made to apply it in other areas,
for example, to record cargo during transportation, to manage
"smart cities", create "smart contracts", and for the Internet -of-
Things (IoT) and Wirele ss Sensor Networks (WSN) etc. [1], [2],
[3], [4], [5], [6], [7], [8] . BC was conceived as a system that is
completely protected from the substitution of information in
existing blocks of the chain. This property makes us look for
ways of using the BC techn ology as a method of protecting the
information that is transmitted from various sensors and mobile
devices. This also implies its storage, without any possibility of
substituting part or all of the information. With respect to cryp-
tocurrency, BC is the ma instay of Bitcoin's [9], [10] financial
strength. It guarantees that information about money transfers
between all the system participants is recorded during the entire
period of the existence of the Bitcoin system.
BC is structured as a chain of blocks that contains infor-
mation, consequently all the blocks of a chain are connected
with each other. A block is filled with a group of records, and
new blocks are always added to the end of the chain, apart of
containing n ew information, new blocks duplicate the infor-mation contained in the previously created structural units of
the system.
Construction of BC chains occurs on the basis of three main
principles - distribution, openness and protection [11], [12 ]. Us-
ers of the system form a computer network. At the same time,
each computer stores a copy of each of the blocks. This struc-
ture is provided by the interaction of "miners" who solve com-
plex, expensive mathematical tasks. To solve them, it is neces-
sary to spend both ma terial resources (electricity, specialized
"farms" for "mining"), and also the hardware capabilities for
complex mathematical calculations known as Proof -of-Work
(POW) [13]. The results of mining are collected in the BC and
as the length of the chain incre ases with time, its reliability in-
creases.
Also, with time, the complexity of the problem solved by the
"miners" increases with the chain. All this requires an increase
in both the computing power of "farms" and in the volume of
devices that store the enti re chain.
However, using BC on mobile devices, for example, in a
smart sensor network, poses problem that makes using BC im-
possible; this is because the sensors do not have the computa-
tional resources to perform POW.
Another well -known problem is that of WSNs nodes, due to
the limited volume of node batteries, have a limited period of
opera tion and as a consequence the entire network is limited. In
[14], [15], [16], [17], [18] , it is shown that dependence on ener-
gy consumption is created by: the algorithm of operation (time
of work, sleep, awake), the use of MAC -protocols, the amount
of tran smitted, received and processed information, data acqui-
sition from sensors, and some other parameters. In the case of
using POW, which is known as a very resource -intensive and
energy -intensive task, the autonomous work of the nodes will
be significantly r educed.
In addition, the standard structure of BC requires a complete
connection between all elements of the network but this is not
always possible and energetically advantageous. ————————————————
S. Kushch, is with the Security and Trust research unit of the Bruno
Kessler Foundation, via Sommarive, 18, Povo, 38123,TN, Italy
E-mail: kushch@yaros.co , skushch@fbk.es
Francisco Prieto -Castrillo with Media Laboratory, Massachusetts Institute
of Technology, Cambridge, MA 02139 -4307, USA; Harvard T.H. Chan
School of Public Health, Harvard, University, Boston, MA 02115, USA;
University of Salamanca, BISITE Research Group, Edificio I+D+i, 37008,
Salamanca, Spain E -mail: fprieto@hsph.harvard.edu , fprie-
to@mit.edu , franciscop@usal.es .
T
2 IEEE TRANSACTIONS O N JOURNAL NAME, MAN USCRIPT ID
All these works provide key insights into the problem of
network resilience , diffusion and consensus from different per-
spectives. But, according to the authors, a model of BC for de-
vices with limited resources, for full and partially connected
BC, is still missing. Therefore, in this paper we make an analy-
sis of the conditions un der which using BC in distributed sensor
networks and IoT devices is possible. The question at issue is
how to design BC without POW with a partial connectivity
while maintaining robustness to failures and attacks. To this
end, we developed several network models.
The paper is organized as follows. In Section 2 we formulate
the problem. The results obtained in the study are presented in
Section 3. Finally, we present the conclusions obtained from
our research and discuss the possibilities for future work in Sec-
tion 4.
2 Problem formulation
Blockchain can be conceived as chains of separated ele-
ments which are interconnected by hashing. There are three key
factors in this process: a) which structure of blocks and chain is
used, b) how the network is built, c) how consensus can be
achieved. We elaborate on these elements below.
2.1 Network structure
The approach proposed in this work is to build a closed
private BC network with unchanged complexity. The number of
new blocks per minute is set by a constant valu e. The entire
database is stored on the server. At the same time, the server is
the node for the distributed P2P network of servers which uses a
BC to account for the information received from the local
sensor networks. This will ensure that the stored inf ormation
remains unchanged, making its storage and recovery more
secure in the event of an attack on a separate data server.
n2n1 n6
n3n5
n4n2n1 n6
n3n5
n4
n2n1 n6
n3n5
n4n2n1 n6
n3n5
n4S3 S4S1 S2
chain 1 1 23456
chain 2 1 23456
chain 3 1 23456
chain 4 1 23456
1234 Full BC
Fig. 1. A general network structure. The total network consists of local segments. Each segment has its own server for storin g local BC. A
distributed network of servers (which is marked -S1,..,S4) also stores a common BC consisting of local fragments.
An examples of such a networks can be: a power distribution
company uses a network of smart meters and sensor s for power
consumption control, to account the use of electricity and
monitor the status of the network; network that controls many
medical devices (including portable), which are grouped into
sub-clusters (eg within the same department of the hospital);
WSN which monitors traffic on highway routes and consists of
several segments etc. (Fig.1).
At the same time, these sensors and counters are combined into a local sensor network for each city. The servers of this or-
ganization in different cities will also be combined into a peer -
to-peer network. It is in this network that a complete BC of all
local sensor networks is stored. This will for example, avoid
attacks on critical infrastructure by substituting information
about the load in local networks, which c an lead to power out-
ages throughout the network.
n2n1n6
n3n5
n4
t 0 1 2 3 4 5 6
n1 0 01 012 0123 01234 012345 0123456
n2 0 01 012 0123 01234 012345 0123456
n3 0 01 012 0123 01234 012345 0123456
n4 0 01 012 0123 01234 012345 0123456
n5 0 01 012 0123 01234 012345 0123456
n6 0 01 012 0123 01234 012345 0123456
BC 0 01 012 0123 01234 012345 0123456
Fig. 2. Structure of the 6 -element distributed network. The table shows the step -by-step formation of the chain. Blockchain is stored in
each node of the chain.
Consider one segment of such a network consisting of six
nodes. Fig.2 illustrates this example. In this case, each new block of information is forwarded to all nodes and BC is built in
parallel on all the elements of the system.
S.KUSHCH ET AL.: A ROLLING BLOCKCHAIN FOR A DYNAMIC WSNS IN A SMART CITY 3
a)
b)n2n1n6
n3n5
n4
t012 3 4 5 6
n1 001012 0122 01222 012222 0123456
n2 001012 0123 01234 012344 0123444
n3 000002 0123 01234 012344 0123456
n4 000012 0123 01234 012345 0123455
n5 000000 0000 01234 012345 0123456
n6 001011 0123 01233 012345 0123456
0123456 BC
t 012 3 4 5
n1 001012 0122 01222 012222 0122226
n2 001012 0123 01234 012344 0123444
n3 000002 0123 01234 012344 0123446
n4 000000 0123 01234 012345 0123455
n5 000000 0000 00004 000045 0000456
n6 001011 0113 01133 011335 0113356
01234566
BC
Fig. 3. The structure of a chain for the six iterations. During each iteration, information from the "active" node is sent to the " neighbors"
which evaluate and give permission to write it to the end of the chain. The remaining nodes retain the previous state: a) a node sends a
renewed BC to its neighbors, b) a node sends only one transaction which is added to the local BC of its neighbors.
It is considered that after the failure of one node the system be-
comes inoperative. However, in real conditions, the system can
continue to work, but some of its parameters may change.
In these circumstances, we can say that the failure of one or
more nodes leads to the trans formation of a fully connected
network to a partially connected network. Also, this situation is
possible if the network was attacked and some connections or
nodes are unavailable or compromised.
Consider the principle of chain formation. Each node con-
tains a list that indicates: the list of nearest "neighbors", the
node’s activation time (sensor interrogation, formation of the
BC block), and the order of sending the block to "neighbors".
The use of such a list will solve at least two problems: it wi ll
optimize the time of sleep -awake -work, it will use the mini-
mum -necessary power for sending -receiving information to and
from neighbors. Also, it should be borne in mind that time of
inclusion of each step differs from the time of inclusion of the
remain ing nodes and must satisfy the inequality: Tn1<
Tn2<∙∙∙<Tnm.
The Blockchain is constructed as follows: step 0, all nodes
record the genesis block 0, step 1 - node 1 creates block 1 and
send it to its neighbors. The inaccessible nodes repeat their pre-
vious state, etc. If a node is a failure node and sends a rejection
to one of its neighbors, in this case it will be able to restore its
state on the basis of consensus with its neighbors. As shown by
tables a) and b) under Fig.3 - the rows represent nodes and the
columns - the time points (iterations). The two BC building var-
iants are possible.
First, the node that created the block, adds it to the BC in its
own memory, and then sends the BC to its neighbors for valida-
tion. After the validation, the next step is to - send a confirma-
tion that the BC is correct, after this confirmation the neighbors
rewrite the renewed BC in their memory (the table a) of Fig.3.
Furthermore, after creating the block, the node sends only
this block for v alidation. Then adds it to the end of the BC. The neighbors add one too (table b) in Fig.3. We see that the second
method is more advantageous in terms of energy efficiency, as
demonstrated above.
The process of BC validation is as follows: after forming t he
chain, the nodes produce an element -by-element verification of
the final chain. If a block written in the chain of each node is
confirmed (was written in) by more than 51% of the nodes at
each iteration, such a block is written in the resultant BC. How-
ever, a possible situation is that there is a partial connection of
nodes, and it is impossible to achieve 51% confirmation for
some blocks. In this case, some blocks may be lost. The analy-
sis of the number of lost blocks, depending on the number of
connect ions in the distributed network, is presented by the au-
thors in [19]
2.2 The structure of the chain
In this section, we discuss the structure of the chain that we
propose to use for resource -constrained devices. The issues of
constructing a distributed net work structure were widely con-
sidered in [20], [21], [22] . In this work, we propose to use a
chain consisting of several parts. Each part contains a limited
number of generated blocks. Their number depends on the pa-
rameters and capabilities of the devices used in the IoT net-
work. Below, we consider the proposed version of the block
structure in more detail.
Notice, that the memory for storing BC is limited. Modern
modules offered by manufacturers of IoT devices are usually
limited from 1 to 8 MB of memory, most of which is used for
storing the software that manages this device. Therefore, we are
limited in the volume of BC (amount of blocks), which can be
stored by each node.
The number of blocks stored by each node is limited by n, after
which the n -1 bloc ks are removed from all nodes. Only the n
block remains as the “zero” block (genesis block) for the next
cycle. In this case, the full BC will be as follows(Fig.4):
4 IEEE TRANSACTIONS O N JOURNAL NAME, MAN USCRIPT ID
Time_bb Id_dev Hash_prev Hash_cur data
Id_dev_1,
…
Id_dev_nTime_start_1,
…
Time_start_ntime_step_1,
…
Time_step_ndata_1,
data_2, …,
data_n
data
Fig. 4. The structure of Blockchain creating in the conditions of limited memory of WSN nodes.
The notation "Bn (B0) New cycle1" indicates that the block
Bn is a zero block for the next cycle, etc. The number of blocks
in the chain depends on the paramet ers of the "worst" memory
device in the network, since the stored chain in each cycle
should fit the available memory. 2.3 Block structure
Below, we consider the structure of each block in detail
(Fig.5).
... ...B0 B1 ... Bn-1Bn (B0)
New cycleBn+1 (B1) ...Bm-1 Bm Bn+2(B2) ...
B0B1 BnBn
(B0)for new cycleBn+1
(B1) of new cycle...Delete blocks
B0-Bn-1Bn+m
B0 for next cycleoperation
“delete”
Fig. 5. General structure of the block. The top part of the figure shows the general block structure.. The bottom part shows the subb lock
"data" (the transactions block
It should be taken into account that each node can have sev-
eral different sensors. Therefore, it is necessary to identify each
sensor and the measurement time. If several measurements
made over a period of time by each sensor (multi -segment
transaction), are recorded in the block, a time stamp "step" must
be added to show the time interval between measurements. In
addition, given that the information transmitted by the sensors
can be "closed", it is encrypted using cryptographic algorithms.
This function is provided by modern IoT modules. In this case,
the size of the Data block is set automatically, depending on the
number of transactions.
2.4 Blockchain formation
There are two options for building this system: 1) When the node memory is "clearing", and only the last
block remains (as B0 for the next cycle);
2) When only the first block is deleted from the stored chain,
and a new block is recorded in the empty space that has been
unallocated in the memory. For example, the block B0 is delet-
ed and the block n+1 is recorded at the end of the circuit. Block
B1 is deleted - the n+2 block is recorded at the end of the chain.
The system is scalable, the order of block formation by de-
vices depends on the assigned id number. There is no possibility
of random block creation. If a node that is ou tside of the queue
offers to add a block to the chain, it is simply ignored. Also,
each new block is checked for compliance with the remaining
nodes, in order to prevent t he substitution of information.
S.KUSHCH ET AL.: A ROLLING BLOCKCHAIN FOR A DYNAMIC WSNS IN A SMART CITY 5
EndStart
Save FullChain
on a server n,m,B0
i=1,mDelete: Bjj=1,m
f(B)=ΣBi
i=nF(B)=f(B)+Bn+j
+
EndStart
n,m,B0
f(B)=ΣBii=1,nSave FullChain
on a serverj=1,m
Delete:
from Bk-nto Bk-1
-
a) b)
Fig. 6. The flowchart of block formation in a chain: a) when a predetermined number of blocks is removed; b) when blocks are re-
moved step -by-step.
Given that this is a closed network, the identification of de-
vices is done by comparing them with the list of authorized de-
vices. The suggestion is that this list should be recorded on a
separate, protected part of the flash memory of each node. The
list is continuously updated in case of a disconnection or the
replacement of the f aulty device. The flowchart of BC for-
mation in a network has the following form (Fig.6).
2.5 Segmented network. Problem formulation
To use the model proposed above, it is necessary that the
number of WSN nodes be fixed at each particular time. This
can be done by dividing the network to segments (Fig.7). An example of such a network can be WSN of a smart city,
where nodes 1 -2-3-4 are stationary, and nodes c1 -c12 can be,
for example, smart cars. Thus, the centers of the subnets are sta-
tionary nodes. At ea ch particular time, each subnet contains a
fixed number of nodes. Also, each subnet builds its own part of
the block, which is sent to the server. Nodes that are elements of
several subnets retain the last block of the previous element of
the chain for ver ifying the next element of the neighboring sub-
net. The server stores all the elements of the chain in a single
blockchain. However, in this case there arises the problem of
estimating the optimal number of elements of each subnet and
the number of connecti ons between its nodes for a given level
of reliability. This will be discussed in the next section .
1C1 C2
C32
C4C5 C7
C63
C8C9 C11
C104
C12
Fig. 7. The structure of WSN separation on a sub -segment when using mobile nodes.
2.5 Mathematical model
Mathematically, the structure of the complete chain is:
6 IEEE TRANSACTIONS O N JOURNAL NAME, MAN USCRIPT ID
𝐹=⋃𝐵𝑖∞
𝑖=0
Then, the part of the chain that will be removed from the
memory of the node in each cycle will be as follows:
𝑓𝑑𝑒𝑙=⋃𝐵𝑖,𝑛=𝑐𝑜𝑛𝑠𝑡𝑛−1
𝑖=0
Here n - the number of nodes in the local netwo rk.
The general function will have the form of a matrix consist-
ing of n columns and m rows.
For example, the matrix for 5 nodes, with end -to-end num-
bering of blocks, will have the following form for the m itera-
tions:
Each of these is the sum of blocks in a chain for one cycle
without taking into account the genesis block B0.
𝐹=
( 𝐵11𝐵12…𝐵1(𝑛−1)𝐵1𝑛
𝐵21𝐵22…𝐵2(𝑛−1)𝐵2𝑛
⋮⋮⋱⋮⋮
𝐵𝑚1𝐵𝑚2…𝐵𝑚(𝑛−1)𝐵𝑚𝑛) . (1)
Then, the analytic expression, which describes the structure
of the complete chain, will have the form of (2).
𝐹=𝐵0⋃𝐵𝑖,𝑗,𝑆=[𝑖=1…𝑛,𝑗=1…𝑚] 𝑖,𝑗∈𝑆 . (2)
where the notation is as follows: j - an amount of elements in
each row of matrix (1); n - number of the elements in the row; i
– number of rows. Also, it should be borne in mind that the el-
ements 𝐵𝑖𝑛=𝐵(𝑖+1)1.
3 MAIN RESULTS
In this section, we consider the implementation of the
proposed method for Blockchain formation.
Fig. 8. The principle of building Blockchain for WSN, which consists of sub -segments, for mobile nodes.
Our procedure works as follows. First we create a linear arrangem ent of sensors as shown in Fig.8.
Fig. 9. Probability of finding a connected path between A and B as a function of the number of nodes “n” and the number of edges “L”. The
dashed blue line represents the limit 0.7Lmax for n=10 (and Lmax=45) .
S.KUSHCH ET AL.: A ROLLING BLOCKCHAIN FOR A DYNAMIC WSNS IN A SMART CITY 7
In this Fig. 9 we plot the probability of finding a connected
path between A and B as a function of the number of nodes “n”
and the number of edges “L”. It is computed using a random
graph model where the connection probability between two
randomly chosen nodes is “p” = Lmax/L, where Lmax=n(n -1)/2. Each curve ranges L from 1 to Lmax for each n. The
dashed blue line represents the limit 0.7Lmax for n=10 (and
Lmax=45) which is the minimum connectivity threshold found
in [19].
Fig. 10. Probability of finding a connected path between A and B as a function of the number of nodes “n” and the number of edges “L”.
The dashed blue line represents the limit 0.7Lmax for n=10 (and Lmax=45) .
These represent fixed sensors along the way. Then we
augment the sensor set by randomly spreading additional sensor
over the area so that we reach a target sensor density. Each
fixed sensor can transmit/receive signals within a radius (see
circles in Fig.8). Therefore we create a network consisting of
the union of the complete graphs of sensors lying inside each
line sensor radius. Then we randomly remove links and check if
a path between the start and the end line nodes can stil l be built.
We did Monte Carlo tests to numerically find the probability of
finding a path when a portion of the links were randomly
removed for different node densities. In Fig.10 we show the
results of this analysis. Here we analyse the length of
alterna tive shortest paths compared to the length AB paths
along the horizontal line.
As it can be noticed, when we increase the level of attack
(proportion of links removed) the network is resilient to provide
alternative paths until its break down. At this poin t no
alternative paths are possible. As expected, in sparse scenarios
(low sensor density) this break down occurs at smaller attack
intensities.
4 SUMMARY AND DISCUSSIO N
The findings outlined in this article can be applied to at least
two fields: Wireless Sensor Networks and the Internet of
Things. Clearly, this contribution is just a first step in the
understanding of short and partially connected BC.
The simulation resul ts showed that with increasing attack
density (increasing the number of lost connections and nodes)
the network remains stable and the Blockchain can be built. It
should be noted that some of the blocks (information from
blocked nodes) can be lost. The num ber of lost blocks depends on the density of the sensors and the intensity of the attack. It
should be taken into account that the reliability of such a
network depends on the number of nodes at each moment of
time in each separate sub -segment. Also, the m inimum value of
nodes is found, which should participate in the construction of
the chain, in order to avoid network interruption.
However, the results of the work clearly show the possibility
of constructing "a Rolling Blockchain", using mobile nodes
when the the start and the end of the route are given. The
problem still needs further elaboration in order to foster more
robust implementations. For instance, we neglected the issues
of security analysis and protection against hacking of the
proposed method. In addition, the issue of having to use the
Merkle tree for this type of network and chain has been left
open. In future works we will research other topological models
and how the use of the Merkle tree in the proposed algorithm,
will affect the resource of the node batteries and what the ratio -
increasing the stability/power consumption of the node is
profitable for using in long Blockchchains. We will also
consider the problem of calculating the optimal size of the
memory used, on the basis of the elem ents available in the
network, in order to optimize their performance.
8 IEEE TRANSACTIONS O N JOURNAL NAME, MAN USCRIPT ID
5 Acknowledgments
This research was partially supported by the Regional
government of Provincia Autonoma di Trento (Italy) and Bruno
Kessler Foundation (Italy) under the Secure Blockchain -based
Application project.
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[13] A. Gervais, G. O. Karame, K. Wst, V . Glykantzis, H. Ritzdorf, and S. Capk un,
“On the security and performance of proof of work blockchains,”Proceedings of
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Sergii Kushch has got Bachelor's Degree in
Radio Engineering at the Electronic Technolo-
gies Faculty of Cherkasy Engineering and
Technological Institute in 1998. In 1999 he
has got a diploma of Radio Engineer. The ar-
ea of his research was statistical Radio Engi-
neering. In 2009 Sergii Kushch has got Mas-
ter's Degree in Radio Engineering at the Fac-
ulty of Electronic Technologies of Cherkasy
State Te chnological University. The title of his diploma thesis was
"Synthesis of statistical pattern recognition algorithms of signals that
have been taken against a background of non -Gaussian noise". In
2009 he was invited to PhD school which he success finished in
2012. The title of his thesis was "Methods of multivariate realization
the components of computer systems". Since 2012 he worked at
Cherkasy State Technological University on the positions of lecturer,
Sr. lecturer and Associate professor of Department of Computer Sci-
ence and Information Security. He published a number of scientific
papers in international and national journals and took part in interna-
tional and national conferences. Currently, he works as researcher in
Foundation Bruno Kessler, Italy i n Security and Trust research unit
with modification Blockchain technology for an increasing protection
personal information EU citizens.
Francisco Prieto Castrillo With a back-
ground in theoretical physics, Francisco Prie-
to Castrillo has worked in many, seemingly
distant fields; statistical physics, artificial in-
telligence, smart energy networks and seis-
mic engineering. The backbone of his in-
volvement in all these activities is his enthu-
siasm for the analysis of the self -organization
phenomena in complex systems. This in-
cludes the way a structure resists an earth-
quake or the patterns of collective social behaviour.
Francisco Prieto Castrillo is a researcher in the BISITE group at Uni-
versity of Salamanca. His former position was as postdoctoral re-
searcher a t the New England Complex Systems Institute (NECSI), a
centre which has been instrumental in the development of complex
adaptive systems. Currently he is affiliated to MIT and to Harvard
University. At MIT he collaborates with the Alex “Sandy” Pentland’s
Human Dynamics Laboratory in the MIT - MediaLab. At the Harvard
T. H. Chan School of Public Health, he analyses different problems
related to social sciences using data mining techniques, machine
learning and complex systems .
|
{
"id": "1806.11399"
}
|
1802.10091
|
Blockchain platform with proof-of-work based on analog Hamiltonian
optimisers
|
The development of quantum information platforms such as quantum computers
and quantum simulators that will rival classical Turing computations are
typically viewed as a threat to secure data transmissions and therefore to
crypto-systems and financial markets in general. We propose to use such
platforms as a proof-of-work protocol for blockchain technology, which
underlies cryptocurrencies providing a way to document the transactions in a
permanent decentralised public record and to be further securely and
transparently monitored. We reconsider the basis of blockchain encryption and
suggest to move from currently used proof-of-work schemes to the proof-of-work
performed by analog Hamiltonian optimisers. This approach has a potential to
significantly increase decentralisation of the existing blockchains and to help
achieve faster transaction times, therefore, removing the main obstacles for
blockchain implementation. We discuss the proof-of-work protocols for a few
most promising optimiser platforms: quantum annealing hardware based on D-wave
simulators and a new class of gain-dissipative simulators.
|
http://arxiv.org/pdf/1802.10091v1
|
Kirill P. Kalinin, Natalia G. Berloff
|
quant-ph
|
quant-ph
|
Blockchain platform with proof-of-work based on analog Hamiltonian optimisers
Kirill P. Kalinin1and Natalia G. Berlo 2;1
1Department of Applied Mathematics and Theoretical Physics,
University of Cambridge, Cambridge CB3 0WA, United Kingdom and
2Skolkovo Institute of Science and Technology Novaya St., 100, Skolkovo 143025, Russian Federation
(Dated: March 1, 2018)
The development of quantum information platforms such as quantum computers and quantum
simulators that will rival classical Turing computations are typically viewed as a threat to secure
data transmissions and therefore to crypto-systems and nancial markets in general. We propose to
use such platforms as a proof-of-work protocol for blockchain technology, which underlies cryptocur-
rencies providing a way to document the transactions in a permanent decentralised public record and
to be further securely and transparently monitored. We reconsider the basis of blockchain encryp-
tion and suggest to move from currently used proof-of-work schemes to the proof-of-work performed
by analog Hamiltonian optimisers. This approach has a potential to signi cantly increase decentral-
isation of the existing blockchains and to help achieve faster transaction times, therefore, removing
the main obstacles for blockchain implementation. We discuss the proof-of-work protocols for a few
most promising optimiser platforms: quantum annealing hardware based on D-wave simulators and
a new class of gain-dissipative simulators.
Blockchain technology with its digital currency, Bit-
coin, was originally introduced about ten years ago [1]
and was quickly followed by the development of many
other cryptocurrencies. Bitcoin was the rst decen-
tralised electronic payment system operated by an open
peer-to-peer network where a nancial transaction hap-
pens directly between two willing parties without the
need for a trusted intermediary such as banks or other
nancial institutions. The deployment and enhancement
of blockchain technology may completely transform the
existing nancial system in the next few years. The world
leading nancial institutions started investing in startups
based on blockchain technologies, exploring its novel ap-
plications and opportunities. The major banks are estab-
lishing a framework for using the blockchain technology
in the nancial markets [2]. The acceptance of bitcoin in
Japan together with a recently established Crypto Valley
in Switzerland are the rst signs that cryptocurrencies
become recognised on the governing level.
In principle, all industries that serve as intermedi-
aries for processing nancial transactions will have to
adapt and compete with the blockchain alternatives. The
blockchain will capture various aspects of our lives in-
cluding non- nancial applications such as the implemen-
tation of a decentralised platform for the Internet of
Things, health records and notary, loyalty payments in
the media industry, private securities. IBM and Samsung
are now developing a new platform called ADEPT (Au-
tonomous Decentralised Peer To Peer Telemetry) based
on the blockchain that will keep a trusted record of
all the messages exchanged between smart devices in a
distributed network. Nasdaq is implementing private
equity exchange on top of Blockchain with a goal to
create a more secure, ecient system to trade stocks
[3]. DocuSign, a company that specializes in electronic
contracts, just unveiled a joint idea with Visa to use
blockchain to track car rentals and reduce paperwork.
Microsoft develops Azure Blockchain to allow the devel-opers and customers to create private, semi-private, pub-
lic and consortium blockchain networks with a single click
on the Azure's cloud platform, thereby enabling them to
distribute blockchain products. Moreover, the majority
of all possible applications can be realised on the same
blockchain by using the "smart contracts", which are
computer programs that can automatically execute the
terms of a contract [4]. When a precon gured condition
in a smart contract among participating entities is met,
the parties involved in the contractual agreement can au-
tomatically make the contractual payments in a trans-
parent manner. Companies like ethereum and Codius
are already enabling Smart contracts using blockchain
technology and many companies which operate using
blockchain technologies are beginning to support Smart
contracts. Many cases where assets are transferred only
after meeting certain conditions, which require lawyers
to create a contract and banks to provide escrow ser-
vices, can be replaced by Smart contracts. Ethereum
is already powering a wide range of early applications
by using Smart contracts in areas such as governance,
autonomous banks, keyless access, crowdfunding and -
nancial derivatives trading.
The investments attracted by main cryptocurrencies
caused an increased interest in understanding the struc-
ture and technological capabilities of the platform. The
blockchain consists of a publicly accessible database of
all transactions that are arranged into blocks of a cer-
tain length in the order preserved by a distributed ledger
(so shared across multiple sites), see Fig. 1. The decen-
tralisation of the blockchain is currently insured by the
distributed computational powers (computational nodes)
verifying all the transactions and agreeing about what
blocks should be on the blockchain, so there is no spe-
ci c computer responsible for a particular transaction.
In order to validate the transaction of a particular block,
one can wait until several newer blocks are added to the
blockchain which will automatically validate all of thearXiv:1802.10091v1 [quant-ph] 27 Feb 2018
2
previously created blocks.
A major technical obstacle, that prevents Visa and
other payment systems to be replaced by digital cryp-
tocurrencies for daily transactions, is the transaction con-
rmation time which is dependent on the processing time
of each new block in a blockchain. In bitcoin, the func-
tions of an intermediary, i.e. the "trust", is based on
the amount of work performed. On one hand, it has to
be hard enough for an ordinary CPU to process it in-
stantaneously (therefore the complexity of the work is
controlled and at the moment is limited to ten minutes
on average to process each block added to a blockchain).
On the other hand, for routine everyday transactions
the time spent per block has to be much shorter than
that. This con
icting requirement prevents all of the ex-
isting cryptocurrencies from becoming a real electronic
payment system that can be used for routine nancial
transactions. Another problem is the violation of de-
centralisation. The 70% of the cryptocurrencies with
the highest capitalisation are already controlled by a few
major computational nodes. Such centralised hubs of
power make it impossible for a new computational node
to join the system unless it matches the huge computa-
tional power of centralized nodes. Possible solutions of
these problems are seemingly mutually exclusive: short-
ening of the processing time has to be accompanied by
restrictions on such processing for modest computational
powers, but not to lead to computational centraliza-
tion! However, we argue in this paper that a concep-
tually di erent scheme of blockchain processing which
relies on using recently emerged analog Hamiltonian op-
timizers (AHO) and quantum simulators may be capable
of ful lling these requirements and revolutionarizing the
blockchain technology. We also argue that the modern
capabilities of such optimizers are already sucient to
implement a proof-of-work to record a public history of
transactions by solving NP-hard problems that are com-
putationally impractical for conventional classical com-
puters and CPUs, but can be easily solved on the pur-
pose built analog Hamiltonian and quantum simulators.
Moreover, our approach does not concentrate on a partic-
ular cryptocurrency such as bitcoin and we do not limit
ourselves to blockchain technology, but we rather focus
on a hard computational problem that can be used as
proof-of-work in future versions of any cryptocurrencies
or other technologies based on proof-of-work principle.
Our paper is organised as follows. In Section 1, we
give an overview of blockchain technology and the way
it drives cryptocurrency payment systems. In Section 2,
we discuss two classes of hard optimization (NP-hard)
problems, namely quadratic unconstrained binary op-
timisation (QUBO) and constant modulus constrained
quadratic continuous optimization (QCO). These prob-
lems are the type of problems that can be solved by
AHO/quantum simulators faster than by classical com-
puter. We discuss the D-Wave simulators based on super-
conducting cubits and recently emerged gain-dissipative
simulators, including polariton, trapped photon conden-
FIG. 1: Blockchain generation by computational nodes in Bit-
coin cryptocurrency. The block is added to a blockchain with
an average interval of ten minutes and it contains approx-
imately 4000-5000 transactions. The output of each block
serves as the name for the next block, thus forming a chain.
sates, and atomic multi-mode QED (quantum electro-
dynamics). In Section 3, we propose how QUBO and
QCO solved by analog simulators can be used in a
blockchain technology as a new generation of proof-of-
work protocol, which ensures much better decentralisa-
tion compared to the existing protocols and provides a
greater transaction rate. Finally, we conclude in Section
4.
1. BLOCKCHAIN AND PROOF-OF-WORK
PRINCIPLE
Adding each new block of transactions to the
blockchain requires solving a computationally demand-
ing problem, i.e. proof-of-work (POW). POW concept
was originally developed to prevent junk mails by requir-
ing the sender to solve a moderately hard computational
problem to allow for the message to be delivered [5]. In
the blockchain, POW concept is implemented by compu-
tational nodes that perform complex mathematical cal-
culations and are rewarded for this by cryptocoins. This
is often the only way for cryptocoins to enter the system,
and hence the computational nodes are called miners and
the process of performing the computations is called min-
ing.
The usual POW problems are based on a function H,
called hash function, which maps an arbitrary sized input
data to a xed size output (called hash) and is designed
to be hard to invert, i.e. the hash ycan be easily com-
puted from the initial data xby calculating y=H(x)
but nding xfrom a given yis computationally hard.
The inversion of the hash function requires an exponen-
tially growing computational time of an order of O(2n)
wherenis the hash size, but when xis found the valida-
tion of the transaction could be easily done by computing
H(x) and comparing the result with the hash y. Every
transaction in the block has a hash associated with it
and each block in the blockchain is identi ed by its block
header hash (see Supplementary Material for the full list
of parameters included in it).
The mining diculty for adding new block, which is
represented by the diculty target value, is dynamically
3
controlled and regularly adjusted by a moving average
giving an average number of blocks per hour xed in
order to compensate the increasing computational power
and varying interest in running nodes involved in mining.
In bitcoin, the diculty target value is updated every
2016 blocks in order to target the desired block interval
accurately, which is now set to be ten minutes. This
rate is chosen as an ad hoc tradeo : the time which is
too short would decrease the stability of the blockchain
(more forks and longer forks in the blockchain tree would
require an increased bandwidth between nodes); the time
which is too long would increase the con rmation time of
transactions. Other cryptocurrencies have di erent times
per block, e.g. Litecoin has a 2.5 minutes block time.
Centralisation problems. The initially supposed global
decentralisation of the bitcoin and other cryptocurrencies
is now under risk since the computational nodes tend
to unite in computational pools followed by emergence
of major cryptocurrencies with highest capitalisation on
the market, i.e. bitcoin and etherium, which are con-
trolled by 5-7 di erent parties. The network remains de-
centralised but with a few centralised hubs. This in turn
leads to a high entry barrier for a new potential min-
ing party since it has to compete with the computational
rate of centralised hubs, which will make the system even
more centralised in the nearest future. For a brief review
of security issues and other problems including energy
consumption, storage of the information, please see the
Supplementary Material.
In the next Section we discuss the type of hard prob-
lems that can be solved { mined { faster on specially
built alternatives to classical computers, namely, analog
Hamiltonian optimisers/quantum simulators. We pro-
pose to put such problems and such solvers at the core
of POW schemes that the agents need to solve in order
to add each new block of transactions to the blockchain.
2. HARD COMPUTATIONAL PROBLEMS AND
THEIR SOLVERS FOR BLOCKCHAIN POW
At the heart of Richard Feynman's idea of quantum
simulator lied the proposal of using one well-tunable
quantum system to simulate another quantum system [6].
To design such quantum simulator [7], one needs to map
the variables of the desired Hamiltonian of the system
into the elements (spins, currents etc.) of the simulator,
tune the interactions between them, prepare the simu-
lator in a state that is relevant to the physical problem
of interest, and perform measurements on the simula-
tor with the required precision. In the past decade, this
original meaning of quantum simulator has been widened
and modi ed to include the platforms that intend to solve
classical optimization problems faster than classical com-
puter for a given problem size (in terms of the number of
variables, and therefore, the dimensionality of the func-
tion to be optimized). Various physical systems have
been proposed and realised as such simulators to a vari-ous extent [8]. Among those are systems that use essen-
tially quantum processes for their operation (e.g. entan-
glement and superpositions) such as trapped ions [9, 10]
or superconducting qubits [11], for others although the
quantum processes are crucial in forming the state of
the system such as Bose-Einstein condensates, the sys-
tem behaves as a classical system, e.g ultracold atoms
in optical lattices [12{15], network of optical parametric
oscillators (OPOs) [16, 17], coupled lasers [18], polariton
condensates [19], multimode cavity QED [20] and photon
condensates [21]. These systems emulate spin Hamilto-
nians such as Ising, XY or Heisenberg (so called n vec-
tor models). Hardness of the problem depends on the
number of nodes (eg. bits, qubits), on the ability to con-
trol couplings between the elements and the overall con-
nectivity of the system. The existence of universal spin
Hamiltonians has been established. Universality means
that all classical n-vector models with any range of in-
teractions can be reproduced within such a model, and
certain simple Hamiltonians such as 2D Ising model on
a square lattice with transverse elds are universal [22].
Indeed, such problems are NP-hard for a general matrix
of couplings { the number of operations grows as an ex-
ponential function with the matrix size. This suggests
that one can formulate a spin Hamiltonian (Ising, XY or
Heisenberg) for which the global minimum can be found
by a simulator optimized for solving such problems much
faster than on classical computer.
Finding the optimal solution of the general nvector
model for a suciently large size may be suitable for
a POW protocol. Here we discuss two of such prob-
lems as the system requirements for simulators men-
tioned above designed to solve these problems. First of
these is the quadratic unconstrained binary optimisation
(QUBO) problem which is an optimization formulation
of a max-3-cut problem for vector z2CNwith compo-
nentszi;i= 1;NandNNreal symmetric matrix
Q
maxzHQz;subject to zi2f 1;1g; (1)
and, second, quadratic continuous optimisation (QCO)
problem
maxzHQz;subject tojzij= 1: (2)
QUBO is a discrete version of QCO for which the decision
variables are constrained to lie on the unit circle,which is
a continuous domain. The Ising model
min X
i<jJijsisjsubject to si2f 1;1g(3)
and XY model
min X
i<jJijsisjsubject to si= (cosi;sini);
(4)
are trivially mapped into QUBO and QCO, respectively,
4
by associating the "spins" siandsiwithz(viazi=
cosi+isinifor the XY model, and zi2f 1;1gfor
the Ising model) and the coupling strengths Jijbetween
the spins with the matrix elements of Q.
These problems are known to be strongly NP-hard in
general and, therefore, even for medium-sized instances
are dicult to solve on a classical computer [23]. The
time required to nd the solution depends on the matrix
structure: its size, number of zero entries (sparsity), the
way the elements are generated, whether it is positive-
de nite or inde nite matrix etc. The algorithms for solv-
ing these problems on a classical computer can be divided
into three types: exact methods that nd the optimal so-
lution to the machine precision, approximate algorithms
that guarantee that the solution will be found within
some approximation ratio and heuristic algorithms where
suitability for solving a particular problem comes from
empirical testing [24]. Exact methods typically involve
a tree search of a general branch-and-bound nature and
the exponential worst-case runtime. They can be used
to solve a limited range of problems for small or sparse
matrices. The heuristic algorithms such as simulated an-
nealing, genetic and evolution algorithms can deliver a
good, but suboptimal (and possibly infeasible) point in a
short amount of time [25]. Approximate algorithms nd
an approximate solution { an optimal value of zHQz,
which is at least a constant times the true optimal value.
Such constant is called a "performance guarantee" of
the algorithm. Both QUBO and QCO are known to
be APX-hard problems [26] meaning that there is no
polynomial-time approximation algorithm that gives the
value of the objective function that is arbitrarily close to
the optimal solution (unless P = NP). For these prob-
lems, therefore, the perfomance guarantee is bounded by
a constant and no approximate algorithms can be de-
vised to do better. The approximate algorithms are typ-
ically based on some form of semide nite programming
relaxation (SDP). The achieved performance guarantee
depends on the structure of the matrix. For instance,
for positive semide nite Qwith the elements of the same
sign the performance guarantee of SDP methods can be
as high as2
min0
1 cos 0:878 [27], however, if
the assumption about the sign of elements is relaxed the
performance guarantee becomes 0.537 for QUBO and =4
for QCO [23] . An approximation algorithm on a more
general inde nite matrix will yield even worse approx-
imate. Furthermore, the task of nding an approxima-
tion for maximizers zinstead of the approximation to the
objective function zHQzeasily becomes non-computable
[28]. The best classical computational algorithms capable
of nding the solution of the QUBO/QCO problems for
a general matrix Qare limited to very modest sizes. For
instance, for N= 200 with only 30% nonzero elements
the state-of-the art algorithms would take on average 80
minutes to solve QUBO [29].
Since the spin Hamiltonian models are straightfor-
wardly mapped into QUBO and QCO, it is natural that
analog/quantum simulators based on condensed matterquantum systems have architecture most suitable for
solving such problems. Below we consider the most
promising platforms for solving QUBO or QCO.
Quantum Annealers. D-Wave is a rst commer-
cially available quantum annealer that is built on su-
perconducting qubits with programmable couplings and
speci cally designed to solve QUBO problems (Eq. 1)
[30]. By specifying the interactions Jijbetween qubits, a
desired QUBO problem is solved by quantum annealing
process [31]. Adiabatic (slow) transition in time from an
initial state of a specially prepared "easy" Hamiltonian
to the objective Ising Hamiltonian guarantees that the
system remains in the ground state, which gives the nal
energy that corresponds to the optimal solution of the
QUBO problem.
First benchmarks on random QUBO problems were
performed on a "D-Wave One" and "D-Wave Two" ques-
tioning the fact of quantum speedup of annealer [32]. It
was demonstrated that although the D-wave One sim-
ulator (with 128 qubits) is a true quantum annealer it
is not yet competitive with classical computing technol-
ogy. No speedup was found for problems of sizes ranging
from 8 to 512 qubits. Later, it was shown [33], that for
carefully crafted problems with rugged energy landscapes
that are dominated by large and tall barriers, the quan-
tum annealer can o er a signi cant runtime advantage
over a classical version of simulated annealing. For some
problems with sizes involving nearly 1000 binary vari-
ables, quantum annealing was up to 108times faster than
classical simulated annealing (SA) or Quantum Monte-
Carlo (QMC) methods running on a single core CPU.
The quantum speedup was not claimed again since a va-
riety of heuristic classical algorithms can solve most in-
stances of problems structured on Chimera architecture
of D-Wave computers more eciently [34].
These results do not exclude a possible quantum
speedup for some other problems that can be speci -
cally created to suit the machine's abilities and which will
bene t from quantum anealing. With the last commer-
cially available D-Wave machine released in 2017, quan-
tum annealers may nally outperform all classical algo-
rithms. For evaluation of the new 2000-qubit D-Wave
QPU a new synthetic problem class was proposed [35]
with more emphasis on creating computational hardness
through frustrated global interactions. Such frustration
creates meaningful diculty for general heuristic algo-
rithms that are unaware of the planted problem class.
The D-Wave team claimed the 2000Q could nd solu-
tions up to 2600 times faster than any known classical
algorithm [35]. This time the D-Wave simulator was com-
peting with the state-of-the-art CPU implementations
of SA, QMC, support vector machine classi cation al-
gorithm, and Selby's CPU implementation of Hamze-de
Freitas-Selby algorithm, making the competition much
stronger than it was in [33]. Three orders of magnitude
speedup over software solvers, reported for pure anneal-
ing time (computation time), translates into a 30 times
speedup in total wall clock time including programming
5
and readout necessary for D-Wave machine.
One of the major limitations of D-wave simulators is
that each qubit can be connected to maximum of six
other qubits and thus N2qubits are needed for encod-
ingN-variable problem. The next generation of D-Wave
quantum computer is expected to be announced in 2018
with new powerful capabilities such as reverse annealing
and virtual graphs. These features are expected to en-
able signi cant performance improvements over the cur-
rent D-Wave simulators by giving users increased control
of the QPU and will probably make the fact of a quantum
speedup even more clear.
OPO-based simulators. Network of coupled optical
parametric oscillators (OPOs) is an alternative physical
system for solving the Ising problem ([36] and references
therein). A scalable optical processor with electronic
feedback was realized for solving Ising problem with up
to 100 spins and 10,000 spin-spin connections [36]. In
this Coherent Ising Machine (CIM), the ground state of
the Ising Hamiltonian corresponds to an oscillation mode
with the minimum network of degenerate OPOs loss. The
fully programmable connections between any two spins is
a signi cant di erence of this model compared to the D-
Wave simulator. The rst promising signs of speedup of
CIM have been reported recently [37], however, the fun-
damental computational power of OPO Ising machines
or the time required to program thousands of connec-
tions and to readout of the nal state have not been fully
explored.
Gain-Dissipative Simulators. We de ne gain-
dissipative simulators as the optimisers based on a
driven-dissipative physical system. The principle of their
operation depends on the gain (pumping) mechanism. As
the gain exceeds some threshold a coherent state of mat-
ter emerges maximising the state occupation and there-
fore solving QUBO or QCO [19]. These are novel sys-
tems and their potential as analog simulators has been
very recently demonstrated [19{21, 38]. These platforms
could be referred to as quantum simulators due to the
quantum-statistical nature of the formation of the coher-
ent state (e.g. Bose-Einstein condensate) during which
a large fraction of bosons occupies the lowest quantum
state and the macroscopic quantum phenomena emerges.
These systems enjoy a quantum speed-up which is asso-
ciated with the stimulated process of condensation i.e.
an accelerated relaxation to the global ground quan-
tum state. However, after the condensate formation
the system behaves classically as non-commutativity of
eld operators becomes insigni cant in the large num-
ber of particles regime. To distinguish these platforms
from quantum computers/quantum simulators that rely
on entanglement and quantum superposition, we refer
to them as "analog Hamiltonian simulator/optimizer"
(AHO). The search for the solution of QUBO or QCO
is via a bottom-up approach which has an advantage
over classical or quantum annealing techniques, where
the global ground state is reached through either a tran-
sition over metastable excited states or via tunnelling be-tween the states in time that depends on the size of the
system. Di erent AHOs rely on di erent quasi-particles
as the basis for bit/qubit and vary by scalability, cou-
pling control, connectivity and the accuracy with which
the result can be read, etc. The microscopics of various
systems we present below can be quite di erent, but the
governing principle for solving QCO is based on the rep-
resentation of each "spin" indexed by iand centered at
the position xiby the wavefunction ( jx xij) exp[ii],
where is the wavefunction of an isolated condensate
centered at the origin. At the condensation threshold
the phase di erences between the individual wavefunc-
tions maximize the total number of particles given by
M Z X
i (jx xij) exp[ii] 2dx
=NZ 2dx+X
j<iJijcos(i j); (5)
whereJij=R
[ (jx xij) (jx xjj)+c:c]dx. Since the
rst term on the right hand side does not depend on the
phases, the maximization of Mis equivalent to minimiza-
tion of Eq. (4), and, therefore, to nding the solution to
QCO. We conclude that such gain-dissipative platforms
solve QCO at the condensation threshold [19]. To under-
stand the formation dynamics of such driven-dissipative
system one can replace the spatially dependent conden-
sate pro le centered at xiby a complex function i(t)
with the dynamics described by a rate equation [39]
_ i= (
i
e ivi j ij2 iUj ij2) i+X
jJij j;(6)
where
i
e is the e ective gain given by the di erence
between the slowly increasing function of pumping and
a constant linear dissipation at site i,virepresents the
blue shift due to the external potential applied at the
sitei,corresponds to the nonlinear losses that allow for
gain saturation and therefore, for a steady state at the
condensation threshold, and Udescribes the strength of
nonlinear interactions at the site i. If one writes iin
terms of the density i(t) and phase i(t) using i=piexp[ii(t)] and separates real and imaginary parts of
Eq. (6) then the resulting equations yield
1
2_i= (
i
e i)i+X
jJijpijcosij+ i(7)
_i= vi Ui X
jJijpjpisinij+i; (8)
whereij=i jand we included a small density and
phase perturbations i(t) andi(t) that respectively rep-
resent the spontaneous and stimulated scattering during
the condensation process, incorporate classical and quan-
tum e ects and disappear at the condensation threshold.
It follows from Eqs. (7-8) that the gradient
ow to the
6
Jij
FIG. 2: A general scheme for a gain-dissipative simulator.
The condensates are pumped in a two-dimensional lattice
with interaction strengths Jijbetween the adjacent vertices.
The nal phase con guration minimizes the XY orIsing
Hamiltonians depeding on a particular physical system.
solution of QCO is realised if all iare the same, which
is achieved by adjusting
i
e during the condensate for-
mation. At the condensate threshold the steady state is
achieved with _ i= 0 and _i== const, where is the
chemical potential of the system. To drive the system to
such a state the adjustment of the pumping of the indi-
vidual nodes has to be accompanied by the adjustment
of the external potentials vi[40].
Next we discuss the the actual physical platforms rep-
resenting the gain-driven analog Hamiltonian optimizers.
Polariton graph optimizers. Polariton graph op-
timizers are based on exciton-polariton condensates ar-
ranged at a particular graph geometry and are used for
solving QCO (Eq. 2). Exciton-polaritons (or polaritons)
are the composed light-matter quasi-particles formed in
the strong exciton-photon coupling regime in semicon-
ductor microcavities [41]. At low densities these quasi-
particles are bosons and condense above some thresh-
old of pumping intensity forming a single coherent state.
Polariton condensates can be imprinted into any two-
dimensional graph by spatial modulation of the pumping
laser. Such two-dimensional graphs of condensates in-
herit high
exibility in engineering any geometrical con-
guration of nodes. By controlling the excitation density,
the pro le of the pump, the graph geometry and the sep-
aration distance between the lattice sites, one can control
the couplings between the sites and realise various phase
con gurations between individual condensates. At the
condensation threshold the phase di erences, i j, be-
tween condensates at di erent nodes indexed by iandj
establish the minimum of the XY model (4) and, there-
fore, solve QCO [19, 39]. Polariton graphs are easily
scalable and the graphs that consist of 45 and 100 nodes
have already been realised [19]. The coupling strengths
Jijin (Eq. 4) and so the elements of Qin (Eq. 2) are
controlled by the graph geometry and go beyond the next
neighbour interactions [42].
Photon-based simulators. Photon condensates as
the elements (bit/qubit) of AHO for solving QCO can be
created by generating variable potentials for light withinan optical high- nesse microcavity [43]. The long pho-
ton lifetime enables the thermalization of photons and
the demonstration of a microscopic photon condensate
in a single localized site. The investigation of e ective
photon-photon interactions as well as the observed tun-
nel coupling between sites makes the system a promising
candidate for AHO. The scalability of hundreds of con-
densates has been already demonstrated [43] suggesting
that thermo-optic imprinting provides a new approach
for variable microstructuring in photonics.
QED-based simulators. A realization of a multi-
mode cavity QED (quantum electrodynamics) system
[44] paves a way to a QED-based AHO thanks to the
strong, tunable-range, and local interactions between
Bose-Einstein condensates trapped within the cavity.
While single-mode cavities provide strong but in nite-
range photon-mediated interactions among intracavity
atoms, it was experimentally shown in [44] that local
couplings can be created using multimode cavity QED.
Moreover, atom-atom couplings may be tuned from short
range to long range which reduces the sparseness of ma-
trixQand therefore increases the potential complexity
of the problem.
The XY model has been previously simulated by other
physical systems: ultra cold atomic optical lattices [45]
and coupled photon lasers network [38], which was also
proposed for solving QCO. We have not discuss here
other universal fully quantum platforms such as based
on superconducting qubits [46] or trapped ions [47] as
their potential for solving QUBO and QCO is not fully
explored.
3. LAYING OUT ANALOG HAMILTONIAN
SIMULATORS ON A BLOCKCHAIN.
In the previous section we considered several possible
analog Hamiltonian simulators that start being compared
to classical state-of-the-art algorithms for solving the
global optimization problems such as QUBO and QCO.
These platforms, have either demonstrated a speedup,
or have a potential to achieve this in the nearest future.
Figure 3 illustrates the schematics of the POW protocols
that can be based on solving QUBO or QCO problems
using the currently available analog Hamiltonian simula-
tors.
The recipe for building a blockchain based on such sim-
ulators is essentially the same for all types of simulators.
The input for each block will include among other pa-
rameters (like timestamp, previous block id, etc.) a pa-
rameter that is speci c for each type of simulator. In case
of the simulators considered here, this additional param-
eter is a matrix of coupling strengths Jij(the elements
of matrixQ). The ways of controlling and modifying the
coupling strengths are system dependent, for instance, by
changing the distance, pumping intensity and the hight of
the trap barrier in gain-dissipative simulators. The cou-
pling matrix has to be formed depending on the block
7
content, so nobody can prepare a coupling matrix in ad-
vance and solve it in order to approve a particular block.
For instance, the numerical expression of the order of
transactions together with the amount of each transac-
tion could be used to form this matrix. The output of
the block consists of the problem optimizers: the result-
ing "spins" sior phasesi, that can be further encoded
and serve as a next block's name. Verifying whether the
block belongs to the chain or not can be done for instance
by checking that the value of the objective function zHQz
for the found optimizers zis larger (better) than the one
found by a classical approximation or heuristic algorithm.
Next we explicitely construct the matrix Q. Suppose
that a blockchain consists of blocks with capacity of X
transactions per block. Depending on the capabilities of
a particular simulator/optimizer a di erent range of cou-
pling strengths Jijcan be realised (for instance, a polari-
ton graph optimizer can achieve the coupling strengths
between 2eV to 2eV [48]). Based on the platform
we de ne a hash function H0(x) =ythat maps a trans-
actionxinto an output ywherey2[minJij;maxJij]:
To generate the coupling matrix Qof sizeNNand
sparsity (100 D)% we need to map the block content
intom=N(N 1)D=200 non-zero matrix elements. If
X=m, each transaction is hashed into a non-zero ma-
trix element directly using H0. IfX > m a necessary
number of transactions has to be hashed together (by a
di erent hash Hthat returns the same size of the out-
put as input) before mapping using H0is performed. If
X < m then the required number of non-zero elements
is generated by rst hashing all transaction individually,
then adding the hashes of their pairs, and so on until the
required number of elements mis reached.
For instance, for bitcoin, the amount of transactions
per block is X5000. The most recent D-Wave sim-
ulator has a coupling matrix of size N2000 which
is quite sparse with D1% which gives about 20000
non-zero matrix elements. We ll the rst 5000 elements
by hashing each of 5000 individual transactions, than by
hashing the hashes of the pairs of the rst, second and
Proof-of-Work based on Superconducting Qubits (D-Wave)Optical Parametric Oscillators(Coherent Ising Machine)Gain-Dissapative Simulatoron polariton condensatesGain-Dissapative Simulatoron photon condensatesGain-Dissapative Simulatoron QEDQuadratic UnconstrainedBinary Optimisation (QUBO)Quadratic Continiuous Optimisation (QCO)Simulators that minimise Ising HamiltonianSimulators that minimise XY Hamiltonian
FIG. 3: The scheme shows that the Proof-of-work protocol
can be realised for solving the QUBO or QCO problems on
purposely built quantum simulators based on superconduct-
ing qubits, OPO-s, polaritons, photons, QED.third transactions with all the rest.
Finally, we note that not only the transaction should
be mapped to the coupling matrix, but the other header
parameters of the block as well (timestamp, the previous
block's id, see Suppl. Mat. for a full list of parameters).
4. CONCLUSIONS
In this article, we propose to use the analog Hamilto-
nian optimisers as a basis for a proof-of-work protocols.
Such simulators are capable of outperforming classical
computers on the timescale of seconds or less as compared
to hours or more, which o ers straightforward bene ts.
In a particular case of blockchain technology, such op-
timisers will allow a faster veri cation process reducing
the time interval between blocks which will signi cantly
decrease the con rmation time of transaction to the or-
der comparable to Visa and even beyond, and a possible
new generation of digital cash will be applicable on day-
to-day basis. A speci c achievable timescale depends on
the nature of simulator and the type of the problem. In
case of a commercially available D-Wave machine, for in-
stance, proof-of-work will take tens of milliseconds. Con-
sequently, the analog simulators considered in this article
will make possible for a blockchain to be operated at a
maximum synchronisation speed between the nodes.
One can think about quantum simulators/analog
Hamiltonian optimizers as the computational black boxes
with approximately the same "hashing power" which is
higher than the power of any classical computer. Dis-
tributing such blackboxes between 20-50 independent
nodes would make the system much more decentralised
than any other existing platforms of current blockchain
technology. The proof-of-work protocol based on ana-
log Hamiltonian simulators would allow better scalability
by making faster the processing of databases (sharding
implementation) while current proof-of-work schemes do
not support it due to security issues.
Supplementary material
The block header. The block header consists of the
bitcoin version number, the hash of the previous block
header, the hash of all the hashes of all the transactions
in the block (the Merkle Root), the timestamp, the di-
culty target (the precision of calculation needed to meet
the required level of POW in under ten minutes) and a
random 32-bit integer number called 'nonce' (the value
that is altered by the miners to try di erent permutations
to achieve the diculty level required).
Bitcoin security. The security of bitcoin is based
on the two cryptographic protocols that prevent it from
being stolen or copied.
The rst is the POW that is required to write trans-
actions to the bitcoin digital ledger. If a majority of
computations are done independently, the honest chain
8
will grow faster and outpace any competing chains since
all the new blocks have to be added to the longest chain.
To modify a past block, an attacker would have to redo
the POW of the block and all blocks after it and then
catch up with and surpass the work of the honest nodes.
The probability of a slower attacker catching up dimin-
ishes exponentially as subsequent blocks are added. So
it's not possible to change the information about any
transactions in the blocks that has been already incorpo-
rated in the blockchain. Moreover, nobody can prepare
a malicious block in advance, since the result of the hash
function will depend on the previous block's hash. This is
why a malicious node has to compete in a computational
power with the whole network of peers in real time with
the only chance to win by exceeding the total power of all
other nodes. Thus, the higher total computation power
of the distributed network of nodes ensures the safety
of the blockchain from creating an alternative history of
transactions by a particular malicious node.
Bitcoin has another cryptographic security feature to
ensure that only the owner of cryptocoins can spend them
which is based on an elliptic curve digital signature algo-
rithm (ECDSA). It is based on assumption that nding
the discrete logarithm in a cyclic subgroup of a random
elliptic curve over a nite eld is infeasible [49]. Following
this signing algorithm, the coin owner generates two ran-
dom numbers for each transaction: a private key which
only the owner knows and a public key which is revealed
to the network. Only the owner is able to produce valid
signatures based on these keys since the public key can be
easily generated from the private key but not the other
way around. In this scenario, the owner signs the hash
of the transaction with the private key, and everyone else
is able to validate the signature with the available public
key by looking at the given signature and transaction. In
this way, a signature can be used to verify that the owner
possesses the private key and therefore has the right to
spend the bitcoin, without revealing the actual private
key.
Security Issues. Knowing the two main security fea-
tures of bitcoin leaves one with the two possibilities to
prevail over the bitcoin's system: either controlling more
than 50% of the computational power on the network
or cracking the cryptographic signature scheme. For the
latter case, the only way to cheat ECDSA is to calculate
the private key using the public key, which is extremely
hard problem with classical computers though its exact
complexity class is not known. Quantum computers run-
ning the Shor's algorithm for number factorization [50]
pose a risk to the encryption schemes such as RSA, and
therefore to cryptocurrencies in particular. However, to
break the relevant RSA codes (say RSA-1024) requires
105qubit quantum computer which is beyond the cur-
rent capabilities of about 50 qubits [51].
At present, there are suggestions of countermeasures
that can be taken to protect bitcoin from a possible fu-
ture attack of quantum computers. Examinations of how
quantum computers could undermine and even exploitbitcoin security protocols have been recently discussed
in [52]. Another proposal [53] positions transmission of
quantum cryptographic keys between a remitter and a
receiver of the eponomous named cryptocurrency, qBit-
coin, where the exchanging qBitcoins requires a transmis-
sion network in place that can send and receive bits of
quantum information, qubits. The alternative signature
schemes that are believed to be quantum safe, are also
discussed, and thus qBitcoin security may rely on a quan-
tum digital signature in future. Quantum-cryptographic
improvements to the current cryptographic schemes that
may be widely used in digital currencies were suggested
as well [54{56].
Energy consumption. In a PoW system, large amounts
of electricity are required in order to power the com-
puting hardware: the bitcoin's current annual electricity
consumption is about 30 TWh which is close to consump-
tion of such countries as Ireland, Buhrain, Slovak Repub-
lic, Oman, Morocco. The bitcoin miners spend around
$50;000 per hour on electricity which is equivalent to
$450 million per year.
Storage problem . Up to now, bitcoin supports about
5-7 tps with a 1 megabyte block limit. Assuming the
unlimited block size and 300 bytes per bitcoin transac-
tion on average, it would require nearly 0.5 gigabytes per
bitcoin block, every ten minutes on average to reach an
equivalent capacity of Visa transaction volume of aver-
age order of2000 tps (56,000 tps at peak) or Paypal of
the order of200 tps (600 tps at peak). Continuously,
that would be over hundred terabytes of data per year.
Clearly, achieving Visa-like capacity on the Bitcoin net-
work is not feasible today since this amount of data has
to be stored not in one place but in all the nodes con-
stituting the decentralised computational network. To
x the issue with the con rmation time and the num-
ber of possible transactions per second, bitcoin lightning
network has been proposed, however, one of the conse-
quences of its implementation could be further unwanted
centralisation [57].
Alternative to proof-of-work: proof-of-stake. In POW
miners invest computing power competing for a chance
to add next block to a Blockchain and win a reward for
doing so. To decrease the energy demands, an alterna-
tive to POW known as the proof-of-stake (POS) scheme
was suggested making the entire mining process virtual:
individual computational nodes reach consensus about
each next block by betting their money. The stakehold-
ers who will form the next block are randomly selected
from a pool of validators with respect to the size of stake
they have, and once the block is added to a Blockchain, a
reward proportional to the stake is given. Betting on the
wrong or the malicious block is to be punished by the
system, which is, in case of etherium's POS suggested
protocol the entire invested stake may be lost [58].
For a blockchain to be secure, the means of selecting
a stakeholder to make a block must be truly random.
Ouroboros introduced the randomness into the leader
election process by way of a secure, multiparty imple-
9
mentation of a coin-
ipping protocol which is realised in Cardano cryptocurrency [59].
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|
{
"id": "1802.10091"
}
|
1906.11946
|
LApps: Technological, Legal and Market Potentials of Blockchain
Lightning Network Applications
|
Following in the footsteps of pioneer Bitcoin, many altcoins as well as
coloured coins have been being developed and merchandised adopting blockchain
as the core enabling technology. However, since interoperability and
scalability, due to high and capped (in particular cases) transaction latency
are deep-rooted in the architecture of blockchain technology, they are by
default inherited in any blockchain based applications. Lightning Network (LN)
is one of the supporting technologies developed to eliminate this impediment of
blockchain technology by facilitating instantaneous transfers of cryptos. Since
the potentials of LN is still relatively unknown, this paper investigates the
current states of development along with possible non-monetary usage of LN,
especially in settlement coloured coins such as securities, as well as creation
of new business models based on Lightning Applications (LApps) and microchannel
payments as well as micro-trades. The legal challenges that may act as
impediment to the adoption of LN is also discussed.
|
http://arxiv.org/pdf/1906.11946v1
|
Mahdi H. Miraz, David C. Donald
|
cs.CR
|
cs.CR
|
THE CHINESE UNIVERSITY OF HONG KONG
FACULTY OF LAW
Research Paper No. 2019 -04
LApps: Technological, Legal and Market Potentials
of Blockchain Lightning Network Applications
Mahdi H. Miraz
David C. Donald
This is a pre -print of the article accepted for oral presentation at and inclusion in the proceedings of the 3rd International Conference on
Information System and Data Mining (ICISDM2019) , proceedings to be published by ACM in the International Confer ence Proceedings
Series. The ACM Digital Library link to the final published version of the article will be provided once the article is published.
LApps: Technol ogical, Legal and Market Potentials of
Blockchain Lightning Network Applications
Mahdi H. Miraz
The Chinese University of Hong Kong
Sha Tin
Hong Kong
m.miraz@ieee.org David C. Donald
The Chinese University of Hong Kong
Sha Tin
Hong Kong
dcdonald@cuhk.edu. hk
ABSTRACT
Following in the footsteps of pioneer Bitcoin, many altcoins as well
as coloured coins have been being developed and merchandised
adopting blockchain as the core enabling technology. However,
since interoperability and scalability, due to high and capped (in
particular cases) transaction latency are deep -rooted in the
architecture of blockchain technology , they are by default inherited
in any blockchain based applications . Lightning Network (LN) is
one of the supporting technologies developed to eliminate this
impediment of blockchain technology by facilitating instantaneous
transfers of cryptos. Since the potentials of LN is still relatively
unknown, this paper investigates the current states of deve lopment
along with possible non-monetary usage of LN, especially in
settlement coloured coins such as securities, as well as creation of
new business models based on Lightning Applications (LApps) and
microchannel payments as well as micro -trades. The lega l
challenges that may act as impediment to the adoption of LN is also
discussed.
CCS Concep ts
• Applied computing → Electronic commerce → Digital
cash • Applied computing → Electronic commerce →
Electronic funds transfer • Applied computing → Law,
social and behavioral sciences • Networks → Network
protocols → Application layer protocols → Peer -to-peer
protocols.
Keywords
Blockchain; Atomic Swap; Lightning Network; Cross -chain
Trading; Cryptocurrencies; Cross -listing; Wallet-to -Wallet
Transfer; On-Chain; Off -Chain; Layer 2; H ashed Timelock
Contracts (HTLC); Payment Channels; State Channels; Coloured
Coins; DAO; ICO; DApps; LApps.
1. INTRODUCTION
Despite several successful new and experimental applications
of blockchain [1] technology , including the conceiving Bitcoin,
there are few inbred limitations that caveat the outright utilisation
of the technology . Lack of interoperability amongst various chains
and restrained scalability by high transact ion latency due to
decentralised consensus approach and network architectural
limitations are the prominent ones [2]. For instance, hitherto
Bitcoin can process 7 transactions per second (TPS) and Ethereum
has the capacity of 15 TPS. While Ripple has a capacity of 1500
TPS, it is far lower tha n that of Visa which is 24,000 [3] and if
demand increases it is likely to go down due to increased network
congestions and augmented load on the consensus process. At
least, this has been the case for Bitcoin and Ethereum: An “unconfirmed” Bitcoin transaction might take a minimum of 10
minutes on an average to get “confirmed” if pulled into the next
availab le “candidate” block, otherwise the process can take up to
several days. Due to increasing popularity of DApps (Decentralised
Apps) , DAOs (Decentralised Autonomous Organization) and
ICO’s (Initial Coin Offering), the waiting time in Ethereum
network is als o exponentially rising. Off-chain scaling, such as
lightning n etwork (LN) [4,5], can play a vital r ole in this regard. In
off-chain scaling [4,6,7,8,9,10,11], a second layer, also known as
layer 2 or payment channel, enables unlimited instantaneous
transactions to take place between two parties. When the channel is
closed, only the netted result of the transactions is broadcast to the
network for consensus. LN thus directly holds the promises of
eliminating the scalability problem of blockchain while atomic
swaps powered by a LN possess the potential of enabling smo other
interoperability.
The rest of the paper briefly describes the fundamental
principle that serves as the foundation for LN . The paper then
evaluates the feasibility of its application in exchanging and settling
of coloured coins – real world assets represented on the Bitcoin (or
more widely any blockchain) networks such as securi ties, bonds,
futures, shares and other commodities. The paper also discusses the
creation of new non-monetary business models laid on the
foundation of LN – mainly utilising Lightning Applications
(LApps) and LN empowered micropayment provisions.
2. FUNDAMENT ALS OF LIGHTNING
NETWORK
2.1 Preamble
A Lightning Network is a second layer Hashed Timelock Contract
(HTLC) based smart contract enabling bi -directional payment
channels built on top of a base layer of blockchain such as Bitcoin.
LN effectuates secure routing of payments or transactions of
coloured coins across multiple peer -to-peer (P2P) payment
channels enabling transactions between two parties who are not
directly connected by any point -to-point channel. Thus, by off -
loading the transactions away from the base layer, LN engineers
instantaneous transfers of assets , cryptocurrencies or other crypto
assets with near -zero transaction fees. The concept of lightning
network was first revealed in December 2015 by Joseph Poon and
Thaddeus Dryja [4], however, it took almost two years to undertake
a series of successful implementations of interoperable test
transactions on Bitcoin core network in December 2017.
Let us consider that Alice and Bob would like to establish a LN
payment channel for transacting Bitcoins ( BTCs ) amongst
themselves. The first step is creating a multisignature wallet which
can be accessed by both of them using their respective private keys.
After the wallets is created, they then need to make a deposit , the
“funding transaction”, a certain amount of BTC (for example 10
BTC) each into the multisignature wallet they have created. A
“commitment transaction” is then required to enable the transfer of
funds using LN. For instance, Alice wants to send 2 BTC to Bob,
she will simply need to transfer ownership of 2 BTC to bob having
the new balance sheet (Alice owns 8 BTC while Bob now owns 12
BTC) signed by the private respective private keys of both parties.
They can now conduct unlimited transac tions between them by
redistributing the funds of the shared wallet. The transfer of
ownership right s is bi -directional and can be performed an
unlimited number of times before the channel is closed by either
party. In this scenario, the funds are actually distributed at the time
of closing the channel and initial as well as final balance are then
broadcasted to the peers of the base blockchain for consensus
approach as normally performed in any base layer transactions.
Thus, the outcome of netted multiple LN transactions is recorded
on the blockchain as a single transaction.
In fact, the commitment transaction fundamentally allocates the
funding transactions as per the current allocation and therefore
comprises two asymmetric transactions. In case of Alice, these are:
one that pays Bob straightaway and the other that is a revocable but
time-locked output , which ultimately pays Alice. The later can be
revoked by Bob if he knows the revocation key. Similarly, Bob’s
commitment transaction will be the converse.
Let us consider the case where Alice is connected with Bob using
one LN payment channel while Bob is connected to Trudy by
another channel. This will enable Alice to indirectly transfer her
coins to Trudy via Bob without needing any dedicated channel
betwe en them. With the widespread adoption of LN, such indirect
channel will automatically increase in scope. However, a routing
algorithm will be required to find an optimal route from the source
to the destination. LN adopts an onion style routing approach
without comprising the privacy where the intermediate nodes only
know the next hop address rather than both the next hop and the
final destination addresses as in traditional routing algorithms. As
of 15:06:40 GMT+0800 (Hong Kong Standard Time) on Thursday
31st January 2019, the number of total number of “reachable”
Bitcoin nodes w as 10,527 with an average of 10,301 in the last 24
hours1, while the number of LN enabled nodes was 5,788 with
23,021 channels of a total network capacity (for LN transfers) of
618.51 BTC . However, the number of “active” LN enabled nodes
was only 2,8702.
So far, there have been several variant implementations of the
originally proposed LN , following recommendations from other
developers of the Bitcoin community. The three major
implementations are: Blockstream’s “c-lightning” implementation
in C , Lightning Labs’ Golang ’s implementation of “Lightning
Network Daemon (LND)” and ACINQ’s Scala implementation of
“eclair”. A complete updated list can be found at GitHub.3 All three
of these have been prove n interoperable by real LN transfers.
Ethereum’s Raiden Network is also an example of off -chain scaling
similar to LN.
2.2 Basic Algorithm
Initially, Alice’s commitment transaction is A1 with a revocation
key of RA1 which is only known by Alice. Similarly, Bob’s
1 https://bitnode s.earn.com/dashboard/
2 https://1ml.com/statistics commitment transaction is B1 with a revocation key of RB1 which
is only known by Bob.
Let us assume Alice wants to send Bob 2 BTC (initially she had 10
BTC).
1. Alice generates a new Bob’s transaction B2
allocating 8 BTC to Alice and 12 BTC to Bob.
2. Alice then signs B2 with her private key and transmit
it to Bob.
3. Once received, Bob signs B2 and temporarily keeps
it
4. Bob generates a new Alice’s transaction A2
allocating 8 BTC to Alice and 12 BTC to Bob.
5. Bob then signs A2 with his private key and transmit
it to Alice.
6. Once received, Alice signs A2 and temporarily
keeps it
7. Alice shares RA1 invalidating A1; A1 can now be
deleted
8. Bob shares RB1 invalidating B1; B1 can now be
deleted
Algorithm 1: Basic LN Algorithm, developed based
on the original LN proposal by Poon and Dryja [ 4].
2.3 Major Advantages
Although the primary intension of developing LN was to
facilitate instantaneous payments over Bitcoin networks, it brings
many other advantages such as :
• Since LN is the enabler of off -chain atomic cross -chain swaps
[2], all the benefits atomic swap can offer are imput ed to LN
including those of sidechains .
• Off-chain scaling such as LN will help cryptocurrencies to
compete with fiat currencies to s ome extent.
• Off-loading some transactions away from the base layer of
chain will shorten the processing queue of “unconfirmed”
transactions which will result in reduc ed on-chain transaction
fees.
• Improved privacy is another key advantage of LN as the
transactions are not recorded on the base DLT. Onion style
nested routing approach adds an extra layer of privacy as t he
intermediate hops can only see the next hop’s address, without
revealing the final destination address.
• Merchants of commodities such as online shops or food
outlets can open a LN Channel and receive instant payments.
• Since the transaction fee is near -zero, LN effectively works as
a micropayment channel.
• LN based LApps possess great potentials to lead the creation
of new ventures and innovative business models.
• LN actuate micro -trading of cryptocurrencies and other crypto
assets.
• LN has the potentials of being used in the settlement of non-
monetary coloured coins such as securities.
2.4 Major Impediments
Considering the fact that off-chain scaling technology such as
LN is still in its infancy, there are many i mpediments to overcome
3 https://github.com/bcongdon/awesome -lightning -network
before it is widely and eff ectively adopted . Thus far, the major
constrains of this technology are as follows:
• LN is considered to be a “resource hog” since both the Bitcoin
and LN nodes need to be run on the same server. This requires
extremely high computational power as well as considerable
amount of time for new installations, especially due to the time
require d for synchronisation of the blockchain, currently sized
at 196.56 GB.4
• Although LN enabled transactions are instantaneous
compared to on-chain transactions , it is still very slow
compared to fiat payment systems such as Visa or Master .
• If at any time either party drops or goes offline, the channel
will be closed and settled .
• Off-chain scaling is not yet supported in many altcoins .
• LN is created as a separate layer (lay er 2) on top of the base
blockchain layer ; therefore, it doesn’t inherit the security
features of blockchain. Considering the fact that the
technology is still not highly proved to be secure, the
supporting networks limit the amount of the currency to be
traded.
• Crypto systems without smart contract support cannot
facilitate off-chain scaling .
• Implementing off-chain scaling requires extensive
programming skill.
• LN, in its current form, is highly vulnerable to distributed
denial of service ( DDoS ) [12] and other cyber -attacks [13]. In
fact, LN already faced a DDoS o n the 20 March 2018 that sent
approximately 200 nodes offline , which is roughly 20% of the
total available LN nodes prior to the attack.
• LN is not light-weight and is highly interdependent on
complex technological configuration. Configuring cluster of
servers , as seen in traditional e-commerce or in banking for
redun dancy, is very complex in the current design of LN.
Therefore, LN for business application are not redundant and
susceptible to single point of failure (SPF).
• Implementing LN in any dynamic cloud environment will
demand significant workaround of the curren t design.
3. APPLICATIONS OF LIGHTING
NETWORK IN NON- MONETARY
TRANSACTIONS AND FUTURE
ADOPTION TRENDS
Lightning network technology is still a new concept -
provisionally developed and implemented with limited scope. The
number and capacity of lightning transactions taking place at this
moment is still very small. However, introduction of LN is gaining
popularity as it solves some of the problems associated with
blockchain technologies, in particular with Bitcoin blockchain.
Currently there are only a few crypto systems that support both
HTLC and the specialised programming functions which are the
minimum technological requirement s to adopt LN. However, they
are expected to implement these features in the future, which will
highly determine the direction of LN’s adoption trends.
Rauchs et al. [14] describes LN as an example of an “interfacing”
Digital Ledge r Technology (DLT) system that “opportunistically ”
implements the elemental functionalities provided by a DLT
technical configuration , which could be adjusted to take advantage
of another or even multiple base DLTs. This feature of LN not only
4 https://www.blockchain.com/en/charts/blocks -size enables off -chain atomic cross -chain swaps [2], but also
materialises the notion of decentralised exchanges , as each LN
node having channels linked to multiple b lockchain networks can
act as a decentralised exchange.
Since nodes in the LN act as a money transmitter, there is a debate
whether registration as a money transfer is a legal requirement.
However, the laws related to money transmission differ across
various countries, even sometimes among states of a country , such
as in the US. Laws of many countries or states even lack clarity on
whether the law for fiat currencies can be applied to cryptos. In fact,
the nodes of LN do not acquire real ownership of the funds while
being transmitted . Therefore, they cannot possibly nobble.
Effectiveness of applying money transfer law in this case thus may
not justify its intention to protect consumers.
In fact, such inter -blockchain interoperability , i.e. atomic swaps
will help boost liquidity of crypto assets arising in these chains by
enabling transfer of assets formulated on one chain into assets
formulated on another chain . Such swaps could include settlement
of securities transactions .
For legal certainty , the current governance approach of standard
framework ag reements applied in international swap transactions
can also be used for atomic swaps , with very little adjustment
necessary once the framework is set in place.
From a regulatory viewpoint, swaps and/or transfers present the
problem that they could traverse geographic boundaries of political
entities or legal jurisdictions . Such transnational activity is harder
to regulate and monitor by the regulatory bodies or similar
government agencies of any jurisdiction . Secrecy is added to this
regulator y difficulty. Due to the implantation of an onion style
routing approach, even if LN channels created by other users are
used to facilitate a transaction or swap, and only the final netted
balance is broadcasted to the base blockchain network for
consensus , the intermediate transfers or swaps remain private. No
one but the transacting parties knows the actual transaction details.
This feature of LN can contribute to the rise of illicit markets.
LN-powered atomic swaps ha ve the potentials of eliminating
legacy crypto exchanges by deploying direct transfers as well as
decentralised exchanges. This could make monitoring and
regulation more difficult. Since the intermediate transactions are
not recorded even by the transacting parties, the inspection of chain
coding is less likely to be sufficient. Therefore, the future adoption
models of crypto assets is highly dependent on how regulatory and
legal provisions are adjusted in different jurisdictions .
In parallel with the pervasive application of blockchain
techn ologies [15,16], LN even holds the promise to widen the
current scope of adoption, especially LN could potentially play a
role in settlement of securities transfers [17] or of other coloured
coins - bringing the direct and transparent holdi ng of assets back to
organis ed markets [18]. LN c ould thus contribute t o higher
transparency leading to better corporate governance.
LN enabled off -chain atomic cross -chain swaps [ 2] could also play
an important role in facilitating cross -listing o n blockchain based
securities exchanges. As per the current cross -listing models, one
company can list its security in multiple exchanges [19]; the
emerging concept of sidechain could be adapted for this purpose. A
sidechain , also known as childchain in Ardor platform, is a
‘loosely’ joined independent blockchain, attached to another (i.e.
parent ) blockchain utilising the “two -way peg” approach. This
enables crypto assets from the parent blockchain to b e securely
moved and used in the sidechains , at an agreed rate, with the option
to move back to the original (i.e. parent) chain.
Analogous to making micro -payments for any commodity, Micro -
trades of cryptocurrencies (as in Foreign Exchange) or other
coloured coins such as shares and securities, can also be feasible
using LN.
Despite the development of the various LN testbeds, seamless
multi-asset conversion is still limited by its technical design.
Therefore, adoption of such trade is foreseeable but subje ct to the
maturity of the technology.
In terms of using LN for American Call Options or for any other
scenarios where significant changes in the traded value of the assets
may happen within the trade windows , with current technological
limitations of HTLC s, it is still problematic. This is primarily
because traders can take unfair advantage of the current contracts
if a rate changes in their favour. However, if LN aims to handle
such trades, either carefully designed modification of the
technology or development of a legal mechanism will be req uired
[20].
As the LN technology has existed in operable form for just around
one year and is still being used in restricted manner, we consider
this still to be a r esearch and development (R&D) phase . However,
as LN technology matures, its more concrete utilisation of the
technology in various applications is expected, especially for
transfer and fungibility of digital assets.
4. DRIVERS OF FUTURE BUSINESS
MODELS
Among all the benefits of LN as described in section 2.2, following
two are currently the enablers of new business models and
innovation:
1. Lightning Applications (Lapps) and
2. Micropayment Channel
4.1 Lightning Applications (LA pps)
One of the major drawbacks of Bitcoin, compared to other
Blockchain 2.0 implementation such as Ethereum, is not being
“turing -complete” and thus having “no” support for smart
contract s. However, implementation of off -chain scaling via LN
Bitcoin blockchain can now support lightning a pplications
(LApps) – similar to decentralised applications (DApps). This is
made possible primarily due to off -chain transactions and
multisignature features of LN. Combined with near -zero
transaction fees, micropayment facilities and instant fund transfer,
many business use -cases are being developed .
4.2 Micropayment Channel
A micropayment, as the name implies, facilitates financial
transactions of a very small amount, usually using online f acilities.
In the 1990s, many micropayment channels were implemented but
could not gain mass popularity , mostly due to limited use of the
Internet. Utilising emerging e -commerce technologies, the concept
of micropayment again came into highlight. In 2010s, the
ecommerce industry has seen a second generation of
micropayment. In fact, the success of a micropayment system
highly depends on the capability of offering extremely low
5http://bitcoinist.com/lightning -network -black -swan -cryptograffiti transaction fees. Since LN can offer near -zero transaction fees, it is
considered to be an enabler of micropayment channels. To help
promote micropayment by LN, a tiny artwork named “Black Swan”
has been auctioned and sold by a user to the lowest bidder for 1
milli-satoshi i.e. $0.000000037 - the lowest possible amount that
could be transferred by Bitcoin LN5. If not the lowest, this is
certainly close to being the lowest price ever paid in an auction .
4.3 Applications and Adoption Trends of
Lightning Network in E -Commerce
Until LN was implemented, the use-cases for Bitcoin end users was
extremely limited to financial activities – mainly managing and
funding wallets as well as exchanges. LN widens the scope and
holds promises to further create new business models empowered
by instant payments, even at m icro level with near -zero transaction
fees. This will thus help significantly lower threshold of entry
barriers for new businesses. Examples of such implementations
include platforms for monetising digital content s at micro level ,
international mobile mess aging services , in-game micro -payments ,
receiving tips, Lightning Jukebox, Gambling, Lightning Point -of-
Sale (PoS) for non -virtual stores, Bitcoin -payable Twitter Bot that
facilitates receiving payments for like, share (retweets) and follows
are few to mention. Lightning App Directory provides a
comprehensive list of applications based on LN.6
5. CONCLUDING REMARKS
By raising the limitations inherent in the two major failures of any
blockchain -based financial application , interoperability and
scalability, this paper argues that a second layer solution such as
lightning network, powered by HTLC and smart contract, has
potential f or addressing these limitations. The paper then discusses
prospective usage of lighting networ ks in the clearing and
settlement of coloured coins and securities . The paper also
discusses future adoption trends of lightning networks and thus the
creation of new business models utilis ing this technology . Legal
and regulatory aspects of LN are discussed and f uture research
directions projected.
6. REFERENCES
[1] Mahdi H. Miraz and Maaruf Ali, "Blockchain Enabled
Enhanced IoT Ecosystem Security ", in proceedings of the
International Conference on Emerging Technologies in
Computing 2018 (iCETiC '18), Part of the Lecture Notes of
the Institute for Computer Sciences, Social Informatics and
Telecommunications Engineering (LNICST), vol. 200,
London, UK, 2018, pp. 38- 46. Available:
https://link.springer.com/chapter/10.1007/978-3 -319-95450 -
9_3
[2] Mahdi H. Miraz and David C. Donald, "Atomic Cross -chain
Swaps: Development, Trajectory and Potential of Non-
monetar y Digital Token Swap Facilities ", Annals of
Emerging Technologies in Computing (AETiC), vol. 3, no.
1, pp. 42 -50, DOI: 10.33166/AETiC.2019.01.005 , January
2019. Avail able:
http://aetic.theiaer.org/archive/v3/v3n1/p5.html
[3] Richard MacManus, "Blockchain speeds & the scalability
debate" , Blocksplain, February 2018. Available:
https://blocksplain.com/2018/02/28/transaction-speeds/
[4] Joseph Poon and Thaddeus Dryja, "The Bitcoi n Lightning
Network: Scalable Off -Chain Instant Payments ", Lightning
6 https://dev.lightning.communi ty/lapps/
Network, White Paper DRAFT Version 0.5.9.2, January 14,
14 January 2016. Available:
https://lightning.network/lightning -network -paper.pdf
[5] Marco Conoscenti, Antonio Vetrò, Juan Carlos De Martin,
and Federico Spini, "The CLoTH Simulator for HTLC
Payment Networks with Introductory Lightning Network
Performance Results ", Information, vol. 9, no. 9, September
2018, 223. Available: https://www.mdpi.com/2078-
2489/9/9/223
[6] Pavel P rihodko, Slava Zhigulin, Mykola Sahno, Aleksei
Ostrovskiy, and Osuntokun Olaoluwa, "Flare: An Approach
to Routing in Lightning Network ", Bitfury White Paper, pp.
1-40, July 2016. Available:
https://bitfury.com/content/downloads/whitepaper_flare_an_
approach _to_routing_in_lightning_network_7_7_2016.pdf
[7] Christian Decker and Roger Wattenhofer, "A Fast and
Scalable Payment Network with Bitco in Duplex
Micropayment Channels ", in Proceedings of the International
Symposium on Stabilization, Safety, and Security of
Distributed Systems (SSS 2015), Part of Lecture Notes in
Computer Science (LNCS), vol. 9212, Cham, Switzerland,
2015, pp. 3- 18. Available:
https://link.springer.com/ch apter/10.1007/978-3 -319-21741 -
3_1
[8] Andrew Miller, Iddo Bentov, Ranjit Kumaresan, Christopher
Cordi, and Patrick McCorry, "Sprites and State Channels:
Payment Network s that Go Faster than Lightning ", arXiv,
November 2017. Available: https://arxiv.org/pdf/170 2.05812
[9] The Raiden Network. Raiden Network. Available:
https://raiden.network
[10] Conrad Burchert, Christian Decker, and Roger Wattenhofer,
"Scalable Funding of Bitcoi n Micropayment Channel
Networks ", in Proceedings of the International Symposium
on Stabilization, Safety, and Security of Distributed Systems
(SSS 2017), Part of Lecture Notes in Computer Science
(LNCS), vol. 10616, Cham, Switzerland, 2017, pp. 361- 377.
Available: https://link.springer.com/chapter/10.1007/978- 3-
319-69084 -1_26
[11] Rami Khalil and Arthu r Gervais, "Revive: Rebalancing Off -
Blockchain Payment Networks ", in Proceedings of the 2017
ACM SIGSAC Conference on Computer and
Communications Security (CCS '17), Dallas, Texas, USA,
2017, pp. 439- 453. Available:
https://dl.acm.org/citation.cfm?id=31340 33
[12] Ahmed S. Abu Daia, Rabie A. Ramadan, and Magda B.
Fayek, "Sensor Networks Attacks Classifications and
Mitigation ", Annals of Emerging Technologies in Computing
(AETiC), vol. 2, no. 4, DOI: 10.33166/AETiC.2018.04.003, pp. 28 -43, October 2018 . Available:
http://aetic.theiaer.org/archive/v2/v2n4/p3.html
[13] Junaid Chaudhry, Kashif Saleem, Paul Haskell -Dowland,
and Mahdi H. Miraz, "A Survey of Distributed Ce rtificate
Authorities in MANETs ", Annals of Emerging Technologies
in Computing (AETiC), vol. 2, no. 3, pp . 11-18, July 2018,
DOI: 10.33166/AETiC.2018.03.002. Available:
http://aetic.theiaer.org/archive/v2/v2n3/p2.html
[14] Michel Rauchs et al., "Distributed Ledger Technology
Systems: A Conceptual Framework ", The Cambridge Centre
for Alternative Finance (CCAF), Judge Business School,
Cambridge, UK, August 2018. Available:
https://www.jbs.cam.ac.uk/faculty -
research/centres/alternative- finance/publications/distributed -
ledger -technology -systems/#.W4YWH- gzZhE
[15] Mahdi H. Miraz and Maaruf Ali, "Applications of
Blockchain T echnology beyond Cryptocurrency ", Annals of
Emerging Technologies in Computing (AETiC), vol. 2, no.
1, pp. 1 -6, DOI: 10.33166/AETiC.2018.01.001, January
2018. Available:
http://aetic.theiaer.org/archive/v 2/v2n1/p1.pdf
[16] Md Mehedi Hassan Onik, Mahdi H. Miraz, and Chul -Soo
Kim, "A Recruitment and Human Resource Management
Technique Using Blockchain Technology for Industry 4.0" ,
in Proceeding of Smart Cities Symposium (SCS -2018),
Manama, Bahrain, pp. 11 -16, DOI : 10.1049/cp.2018.1371 ,
22-23 April 2018. Available:
https://ieeexplore.ieee.org/document/8643177
[17] Mahdi H. Miraz and David C. Donald, "Application of
Blockchain in Booking and Registration Systems of
Securities Exchanges ", in proceedings of the IEEE
Intern ational Conference on Computing, Electronics &
Communications Engineering 2018 (IEEE iCCECE '18),
Southend, United Kingdom, 16- 17 August 2018.
[18] David C. Donald and Mahdi H. Miraz, " Restoring Direct
Holdings and Unified Pricing to Securities Markets with
Distributed Ledger Technology ", Manuscript on file with
authors .
[19] David C. Donald, "Networked Securities Markets: From
Cross-Listing to Direct Connection ", in The Research
Handbook on Asian Financial Law, Doug Arner et al., Eds.:
Edward Elgar, 2019.
[20] Paul H. Madore, "Bitcoin: Developer Explains Why a Multi-
Asset Lightning Network Might Not Work" , CNN,
December 2018. Available: https://www.ccn.com/bitcoin -
dev-explains -why -a-multi-asset -lightning -network -might -
not-work/
|
{
"id": "1906.11946"
}
|
2102.08107
|
Interdependencies between Mining Costs, Mining Rewards and Blockchain
Security
|
This paper studies to what extent the cost of operating a proof-of-work
blockchain is intrinsically linked to the cost of preventing attacks, and to
what extent the underlying digital ledger security budgets are correlated with
the cryptocurrency market outcomes. We theoretically derive an equilibrium
relationship between the cryptocurrency price, mining rewards and mining costs,
and blockchain security outcomes. Using daily crypto market data for 2014-2021
and employing the autoregressive distributed lag approach - that allows
treating all the relevant moments of the blockchain series as potentially
endogenous - we provide empirical evidence of cryptocurrency price and mining
rewards indeed being intrinsically linked to blockchain security outcomes.
|
http://arxiv.org/pdf/2102.08107v1
|
Pavel Ciaian, d'Artis Kancs, Miroslava Rajcaniova
|
econ.GN, cs.IT, math.IT, q-fin.EC, q-fin.TR
|
econ.GN
|
1
Interdependencies between Mining Costs, Mining Rewards and Blockchain
Security1
Pavel Ciaian
European Commission, Joint Research Centre (JRC)
Via E. Fermi 2749, 21027 Ispra, Italy
e-mail: pavel.ciaian@ec.europa.eu
d'Artis Kancs
(corresponding author)
European Commission, Joint Research Centre (JRC)
Via E. Fermi 2749, 21027 Ispra, Italy
e-mail: d'artis.kancs@ec.europa.eu
Miroslava Rajcaniova
Slovak University of Agriculture in Nitra (SUA) and University of West Bohemia (UWB)
Tr. Andreja Hlinku 2, 949 76 Nitra, Slovakia
e-mail: miroslava.rajcaniova@uniagr.sk
1 The authors gratefully acknowledge financial support received from the Slovak Research and Development
Agency under the contract No. APVV -18-0512 and VEGA 1/0422/19. The authors would like to thank two
anonymous referees for excellent suggestions as well as participants of the International Conference on
Macroeconomic Analysis and International Finance as well as seminar participants at the European Commission
for comments and useful suggestions. The conceptual framework of this paper is based on Ciaian et al. (2021).
The authors are solely responsible for the content of the paper. The views expressed are purely those of the authors
and may not in any circumstances be regarded as stating an official position of the European Commission.
2
Interdependencies between Mining Costs, Mining Rewards and Blockchain Security
Abstract
This paper studies to what extent the cost of operating a proof -of-work blockchain is
intrinsically linked to the cost of preventing attacks , and to what extent the underlying digital
ledger’s security budgets are correlated with the cryptocurrency market outcomes. We
theoretically derive an equilibrium relationship between the cryptocurrenc y price , mining
rewards and mining costs, and blockchain security outcomes . Using daily crypto market data
for 2014 –2021 and employ ing the autoregressive distributed lag approach – that allows treating
all the relevant moments of the blockchain series as potentially endogenous – we provide
empirical evidence of cryptocurrenc y price and mining rewards indeed being intrinsically
linked to blockchain security outcomes.
Keywords : Cryptocurrenc y, ARDL , blockchain, proof -of-work, security budget , institutional
governance technology , network externalities
JEL codes : D82, E42, G12, G15, G18, G29.
3
1 Introduction
Blockchain – a distributed network of anonymous record -keeping peers (miners) – is an
inherently ‘trustless’ ledger . In the proof -of-work (PoW) blockchain, the trust problem among
non-trusting parties is solved by requiring miners to pay a cost (in form of computing capacity )
to record transaction information on a chain of block s and requiring that other record -keepers
(miners) validate those blocks . Mining incentives are ensured via rewards for a correct and
secure record keeping – the reward for every block is allocated to the miner that first solves the
computational problem (hash function) by using guess and check algorithms based on the new
and previous blocks of transactions.
Cryptocurrency price shocks and hence changes in mining rewards (in a fiat currency
denomination ) affect mining incentives for the ledger record -keepers and hence the underlying
ledger’s security . Blockchain users – who value the network security – in turn adjust their
crypto coin portfolio exercising in such a way upward or downward pressure on
cryptocurrency ’s price (see Figure s 2 and 3 ). The literature suggests that these
interdependencies between cryptocurrency’s value , mining costs and blockchain security may
contribute to the extreme volatil ity of cryptocurrency return (Ciaian et al. 2021 b; Pagnotta
2021 ).
The present paper studies these interdependencies between mining costs, mining rewards and
blockchain immutability . We attempt to answer the following questions. T o what extent the
cost of operating blockchain s is intrinsically linked to the cost of preventing attacks ? How
closely interrelated are the digital ledger’s record -keeping security budgets (measured by
mining rewards in a fiat currency nomination) of cryptocurrencies with the cryptocurrency
market outcomes? We focus on the proof -of-work blockchain, which is a particularly
interesting blockchain to study as the involved physical resource expenditures provide a
distinct advantage in achieving consensus among distributed miners . Our results suggest that
the cryptocurrenc y price and mining rewards are indeed intrinsically linked to blockchain
securi ty outcomes , the elasticity of mining rewards being higher than that of mining costs with
respect to the network security equilibrium.
The previous literature has mostly studied b lockchain security concerns from a crypto -coin
user perspective (see Lee 2019, for a survey). It has found that crypto -coin users value the
underlying ledger security , they internalize and price the risk of a blockchain attack that could
compromise the ability to e xchange crypto -coins for goods. Blockchain users who engage in
on-chain transactions – only the on -chain transactions are secured by the mining rewards –
value security measured by the amount of computational power committed to the blockchain ;
ceteris pari bus they prefer more computing power being committed to the ledger. There is little
empirical evidence , however, available in the literature about the i nterdependencies between
mining costs, mining rewards and blockchain security . Moreover, there is confusion in the
4
literature that the blockchain security would be an embedded property of the underlying
institutional governance’s technology .
Our main contribution to the literature is formally establish ing a link between the probabili ty
distribution over security outcomes depend ing permanently on the underlying distribution of
cryptocurrency market outcomes and providing a supporting empirical evidence . The paper s
most closely related to ours are from the theoretical literature on the blockchain mining and
security (Iyidogan 2020 ; Pagnotta 202 1; Ciaian et al. 2021a ). In particular, our paper is related
to the emerg ing literature on the economic properties and implications of blockchains (Abadi
and Brunnermeier 2018 ; Budish 2018 ; Biais et al. 201 9). This literature studies coordination
among miners in a blockchain -based system and show s that while the strategy of mining the
longest chain is in fact an equilibrium, there are other equilibria in which the blockchain forks,
as observed empirically. Whereas Abadi and Brunnermeier (2018 ) place most of the focus on
coordination among users ; record -keepers' payo ffs are determined by users' actions, and a
global games re finement of the game played among users puts more discipline on exactly how
and when a fork may occur , in Biais et al. (2019) forks occur for several reasons and are
interpreted as causing instability ; record -keepers' payo ffs when forking depend exogenously
on the number of record -keepers who c hoose a given branch of the fork. Budish (2018) studies
the costs of incentivizing honesty for cryptocurrency blockchains . Cong and He (201 9) focus
mostly on the issue of how ledger transparency leads to a greater scope for collusion between
users of the system. An alternative perspective studied in the literature is the collusion between
the blockchain's record -keepers rather than between users , which show s that collusion can
occur only when entry of record -keepers is constrained.
Our results complement the findings of this emergent literature by quantifying how the
probability distribution over security outcomes permanently depend s on the underlying
distribution of cryptocurrency market outcomes . Due to the extremely high cryptocurrenc y
volatility , also the PoW -blockchain security budget is exposed to high volatility and may result
in a series of low-security equilibriums and high -security equilibriums. In contrast, t he physical
resource cost to write on the blockchain – the cost of operating the PoW -blockchain – is only
weakly cointegrated with the strength of the network security . This cointegration relation ship
is geographically differenced – it is more significant for the world global mining leader China
than for other world regions. The mining cost effect seems to trigger a downward pressure on
the extensive margin of mining more in North America and Europe than in China, where the
increasing intensive margin of mining more than offsets the negative effects on the network
hash rate and hence the blockchain security. The ARDL estimates for the speed of adjustment
of the PoW -blockchain security suggest that after temporary shocks to crypto markets any
disequilibria is corrected and the security equilibrium revert s back to mean in the long -run.
5
The paper proceeds as follows. First, we provide a background literature overview about the
PoW -blockchain (section 2). Second, we establish a theoretical relationship between the cost
of proof -of-work, cryptocurrency market outcomes and blockchain security outcomes (section
3). Third, we estimate empirically the derived structural PoW -blockchain security relationships
for crypto currencies that rely on application -specific integrated circuits (ASICs), such as
Bitcoin . We use daily Bitcoin data for 2014 –2021 and employ an autoregressive distributed lag
approach that allows treating all the relevant moments of the blockchain series as potentially
endogenous (section s 4 and 5 ). To examine the extent to which this relationship is contingent
upon exogenous price shocks, the role of the cryptocurrenc y mining reward and the proof -of-
work cost for each of the respective moments is estimated after accounting for the information
embedded i n the lags of the entire distribution of blockchain security outcomes . The main
results are presented in section 6. They suggest that the cryptocurrenc y price , mining rewards
and mining costs are intrinsically linked to blockchain security outcomes. The fi nal section
concludes.
2 The record -keeping of digital transaction s
2.1 Blockchain and the proof -of-work
Blockchain is a distributed alternative to centralized transaction -recording and record -keeping
system s by enabling trustworthy interactions , recording transactions among non -trusting
parties and storing interaction records . The underlying ledger that creates and stores records of
transactions is a digital chain of blocks , where information is recorded sequentially in data
structures known as ‘blocks ’ stored into a public database ('chain') . Being distributed,
blockchain is run by a peer-to-peer network o f nodes (computers) who collectively adhere to
an agreed distributed validation algorithm ( protocol ) to ensure the validity of transactions. A
distributed network of anonymous record -keeping peers (miners) with free entry and exit is
inherently ‘trustless’ and thus requires a trust -enhancing mechanism . To solve t he trust problem
among non -trusting parties , miners are obliged to pay a cost (in form of computing power for
blockchain ) to record transaction information and requiring that future record -keepers (miners)
validate those reports. Under a well -functioning institutional governance technology,
blockchain is immu table, me aning that once data have been recorded on the blockchain, it
cannot be altered.
The key preconditions for a well-functioning market are accuracy and security of transaction s
and enforcing property rights and contracts. In traditional centralized institutional governance
systems, usually , state or other type of centralized authority ( intermediary ) guarantees the
transfers of ownership, ensures transfers of possession s, guarantees the security of property
rights and contract enforcement. The honest behavior of the centralized intermediary is
incentivized through monopoly rents. A comparative advantage of distributed institutional
6
governance systems such as blockchain is the ability to achieve and enforce a uniform view
(agreement ) among non -trusting parties with divergent interests and incentives on the state of
transactions in a cost -efficient and consensus -effective way. Blockchain security algorithms
make it possible for distributed record -keepers to ensure that the network rules are being
followed, i.e. all other record -keepers disregard any chain containing a block that does not
conform to the network rules. The correctness and security is incentivized via physical resource
costs : proof -of-work (PoW) makes it costly to extend invalid chains of blocks (Davidson, De
Filippi and Potts 2016; Cong and He 201 9).2
Given it s cost-efficie ncy and consensus -effective ness advantages , blockchain’s potential
applications go far beyond the creation of decentralized digital currencies, it can be used to
achieve consensus of virtually any type of records or transactions, particularly of those related
to property rights, transfer of property rights and contract execution (Davidson, De Filippi and
Potts 2016). Blockchain technology has the potential to serve as an irreversible and tamper -
proof public record repository for documents, contracts, properties, and assets as well as it can
be used to embed and digitally store information and instructions with a wide range of
applications. For instance, smart c ontracts (self -executing actions in the agreements between
two or multiple parties), smart properties (digitally recorded ownership of tangible and
intangible assets) or decentralized autonomous organizations (DAOs) (Atzori 201 7). Such a
system might susta in various activities spanning from financial transactions, identity
management, data sharing, medical recordkeeping, land registry up to supply chain
management and smart contract execution and enforcement. Blockchains can also record
obligations; distrib uted ledgers could be used in the fintech space to track consumers'
transaction s and credit histories (e.g. Davidson, De Filippi and Potts 2016; Nascimento,
Pólvora and Sousa -Lourenço 2018) .
In the same time , ensuring a transaction correctness and security may be more cha llenging for
distributed digital ledgers than for traditional centralized ledgers (Abadi and Brunnermeier
2018) .3 First, because digital goods are non-rival and non -excludab le, which unlike t raditional
private goods do not prevent a double spending. Second, the security budget of distributed
ledgers is endogenous and fluctuate s over time (in a fiat currency nomination – see Figure 1 ),
implying that the underlying institutional governance technology may become vulnerable to
attacks during cryptocurrency’s low -price low -security -budget periods. Hence, marinating the
correctness and security of transaction s may become a challenge especially in periods of low
security budget. Indeed , a number of cryptocurrency -blockchains with a comparably small
2 There are two main types of validation mechanism – proof -of-work (PoW) and proof -of stake (PoS) – with each
having different incentive scheme in achieving consensus. This paper focuses on the PoW linked to Bitcoin which
is the largest and most popular cry ptocurrency.
3 A strong security of information in the context of a distributed ledgers implies immutable records of transactions,
including ownership rights and smart contracts.
7
security budget of preventing attacks have experienced successful majority (hash rate )4 attacks
in recent years, e.g. Bitcoin Gold, Ethereum Classic.5
2.2 Blockchain mining
The blockchain mining consists of nodes (called miners or record -keepers ) of a distributed
network competing for the right to record sequentially information about new transactions to
the digital ledger. In the case of PoW, miners have to solve a computationally challenging
problem in order to record information and validate o thers' records on the ledger (in intervals
of around ten minutes in Bitcoin ). Solving the computational problem (puzzle) is energy
intensive and thus costly. First, miners have to invest in a computing capacity ; these costs are
fixed and independent of the success rate. Second, miners have to incur variable costs, such as
energy (and time) for the computationally -intense mining process, and rental expenses for the
location of the mining equipment. On the revenue side, mining incentives are ensured via
rewar ds for a correct and secure record keeping. The reward for every block is allocated to the
miner that first solves the computational problem (hash function), by using guess and check
algorithms based on the new and previous blocks of transactions. The winning miner
broadcasts both the new block of transactions and the solution to the computational problem to
the entire decentralized network; all other network participants “ express their acceptance of
the [new] block by working on creating the next block in the chain, using the hash of the
accepted block as the previous hash ” (Nakamoto 2008).
The miner's computing capacity is the main mining input, its performance is measured in a
hash rate, which measures the speed at which a given mining machine o perates. Usually, the
hash rate is expressed in hashes per second (h/s). For example, a mining machine operating at
a speed of 100 hashes per second makes 100 guesses per second. Thus, the hash rate measures
how much computing capacity blockchain is deploy ing to continuously solve the
computational problem and generate/record blocks. Given that a mining computer has to make
many guesses to solve the computational problem; higher hash rate allows a miner to have
higher number of guesses per second, thus incr easing his/her chance to first solve the
computational problem and receive the reward.
The computational problem to be solved by miners adjusts endogenously, depending on the
number of network participants and the aggregate blockchain computing capacity it is adjusted
to become more difficult or less difficult. Bitcoin’s mining (hashing) difficulty algorithm is
designed to adjust after every 2 016 blocks (approximately every 14 days) to maintain an
4 The hash rate measures the speed at which a given mining machine operates. The hash rate is expressed in hashes
per second (h/s) (or number of guesses per second), which measures how much computer capacity a
cryptocurrency network is devoted to solve the computational problem and generate/record blocks.
5 For example, Bitcoin Gol d, a hard fork of Bitcoin, was subject to several double -spending attacks in 2018 causing
a price reduction (in USD) by around 40%. Similarly, Ethereum Classic also experienced a double -spend attack
in 2019 and 2020 also leading to its price decrease.
8
interval of approximately 10 -minutes between blocks. When t he aggregate blockchain
computing capacity increases, the computation problem difficulty adjusts upwards (i.e. the
required hash rate to ‘mine’ a block increases), whereas in periods of a low mining network
participation, it decreases. The adjustment in th e mining difficulty level is done for the purpose
to compensate / counterbalance changes in the aggregate blockchain computing capacity
employed by miners (Joudrey 2019; BitcoinWiki 20 21).
The network hash rate also determines the security and stability o f the underlying blockchain
institutional governance technology (Figure s 2 and 3 ). The physical resource cost to write on
the PoW -blockchain – the cost of operating the blockchain – is intrinsically linked to the cost
of preventing attacks.6 Higher hash rate implies stronger security, because any dishonest miner
(attacker) would need to employ more resources ( computing capacity ) to attack the institutional
governance technology of blockchain.7 In the context of creating and maintaining distributed
ledgers of information, a strong security implies immutable records of transactions, including
ownership rights and smart contracts.
2.3 Digital goods and decentralized ledgers vis -à-vis physical goods and centralized legers
Correctness and security . The system security reflects the probability of an attack; security is
paramount to any financial or non -financial network since transfers of ownership and
enforcement of property rights require verifications, and it should be difficult for an at tacker to
manipulate historical or/and new records. In a centralized system, a specific trusted agent
assumes such responsibility. In blockchain, however, verification and updates to the system
ledger rely on self -selected non -cooperating agents – miners. In cryptocurrencies such as
Bitcoin, the reward to successful miners includes transaction fees and a predetermined number
of newly minted bitcoins, which role is to incentivize miners to devote computing capacity for
block validation and thus to provide th e system’s security. The probability of an attack is driven
by the balance of a computing power between potential attackers and honest miners. Greater
6 For illustration purpose, an example of a majority attack and gain from double -spending as provided by Van
Valkenburgh (2018) could look as follows: An attacking miner with the majority computing power compiles a
secret (private) version of the Bitcoin bl ockchain. At the same time the attacking miner sends, for example, 100
Bitcoins to a Bitcoin exchange, sells them and sends the received money (dollars) to his/her bank account. This
Bitcoin transaction is incorporated into the public blockchain run by hon est miners. The exchange observes the
transaction on the public (honest) blockchain, thus assumes it has the 100 Bitcoins and initiates money transfer to
the attacker’s bank account. However, the attacker does not send the 100 Bitcoins to the exchange in h is/her own
secret blockchain version. Once the attacker receives the money to his/her bank account, the private version of
the Bitcoin blockchain can be broadcasted to the network. Because the attacker has more computing power than
the rest of the network combined, the attacker private chain will be longer (more cryptographic problems solved)
and the rest of the network will recognize this new blockchain as the valid one. According to the new reorganized
chain, the exchange that accepted the 100 Bitcoins fo r money no longer has those 100 Bitcoins as well as it lost
their dollar value of Bitcoins which were sent to the attacker bank account. In contrast, the attacker has both the
100 Bitcoins and the dollar value of 100 Bitcoins (Van Valkenburgh 2018).
7 Different types of blockchain attacks include selfish mining, the 51% attack, Domain Name System (DNS)
attacks, distributed denial -of-service (DDoS) attacks, consensus delay (due to selfish behavior or distributed
denial -of-service attacks), blockchain forks, orphaned and stale blocks, block ingestion, smart contract attacks,
and privacy attacks.
9
number of (honest) miners and more computing capacity imply smaller probability of a
successful attack (F igure 2).
Compared to traditional centralized ledgers, ensuring a transaction correctness and security
may be more challenging for distributed digital ledgers like Bitcoin, because digital goods are
non-rivalry and non -excludability which compared to tradi tional private goods do not prevent
a double spending, and the security budget of distributed ledgers is endogenous and fluctuates
over time (i.e. if the value mining rewards changes in a fiat currency nomination). Miners react
to expected profit incentive s by adjusting their computing capacity. For example, low
expectations of a cryptocurrencies price would induce reducing computing capacity devoted to
mining, thus rendering the network more vulnerable and potentially further magnifying the
cryptocurrency’ s price decrease. Due to miners’ rational responses, the realization of
pessimistic cryptocurrency’s market outcomes also implies that a cryptocurrency’s security can
severely worsen resulting in a low -security equilibrium and lowering the network’s life
expectancy as measured by the average time until a successful attack (Pagnotta 202 1).
Double -spending attacks are one of the largest security concerns among blockchain users.
Cryptocurrencies that have a relatively small security budget of preventing attack s have
experienced a number of successful majority hash rate attacks in recent years. For example,
Bitcoin Gold, a hard fork of Bitcoin, experienced a sequence of double -spending attacks in
May 2018. Its price measured in USD at the end of that month was 4 0% lower. Ethereum
Classic also experienced a double -spend attack and several deep block reorganizations,
following a 50% decline in its price and hash rate in January 2019. Double -spending attack is
also possible when the blockchain in question handles as sets other than currency. For example,
a financial institution that loses money on a trade may wish to reverse the history of transactions
including that trade.
Competition and costs. Blockchain differs from centralized legers (e.g. notary offices, cadastr al
offices, banks) along several dimensions of the market structure, competition being one of
them. Typically, centralized ledgers are managed by monopolists (e.g. central banks) that
extract distortionary rents from the ledger's users, because the entry i s not free and switching
between legers is costly for users. Traditional centralized record -keeping systems provide
incentives to record honestly by monopoly profits and the fear to lose the future monopoly
rents (Abadi and Brunnermeier 2018). In contrast, distributed record -keeping system allow for
competition: there is a free entry (every miner can write on the ledger, subject to network rules)
and switching between ledgers (e.g. ‘forks’) is costless for users.8 The blockchain market
structure with free entry and fork competition eliminates the rents that a monopolist would
extract in an identical market and eliminates the inefficiencies arising from switching costs in
8 For example, a hard fork preserves all of the data in the parent blockchain: e.g. Bitcoin Gold and Bitcoin Cash
in the case of hard forks of Bitcoin.
10
centralized record -keeping systems (Huberman, Leshno, and Moallemi 201 9). There is also
competition between a potential attacker and all honest miners. Incentives to record honestly –
that are provided through the imposition of a physical resource cost to write on the blockchain
– make it costly for a potential attacker to distort the ledger .
Free entry and competition ensure that distributed ledgers can be more efficient and
transactions less costly than centralized ledgers. Blockchain miners can enter freely, meaning
that any agent who wishes to write on the ledger may do so by following a n agreed set of rules.
However, free entry of anonymous record -keepers is ‘trustless’ and thus requires a trust -
enhancing mechanism. Public blockchains typically solve the trust problem by forcing record -
keepers to pay a physical resource cost to record in formation and requiring that future record -
keepers validate those reports. In the case of the proof -of-work (PoW), miners have to solve a
computationally challenging problem in order to record information and validate others'
reports. The physical resource cost to write on the blockchain is the main the cost of operating
a blockchain. Compared to distortionary rents of centralized ledgers, in distributed blockchain -
based record -keeping systems, welfare losses stem mainly from the waste of computational
resources, computing and electricity costs of mining. Enforcing the execution of transactions
by a cryptographic code ensures a significant reduction of transaction costs. According to
Huberman, Leshno, and Moallemi (201 9), the physical resource costs of compe ting non -
cooperating miners are significantly lower than monopoly rents of centralized ledgers. Hence,
an important cost advantage of the blockchain technology compared to centralized record -
keeping systems consists of avoiding centralized intermediaries (e.g. a notary, cadastral office,
banks) and the associated rents.
Network externalities. Digital distributed ledgers such as blockchain are subject to network
externalities, which are not present under centralized ledgers. When miners engage in the
minin g of blockchains, both positive and negative network externalities related to the
blockchain security emerge . The positive network externality suggests higher blockchain
security as the number of miners increases , because each additional node strengthens the
chain’s security, by making it more difficult for any individual miner to launch an attack or to
guess who will be the winning miner (Waelbroeck 2018). The negative network externality
occurs because each individual miner invests in the mining -computing power, which increases
both the individual miner’s marginal income though al so mining costs, as the difficulty of the
computational problem increases in the number of miners and their computing power (“hash -
power”). Subsequently , higher difficulty of mining reduces the incentives for mining and
increases the concentration of mining activities, as miners are l earning by mining , resulting in
reduc ed blockchain security (Parra -Moyano, Reich and Schmedders 2019). When many small
miners enter the blockchain network, likely, the positive network externality will dominate and
the blockchain security outcomes will be superior compared to a highly skewed distribution of
11
computing power across miners (few mining pools having a large share of the total network
hash rate).
Individual non -cooperating agents do not internalize these network externalities when making
their optimal decisions. Blockchain users (agents who engage in transactions) take the price
and security levels as given and, unlike the central planner, do not internalize the impact of
their decisions on mining costs. Similarly, miners do not internalize the effect of their hash rate
choice on the blockchain security. For equilibria that display high security levels, given the
decreasing security gains f rom a mining investment, the part that miners fail to internalize is
decreasing. In contrast, the part that blockchain users fail to internalize is not decreasing,
because marginal mining costs are not decreasing (Pagnotta 202 1).
Fourth, being a distribute d institutional governance technology for creating and maintaining
distributed ledgers of information, it is different from a centralized institutional governance.
According to Davidson, De Filippi and Potts (2016), blockchain provides a new “institutional
technology” or a “governance technology”. “[Blockchain can be understood] as a revolution
(or evolution) in institutions, organization and governance ” (Davidson, De Filippi and Potts
2016). Compared to traditional centralized intermediaries, digital rules are enforced by a
distributed network of interconnected non -trusting parties. Trustworthily interactions between
non-trusting parties are executed and recorded on a distributed network by eliminating the need
for a centralized intermediary. A consensus mechanism ensures that the true history is recorded
on the ledger, rejecting fraudulent records. The build -in validation processes in the PoW
consensus algorithm and the use of cryptographic signatures and hashes ensures the network
governance, disincentivizes dishonest nodes to insert fake or malformed transactions in the
blockchain, and ensures the trustworthiness of transactions among no n-trusting parties on
blockchain. The computationally established trustworthiness of the institutional governance
technology ensures accuracy in establishing, delineating and protecting ownership rights (i.e.
it allows owners to exercise ownership rights i n terms of use, transfer, or exploitation of assets);
it can execute and enforce contracts (through smart contracts); and it can encompass various
types of organizations through DAOs (e.g. firms, venture capital funding, non -profit
organization).
Followin g North (1990), institutions are “ the rules of the game in a society ” and includes both
formal rules such as laws and informal constraints such as “ codes of conduct, norms of
behavior, and conventions ”. Formal rules are enforced by state, while informal ru les are
enforced by the members of the relevant group (North 1990; Kingston and Caballero 2009;
Greif and Kingston 2011). According to Hodgson (2006) “ institutions are systems of
established and embedded social rules that structure social interaction s”. Fr om this point of
view, blockchain is a type of distributed (informal) institution with digitally embedded rules of
the game – defined within the validation algorithm and enforced through a decentralized
12
network of participants – that structure digitally re corded interactions between agents.
According to Davidson, De Filippi and Potts (2016) “[blockchain is] an ‘institutional
technology’, a governance technology for making catallaxies, or rule -governed economic
orders. Blockchains thus compete with firms, ma rkets and economies, as institutional
alternatives for coordinating the economic actions of groups of people, and may be more or
less efficient depending upon a range of conditions (behavioural, cultural, technological,
environmental, etc). ”
Fifth, signifi cant changes in the global technological development (e.g. new/faster technologies
become available) or macroeconomic environment, require adjustments in the institutional
governance – either by a central authority or endogenously. Technological changes un derlying
digital institutional governance systems are considerably faster compared to traditional
institutional governance systems. In traditional centralized record -keeping systems, the
frequency of important technological changes and the required institu tional governance
adjustments is low; the key role in adjusting institutions to changes in the external environment
plays the centralized intermediary . In digital self -enforcing record -keeping systems, the
frequency of technological changes and the require d institutional governance adjustments is
high; the institutional governance is adjusted endogenously. For example, the blockchain
network governance employs the PoW consensus algorithm and uses cryptographic signatures
and hashes. The institutional govern ance technology of Blockchain is frequently adjusted
(every 14 days) to changes in the technological development (e.g. growth of the mining
processor computing speed) or macroeconomic environment (e.g. significant increase in the
cryptocurrency’s value and hence mining rewards in a fiat currency denomination) by adjusting
mining incentives for the network record -keepers (Dollar and Kraay 2003; Hodgson 200 6;
Glaeser et al. 2004; Kingston and Caballero 2009; Greif and Kingston 2011).
Blockchain provides a particularly interesting case to study, as the institutional governance
system (i.e. the security of the enforcement of property rights and contracts) is determined
endogenously by the underlying governance technology . Whereas t raditional centralized
institutional governance systems often are path-dependent and face many impediments to bring
about evolutionary developments (e.g. interest group pressure, bargaining and political conflict
between interest groups) which makes them less sensitive to economic changes (North 1990;
Kingston and Caballero 2 009; Greif and Kingston 2011) , the flexibility in institutional
adjustment of digital distributed ledgers allow them to adopt and accommodate changing
market conditions such as integrating and stimulating the growth of new technologies and
"non -tangible" i nnovations . Institutional rigidity (neutrality) could be desirable in certain
situations particularly when underlying factors (e.g. economic crisis, political crisis, civil
conflicts, wars) pressure towards lower -quality institutions , but not in others ( uni-directional
institutional change towards its improvement is desirable, not vice versa ). Hence , it is important
13
to understand the interdependencies between mining costs, mining rewards and blockchain
security .
3 Conceptual Framework
3.1 The model
We want to determine theoretically the equilibrium relationship s between blockchain security,
cryptocurrency market outcomes and resources devoted to the blockchain mining . Building on
the mining model s of Thum (2018) and Budish (2018) and considering the PoW of the most
popular cryptocurrenc ies as example , we model a rational miner i that decid es on the quantity
of computing capacity , mit (e.g. expressed by the number of computer operations) , to devote
for mining each block t (represented in block time measured in 10 minute interval which is the
average time needed to mine a block in blockchain ). The mining output is measured in capacity
of blockchain security units.
The probability of miner i winning the contest (i.e. the right to generate a new block and collect
reward) depends on his/her computing capacity devoted for each block relative to the
computing capacity of other miners . Previous studies assume that the probability of winning
the contest and validating a block is independent of the miner size: 𝑚𝑖𝑡/(𝑚𝑖𝑡+∑ 𝑚𝑗𝑡𝑛𝑡
𝑗≠𝑖), where nt
is the total number of miners and ∑ 𝑚𝑗𝑡𝑛𝑡
𝑗≠𝑖 is the total blockchain computing capacity of other
miners devoted to the block t (e.g. Cocco and Marchesi 2016; Thum 2018) . However, Parra -
Moyano , Reich and Schmedders (2019) show that the probability of relatively bigger miners
winning the mining contest is higher than that of relatively smaller miners because t here is a
“learning" effect when mining a particular block with larger mining computers learn ing faster
than smaller mining computers . To account for the learning by mining , we assume the
following transformation of the probability for a miner winning a block : 𝑒𝑚𝑖𝑡𝛾
/(𝑒𝑚𝑖𝑡𝛾
+∑ 𝑒𝑚𝑗𝑡𝛾𝑛𝑡
𝑗≠𝑖),
where 𝛾 is a transformation parameter (with 0<𝛾≤0), which implies that the ratio of odds
between big and small miners (mining computers ) of winning a block increases with the
miner s’ size, mit, while keeping the ratio of miner ’ size between miners fixed .
The purchase price of one unit of a computer equipment of a given efficiency, 𝜀, is denoted by
𝑞𝑡. The successful miner receives rew ard ptRt, where Rt is cryptocurrency quantity and pt is the
cryptocurrency price per one unit expressed in monetary values (e.g. US dollar). Miner i
chooses computing capacity , mit, for a given computer efficiency, 𝜀, so that to maximize the
present discounted value of the flow of profits over the infinite time horizon :
(1) 𝜋𝑖=∑(1
1+𝜌)𝑡
(𝑒𝑚𝑖𝑡𝛾
𝑒𝑚𝑖𝑡𝛾
+∑ 𝑒𝑚𝑗𝑡𝛾𝑛𝑡
𝑗≠𝑖𝐸(𝑝𝑡)𝑅𝑡−𝑐𝑡𝑚𝑖𝑡−𝑞𝑡𝐼𝑖𝑡)−𝐹 𝑡
Subject to 𝑚𝑖𝑡+1 units of computing capacity :
14
(2) 𝑚𝑖𝑡+1=(1−𝛿)𝑚𝑖𝑡+𝐼𝑖𝑡
where ct denotes variable costs per computer operation (e.g. energy cost), E(pt) is the expected
cryptocurrency price , 𝛿 is depreciation rate, 𝐼𝑖𝑡 is investment in computer equipment , F are
one-time fixed costs (e.g. building – see Garratt and van Oordt 2020 ), and 𝜌 is a discount rate
for time preference . Deviations from the expected price are random shocks, ν, with an expected
value of zero: E(pt) = pt*, where pt = pt* + ν. We assume a rational price expectation framework
of Muth (1961) in which miners base their cryptocurrency price formation on all the available
information at the time when making their decision s on the investment in mi. Miners are
identical, risk -neutral, non -cooperative and profit -driven agents that invest accor ding to the
anticipated real value of block rewards.
Maximi zing miner i’s profits for the given blockchain computing capacity of all other miners
yields the following o ptimal conditions :
(3) −𝑞𝑡+1
1+𝜌𝜆𝑡+1=0
(4) 𝛾𝑚𝑖𝑡𝛾−1𝑒𝑚𝑖𝑡𝛾
∑ 𝑒𝑚𝑗𝑡𝛾𝑛𝑡
𝑗≠𝑖
(𝑒𝑚𝑖𝑡𝛾
+∑ 𝑒𝑚𝑗𝑡𝛾𝑛𝑡
𝑗≠𝑖)2𝐸(𝑝𝑡)𝑅𝑡−𝑐𝑡+1
1+𝜌(1−𝛿)𝜆𝑡+1=𝜆𝑡
(5) 𝑚𝑖𝑡+1=(1−𝛿)𝑚𝑖𝑡+𝐼𝑖𝑡
where 𝜆𝑡 is a shadow price for a unit of computer resources .
Assuming a steady state equilibrium with 𝑚𝑖𝑡=𝑚𝑖𝑙, 𝑅𝑡=𝑅𝑙, 𝐸(𝑝𝑡)=𝐸(𝑝𝑙), 𝑞𝑡=𝑞𝑙, and
𝑛𝑡=𝑛𝑙 for 𝑡≠𝑙 and a symmetric equilibrium with mit = mjl, the equilibrium computing
capacity per miner can be derived from equations (3) to (5) as follows:
(6) 𝑚𝑡=[𝛾(𝑛𝑡−1)
𝑛𝑡2𝐸(𝑝𝑡)𝑅𝑡
𝑐𝑡+(𝜌+𝛿)𝑞𝑡]1
1−𝛾
Rewriting equation (6) in terms of the total blockchain computing capacity devoted to mining,
𝑛𝑡𝑚𝑡∗, yields the mining equilibrium :
(7) 𝑛𝑡𝑚𝑡=(1
𝑛𝑡)1+𝛾
1−𝛾[𝛾(𝑛𝑡−1)𝐸(𝑝𝑡)𝑅𝑡
𝑐𝑡+(𝜌+𝛿)𝑞𝑡]1
1−𝛾
Equation (7) implies that the total blockchain computing capacity increases in the relative gain
from mining, 𝐸(𝑝𝑡)𝑅𝑡/(𝑐𝑡+(𝜌+𝛿)𝑞). The mining equilibrium implies that the blockchain
computing capacity devoted to mining fluctuates with the cryptocurrency price. This model
feature reflects the intuition that, ceteris paribus, higher nominal reward or higher
cryptocurrency price induces miners to invest in more computing capacity . The opposite is true
when agents anticipate the value of cryptocurrency to be low, miners have little incentive to
invest in computational resources, and the security of the network is low.
Second , the mining equilibrium (7) implies that the total blockchain computing capacity
increases at a decreasing rate in the level (intensity) of competition, (𝑛𝑡−1)/𝑛𝑡2.
15
Third, equation (7) implies that miners have incentives to revert to the equilibrium level of the
blockchain computing capacity as a response to cryptocurrency price shocks because otherwise
miners would experience losses.
We follow Abadi and Brunnermeier (2018) and assume a free entry equilibrium where miners
enter until profits are driven to zero. In the blockchain system, miners don’t compete in prices
but in capacity, similar to Cournot -type firms. An increase in the processing power of
competing miners results in the expansion of the total blockchain computing capacity . In the
presence of network externalities, free entry of miners serves to pin down the strength of the
security.
Using equations (5) and (6), it is possible to derive the equilibrium number of miners, nt,
depending on mining return s, variable costs , fixed costs and the level (intensity) of
competition, (𝑛𝑡−1)/𝑛𝑡2:
(8) 𝑛𝑡=𝐸(𝑝𝑡)𝑅𝑡/(𝜌𝐹+(𝑐𝑡+𝛿𝑞𝑡)[𝛾(𝑛𝑡−1)
𝑛𝑡2𝐸(𝑝)𝑅
𝑐𝑡+(𝜌+𝛿)𝑞𝑡]1
1−𝛾)
Fixed costs are related to credit constraint and rigi dities to increase capacity related to financing
the entry costs into the mining .
Equations (3) to (7) define the equilibrium behavior of honest miners by pinning down the level
of computer resources they would allocate for mining at a given level of reward and
competition from other miners. The total blockchain computing capacity devoted to the
blockchain mining, 𝑛𝑡𝑚𝑡, determines the security of blockchain . As discussed above, the more
challenging is the computational mining puzzle to solve, the safer and more stable is the
institutional governance technology because it becomes more costly for a potentially dishonest
miner to conduct an attack. Such an attack may adversely affect the perception of
cryptocurrency by its users reducing their trust and hence valuation of the cryptocurrency . If
the reduction of the trust is large , it may cause a collapse in the economic value (price) of
cryptocurrency . As equation (7) implies, the blockchain computing capacity for mining and
hence the hash rate of the network would reduce , which might eventually lead to a collapse of
blockchain. Thus, the security of the PoW blockchain depend s on the size of mining reward
received by miners which also determines the total blockchain computing capacity determined
in equation (7).
3.2 Blockchain security and a ttacks
The probability of a (successful) attack on blockchain is reflected in the underlying ledger’s
security , it is inversely related the blockchain ’s security budget. This probability is driven by
the balance of computing power between an attacker and honest miners. As noted by
BitcoinWiki (20 21), “Bitcoin's security model relies on no single coalition of miners
16
controlling more than half the mining intensity ”.9 A miner who controls more than 50% of the
total blockchain computing capacity could exercise attack on blockchain that involves the
addition of blocks that are somehow invalid or reverse previous accepted transactions
(“majority attack” ). Either the blocks contain outright fraudulent transactions, or they are added
somewhere other than the end of the longest valid chain. A successful majority attacker could
prevent (for the time that the attacker controls mining) confirmation of new transactions (e.g.
by producing empty blocks) and rev erse own transactions which potentially allows double -
spending thus affecting all transactions that share the history with reversed transactions
(BitcoinWiki 20 21).
In our model, to control a majority power, equation (7) implies that an attacker must control
more than 50% of the total blockchain computing capacity , 𝐴𝑛𝑡𝑚𝑡, where A > 1. If we assume
that the attack takes the duration equal to s block time, then the attacker’s costs10 are
𝑠𝐴(𝑐𝑛𝑡𝑚𝑡+𝑞𝑡𝑛𝑡𝐼𝑡)−(1−𝜃)𝑞𝑛𝑡𝑚𝑡 and the mining reward during the attack is sptRt, where
𝜃 (0≤𝜃≤1) represents the proportion of the mining technology , mt, that can be recovered
(reused, resoled, repurposed) after the attack .11 The first term of the attacker’s costs,
𝑠𝐴(𝑐𝑡𝑛𝑡𝑚𝑡+𝑞𝑛𝑡𝐼𝑡), includes energy and investment co sts, while the second term, (1−
𝜃)𝑞𝑛𝑡𝑚𝑡, represents the loss related to the part of mining technology that cannot be recovered
after the attack.
To des -incentivize and deter attacks on blockchain , the cost of an attack must be greater than
the potential gain from an attack. Using the optimal condition (5), this implies the following
incentive compatibility condition for blockchain against attack s:
(9) 𝑠𝐴𝑛 𝑚∗[(𝑐𝑡+𝑞𝑡𝛿)−(1−𝜃)𝑞𝑡]≥(1−∆)𝑠𝐸(𝑝𝑡)𝑅𝑡+𝑉𝐴(∆)
where ∆ (0≤∆ ≤1) is the proportional decrease in the price of cryptocurrency after the attack
and VA is the expected payoff of the attack which is dependent on ∆ and is equal to the sum of
gains, 𝑉𝑡(∆), obtained over the duration of attack s with 𝑉𝐴(∆)=∑𝑉𝑡(∆)𝑠 .12 The payoff from
the attack, VA, can represent the gain from a cryptocurrency double -spending or other type of
gains (e.g. gain from a short sale of cryptocurrency , gain in cryptocurrency future markets from
price fluctuation caused by the attack).
Using equation (6), the incentive compatibility condition (9) can be rewritten as:
9 Although Bit coin has not suffered from a majority attack, a number of Altcoins were subject to successful attacks
in the past. For example, this was the case of the Bitcoin hard fork (Bitcoin Gold) in May 2018 (stealing $18
million worth of Bitcoin and other cryptos ) , Ethereum Classic (ETC) in January 2019 (double spending to over
200,000 ETC worth around $1.1 million), and Verge (XVG) was attacked several times in 2018 (with the biggest
attack extracting about 35 million of XVG) (ViewNodes 2019).
10 According to Crypt o51 (202 1), the theoretical cost of a 51% attack on Bitcoin is $ 413,908 per one hour.
11 Note that if Bitcoin does not collapse after the attack, the mining equipment can be reused in continuing mining
Bitcoin.
12 Note that in the steady state situation as sumed in the incentive compatibility condition (9), implies that the
discount rate ρ cancels out with 𝑉𝑡(∆)=𝑉𝑙(∆) for 𝑡≠𝑙.
17
(10) {[𝐸(𝑝𝑡)𝑅𝑡]𝛾
1−𝛾−(1−∆)
𝛽}𝑠𝐸(𝑝𝑡)𝑅𝑡≥𝑉𝐴(∆)
𝛽
where 𝛽=𝐴𝑛𝑡[(𝑐𝑡+𝑞𝛿)−(1−𝜃)𝑞][𝛾(𝑛𝑡−1)
𝑛𝑡21
𝑐𝑡+(𝜌+𝛿)𝑞𝑡]1
1−𝛾
Consider an attack where the only gain, VA, is double spending . The attacker acquires 𝑋 units
of crypto coins which (s)he double spends during the attack by exchanging them for the
standard fiat currency . This implies that the gain from attack is 𝑉𝐴(∆)=𝐸(𝑝𝑡)𝑋−∆𝐸(𝑝𝑡)𝑋.
After the attack , the attacker keeps the value of (double spent) X cryptocurrency in the standard
fiat currency, 𝐸(𝑝𝑡)𝑋, but lose s partially or fully (value of ) cryptocurrency acquired for the
attack, ∆𝐸(𝑝𝑡)𝑋. If ∆ is sufficiently small (i.e. cryptocurrency does not collapse after the
attack), then the system is vulnerable to the double -spending attack. However, if ∆=1 there is
no gain from double -spending attack because the double spending attacker loses exactly as
much value as (s)he gains from double spen ding. That is, 𝑉𝐴(∆)=0 and equation (10)
collapses to [(𝛾(𝑛𝑡−1)/𝑛𝑡2)(𝐸(𝑝𝑡)𝑅𝑡/𝑐𝑡+(𝜌+𝛿)𝑞𝑡)]1/(1−𝛾)=𝑚𝑡≥0. If ∆ is sufficiently
large, then the attack can sabotage the blockchain and lead to its complete collapse if ∆=1. In
this case , the motivation of the attacker may be other than the gain (profit) from double
spending (e.g. adversary power interested to damage the cryptocurrency which could include
a competing centralized intermediary , a competing cryptocurrency, or other entity) (Budish
2018) .
In line with Abadi and Brunnermeier (2018); Budish (2018) , equation (10) implies that the
equilibrium block reward to miners must be sufficiently large relative to the one -off gain fr om
the attack. Given that the gain from the attack, 𝑉𝐴(∆), is unknown (e.g. in the case of the double
spending attack , X an thus 𝑉𝐴(∆)=𝐸(𝑝𝑡)𝑋−∆𝐸(𝑝𝑡)𝑋 could be large for ∆<1) and its value
might be substantial , the equilibrium mining intensity need s to be larger than the one implied
by equation (7) in order to deter an attack . This is induced by the fact that the payoff from the
blockchain attack, VA, does not affect the economic behavior (incentives ) of honest miners in
allocat ing their computing capacity for mining (i.e. VA does not enter in equation (7)).
3.3 Testable hypotheses
From equations (7) -(10), we can derive three empirically testable hypotheses:
• Mining reward h ypothesis : Security outcomes of the PoW -blockchain and the
cryptocurrency price. When agents anticipate the value of cryptocurrency to be low,
miners have little incentive to invest in computational resources, and the security of the
network is low. The opposite is true when agents anticipate the value of cryptocurrency
to be high. Ceteris paribus , the blockchain security is sensitive (elastic) to the mining
reward.
• Mining cost h ypothesis : The physical resource cost to write on the PoW -blockchain is
intrinsically linked to the cost of preventing attacks; the security of blockchain is
18
structurally linked to the ledger’s security budget and mining costs . Ceteris paribus, the
blockchain security is sensitive (elastic) to mining costs.
• Mean -reverting hypothesis : The mean -reverting behavior of the PoW -blockchain
security implies that temporary cryptocurrency price shocks and mining cost shocks do
not affect the long -run blockchain security. Ceteris paribus, the PoW -blockchain
security reverts back to mean in the long -run.
4 Estimation strategy
4.1 Empirical PoW blockchain security model
The theoretical analysis established interdependencies between blockchain security,
cryptocurrency market outcomes and resources devoted to the blockchain mining. Equation (7)
implies that the security ( measured by the allocated computing capacity ) of the PoW -
blockchain depend s on mining rewards, the intensity of miners’ competition, mining costs,
discount rate and the computer equipment cost-efficiency.
In this section , we assess empirically the interdependencies between mining costs, mining
rewards and the PoW -blockchain security outcomes on cryptocurrency market outcomes and
mining resources. For the sake of tractability, i t is useful to apply a logarithmic transformation
to equation (7), which yields the following equilibrium relationship :
(11) 𝑦𝑡=𝑏0+𝛽𝑥𝑡+𝑢𝑡
where y represents the dependent variable – the PoW blockchain security ( computing capacity
devoted to mining), 𝛽 is a vector of coefficients to be estimated, x is a vector of explanatory
covariates – mining reward s, 𝑝𝑡𝑅𝑡, the number of miners, 𝑛𝑡, the intensity of miners’
competition, (𝑛𝑡−1)𝑛𝑡2⁄, the cost of mining (including the discount rate ), 𝑐𝑡+(𝜌+𝛿)𝑞𝑡 and
the com puter equipment efficiency, 𝜀𝑡, and 𝑢𝑡 is an error term .
The expected signs of coefficients for the mining reward and the intensity of miners’
competition in equation (11) are expected to be positive ( number of miners and mining reward
effects in Figure 2). The sign of coefficient associated with the cost of mining (energy costs
and discount rate) is expected to be negative (mining cost effect in Figure 3). The computer
equipment cost-efficiency coefficient is expected to have a positive relationship with the
blockchain computing capacity , because everything else constant, higher computing efficiency
implies that less energy is needed to achieve a certain computing hash rate. Our primary interest
is on coeff icients associated with the mining reward and the cost of mining : the first coefficient
measure s the elasticity of the PoW -blockchain security (mining network hash rate) with respect
to the mining reward ( Mining reward hypothesis ) and the second one with t he mining costs
(Mining cost hypothesis ). They reflect the level of endogeneity of the security of the PoW -
blockchain with respect to cryptocurrency market outcomes.
19
4.2 Estimation issues
The estimation of interdependencies between mining costs, mining rewards and blockchain
security determined in equation (11) is subject to several econometric issues. The first aspect
to consider is the problem of endogeneity. The endogeneity issue is particularly relevant for
distributed digital ledger series , as the security outcomes of the PoW -blockchain can be
determined con currently with the cryptocurrency mining reward . For example, when
distributed agents anticipate the value of cryptocurrency to be low, miners are not motivated to
invest in computational resources, and the security of the blockchain would be low. In that
case, crypto -coin users do not wish to accumulate large real balances, and the resulting market
valuation for cryptocurrency would be low. The opposite would be true if the value of
cryptocurrency is expected to be high.
To address the endogeneity problem , we rely on the Autoregressive Distributed Lag (ARDL)
methodology that is being increasingly used for studying cryptocurrencies (e.g. Bouoiyour and
Selmi 2015) and financial markets more generally (e.g. Stoian and Iorgulescu 2020) . The
ARDL bounds testing approach developed by Pesaran and Shin (1999 ) is particularly
appropriate for estimat ion of the blockchain security equilibrium relationship (11) as it enables
to model the long - and short -run relationships simultaneously and has several ad vantages over
the standard cointegration methods . A key advantage for our analysis is that t he ARDL
approach allows treating all the relevant moments of blockchain series as potentially
endogenous. As noted by Pesaran and Shin (1999, p. 16), t he use of ARDL is well suitable to
address the endogeneity problem : ‘‘appropriate modification of the orders of the ARDL model
is sufficient to simultaneously correct for residual serial correlation and the problem of
endogenous regressors ’’.
In the contex t of cryptocurrencies , another important advantage is that the ARDL approach
permits different number of lags for each series. Contrary to other cointegration techniques
(see Engle and Granger, 1987; Phillips and Ouliaris, 1990 ; Johansen, 1991) , the ARDL
methodology does not require testing for the order of integration ; it can be applied irrespective
of whether the regressors are purely I(0), purely I(1) or mutually cointegrated variables
(Pesaran et al., 2001). However, as point ed out by Ouattara (2004) , if I(2) variables are present
in the data, the computed F statistics of Pesaran et al. (2001) become invalid. To make sure
that none of the variables is integrated of order I(2) or beyond, we investigate the integration
status of the series by using the augmented Dickey –Fuller (ADF) test, the Dickey –Fuller GLS
test (DF -GLS) and Phillips –Perron (PP) test. In order to find the appropriate number of lags
for the series we follow the Akaike Information Criterion. Accordingly, the role of the
cryptocurrency mining reward and the proof -of-work cost for each of the respective moments
can be estimated after accounting for the information embedded in the lags of the entire
distribution of blockchain security outcomes.
20
Second, t here is also a potential errors -in-variables problem because part of the series is
obtained from primary non -harmonized data sources and it is not straightforward to judge the
reliability of these series . This concerns mainly the series that are not recorded on blockchain,
(e.g. mining cost data). Indeed, the mining unit cost s time series for different world regions
are collected by using different sampling methodologies and different weights. These issues
can be partially addressed by first differen cing the data . Nevertheless , part of potential errors -
in-variables issues remain. To address the remaining potential errors -in-variables , we create
alternative proxies for the dependent variable – blockchain security – and key explanatory
variable s – mining competition – and estimate these otherwise identical mining models for
robustness . The robustness check s results d o not indicate any abnormal deviations in the
estimated coefficients when cointegrating alternative proxies for the critical series.
4.3 Econometric strategy
The ARDL procedure involves two steps. First, we check for the existence of a long -run
relationship by comparing the calculated F -statistic with the critical value tabulated by Pesaran
et al. (2001). We begin with t he general form of an ARDL( p, q) model:
(12) 𝑦𝑡=𝑏0+∑ 𝜙𝑦𝑡−1+∑ 𝛽𝑖𝑥𝑡−1+𝑢𝑡𝑞
𝑖=0𝑝
𝑖=1
where y represents the dependent variable – security ( computing capacity ) of mining , x is a
vector of independent variables – mining rewards, intensity of miners’ competition, energy
costs, discount rate and the com puter equipment efficiency, b0 is the intercept, p is the number
of optimal lags of the dependent variable and q represent the number of optimal lags of each
explanatory variable.
Pesaran et al. (2001) proposed two types of critical values for a given significance level . The
first type assumes that all variables in the model are I(1), whereas the second one assum es that
all series are I(0). If the computed F statistic is below the lower bound, the null hypothesis of
no long-run relationship fails to be rejected . In such case , an ARDL model in first differences
without an error correction term should be estimated. If the F-statistic lies between the two
bounds, the result is inconclusive. And finally, i f the computed F-statistic exceeds the upper
bound, the null hypothesis of no cointegration is rejected . In this case , the error correction
model to be estimated is:
(13) ∆𝑦𝑡=𝑏0+𝛼(𝑦𝑡−1−𝜃𝑥𝑡)+∑ 𝜓𝑦𝑖∆𝑦𝑡−𝑖+𝑝−1
𝑖=1∑ 𝜓𝑥𝑖∆𝑥𝑡−𝑖+𝑢𝑡𝑞−1
𝑖=0
where θ represent the long-run coefficients, ∆ is the first difference operator , ψ are short -run
multipliers and α shows the speed of adjustment of the dependent variable to a short -term
shock. It measures how quickly the blockchain security adjusts to deviations from the
equilibrium ( Mean -reverting hypothesis ).
21
4.4 Specification tests
Following the standard approach in the literature ( Pesaran et al. 2001 ), we apply a set of
diagnostic tests, as the validity of ARDL results is based on the assumption of normally
distributed error terms, no serial correlation, heteroscedasticity and stability of the coefficients.
The empirically estimable model specification s and the number of lags is determined based on
the results from diagnostic tests , i.e. Breusch -Godfrey LM test and Durbin’s alternative test for
autocorrelation, Breusch -Pagan/Cook -Weisberg test for heteroscedasticity, normality testing
and cumulative sum test for the parameter stability.
5 Data
In empirical estimations, we use Bitcoin daily data for the period 27/12/2014 – 10/01/2021.
The details of data series used in estimations and their sources are reported in Table 1. All time-
series are transformed in a log -form in the estimations, implying that the estimated coefficients
can be interpreted as elasticities. Table 2 provides a descriptive statistic of the data used. The
construction of dependent and explanatory variables is explained in the following.
Our principal data source is blockchair.com that contains records for the entire Bitcoin mining
history starting from 2009 until latest transactions in 2021 . For each b lock successfully mined,
blockchair.com contains 36 block -specific characteristics: block_id, hash, time, median_time,
size, stripped_size, weight, version, version_hex, version_bits, merkle_root, nonce, bits,
difficulty, chainwork, coinbase_data_hex, tran saction_count, witness_count, input_count,
output_count, input_total, input_total_usd, output_total, output_total_usd, fee_total,
fee_total_usd, fee_per_kb, fee_per_kb_usd, fee_per_kwu, fee_per_kwu_usd, cdd_total,
generation, generation_usd, reward, reward _usd, miner. We aggregate single blocks into daily
mining output, to align with the rest of the data.
5.1 Dependent variable
The dependent variable is the PoW -blockchain security. According to the theoretical model,
the ledger security reflects the probability of an attack ; a high security implies that it should be
difficult for an attacker to manipulate historical or/and new records. The probability of an attack
is determined by the balance of the computing power between poten tial attackers and honest
miners. More (honest) miners and higher computing capacity imply smaller probability of a
successful attack (Figure 2).
In the empirical analysis, we measu re the blockchain computing capacity devoted to mining by
hash rate , it is expressed in average daily hashes per second. According to CoinMetrics, there
are several drawbacks with the hash rate index.13 The most important one relates to the random
13 https://coinmetrics.io/coin -metrics -state-of-the-network -issue -49
22
block generation process, because of which the implied hash rate tends to f ollow an oscillating
pattern. On the one hand, there is randomness as to whether or not a contract would settle at
the top or bottom of an oscillation, which could significantly impact the outcome of a
transaction . On the other hand, the hash rate can be manipulatable by large miners that control
significant portions of the network hash rate. To circumvent these issues, we use difficulty as
an alternative proxy for measuring the blockchain computing capacity . The alternative
dependent variable mining difficulty measure s the effort required to mine a new block on the
blockchain. Both proxies for the network security – hash rate and difficulty – have been
extracted from bitinfocharts.com (see Table 1). Both series were verified against data from
blockchair.com .
5.2 Explanatory covariates
Mining reward . PoW -blockchain m ining incentives are ensured via rewards for a correct and
secure record keeping. The reward for every block is allocated to the miner that first solves the
computational problem (hash function), by using guess and check algorithms based on the new
and previous blocks of transactions. The mining reward of distributed ledgers is endogenous
and fluctuates over time (in a fiat currency nomination – see Figure 1 ), implying that the
underlying institutional governance technology may be contingent on the mining reward. In
the empirical analysis, t he variable mining reward is measured as the average daily value of
the reward per block calculated by dividing the total mining reward per day (in US dollars) by
the total number of blocks per day. Both variables – the total mining reward /day and the total
number of blocks /day – have been extracted from blockchair.com (see Table 1).
Proof-of-work costs . Free entry and competition ensure that distributed ledgers can be more
efficient and transactions less costly than cent ralized ledgers. As determined in the theoretical
model (section 3), blockchain miners can enter freely, meaning that any agent who wishes to
write on the ledger may do so by following an agreed set of rules. However, free entry of
anonymous record -keepers is ‘trustless’ and thus requires a trust -enhancing mechanism. PoW -
blockchains solve s the trust problem by forcing record -keepers to pay a physical resource cost
to record information and requiring that future record -keepers validate those reports. The
physical resource cost to write on the blockchain is the main the cost of operating a distributed
digital ledger and forms the PoW -blockchain ’s security budget.
In the empirical analysis, we construct a separate resource cost proxy for each global world
region to measur e the v ariable mining unit costs . According to Ciaian et al. (2021 b), electricity
costs account for 94 -97 percent of variable mining costs of PoW blockchains . Hence, we use
electricity prices in Europe ( electricity Europe ), China ( electricity China ) and North America
(electricity N. America ) to measure a region -specific cost of mining . These series have been
constructed from three distinct sources: European Electricity Index (epexspot.com ), Chengdu's
Usage Price Electricity for Industry (ceicdata.com ) and Electricity Price in North America
23
(reports.ieso.ca ) (see Table 1) . To address potential errors -in-variables issues, we construct
alternative proxies for measuring the variable mining unit costs and estimate these otherwise
identical mining models for robustness.
As regards the fixed costs of mining, the mi ning equipment efficiency is proxied with the most
efficient mining hardware available in each time period measured by the energy efficiency of
the hardware (see Table 6 for an overview) . This approach follows closely the literature (Zad e
and Myklebost 2018; CBEI 20 21). We proxy the discount rate with the US 10 -year treasury
constant maturity rate ( 10-year-treasury ). The 10-Year Treasury Constant Maturity Rate
(DGS10) is extracted from fred.stlouisfed.org (see Table 1).
Number of miners . The total number of blockchain miners affects the blockchain security both
directly and indirectly via n etwork externalities (see Figure s 2 and 3 ). When miners engage in
the mining of blockchains, two types of opposite network externalities of the blockc hain
security arise, one positive and one negative. The positive network externality implies that the
blockchain security is increasing with the number of miners, because each additional node
reinforces the chain’s security, by making it harder for any ind ividual miner to launch an attack
or to guess who will be the winning miner (Waelbroeck 2018). The negative network
externality occurs because each individual miner invests in the mining -computing power,
which increases both the individual miner’s marginal income though also mining costs, as the
difficulty of the computational problem increases in the number of miners and their computing
power (“hash -power”). Increasing the difficulty of mining reduces the incentives for mining
and – in the presence of lear ning by mining – increases the concentration of mining activities,
reducing in such a way the blockchain security (Parra -Moyano, Reich and Schmedders 2019).
When many small miners enter the blockchain network, likely, the positive network externality
will dominate and the blockchain security outcomes will be superior compared to a highly
skewed distribution of computing power across miners (few mining pools having a large share
of the total network hash rate).
In the empirical analysis, we compute the total number of miners from the blockchair.com (see
Table 1) . Note that using B lockchair data we are able to distinguish between the total number
of active miners and successful miners in every period. Although, the two series are correlated,
their moments are different – the speed of adjustment to exogenous input and output price
shocks is different between the two series. Distinguishing between the total number of active
miners and successful miners is an important innovation compared to previous studies, Parra -
Moyano, Reich and Schmedders (2019) is the only study we are aware of that uses a
comparable decomposition technique.
Competition intensity . As discussed in section 2 and derived in the theoretical model , PoW -
blockchain -based distributed record -keeping system s allow for competition: there is a free
entry (every miner can write on the ledger, subject to network rules) and switching between
24
ledgers (e.g. ‘forks’) is costless for users.14 There is also competition between a potential
attacker (s) and all honest miners. Incentives to record honestly make it costly for a potential
attacker to distort the ledger. In the empirical analysis, we consider two alternative proxies for
the competition intensity – Herfindahl -Hirschman index ( hhi) and normalized Herfindahl -
Hirschman index ( hhi normalised ) – in order to account for the unequal distribution of the
blockchain computing capacity between different miners . Both Herfindahl -Hirschman
concentration indices are computed based on the number of miners and the network hashrate .
Both series – the total number of active miners and the network hashrate – have been extracted
from blockchair.com (see Table 1).
6 Results
Before proceeding with the ARDL bounds testing we determine the order of integration of the
variables . The test results summarized in Table 4 indicate that there are no variable s integrated
of the second order , which validates the use of the ARDL approach.
Table 3 summarizes the three estimated mining models with alternative specification of
explanatory variables and for each of the 3 model s we include 2 sub-models with alternative
measur es of the PoW -blockchain security , i.e. hash rate and difficulty . The three estimated
mining models differ by the proxy measuring the computer intensity. Model 1 use s competition
intensity variable, (𝑛𝑡−1)𝑛𝑡2⁄, as derived in equation (7), whereas models 2 and 3 use the two
alternative proxies for competition intensity: the Herfindahl -Hirschman index ( hhi) and the
normalized Herfindahl -Hirschman index ( hhi normalised ), respectively. The rest of variables
are uniform across all estimated mining models.
6.1 Mining reward and PoW -blockchain security
The m ining reward hypothesis says that, c eteris paribus, the blockchain security is sensitive
(elastic) to the mining reward. The long-run ARDL estimat es tend to confirm a structural
relationship between the mining reward and security outcomes of the PoW -blockchain (Table
4). This holds for both security variables measuring the blockchain computing capacity , hash
rate and difficulty , and across all estimated models. The estimated elasticities of the mining
reward variable range from 1.38 to 1.85, indicating an elastic response in the blockchain
computing capacity to permanent change s in the mining reward : 1% permanent increase in the
mining reward increases the underlying blockchain security by 1.38% to 1.85% in the long -
run. Hence, our estimates fail to reject the mining reward hypothesis : the PoW -blockchain
security is overly sensitive (elastic) to the cryptocurrency mining reward. A change in the
payoff from mining causes more than proportionate change in the PoW -blockchain security.
14 For example, a hard fork preserves all of the data in the parent blockchain: e.g. Bitcoin Gold and Bitcoin Cash
in the case of hard forks of Bitcoin.
25
As discussed in section 2 and derived in the theoretical model, given that mining costs are
incurred in standard fiat currencies in most cases (e.g. US dollar, Euro), the value of the mining
reward fluctuates with the price of cryptocurrency ,15 which in turn affects the mining reward
and mining incentives. Thus, if the expected cryptocurrency price decreases, lower mining
incentives reduce the equilibrium computer mining capacity and hence the cryptocurrency
security. Our estimates also imply that the reverse is valid in the case of a cryptocurrency price
increase.
As regards th e short -run estimates for the m ining reward , they are less significant across the
estimated ARDL models than the long-run results and the estimated elasticity is rather small
(Table 5). A 1% positive shock in the Bitcoin mining reward (the third lag) decreases the
blockchain computing capacity in the short -run by between 0.01% and 0.02%. Generally, also
the short -run estima tes tend to support the mining reward hypothesis : although an inverse
relationship is found in our data , the security outcomes of the PoW -blockchain shows
sensitivity to the Bitcoin mining reward even in the short -run. This negative relationship
between the mining reward and mining intensity could be a result of other short -run effects
such as mining optimization across cryptocurrencies , i.e. switching mining to other
cryptocurrencies (e.g. to Bitcoin cash) when the rela tive price of Bitcoin to cryptocurrencies
decreases.16 This short -run inverse relationship could also be caused by secondary spiral effects
induced by Bitcoin price changes – decrease (increase) – as suggested by Kroll, Davey and
Felten (2013), through the subsequent loss (gain) of confidence (trust) in Bitcoin when Bitcoin
mining intensity decreases (increases) which might further reduce (increase) the Bitcoin price.
6.2 Proof -of-work cost and blockchain security
The m ining cost hypothesis says that, c eteris paribus, the blockchain security is sensitive
(elastic) to mining costs. To capture a region -specific cost of mining, we have constructed
distinct electricity price variables for Europe, China and the North America.
The long-run estimates for the p roof-of-work cost are less significant across the estimated
ARDL models than mining reward results and the estimated elasticity shows a substantial
variation across world mining regi ons (Table 4). In line with the m ining cost hypothesis , the
estimated impact of variable mining unit cost s is negative and statistically significant for North
15 Note that the change in Bitcoin price is the main factor deriving the change in the value of mining reward
because according to the algorithm the quantity of mining reward in Bitcoins, Rt, changes (halves) only
approximately every 4 years, whereas Bitcoin price changes daily.
16 There is some evidence of asymmetric change in Bitcoin and altcoin prices: shocks to altcoins prices tend to be
greater than Bitcoin price shocks (Reiff 2018; Cheikh, Zaied a nd Chevallier 2020). This implies that the relative
prices of Bitcoin to altcoins are inversely related with the Bitcoin price changes which may incentivize miners to
shift some Bitcoin computer capacity to mining altcoins when Bitcoin price increase, and shift back the computer
capacity to Bitcoin mining when Bitcoin price declines. Note that the shift in mining between different cryptos is
less relevant for ASIC mining hardware, commonly used for Bitcoin mining, which is more efficient in mining
specific cryptocurrencies (specific cryptographic hash algorithm) and cannot be used for mining other types of
cryptocurrencies.
16 https://www.vox.com/2019/6/18/18642645/bit coin-energy -price -renewable -china
26
America and Europe. In contrast , the long-run estimat es for the global mining leader China –
71.70 % of the g lobal Bitcoin hash rate are c oncentrated in China (Song and Aste 2020 ) –
suggest a statistically significant and positive relationship between the proof -of-work cost and
the security of the PoW -blockchain . This result is contrary to the theoretical predictions and
requires some explanation.
One explanation for these geographically differentiated results could be that the intensive
margin of mining is larger in China, where all major mining pools are concentrated . Given that
variable mining costs are lower in China than in Europe and North America, positive shocks
to electricity prices may actually increas e the global share of Chinese miners. Indeed , our
estimates capture other long -term behavioral effects of miners induced by a permanent change
in electricity prices such as shifting mining location to places with cheaper energy (e.g. to
remote regions of C hina, from mainland Europe to Iceland to harvest geothermal power).17
Such long -term behavioral effects may actually increase the blockchain computing capacity , if
the energy cost savings more than offset the price increase. Further, these results may also
reflect the fact that the mining input cost data (which are location -specific ) are less reliable
than the mining reward data, which are publicly available for every single historical
cryptocurrency transaction.
The short -run results for the variable mining unit costs and security outcomes of the PoW -
blockchain are available for China, they cannot be examined for North America and Europe
due to the estimated ARDL specifications (Table 5). In line with the theoretical model in
equation (7), positive shocks to electricity prices in China have a statistically significant and
negative impact on the blockchain computing capacity of the PoW -blockchain in the short -run.
This result co ntrasts long-run estimate s, where a permeant increase in electricity prices in
China led to an increase in the mining intensity suggesting that other structural changes in
miners’ behavior might take place when the cost changes are permanent. Thus , our short -run
PoW cost estimates tend support the mining cost hypothesis that the security outcomes of the
PoW -blockchain is sensitive to mining costs. In the long -run, however, structural shifts and
relocation of mining farms – to reduce mining operating costs – may take place and offset the
short -run mining cost effect.
Overall, these ARDL bounds testing results suggest that the blockchain security is sensitive to
proof-of-work cost s. However, we cannot provide a definite and robust answer to the m ining
cost hypothesis . Instead , these results call for further analysis using more disaggregated
location -specific mi ning cost data . Indeed, looking into p roof-of-work costs and blockchain
security outcomes using geographically disaggregated data offers a promising avenue for the
future research.
17 https://www.vox.com/2019/6/18/18642645/bitcoin -energy -price -renewable -china
27
A further variable capture mining costs in ou r model is the hardware efficiency . In line with
the theoretical model, the hardware efficiency variable has a statistically significant positive
impact on the blockchain computing capacity in all estimated models. The ARDL results imply
that an increase in the efficiency of mini ng equipment (decrease in the input units of the
computing capacity per security output unit) leads to an upgrade of security outcomes of the
PoW -blockchain in the long -run. The estimated elasticities vary between 0. 23 and 0.8 3,
implying that a 1% permanen t increase in the efficiency of mining equipment increases the
blockchain computing capacity in the long -run by between 0.23% and 0.8 3%. Hence, the PoW -
blockchain mining security is dependent of the mining technology available in each given point
of time.18
6.3 Competition and network externalities
The theoretical mining model in (7) implies that the total blockchain computing capacity
increases at a decreasing rate in the level (intensity) of competition. Our long-run estimates
suggest that the miners’ competition intensi ty (number of miners , competition intensity ) has a
negative impact on security outcomes of the PoW -blockchain ; all long-run estimates are
significant ly different from zero in Table 4. These results suggest that a permanent increase in
the competition intensity exercises a downward pressure on the blockchain computing capacity
in the long -run.
As discussed in Section 2, digital distributed ledgers such as blockchain are subject to a number
of network externalities. When new miners enter the blockchain mining, two types of direct
network externalities related to the blockchain security arise, one positive and one negative.
The positive network externality implies that the blockchain security is increasing with the
number of miners, because each additional node reinforces the chain’s security . In line with
the previous literature (Waelbro eck, 2018), t he negative network externality occurs because
each individual miner invests in the mining -computing power, which increases both the
individual miner’s marginal income though also mining costs, as the difficulty of the
computational problem in creases in the number of miners and their computing capacity (“hash -
power”). Increasing the difficulty of mining reduces the incentives for mining and – in the
presence of learning by mining – increases the concentration of mining activities, which in turn
reduc es the blockchain security . Our estimate s suggest that the negative network externality
dominates of the positive network externality. Our results are in line with th ose of Parra -
Moyano, Reich and Schmedders (2019) who find that the probability of winning a mining
contest increases with the miner size. This motiv ates miners to join mining pools to increase
18 The short -run effects of electricity prices, hardw are efficiency , 10-year-treasury and alternative proxies for
competition intensity cannot be examined due to the ARDL specifications, as no lags of these dependent variables
entered the model.
28
their probability to win the mining contest and receive reward .19 Indeed , our competition proxy
variable s are constructed based on the observed number of miners but not on the number of
members within mining pools . And since a greater competition may imply fewer miners
(because many individual miners join mining pools) , the implied actual long -run relationship
between the competition intensity and mining intensity may become negative.
In the short -run, the mining competition intensity has a statistically positive impact on the
blockchain computing capacity in all estimated models. These results also indicate that in the
short -run, the competition among miners encourages deployment of more mining capacity in
line with the model derived in equation (7). While in the short -run the miners’ competition
leads to expansion of the blockchain mining capacity , in the long -run the inverse relationship
is valid indirectly suggesting reduced competition level as individual miners have the incentive
to join mining pools.
6.4 Dynamics and the mean -reverting of the blockchain security
The m ean-reverting hypothesis says that, c eteris paribus, the PoW -blockchain security reverts
back to mean in the long -run. The estimates of the error correction term – which measure the
speed of adjustment of the short -run dynamics of mining to the long -run equilibrium path – are
statistically significant across all models. The error correction terms vary between -0.002 and
-0.009 , imply ing that between 0.2 0% and -0.90% of the long -run disequilibrium in mining
intensity is corrected by the short -run adjustment on the same day. In other words, the
disequilibrium corrects at a n average speed of convergence of between 0.2 0% and 0.9 0% per
day. In terms of the duration, any deviation from the long -run equilibrium is corrected in around
109 to 447 days. These results provide support for the m ean-reverting hypothesis saying that
in response to shocks and short -run deviations security outcomes of the PoW -blockchain revert
back to the equilibrium security level in the long -run.
The lagged dependent variable ( proxied by hash rat e and difficulty ) is statistically significant
in all estimated mining models. The coefficient estimates vary between -0.03 and -0.47. The
relatively high values of these coefficients indicate that the temporary shocks in the mining
computing capacity disappear over time relatively fast: in around 2 to 29 days. These results
support the mean-reverting hypothesis that the security outcomes of the PoW -blockchain are
sensitive to Bitcoin market outcomes in the short -run to fluctuation s with instant shocks
disappear ing over a short time period (within few days) .
The 10-year-treasury variable, which is a proxy for the discount rate, has a statistically
significant positive effect on the blockchain computing capacity . This result suggests that that
the 10-year-treasury actually captures a miner investment competition effect , i.e. miner s
19 Other benefit of joining mining pools is that it creates a st eady stream of income, rather than greater income but
at lower frequency (i.e. due to lower odd of winning the mining contest) with individual mining (Liu and Wang
2017).
29
perceive it as an alternative investment asset. As far as cryptocurrency is perceived as an
investment asset, shocks to competing investment asset returns (including 10-year-treasury )
are expected to impact positively miners’ choices to invest in the mining of cryptocurrency .
Our results confirm that miners perceiv e cryptocurrency to be competing for investment with
other financial assets and thus need to generate a competitive return. The return arbitrage
among alternative potential investment opportunities implies a positive price relationship
between cryptocurrency and alternative financial assets (Murphy 2011; Ciaian et al. 2018 ,
Ciaian et al. 2021a ). Thu s, the positive coefficient associated with the 10-year-treasury variable
implies that miners are motivated to invest in more computing capacity for mining when the
returns to financial assets increase.
7 Discussion and concluding remarks
The present paper has studied the i nterdependencies between mining costs, mining rewards and
blockchain security . We have attempted to answer the following questions. T o what extent the
cost of operating blockchain s is intrinsically linked to the cost of preve nting attacks ? To what
extent the digital ledger’s record -keeping security budgets (measured by mining rewards in a
fiat currency nomination) of cryptocurrencies are correlated with the cryptocurrency market
outcomes? In this paper, we have focused on the proof -of-work (PoW) blockchain, which is a
particularly interesting blockchain to study as the involved physical resource expenditures
provide a distinct advantage in achieving consensus among distributed miners .
First, we have theoretically derive d an equilibrium relationship between cryptocurrenc y price ,
mining rewards and mining costs, and blockchain security outcomes . Second, u sing daily
Bitcoin data for 2014 –2021 and employ ing the autoregressive distributed lag approach – that
allows treating all the relevant moments of the blockchain series as potentially endogenous –
we have provide d empirical evidence about interdependencies between mining costs, mining
rewards and blockchain security . Our resu lts suggest that the cryptocurrenc y price and mining
rewards are intrinsically linked to blockchain security outcome s. In contrast, t he physical
resource cost to write on the blockchain – the cost of operating the PoW -blockchain – is only
weakly cointegrated with the strength of the network security; the ARDL results for mining
costs are geographically differentiated, implying heterogen eities in variable mining costs
across global world mining regions .
Our main contribution to the literature is formally establishing a link between the probability
distribution over security outcomes that permanently depend on the underlying distribution of
cryptocurrency market outcomes and providing a supporting empirical evidence . Our results
complement findings of this emergent literature by quantifying how the probability distribution
over security outcomes permanently depend s on the unde rlying distribution of cryptocurrency
market outcomes . Due to the extremely high cryptocurrenc y return volatility, the PoW -based
30
blockchain security budget is exposed to high volatility and may result in a series of low-
security equilibriums and high -secur ity equilibriums.
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Figure 1. Bitcoin price and aggregate security spend , USD
Source: coinmetric.com and studio -glass -node. Notes: Bitcoin’s cumulative miner revenue – Thermocap – is
calculated by taking the running sum of daily miner revenue in USD . Market cap italization to Thermocap
provides an indicat ion of the B itcoin’s current market value compared to the aggregate amount spent to secure
the network. Note that, due to lags in the adjustment of mining investment, Thermocap is relatively slow
moving and does not have the same level of volatility as the market cap italization .
Figure 2. Interdependenc ies between bitcoin price and blockchain security
Source: Conceptual framework (section 3).
Mining reward effect
Increases blockchain security (+)
users value
blockchain security
probability of
successful attacksblock -reward in
fiat currency
intensive margin +
extensive margin
difficulty to mine a
new block↑ Bitcoin price
immutability of
blockchain↓ attack chance
↑ ledger security↑ mining reward
↑ computing capacity
↑ hash rate
34
Figure 3. Interdependenc ies between proof -of-work cost and blockchain security
Source: Conceptual framework (section 3).
Table 1. Data sources
Variable Unit Description of variable Source
Dependent variable
hash rate Hash/second Total computing capacity bitinfocharts.com
difficulty Average difficulty per
day Mining difficulty bitinfocharts.com
Explanatory variables
mining reward USD per block Mining reward per block
(reward_usd/ no_bl_total ) blockchair.com
PoW cost: electricity
Europe EUR/MWh European Electricity Index www.epexspot.com
PoW cost: electricity
China USD/kWh Chengdu's Usage Price
Electricity Industry, USD www.ceicdata.com
PoW cost: electricity N.
America CAD/MWh Electricity price in North
America ieso.ca
hardware efficiency J/Giga hash Mining equipment
efficiency – Bitcoin mining
hardware generation ( the
most efficient device in each
period) Constructed based on: Zad e and
Myklebost (2018), CBEI (2019)
number of miners No Number of miners, 𝑛𝑡 blockchair.com
competition intensity Index Competition intensity computed (𝑛𝑡−1)𝑛𝑡2⁄
hhi Index Herfindahl -Hirschman index computed based on 𝑛𝑡 and
hashrate
hhi normalised Index Normalised Herfindahl -
Hirschman index computed based on on 𝑛𝑡 and
hashrate
10-year-treasury % 10-Year Treasury Constant
Maturity Rate (DGS10) fred.stlouisfed.org
Mining cost effect
Reduces blockchain security ( –)
variable cost in
fiat currency
probability of
successful attacks
immutability of
blockchainblock -reward in
fiat currency
intensive margin +
extensive margin
difficulty to mine a
new block↑ mining costs
↑ attack chance
↓ ledger security↓ mining profits
↑ computing capacity
↓ hash rate
35
Table 2. Descriptive statistics of used data
Variable Obs Mean Std. Dev. Min Max
Dependent variable
hashrate 2207 40.612 6.475 25.442 52.332
difficulty 2207 24.028 6.923 9.581 32.709
Explanatory variables
mining_reward 2207 9.954 2.387 -0.692 14.713
PoW cost: electricity Europe 2207 3.961 0.981 -6.908 5.429
PoW cost: electricity China 2207 -2.506 0.067 -2.402 -2.409
PoW cost: electricity N. America 2207 2.809 2.177 -6.908 6.010
hardware efficiency 2207 0.172 3.090 -3.219 6.552
number of miners 2207 3.068 0.934 0.000 3.984
competition intensity 2207 -3.301 1.089 -6.908 -1.497
hhi 2207 -1.851 0.770 -2.608 0.000
hhi normalised 2207 -2.173 0.946 -3.176 0.000
10-year-treasury 2207 0.893 0.229 0.315 1.428
Table 3. Specification of empirical models
Dependent variable: hashrate Dependent variable: difficulty
M1.1 M2.1 M3.1 M1.2 M2.2 M3.2
Dependent variable
hashrate X X X
difficulty X X X
Explanatory variables
mining reward X X X X X X
PoW cost: electricity Europe X X X X X X
PoW cost: electricity China X X X X X X
PoW cost: electricity N. America X X X X X X
hardware efficiency X X X X X X
number of miners X X X X X X
competition intensity X X
hhi X X
hhi normalised X X
10-year-treasury X X X X X X
Table 4. Estimation results: long -run interdependencies
Dependent variable: hashrate Dependent variable: difficulty
M1.1 M2.1 M3.1 M1.2 M2.2 M3.2
mining reward 1.398 *** 1.428 *** 1.379 *** 1.703 *** 1.848 *** 1.845 ***
PoW cost: electricity Europe -0.236 ** -0.331 ** -0.340 ** -0.137 * -0.155 * -0.183 *
PoW cost: electricity China 1.716 ** 1.206 ** 2.568 ** 1.238 * 2.857 * 3.108 *
PoW cost: electricity N. America -0.132 ** -0.214 ** -0.240 ** -0.061 * -0.120 * -0.142 *
hardware efficiency 0.839 *** 0.546 *** 0.650 *** 0.494 *** 0.237 *** 0.372 ***
number of miners -1.154 *** -2.824 *** -2.487 *** -2.112 *** -4.027 *** -3.393 ***
competition intensity -1.143 *** -1.257 ***
hhi -3.596 ** -5.756 ***
hhi normalised -2.285 *** -4.595 ***
10-year-treasury 2.204 ** 4.869 ** 5.994 * 2.556 * 5.675 ** 7.364 *
Error correction term
hash rate ( -1) -0.009 *** -0.008 *** -0.007 ***
difficulty ( -1) -0.003 ** -0.004 ** -0.002 **
Speed -of-adjustment (days) 109 121 135 317 350 447
Notes: Speed -of-adjustment is calculated based on the error correction rate. ***significant at 1% level,
**significant at 5% level, *significant at 10% level. Empty cells indicate absence of a variable in the respective
model.
36
Table 5. Estimation results: short -run interdependencies
Dependent variable: hashrate Dependent variable: difficulty
M1.1 M2.1 M3.1 M1.2 M2.2 M3.2
Δ dependent variable ( -1) -0.444 *** -0.441 *** -0.446 *** 0.192 *** 0.214 *** 0.202 ***
Δ dependent variable ( -2) -0.469 *** -0.452 *** -0.431 *** -0.146 *** -0.148 *** -0.148 ***
Δ dependent variable ( -3) -0.317 *** -0.293 *** -0.291 *** -0.039 *** -0.034 *** -0.034 ***
Δ dependent variable ( -4) -0.230 *** -0.196 *** -0.203 *** -0.075 *** -0.071 *** -0.073 ***
Δ dependent variable ( -5) -0.164 *** -0.143 *** -0.152 *** -0.058 ** -0.057 *** -0.056 ***
Δ dependent variable ( -6) -0.099 ** -0.096 *** -0.092 *** -0.053 ** -0.047 *** -0.049 **
Δ dependent variable ( -7) -0.074 ** -0.064 ** -0.064 ** -0.061 ** -0.057 ** -0.051 **
Δ mining reward -0.019 *** -0.017 *** -0.017 *** -0.001 *** -0.001 *** -0.001 ***
Δ mining reward ( -1) -0.020 ** -0.034 ** -0.016 ** -0.003 ** -0.002 * -0.002 *
Δ mining reward ( -2) 0.001 * 0.001 * 0.001 *
Δ mining reward ( -3) -0.014 * -0.015 -0.013 *
Δ mining reward ( -4) -0.015 -0.015 -0.015
Δ PoW cost: electricity China -1.258 *** -1.319 *** -1.181 ***
Δ number of miners 0.002 ** 0.003 **
Δ number of miners ( -1) 0.077 0.076
Δ number of miners ( -2) 0.092 0.094
Δ competition intensity 0.010 ** 0.001 **
Δ competition intensity ( -1) 0.063 ** 0.033 *
Δ competition intensity ( -2) 0.041 * 0.033
constant 0.292 ** 0.350 ** 0.383 ** 0.046 ** 0.074 ** 0.103 **
Notes: ***significant at 1% level, **significant at 5% level, *significant at 10% level. Empty cells indicate
either absence of a variable in the respective model or the coefficient or the variable is not selected in the
estimation; Δ is difference.
Table 6. Development of the PoW mining hardware efficiency
Type Hardware name Date J/Th
CPU ARM Cortex A9 3 Oct 2007 877,193
GPU ATI 5870M 23 Sep 2009 264,550
FPGA X6500 FPGA Miner 29 Aug 2011 43,000
ASIC Canaan AvalonMiner B1 1 Jan 2013 9,351
ASIC KnCMiner Jupiter 5 Oct 2013 1,484
ASIC Antminer U1 1 Dec 2013 1,250
ASIC Bitfury BF864C55 3 Mar 2014 500
ASIC RockerBox 22 Jul 2014 316
ASIC ASICMiner BE300 16 Sep 2014 187
ASIC BM1385 19 Aug 2015 181
ASIC PickAxe 23 Sep 2015 140
ASIC Antminer S9-11.5 1 Jun 2016 98
ASIC Antminer R4 1 Feb 2017 97
ASIC Ebang Ebit 10 15 Feb 2018 92
ASIC 8 Nano Compact 1 May 2018 51
ASIC Antminer S17 9 Apr 2019 36
ASIC Antminer S19 Pro 23 Mar 2020 30
Source: Song and Aste (2020)
|
{
"id": "2102.08107"
}
|
2201.11370
|
An IoT Blockchain Architecture Using Oracles and Smart Contracts: the
Use-Case of a Food Supply Chain
|
The blockchain is a distributed technology which allows establishing trust
among unreliable users who interact and perform transactions with each other.
While blockchain technology has been mainly used for crypto-currency, it has
emerged as an enabling technology for establishing trust in the realm of the
Internet of Things (IoT). Nevertheless, a naive usage of the blockchain for IoT
leads to high delays and extensive computational power. In this paper, we
propose a blockchain architecture dedicated to being used in a supply chain
which comprises different distributed IoT entities. We propose a lightweight
consensus for this architecture, called LC4IoT. The consensus is evaluated
through extensive simulations. The results show that the proposed consensus
uses low computational power, storage capability and latency.
|
http://arxiv.org/pdf/2201.11370v1
|
Hajar Moudoud, Soumaya Cherkaoui, Lyes Khoukhi
|
cs.NI, cs.CR
|
cs.NI
|
An IoT Blockchain Architecture Using Oracles and
Smart Contracts: the Use-Case of a Food Supply
Chain
Hajar Moudoudy, Soumaya Cherkaoui, Lyes Khoukhiy
Department of Electrical and Computer Engineering, Universit ´e de Sherbrooke, Canada
yUniversity of Technology of Troyes, France
fhajar.moudoud, soumaya.cherkaoui g@usherbrooke.ca, lyes.khoukhi@utt.fr
Abstract —The blockchain is a distributed technology which
allows establishing trust among unreliable users who interact
and perform transactions with each other. While blockchain
technology has been mainly used for crypto-currency, it has
emerged as an enabling technology for establishing trust in the
realm of the Internet of Things (IoT). Nevertheless, a naive
usage of the blockchain for IoT leads to high delays and
extensive computational power. In this paper, we propose a
blockchain architecture dedicated to being used in a supply chain
which comprises different distributed IoT entities. We propose a
lightweight consensus for this architecture, called LC4IoT. The
consensus is evaluated through extensive simulations. The results
show that the proposed consensus uses low computational power,
storage capability and latency.
Keywords —Blockchain; Internet of Things (IoT); Consensus;
Supply chain.
I. I NTRODUCTION
With the rapid rise of smart devices, smart homes, smart
cities, and smart everything, the Internet of Things (IoT) has
gained popularity among users [1][2]. IoT can be defined as
a group of interconnected things or devices, in a private or
a public network, sharing data to provide a service such as
automation or monitoring. However, in order to fully take
advantage of the IoT paradigm, several problems should be ad-
dressed [3]. These problems include particularly, security and
privacy issues, especially when private and business-related
information are collected and shared among different entities.
Another set of related problems are integrity, trustworthiness,
and non-repudiation of the data shared among the different
entities [4]. For example, in a smart hospital, the subject will
receive treatment based on data provided by sensors; but could
we trust the information provided by these sensors?
Blockchain (BC) has been proposed as a solution to over-
come these problems [5]. Because it is decentralized, BC
eliminates the need of having a third party verify data integrity,
trustworthiness and non-repudiation. Also, BC does not have
a single point of failure. BC is a distributed technology that
allows transaction verification by members which could be
dishonest. It is an immutable ledger (chain) that maintains a
continuously growing set of data records called blocks. Data
records store information about each transaction performed bythe users. Once a valid block is gathered, it is connected to
the last block of the chain.
BC uses cryptography signatures, public/private keys, and
a consensus mechanism for appending any new block into the
chain. A consensus corresponds to a protocol that establishes
an agreement among independent entities about the state
of the BC. The ability of BC to reach consensus among
dishonest distributed peers provides a high system availability
and security for IoT systems involving numerous entities. Still,
the computing power needed to run the BC is somehow in-
compatible with the restrictive features of several IoT systems
[6] [7]. For example, IoT endpoints generally have limited
resources; they are power-constrained with limited computing
energy, storage, and bandwidth.
There have been several works that tried to address the
challenges of using BC in an IoT context [8]. However,
most of these works, if not all, display shortcomings in one
or several of the following aspects; openness, lightweight
consensus, use of smart contracts, and Oracles. We define
openness as the capacity of non-members of BC to access
stored data. For example, when using an architecture such
as the one proposed in [9], only members of the BC can
access data, which can restrict access to information that
can be of interest to the public in general. A consensus for
IoT BC should be non compute-intensive; in other words,
lightweight. This aspect has not been addressed in several
works [10][11]. Smart contracts implement a formal model to
provide a division of labour between stakeholders, which can
be useful for implementing rules and policies. Smart contracts
are absent in several works like [12]. An Oracle is a third-
party agent who verifies data that cannot be reached or fetched
directly by the BC [13]; in other terms, data that comes from
the physical world (e.g., sensor data). This aspect has not been
addressed in many works [14][15].
In this paper we propose a secure architecture that over-
comes the challenges of using BC in an IoT context, that
ensures openness, uses a lightweight consensus for IoT
(LC4IoT), smart contracts, and Oracles. To accurately illus-
trate the architecture, a food supply chain use-case will be
adopted throughout the paper. Still, the concepts presented inarXiv:2201.11370v1 [cs.NI] 27 Jan 2022
this paper are well suited for other types of IoT applications.
The remainder of this paper is organized as follows. In
section II, we present the proposed architecture. Section III
details LC4IoT used in the architecture. Section IV studies
the performance of LC4IoT in comparison with the consensus
used by the Bitcoin architecture. In section V , we give an
overview of some proposed BC IoT architectures. Finally,
section VI concludes the paper.
II. FOOD SUPPLY BLOCKCHAIN
The food supply chain system includes multiple stakehold-
ers such as farmers, distributors, retailers, and consumers
(see Fig. 1). These stakeholders can be located in different
regions, with produce possibly transiting between several
countries before arriving at a consumer. Due to the lack of
trust and transparency among tiers, produce tracing becomes
challenging. In this context, BC ability to permanently record
data and provide real-time access to information could help
overcoming the problem of produce traceability. The proposed
architecture involves four tiers: overlay IoT network, smart
farm, Oracle’s network, and Cloud. Thereafter, we define each
tier of the architecture.
A. Overlay network
We introduce a peer-to-peer overlay network including
supply chain members. The overlay network forms a dis-
tributed network involving multiple stakeholders. This peer-
to-peer network is built on top of the supply chain system,
enabling stakeholders connected to it to communicate. All
members of the supply chain are initialized at the beginning of
communication and identified by a public key. A new member
is accepted in the overlay network if she is approved by a
quorum. A “quorum” is the minimal number of members
required to reach an agreement. All members of the overlay
network have a list of approved member keys stored in their
local storage.
We use smart contracts to ensure that rules and policies
are respected by parties in the overlay network. In this
architecture we use smart contracts in two ways; first, they are
applied by a third party who offers transparency and efficiency,
second, they are implemented to govern operations between
stakeholders.
B. Smart farm
The smart farm is comprised of IoT devices, proxy nodes,
storage and BC.
In general, BC has three main ledger types: public, consor-
tium, and private BC [16]. In a public BC, everyone can
join the network and all BC members are responsible for
transaction validation. The consortium BC is different in that,
it is partly decentralized; only some members oversee the
consensus determination. Generally, consortium BC is built
by several organizations. Lastly, private BC restricts access to
network members only. Private BC is implemented by a single
company or an organization. In the proposed architecture we
use a public/private BC. The private BC is used to store private
Fig. 1: Food supply chain overlay network
information of the smart farm. The public BC is used for
tracking produce, and for providing information to the general
public. Fig. 2 illustrates the smart farm BC architecture.
IoT sensors are responsible for gathering data from the
field in a smart farm (e.g. RFID tags for cattle). Since
IoT devices have limited computing power and energy, we
use proxy nodes to outsource computing. The imparting of
information between the smart farm IoT devices is referred to
as a transaction. Every produce is identified by a public key
that changes with every transaction. Data are stored centrally
in designated storage, while transactions are recorded in the
private/public BC.
C. Oracle network
In the food supply chain, data is generally collected from
sensors scattered across multiple locations. We use Oracles to
check the veracity of sensor data. For example, an Oracle
can inform whether the temperature inside a refrigerated
track transporting produce has come above a certain threshold
during the transportation journey.
To verify data, an Oracle Oineeds to compare received data
xfrom a sensor with fetched data yby the Oracle (see Eq.
1).
Oi(x) =(
1ifOi:verify (x;y) ==True
0otherwise:(1)
In our architecture, multiple Oracles can be used. The
Oracles’ network is able to divide the approval process for
data veracity among multiple parties. We define the Oracles’
network data verification process as follows: Mout ofN
multi-signature transaction should be reached among Oracle
parties. A transaction is valid when Mout ofNOracles sign
it, that is, the sum is greater than a threshold (Eq.2).
8Oi;Oi2f0;1g!f0;1gk;1iM;
MX
i=1Oi(x) (2)
Fig. 2: Smart farm architecture based on BC
Thevalue depends on the number of active Oracles and
the fault tolerance of the system. Where Mis the number of
active Oracles in the network and kis a security parameter.
D. Cloud
Cloud stores raw data received from the Oracles’ network.
Data is either publicly accessible, ensuring data transparency
or has limited access to preserve the privacy of stakeholders.
Each member of the food supply chain allocates a Cloud space
for personal usage, linked with a Cloud public key CPk . This
process guarantees that data is correctly routed and the source
is identified.
We propose using a private BC in the Cloud to store data
hash, enabling data trustworthiness and non-repudiation. In
BC, the hash function is the algorithm used to write a new
transaction through the mining process [17]. It maps data and
generates a summary; the unique fingerprint substitutes an
input string from arbitrary to a fixed size. The hash enables
data integrity by comparing the hash value along with some
additional inputs, like timestamp and previous block hash.
BC uses the SHA-256 secure hash algorithm, which provides
almost a unique and fixed size of 256 bits (32 bytes), requiring
low computing power.
III. LIGHTWEIGHT CONSENSUS
In this section, we explain the transaction verification pro-
cess and how off-chain storage is performed in the proposed
architecture. Also, we detail LC4IoT.
A. Transaction verification
In order to append a new transaction in BC, miners need
to verify some conditions. This verification process is divided
into three steps:
1) First, miners verify the sender’s signature to validate
transaction authenticity. This digital signature authen-
ticates the sender, using the public key stored in the
transaction.
2) Second, miners check if the public key is predefined;
which means, the sender public key has a stored trans-
action in the BC. Otherwise, it is a genesis transaction
(see section C).
3) Third, miners verify the Oracles’ network signatures.If all conditions are validated, data is then transferred to the
verified transaction pool for mining.
B. Data transfer off-chain
The BC could be used as a mediator for data transfer.
Generally, there are two methods used to store data within BC;
first, data is sent within a transaction, it is the case with Bitcoin
[18], or, data is stored in smart contracts, it is the case with
Ethereum [19]. Both approaches submit a transaction in the
BC. Nevertheless, the BC block size is limited. To solve this
problem, several proposals have been put forward. A trivial
solution consists in increasing the block size. For example,
Bitcoin Cash [20] upgraded the block size from 1 MB to 8
MB. Still, this solution may affect the operation of nodes and
tends to be more expensive. For our architecture, we store raw
data off-chain and metadata on-chain, to improve latency and
system scalability.
In this section, we outline how data transfer is performed in
BC. We explain mining steps in the case where Alice wants to
transfer 5GB of data to Bob. Let Alice be a member of the BC,
which means that her public key exists in a previous block of
the chain. First, Alice will store the 5GB of data in the Cloud.
Alice is identified by the triple ( Pk,Prk,a) wherePkis the
distributed public key, Prk is the secret private key, and ais
the address where she stored data. Since block size in BC is
limited, Alice will only store metadata on-chain and the actual
data off-chain. Alice will share the private key with Bob and
encrypt metadata with the public key. Once the transaction is
created, Alice will sign it using the private key. A node in the
network will verify the state of the transaction, by verifying
the signature, public key of both the sender and the receiver.
Afterwards, the transaction is sent to the verified transaction
pool.
A node in the network can also act as a miner. Miners of the
network choose a transaction form the pool and build a block.
Different miners could pick the same transaction. Miners in
the network will attempt to reach a consensus to append a
block. The first miner who reaches a consensus will broadcast
the new block to other miners. The transaction becomes a
permanent part of the ledger, Bob can now have access to the
address where Alice has stored data with her private key.
C. Lightweight consensus for IoT (LC4IoT)
We propose LC4IoT algorithm that integrates the use of
Oracles for block appending in the BC. In step 1, nodes
in the network fetch a random transaction Tkfrom the
verified transaction pool and try to create a new block Bi+1.
To simplify, we admit that every block contains only one
transaction. Every transaction has the following arguments,
Tk(Pk;CPk;O i:sig;metadata ), wherePkis the public key
of the data provider, CPk is the Cloud public key, Oi:sig is
the Oracles signature and metadata is the metadata that we
want to store. The output of the algorithm will be a new block
that contains the transaction. In step 2, the algorithm verifies if
the stored signature in the transaction belongs to the Oracles’
network or not. If the provided signature does not belong to
the list of the Oracles’ network, the algorithm returns False.
Otherwise, the algorithm verifies if the transaction is a storing
transaction or a genesis transaction.
To store a transaction, miners should calculate the times-
tampTSand the hash of the previous block (step 14). Finally,
in steps 15 to 16, the algorithm return the new block Bi+1,
which contains the hash of the previous block, transaction and
the hash of the new block.
To ensure the system liveness, which means the growth
of the system, we will use PBFT [21] consensus for genesis
transaction. This consensus handles ffaulty members of the
network. If the sum of stakeholders Siin the overlay network
is superior to a min 3f+ 1 we accept the demand. For a
genesis transaction, the system allocates a new public key to
the requester, which will be stored in a new block.
IV. PERFORMANCE ANALYSIS
In this section, we provide a quantitative performance eval-
uation of LC4IoT. First, we conduct a simulation to evaluate
the computing power and time consumed by the proof of work
consensus. Second, we assess LC4IoT: computational power,
memory footprint, and latency.
A. Proof of work evaluation
The proof of work is the consensus algorithm that secures
the network by demanding to the requester some work. An
example of BC that uses the proof of work consensus is
Bitcoin [22]. To reach an agreement among users, each node
of the network calculates a hash value called “Nonce” in the
block header. Miners in the network will try to estimate a
secret value, then embed it in the block. All information inside
the block header will be combined, next inputted to a SHA-
256 hash function. The first miner who will reach a hash
function output less than a threshold can add the new block
to the chain. The new block is then broadcast to network
users. In Bitcoin, a valid block hash requires that it starts
with several zeros, which refer to difficulty level, adjusted to
limit block generations. Currently, the difficulty reached by the
Bitcoin is /tildelow18 zeros [23]. Since we cannot choose the hash
value of a block, miners try several combinations to solve this
puzzle. To demonstrate the drawbacks of the proof of work
consensus, we implement a proof of work using JavaScriptAlgorithm 1 Block appending algorithm
Input8Tk2T,8Bi2B,8Oi2O;
Output New blockBi+1;
1:Initialize:
2:Tk: A transaction from the verified transaction pool.
3:Bi: The last appended block in the blockchain B.
4:Oi: Oracle,
5:m: The number of Oracles in the network
6:j= 0
7:whilejmjjTk:output (2)6=Oj:sigdo
8:j j+ 1
9:
10:end while
11:ifj=m+ 1
12:Return False
13:else
14: whilei0jj
15: Bi:Ti:outputs (2) ==Tk:outputs (o)do
16:
17:i i 1
18: end while
19: ifi= 0then
20:Tk GenesisTransaction (Tk)
21: update transaction pool
22: else
23:TS date:gettime ()
24:Bi+1:H CalH (i1;Bi:H;TS;T k)
25:Bi+1 Bi+1(Bi:H;TS;T k;Bi+1:H)
26:Return B i+1
27: end if
28:end if
Fig. 3: Proof of work evaluation
enabling change of difficulty. We simulate the appending of
one block on Intel machine Core™ i7-8550U CPU @ 1.80
GHz 2.00 GHz with 16.0 RAM. Fig. 3 shows the results of
processing time and computational power. Simulation result
proves that we cannot use a proof of work consensus in the
context of the food supply chain.
Fig. 4: computational power evaluation
B. Performance evaluation
In this section, we evaluate LC4IoT in contrast to the
proof of work consensus. In our experiments, we fixed the
difficulty variable to 4 for the proof of work. We use the
following evaluation metrics: computational power, memory
footprint and processing time. We run the simulation 3 times
to add 10 blocks into the BC and record CPU evolution
during the time. Fig. 4 shows the results of computational
power used by both consensuses. Localmax annotation refers
to the consensus execution peak. As we mentioned before,
we separate transaction verification from block appending,
Oracles network oversees transactions, whereas miners append
blocks to the ledger. To quantify the advantages of this design
decision, we perform simulation while we alter the number of
blocks to add to the BC; simulation results are presented in
Fig. 5.
Some BC mining algorithms are memory intensive; RAM
footprint required by consensus is higher than system avail-
ability. By comparing the memory usage of both consensuses,
we display the need for a high-powered computer to perform
mining and solve the complex puzzle for the proof of work.
Even if proof of work algorithm makes memory requirements
seem secondary in comparison with the high-computational
power consumed, a lot of RAM usage will make mining run
more slowly; leading to an increase in the delay. LC4IoT
algorithm improves block verification process and entails a
low memory growth rate of 0:362% per block. The delay
metric, shown in Fig. 6, refers to the time consumed by the
algorithm to append blocks into the ledger. In Fig. 6 the delay
evaluation of the proof of work and LC4IoT are illustrated.
As evidenced by the figure, the proof of work delay increases
with the number of blocks to add in the BC. The proof of
work consumes 7.81s to append 10 blocks, while LC4IoT
only needs 0.139s for the same number of blocks.
V. R ELATED WORK
Various works have been conducted for using BC in the IoT
context. Fern ´andez-Caram ´es et al. reviewed the state of the
art of BC and IoT applications and evaluated the limitations
of BC usage for IoT applications [8]. Likewise, BC who
implements smart contracts were used for several purposes.
Choi et al. utilized BC smart contracts to secure the control
of IoT sensors [15]. Novo et al. proposed a distributed IoT
Fig. 5: Memory evaluation
Fig. 6: Delay evaluation
architecture based on BC smart contracts to ensure access
management. This architecture uses a proof of concept con-
sensus [9] and private BC to secure information. Yet, using a
private BC may restrict access to information with a general
interest for users. Michelin et al. designed a SpeedyChain
framework based on BC, allowing smart vehicles to share data
in a secure matter [12]. Liu et al. presented a BC framework
ensuring data integrity [14]. Further, BC has helped the supply
chain overcoming its limitations such as transparency, relia-
bility and integrity. Tian et al. in [24] proposed a conceptual
framework for agricultural produce traceability using BC and
Radio-Frequency Identification (RFID) technology. The BC
stores information provided by the RFID technology to help
track produce. Korpela et al. in [25] studied the requirement
for supply chain integration with the BC. This investigation
proposed the usage of Cloud applications to achieve inter-
operability in the supply chain. However, both proposals in
[24] and [25] do not take into consideration the high number
of transactions managed by BC. The supply chain conveys a
high volume of communications and requires real-time access
to information. A conventional BC needs from seconds to
minutes to append a block into the chain. As an example, for
Bitcoin BC, block generation takes 10 minutes [26]. Several
BC consensuses require high computational power, which is
not suitable for the constrained nature of IoT sensors. In their
work, Su et al. developed and implemented a supply chain
based on BC technology, using Ethereum consensus [10].
Pass et al. proposed a fruitChain protocol using the proof of
work consensus [11]. Both the proof of work consensus and
the Ethereum consensus need high computing resources for
block appending. Dorri et al. proposed a lightweight scalable
blockchain (LSB) that reduces the delay for appending data to
the BC [27]. In [27], however, the proposed BC architecture
does not address how external data outside the network is
accessed.
VI. C ONCLUSION
Blockchain (BC) establishes trust among different parties
who could be dishonest, enabling data sharing in a secure
matter. The use of BC in an IoT context may offer several ben-
efits like trustworthiness and data non-repudiation. However,
the constrained nature of IoT sensors is incompatible with the
high computational power needed for the BC. In this paper, we
presented a secure IoT BC architecture whose usage was illus-
trated for the use case of a food supply chain. The proposed
architecture uses an Oracles’ network and smart contracts,
ensuring produce traceability and system openness. Further,
we proposed using a lightweight consensus for IoT (LC4IoT),
which reduces computational power, storage capability, and
latency. Simulation results highlighted the effectiveness of the
proposed consensus.
ACKNOWLEDGEMENT
The authors would like to thank the Natural Sciences and
Engineering Research Council of Canada, as well as FEDER
and GrandEst Region in France, for the financial support of
this research.
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[25] K. Korpela, J. Hallikas, and T. Dahlberg, “Digital Supply Chain
Transformation toward Blockchain Integration,” Jan. 2017. [Online].
Available: http://scholarspace.manoa.hawaii.edu/handle/10125/41666
[26] I. Eyal, A. E. Gencer, E. G. Sirer, and R. van Renesse, “Bitcoin-NG:
A Scalable Blockchain Protocol,” p. 16.
[27] A. Dorri, S. S. Kanhere, R. Jurdak, and P. Gauravaram, “LSB:
A Lightweight Scalable BlockChain for IoT Security and Privacy,”
arXiv:1712.02969 [cs] , Dec. 2017.
|
{
"id": "2201.11370"
}
|
2001.07091
|
Blockchain Consensus Algorithms: A Survey
|
In recent years, blockchain technology has received unparalleled attention
from academia, industry, and governments all around the world. It is considered
a technological breakthrough anticipated to disrupt several application
domains. This has resulted in a plethora of blockchain systems for various
purposes. However, many of these blockchain systems suffer from serious
shortcomings related to their performance and security, which need to be
addressed before any wide-scale adoption can be achieved. A crucial component
of any blockchain system is its underlying consensus algorithm, which in many
ways, determines its performance and security. Therefore, to address the
limitations of different blockchain systems, several existing as well novel
consensus algorithms have been introduced. A systematic analysis of these
algorithms will help to understand how and why any particular blockchain
performs the way it functions. However, the existing studies of consensus
algorithms are not comprehensive. Those studies have incomplete discussions on
the properties of the algorithms and fail to analyse several major blockchain
consensus algorithms in terms of their scopes. This article fills this gap by
analysing a wide range of consensus algorithms using a comprehensive taxonomy
of properties and by examining the implications of different issues still
prevalent in consensus algorithms in detail. The result of the analysis is
presented in tabular formats, which provides a visual illustration of these
algorithms in a meaningful way. We have also analysed more than hundred top
crypto-currencies belonging to different categories of consensus algorithms to
understand their properties and to implicate different trends in these
crypto-currencies. Finally, we have presented a decision tree of algorithms to
be used as a tool to test the suitability of consensus algorithms under
different criteria.
|
http://arxiv.org/pdf/2001.07091v2
|
Md Sadek Ferdous, Mohammad Jabed Morshed Chowdhury, Mohammad A. Hoque, Alan Colman
|
cs.DC
|
cs.DC
|
1
Blockchain Consensus Algorithms: A Survey
Md Sadek Ferdous, Member, IEEE, Mohammad Jabed Morshed Chowdhury,
Mohammad A. Hoque, Member, IEEE , and Alan Colman
F
Abstract —In recent years, blockchain technology has received unpar-
alleled attention from academia, industry, and governments all around
the world. It is considered a technological breakthrough anticipated to
disrupt several application domains touching all spheres of our lives.
The sky-rocket anticipation of its potential has caused a wide-scale
exploration of its usage in different application domains. This has resul-
ted in a plethora of blockchain systems for various purposes. However,
many of these blockchain systems suffer from serious shortcomings
related to their performance and security, which need to be addressed
before any wide-scale adoption can be achieved. A crucial component
of any blockchain system is its underlying consensus algorithm, which
in many ways, determines its performance and security. Therefore, to
address the limitations of different blockchain systems, several existing
as well novel consensus algorithms have been introduced. A systematic
analysis of these algorithms will help to understand how and why any
particular blockchain performs the way it functions. However, the existing
studies of consensus algorithms are not comprehensive. Those studies
have incomplete discussions on the properties of the algorithms and
fail to analyse several major blockchain consensus algorithms in terms
of their scopes. This article fills this gap by analysing a wide range of
consensus algorithms using a comprehensive taxonomy of properties
and by examining the implications of different issues still prevalent in
consensus algorithms in detail. The result of the analysis is presented in
tabular formats, which provides a visual illustration of these algorithms
in a meaningful way. We have also analysed more than hundred top
crypto-currencies belonging to different categories of consensus al-
gorithms to understand their properties and to implicate different trends
in these crypto-currencies. Finally, we have presented a decision tree
of algorithms to be used as a tool to test the suitability of consensus
algorithms under different criteria.
Index Terms —Blockchain, Distributed Consensus, Proof of Work, PoW,
Proof of Stake, PoS, Delegated Proof of Stake, DPoS.
1 I NTRODUCTION
In the last few years, blockchain has received wide-spread
attention among the industry, the Government, and aca-
demia alike. This interest has been piqued by the success
M. S. Ferdous is with Shahjalal University of Science and Technology, Sylhet
3114, Bangladesh and Imperial College London, London SW7 2AZ, U.K. E-
mail: sadek-cse@sust.edu.
Mohammad Jabed Morshed Chowdhury is with La Trobe University, Mel-
bourne, Victoria-3086, Australia. E-mail: m.chowdhury@latrobe.edu.au.
Mohammad A. Hoque is with University of Helsinki, 3835 Helsinki, Helsinki
Finland. E-mail: mohammad.a.hoque@helsinki.fi.
Alan Coleman is with Swinburne University of Technology, Hawthorn,
Australia. E-mail: acolman@swin.edu.au.of Bitcoin [1] that was introduced in 2008. While crypto-
currencies have emerged as the principal and the most pop-
ular application of blockchain technology, many enthusiasts
from different disciplines have identified and proposed a
plethora of applications of blockchain in a multitude of
application domains [2], [3]. The possibility of exploiting
blockchain in so many areas has created huge anticipation
surrounding blockchain systems. Indeed, it is regarded as
one of the fundamental technologies to revolutionise the
landscapes of the identified application domains.
A blockchain system is, fundamentally, a distributed
system that relies on a consensus algorithm that ensures
agreement on the states of certain data among distributed
nodes. A consensus algorithm is the core component that
directly dictates how such a system behaves and the per-
formance it can achieve. Distributed consensus has been
a widely studied research topic in distributed systems,
however, with the advent of blockchain, it has received
renewed attention. A wide variety of crypto-currencies
targeting different application domains has introduced an
array of unique requirements that can only be satisfied by
their corresponding consensus mechanisms. This fact has
fuelled the need not only to examine the applicability of
existing consensus algorithms in newer settings, but also to
innovate novel consensus algorithms. Consequently, several
consensus algorithms have emerged, each of which pos-
sesses interesting properties and unique capabilities.
As the characteristics of various types of blockchain
systems are fundamentally dependent on the consensus
algorithms they use, a systematic analysis of existing con-
sensus algorithms is required. It is necessary to examine,
compare, and contrast these algorithms. There have a been
a number of attempts aiming to fulfil this goal can be found
in [4], [5], [6], [7], [8], [9], [10]. In particular, the works carried
out by Cachin et al. [4] and Bano et al. [5] are noteworthy as
they represent the pioneer works in this scope. Cachin et al.,
in their work, have explored different aspects of distributed
systems and consensus and focused on consensus algorithm
deployed in blockchain systems that are not to open to
the public. On the other hand, the focus of the work by
Bano et al. is more general in the sense they have explored
consensus algorithms used both in public as well as private
systems. Another exceptional work is by Wang et al. [6] in
which the authors have presented a comprehensive survey
of different aspects of consensus, mining, and blockchains
in a detailed fashion.
However, all these works have some major shortcom-arXiv:2001.07091v2 [cs.DC] 7 Feb 2020
2
ings. For example, the factors upon which the consensus
algorithms have been analysed are not comprehensive.
Importantly, a wide range of consensus algorithms and
their internal mechanisms utilised in many existing crypto-
currencies have not been considered at all. In addition, all
of these studies have failed to capture the practical interrela-
tion between blockchain systems (mostly crypto-currencies)
and their corresponding consensus algorithms. All in all,
there is a pressing need for a study that analyses a wide
range of existing consensus algorithms and the blockchain
systems in a practical-oriented way and synthesises this
analyses into a conceptual framework in a concise yet com-
prehensive manner. The principal motivation of this article
is to fill in this gap.
Contributions. The main contributions of the article are
presented below:
A novel taxonomy of consensus properties, capturing
different aspects of a consensus algorithm, has been cre-
ated. In this taxonomy, consensus algorithms have been
categorised in two major categories: incentivised and
non-incentivised algorithms, which have been again
sub-divided as per different considerations. Consensus
algorithms belonging to each sub-category analysed
together using the taxonomy of consensus properties.
The analysis of each sub-category has been summarised
in tabular formats so as to visually represent it in a
comprehensible way.
For each category (and the sub-category, if any), the cor-
responding blockchain systems (predominantly crypto-
currencies) have been analysed as well. The ana-
lysis result has been presented in a concise fashion,
which can be used to understand the inter-relation
between these systems and their underlying consensus
algorithms.
The major issues in each category of consensus al-
gorithm have been examined in detail, and their im-
plications have been further analysed.
Over hundred crypto-currencies, belonging to different
consensus algorithms, have been examined to under-
stand their different properties. These properties then
have been utilised to analyse and identify different
trends among these crypto-currencies.
Finally, a decision tree of consensus algorithms have
been presented. This tree can be utilised to test the suit-
ability of a consensus algorithm under certain criteria.
In short, with these contributions, this article represents one
of the most comprehensive studies of blockchain consensus
algorithms as of now.
Structure. In Section 2 we present a brief background on dis-
tributed consensus, highlighting its different components,
types and properties. Section 3 outlines a brief presentation
on blockchain covering its different aspects such as types,
properties, layers. A taxonomy of consensus algorithms and
their underlying properties is presented in Section 4. Section
5 and Section 6 analyse different incentivised consensus
algorithm whereas Section 7 examines the different non-
incentivised consensus algorithms. Finally, we conclude in
Section 8 with a detailed discussion on different issues
involving the analysed consensus algorithms and the cor-responding crypto-currencies.
2 B ACKGROUND : DISTRIBUTED CONSENSUS
Consensus mechanisms in distributed systems have been
a well studied research problem for nearly three decades.
Such mechanisms enable consensus to be achieved regard-
ing a shared state/data among a set of distributed nodes.
The need for a shared state originated the notion of replic-
ated database systems in order to ensure resilience against
node failures within a network. Such database systems
ensure that data is not lost when one or more nodes fail
to function in an excepted fashion.
The notion of the replicated database can be generalised
with the concept of State Machine Replication (SMR) [11].
The core idea behind SMR is that a computing machine can
be expressed as a deterministic state machine . The machine
accepts an input message, performs its predefined computa-
tion, and might produce an output/response. These actions
essentially change its state. SMR conceptualises that such a
state machine, with an initial state, can be replicated among
different nodes. If it can be ensured that all the participating
nodes receive the same set of input messages in the exact
same order (the phenomenon known as atomic broadcast ),
then each node would be able to evolve the states of its
state machine individually in exactly the same fashion. This
can guarantee consistency and availability regarding the
state of the machine (as well as data it holds) among all
(applicable) nodes even in the presence of node failures.
Once this occurs, it can be said that a distributed consensus
has emerged among the participating nodes. It is imperative
that a protocol is defined to ensure timely dissemination
and atomic broadcast of input messages among the nodes
and, in many ways, dictates how a distributed consensus is
achieved and maintained. Hence, such a protocol is aptly
called a consensus protocol.
Designing and deploying a consensus protocol is a chal-
lenging task as it needs to consider several crucial issues
such as resiliency against node failures, node behaviour,
network partitioning, network latency, corrupt or out-of-
order inputs, and so on [7]. Schneider pointed out that
there are two crucial requirements to reach and maintain
consensus among distributed nodes. The first requirement
is a deterministic state machine. The second requirement is
aconsensus protocol to disseminate inputs in a timely fashion
and to ensure atomic broadcast among the participating
nodes. At the same time, the consensus protocols must
ensure the properties of the atomic broadcast [12], [13],
[4], [5]. The properties of atomic broadcast in distributed
consensus is illustrated in Table 1.
One way to achieve the design goals of such a protocol
is to make certain assumptions under which the protocol is
proved to function properly. These assumptions influence
the critical characteristics of a consensus protocol. Next,
we explore two sets of widely-used assumptions for any
distributed consensus protocol.
The first set of assumptions are about the underly-
ing networking type. Dwork et al. categorised three types
of networks exhibiting different properties: synchronous,
asynchronous, and partially/eventually synchronous [22].
The latency involved in delivering a message to all nodes
3
PropertiesNote
ValidityThis guarantees that if a message is broadcast by
a valid node, it will be correctly included within
the consensus protocol.
AgreementThis is to guarantee that if a message is de-
livered to a valid node, it will ultimately be
delivered to all valid nodes.
IntegrityThis is to ensure that a message is broadcast
only once by a valid node.
Total OrderThis is to ensure that all nodes agree to the order
of all delivered messages.
Table 1: Atomic broadcast Properties of Distributed Con-
sensus Protocols.
Properties Note
Safety/ Con-
sistencyA consensus protocol is considered safe (or con-
sistent) only when all nodes produce the same
valid output, according to the protocol rules, for
the same atomic broadcast.
Liveness/
availabilityIf all non-faulty participating nodes produce an
output (indicating the termination of the pro-
tocol), the protocol is considered live.
Fault
ToleranceIt exhibits the network’s capability to perform
as intended in the midst of node failures.
Table 2: Properties of Distributed Consensus Protocols.
in a synchronous network is bound by some time denoted
as. On the other hand, the latency in an asynchronous
network cannot be reliably bound by any . Finally, in a par-
tially/eventually synchronous network, it is assumed that
the network will eventually act as a synchronous network,
even though it might be asynchronous over some arbitrary
period of time.
The second set of assumptions is about the different
properties of a consensus protocol. According to [7], a con-
sensus protocol should have the following three properties;
namely consistency, availability, and fault tolerance. These
properties are elaborated in Table 2 A well-known theorem,
by Fischer, Lynch and Paterson [23], called FLP Impossibility
has shown that a deterministic consensus protocol cannot
satisfy all three properties described above in an asynchron-
ous network. It is more common to tend to favour safety and
liveness over fault tolerance in the domain of distributed
system applications. A related theorem is the CAP theorem
[24], which states that a shared replicated datastore (or, more
generally, a replicated state machine) cannot achieve both
consistency and availability when a network partitions in
such a way that an arbitrary number of messages might be
dropped.
In addition to the above assumptions, there are two
major fault-tolerance models within distributed systems:
crash failure (or tolerance) and Byzantine failure [5], [7],
[4]. The crash failure model deals with nodes that simply
fail to respond due to some hardware or software failures.
It may happen any time without any prior warning, and
the corresponding node remains unresponsive until further
actions are taken. Byzantine failure, on the other hand, deals
with nodes that misbehave due to some software bugs or
because of the nodes being compromised by an adversary.
This type of failure was first identified and formalised by
Leslie Lamport in his seminal paper with a metaphoricalByzantine General’s problems [14]. A Byzantine node can
behave maliciously by arbitrarily sending deceptive mes-
sages to others, which might affect the security of distrib-
uted systems. Hence, such nodes are mostly relevant in
application with security implications.
To handle these two failure models, two corresponding
major types of consensus mechanisms have emerged: Crash-
tolerant consensus and Byzantine consensus [4]. Next, we
briefly discuss each of them, along with their associated
properties.
1)Crash-tolerant consensus: Algorithms belonging to this
class aim to guarantee the atomic broadcast (total or-
der) of messages within the participating nodes in the
presence of certain number of node failures. These
algorithms utilise the notion of views or epochs, which
imply a certain duration of time or events. A leader
is selected for each epoch who takes decisions regard-
ing the atomic broadcast, and all other nodes comply
with its decision. In case a leader fails due to a crash
failure, the protocols elect a new leader to function.
The best known algorithms belonging to this class can
continue to function if the following condition holds:
t < n= 2where tis the number of faulty nodes and n
is the total number of participating nodes [4]. Examples
of some well-known crash-tolerant consensus protocol
are: Paxos [15], [16], Viewstamped Replication [17],
ZooKeeper [18], and Raft [19].
2)Byzantine consensus: This class of algorithms aim to
reach consensus amid of certain nodes exhibiting Byz-
antine behaviour. Such Byzantine nodes are assumed
to be under the control of an adversary and behave un-
predictably with malicious intent. Similar to any crash-
tolerant consensus protocol, these protocols also utilise
the concept of views/epochs where a leader is elected
in each view to order messages for atomic broadcast,
and other honest nodes are assumed to follow the in-
structions from the leader. One of the most well-known
algorithms under this class is called Practical Byzantine
Fault Tolerant (PBFT), which can achieve consensus in
the presence of a certain number of Byzantine nodes
under an eventual synchronous network assumption
[20]. The tolerance level of PBFT is f < n= 3, where
fthe number of Byzantine nodes and ndenotes the
number of total nodes participating in the network [4].
As we will explore later, PBFT algorithms have been
widely utilised in different blockchain systems.
3 B ACKGROUND : BLOCKCHAIN
In this section, we present a brief introduction to the block-
chain technology and it related terminologies. At the centre
of the blockchain technology is the blockchain itself stored
by the nodes of a P2P network. A blockchain is a type of
distributed ledger consisting of consecutive blocks chained
together following a strict set of rules. Here, each block is
created at a predefined interval, or after an event occurs, in
a decentralised fashion by means of a consensus algorithm.
Within each block, there are transactions by which a value is
transferred in case of crypto-currencies or a data is stored for
other blockchain systems. The consensus algorithm guar-
4
antees several data integrity related properties (discussed
below) in blockchain.
Even though the terms blockchain and DLT (Distributed
Ledger Technology) are used inter-changeably in the literat-
ure, there is a subtle difference between them which is worth
highlighting. A blockchain is just an example of a particular
type of ledger, there are other types of ledger. When a ledger
(including a blockchain) is distributed across a network, it
can be regarded as a Distributed Ledger.
Since the blockchain technology has been introduced
with Bitcoin, it will be useful to understand how Bitcoin
works. In Section 3.1, we discuss a brief primer of Bitcoin
and its associated terminologies. Then, we describe differ-
ent properties and types of blockchains in Section 3.2 and
Section 3.3 respectively. Finally, we present the concept of
blockchain layers in Section 3.4.
3.1 Bitcoin
The Bitcoin network consists of nodes within a P2P (Peer-
to-Peer) network. Each node needs to download the Bitcoin
software to connect to the network. There are different types
of nodes in the network, with miner nodes and general
nodes being the major ones. A general node is mostly used
by users to transfer bitcoin in the network, whereas a miner
node is a special node used for mining bitcoins (see below).
Each user within a node needs to utilise wallet software
to create identities. An identity in the Bitcoin network con-
sists of a private/public key pair, and a bitcoin address is
derived from the corresponding public key. A sender needs
to know such an address of the receiver to transfer any
bitcoin. Bitcoin is transferred between two entities using the
notion of a transaction where the sender utilises a wallet
software for this purpose. This transaction is propagated to
the network, which is collected by all miner nodes. Each
miner node combines these transactions into a block and
then engages in solving a cryptographic puzzle, with other
miners, in which it tries to generate a random number which
satisfies the required condition (the random number must
be less than a target value called the difficult target ). When
a miner successfully solves the puzzle, that miner is said
to have generated a valid block which is then propagated
in the network. The Bitcoin protocol generates a certain
amount of new Bitcoins for each new valid block and
rewards the miner for its effort in creating the block. Other
miners validate this newly mined block and then add it to
the blockchain. Each new block refers to the last block in the
chain, which in turn refers to its previous block, and so on.
The very first block in the chain, known as the genesis block ,
however, has no such reference.
The decentralised nature of this mining process might
result in multiple valid blocks generated by different miners
and propagated at the same time in the network. All of them
are added to the blockchain and they refer to the same last
block in the chain. Consequently, multiple branches emerge
from the same blockchain. This is a natural phenomenon
in blockchain and is aptly known as fork. The fundamental
goal of the corresponding consensus protocol is to resolve
this fork so that only one branch remains and other branches
are discarded. The consensus algorithm utilised in Bitcoin
follows a simple rule: it lets the branches grow. As soon asFigure 1: Block Generation Process of Bitcoin
A Each miner collects transactions that are broadcast
in the network and uses her hashpower to try to
generate a block via repeated invocation of a hash
function on data. The data consists of the transactions
that she saw fit to include, the hash of the previous
block, her public key address, and a nonce.
B When a miner succeeds in generating a block, mean-
ing that the hash of her block data is smaller than the
current difficulty target, she broadcasts her block to
the network.
C The other miners continue to extend the blockchain
from this new block, only if they find that this block
is valid, i.e., it refers the hash of the previous block
of the longest chain and meets the current difficulty
target.
D The block reward (newly minted coins) and the fees
from the transactions go to the miner’s public address.
This means that only the miner can spend those by
signing with her corresponding private key.
E The difficulty level readjusts according to the mining
power of the participates, by updating the hash target
value every 2016 blocks ( 2 weeks) so that blocks get
generated once every 10 minutes on average.
F The block reward starts at 50 coins and halves in every
210, 000 blocks, i.e., about every 4 years.
one branch grows longer than the others (more specifically,
the total cumulative computational effort of one branch ex-
ceeds the others), all miners select the longest branch (or the
branch with the highest computational effort), discarding all
other branches. Such a branch is known as the main branch
and other branches are known as orphan branches. Only
the miners in the main branch are entitled to receive their
Bitcoin rewards. When a fork is resolved across the network,
a distributed consensus emerges in the network.
The frequency of Bitcoin block generation depends on
the difficulty parameter, which is adjusted after 2016 blocks.
The protocol adjusts the difficulty parameter in such a way
that a block is generated in every 10 minutes on average.
However, the Bitcoin reward is halved after every 210;000
blocks, or approximately after every 4years. At the initial
stage, the reward for generating a valid block had been
50Bitcoins, which was halved to 25Bitcoins in 2012 and
12:5Bitcoins in 2016 . The next halving will occur in 2020
where the reward will be reduced to 6:25bitcoins per block.
This geometric reduction in every four years underlines
a maximum total supply of 21million of Bitcoins. It is
expected that this supply will be exhausted in the year of
2140 when the rewarded bitcoin will be infinitesimally small
for each block.
The process of Bitcoin protocol is presented in Figure 1.
3.2 Properties of blockchain
A blockchain exhibits several properties that make it a
suitable candidate for several application domains [25]. The
properties are discussed below.
5
Distributed consensus on the chain state : One of the
crucial properties of any blockchain is its capability to
achieve a distributed consensus on the state of the chain
without being reliant on any trusted third party. This
opens up the door of opportunities to build and utilise
a system where states and interactions are verifiable
by the miners in public blockchain systems or by the
authorised entities in private blockchain systems.
Immutability and irreversibility of chain state :
Achieving a distributed consensus with the participa-
tion of a large number of nodes ensures that the chain
state becomes practically immutable and irreversible
after a certain period of time. This also applies to smart-
contracts and hence enabling the deployment and exe-
cution of immutable computer programs.
Data (transaction) persistence : Data in a blockchain is
stored in a distributed fashion, ensuring data persist-
ence as long as there are participating nodes in the P2P
network.
Data provenance : The data storage process in any
blockchain is facilitated by means of a mechanism
called the transaction. Every transaction needs to be
digitally signed using public key cryptography, which
ensures the authenticity of the source of data. Combin-
ing this with the immutability and irreversibility of a
blockchain provides a strong non-repudiation instru-
ment for any data in the blockchain.
Distributed data control : A blockchain ensures that
data stored in the chain or retrieved from the chain can
be carried out in a distributed manner that exhibits no
single point of failure.
Accountability and transparency : Since the state of
the chain, along with every single interactions among
participating entities, can be verified by any authorised
entity, a blockchain promotes accountability and trans-
parency.
3.3 Blockchain type
Depending on the application domains, different blockchain
deployment strategies can be pursued. Based on these
strategies, there are predominantly two types of block-
chains, namely Public and Private blockchain, as discussed
below:
Public blockchain : A public blockchain, also known as
theUnpermissioned or permissionless Blockchain , allows
anyone to participate in the blockchain to create and
validate blocks as well as to modify the chain state by
storing and updating data through transactions among
participating entities. This means that the blockchain
state and its transactions, along with the data stored
is transparent and accessible to everyone. This raises
privacy concerns for particular scenarios where the
privacy of such data needs to be preserved.
Private blockchain : A private blockchain, also known
as the Permissioned Blockchain , has a restrictive notion in
comparison to its public counterpart in the sense that
only authorised and trusted entities can participate in
the activities within the blockchain. By allowing only
authorised entities to participate in activities within the
blockchain, a private blockchain can ensure the privacyof chain data, which might be desirable in some use-
cases.
3.4 Blockchain Layers
There are several components in a blockchain system whose
functionalities range from collecting transactions, propagat-
ing blocks, mining, achieving consensus and maintaining
the ledger for its underlying crypto-currencies, and so on.
These components can be grouped together according to
their functionalities using different layers similar to the
well-known TCP/IP layer. In fact, there have been a few
suggestions to design a blockchain system using a layered
approach [26], [27]. The motivation is that a layered design
will be much more modular and easier to maintain. For
example, in case a bug is found in a component of a layer
in a blockchain system, it will only affect the functionalities
of that corresponding layer while other layers remain unaf-
fected. For example, David et al. [27] suggest four layers:
consensus, mining, propagation, and semantic. However,
we believe that the proposed layers do not reflect the proper
grouping of functionalities. For example, consensus and
mining should be part of the same layer as mining can
be considered an inherent part of achieving consensus. In
addition to this, some blockchain systems might not have
any mining algorithms associated with it. In this paper,
we, therefore, will define four layers (Figure 2): network,
consensus, application, and meta-application. The function-
alities of these layers are briefly presented below.
Meta-Application Layer: The functionalities of the meta-
application layer in a blockchain system (see Figure 2) is to
provide an overlay on top of the application layer to exploit
the semantic interpretation of a blockchain system for other
purposes in other application domains. For example, Bitcoin
has been experimented to adopt in multiple application
domains, such as DNS like decentralised naming system
(Namecoin [28]), decentralised immutable time-stamped
hashed record (Proof of Existence [29]), and decentralised
PKI (Public Key Infrastructure), such as Certcoin [30].
Application Layer: The application layer (in Figure 2)
defines the semantic interpretation of a blockchain system.
An example of a semantic interpretation would be to define
a crypto-currency and then set up protocols for how such
a currency can be exchanged between different entities.
Another example is to establish protocols to maintain a state
machine embodying programming capabilities within the
blockchain, which can be exploited to create and deploy
immutable code (the so-called smart contract ). The applic-
ation also defines the rewarding mechanism, if any, in the
blockchain system.
Consensus Layer: The consensus layer, as presented in Fig-
ure 2, is responsible for providing the distributed consensus
mechanism in the blockchain that essentially governs the
order of the blocks. A critical component of this layer is the
proof protocol (e.g., proof of work and proof of stake) that is
used to verify every single block, which ultimately is used
to achieve the required consensus in the system.
Network Layer: The components in the network layer are
responsible for handling network functionalities which in-
clude joining in the underlying P2P network, remaining
6
ConsensusOrphanedbranchXXOrphanedbranchMainbranchCrypto-currencySMART Contracte-VotingPKIEscrowingCasinoDNSNetworkLayerConsensusLayerApplicationLayerMeta-ApplicationLayer
Figure 2: Blockchain Layers
in the network by following the underlying networking
protocol, disseminating the current state of the blockchain to
newly joined nodes, propagating and receiving transactions
and blocks and so on.
4 C ONSENSUS TAXONOMY &PROPERTIES
With the introduction and advancement of different block-
chain systems, there has been a renewed interest in distrib-
uted consensus with the consequent innovation of different
types of consensus algorithms. These consensus algorithms
have different characteristics and functionalities. In this
section, we first distinguish between two major types of
consensus and then present a taxonomy of their properties.
Later, in Section 5 and 6, we explore numerous crypto
currencies and discuss incentivised consensus algorithms.
Similarly, we focus on non-incentivised consensus and the
blockchain applications in Section 7.
Consensus mechanisms used by the various blockchain
systems can be classified based on the reward mechanism
that participating nodes might receive. Therefore, we first
classify the consensus mechanisms in blockchain systems
into two categories: incentivised and non-incentivised al-
gorithms.
Incentivised Consensus. Some consensus algorithms re-
ward participating nodes for creating and adding a new
block in the blockchain. Such algorithms belong to this
category. These algorithms are exclusively used in public
Consensus propertiesStructural propertiesBlock & reward propertiesSecurity propertiesPerformance propertiesFigure 3: Taxonomy of consensus properties.
blockchain systems and the reward provided acts as an
incentive for participating nodes to behave accordingly and
to follow the corresponding consensus protocol rigorously.
Non-incentivised Consensus. Private blockchain systems
deploy a type of consensus algorithms that do not rely
on any incentive mechanism for the participating nodes
to create and add a new block in the blockchain. Such
algorithms belong to this category. With the absence of any
reward mechanism, these nodes are considered trusted as
only authorised (allowed) nodes can participate in the block
creation process of the consensus algorithm.
4.1 Consensus properties
Each consensus algorithm has different characteristics and
serves different purposes. To compare these disparate
groups of consensus algorithms, we need to define eval-
uation criteria. In this section, we present this evaluation
criteria in the form of taxonomies of consensus proper-
ties. These properties have been collected from existing
researches, such as [5], [4], and compiled as a taxonomy in
this work.
The taxonomy is presented in Figure 3. According to this
taxonomy, a consensus mechanism has four major groups of
properties: Structural ,Block & reward ,Security and Perform-
ance properties. Each of these properties is briefly discussed
below.
4.1.1 Structural properties
Structural properties define how different nodes within a
blockchain network are structured to participate in a con-
sensus algorithm. These properties can be sub-divided into
different categories as illustrated in Figure 4. We briefly
describe each of these categories below.
Node types: It refers to different types of nodes that
a consensus algorithm is required to engage with to
achieve its consensus. The types will depend on the
consensus algorithm which will be presented in the
subsequent section.
Structure type: It refers to the ways different nodes are
structured within the consensus algorithm using the
concept of a committee. The committee itself can be
of two types: single and multiple committees. Each of
these committees is described below.
Underlying mechanism: It refers to the specific mechan-
ism that a consensus algorithm deploys to select a particular
node . The mechanism can utilise lottery, the age of a
particular coin or a voting mechanism. A lottery can
7
Structural propertiesNode typeStructure typeSingleTypeOpenCloseFormationImplicitExplicitConfigurationStaticDynamicMultipleTopologyFlatHierarchichalConfigurationStaticDynamicUnderlying mechanismLotteryProbabilisticRandomisedVotingSingleMultipleCoin-age
Figure 4: Taxonomy of structural properties.
utilise either a cryptography based probabilistic mech-
anism or other randomised mechanisms. In a voting
mechanism, voting can be carried out either in a single
or multiple rounds. The coin-age, on the other hand,
utilises a special property, which depends on how long
a particular coin has been owned by its owner.
Next, we explore different types of voting committees for
existing consensus algorithms.
Single committee. A single committee refers to a spe-
cial group of nodes among the participating nodes which
actively participate in the consensus process by producing
blocks and extending the blockchain. Each single committee
can have different properties. Next, we briefly explore these
properties.
Committee type: A committee can be open or close. A
committee is open if it is open to any participating nodes
or closed if it is restricted to a specific group of nodes.
Committee formation: A committee can be formed
either implicitly or explicitly. An implicit formation
does not require the participating nodes to follow
any additional protocol rules to be in the committee,
whereas an explicit formation requires a node to follow
additional protocol steps to be a part of the committee.
Committee configuration: A committee can be con-
figured in a static or a dynamic fashion.
– Static: In a static configuration, the members of the
committee are pre-selected and fixed. No new mem-
bers can join and participate in the consensus process.
– Dynamic: In a dynamic configuration, the committee
members are defined for a time-frame (known as
epoch), after which new members are added, and
old members are removed based on certain sets of
criteria. In such a committee, nodes are selected using
a voting mechanism where voting is carried either
in a single or multiple rounds. Some consensus al-
Block & Reward propertiesGenesis dateBlock rewardTo tal su p p lyBlock timeFigure 5: Taxonomy of block & reward properties.
gorithms, however, do not specify any specific time-
frame, and hence, members can join or leave any
time at will. Nodes in such configuration are selected
using a lottery mechanism which utilises either a
cryptography based probabilistic mechanism or other
randomised mechanisms.
Multiple committee. It has been observed that the time
it takes to achieve consensus in a single committee tends
to increase as the number of the member starts to increase
[5], thereby reducing performance. To alleviate this problem,
the concept of multiple committee has been introduced,
where each committee consists of different validators [5].
A multiple committee can have different properties. Next,
we explore two properties.
Topology: It refers to the way different committees
are organised. For example, the topology can be flat
to indicate that different committees are at the same
level or can be hierarchical where the committees can be
considered in multiple layered levels.
Committee configuration: In addition, like a single
committee, the multiple committees can be configured
in a static or dynamic way.
4.1.2 Block & reward properties
Properties under this category can be utilised as quantitat-
ive metrics to differentiate different crypto-currencies. The
properties are (Figure 5): genesis date, block reward, total
supply, formula, and block creation time. These proper-
ties do not necessarily characterise different consensus al-
gorithm directly, however, most of them (except the genesis
date) have a direct and indirect impact on how consensus is
achieved in a particular crypto-currency based blockchain
system. For example, block reward incentivises miners to
act accordingly by solving a cryptographic puzzle, which is
then ultimately used to achieve consensus. The properties
are described below:
Genesis date represents the timestamp when the very
first block was created for a particular crypto-currency.
Block reward represents the reward a node receives for
creating a new block.
Total supply represents the total supply of a crypto-
currency.
Block time represents the average block creation time
of a crypto-currency.
4.1.3 Security properties
A consensus algorithm must satisfy a number of security
properties as shown in (Figure 6) and are described below:
Authentication: This implies if nodes participating in
a consensus protocol need to be properly verified/au-
thenticated.
8
Security propertiesAuthenticationNon-repuditationCensorship reistanceAttack vectorsAdversary toleranceSybil protectionDoSNothing At Stake (NAS)BribingLong-rangeAccumulationGrindingCartel
Figure 6: Taxonomy of security properties.
Non-repudiation: This signifies if a consensus protocol
satisfies non-repudiation.
Censorship resistance: This implies if the correspond-
ing algorithm can withstand against any censorship
resistance.
Attack vectors : This property implies the attack vectors
applicable to a consensus mechanism. Here, we present
a set of attack vectors that are applicable to any con-
sensus algorithm. The other attack vectors presented in
Figure 6 are applicable to a specific class of consensus
algorithm. Therefore, we will discuss them in the up-
coming sections, when we explore such algorithms.
– Adversary tolerance: This signifies the maximum
byzantine nodes supported/tolerated by the respect-
ive protocol.
– Sybil protection: In a Sybil attack [34], an attacker
can duplicate his identity as required in order to
achieve illicit advantages. Within a blockchain sys-
tem, a sybil attack implicates the scenario when an
adversary can create/control as many nodes as re-
quired within the underlying P2P network to exert
influence on the distributed consensus algorithm and
to taint its outcome in her favour.
– DoS (Denial of Service) resistance: This implies if
the consensus protocol has any built-in mechanism
against DoS attacks.
4.1.4 Performance properties
The properties belonging to this group can be utilised
to measure the quantitative performance of a consensus
protocol. A brief description of each property is presented
below with its illustration in Figure 7
Fault tolerance: signifies the maximum faulty nodes the
respective consensus protocol can tolerate.
Performance propertiesFault toleranceThroughputScalabilityLatencyEnergy consumptionFigure 7: Taxonomy of performance properties.
Throughput: implies the number of transactions the
protocol can process in one second.
Scalability: refers to the ability to grow in size and
functionalities with- out degrading the performance of
the original system [31].
Latency (Finality): refers to ” the time it takes from when a
transaction is proposed until consensus has been reached on
it” [5]. It is also known as finality.
Energy consumption: indicates if the algorithm (or
the utilising system) consumes a significant amount of
energy.
5 I NCENTIVISED CONSENSUS : POW & P OS
In this section, we explore different incentivised consensus
algorithms. Such algorithms can be grouped in three major
categories: Proof of Work (PoW), Proof of Stake (PoS), and
Hybrid Consensus. Among them, this section discusses
PoW and PoS algorithms in Section 5.1 and Section 5.2 re-
spectively. For readability, hybrid algorithms are presented
in Section 6.
5.1 Proof of Work (PoW)
A Proof of Work (PoW) mechanism involves two different
parties (nodes): prover (requestor) and verifier (provider).
The prover performs a resource-intensive computational
task intending to achieve a goal and presents it to a verifier
or a set of verifiers for validation that requires significantly
less resource. The core idea is that the asymmetry, in terms
of resource required, between the proof generation and
validation acts intrinsically as a deterrent measure against
any system abuse.
Within this aim, the idea of PoW was first presented
by Dwork and Naor in their seminal article in 1993 [33].
They put forward the idea of use PoW to combat email
spamming. According to their proposal, an email sender
would be required to solve a resource-intensive mathem-
atical puzzle and attach the solution within the email as a
proof that the task has been performed. The email receiver
would accept an email only if the solution can be success-
fully verified.
Within the blockchain setting, a similar concept has been
adopted. Each PoW mechanism is bound to a threshold,
known as the difficulty parameter in many blockchain sys-
tems. The prover would carry out the computational task in
several rounds until a PoW is generated that matches the
required threshold, and every single round is known as a
single proof attempt.
PoW has been the most widely-used mechanism to
achieve a distributed consensus among the participants
9
regarding the block order and the chain state. In particu-
lar, a PoW mechanism in a blockchain serves two critical
purposes:
A deterrent mechanism against the Sybil Attack . In
PoW, every mining node would require a significant
monetary investment to engage in a resource-intensive
PoW mechanism during the block creation process. To
launch a Sybil attack, the monetary investment of an
attacker will be proportional to the number of Sybil
identities, which might outweigh any advantage gained
from launching a Sybil attack.
The PoW mechanism is used as an input to a function
which ultimately is used to achieve the required distrib-
uted consensus when a fork happens in a blockchain
[44].
We differentiate between three major classes of PoW
consensus mechanisms: Compute-bound PoW, Memory-bound
PoW and Chained PoW. Each of these is explored in the
following sections.
5.1.1 Compute-bound PoW
ACompute-bound PoW , also known as CPU-bound PoW ,
employs a CPU-intensive function that carries out the re-
quired computational task by leveraging the capabilities of
the processing units (e.g., CPU/GPU), without relying on
the main memory of the system. These particular charac-
teristics facilitate the scenario in which the computation
can be massively optimised for faster calculation using
Application-specific Integrated Circuit (ASIC) rigs. This has
drawn criticisms among the crypto-currency enthusiasts as
general people cannot participate in the mining process with
their general purpose computers, and the mining process is
mostly centralised among a group of mining nodes.
Hashcash by Back et al. [45] is the earliest example to
leverage a PoW mechanism in practical systems. Similar to
the proposal of Dwork and Naor in [33], Hashcash is also
designed to combat spams. In this scheme, the email sender
would require to generate a SHA-1 hash with a certain prop-
erty using as the input a number of information including
recipient’s email address and date. The property dictates
that the generated hash must have at least 20 bits of leading
zeroes. Generating an SHA-1 hash with this property would
require the senders to engage in several proof attempts in
a pseudo-random fashion. Once the hash is generated, it
is added within the email header. The verification on the
recipient’s side is rather trivial, which requires comparing a
newly generated hash using the required information with
the supplied hash. If they match, it proves that the email
sender has engaged in the required amount of computa-
tional work. The effectiveness of this approach of fighting
spams depends on the hypothesis that spammers rely on
the revenue model requiring a mere amount of cost to send
a single email. When they would need to engage in such
a computationally intensive task for sending every single
email, the aggregated associated cost might heavily affect
their profit margin and thus deter them from spamming.
Nakamoto consensus is the compute-bound PoW con-
sensus algorithm leveraged in Bitcoin. It is based on the
approach of Hashcash, modified to be applied within theblockchain setting. As discussed in Section 3.1, all mining
nodes (miners) compete with each other to generate a valid
block by finding a solution smaller than the difficulty target.
Similar to the idea of HashCash, the miners need to engage
in several proof attempts, until the solution is found. In
each of these proof attempts, each miner generates a hash
using either the SHA-256 or SHA-256d (a double hashing
mechanism using SHA-256) algorithm and checks if the
generated hash is smaller than the difficulty target. The
effect of this distributed engagement is that forks happen,
and then the Nakamoto consensus algorithm is utilised to
resolve the fork and to achieve a network-wide distributed
consensus. The reader is referred back to Section 3.1 (and
Figure 1) for a brief description of Nakamoto consensus.
Currently, there are many crypto-currencies that utilise
the Nakamoto consensus algorithm. Table 3 shows the top
10 of such currencies according to their market capitalisation
as rated by CoinGecko1(a website which tracks different
activities related to crypto-currencies) as of July 24, 2019.
The table also presents their Block and reward properties
as presented in Figure 5. It is to be noted that information
regarding the properties in Table 3 for these (and other
subsequent) currencies has been collected by consulting
their corresponding whitepapers, websites and introductory
announcements on Reddit website2.
5.1.2 Memory-bound PoW
To counteract the major criticism of compute-bound PoWs
allowing the utilisation of ASIC-based rigs for the mining
purpose (see Section 5.1.1), memory-bound PoWs have been
proposed. A memory-bound PoW requires the algorithm to
access the main memory several times and thus ultimately
binds the performance of the algorithm within the limit
of access latency and/or bandwidth as well as the size of
memory. This restricts ASIC rigs based on a memory-bound
PoW to have the manifold performance advantage over
their CPU/GPU based counterparts. In addition, the profit
margin of developing ASIC with memory and then building
mining rigs with them is not viable as of now for these
classes of PoWs. Because of these, memory-bound PoWs
are advocated as a superior replacement for compute-bound
PoWs in de-monopolising mining concentrations around
some central mining nodes.
There is a large variety of consensus algorithms be-
longing to this class, unlike the consensus algorithms of
compute-bound PoW which are largely based on Hashcash.
These algorithms can be further categorised as follows:
Cryptonight; Scrypt and its variants; Equihash; Ethhash/D-
agger; Neoscript; and Timetravel. We now describe each
of these different types of memory-bound PoW consensus
algorithm.
1) C RYPTONIGHT .Cryptonight is a class of PoW consensus
algorithms that, in principle, is a memory-hard hash func-
tion [32]. It utilises the Keccak hashing function [46] intern-
ally and relies on a 2MB scratchpad residing on the memory
of a computer. The scratchpad is extensively used to per-
form numerous read/write operations at pseudo-random
1. https://www.coingecko.com/
2. https://www.reddit.com/
10
Table 3: Top ten crypto-currencies that utlise Nakamoto consensus algorithm.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Bitcoin/Bitcoin Cash
[69] [70]03.01.2009 12.5 21 10m
Syscoin [71] 16.08.2014 80.04659537 888 1m
Peercoin [72] 19.08.2012 55.17265345 2000 10m
Counterparty [73] 01.02.2014 All currency in circulation 2.6m -
Emercoin [75] 11.12.2013 Smooth emission 41 10m
Namecoin [76] 19.04.2011 12.50000000 21 10m
Steem Dollars [77] 04.06.2016 Smooth emission Unlimited 3s
Crown [78] 08.09.2014 1.8 42 1m
XP 24.08.2016 2220 NA
Omni (Mastercoin) [79] 31.07.2013 16.71249999 Omni 0.6 20s
addresses within that scratchpad. In the final step, the
desired hash is generated by hashing the entire scratchpad.
Its reliance on a large scratchpad on the memory of a
system makes it resistant towards FPGA and ASIC mining
as the economic incentive to create FPGA, and ASIC mining
hardware might be too low for the time being. As such,
Cryptonight introduces the notion of so called Egalitarian
proof of work [32] or proof of equality, which enables anyone
to join in the mining process using any modern CPU and
GPU.
One prominent property of the coins belonging to this
class is that all of them support stronger sender-receiver
privacy by facilitating anonymous transactions.
Current currencies utilising Cryptonight according to
Coingeko as of July 24, 2019 is presented in Table 4. Like
Table 3, Table 4 also presents their Block and reward prop-
erties as presented in Figure 5.
2) S CRYPT AND ITS VARIANTS .Scrypt is a password based
key driving function (KDF) that is currently used in many
crypto-currencies [47]. A KDF is primarily used to gen-
erate one or more secret values from another secret key
and is widely used in password hashing. Previous key
deriving functions such as DES-based UNIX Crypt-function,
FreeBSD MD5 crypt, Public-Key Cryptography Standards#5
(PKCS#5), and PBKDF2 do not impose any specific hard-
ware requirements. This enables any attacker launch attacks
against those functions using specific FPGA or ASIC en-
abled hardware, the so-called custom hardware attacks [48].
Scrypt has been designed to counteract this threat.
Toward this aim, one of the core characteristics of Scrypt
is its reliance on the vast memory of a system, making it
difficult to perform using FPGA and ASIC enabled cus-
tom hardware. In the underneath, Scrypt utilises Salsa20/8
Core [49] as its internal hash function. A simplified version
of Scrypt is used in the corresponding crypto-currencies,
which is much faster and easier to implement, and can be
performed using any modern CPU and GPU. Hence, anyone
can join in the mining process for crypto-currencies using
this function. However, the ever-increasing price of crypto-
currencies has incentivised miners to produce custom ASIC
hardware for some crypto-currencies utilising Scrypt in
recent times. An example of such hardware that can be used
to mine different Scrypt crypto-currencies is Antminer L3+
[50].
To tackle this issue of exploiting ASIC for mining, sev-
eral Scrypt variants have been proposed: Scrypt-N/ScryptJane/Scrypt Chacha and Scrypt-OG, each providing particu-
lar advantages over others. Scrypt-N and Scrypt Chacha rely
on SHA256 and ChaCha [52] as their internal hash functions,
respectively, whereas Scrypt Jane utilises a combination of
different hash functions. All of them support progressive
and tunable memory requirements, which can be adjusted
after a certain period. This is to ensure that custom ASIC
hardware is rendered obsolete once the memory require-
ment is changed. Finally, Scrypt-OG (Optimised for GPU)
is optimised to be eight times less memory intensive than
Scrypt [51].
Table 5 shows the top 10 currencies, which either use
Scrypt or one of its variants, as per their market capitalisa-
tion according to CoinGecko as of July 24, 2019.
3) E QUIHASH Equihash is one of the recent PoW algorithms
that has been well received in the blockchain community
[55]. It is a memory-bound PoW that requires to find a solu-
tion for the Generalised Birthday problem using Wagner’s
algorithm [56]. Equihash has been designed to decentralise
the mining procedure itself, similar to other memory-bound
approaches. However, so far, very small portions of such
algorithms have succeeded. One of the crucial reasons for
this is that their underlying time-memory complexity trade-
off is largely constant. This means that reducing memory
requirement in these algorithms have little effect on their
corresponding time complexity.
Wagner’s solution has a steep time-memory complexity
trade-off, reducing memory increases time complexity sub-
stantially. This premise has been exploited by Equihash to
ensure that mining is exclusively proportional to the amount
of memory a miner has. Thus, it is more suitable for a gen-
eral purpose computer, rather than any ASIC-enabled hard-
ware which can only have relatively small memory in order
to make their production profitable for the mining process.
Due to this reason, it has been claimed that Equihash can
support ASIC resistance, at least for the foreseeable future.
In addition, the verification is extremely lightweight and
even can be carried out in resource-constrained mobile
devices. Table 6 shows the eight currencies which utilise
Equihash according to CoinGecko as of July 24, 2019.
4) E THASH (DAGGER -HASHIMOTO )/D AGGER .Ethash is a
memory-bound PoW algorithm introduced for Ethereum
with the goal to be ASIC-resistant for a long period of
time [58]. It was previously known as Dagger-Hashimoto
algorithm [59] because of its utilisation of two different
algorithms: Dagger [60] and Hashimoto [60].
11
Table 4: Top ten crypto-currencies that utilise Cryptonight, with Bytecoin being the first to use this algorithm.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply (Million) Block Time
Monero 18.04.2014 4.86930501 Starting at M= 264 1
infinite supply2.0m
Bytecoin 04.07.2012 666.76 184.46 billion 2.0m
Aeon 06.06.2014 5.48 Starting at M= 264 1,
infinite supply.4.0m
Boolberry 17.05.2014 4.85 18.5 Million 2.0
Karbowanec 30.05.2016. 8.83 Starting with 10 Million, in-
finite supple4.0m
Fantomcoin 06.05.2014 smooth emission, 50% coins will be
emitted in 6 years and block reward
decreases with a similar Starting at
M= 264 1infinite supply 1.0m
Dashcoin fork of
Bytecoin05.07.2014 1.55 2.0m
QuazarCoin 08.05.2014 smooth emission 2.0
BipCoin 20.08.2016 smooth emission 2.0
Cannabis Industry
Coin16.10.2016 70.00000000 21 M 2.0
Table 5: Top ten crypto-currencies using Scrypt.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Litecoin [80] 13.10.2011 25.00 84 million 2.5m
Verge [82] 15.02.2016 730.00 16.5 billion 0.5m
Bitmark [83] 13.07.2014 (no longer monitored
after 2016)27.58 million 2.0m
Dogecoin [84] 06.12.2013 10000.00 Total supply NA
GameCredits [85] 01.06.2015 fixed (12.5 coins) 84 million 1.5m
Einsteinium [86] 01.03.2014 2 2.9 billion 1.0m
Voxels [87] 03.11.2015 smooth emission 2.1 billion 2.5m
Viacoin [88] 18.07.2014 0.63 23 million 0.5m
Hempcoin [89] 9.03.2014 smooth emission 2.5 billion 1.0m
Table 6: Crypto-currencies utilising Equihash algorithm.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Zcash [90] 28.10.2016 10 21 million 2.5m
Bitcoin Gold [91] 24.10.2017 12.5 21 million 10m
Komodo [92] 15.10.2016 3 200 million 1m
Zclassic [93] 6.11.2016 12.5 21 million 2.5m
ZenCash [94] 30.05.2017 7.5 21 million 2.5m
Hush [95] Genesis date 11.25 21 million 2.5m
BitcoinZ [96] 10.09.2017 12500.00 21 billion 2.5m
VoteCoin [97] 31.08.2017 125 2.2 billion 2.5m
Dagger is one of the earliest proposed memory-bound
PoW algorithm which utilises Directed Acyclic Graph
(DAG) for memory-hard puzzle solving with trivial verific-
ation that requires less memory to be used in resource con-
strained devices. However, the Dagger algorithm is proven
to be vulnerable towards a shared memory hardware ac-
celeration attack, as discussed in [61]. That is why it has
been dropped as a PoW candidate for Ethereum. Hashimoto
algorithm, on the other hand, relies on the delay incurred for
reading data from memory as the limiting factor and thus,
is known as an I-O bound algorithm.
Ethash combines these two algorithms to be ASIC-
resistant and functions as follows. Ethash depends on a
large pseudo-random dataset, which is recomputed during
each epoch. Each epoch is determined by the time it takes
to generate 30,000 blocks in approximately five days. This
dataset is essentially a directed acyclic graph and hence, iscalled DAG. During the DAG generation process, a seed is
generated at first, which relies on the length of the chain.
The seed is then used to compute a 16 MB pseudo-random
cache. Then, each item of the DAG is generated by utilising
a certain number of items from the pseudo-random cache.
This entire process enables the DAG to grow linearly with
the growth of the chain. Then, the latest block header and
the current candidate nonce are hashed using Keccak (SHA-
3) hash function, and the resultant hash is mixed and hashed
several times with data from the DAG. The final hashed
digest is compared to the difficulty target and accepted or
discarded accordingly.
Every mix operation in Ethash requires to have a read in
a pseudo-random fashion from the DAG, which is randomly
accessed from the memory. This serves two purposes:
The read operation is limited by the speed of the
memory access bandwidth, which is thought to be
12
theoretically optimal, and thus, more optimisation is
less likely.
Even though the mixing circuitry can be built within an
ASIC, the bottleneck would still be the memory access
delay.
That is why Ethash is thought to be suitable for use on
commodity computing capacity with good powerful GPUs.
To achieve the same level of performance, an ASIC would
require to accommodate as large memory as a general
purpose computer providing a financial disincentive.
There are currently two currencies utilising Ethereum
according to coingecko as of July 24, 2019 [62]. Even though
Dagger algorithm is proven not to be ASIC resistant, it is
being used in 6 currencies [63]. All of these are presented in
Table 7.
5) N EOSCRYPT .NeoScrypt, an extension of Scrypt, is a key
derivation function that aims to increase the security and
performance on CPUs and GPUs while being strong ASIC
resistant [146]. Internally it utilises a combination of Salsa
20/20 [49] and ChaCha 20/20 [52] along with Blake2s [74].
Its constructions impose larger memory segment size, and
hence, larger temporal buffer requirements. This makes it
1.25 times more memory intensive than Scrypt. The motiva-
tion is that this higher requirement of memory will act as a
deterrent towards building ASICs for NeoScrypt.
Currently, there are 10 currencies utilising NeoScrypt
according to Coingecko as of 18 July, 2019 [147] which are
presented in Table 8.
5.1.3 Chained PoW
A chained PoW utilises several hashing functions chained
together in a series of consecutive steps. Its main motiv-
ation is to ensure ASIC resistance, which is achieved by
the underlying mechanisms by which the corresponding
hashing functions are chained together. In addition to this,
the PoW algorithms belonging to this series aim to ad-
dress one particular weakness of any compute-bound and
memory-bound PoW algorithm: their reliance on a single
hashing function. With the advent of quantum computing,
the security of a respective hashing algorithm might be
adversely affected, which undermines the security of the
corresponding blockchain system. If this happens, the old
algorithm needs to be discarded, and a new quantum res-
istant hashing algorithm needs to be incorporated to the
respective blockchain using a mechanism called hard-fork.
A hard-fork is a mechanism by which a major update is
enforced in a blockchain system. This is quite a disruptive
procedure that has negative effect on any blockchain system.
In such scenarios, a chained PoW algorithm would continue
to function until all its hashing functions are broken.
There are several chained PoW algorithms that are cur-
rently available.
1) X11/X13/X15. X11 is a widely-used hashing algorithm in
many crypto-currencies. In X11, eleven hashing algorithms
are consecutively carried our one after another. The hash-
ing algorithms are blake, bmw, groestl, jh, keccak, skein, luffa,
cubehash, shavite, simd, and echo .
One advantage of X11 is that it is highly energy effi-
cient: GPUs computing X11 algorithm requires approxim-
ately 30% less wattage and remains 30 50% cooler incomparison to Scrypt [54]. Even though the algorithm has
been designed in such a way that it can only be used with
CPUs and GPUs, the economic incentives have allowed the
creation of ASIC to be used during the mining process.
It has different variants where the number of chained
hashing functions differs. For example, X13 utilises 13 hash-
ing functions, and X15 utilises 15 hashing functions.
Table 9 presents the top 10 crypto-currencies utilising
these three algorithms, as per their market capitalisation as
of July 24, 2019 according to CoinGecko.
2) Q UARK .Quark PoW algorithm relies on six different
hashing functions: BLAKE [74], Blue Midnight Wish [64],
Grøstl [65], [140], JH [66], Keccak and Skein [67]. These
functions are implemented in mixed series with nine steps
[138]. Within these nine steps, three functions are randomly
applied in three steps depending on the value of a bit. The
main motivations of mixing these six functions in nine steps
are as follows:
To alleviate the risk of a compromised system in light of
its underlying single hashing algorithm being broken.
To impose restrictions so that Quark can only be
mined using CPUs while making it difficult to mine
using GPUs and ASICs, because of the usage of ASIC-
resistant mechanisms (e.g. Keccak).
However, it did not take long before ASIC mining
hardware for Quark appeared in the market, so that this
could be mined using a GPU and ASIC [68]. However,
the profitability and performance of such hardware are not
obvious.
The currencies utilising Quark according to CoinGecko
as per July 24, 2019 [139] are presented in Table 10.
3) L YRA 2RE. Lyra2RE is a class of chained PoW which util-
ises five hash functions: BLAKE, Keccak, Lyra2,[13] Skein,
and Grøstl. It has been developed by the developers of
Vertcoin, a currency based on Lyra2RE. It was designed
to be CPU friendly, however, it was discovered in 2015
that the majority of the hashing power utilised for mining
VertCoin in its network was facilitated by a botnet stealing
CPU cycles from a large number of infected computers. This
motivated the Vertcoin developers to release Lyra2REv2,
which utilises six hash functions, BLAKE, Keccak, Cube-
Hash, Lyra2, Skein, and Blue Midnight Wish with GPU
only PoW. Currently, there are only three currencies utilising
Lyra2REv2 according to CoinGecko as of 31 December 2017
which are presented in Table 11.
4) M AGNIFICENT 7.Magnificent 7 (M7) is a class of chained
PoW which utilises seven hash functions to generate the
candidate hash during the mining process of Cryptonite
coin (not to be confused with the Cryptonight PoW al-
gorithm) [143]. The utilised hash functions are SHA-256,
SHA-512, Keccak, RIPEMD, HAVAL, Tiger and Whirlpool.
Internally, the header of the candidate block sequentially
hashed by the corresponding functions and then multiplied
to generate the final hash, which is then compared against
the difficulty threshold. Even though it a not memory-
bound PoW, it has been claimed that the multiplication
operation enables it to run on a general purpose CPU easily,
however, makes it difficult to run on GPUs and ASICs
[143]. Even so, there are is at least one GPU miner available
13
Table 7: Crypto-currencies utilising Ethash algorithm. The block rewards are in the corresponding currencies.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Ethereum [98] 30.07.2015 2 infinite supply 10-20s
Ethereum Clas-
sic [99]30.07.2015 3.88 10-20s
Ubiq [100] 28.01.2017 6 NA 88s
Shift [101] 01.08.2015 1 infinite supply 27s
Expanse [102] 13.09.2015 4 31.4 Million 1.0m
DubaiCoin-
DBIX [103]27.03.2017 6 Total supply 1.5m
SOILcoin [104] 03.10.2015 3.0 Total supply 52s
Krypton [105] 15.02.2016 0.25 2.67 Million 1m 44s
Table 8: Crypto-currencies utilising NeoScrypt and Timetravel 10 algorithms. The block rewards are in the corresponding
currencies.
Currency Algorithm Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Red Pulse [106] 17.10.2017 NA 1.36 Billion NA
Feathercoin [107] NeoScrypt 16.04.2013 40 336 Million 1.0m
GoByte [108] NeoScrypt 17.11.2017 3.71 31.8 Million 2.5m
UFO Coin [109] NeoScrypt 03.01.2014 625 4 Billion 1.5m
Innova [110] NeoScrypt 19.10.2017 2.64 1.29 Million 2m
Vivo [88] NeoScrypt 20.08.2017 4.5 1.1 Million 2m18s
Desire [113] NeoScrypt Genesis date 10.45 1.17 Million 2.5m
Orbitcoin [114] NeoScrypt 28.07.2013 0.5 3.77 Million 6.0m
Phoenixcoin [115] NeoScrypt 08.05.2013 12.5 98 Million 1.5m
Bitcore [116] Timetravel 10 April 24, 2017 3.13 21 Million 2.5m
Table 9: Crypto-currencies utilising X11/X13 algorithms. The block rewards are in the corresponding currencies.
Currency Algorithm Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Dash [117] X11 January 19, 2014 1.55 22 Million 2.5m
Stratis [118] X13 August 09, 2016 NA NA NA
Cloakcoin [119] X13 Genesis date 496.00 4.5 Million 1.0m
Stealthcoin [120] X13 July 04, 2014 NA 20.7 Million 1.0m
DeepOnion [121] X13 July 13, 2017 4 18.9 Million 4m
HTMLcoin [122] X15 September 12, 2014 NA 90 Billion 1.0m
Regalcoin [123] X11 September 28, 2017 NA 7.2 Million NA
Memetic [124] X11 March 05, 2016 NA NA NA
ExclusiveCoin [125] X11 June 12, 2016 NA NA NA
Creditbit [126] X11 November 02, 2015 NA 100 Million 1.0m
Table 10: Crypto-currencies utilising Quark algorithm. The block rewards are in corresponding currencies.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Quark [67] July 21, 2013 1 247 Million 0.5s
PIVX [128] NA 5 NA 1.0m
MonetaryUnit [129] July 26, 2014 18 1 Quadrillion 0.67m
ALQO [130] October 30, 2017 3 NA 1m
Bitcloud [131] August 15, 2017 22.5 200 Million 6.5m
Zurcoin [132] NA 12.5 NA 0.75m
AmsterdamCoin [133] November 01, 2015 10 84 Million 1.0m
Animecoin [134] NA NA NA NA
Table 11: Crypto-currencies utilising Lyra2RE algorithm. The block rewards are in corresponding currencies.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Vertcoin [135] January 10, 2014 25 84 Million 2.5m
Monacoin [136] January 01, 2014 25 105 Million 1.5m
Crypto [137] April 30, 2015 NA 65.8 Million 0.5m
for M7 [144]. Its performance, though, is not known. The
corresponding information for Cryptonite is presented in
Table 12.5.1.4 PoW Limitations
PoW (Nakamoto) consensus algorithm has been widely
accoladed for its breakthrough in the distributed consensus
14
Table 12: Information regarding Cryptonite utilising M7 algorithm.
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply
(Million)Block Time
Cryptonite July 28, 2014 Dynamic 1:84Billion 1 Minute
010203040506070802017-02-10…2017-03-27…2017-05-11…2017-06-25…2017-08-09…2017-09-23…2017-11-07…2017-12-22…2018-02-05…2018-03-22…2018-05-06…2018-06-21…2018-08-05…2018-09-19…2018-11-03…2018-12-18…2019-02-01…2019-03-18…2019-05-02…2019-06-16…Estimated TWh per YearMinimum TWh per Year
Figure 8: Bitcoin energy consumption over the last years.
paradigm, starting with Bitcoin. It had laid down the
foundation for the subsequent advancement, which resul-
ted in different PoW algorithms and crypto-currencies as
discussed in the earlier sections. Even so, there are some
significant limitations. Next, we briefly discuss these limita-
tions:
Energy consumption: Each PoW algorithm needs to
consume electricity to compute the hash. As the dif-
ficulty of the network starts to increase, so does the
energy consumption. The amount of consumed energy
is quite significant when calculated over the whole net-
work consisting of ASIC/GPU mining rigs all around
the world. Digiconomist3website tracks the electricity
consumption of Bitcoin and Ethereum. According do
it, the energy consumption of Bitcoin and Ethereum
are around 40 TWh (Tera-Watt Hour) and 10 TWh,
respectively. Their energy consumption graphs for the
last one year are presented in Figure 8 [148] and Figure
9 [149].
Figure 9: Ethereum energy consumption over the last year.
3. https://digiconomist.net/To put this into perspective, we present Figure 10,
whose data has been collected from [148]. This figure
illustrates Bitcoin’s energy consumption relative to the
electricity consumption of different countries. For ex-
ample, the electricity consumed by Bitcoin in a year
could power up 6;770;506 American households and
is much more than what Czech Republic consumes in
a year [148]. The utilisation of this huge amount of
electricity has raised the question of sustainability of
PoW-based crypto-currencies.
Figure 10: Bitcoin energy consumption relative to different
countries.
Mining centralisation: With the ever-increasing diffi-
culty rate, miners within a PoW-based crypto-currency
network need to upgrade the capability of their AS-
IC/GPU mining rigs to increase their chance of cre-
ating a new block. Even so, it becomes increasingly
difficult for a single miner to join in the mining process
without substantial investment in the mining rigs. The
consequence is that the economies of scale phenomenon
strongly impacts the PoW algorithms. The economies
of scale in economic theory is the advantage a pro-
ducer can gain by increasing its output [150]. This
happens because the producer can spread the cost of
per-unit production over a larger number of goods,
which increases the profit margin. This analogy also
applies to PoW mining as explained next. A mining
pool can be created where the mining resources of
different miners are aggregated to increase the chance
of creating a new block. Once a mining pool receives
a reward for creating the next block, the reward is
then proportionally divided among the participating
miners. Unfortunately, this has led to the centralisation
problems where block creations are limited among a
handful of miners. For example, Figure 11 illustrates
the distribution of network hashrate among different
miners in Bitcoin [152]. As evident from the figure, only
five mining pools control the 75% of hashrate of the
15
whole network. There is a fear that they could collude
with each other to launch the 51% attack to destabilise
the whole bitcoin network.
Known Blocks.
Relayed By count
BTC.com 74
Unknown 41
F2Pool 39
Poolin 38
AntPool 37
SlushPool 27
BTC.TOP 26
ViaBTC 16An estimation of hashrate
distribution amongst t he largest mining pools
The graph below shows the market share of the most popular bitc oin mining pools. It should only be used as a
rough estimate and for various reasons will not be 100% accurat e. A large portion of Unknown blocks does not
mean an attack on the network, it simply means we have been una ble to determine the origin.24 hours - 48 hours - 4 DaysBitcoin Hashrate Distribution - Blockchain.info https://www.bloc kchain.com/en/pools?timespan=48hours
1 of 1 27/07/2019, 10:08 pm
Figure 11: Bitcoin hashrate distribution of mining pools.
Tragedy of commons: Many PoW algorithms suffer an
economic problem called the Tragedy of the commons . In
economic theory, the tragedy of the commons occurs
when each entity rushes to maximise its profit from a
depleting resource without considering the well-being
of all that share the same resource [151]. This situation
occurs in a crypto-currency if it is deflationary in nature
with limited supply, e.g. Bitcoin. It has been argued
when the reward of creating a new block in Bitcoin
will reach nearly zero; the miners will have to solely
rely on the transaction fees to cover their expenses.
This might create an unhealthy competition among the
miners to include as many transactions as possible, just
to maximise one’s profit. The consequence of this is
that transaction fees will keep decreasing, which might
lead to a situation that miners cannot make enough
profit to continue the mining process. Eventually, more
and more miners will leave the mining process, which
might lead toward 51% attacks or other scenarios that
de-stabilise the Bitcoin network.
Absence of penalty: All PoW algorithms (both com-
pute and memory bound) are altruistic in nature in
the sense that they reward behaving miners, however,
do not penalise a misbehaving miner. One example is
that a miner can collude with a group of miners (a
phenomenon known as selfish mining ) to increase its
profitability in an illegitimate way [153]. In addition,
a miner can engage in Denial-of-Service attack by just
not forwarding any transaction or block within the
network. Furthermore, such malicious miners can join
forces to engage in the spawn-camping attack, in which
they launch DoS attacks simultaneously over and over
again to render the network useless for the corres-
ponding crypto-currency [156]. A penalty mechanism
would disincentivize any miner to engage in any type
of malicious misbehave.5.1.5 Analysis
In this section, we summarise the properties of different
PoW algorithms in Table 13, Table 14 and Table 15 utilising
the taxonomies presented in Section 4. In these tables, a
‘’ symbol is utilised to indicate if a particular property is
supported by the corresponding algorithm. For other prop-
erties, explanatory texts have been used for any particular
property.
As presented in Table 13, different types of PoW al-
gorithms share exactly similar characteristics. In these al-
gorithms, they are mainly two types of nodes: clients and
miners. Miners are responsible for creating a block using
a randomised lottery mechanism. Conversely, clients are
the nodes that are responsible for validating each block as
well as utilised to transact bitcoin between different users.
Committees in these algorithms represent the set of miners,
exhibiting the property of a single open committee structure
where anyone can join as a miner. The respective committee
is formed implicitly in a dynamic fashion, indicating any
miner can join or leave whenever they wish.
As per Table 14, none of the algorithms requires any
node to be authenticated to participate in the algorithm. All
of them have strong support for non-repudiation in the form
of digital signature as part of every single transaction. These
algorithms have a high level of censorship resistance, which
means that it will be difficult for any regulatory agency
to impose any censorship on these algorithms. As for the
attack vector, each PoW algorithm requires every miner
node to invest substantially for mining hardware in order
to participate in these consensus algorithms. This feature,
thus, acts as a deterrent against any Sybil or DoS attack in
any PoW algorithm. The adversary tolerance is based on the
assumption that PoW suffers from 51% attacks, and thus,
adversary nodes need to have less than 50% of the total
hashing power of the network.
According to Table 15, these algorithms have low
throughput, and unfortunately, do not scale properly. Fur-
thermore, most of the algorithms require a considerable time
to reach finality, and their energy consumption is consider-
ably high, as explained in Section 5.1.4. The fault tolerance in
these algorithms is 2f+ 1like any BFT algorithm, implying
they can achieve consensus as long as more than 50% of
nodes function correctly.
5.2 Proof of Stake
To counteract the limitations of any PoW algorithm, another
type of consensus algorithm, called Proof of Stake (PoS) has
been proposed. The earliest proposal of a PoS algorithm
can be found on the bitcointalk forum in 2011 [154]. Soon
after, several projects started experimenting with the idea.
Peercoin [72], released in 2012, was the first currency to
utilise the PoS consensus algorithm.
The core idea of PoS evolves around the concept that the
nodes who would like to participate in the block creation
process must prove that they own a certain number of coins
at first. Besides, they must lock a certain amount of its
currencies, called stake , into an escrow account in order to
participate in the block creation process. The stake acts as
a guarantee that it will behave as per the protocol rules.
The node escrows its stake in this manner is known as the
16
Table 13: Structural properties of PoW consensus algorithms.
Single committeeNode typeType Formation ConfigurationMechanism
Clients & Miners Open Implicit Dynamic Lottery, Randomised
Table 14: Security properties of PoW consensus algorithms.
Attack VectorsAuthentication Non-repudiationCensorship
resistance Adversary tolerance Sybil protection DoS Resistance
x High 2f+ 1
Table 15: Performance properties of PoW consensus algorithms.
Fault tolerance Throughput Scalability Latency Energy consumption
2f+ 1 Low Low Medium-High High
stakeholder, leader, forger, or minter in PoS terminology.
The minter can lose the stake, in case it misbehaves.
In essence, when a stakeholder escrows its stake, it
implicitly becomes a member of an exclusive group. Only a
member of this exclusive group can participate in the block
creation process. In case the stakeholder gets the chance to
create a new block, the stakeholder will be rewarded in one
of the two different ways. Either it can collect the transaction
fees within the block, or it is provided a certain amount of
currencies that act as a type of interest against their stake.
It has been argued that this incentive, coupled with any
punitive mechanism, can provide a similar level of security
of any PoW algorithm. Moreover, it can offer several other
advantages. Next, we explore a few benefits of a PoS mech-
anism [156].
Energy Efficiency: A PoS algorithm does not require
any node to solve a resource-intensive hard crypto-
graphic puzzle. Consequently, such an algorithm is
extremely energy efficient compared to their PoW coun-
terpart. Therefore, a crypto-currency leveraging any
PoS algorithm is likely to be more sustainable in the
long run.
Mitigation of Centralization: A PoS algorithm is less
impacted by the economies of scale phenomenon. Since
it does not require to build up a mining rig to solve
any resource-intensive cryptographic puzzle, there is
no way to maximise gain by increasing any output.
Therefore, it is less susceptible to the centralisation
problem created by the mining pool.
Explicit Economic Security: A carefully designed pen-
alty scheme in a PoS algorithm can deter any misbe-
having attack, including spawn-camping. Anyone en-
gaging in such attacks will lose their stake and might
be banned from any block creation process in the fu-
ture, depending on the protocol. This eventually can
strengthen the security of the system.
Initial supply: One of the major barriers in a PoS
algorithm is how to generate the initial coins and fairly
distribute them among the stakeholders so that they can be
used as stakes. We term this barrier as the bootstrap problem.
There are two approaches to address the bootstrap problem:
Pre-mining: A set of coins are pre-mined, which are
then sold before the launch of the system in an IPO(Initial Public Offering) or ICO (Initial Coin Offering).
PoW-PoS transition: The system starts with a PoW
system to fairly distribute the coins among the stake-
holders. Then, it slowly transitions towards the PoS
system.
Reward process: Another important aspect is the re-
warding process to incentivise the stakeholder to take part
in the minting process. Unlike any PoW, where a miner is
rewarded with new coins for creating a valid block, there is
no reward for creating a valid block. Instead, to incentivise
a minter, two types of reward mechanisms are available
within a PoS algorithm:
Transaction Fee: The minter can collect fees from the
transactions included within the minted block.
Interest rate: A lower interest rate is configured, which
allows the currency to inflate over time. This interest
is paid to the minter as a reward for creating a valid
block.
Selection process: A crucial factor in any PoS algorithm
is how to select the stakeholder who can mint the next block.
In a PoW algorithm, a miner is selected based on who can
find the resource-intensive desired hash. Since PoS does not
rely on hind such a hash as the mechanism to find the
next block, there must be a mechanism to select the next
stakeholder.
Currently, there three different approaches to Proof of
Stake: Chained, BFT, and Delegated.
CHAINED POS.The general idea of a chained PoS is to
deploy a combination of PoW and PoS algorithms chained
together to achieve any consensus. Because of this, there
can be two types of blocks, PoW and PoS blocks, within
the same blockchain system. To accomplish this, the cor-
responding algorithm relies on different approaches to se-
lect/assign a particular miner for creating a PoW block or
select a set of validators for creating a PoS block in different
epochs or after a certain number of blocks created. In
general, a chain based PoS can employ any of the following
three different approaches to select the miner/stakeholder:
Randomised PoW Mining: A miner who can solve the
corresponding cryptographic PoW puzzle is selected in
a random fashion.
Randomised Stakeholder Selection: A randomised PoS util-
ises a probabilistic formula that takes into account the
17
staked currencies and other parameters to select the
next stakeholder. The other parameters ensure that a
stakeholder is not selected only based on the number of
their staked coins and act as a pseudo-random seed for
the probabilistic formula.
Coin-age based selection. A coin-age is defined as the
holding period of a coin by its owner. For example, if
an owner receives a coin from a sender and holds it for
five days then the coin-age of the coin can be defined
as five coin-days. Formally,
coin age=coinholdingperiod
Algorithms belonging to this class select the stake-
holder using staked coins of the stakeholders and their
corresponding coin-age.
In general, a chained PoS algorithm favours towards
availability over consistency when network partition occurs,
as per the CAP theorem.
BFT P OS.BFT PoS is a multi-round PoS algorithm. In the
first step, a set of validators are pseudo-randomly selected to
propose a block. However, the consensus regarding commit-
ting this block to the chain depends on the >2=3quorum
of super-majority among the validators on several rounds. It
inherits the properties of any BFT consensus, and as such, it
tolerates up to 1=3of byzantine behaviour among the nodes.
In general, a BFT PoS algorithm favours towards con-
sistency over availability when network partition occurs,
within the setting of CAP theorem.
DELEGATED PROOF OF STAKE .Delegated Proof of Stake
(or DPoS in short) is a form of consensus algorithm in which
reputation scores or other mechanisms are used to select the
set of validators [184]. Even though it has the name Proof of
Stake associated with it, it is quite different from other PoS
algorithms.
In DPoS, users of the network vote to select a group
of delegates (or witnesses) who are responsible for creating
blocks. Users utilise reputations scores or other mechanisms
to choose their delegates. Delegates are the only entities
who can propose new blocks. For each round, a leader is
selected from the set of delegates who can propose a block.
How such a leader is chosen depends on the respective
system. The leader gets rewards for creating a new block,
and is penalised and de-listed from the set of validators if it
misbehaves.
The delegates themselves compete with each other to get
included in the validator list. In such, each validator might
offer different levels of incentives for the voters who vote for
it. For example, if a delegate is selected to propose a block,
it might distribute a certain fraction of its reward among the
users who have selected it. Since the number of validators
is small, the consensus finality can be fast.
Next, we explore several crypto-currencies or mechan-
isms that use the above mentioned PoS approaches.
5.2.1 Chained PoS
Next, we present two examples of a chained PoS algorithm
to illustrate how this approach has been applied in practice.
1) P EERCOIN (PPC OIN).Peercoin is the first crypto-
currency to formalise the notion of PoS by utilising a hybridPoW-PoS protocol [174]. The Peercoin protocol is based on
the assumption that coin-age can be leveraged to create a
PoS algorithm which is as secure as any PoW algorithm
while minimising the disadvantages associated with a PoW
algorithm.
Peercoin protocol recognises two different kinds of
blocks: PoW blocks and PoS blocks, within the same block-
chain. These blocks are created by two separate entities:
miners and minters. Miners are responsible for creating
PoW blocks, similar to Bitcoin whereas minters are respons-
ible for creating PoS blocks. Irrespective of the last block
type, the next block either can be a PoW block or a PoS
block, and these entities compete with each other to create
the next block [175]. Miners compete with other miners to
find a valid PoW block that matches the PoW difficulty
target, similar to Bitcoin. Similarly, minters compete among
themselves to find a valid PoS block that matches the PoS
difficulty target (similar to a PoW algorithm but requires
much less computation). As soon as any PoW or PoS block
is found, it is broadcast to the network, and other nodes
validate it.
Within a PoS block, a minter utilise their holding coins
as a stake, and the minter is rewarded approximately 1%
per annum based on the coin-age of the staked coins. The
reward is paid out for each block in a newly created special
transaction called the coinstake transaction. Each coinstake
transaction consists of the number of staked inputs and a
kernel , containing the hash that meets the PoS difficulty.
The hash itself is calculated over a small space and hence
not computationally intensive at all. It utilises the number
of staked inputs and a probabilistic variable, whereas the
difficulty condition is calculated utilising the coin-age of
the staked inputs as well as a difficulty parameter. This
parameter is adjusted dynamically to ensure that one block
is created in 10 minutes. In other words, the valid kernel
depends on the coin-age of the staked inputs, and the higher
the coin-age, the higher is the probability to match the
difficulty.
The coinstake transaction is paid to the minter, which
contains the coins staked along with the reward. Once a
PoS block is added to the chain, the coin-age of the staked
coins is reset to zero. This indicates that all the stacked coins
are consumed. This ensures that the same coins cannot be
used over and over again to create a PoS block within a
short period of time. The main chain in Peercoin is selected
based on the highest total coin-age consumed in all blocks.
That means, if a PoW block and PoS block are received
simultaneously as the next block by a node, the algorithm
dictates the PoS block to be selected over the PoW block.
The block reward for a PoW block in Peercoin decreases
and will cease to be significant after a certain period of time.
It is currently used for the coin generation and distribution
purpose and will be completely phased out in the future
[205]. It has no role whatsoever on securing the network,
which is largely based on the PoS algorithm. Once the PoW
algorithm is phased out, it is suggested that the energy
consumption of Peercoin will be significantly low while
providing similar security as any PoW algorithm.
Peercoin is highly regarded for formalising the first
alternative mechanism to PoW, however, it suffers from all
the attack vectors of PoS, as presented in Section 5.2.4. Two
18
other coins Black and Nxt removes age from the equation
in order to avoid the exploitation of the system by the
dishonest entities having a significant amount of coins.
2) CASPER FFG. Casper the Friendly Finality Gadget
(CFFG) is a PoW-PoS hybrid consensus algorithm proposed
to replace the Ethereum’s PoW consensus algorithm [181]. In
fact, CFFG provides an intermediate PoS overlay on top of
its current PoW algorithm so that Ethereum is transformed
to a pure PoS protocol called Casper the Friendly Ghost
(CTFG) described below (Section 5.2.2).
The PoS layer requires the participation of validators.
Any node can become a validator by depositing some Eth-
ereum’s native crypto-currency called Ether to a designated
smart-contract, which acts as a security bond. The network
itself will mostly consist of PoW miners who will mine
blocks according to its current PoW algorithm. However,
the finalisation/check-pointing of blocks will be carried out
by PoS validators. The check-pointing/finalisation is the
process to ensure that the chain becomes irreversible up
to a certain block and thus, short and low range attacks
(particular types of PoS only attacks presented in Section
5.2.4) as well as the 51% attack cannot be launched beyond
the check-pointing block.
The check-pointing occurs every 50 blocks, and this
interval of 50 blocks is called an epoch [158]. The finalisation
process requires two rounds of voting in two successive
epochs. The process is as follows. In an epoch, the validators
vote on a certain checkpoint c(a block). A super-majority
(denoted as +2=3) occurs when more than 2=3of the valid-
ators vote for the checkpoint c. In such a case, the checkpoint
is regarded as justified . If in the next epoch, ( +2=3) of the
validators vote on the next checkpoint c0(a block which
is a child of the block belonging to C),c0is considered
justified whereas cis considered finalised. A checkpoint
created in this manner for each epoch is assumed to create
a checkpoint tree where c0is a direct child of c. The process
can be summarised in the following way: +2=3Votec!
Justify c!+2=3Votec0!Finalize cand Justify c0
Once a checkpoint is finalised, the validators are paid.
The payment is interest-based and is proportional to the
number of ethers deposited. If it occurs that there are two
checkpoints, it signifies that a fork has occurred. This can
only happen when a validator or a set of validators has de-
viated from the protocol. In such cases, a penalty mechanism
is imposed in which the deposit of the violating validator(s)
is destroyed.
In essence, CGGF is a combination of Chained and BFT
consensus mechanisms with strong support for availability
over consistency. Its properties ensure that block finalisation
occurs quickly, and the protocol is mostly secure against all
PoS attacks except the cartel formation attack (a particular
type for PoS only presented in Section 5.2.4). However, it is
to be noted that this consensus mechanism has not been
implemented yet. Therefore, it is yet to be seen how it
performs in reality.
5.2.2 BFT PoS
In this section we describe three notable BFT PoS algorithms
that have had significant uptake in practice: Tendermint,
CTFG and Ouroboros.1) T ENDERMINT .Tendermint is the first to showcase how
the BFT consensus can be achieved within the PoS setting
of blockchain systems [178], [179], [180]. It consists of two
major components: a consensus engine known as Tender-
mint Core and its underlying application interface, called
theApplication BlockChain Interface (ABCI). The Tendermint
core is responsible for deploying the consensus algorithm,
whereas the ABCI can be utilised to deploy any blockchain
application using any programming language.
The consensus algorithm relies on a set of validators. It is
a round-based algorithm where a proposer is chosen from
a set of validators. In each round , the proposer proposes
a new block for the blockchain at the latest height. The
proposer itself is selected using a deterministic round-robin
algorithm, which ultimately relies on the voting power of
the validators. The voting power, on the other hand, is
proportional to the security deposit of the validators.
The consensus algorithm consists of three steps (pro-
pose, pre-vote, and pre-commit) in each round bound by
a timer equally divided among the three steps, thus making
it a weakly synchronous protocol. These steps signify the
transition of states in each validator. Figure 12 illustrates the
state transition diagram for each validator. At the beginning
of each round, a new proposer is chosen to propose a new
block. The proposed block needs to go through a two-stage
voting mechanism before it is committed to the blockchain.
When a validator receives the proposed block, it val-
idates the block at first, and if okay, it pre-votes for the
proposed block. If the block is not received within the
propose timer or the block is invalid, the validator submits
a special vote called Prevote nil . Then, the validator waits
for the pre-vote interval to receive pre-votes from the super-
majority (denoted as +2=3) of the validators. A +2=3pre-
votes signifies that the super-majority validators have voted
for the proposed block, implying their confidence on the
proposed block and is denoted as a Polka in Tendermint
terminology. At this stage, the validator pre-commits the
block. If the validator does not receive enough pre-votes for
the proposed block, it submits another special vote called
Precommit nil . Then, the validator waits for the pre-commit
time-period to receive +2=3pre-commits from the super-
majority of the validators. Once received, it commits the
block to the blockchain. If +2=3pre-commits not received
within the pre-commit time-period, the next round is initiated
where a new proposer is selected, and the steps are repeated.
To ensure the safety guarantee of the algorithm, Tender-
mint is also coupled with locking rules. Once a validator
pre-commits a block after a polka is achieved, it must lock
itself onto that block. Then, it must obey the following two
rules:
it must pre-vote for the same block in the next round
for the same blockchain height,
the unlocking is possible only when a newer block
receives a polka in a later round for the same blockchain
height.
With these rules, Tendermint guarantees that the con-
sensus is secure when less than one-third validators exhibit
byzantine behaviour, meaning conflicting blocks will never
be committed at the same blockchain height. In other words,
Tendermint guarantees that no fork will occur under this as-
sumption. Since Tendermint favours safety over availability,
19
Propose
Pre-Vote Nil Pre-commit Nil
Propose BlockNew Height
Wait for
pre-
commits
from +2/3Propose BlockCommit New Round
Wait for
pre-vote
from +2/3Valid block
No +2/3 pre-vote
for block
+2/3 pre-vote
for block+2/3 pre-commit
for blockNo +2/3
pre-commit for blockInvalid block
or not received
in time
Figure 12: Tendermint consensus steps.
it has one particular weakness. It requires 100% uptime of
its+2=3(super-majority) validators. If more than one-third
(+1=3) are validators are offline or partitioned, the system
will stop functioning [178]. In such cases, out-of-protocol
steps are required to tackle this situation.
Unlike PoW or other PoS algorithms that come with
defined reward mechanisms and crypto-currency applica-
tions, the latest version of Tendermint more likely acts as the
consensus plugin, which can be retro-fit to other blockchain
systems. For example, Tendermint has been integrated with
a private instantiation of Ethereum in a Hyperledger project
called Burrows [209]. That is why there is no reward/pun-
ishment mechanism defined in Tendermint. However, it can
be easily introduced in the application layer via the ABCI.
For example, a reward mechanism can be introduced for
the proposer and the validator to motivate them to engage
in the consensus process. A node can become a validator by
bonding a certain amount of security deposit. The deposit is
destroyed, in case the corresponding validator misbehaves,
and thus acts as a deterrent for the validator to launch
any attack in the network. Together with the consensus
algorithm and a carefully designed reward and punishment
mechanism, all PoS attacks can be effectively handled.
2) C ASPER THE FRIENDLY GHOST (CTFG). CTFG is a pure
BFT PoS algorithm that aims to transform Ethereum to a
PoS-only blockchain system in the future [182]. As described
above, CFFG is geared towards a gentle transition from a
PoW to a PoS model for Ethereum, where CTFG will take
control of the consensus mechanism ultimately.
CTFG is based upon a rigorous formal model called
Correction by Construction (CBC) that utilises the GHOST
(Greedy Heaviest-Observed Subtree) primitive as its con-
sensus rule during fork [183]. The idea is that the CTFG
protocol will be partially specified at the initial stage along
with a set of desired properties. Then, the rest of the protocol
is dynamically derived in such a way that it satisfies the de-
sired properties - hence the name correction by construction.
This is in contrast to the traditional approach for designing
a protocol where a protocol is fully defined at first, and then
it is tested to check if it satisfies the desired properties [156].
To achieve this, CTFG introduces a safety oracle, acting
as an ideal adversary, which raises exceptions when a fault
occurs and also approximates the probability of any futurefailure. Based on this, the oracle can dynamically fine-tune
the protocol as required to evolve it towards its completion.
Similar to CFFG, CTFG also requires a set of bonded
validators that will bond ethers as a security deposit in a
smart-contract. However, unlike any other PoS mechanisms,
the validators will bet on the block, which has the highest
probability to be included in the main chain according to
their own perspective. If that particular block is included in
the main chain, the validators receive rewards for voting in
favour of the block. Otherwise, the validators receive certain
penalties.
Like any PoW algorithm, CTFG favours availability over
consistency. This means that blocks are not finalised in-
stantly, like Tendermint. Instead, as the chain grows and
more blocks are added, a previous block is considered impli-
citly final. A major advantage of CTFG over Tendermint is
that it can accommodate dynamic validators. This is because
the finality condition in Tendermint requires that its block
interval is short, which in turn demands a relatively lower
number of pre-determined validators. Since CTFG does not
rely on any instant finality, it can theoretically accommodate
a higher number of dynamic validators.
CTFG is currently is the most comprehensive proposal
which addresses all PoS attack vectors. However, it is to be
noted that this is just a proposal at the current stage. There-
fore, its performance in real settings is yet to be analysed.
3) O UROBOROS .Ouroboros is a provably secure PoS al-
gorithm [185], [186] utilised in the Cardano platform [187].
Cardano is regarded as third-generation blockchain system
supporting smart-contract and decentralised application
without relying on any PoW consensus algorithm.
In Ouroboros, only a stakeholder can participate in the
block minting process. A stakeholder is any node that holds
the underlying crypto-currency of the Cardano platform
called Ada . Ouroboros is based on the concept of epoch ,
which is essentially a predefined time period. Each epoch
consists of several slots. A stakeholder is elected for each
slot to create a single block, meaning a block is created in
each slot. The selected stakeholder is called a slot leader and
is elected by a set of electors . An elector is a specific type of
stakeholder which has a certain amount Ada in its disposal.
In each epoch, the electors select the set of stakeholders
for the next epoch using an algorithm called Follow the
20
Satoshi (FTS). The FTS algorithm relies on a random seed
to introduce a certain amount of randomness in the elec-
tion process. A share of the random seed is individually
generated by all electors who participate in a multiparty
computation protocol. Once the protocol is executed, all
electors posses the random seed, constructed with all of
their shares. The FTS algorithm utilises the random seed
to select a coin for a particular slot. The owner of the coin is
then elected as the slot leader. Intuitively, the more coins a
stakeholder possesses, the higher is its probability of being
selected as the slot leader.
Ouroboros is expected to provide a transaction fee based
reward to incentivise stakeholders to participate in the
minting process. However, the details are in the process
of being finalised. It has been mathematically proven to
be secure against almost all PoS attack vectors except the
cartel formation [185]. Nevertheless, how it will perform
once deployed is yet to be seen.
5.2.3 DPoS
There are several mechanisms deployed by different crypto-
currencies under the general category of DPoS. Next, we
present a few prominent approaches of some well-known
DPoS based crypto-currencies. Our analysis of these crypto-
currencies are summarised in Table 16.
1) EOS. EOS is the first and the most widely known
DPoS crypto-currency and smart-contract platform as of
now [188]. With the promise of greater scalability and
higher transactions per second than Ethereum, it raised 4
billion USD in the highest ever ICO event to date [190].
Initial EOS currency was created on the Ethereum platform,
and later migrated to their own blockchain network. The
DPoS consensus algorithm of EOS utilises 21validators,
also known as Block Producers (BPs). These 21validators are
selected with votes from EOS token (currency) holders. The
number of times a particular BP is selected to produce a
block is proportional to the total votes received from the
token holders.
Evey DPoS currency must create an initial supply before
the network is operational. This supply is used to select 21
BPs (with voting) as well as to reward the BPs for creating
blocks, and thus, securing the network. EOS had an initial
supply of 1Billion EOS tokens with an annual inflation of
5%. Among the inflated currencies, 1%is used to reward
the block producers, whereas the rest of the 4%are kept
for future R&D for EOS [191]. Currently, an EOS block is
created in 0:5s. Blocks in EOS are produced in rounds where
each round consists of 21 blocks [192]. At the beginning of
each round, 21 BPs are selected. Next, each of them gets a
chance to create a block in pseduo-random fashion within
that particular round. Once a BP produces a block, other
BPs must validate the block and reach into a consensus. A
block is confirmed only when ( +2=3) majority of the BPs
reach the consensus regarding the validity of the block. Once
this happens, the block and the associated transactions are
regarded as confirmed or final, so no fork can happen.
2) T RON .Tron is another popular DPoS based crypto-
currency [193]. With an initial supply of 99Billion Tron
tokens (represented with TRX ), it is another smart-contract
supported blockchain platform, very similar to Ethereumand EOS in functionality. Its consensus mechanism utilises
27validators, known as Super Representatives (SRs) [194].
The SRs are selected in every six hours with votes by TRX
holders who must freeze a certain amount of TRX to vote for
an SR. The deposits amount can be frozen back after three
days once the voting is cast [195]. A block in Tron is created
in every 3s for the corresponding SR receives a reward of 32
TRX. Another important feature of Tron is that there is no
in-built inflation mechanism in the protocol, which implies
that the total supply will remain constant throughout its
lifespan.
3) T EZOS .Tezos is, like EOS and Tron, a smart-contract plat-
form which utilises a variant of DPoS consensus algorithm
[196]. With a block reward of 16XTZ (Tezos currency) and
block creation time of 60s, Tezos does not require any pre-
defined number of stakeholders (or Bakers as defined in
Tezos) [197]. This differs Tezos from other DPoS currencies.
Instead, the consensus mechanism utilises a dynamic range
of stakeholders where anyone holding a substantial amount
of XTZ can be a stakeholder. This limits general users
to participate in the consensus mechanism. To rectify this
problem, Tezos provides a mechanism by which anyone can
delegate their XTZ to someone so that it can accumulate the
required number of XTZ to be a baker. In return, the baker
would return a certain proportion of their received block
reward to the delegating party. Tezos started with an initial
supply of 765 Million XTZ tokens. It relies on an annual
inflation of 5:51% and the inflated currencies are used to
reward the bakers.
4) L ISK.Lisk is a unique DPoS blockchain platform which,
enables the development of DApps using JavaScript [200].
Another unique feature of Lisk is its ability to accommodate
and then to operate with multiple blockchains, known as
sidechains along with a central blockchain called mainchain .
Each sidechain can be deployed and maintained by a
particular application provider, which needs to be synced
with the mainchain as per the Lisk’s protocol rule. In this
way, different applications can leverage different sidechains
simultaneously without burdening off the mainchain. Even
though the responsibility of maintaining a sidechain relies
on the particular application provider, the mainchain must
be maintained with the Lisk DPoS consensus protocol,
which utilises 101delegates [201]. Only these delegates can
produce a block. These delegates are selected using votes
from Lisk currency (denoted with LSK ) owners, where
each holder has 101 votes. The weight of each vote is
proportional to the amount of LSK owned by the respective
owner. The selection of delegates happens before a round,
where each round consists of 101 block generation cycle.
Thus, in a round, each delegate is randomly selected to
create a block. It has a block creation time of 10seconds
and block reward of 5LSK. Started with an initial supply
of100million LSK, Lisk has a current supply of 132million
with an annual inflation of 5:65% .
5) A RK.Ark is yet another DPoS based blockchain platform
[202]. It utilises 51delegates to create 51blocks in each
round [203]. With a block creation time of 8s, each round
lasts for 408s. Each delegate receives 2ARK (the native
currency of the ARK platform) for creating a block. It had
21
an initial supply of 125 million. With an annual inflation
of5:55, the supply was around 142 million (as of June
2019). Like other DPoS blockchains, the delegates in Ark
are also selected with votes by Ark currency owner, where
the weight of each is proportional to the amount of ARK
owned by the voter.
5.2.4 Limitations of PoS
Even though the variants of different PoS algorithms offer
several significant advantages, there are still a few disad-
vantages in these classes of algorithms. We explore these
disadvantages below.
Collusion: If the number of validators is not large
enough, it might be easier to launch a 51% attack on the
corresponding consensus algorithm by colluding with
other validators.
Wealth effect: The sole reliance on coin-wealth in a
consensus algorithm or for the selection of validators
creates an environment where people with a large por-
tion of coins can exert greater influence.
In addition to these disadvantages, there have been a few
other attack vectors identified for the PoS algorithms:
Nothing-at-stake (NAS) attack [157]: During a block-
chain fork, an attacker might attempt to add its newly
created block in all forked branches to increase their
probability to add their block as the valid block. Such
scenario is unlikely to occur in any PoW algorithm.
This is because a miner would need to share their
resources in order to mine at different branches. This
would eventually decrease their chance of finding a
new block because of the resources shared in multiple
branches. Since it does not cost anything for a minter
in a PoS algorithm to add blocks in multiple parallel
branches, the attacker is motivated to do so. Applying
a penalty for such misbehaviour could effectively tackle
this problem.
Bribing (short-range, SR) attack [157], [176]: In this
attack, an attacker tries to double spend by creating a
fork. An example of this attack would be as follows. The
attacker pays to a seller to buy a good. The seller waits
for a certain number of blocks (e.g., six blocks) before
the good is delivered to the attacker. Once delivered,
the attacker forks the main chain at the block (e.g., six
blocks back, which is relatively short and hence the
name) in which the payment was made. Then, the at-
tacker bribes other minters to mint on top of the forked
branch. As long as the bribed amount is lower than
the price of the delivered good, it is always profitable
for the attacker. The colluding minter has nothing to
lose if it is coupled with the nothing-at-stake attack on
their part but can gain from the bribery. Again, it can
be tackled by introducing a penalty mechanism for all
misbehaving parties.
Long-range (LR) attack [157]: In this attack, the attacker
attempts to build an alternative blockchain starting
from the earliest blocks if the attacker can collude with
the majority of the stakeholders. The motivation might
be similar to double spending or related issues provid-
ing advantages to the attacker as well as the colluded
stakeholders. As explained above, the colluded stake-
holder has nothing to lose if it can be coupled with thenothing-at-stake attacks. Check-pointing is one of the
methods by which it can be tackled. The check-pointing
codifies a certain length of the blockchain to make it
immutable up to that point, and thereby undermining
the attack. This is because the attacker cannot fork the
blockchain before that check-point.
Coin-age accumulation (CAC) attack [157], [176]: The
PoS algorithms that rely on the uncapped coin-age
parameter are susceptible to this attack. In this attack,
the attacker waits for their coins to accumulate enough
coin-age to exploit the algorithm for launching double
spends by initiating a fork. This attack can be tackled
by introducing a cap on the coin-age which minimises
the attack vector.
Pre-computing (PreCom) attack [157], [155]: A pre-
computing attack, also known as Stake-grinding attack,
would allow an attacker to increase the probability of
generating subsequent blocks based on the information
of the current block. If there is not enough randomness
included in the PoS algorithm, the attacker can attempt
to pre-compute subsequent blocks by fine-tuning in-
formation of the current block. For a particular set of
information (e.g., a set of transactions), if the attacker
finds that the probability of minting a few subsequent
blocks is less than desired, the attacker can update
the set of transactions to increase their probability of
determining the next few blocks. It can be effectively
tackled by introducing a secure source of randomness
in the algorithm.
Cartel formation (CAF) attack [158]: In economic the-
ory, an oligopoly market is dominated by a small set of
entities having greater influence or wealth than other
entity. They can collude with one another by forming a
cartel to control price or reduce competition within the
market. It has been argued that ” Blockchain architecture
is mechanism design for oligopolistic markets. ” [159] which
affects both PoW and PoS algorithms. Such a cartel can
launch 51% attacks on the PoS algorithm or exploit the
stakes to monopolise the PoS algorithm.
5.2.5 Analysis
In this section, we summarise the properties of different
PoS algorithms utilising the taxonomies and PoS attack
vectors in Table 17, Table 18, Table 19 and Table 20. Like
before, a ‘ ’ symbol has been utilised to indicate if the
corresponding algorithm supports a particular property,
and the ‘X’ symbol signifies that the particular property
is not supported. The ‘-’ symbol implies that the property
is not applicable, whereas the symbol ‘?’ indicates that no
information has been found for that particular feature. For
other properties, explanatory texts have been used as well.
From Table 17, only chained algorithms are based on
multiple committee utilising a flat topology with a dynamic
configuration. These algorithms also use a probabilistic lot-
tery to select a minter. Conversely, other PoS algorithms, ex-
cept Tendermint, are based on the single committee having
an open type and explicit formation with a dynamic config-
uration and mostly rely on voting mechanisms. Tendermint
uses a closed committee with a static configuration.
As per Table 18, none of the algorithms, except Tender-
mint requires any node to be authenticated to participate
22
Table 16: Comparison of DPoS Currencies with ‘-’ signifying not applicable.
Currency Genesis date
(dd.mm.yyyy)Initial supply Inflation Current supply
(23.05.2019)Block reward Block Time Validator
nos
EOS 01:07:2017 1Billion 5% 1:04Billion 1% of inflated
currency divided
among 21
validators0:5s 21
Tron 28:08:2017 99Billion - 99Billion 32TRX 3s 27
Tezos 30:06:2018 765Million 5:51% 795Million 16XTZ 60s Not pre-
defined
Lisk 24:05:2016 100Million 5:67% 132Million 5LSK 10s 101
Ark 21:03:2017 125Million 5:55% 142Million 2ARK 8s 51
in the algorithm. All of them have strong support for non-
repudiation in the form of digital signature as part of every
single transaction. These algorithms have a high level of
censorship resistance, as do all PoW algorithms. As for
the attack vector, each PoS algorithm requires every miner
node to invest substantially to participate in this algorithm.
This feature, thus, acts as a deterrent against any Sybil or
DoS attack in any PoS algorithm. The adversary tolerance
for Chained systems can be calculated using this formula:
min(2f+ 1;3f+ 1) = 3 f+ 1. This is because a chained
algorithm utilises both PoW and PoS algorithms and thus
needs to consider the adversary tolerance for both of them.
We consider the minimum of these two ( 3f+ 1). The
supported adversary tolerance for other algorithms is 3f+1
except BFT Ouroboros whose adversary tolerance is 2f+ 1.
According to Table 20, all BFT, and DPoS algorithms
have considerably high throughput, low latency, and high
scalability. Their energy consumption is negligible. How-
ever, the chained algorithms have a comparatively lower
throughput, lower scalability, and higher latency with re-
spect to their BFT and DPoS counterparts. The fault toler-
ance of chained and BFT algorithms is 2f+ 1 like any BFT
algorithm, implying they can achieve consensus as long as
more than 50% of nodes function properly. However, DPoS
algorithm requires a 3f+ 1fault tolerance.
Table 19 outlines a comparison of additional attack vec-
tors with symbols representing the usual semantics. CTFG,
Tentermint, and Ouroboros have mitigation mechanisms
against these attack vectors. However, Casper FFG, and
any DPoS algorithms cannot successfully defend against
the cartel formation attack. Peercoin, on the other hand,
has mechanism against this cartel formation attack, unfor-
tunately, suffers from all other attack vectors.
Finally, a comparison of the selected DPoS crypto-
currencies is presented in Table 16.
6 I NCENTIVISED CONSENSUS : BEYOND POWAND
POS
Some consensus algorithms take a different approach in
which they do not solely rely on any PoW or PoS mech-
anism. Instead, they use an approach in which a PoW/-
PoS mechanism is combined with another approach. We
consider such algorithms as hybrid algorithms which are
presented in Section 6.1. Other approaches adopt a more
drastic approach in which they do not leverage any
type PoW/PoS algorithm whatsoever. Such algorithms aretagged as N-POS/POW (to symbolise Non-PoS/PoW) al-
gorithms and discussed in Section 6.2.
6.1 Hybrid Consensus
In this section, we outline a new breed of consensuses
algorithms that combine either a PoW or PoS algorithm
or both with another novel algorithm or mechanism, thus
creating a hybrid mechanism.
1) P ROOF OF RESEARCH (POR). Proof of research is a
hybrid approach that combines proof-of-stake with the
proof-of-BOINC [160]. BOINC stands for Berkeley Open
Infrastructure for Network Computing [162]. It is a grid
computing platform widely used by scientific researchers
in different domains by allowing them to exploit the idle
computing resources of personal computers around the
world. With the proof-of-BOINC, a researcher has to prove
his contribution for the BOINC research work.
The PoR mechanism is leveraged by Gridcoin [160],
[161], a crypto-currency that can be earned by anyone by
sharing their computing resources with the BOINC pro-
ject. The mechanism by which PoS and Proof-of-BOINC
are tied together for the PoR is explained next [161]. The
PoS mechanism is similar to the traditional PoS algorithm.
Anyone can become a minter, known as Investor in Gridcoin
terminology, by owning a certain amount of Gridcoin and
participating in the minting process. In addition to this,
other users, known as Researchers in Gridcoin terminology,
can also participate in the minting process. Interestingly, an
investor can also be a researcher and thus, can increase their
amount of grid coin earned.
For this, a researcher installs the BOINC software and
registers a project from the BOINC whitelist with his email
address. The researcher is assigned a unique cross project
identifier (CPID) and starts downloading the work share.
Once the computation is completed, the researcher returns
the result with a credit recommendation for the completed
workload. The recommendation is compared with that of
another researcher, and the minimum credit is rewarded.
This workload credit data is stored in the header of each
block and the researcher is rewarded with the corresponding
amount of Gridcoin. To summarise, the consensus mechan-
ism is mostly dominated by the PoS mechanism with Proof-
of-BOINC acts as a reward mechanism for sharing unused
computing resources available to the researchers. Hence, its
security is similar to that of the traditional PoS algorithm.
2) S LIMCOIN ’SPROOF -OF-BURN (POB).The Proof-of-Burn
is a consensus algorithm proposed by Ian Stewart as an
23
Table 17: Comparing structural properties of PoS Consensus Algorithms.
Single committee Multiple committee Consensus
/SystemNode typeType Formation Configuration Topology ConfigurationMechanism
Chained
(PeerCoin)Clients, Miners
& Minters- - - Flat Dynamic Probabilistic lottery
Chained
(CFFG)Clients, Miners
& Validators- - - Flat Dynamic Probabilistic lottery
BFT
(Tendermint)Clients &
ValidatorsOpen (Close) Explicit Dynamic (Static) - - Voting
BFT (CTFG) Clients &
ValidatorsOpen Explicit Dynamic - - ?
BTFG
(Ouroboros)Clients,
Electors &
StakeholdersOpen Explicit Dynamic Voting
DPoS Clients &
ValidatorsOpen Explicit Dynamic - - Voting
Table 18: Comparing security properties of PoS Consensus Algorithms.
Attack Vectors Consensus
/SystemAuthentication Non-repudiationCensorship
resistance Adversary tolerance Sybil protection DoS Resistance
Chained
(PeerCoin)X High 3f+ 1
Chained
(CFFG)X High 3f+ 1
BFT
(Tendermint)(In close
type), X (In
open type)High 3f+ 1
BFT (CTFG) X High 3f+ 1
BFT
(Ouroboros)X High 2f+ 1
DPoS X High 3f+ 1
Table 19: Comparison of additional attack vectors protection among PoS Consensus Algorithms
ConsensusnSystem Nothing-at-Stake Bribing Long-range Coin-age Pre-computing Cartel formation
Chained (PeerCoin) X X X X X
Chained (Casper FFG) X
BFT (Tendermint)
BFT (CTFG)
BFT (Ouroboros)
DPoS X
Table 20: Comparing performance properties of PoS Consensus Algorithms.
ConsensusnSystem Fault
toleranceThroughput Scalability Latency Energy consumption
Chained (PeerCoin,
CFFG)2f+ 1 Medium Medium Medium Medium
BFT (Tendermint,
CTFG, Ouroboros)2f+ 1 High High Low Low
DPoS 3f+ 1 High High Low Low
alternative to PoW [163]. In PoW, miners need to invest in
building a mining rig in order to participate in the mining
process. In PoB, miners need to burn their coins in order to
participate in the mining process. Burning coins mean that
sending coins to an address without the private key and
thus never usable. Thus, burning coins is an analogous idea
to the investment for building a mining rig. The amount
of burning has a positive correlation with the possibility
of being selected for mining the next block. This is similar
to the PoW system, where the miners increasingly invest
in modern equipment to maintain the hash power, as the
incentive decays with the complexity.
Slimcoin is a crypto-currency which utilises the idea ofPoB in combination with PoW and PoS [164], [165], thus
creating a hybrid consensus mechanism. Algorithmically,
their idea is similar to the chained PoS algorithm of Peercoin
as presented in in Section 5.2.1 with additional PoB mech-
anism sandwiched in between PoW and PoS algorithms.
The PoW is used to generate the initial coin supply using
the mechanism of Bitcoin. When the system has sufficient
amount of money supply, it plans to switch to a hybrid of
PoW and PoS mechanism similar to Peercoin where PoB will
be used to select the miner. As this happens, the minters
will need to burn their accumulated coins in order to be
eligible to participate in the PoS minting process. Since PoB
24
algorithm is mostly used for minter selections, it has hardly
any effect on the security of the system. Hence, its security
and other properties are mostly similar to that of Peercoin.
3) P ROOF OF STAKE -VELOCITY (POSV). One of the major
limitations of coin-age based PoS is that there is no incentive
(or lack of penalty thereof) for the minters to be online to
participate in the staking process. This is because that the
coin-age increases linearly over time, without the need for
the stakeholders to be online and participate in the staking
process. They can, therefore, choose to participate for a short
period and then collect the reward and may go offline again.
The lack of participants may facilitate attacks at a certain
time.
To counteract this problem, a crypto-currency called
Reddcoin proposed a novel hybrid algorithm called Proof of
Stake-Velocity (PoSV) [166], [168]. The central to the PoSV is
the idea of a mechanism called the velocity of stakes coupled
with any traditional PoS algorithm. Conceptually, the velo-
city of stake mirrors the notion of the velocity of money,
a terminology from Economics implying the frequency of
money flow within the society [169]. Indeed, the velocity
of stakes evolves around the idea of increasing the flow of
stakes during the PoS consensus mechanism [167]. This (the
flow of stakes) can be achieved if the minters are encour-
aged to actively participate in the consensus mechanism by
staking their crypto-currency, instead of holding their coins
offline. This process in a way will also increase the overall
security of the system and counteract the lack of participant
issue in PoS.
To facilitate this PoSV introduces a non-linear coin-
ageing function in which the coin-age of a particular coin
is gained much faster in the first few days and weeks than
the gain in later weeks. For example, it has been estimated
that minters who stake their coins every two weeks or less,
can earn up to 20% more than people who do not participate
in the staking process [167]. Such incentives encourage the
minters to increase the velocity of stakes in the whole
network. Note that PoSV is similar to any PoS mechanism
along with its properties and hence, not explored in detail
here.
6.2 N-POS/POW
The consensuses algorithms presented in this category do
not rely any way on either PoW or PoS algorithms. Instead,
they rely on completely novel mechanisms. Therefore, we
call them N-POS/PoW algorithms for the convenience of
group naming.
1) P ROOF -OF-COOPERATION (POC). The Proof-of-
Cooperation is a consensus algorithm introduced by the
FairCoin crypto-currency [170], [171]. This consensus
algorithm relies on several special nodes known as Certified
Validating Nodes (CVNs). CVNs function similar to the way
validators act in a DPoS consensus algorithm as utilised by
EOS or Tron crypto-currencies, as they are nodes which can
create blocks in Faircoin using the PoC consensus algorithm.
However, unlike any DPoS validators, each CVN node is
authenticated by their corresponding Faircoin identifier as
well as trusted following a set of community-based rules
and technical requirements [171]. The community rules
state that a candidate node willing to be a CVN mustparticipate in Faircoin community activities by performing
some tasks. Examples of these tasks are running a local
node or contributing to any technical or management issue
related to Faircoin which must be confirmed by at least two
active members of the community. Besides, the candidate
node must follow a set of technical requirements such
as 24/7 network availability and a special cryptographic
hardware used for signature generation.
With the involvement of CVNs selected in the previously
discussed manner, the core mechanism for PoC consensus
algorithm is briefly discussed next. Blocks in Faircoin are
created in a round-robin fashion in every three minutes
of epoch by one of the CVNs. To create a new block,
a CVN needs to be selected using a deterministic voting
mechanism individually carried out by every single CVN in
the network. The steps of this mechanism are:
Each CVN finds the CVN, which has created a block
furthest in the chain by traversing backwards through
the chain.
Next, it is checked if the found CVN has been active
recently in the network by looking for its signature in
the last few blocks. If so, this CVN will be selected as
the next CVN.
Then, each node creates a data set consisting of the hash
of the last block, the ID of the selected CVN for the next
block, and its own CVN ID, which is then signed by the
specified cryptographic hardware. The created dataset,
along with the signature, is then propagated through
the network.
The selected CVN receives this dataset along with their
signature from multiple CVNs and verifies each signa-
ture. As soon as the selected CVN finds that more than
50% CVNs have selected it to be the next block creator,
it can be certain that its turn is next at the end of the
current epoch, i.e., three minutes.
The selected CVN adds all pending transactions into a
new block, along with all the received signatures, and
propagates the block in the network.
Upon receiving the block, other CVNs verify the block
by checking the if the CVN who created the block is
actually the one selected as the block creator as well
as validating all signatures in it and its transactions. If
the verification is successful, the block is added to the
blockchain and the same mechanism continues.
2) P ROOF OF IMPORTANCE (POI).PoS gives an unfair
advantage to coin hoarders. The more coins they keep in
their accounts, the more they earn. This means the rich get
richer and everyone has an incentive to save coins instead of
spending them. To solve these issues NEM has introduced
a novel consensus mechanism called “Proof of Importance
(PoI)” [172]. It functions similarly to PoS: nodes need to
’vest’ an amount of currency to be eligible for creating blocks
and are selected for creating a block roughly in proportion to
some score. In Proof-of-stake, this ’score’ is one’s total vested
amount, but in PoI, this score includes more variables. All
the nodes that have more than 10000 XEM (the correspond-
ing crypto-currency of XEM) are theoretically given equal
positive importance and with 9B XEM coins there can be
maximum 900Ksuch nodes. However, the actual number
of such nodes and their importance vary with time and their
25
amount of transaction in XEM.
The calculations borrow from the math of network clus-
tering and page ranking. At a high level, the primary inputs
are:
Net transfers: how much has been spent in the past
30 days, with more recent transactions weighted more
heavily.
Vested amount of currency for purposes of creating
blocks.
Cluster nodes: accounts that are part of interlinked
clusters of activity are weighted slightly more heavily
than outliers or hubs (which link clusters but not part
of them).
In NEM, the importance of an account depends only
on the net transfers of XEMs from that account. To be
considered for the importance estimation at a certain block
height, h, a node must have transferred at least 100 XEMs
during the last 30days or 43;200 blocks. The “importance
score” addresses two primary criticisms of proof-of-stake.
One risk is that people hoard many coins as possible
and reap the rewards from block creation. This concentrates
wealth while discouraging transactions. The importance
score means that hoarding will result in a lower score while
spreading XEM around will increase it. Being a merchant
pays better than having a hoard.
6.3 Analysis
In this section, we summarise the properties of different Hy-
brid and N-Pow/PoS algorithms utilising the taxonomies in
Table 21, Table 22, Table 23 and Table 24. Like before, ‘-’
signifies that the corresponding property is not applicable
for the respective consensus algorithm, ‘?’ indicates that the
information the property has not been found, a ‘ ’ is used
to indicate an algorithm satisfies a particular property and
‘X’ is used to imply the reverse (not satisfied).
Table 21 presents the comparison of structural properties
for the corresponding consensus algorithms. Among them,
PoR and PoB depend on a multiple committee formation
with a flat topology and dynamic configuration. Conversely,
PoSV and PoI use an open single committee with a dynamic
configuration, and probabilistic lottery as their underlying
mechanism. PoC has an implicit, open, and dynamic single
committee, which relies on voting mechanism.
All these algorithms have an adversary tolerance of
3f+1with the support of non-repudiation, Sybil protection,
DoS resistance, and high censorship resistance as reported
in Table 22. Entities in PoB, PoSV , and PoI do no require to be
authenticated while PoC entities must be authenticated, and
researchers in PoR need to be authenticated. However, other
entities in PoW can remain non-authenticated, as indicated
with the ‘X’ symbol in the table. All of them except PoC
and PoI have 3f+ 1 adversary tolerance because of their
usage of PoS algorithms. We have not found any regarding
adversary tolerance for PoC and PoI.
Table 23 presents the comparison of some additional
attack vectors for the Hybrid algorithms. As evident from
the table, since these algorithms utilise PoS as one of their
consensus algorithms, they suffer from the similar limita-
tions of any PoS algorithm. For example, none of them has
any guard against most of these additional attack vectors.The only exception is PoB which is because of its use of
Peercoin like functionality, can resist the cartel formation
attack.
The comparison of the performance properties for these
algorithms is presented in Table 24. All of them have 2f+ 1
fault tolerance except PoC and PoI as we have not found
any information fault tolerance for PoC and PoI. In terms of
Scalability, Latency and Energy, every algorithm except PoB
exhibits similar characteristics: they have high throughput,
consume low energy, and have low latency, meaning they
reach finality quickly. Because of its reliance on PoW, PoB
has low scalability, low latency, and also consume meidum
energy. In terms of throughput, PoR, PoSV and PoI have
high throughput, whereas PoC has a low throughput and
PoB has a medium throughput.
Finally, a comparison of the selected Hybrid and N-
PoW/PoS crypto-currencies is presented in Table 25.
7 N ON-INCENTIVISED CONSENSUS
In this section, we present non-incentivised consensus
algorithms that are used in private blockchain systems
well-suited for non-crypto-currency applications. These al-
gorithms are mostly based on classical consensus algorithms
with special features added for their adoption for the corres-
ponding blockchain systems.
One of the major initiatives within the private blockchain
sphere is the Hyperledger project, which is an industry-
wide effort [206]. Founded by the Linux Foundation, it is
a consortium of some of the major tech vendors of the
world. It provides an umbrella to facilitate the development
of different types of open source projects utilising private
blockchains with a specific focus to address issues involving
business and governmental use-cases. Currently, there are
six major projects within Hyperledger: Hyperledger Fabric
[207], Hyperledger Sawtooth [208], Hyperledger Burrow
[209], Hyperledger Iroha [210] and Hyperledger Indy [211].
Each of them is analysed below with a brief introduction.
7.1 Hyperledger Fabric
Hyperledger Fabric is the first major private blockchain
system that originated from the Hyperledger ecosystem
[207]. It has been designed with strong privacy in mind
to ensure that different businesses organisations, including
governmental entities, can take advantage of a blockchain
system in different use-cases. A crucial capability of Fabric
is that it can maintain multiple ledgers within its ecosystem.
This is a useful feature, which separates Fabric from other
blockchain systems consisting of only one ledger in each of
their domains.
A key strength of Fabric is its modular design and
pluggable features. For example, Fabric is not dependant
on a particular format of ledger data, which is useful in
several use-cases. In addition, the consensus mechanism
is fully pluggable. Therefore, different types of consensus
algorithms can be used in different situations.
As part of its consensus process, Fabric utilises a special
entity called Orderer, which is responsible for creating a
new block and extending the ledger by adding the block
in the appropriate order. In addition, there are other entit-
ies known as endorsers. Each endorser is responsible for
26
Table 21: Comparing structural properties of Hybrid and N-POS/POW Consensus Algorithms.
Single committee Multiple committee Consensus
/SystemNode typeType Formation Configuration Topology ConfigurationMechanism
PoR Clients
(Researchers) &
Minters- - - Flat Dynamic Probabilistic lottery
PoB Clients, Miners
& Minters- - - Flat Dynamic Probabilistic lottery
PoSV Clients &
MintersOpen Implicit Dynamic Probabilistic lottery
PoC Clients & CVNs Open Explicit Dynamic - - Voting
PoI Clients &
transaction
partnersOpen Implicit Dynamic - - Probabilistic lottery
Table 22: Comparing security properties of Hybrid and N-POS/POW Consensus Algorithms.
Attack VectorsConsensus Authentication Non-repudiationCensorship
resistanceAdversary tolerance Sybil protection DoS Resistance
PoR X/ High 3f+ 1
PoB X High 3f+ 1
PoSV X High 3f+ 1
PoC High ?
PoI X High ?
Table 23: Comparison of additional attack vectors protection for Hybrid and N-POS/POW Consensus Algorithms
Consensus
/SystemNothing-at-Stake Bribing Long-range Coin-age Pre-computing Cartel formation
PoR X X X X X X
PoB X X X X X
PoSV X X X X X X
Table 24: Comparing performance properties of Consensus Algorithms of Hybrid and N-POS/POW.
Consensus Fault
toleranceThroughput Scalability Latency Energy
PoR 2f+ 1 High Medium Low Low
PoB 2f+ 1 Medium Low Medium Medium
PoSV 2f+ 1 High Medium Low Low
PoC ? LoW (10.6 TPS [173]) Medium Low LoW
PoI ? High Medium Low Low
Table 25: Hybrid & Non-PoW/PoS currencies
Currency Genesis date
(dd.mm.yyyy)Block reward Total supply Consensus Block Time
Gridcoin 24 Mar 2016 Minting 42 Million PoR, PoS 1 minute
Slimcoin May 2014 50-250 coins 133 Million PoB, PoW, PoS 1.5 minutes
Reddcoin January 20,
2014Block reward 2.8 Billion PoSV 1 minute
Faircoin 6th of March,
2014.Block reward 5.3 Million PoC Depends on
Time-weight
Parameter
Burst 11 August 2014 Reduces at a fixed rate
of 5 percent each month204 Million PoC 4 minutes
NEM March 31st,
2015transaction fees only +
node rewards899 Million PoI 1 minute
validating and endorsing a transaction where it checks if
an entity is allowed to perform a certain action in a ledger
encoded within the transaction. Other participating entities
are general users who create transactions. All the entities,
including the Orderer(s) and the endorsers, are registered
and authenticated via a Fabric specific special entity called
Membership Service Provider (MSP). The MSP is responsible
for managing the identities of all participants in the ledger.Using this identity layer, it is possible to create security
policies that dictate which entities can perform what actions
within a specific ledger. A simple flow of a consensus
process in Fabric is illustrated in Figure 13.
The number of Orderer can be increased to distribute the
ordering service. Currently, it supports SOLO and Kafka. A
SOLO ordering service consists of just one single orderer
and hence, cannot provide any type of fault tolerance. That
27
A All required entities are registered in the MSP .
B A channel with a ledger is initiated. In addition, a
policy is created containing the endorsement criteria
as well as other security and privacy criteria.
C A chaincode (smart-contract written either in Java or
Go) is deployed in the ledger.
D When an entity wishes to invoke certain functions in
the chaincode to read data from the ledger or to write
data into the ledger, it submits a transaction proposal
to all the required endorsers as dictated in the policy.
E Each endorser validates the proposal, executes the
chaincode and returns a proposal response consisting
of other ledger data.
F The proposal, its response, and other ledger data are
encoded as a transaction and sent to the Orderer.
G The Orderer creates a block using the transaction and
returns the block to the endorsers.
H Each endorser validates the block and, if validated,
extends the ledger by attaching the new block. This
essentially updates the state of the ledger.
Figure 13: A simple flow of a consensus process in Fabric.
is why it is not recommended to utilise the SOLO model
in the deployed system and has only been provided for
initial testing. On the other hand, the Kafka Orderer utilises
a Kafka cluster for deploying distributed Orderers. Kafka
is a distributed streaming platform with a pub-sub archi-
tecture [212] and is coupled with Zookeeper, a distributed
coordination service [213]. At this point, the Kafka Orderer
is the only recommended setting for achieving consensus
in Fabric. An SBFT (Simplified Byzantine Fault Tolerance)
based consensus algorithm is currently being developed and
is to be released soon.
7.2 Hyperledger Sawtooth
Hyperledger Sawtooth, initially developed by Intel, is a
software framework for creating distributed ledgers suitable
for a variety of use cases [208]. Sawtooth utilises a novel
consensus algorithm called Proof-of-Elapsed-Time (PoET),
which depends on Intel SGX (Software Guard Extension).
Intel SGX is a new type of Trusted Execution Environment
(TEE) integrated into the new generation of Intel processors.
SGX enables the execution of code within a secure enclave
inside the processor, whose validity can be verified using a
remote attestation process supported by the SGX.
PoET, similar to the Nakamoto consensus algorithm in
Bitcoin, relies on the concept of electing a leader in each
round to propose a block to be added in the ledger. The
difference is that the Nakamoto algorithm and its variants
select a leader by a lottery mechanism, which utilises com-
puting power to generate a proof, as described previously.
However, PoET solely relies on the Intel SGX capability to
elect a leader. During each round, every validator node
in the network, requests for a wait time from a trusted
function in the SGX enclave. The validator that is assigned
the shortest waiting time is elected as the leader for that
round. The winning validator then can propose a block,consisting of a series of transactions from the defined trans-
action family. Other validators can utilise a trusted function
supported by SGX to assess whether a trusted function has
assigned the shortest time to the winning validator, and
the winning validator has waited the specified amount of
time. Furthermore, other validators verify the validity of
the block before it is included in the ledger. The inclusion
of the PoET as a consensus algorithm enables Sawtooth to
achieve massive scalability as it does not need to solve a
hard, computationally intensive cryptographic puzzle. In
addition, it allows Sawtooth to be used not only for a
permissioned ledger, but also for a public ledger.
7.3 Hyperledger Burrow
Hyperledger Burrow is a private (permissioned) deploy-
ment of the Ethereum platform [209]. It has been created
and then deposited to the Hyperledger code-base by Monax
Industries Limited [214]. The core component in Burrow
is a permissioned version of the EVM (Ethereum Virtual
Machine) to ensure that only authorised entities can execute
code. Two additional components have been added: Byz-
antine fault-tolerant Tendermint protocol [179], [221] and
the RPC gateway.
The Tendermint consensus falls under the category of
a Byzantine Fault Tolerance (BFT) algorithm, which can
be used to achieve consensus even under the Byzantine
behaviour of a certain number of nodes as presented in
Section 5.2.2.
Burrow depends on several validators, which are known
(authorised) entities with the duty to validate each block
utilising the Tendermint consensus algorithm. This al-
gorithm allows consensus to be achieved in Burrow with
1/3 nodes exhibiting Byzantine behaviour, either acting
maliciously or having been down due to network or system
failure.
Since Burrow utilises the EVM, a wide-range of smart-
contracts and DApps (Decentralised Applications) could
be deployed. Using the Tendermint algorithm with a set
of known validators allows Burrow to scale at a much
faster rate than Ethereum while preserving the privacy of
transactions by allowing only known entities to participate
in the network.
7.4 Hyperledger Iroha
Hyperledger Iroha is a private blockchain system initially
developed by Soramitsu, Hitachi, NTT Data, and Colu
and is currently hosted by Linux foundation under the
Hyperledger Project [210], [215]. Iroha aims to create a
simple blockchain infrastructure which can be incorporated
into any system which requires a blockchain architecture
underneath to function. The major emphasis while design-
ing Iroha is on a simpler construction with a strong focus
on mobile-friendly application development using a novel
consensus mechanism called YAC (Yet Another Consensus)
[215], [216]. One fundamental different of Iroha from other
Hyperledger project is its fine-grained permission control
mechanism which allows defining permissions for all relev-
ant commands, queries, and even joining in the network.
The core architecture consists of several components
[216], [219]. A brief description of its major components is
presented below:
28
Troii represents the entry point of any application to
the Iroha network. It utilises gRPC (gRPC Remote Pro-
cedure Calls [218]), an open source RPC framework,
to interact with different peers and entities within the
blockchain network.
Model represents how different entities are represented
within the system and defines the mechanism to inter-
act with them.
Network provides the network functionalities required
to maintain the P2P network and to propagate transac-
tions in the network.
Consensus facilitates the functionalities related to
achieving consensus in the network using the YAC
consensus protocol, a practical byzantine fault-tolerant
algorithm (discussed below).
Simulator provides a mechanism to simulate the effects
of transactions on the chain by creating a temporary
snapshot of the chain state.
Validator allows the validation of transactions by veri-
fying its formats and signature along with the veri-
fication of business rules and policies involved in the
transactions. There are two types of validations in Iroha:
–Stateless validation checks for transaction formats and
signature.
–Stateful validation checks the business rules and
policies, e.g., if a certain action is allowed by an entity.
Synchroniser is a part of the consensus component and
is responsible for synchronising the chain to a new or
disconnected node.
Ametsuchi is the storage component of Iroha and is
used to store the blocks and the chain state known as
World State View (WSV).
These components are used by three core entities within
the architecture [216]:
Clients are applications that they can query data from
the allowed Iroha chain as well as can perform certain
actions, called commands, by which the state of the
chain is updated. For each of these, clients need to
interact with the peer.
Peers are nodes that have the following two functional-
ities:
–To maintain a copy of the ledger. Applications can
thus interact with a peer to query a chain or to submit
transactions to update the chain.
–To participate in the consensus process by maintain-
ing its address, identity and trust as a single entity in
the network.
Ordering service node(s): Like Fabric, ordering service
nodes are responsible for ordering transactions and
creating a proposal of a block.
With these components and entities, a flow of transac-
tions in Iroha is briefly presented in Figure 14 [216].
7.5 Hyperledger Indy
Hyperledger Indy is a private blockchain system purpose-
fully built for providing an ecosystem for blockchain-based
self-sovereign identity [211], [222]. The concept of Self-
Sovereign Identity has been initially promoted by the Sovrin
foundation [223], a non-profit international entity consistingA A client prepares and sends a transaction to a peer
using Troii.
B The peer performs stateless validation to the trans-
action and forwards the transaction to the ordering
service using an ordering gate.
C The ordering service combines and orders transac-
tions from different peers in a transaction proposal
which is then broadcast to the peers.
D Each peer performs a stateful validation of the pro-
posal using the simulator and creates a block con-
sisting of only verified transactions. Each peer signs
the block, generates a hash of the proposed block
and finally, creates a tuple containing the hash and
the signature. Such a tuple is called a vote. The block
and the vote are then internally sent to the consensus
gate to initiate the YAC mechanism.
E The YAC mechanism in each peer prepares an
ordered list of voting peers utilising the hashes
created in the previous step. The first peer in the
list is regarded as the leader and is responsible for
aggregating votes from other voting peers.
F After aggregating all votes from the voting peers, the
leader computes the supermajority (usually 2/3rd)
of votes for a certain hash (signifying a block).
G Once a supermajority for a proposed block is
achieved, the leader propagates a commit message
for this particular block to all voting peers.
H Each voting peer verifies the commit message and
adds the block to the blockchain.
Figure 14: A flow of transactions in Iroha .
of several private organisations to promote the notion of
Self-sovereign Identity. The Indy project is closely associated
with the Sovrin foundation focusing on materialising this
notion of a self-sovereign identity system as a public identity
utility.
Currently, Indy consists of the following two major com-
ponents:
Indy-plenum : Plenum is the underlying distributed
ledger (blockchain) construct of the Indy platform. Like
any distributed ledger, the Plenum ledger is fundament-
ally an ordered log of transactions. In addition, it consists
of several nodes, among which a single or a few chosen
ones act as the leader responsible for ordering the trans-
actions. The nodes execute a consensus protocol which
utilises a three-phase commit to reach agreement among
themselves regarding the order of the transactions.
Indy-SDK : This provides the required software APIs and
tools to enable other software to interact with the Plenum
ledger. It hides all the intricate internals from the users of
the platform so that the platform can be utilised without
even knowing the complexities of the ledger and its asso-
ciated consensus protocol.
The consensus protocol utilised in Indy is called RBFT
(Redundant Byzantine Fault Tolerance) [224]. Like any other
byzantine fault tolerance protocol, it relies on 3f+ 1 nodes
(a participant in the consensus protocol) in order to handle
fbyzantine nodes [224], [225]. For example, it requires
29
four deployed nodes in order to handle a single byzantine
node. Each participating node in RBFT deploys two (or
more) protocol instances, aptly called Master and Backup
protocol instance, each of which is executed in parallel. A
separate primary node (also called a leader) is selected from
the master and the backup protocol instance. The leader is
responsible for ordering the transactions. Its performances,
i.e., latency and throughput, are periodically observed by
the other instances. If its performance degrades, a different
leader is selected from the backup instance.
Indy maintains a number of ledgers for different pur-
poses, unlike many other blockchain systems which employ
a solo ledger. For example, separate ledgers are maintained
for node maintenance, for identity transactions and so on.
Clients (users via their appropriate software interfaces) can
interact with these ledgers via different nodes for updating
the ledger via transactions and for reading from the ledger
via queries. A fine-grained permission mechanism can be
used to dictate which client has to write permissions, how-
ever, any client can read from the ledger.
Once a node receives a transaction from a client, it per-
forms some validation and then broadcasts the transaction
to other nodes in the network. When the transaction reaches
enough nodes, the primary node starts a new consensus
round using a three-phase commit mechanism. In the end,
all nodes agree to the order proposed by the primary node
and add the transaction into the corresponding ledger.
7.6 Analysis
In this section, we analyse the non-incentive consensus pro-
tocols against the criteria selected before. Block and reward
properties are not considered as they are not relevant for
non-incentivised consensus protocols. We use the notation
‘’ to indicate an algorithm satisfies a particular property
and the notation ‘-’ to indicate that there is no informa-
tion regarding that specific property. For other properties,
explanatory texts are added.
STRUCTURAL PROPERTIES .Tn Table 26, we present the
comparison of structural properties among the non-
incentivised consensus algorithms discussed in this section.
As evident from the table, different algorithms use different
types of nodes, and all algorithms are based on single com-
mittee with closed committee type and explicit committee
formation. Only YAC relies on a dynamic configuration
which utilises the reputations of the nodes from previous
interactions; all others have the static configuration.
Voting is the predominantly used underlying mechan-
ism, which is utilised by Tendermint Burrow, YAC, and
RBFT, whereas PoET relies on a lottery mechanism. Fabric
currently utilises the ordering services by the orderer. In
the future, it might utilise SBFT, which leverages the voting
mechanism.
SECURITY PROPERTIES .The comparison of security prop-
erties among the non-incentivised consensus algorithms
is presented in Table 27. All algorithms support non-
repudiation via digital signature and have a significantly
low censorship resistance. This is because the identities of
all participating nodes are known. In case any node starts
misbehaving, because of an attacker taking control of thatnode, it can be easily identified, and proper actions can be
taken. The same logic applies for the Sybil protection and
towards DoS resistance. Being mostly based BFT algorithms,
all algorithms, except PoET, have 3f+ 1 adversarial tol-
erance. It has been found that PoET has an adversarial
tolerance of
log log n
log n
[220], where nis the number of
nodes.
PERFORMANCE PROPERTIES .The comparison of perform-
ance properties among the non-incentivised consensus al-
gorithms is presented in Table 28. All algorithms can
provide a good throughput and do not require to consume
any significant amount of energy. PoET, utilising a lottery
mechanism, can be scaled with a large number of valid-
ators, however, this will increase the latency (finality) of
transactions [217]. All other algorithms employing a voting
mechanism cannot be scaled with a large number of val-
idators, providing low latency for the transactions. Fabric,
YAC, and RBFT provide a 2f+ 1 fault tolerance, whereas
the information regarding the fault tolerance for PoET and
Tendermint Burrow is not specified formally.
8 D ISCUSSION
As per our analysis in different sections, it is clear that PoW
consensus algorithms have major limitations, specifically in
terms of power consumption and scalability. Many regard
PoS, and it is variant DPoS, to be the most suitable altern-
atives. To understand the applications of these algorithms
in public blockchain systems, we have analysed the top 100
crypto-currencies, as reported on CoinMarketCap4as of 18
July, 2019.
In the first analysis, we have calculated the number
of consensus algorithms used in these (top 100) crypto-
currencies. The distribution of consensus algorithms is
presented in Figure 15 . As per our analysis, PoW is still
the most widely used ( 57% ) consensus algorithms to date,
whereas DPoS is the second most with 11% , and PoS is the
third most with 6%used consensus algorithms. All other
consensus algorithms represent the remaining 26% . This
means that, even though many consider that PoS and DPoS
are the best alternatives to PoW, their adoption is still far
behind PoW.
To investigate it further, we have analysed a year-
wise distribution of the genesis dates of different crypto-
currencies. It is to understand if there is any inclination
towards an alternative consensus algorithm over PoW in
recent years. The distribution is illustrated in Figure 16,
which represents a surprising observation: PoW is still the
most widely used algorithms for crypto-currencies which
have been created in recent years. For example, the numbers
of crypto-currencies created with PoW algorithms in last
three years (2017, 2018 & 2019) are 11,19and4respectively,
in comparison to 4,2and2for PoS and DPoS combinedly.
This implies that PoW is still the most popular consensus
algorithm among the crypto-currency community. A deeper
investigation reveals another insight though. The top 100 list
retrieved from Coinmarketcap also contains crypto-tokens
generated on top of any smart-contract platform such as
Ethereum, EOS and Tron with majority tokens are built on
4. https://coinmarketcap.com/
30
Table 26: Comparing structural properties of Consensus Algorithms of Hyperledger Systems.
Single committee Consensus
/SystemMechanism Node typeType Formation Configuration
Fabric Ordering/Voting (SBFT) Client (Regular peer), En-
dorsing peer & OrdererClose Explicit Static
PoET Lottery Client, Transaction
processor & ValidatorClose Explicit Static
Tendermint Burrow Voting As Tendermint (3) Close Explicit Static
YAC Voting Client, Peer & Ordering Ser-
vice nodeClose Explicit Dynamic
RBFT Voting Client & Node Close Explicit Static
Table 27: Comparing security properties of Consensus Algorithms of Hyperledger Systems.
Attack VectorsConsensus Non-repudiation Censorship resistanceAdversary tolerance Sybil protection DoS Resistance
Fabric Low 3f+ 1
PoET Low
log log n
log n
Tendermint Burrow Low 3f+ 1
YAC Low 3f+ 1
RBFT Low 3f+ 1
Table 28: Comparing performance properties of Consensus Algorithms of Hyperledger Systems.
Consensus Fault tolerance Throughput Scalability Latency Energy
Fabric 2f+ 1 Good Medium Low Low
PoET - Good Good Medium Low
Tendermint Burrow - Good Medium Low Low
YAC 2f+ 1 Good Medium Low Low
RBFT 2f+ 1 Good Medium Low Low
top of Ethereum. Most of these tokens have emerged after
2016 with Ethereum utilising PoW . This could be the reason
why the most recent crypto-currencies have been found to
utilise PoW.
Another indication of PoW domination over other al-
gorithms is the market-cap distribution of their correspond-
ing crypto-currencies. The distribution is presented in Table
29 and illustrated in Figure 17. Not surprisingly, PoW cur-
rencies with a market-cap of around 221 Billion USD have
a massive 93% dominance over other currencies. DPoS and
PoS currencies are the nearest rivals with a market-cap of
around 6Billion USD and dominance of only 3%for each
group.
Table 29: Market capitalisation of major consensus al-
gorithms in top 100 Crypto-currencies
Consensus Algorithms Market-cap (USD)
PoW 221;238;526;412
DPoS 6;483;606;020
PoS 6;287;224;485
PoW+PoS 2;436;683;929
Proof of Authority 572;188;935
Proof of Activity 274;066;240
From our investigation, it is clearly evident that PoW
algorithm, even with its major limitations, is still the most
popular consensus algorithm to be utilised in different
crypto-currencies. Currencies which utilise PoW algorithms
consume a significant amount of energy as illustrated in
Section 5.1.4. Besides, they have a reduced throughput (in
terms of transaction number) compared to PoS and DPoScurrencies. For example, the reported TPS (Transactions Per
Second) for Bitcoin and Ethereum are 7and15 25, respect-
ively [226], while DPoS currencies EOS has a reported and
estimated TPS of 50 and 4000 respectively [226] and Tron
has a claimed TPS of 2000 [227]. Clearly, DPoS currencies
have better performance, at least in terms of TPS, over
any PoW currency. Therefore, one might ask the underlying
reason behind this counter-intuitive trend of PoW being the
most popular consensus algorithm. We have identified a few
reasons behind this as presented below:
Bitcoin is the most dominant crypto-currency in terms
of market-cap. As of 18 July, it has a market cap of
around 171Billion USD. In addition to this, its different
forked variants (Bitcoin Cash5and Bitcoin Satoshi
Vision6) also have a combined market-cap of 8Billion
USD. If we exclude Bitcoin and its variants, we have
a slightly different distribution of market-cap, as illus-
trated in Figure 18. Here, the market-cap percentage of
PoW algorithm is reduced from 93% to71% percent,
which is still significant in comparison to DPoS and
PoS, its nearest rivals.
PoW has the first-mover advantage because of Bit-
coin and Ethereum, both being the pioneer in their
respective domain. Bitcoin has been the first successful
crypto-currency, while Ethereum is the first blockchain-
based smart-contract platform. Other crypto-currencies,
being motivated by their success, might have adopted
the approach of utilising PoW as their corresponding
consensus algorithm.
5. https://www.bitcoincash.org/
6. https://bitcoinsv.io/
31
PoW58%DPoS9%PoS8%PoW+PoS4%Proof of Activity (PoA)1%Delegated Byzentine Fault Tollerance (dBFT)3%Practical Byzentine Fault Tollerance (PBFT)2%Loop Fault Tolerance (LFT)1%XRP Ledger Protocol1%Stellar Consensus Protocol (SCP)1%Tangle1%VBFT (verifiable random function)1%Proof of authority2%Proof-of-Service (PoS)1%Other1%POI1%Proof of retrivility1%BFT3%Proof-of-Believability (PoB)1%Other10%
PoWDPoSPoSPoW+PoSProof of Activity (PoA)Delegated Byzentine Fault Tollerance (dBFT)Practical Byzentine Fault Tollerance (PBFT)Loop Fault Tolerance (LFT) XRP Ledger ProtocolStellar Consensus Protocol (SCP)TangleVBFT (verifiable random function)Proof of authorityProof-of-Service (PoS)OtherPOIProof of retrivilityBFTProof-of-Believability (PoB)
Figure 15: Consensus algorithms in Top 100 Crypto-currencies
Another strong argument in favour of PoW is its un-
derlying security. The number of miners is far greater
in Bitcoin than the number of validators in PoS and
DPoS. This implies a better decentralisation in Bitcoin
than PoS or DPoS. For example, EOS has only 21 valid-
ators, while Tron has 27 validators. The probability of
collusion among these validators is far greater than that
of any popular PoW currency. For this reason, many in
the blockchain community have been doubtful of the
security of any PoS/DPoS currency. However, there is
a counter argument against this. Because of the mining
centralisation issue ( highlighted in Section 5.1.4), many
point out that a PoW algorithm might also be prone
to centralisation. Therefore, a PoW currency might also
suffer from collusion attack.
With the dominance of PoW over other consensus al-
gorithms, one might wonder what lies ahead and might ask
if there will be any shift of balance among the consensus
algorithms. We believe that we will most definitely experi-
ment with a shifting of balance in the near future. In this
regard, the PoS transformation process of Ethereum will
be a crucial factor. The proposed Ethereum PoS consensus
mechanisms, both CFFG and CTFG, are highly regarded
by the academics and industrial enthusiasts for their strong
guarantee of security. With their strong focus on economic
incentive and game-theoretic based approach, it is believed
that their security will be as close as PoW and much better
than any current PoS/DPoS algorithm can provide. In par-
ticular, the number of validators will be much higher than
any number leveraged in the current PoS/DPoS algorithms.However, it is yet to be seen how they will perform once
deployed in real-life settings.
The existence of numerous algorithms and wide vari-
ations in their properties impose a major challenge to com-
prehend them properly. In particular, it is often difficult to
test the suitability of a particular algorithm under certain
criteria. A visual tool would be a great help in this regard.
Towards this aim, we present a decision tree in Figure 19,
which can be used to determine the suitable consensus
algorithms under certain criteria in different scenarios. For
example, such a decision tree diagram can be leveraged
to select a particular consensus algorithm while design-
ing/developing a new blockchain system.
The tree utilises five critical criteria to achieve its goal:
incentives, energy consumption, scalability, security (with
respect to adversary tolerance), and ASIC-resistance. If the
system needs to incentivise the miner/validating nodes,
then proof-of-work(PoW) and proof-of-stake (PoS) con-
sensus are appropriate choices. Because of their underlying
incentives mechanisms, the primary applications of these
consensus algorithms are public crypto-currencies. On the
other hand, a private blockchain network usually does not
rely on any crypto-currencies to motivate or incentivise
any validators to run the blockchain network. In addition
to incentives, energy consumption is another determining
factor in choosing appropriate consensus algorithms. PoW-
type algorithms consume high energy, whereas PoS al-
gorithms and their derivatives consume a moderate amount
of energy. PoW-types algorithms are very slow as of now
and can process only a limited number of transactions.
32
PoWDPoSdBFTPoW + PoS02468101214161820
20092010201120122013201420152016201720182019PoWPoSDPoSProof of ActivitydBFTProof of AuthorityPoW + PoS
Figure 16: Year-wise distribution of consensus algorithms in Top 100 Crypto-currencies
93%3%1%0%0%0%3%3%PoWDPoSPoW+PoSProof of ActivitydBFTProof of AuthorityPoS
Figure 17: Percentage of market capitalisation of consensus algorithms in top 100 Crypto-currencies
However, compromising a popular PoW-based blockchain
network is very difficult, and therefore, they are more secure
than their counterparts. PoW-based algorithms can also be
classified based on computational complexity. As discussed
earlier, ASIC is a specialised hardware, designed and used
to solve hash-based computational problems. ASIC is ex-
pensive and hinders common people from participating
in the blockchain network. Therefore, memory-based PoW
has been designed. Now it is widely used in different
crypto-currencies. Non-incentivised consensus algorithms
are mostly used in private blockchain systems. They con-
sume a very low amount of energy compared to other types
of consensus algorithms and are also very scalable. That
means the miners can verify the transactions and create
blocks really fast. However, a comparatively low number ofvalidating nodes makes these algorithms more vulnerable
to attacks.
For clarity, we provide a few examples to utilise the
decision tree diagram presented in Figure 19. If an incentiv-
ised algorithm is required for a highly scalable blockchain
system that aims to consume low energy DPoS and BFT
derivatives such as Tendermint, CTFG, and Ouroboros are
the preferred options. However, they will have moderate
security as described earlier. On the other hand, if security
is of the highest priority, PoW algorithms are more suitable.
In this scenario, there are two options: memory-bound or
CPU bound. If ASIC resistance is desired, one should opt
for memory-bound PoW algorithms. However, in such a
case, one has to sacrifice scalability, and such algorithms
will consume high energy.
33
71%11%4%0%2%1%11%14%PoWDPoSPoW+PoSProof of ActivitydBFTProof of AuthorityPoS
Figure 18: Percentage of market capitalisation excluding Bitcoin and its variants
Note that this is just an example of how such a diagram
can be developed using our selected four criteria. Other
criteria can be utilised to generate a different diagram which
might be suitable for other specific scenarios. Whenever
such a diagram is to be developed, the tables (Table 13, Table
14, Table 15, Table 17, Table 18, Table 19, Table 20, Table 21,
Table 22, Table 23, Table 24, Table 26, Table 27 and Table 28)
utilised to compare different consensus algorithms against
the defined properties in the taxonomy will be crucial as
the these tables will provide the required template by which
such a diagram can be created.
9 C ONCLUSION
With the popularisation of crypto-currencies, and block-
chain in general, there has been a renewed interest in the
practical implications of different distributed consensus al-
gorithms. Most of the existing systems struggle to properly
satisfy the need for any wide-scale real-life deployment as
they have serious limitations. Many of these limitations are
due to the underlying consensus algorithm used in a partic-
ular system. Therefore, in the quest to create more suitable
practical blockchain systems, the principal focus has been
on distributed consensus. This has led to the explorations;
either existing consensus algorithms have been exploited
or novel consensus mechanisms have been introduced. The
ultimate consequence of this phenomenon is a wide-range
of consensus algorithms currently in existence. To advance
the knowledge of this domain, it is essential to synthesise
these consensus algorithms under a systematic study, which
is the main motivation of this article.
Even though there have been several similar works, this
is the first paper to introduce a taxonomy of properties
desirable for a consensus algorithm and then utilise that
taxonomy to analyse each algorithm in a detailed fash-
ion. In addition, different consensus algorithms have been
grouped into two major categories: Incentivised and Non-
incentivised consensus algorithms. An incentivised con-
sensus algorithm, exclusively utilised by public blockchain
systems and crypto-currencies, relies on incentives for the
participants in order to motivate them to behave as inten-
ded. On the other hand, in any non-incentivised algorithm,the participants are considered as trusted, and hence, it is
assumed that no incentives are required to ensure intended
behaviour. As such, these algorithms are mostly used in the
private blockchain sphere. We have again grouped incentiv-
ised algorithms into three major sub-categories: PoW (Proof
of Work), Proof of Stake (PoS) and consensus algorithms
beyond PoW and PoS.
A PoW algorithm relies on computational complexit-
ies or memory size/performance to solve a cryptographic
puzzle. There are three major approaches followed by PoW
consensus algorithms: i) a compute-bound PoW leveraging
the capabilities of the processing unit, ii) a memory-bound
PoW which is more reliant on the size and performance of
the main memory, and iii) a chained PoW utilises a num-
ber of hashing algorithms executed consecutively one after
another. Blockchain systems utilising such a mechanism has
special nodes, called miner nodes, who are responsible for
solving this puzzle and creating a new and valid block
and extending the chain by appending this block in the
existing chain. The probability to solve this puzzle depends
on a network parameter, called difficulty, which is adjusted
automatically after a certain period of time. As more miners
participate in the network, the network parameters are
adjusted in such a manner that requires more computa-
tional power to mine a new block. As the corresponding
systems become more popular, it attracts more miners,
which increases the security of the system. However, the
increased computational power results in more energy being
consumed. Apart from this, PoW systems generally have a
low throughput and do not scale properly. PoS algorithms
and their corresponding mechanisms have been analysed in
greater detail in Section 5.1.
To alleviate the major issues of PoW, Proof of Stake (PoS)
has been proposed. In PoS, the nodes who would like to
participate in the block creating process are called minters,
and they need to own and lock a certain amount of the
corresponding crypto-currency, called stake. Such a stake is
used to ensure that the minters will act as required since
they will lose their stakes when acting maliciously. PoS has
several variants: Chained PoS, BFT PoS and DPoS. The core
idea of a chained PoS is to leverage a combination of PoW
and PoS algorithms chained together to achieve consensus.
34
Consensus
algorithm
Yes
High
LowNo
LowLow
HighMedium
Medium
High
Yes NoMediumIncentivised
Energy
Scalable
Security
AISC
ResistantRBFT
SBFT
YAC
PoET
Burrow
Chained
PoW
Memory Bound PoW (e.g, Cryptonight, Scrypt, and
NeoScrypt consensus)
Chained PoW (e.g, X11/X13/X15, Quark, and Lyra2RE
consensus)CPU -Bound PoW (e.g, Nakamoto consensus)DPoS
Tendermint
CTFG
OuroborosLow
PoR
PoSV
PoC
PoIMedium
MediumLow
Medium
Figure 19: Decision tree to choose appropriate consensus algorithms
BFT PoS uses a multi-round PoS mechanism in which a
validator (minter) is selected, from a set of validators, by
the agreement of super-majority quorum among other val-
idators. On the other hand, DPoS selects a minter, from a
set of minters, using votes from other clients of the network.
PoS algorithms are generally fast and scalable, having high
throughput. However, they also need to consider several
other attack vectors such as Nothing-at-stake, bribing, long-
range attack, cartel formation, and so on. Detailed analysis
of different aspects of PoS algorithms has been presented in
Section 5.2.
There are also some Hybrid consensus algorithms that
combine the mechanisms of PoW and/pr PoS with another
novel algorithm. Proof of Research, Proof of Burn, Proof of
Stake-Velocity are examples of such an algorithm. Again,
there are mechanisms that are novel and have no reliance
on PoW/PoS whatsoever. Proof of Cooperation and Proof
of Importance are examples of such novel algorithms. The
discussion and analysis of these consensus algorithms have
been presented in Section 6.
Finally, there are also a few non-incentivised consensusalgorithms which are exclusively utilised in private block-
chain systems. Hyperledger is the leading private block-
chain foundation under which different private blockchain
systems such as Hyperledger Fabric, Hyperledger Sawtooth,
Hyperledger Burrow, Hyperledger Iroha, Hyperledger Indy,
and so on. These systems rely on different other consensus
mechanisms such as SBFT, PoET, Tendermint Burrow, YAC,
and RBFT. Key characteristics of these consensus algorithms
are high throughput and low latency with acceptable scalab-
ility. Also, the algorithms require that every entity that
participates in the network must be properly authenticated.
A detailed analysis of these algorithms has been presented
in Section 7.
Our analysis in Section 8 suggests that PoW, with its
many disadvantages, still is the most dominant in terms of
market capitalisation (indicating its adoption) and crypto-
currency in the world. As discussed earlier, DPoS and PoS
algorithms, PoW’s closest rivals, aim to tackle many of
PoW’s limitations. However, their adoption is still limited.
In addition to this analysis, we have presented an exemplary
35
decision tree-based figure which can be utilised to filter out
or select consensus algorithms that fit certain criteria. Such
a figure will be a useful tool for any who would like to test
the suitability of a certain consensus algorithm under certain
criteria.
There is one issue that must be highlighted before we
conclude this article. The principal focus of this article has
been to explore and synthesise the consensus algorithms
available in different blockchain systems. However, there
are other distributed ledger systems, which do not rely on
any blockchain-type structure. Instead, they utilise other
structures to represent their respective ledgers. Examples
of two such prominent crypto-currencies are IoTA7and
NANO8. Both of their ledgers are based on DAG (Directed
Acyclic Graph), a specific type of directed graph with no
cycle. However, IoTA uses a novel consensus algorithm
called Tangle [229] while NANO utilises a representative
based consensus mechanism [228]. These two systems have
received significant attention because of their fee-less struc-
ture and fast transaction rates. However, we do not consider
these systems any further as they are out of scope for this
article. We plan to investigate such novel systems in the
future in a different exploration.
There is high anticipation among the blockchain enthu-
siasts that blockchain technology will disrupt many existing
application domains. However, to unlock its true potential, a
blockchain system must adopt a suitable consensus that can
enable it to satisfy its intended properties. This is because
a consensus algorithm is the core component of any block-
chain system, and it dictates how a system behaves and the
performance it can achieve. However, as our analysis in this
article suggests, an ideal consensus algorithm is still elusive
as almost all algorithms have significant disadvantages in
one way or another with respect to their security and
performance. Until a consensus algorithm finds the correct
balance between these crucial factors, we might not see the
wide-scale adoption as many crypto-currency enthusiasts
are hoping.
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Accessed on July 27, 2019.
|
{
"id": "2001.07091"
}
|
2012.14481
|
A Survey on Vulnerabilities of Ethereum Smart Contracts
|
Smart contract (SC) is an extension of BlockChain technology. Ethereum
BlockChain was the first to incorporate SC and thus started a new era of
crypto-currencies and electronic transactions. Solidity helps to program the
SCs. Still, soon after Solidity's emergence in 2014, Solidity-based SCs
suffered many attacks that deprived the SC account holders of their precious
funds. The main reason for these attacks was the presence of vulnerabilities in
SC. This paper discusses SC vulnerabilities and classifies them according to
the domain knowledge of the faulty operations. This classification is a source
of reminding developers and software engineers that for SC's safety, each SC
requires proper testing with effective tools to catch those classes'
vulnerabilities.
|
http://arxiv.org/pdf/2012.14481v1
|
Zulfiqar Ali Khan, Akbar Siami Namin
|
cs.CR
|
cs.CR
|
1
A Survey on Vulnerabilities of Ethereum Smart
Contracts
Zulfiqar Ali Khan and Akbar Siami Namin
Department of Computer Science
Texas Tech University
zulfi.khan, akbar.namin@ttu.edu
Abstract
Smart contract (SC) is an extension of BlockChain technology. Ethereum BlockChain was the first to incorporate SC and thus
started a new era of crypto-currencies and electronic transactions. Solidity helps to program the SCs. Still, soon after Solidity’s
emergence in 2014, Solidity-based SCs suffered many attacks that deprived the SC account holders of their precious funds. The
main reason for these attacks was the presence of vulnerabilities in SC. This paper discusses SC vulnerabilities and classifies them
according to the domain knowledge of the faulty operations. This classification is a source of reminding developers and software
engineers that for SC’s safety, each SC requires proper testing with effective tools to catch those classes’ vulnerabilities.
Index Terms
Smart Contract, Ethereum, EVM, vulnerabilities, Solidity, tools
I. I NTRODUCTION
BlockChain is the most significant development to promote crypto-currencies. There are variations of BlockChain. Ethereum
BlockChain, also known as Ethereum Virtual Machine (EVM) allows various unknown individuals to join hands and work
together under a digital agreement known as a SC. Contracts require rules, but in this case, a programming language called
Solidity embeds the rules within the SC itself. SC does not contain any ”main” method, so it’s not self-executable.
SC is deterministic. This constraint generates the same output when any node of the Ethereum network executes the SC.
Nodes can be users, or nodes can be miners responsible for validating SCs by solving a mathematical puzzle. The mathematical
puzzle should generate the same result on all nodes, and this validation process follows the consensus protocol.
Once the mining process completes, its owner uploads the SC on the BlockChain. If the contract does not fit according to
Ethereum rules, miners discard it. This process might follow the re-submission of SC. Thus the consensus protocol becomes
a method for developing trust among the parties.
The trust is that there exists no error or fraud. But once the owner uploads the SC, it becomes immutable. Thus an unsafe
SC can shatter the trust. Hackers can misuse it, causing considerable losses to the SC account holders. Therefore it is necessary
to identify the vulnerabilities of SC before one uploads the SC on the BlockChain.
This survey research paper focuses on 20 vulnerability patterns. We have also provided the Solidity code corresponding
to each vulnerability. We have used the context of SC’s faulty operation for the classification of vulnerabilities. For brevity
reasons, we have skipped the mitigation techniques.
II. M OTIVATION
Previous research work has created several taxonomies related to vulnerabilities of SCs. The typical approach is to categorize
the vulnerabilities based upon EVM, BlockChain, and Solidity associated issues as discussed in [1]. The above three are broader
classes and give less information to the readers about the code’s internal drawback.
Similarly, the survey in [2] classifies the SC vulnerabilities in the context of EVM and Solidity. Other significant survey
grouped the vulnerabilities based upon layering [3] like application layer, data layer, and consensus layer. Furthermore, the
work in [4] used NIST Bugs Framework for the classification. However, SC introduced new kinds of vulnerabilities [3] not
common in traditional programming paradigms.
We are the first to classify SC’s security issues according to the vulnerable operation’s domain knowledge. A good
understanding of vulnerabilities requires coding examples along with description. Work in [2], [3] also provided coding
examples, but we address a different set of vulnerabilities. It is worth mentioning that the most relevant survey on the SC
attacks with vulnerable SC code is available in [5], published in 2017. Since then, the Solidity programming language has
undergone major changes. In this survey paper, we replicate vulnerabilities in Solidity code using the “solc” compiler version
5.0. The key contributions of this survey paper are:
– A classification of the SC vulnerabilities according to the domain knowledge.
– A description of SC vulnerabilities through updated sample code.
– A discussion of twenty SC vulnerabilities along with twenty-four SC vulnerability detection tools.
– A discussion on the regeneration of deprecated Var vulnerability (section IV-D2).arXiv:2012.14481v1 [cs.CR] 28 Dec 2020
2
Fig. 1. Classification of Ethereum SC vulnerabilities into seven categories.
The remainder of this paper is organized as follows: section III presents our classification of SC’s vulnerabilities. Section
IV presents the description of SC vulnerabilities with respect to domain knowledge along with some sample code. Section V
lists the testing tools developed for SCs. Section VI concludes the paper and highlights the future research directions.
III. A C LASSIFICATION OF SC’ SVULNERABILITIES BASED ON DOMAIN KNOWLEDGE
SCs are susceptible to coding threats. One research classifies the vulnerabilities into security, functional, operational, and
developmental categories [6]. These categories do not necessarily reflect any knowledge specific to SC.
Our research classifies the vulnerabilities using the domain knowledge of operations performed by vulnerable SC. For
instance, SCs communicate with each other by invoking methods. This inter-SC communication operation can pave the way
for several coding irregularities like reentrancy, denial of service, mishandled exception, and so on. In the same way, other
SC specific operational knowledge helped us to create vulnerability categories like Contractual, Arithmetic, Gas Related,
Transactional, Randomization and Deprecated vulnerabilities. The domain knowledge-based classification provides (i) more
knowledge about the cause of vulnerability (as compared to previous taxonomies discussed above) and (ii) we can drill down
to the specific instructions (in some cases). For instance, in case of inter-contractual vulnerabilities, we can infer that call
is behind this vulnerability. Due to space limits, we have excluded randomization and deprecated vulnerabilities from the
discussion.
Figure 1 depicts our classifications of SC’s vulnerabilities. As shown in Figure 1, the SC’s vulnerabilities are grouped
into 1) inter-contractual, 2) contractual, 3) integer bugs, 4) gas related, 5) transnational, 6) deprecated, and 7) randomization
vulnerabilities.
IV. D ESCRIPTIONS OF SC V ULNERABILITIES
A. Inter-Contractual Vulnerabilities
Listing 11shows an example of inter-contractual communication between ModifiedBank andModifiedMalicious
SCs using call modified from [7] in the context of a bank and an attacker. call can also generate reentrancy attack. Solidity
provides send ,transfer , and call functions for contract-wide Ether transfers along with an external payable fallback
function (FF) at the receiving SC. FF is an anonymous function, which retrieves the transferred Ether in the global state
variable msg.value . Transferring Ether incurs gas charges. send andtransfer can compensate for 2300 [1] amount of
gas which is sufficient only for executing FF not linked to state changes. call can transfer entire gas, which can initiate
reentrancy attack, as discussed below:
1pragma solidity ˆ0.5.1;
2contract ModifiedMalicous{
3 ModifiedBank mb;
4 constructor (address payable addressOfBank, uint amount) public {
1This paper lists the EVM opcodes in capital letters and references the Solidity functions and contents of Listings in this font .
3
Fig. 2. The reentrancy cycle, (Solidity-like) code modified from [7], [12].
5 mb=ModifiedBank(addressOfBank);
6 mb.withdraw(amount);
7 }
8 function ()external payable { mb.withdraw( msg.value );}
9}
10contract ModifiedBank{
11 mapping (address =>uint )bal;
12 //more
13 function withdraw( uint _amount) public returns (bytes memory message) {
14 if(bal[ msg.sender ]>= _amount){
15 (bool success, bytes memory returnsMessage) = msg.sender .call .value (_amount)());
16 bal[ msg.sender ]-=_amount;
17 /*more */}}
18 function ()external payable {}}
Listing 1. Example Solidity Code of Reentrancy, modified from [7].
1) Reentrancy: Reentrancy is a devastating attempt to deprive the investors of their precious cash. Even though the reentrancy
attack (or the DAO attack [8]) did not knock out the newly born SC technology, but created a stir in the Ethereum community.
Reentrancy problem soon became an exciting topic of discourse among the BlockChain researchers because of two reasons:
its inherent characteristics (discussed below) and the enormity of Ether flown out from SC as a result of it.
Reentrancy problem occurs when an attacker reenters the SC repeatedly. This reentrance sparks of multiple issues like fueling
out the entire gas of the victim, hijacking of the victim’s SC , execution of two functions at the same time and undue transfer
of funds from victim’s SC to the attacker’s account. Developmental issues coupled with the built-in implicit invoking nature
of FF provide a stimulus for this attack.
There are several variants of this type of attacks like same or cross-function reentrancy ( [9]), “Single-entrancy” ( [10]),
and the state-of-the-art reentrancy attacks discussed in [11]. Reentrancy is a repeated operation and the statements execute in
a cycle as shown in Figure 2. As discussed above, Listing 1 tries to simulate the banking application. However, the banking
contract provides only the withdraw(...) method with a flaw. The attacker ModifiedMalicious exploits this flaw to
drain the Ether from the bank into its own account.
Listing 1 demonstrates how the ModifiedMalicious contract exploits the flaw in line#16. Firstly, the attacker’s SC
ModifiedMalicious calls the ModifiedBank SC’swithdraw method in line#6 to retrieve balance amount from her
SC account. As the SC ModifiedBank executes line#15 (using call ) for transferring, the action results in the voluntary
invoking of the costly FF of the attacker in line#8. It is costly because the FF in line#8 is not empty and contains the code
to invoke the withdraw method of ModifiedBank in line#8. This process repeats until the bank or the attacker reaches out
of gas state. However, in the meantime attacker may drain handsome amount from the bank because the debit process never
deducts her balance, i.e., line#16 never executes.
2) Denial of Service (Unexpected throw) :Denial of Service occurs due to various reasons. External functions may contain
broken linkage due to the use of throw (deprecated). Listing 2 (excerpted from [5]) shows the contracts (i.e., MKotET1 ,
MKotET1_1 ). Both are related to a game to acquire the throne of KingofEther . The throne’s price is the money required by
current king to leave the throne along with some processing fee to the SC’s owner. However both the contracts suffer from
Denial of Service vulnerability. MKotET1 (starting from line#1) exhibits Denial of Service threat using transfer in line#7
andMKotET1_1 (starting from line#12) displays Denial of Service threat using call in line#17. MKotET1 andMKotET1_1
usetransfer andcall respectively to deliver Ether to SC MMallory (starting from line#22), which contains a risky FF,
4
in line#23, which reverts in line#24, throwing an exception unconditionally. Both transfer andcall fail to deliver Ether
to ‘MMallory’ due to the revert in line#24.
1 contract MKotET1{
2 address payable emperor; uint public rewardPrice = 500;
3 //declarations for MfindCrownPrice( ) & MNRewardPrice( )
4 function ( ) external payable {
5 require (msg.value >= rewardPrice);
6 uint MCrownPrice = MfindCrownPrice();
7 emperor. transfer (MCrownPrice);
8 emperoror = msg.sender ;//Unreachable
9 rewardPrice= NRewardPrice();//Unreachable
10 /*more */}//MKotET1 ends
11 //modified KotET using Call
12 contract MKotET1_1{
13 // some declarations
14 function ( ) external payable {
15 require (msg.value >= rewardPrice);
16 uint MCrownPrice = MfindCrownPrice( );
17 (bool success,) = emperor. call .value (MCrownPrice)("");
18 require (success);//throw if !success
19 emperor= msg.sender ;//Unreachable
20 rewardPrice = NewRewardPrice();//Unreachable
21 /*more */}//MKotET1_1 ends
22 contract MMallory {
23 function ( ) external payable {
24 revert ( ); } }//MMallory ends
Listing 2. KingOfEtherThrone threat variants:Transfer, Call [5].
3) Mishandled Exception: Mishandled Exception [13] is a frequently occurring threat [14] and appears by other names
in the surveyed literature such as “ Unchecked send ”, “Unchecked External Call ”, and “ Exception Disorders .” Exceptions are
run-time errors. One of the well-known exceptions in the Ethereum network is the out-of-gas exception. However, when a SC
invokes an untrusted external function, a programmer must take extra care. For instance, sending Ether, implicitly invokes the
FF of another SC. FF may fail and the reason might not be the out-of-gas exception [15].
In Solidity, we can use transfer ,send , and call for sending Ether to another SC. If an exception occurs in the
callee, transfer propagates the exception in the caller’s SC, which is safe. APIs like call ,send , and delegatecall
return false and the execution continues [16]. Thus, if the programmer skips the checking of the false returned value, execution
would continue resulting in an inconsistent state [17]. Surveyed literature argues that the owner deliberately does not throw an
exception if the send operation fails. This attitude may result in exceeding the call-depth stack (i.e. CDS, which is a deprecated
vulnerability) [5], [18]. MMallory contract in Listing 3 undo the transfer of Ether to itself. The payable directive facilitates
the importing of Ether, as discussed in section IV-B under frozen Ether vulnerability. However, anybody sending Ether to
MMallory will suffer from exception due to revert in line#2.
1 contract MMallory{
2 function ()external payable {revert (); }}
Listing 3. ’revert’ in line#2, causes mishandled exception [5] to the caller.
4) Gasless send [13]: send is associated with a fixed gas stipend of 2300 [12], which is enough to execute an empty FF.
If the FF modifies the state of the SC, then the required gas can increase beyond 2300. This costly FF results in an out-of-gas
exception [5]. Costly FF can be due to the developer’s mistake instead of a malicious activity [19]. In other words, if the “gas
used” is high, an exception occurs, and the malicious miner may keep the untransferred amount.
1 contract Sender {
2 function pay( uint val, address payable _recv) public { //_recv points to contract Receiver
3 if(_recv. send (val)){ } else { } }}
4 contract Receiver {
5 uint public TotalBal = 0;//state variable
6 function ()external payable {
7 TotalBal = TotalBal + msg.value ; } }
Listing 4. Receiver SC’s FF is costly: alters ’TotalBal’, line#7, [20].
But in some cases, the malicious SC owner may keep the non-transferred amount and miner only gets his fee. call mitigates
this threat but transfers all gas which causes reentrancy. Listing 4 shows two SCs, Sender line#1 and Receiver line#4
modified from [5]. Sender transfers funds to Receiver using send in line#3 but Receiver retrieves the Ether using a costly
FF in line#6. Transfer increments the state variable TotalBal in line#7. But transfer in line#3 becomes a threat as send
uses 2300 amount of gas, which is not enough to execute a costly FF.
5
5)call Transfers All Gas: At the bytecode level, Solidity’s send andtransfer translate into CALL (EVM bytecode).
CALL executes the FF of the calling SC and can fail due to insufficient gas. It consists of four stack arguments: 1) the amount
of gas required for the transaction, recipient’s address, 2) the amount of Ether to be transferred , 3) size, and 4) the location
of input data along with the size and location of return data [21], [22]. call can provide the stimulus for reentrancy attack
by forwarding all the gas [6]. However, implicit call statements are also possible. Listing 5 shows the code modified from
[23]. Line #7 uses call implicitly, which is vulnerable; the first parameter is the Ether value, while the second parameter is
the argument of f(..) .
1 contract test1 {
2 function f(int x)public payable returns (int){return x;} }
3 contract test2 {
4 function testA( address _add1) public {
5 test1 a = test1 (_add1);
6 uint amount = 500;
7 a.f. value (amount)(2);//vulnerable
8 a.f. value (amount);//non-vulnerable
9 } }
Listing 5. test2 : implicit CALL sends amount to f(), in line#7 [23].
B. Contractual Vulnerabilities
Contractual bugs impact the SC itself. Both the attacker and owner exploit them for causing harm to the SC users. The
owner can design the SC to prevent leakage (or transfer) of any Ether, thus turning the SC into a black hole. However, the
worst happens when an attacker uses the unprotected selfdestruct command to destroy the SC. The attacker enjoys the
balance of the account if the attacker changes the ownership of SC before destroying it.
1) Frozen Ether: Also known as “ Locked Money ” [6] or “ be no black hole ” [24]. The frozen Ether threat deprives the SC
account holders of Ether worth millions of dollars, as in the case of parity wallet SC. One atypical impact of the above exploits
resulted after the accidental killing of the library SC, which provided an external route to Ether in parity Wallet SC (and to
other multisigWallet -like SCs [25]).
Apart from the accidental execution of suicide command, which the hobbyist confessed of doing in his issue# 6995 on
Github [26], there could be coding loopholes in the SC, which can prevent exporting of Ether from the SC. Programmatically,
exporting Ether weakness applies to SCs, which lack statements like call ,send , ortransfer , which move the funds
outside the SC, along with the presence of payable directive in the SC, which on the other hand facilitates importing of
Ether. To summarize, “frozen Ether” vulnerability occurs when:
1) SC permits inbound Ether traffic but shuts the outbound Ether traffic. Reference [25] labels such SCs as greedy. FF handles
inbound Ether traffic but call ,send andtransfer handle outbound Ether traffic. Listing 6 uses a SC modified from
[24], which shows the freezing Ether vulnerability because the SC contains a method having payable directive in
line#2 but does not contain program paths leading to CALL, DELEGATECALL, or SELFDESTRUCT opcodes:
1 contract ModifiedBitway{
2 function ( ) external payable { } }
Listing 6. payable directive creates an Ether receiving FF in line#2.
2) Wallet SC relies on another SC or library. The library SC provides functions to support the Wallet, for instance, the library
can provide function for transferring Ether. But if the library SC eventually kills herself by executing selfdestruct
command or some other SC (or an attacker) accidentally (or deliberately) kills the library SC, then it would close the
doors of Ether extraction from the wallet SC. Listing 7, modified from [27], shows the use of delegatecall in line#4
to load the code from the address 0xNewLibrary , line#2, containing the library’s withdraw() method.
1 contract testDC{
2 address _nl = 0xNewLibrary;
3 function withdrawM() {
4 _nl. delegatecall (msg.data ); } }
Listing 7. withdrawM() uses library through hardcoded address(line#2) and delegatcall (line#4) using that address [27].
2) Self-Destructible: selfdestruct (previously known as suicide ) allows a SC to destroy itself by releasing the Ether
of account holders. SC uses this alternative in emergencies. Research conducted by [25] terms a SC suicidal (i.e. vulnerable for
SELDESTRUCT opcode) if the SC does not correctly guard the selfdestruct command. Attacker requires two things to
kill a SC: (1) reaching into the conditional statement enclosing the selfdestruct statement, and (2) attaining the ownership
of SC.
selfdestruct causes unresponsiveness [8] of the SC resulting in “Denial of Service.” selfdestruct deletes the SC’s
code permanently [28], [29]. All the SC’s funds would transfer to the account associated with SC. This transfer will not trigger
6
FF [28]. A beneficiary can be an existing account or the account may not exist. In the latter case, the destruction process
creates the account and charges fees for it [21]. Contract MDiscontinue in Listing 8 provides a TerminateMe function
to destroy the contract with unprotected selfdestruct (i.e., without any ifblock) in line#6, modified from [30]:
1contract MDiscontinue{
2 address payable owner;
3 constructor ()public {
4 owner = msg.sender ; }
5 function TerminateMe() public {
6 selfdestruct (owner);}}//suicidal
Listing 8. selfdestruct , line#6, without any guard, suicidal SC [30].
3) Stealing Ether: Stealing Ether vulnerability (or Unsecured Balance) surfaces when the SC initializes the owner field
indirectly as in line#4 of Listing 9 in a function other than the constructor. The use of a function for initialization of owner
address (as in line#3) can lead to a problematic situation. This situation is similar to parity SC, which doomed the multi-signature
SC of 30m dollars [31].
1contract CompWallet{
2 address payable owner;//state variable
3 function initComWallet( address payable _owner) public {
4 owner = _owner;}//any user can change owner
5 function withdraw( uint _amount) public {
6 if(msg.sender == owner){
7 if(!owner. send (_amount)){}
8 else {}}}}
Listing 9. owner initialized in line#4, outside constructor [27].
C. Arithmetic Bugs
This problem occurs as a result of a mathematical operation. Most significant bug is the integer overflow/underflow, which
is a common problem in programming languages. Increments beyond the maximum or decrements below the lowest value
(i.e. wrap-around) may generate wrong results. Thus, developers must perform manual checking (i.e., employ a human expert
to check the code); otherwise, code may create a wrap-around error. One solution is to use SafeMath library. The latest
research recommends using the “ solc-verifier ” tool.
1) Integer Overflow/Underflow: Ethereum has nothing to do with data types leaving the compiler responsible for catch-
ing integer overflows and under-flows [17], [32]. Solidity is rich in integer data types with flexible sizes (uint8, uint16,
uint24,..uint256, int8, int16, int24,..int256) but does not support floating-point math [6]. Thus, the diversity of integer data
types provides no benefit to SC. Work in [17], [33] provides an example code generating integer bugs. Figure 3 shows an
underflow SC and its output on Remix (0 changes to 255 on decrements).
Fig. 3. Underflow SC, MUFTest1, modified from [34], and the result of debugging it on REMIX IDE.
SafeMath [6], [35] provides several functions to replace the ordinary arithmetic operations in SCs. Listing 10, lines#2-4,
show the logic of SafeMath library’s method sub(..) . Figure 3 SC, MUFTest1 , is modified from [34]. We replaced the
line#5 in Figure 3 by sub(..) method of SafeMath library as shown in Listing 10, line#9, of SC MUFTest2 .
7
2) Unchecked Maths: Unchecked math means that a SC is not using strategies to protect mathematical statements from
overflows/underflows. A good practice is to protect the code using assertions and SafeMath library as shown again in Listing
10, line#9.
1 library SafeMath {
2 function sub( uint8 x,uint8 y)internal pure returns (uint8 ) {
3 assert (y <= x);
4 return x - y; } }
5 contract MUFTest2 {
6 using SafeMath for uint8 ;
7 uint8 testVal= 0;
8 function Utest() public returns (uint8 ){
9 testVal= testVal.sub(1);
10//instead of : testVal- - (in line#5, Fig. 3)
11 return testVal; } }
Listing 10. Use of sub(..) function, line#9, to avoid underflow [34].
D. Gas Related Issues
The gas serves two essential purposes for the EVM network. Firstly, gas serves as compensation to the miners’ efforts for
recording transactions. Secondly, the gas acts as a fuel for running a transaction and thus prevents long transactions from
hijacking the EVM scheduling scheme. Logically, it means that if the user does not pay enough gas fee as required for the
transaction, the transaction will fail by generating an “ out of gas ” exception. Surprisingly, an integer overflow can also cause
“out of gas.” Other examples are Denial of Service, and Wallet Griefing, as discussed below:
1) Denial of Service (Costly Loops Causing Out of Gas Exception): EVM protects programs from Denial of Service attacks
by forced termination. EVM allocates gas at the start of execution, and each execution step results in some deduction. If the
remaining amount after deduction is less than the amount required for execution, EVM [15] terminates the SC’s execution,
causing partial or full rollback [36]. The termination can occur even without the influence of an attacker [37].
An attacker can manipulate the arr, line#4, in Listing 11 by adding additional addresses. Thus, increasing the execution
cost and transfer to manipulated addresses. In the worst case, the entire gas may be exhausted, resulting in full revert .
Denial of Service can occur due to the presence of revert in external function, as in Listing 2, line#24.
1 contract testLL{
2 uint constant LARGEGAS = 100000;
3 address payable addrArr;
4 function LongList( uint256 memory nextV, uint [ ] memory arr, address payable _addr) public {
5 uint256 i= nextV;
6 addrArr = _addr;
7 for ( ;i < arr. length && gasleft() > LARGEGAS; i++) {
8 addrArr. send (arr[i]); }
9 nextV = i; } }
Listing 11. Unbounded mass operation: traversing an unsized array [15].
Another example is the Denial of Service due to a costly for or awhile loop. This may cause depletion of gas in each
iteration and finally resulting in Denial of Service. Work presented by [15] renames this vulnerability, as “ Unbounded mass
operations due to an unsized array variable arr in line#7, Listing 11, and [15] have proposed resumable loops. Function
LongList(..) , line#4, in Listing 11, is excerpted from [15]. In the case of revert , due to “out of gas” threat, nextV ,
in line#9, points to the arr index, from where to resume.
2) Integer Overflow (Causing Out of Gas Exception): Authors of SmartCheck [6] and MadMax [15] discuss about the
integer overflow issue in a loop in the context of Var (deprecated, Solidity used Var for Type Inference [6], [9]). However,
an integer overflow can occur even when Var does not determine the type of loop index (i.e., uint8 ) variable. By this, we
mean that integer overflow can occur if the type of loop index variable is a short integer at run-time. Listing 12 shows an
overflow without using Var, as in line#4 in Listing 12 when the loop counter variable becomes greater than or equal to 255.
1contract Overflow {
2 int [300] emp;
3 function testOF() public returns (bool ) {
4 for (uint8 i = 0; i < emp. length ; ++i) { }}}
Listing 12. Short integer overflows even without Var [38] in line#4.
3) Wallet Griefing Causing Out of Gas Exception: This vulnerability occurs if a SC uses a loop to send Ether to multiple
SCs. Thus, if one receiver fails, then the entire transaction fails. The receiver can fail due to a bad FF (line#2, Listing 3).
If the sending SC uses a throw (i.e.,revert ) to handle the failure of send then it can exacerbate the situation because
throw consumes the entire gas [14], [39]and locks the sender’s SC [15], [40]. Repeated attempts may also fail due to “out
of gas” situation, as in line#6, Listing 13, modified from [15]. However, the latest version in can achieve the same effect by
using require andtransfer instead of send andrevert .
8
1for (uint i = 0; i < employee. length ; i++) {
2 if( employee [i].paid < min_salary ) {
3 // sentinel for making a payment.
4 // code may lock the SC
5 // due to {\tt revert} consuming all gas.
6 if(!( employee [i].addr. send ( employee [i].bonusAmount ))) revert () ;
7 employee [i] = newEmployee ; } }
Listing 13. Wallet Griefing using revert ,send fails in line#6, [15].
E. Transactional Irregularities
EVM transactions become a source of greed for the miner, which validates them. Thus some miners can influence the
transactions resulting in vulnerabilities like Transaction Ordering Dependence and Time Stamp Dependence.
1) Transaction Ordering Dependence (TOD): TOD is also known as “ front running race condition ” [16]. This attack,
“selfish mining attack”, occurs due to the mishandling of transaction queue by miners. The owner/user incentivizes the miner
to change the order of the transaction [18].
One example of TOD is the case of marketplace SC as shown in Listing 14 modified from [18] in which a miner may
not honor a leading buyer’s request at the cost of some other transaction. The buyer sends the transaction to buy at cost 100,
line#7, Listing 14. At the same time, owner sends the transaction with a high gas fee to increase the price, line#4, Listing 14.
The owner’s transaction executes first due to the higher gas price incentive. The buyer’s transaction completes next but the
buyer pays more.
1 contract MMarketPlace {
2 uint private cost=100 ;
3 uint private inventory= 100; //more declarations
4 function incPrice ( uint _incCost ){
5 require (msg.sender == owner )
6 cost = cost + incCost ; }
7 function buy ( ) returns (uint ){
8 require (msg.value == cost);
9 require (inventory > 0));
10 inventory -= 1; / *more */ } }//use of SafeMath recommended
Listing 14. Miner alters lines#5-8, causing TOD: marketplace [18].
Contrary to the general notion about a miner in connection with TOD, the recent research conducted by [17] argues that it
is hard to exploit TOD threat because the attacker should be a miner, and there are less financial gains. Apart from this, there
are some other programming problems. State variables often have a dependency on the function, which changes their values.
Thus, coding such a function when multiple users are invoking that function is a concurrent programming issue.
The concurrency in SCs requires some mechanism like semaphore to control access to the state variable. Reference [9] argues
that Solidity does not support concurrency. Thus, manipulation by a miner is a limitation of BlockChain rather than a bug.
However, EVM must provide some solution for miner’s problems, as the miners also contribute to immense power consumption.
One solution proposed by [41] is to delegate miners’ role to a SC. Another name for this problem is the unpredictable state
problem. This is because multiple invocations of a dependent function make it difficult to predict what the state and the values
stored within a SC will be when a user executes the function.
2) TimeStamp Dependence [13]: Each block within the BlockChain contains three pieces of information: 1) timestamp, 2)
cryptographic hash, and 3) the transaction data.
– Timestamp represents the time when the miner verifies all the transactions within the block after computing the proof-of-
work puzzle. A miner can manipulate the block timestamp but has to complete the validation within 900s [18]; otherwise
other miners would reject the block.
– Cryptographic hashesare deterministic functions. Miners exploit the deterministic hash values to verify the integrity of the
block’s data. Cryptographic hash chains the current block with the previous block as there is a dependency between hash
values of the two said blocks.
– Transaction data can vary based upon transactions. Still, for the most straightforward transactions between two SC accounts,
the transaction data would be the sender’s and receiver’s SC account addresses and the amount of Ether sender transfers
to the receiver.
Despite the doubtful accuracy, SCs use the timestamp for random number generation. Due to the miner’s involvement in
setting the timestamp’s value, the timestamp becomes a so-called deterministic random value. Hence, the usage of the timestamp
as a random value in lottery implementation is vulnerable [21]. Programmatically, block.timeStamp retrieves the timestamp
associated with a block. However, one can just use now (alias for block.timestamp) also to retrieve the timestamp, as in Listing
15, lines#3-4. Solidity uses now for simplicity and bytecode implementation. But Solidity does not discriminate between now
andblock.timeStamp and hence both are vulnerable.
9
1 contract TSD{
2 function Mpay () public {
3 uint tTime = now;
4 if(tTime > ( now + 2) ){ if(!msg.sender .send (200)) { } else { } } } }
Listing 15. Miner’s misuse of now (lines#3-4) causes TSD.
3) tx.origin: tx.origin is a transaction state variable which indicates the originator of the transaction. Other transaction
state variables also exist like tx.GasPrice . But tx.GasPrice has a fixed value so the adversary cannot change it [9].
On the other hand, tx.origin can vary. This variation can lead to attacks because we cannot use tx.origin to ratify the
contract’s owner, as shown in Listing 16.
Lines#1-9 in Listing 16 show the victim’s SC (i.e., TxUserWallet ) and lines#10-11 show the interface (i.e., TxUserWallet ).
Note that the names of the victim’s SC and the interface are the same but they are in different files.
1contract TxUserWallet {
2 address owner;
3 constructor ()public {
4 owner = msg.sender ; }
5 function sendTo( address dest, uint256 amount) payable public returns (bytes memory theMessage)
6 {require (tx.origin == owner);
7 (bool success, bytes memory returnMessage) = dest. call .value (amount)();
8 require (success);
9 return returnMessage; } / *more */}
10interface TxUserWallet {
11 function sendTo( address dest, uint amount) external ;}
12contract TxAttackWallet {
13 address owner;
14 constructor () public {
15 owner = msg.sender ; }
16 function ()external payable {
17 TxUserWallet( msg.sender ).sendTo(owner, msg.sender .balance ); } / *more */}
Listing 16. Example of tx.origin [42], Victim’s SC (i.e. TxUserWallet ).
The rest of the code from lines#12-17 show the attacker’s SC, TxAttackWallet . The surveyed literature recommends the
replacement of tx.origin withmsg.sender particularly for authenticating the sender of a message [14]. tx.origin
represents the address of the first account in the call chain (i.e., the list of calls related to the currently executing transaction),
whereas msg.sender is the original caller [6], [25].
V. T OOLS FOR TESTING SMART CONTRACTS
we provide a brief description of SC tools developed for detecting above mentioned vulnerabilities. We grouped the testing
tools into dynamic and static-based tools.
A. Testing Tools based on Static Analysis of SCs
There are a good number of static-analysis for testing SCs:
–Zeus [9]. Zeus is a tool for formal verification of SCs using abstract interpretation and symbolic model checking. Zeus
works directly on the high-level of SC code. Zeus detects threats like reentrancy (section IV-A1), Unchecked and Failed
send (section IV-A3), Integer Overflows (section IV-C1), and Timestamp dependency(section IV-E2).
–VeriSolid [43]. VeriSolid is a SC development tool. VeriSolid uses a transition system model to generate Solidity-based
formally verified SCs. Automatic code generation is an important achievement in the context of formal verification tools.
VeriSolid prevents reentrancy (section IV-A1) by design and uses liveness property to prevent Denial of Service (section
IV-A2, IV-D1).
–Vandal [14]. Vandal is a static analysis tool. Vandal performs security analysis of EVM bytecode using a logic language,
called Souffle, to transform the analysis into C++. Vandal’s static analysis library functions detect threats like “Unchecked
Send” (section IV-A3), Reentrancy (section IV-A1), UnSecured Balance (section IV-B3), Destroyable SC (section IV-B2)
[14].
–Teether [22]. Teether focuses on the same idea of critical paths as in [24]. Teether constructs the CFG of the SC using
the EVM bytecode, which helps to detect critical paths. The authors discussed the peculiar problem of backward traversal
associated with JMP because of JMP’s similarity with x86’s return statement.
–SmartScopy [44]. SmartScopy is an attack synthesizer. SmartScopy performs summary-based symbolic evaluation, which
reduces the program size for symbolic analysis (SA) required for automatic generation of adversarial SC. The adversarial
SC confirms the presence of vulnerability in the victim SC, detected by manual analysis. SmartScopy detects threats like
reentrancy (section IV-A1), timestamp dependence (section IV-E2), and Gasless send (section IV-A4).
10
–SmartCheck [6]. SmarkCheck helps to remove the simple bugs quickly. However, for removing non-trivial bugs, [6]
recommends using more sophisticated techniques like taint analysis (TA). The authors identified several coding threats
like reentrancy (section IV-A1, Mishandled Exception IV-A3, call transfers all gas, and so on. The SmartCheck [6]
related research provides a comprehensive list of threats based upon exploits related to security, functional, operational,
and developmental issues.
–Securify [27]. Securify is a static analysis tool, focusing on patterns (compliance or violation). The tool extracts the
domain knowledge from patterns related to a security property. Securify detects threats like Stealing (section IV-B3) and
frozen Ether (section IV-B1), Reentrancy (section IV-A1), Mishandled Exception (section IV-A3) and Transaction Ordering
Dependence (section IV-E1). Related violation properties with regarding threats are restricted write, Ether liquidity, no
writes after calls, handled exception, restricted transfer and TOD [27].
–Oyente [18]. Oyente performs static analysis of SCs. [18] recommend the extension of Oyente and is a reality when one
reads the details of the tools mentioned in [45]. Oyente detects threats like TOD (section IV-E1), TimeStamp Dependence
(section IV-E2), and Mishandled Exception (section IV-A3).
–OSIRIS [32]. OSIRIS employs a strategy based upon taint analysis and symbolic execution and consists of an integer de-
tection module. Symbolic execution module constructs a control flow graph (CFG) from the bytecode. The CFG processes
different paths of the SC using symbolic values, as in Maian [25]. Osiris [32] detects arithmetic bugs (overflow/underflow
(section IV-C1) and division by 0), truncation bugs (converting from a larger to smaller data size, e.g., 64-bit data to
16-bit data), and signedness bugs (converting a signed integer to an unsigned integer of the same width and vice versa).
–MadMax [15]. MadMax focuses on automatic detection of gas-focused threats. Like Vandal [14], MadMax performs the
de-compilation of EVM bytecode and similarly uses a logic-based approach to produce a high-level representation of the
program model. MadMax defines strategies for surviving out of gas conditions in the context of resumable loops (section
IV-D1), loops bounded by induction variable, and dynamically bounded loops.
–KFrameWork-EVM-Semantics (KEVM) [36] :KEVM is a semantic analysis tool based upon SE. KEVM’s development
framework integrates a semantic debugger and a program verifier. Tool encapsulates a gas analyzer that computes gas
bounds during execution and can help in detecting “Denial of Service”(section IV-A2, section IV-D1) threats.
–Interactive Theorem Provers (ITP) [46]. Interactive Theorem Prover combines the idea of theorem proving with testing.
Initially, this tool presents the desired behavior of EVM in LEM [47]. Authors used both the community-based test suits
and interactive theorem provers like Isabelle/HOL to test their EVM definitions. The authors divided the formalization
into deterministic and non-deterministic formalization. The basic assumption for non-deterministic formalization was to
segregate the environment from the system. This helped to reason about the adversarial attack and to model the reentrancy
attack (section IV-A1).
–Gasper [48]. Gasper uses Oyente Engine to generate the CFG and identifies the code for optimization. [48] points out
seven gas costly-patterns. Due to shortage of space, we have not discussed Gasper patterns in this paper.
–FSolidM [49]. FSolidM allows development of secure SCs using Finite State Machines (FSMs) [16]. FSolidM uses a set of
plugins and design patterns that developers can add to the SC for implementing locking, maintaining transaction counter,
and enforcing timed transitions to safeguard against reentrancy (section IV-A1), transaction ordering dependence (section
IV-E1), and time constraint, respectively.
–FVF* [7]. The research work in [7] describes the verification of Ethereum SCs using F* (FVF* stands for formal
verification using F*). The work in [7] added an effect system in F* which helps in the detection of “unchecked send”
(section IV-A3) and destructive patterns like reentrancy (section IV-A1).
–EtherTrust [50]. Authors used the tool to prove the reachability property for SC’s bytecode. EtherTrust ensures two things
about the SC: (i) FFs should not result in DAO type of attack (section IV-A1) (ii) data is not vulnerable to miner’s
manipulation.
–DappGaurd [19]. DappGaurd detects the diverse type of threats. However, instead of relying on bytecode or Solidity
code, DappGaurd focuses on Transaction Receipts, which analyze live SCs, but the authors do not provide the source for
retrieving TRs. DappGaurd’s prototype version detects several threats, and for this purpose, DappGard incorporates the
Oyente engine.
–sCompile [24]. sCompile exploits the notion of “critical paths” i.e., in place of identifying a program to be vulnerable,
sCompile identifies critical paths (i.e., money related inter-contractual paths involving call ) in the program. Developed
inC+ +, sCompile uses Z3 SMT Solver for SE. sCompile detects threats like reentrancy (section IV-A1), be no black
hole (section IV-B1, and unguarded selfdestruct (section IV-B2).
B. Testing Tools based on Dynamic Analysis of SCs
–Vultron [51]. Vultron is still in infancy stages and the authors have tested the prototype using truffle suite. Vultron is a
test oracle, which stores bookkeeping information in variables. Vultron compares these variables with account balances
related to SCs to determine the inconsistencies. Vultron can identify threats like reentrancy (section IV-A1), exception
disorder (section IV-A3), integer overflow/underflow (section IV-C1), and “gasless send” (section IV-A4).
11
–Sereum [11]. Sereum modifies the “goethereum” client “geth” and adds an attack detector and taint engine. Sereum works
at the bytecode level and the binary level does not keep type information. This fact makes it challenging to infer about
the sensitivity of data [30]. Sereum related research reports a reentrancy attack (section IV-A1).
–Regaurd [52]. Regaurd is a dynamic analysis tool and incorporates a fuzzing based analyzer. Regaurd focuses on automatic
detection of common threats in SCs like reentrancy bugs (section IV-A1). Regaurd transforms the code into an intermediate
representation (IR) (Abstract Syntax Tree). Finally, Regaurd executes the SC (with transactions as input) and forwards
the dump of relevant operations of run-time analysis to the core detector to detect reentrancy bugs.
–Maian [25]. Maian is a static analysis tool but also performs dynamic analysis of SC. For offline inspection, Maian’s
input is the EVM bytecode and the SC’s initial state retrieved from the BlockChain. SA generates actual values for the
transaction given SC’s bytecode and analysis specification i.e., vulnerability category to search like Suicidal (same as
selfdestruct , IV-B2) or Greedy (same as Frozen Ether, section IV-B1) as input. Maian then uses the actual values
in the validation step.
–EasyFlow [35]. EasyFlow is a specialized tool focusing on integer overflow. EasyFlow uses taint analysis for overflow
detection. Detection algorithm analyzes transaction instructions and mathematical instructions (at bytecode level) like EXP,
ADDMOD, and MULMOD and even the instructions protected by SafeMath library.
–ContractFuzzer [12]. Fuzzing is a technique which can perform both static and dynamic analysis independently or at the
same time. [53] discusses an example of fuzzing with SC by mounting the Truffle project to a docker image. ContractFuzzer
detects threats like “Gasless send” (section IV-A4), “Exception disorder” (section IV-A3), Reentrancy (section IV-A1),
TSD (section IV-E2), and Freezing Ether (section IV-B1).
VI. C ONCLUSION AND RESEARCH DIRECTIONS
We have provided classification of SC vulnerabilities but this can be further enhanced. In fact, security of SC is vital for
strengthening the concept of BlockChain. Consistent efforts from academia have provided great solutions and this should
continue. Our future research would be related to identifying randomization vulnerabilities. Following are the suggestion to
fill the gaps in previous research and to advance the current research:
– For EVM researcher: a) there is a need for run-time environment within the Ethereum (i.e. EVM) and all the newly
launched SC must be tested using this environment. This would take care of SC which are launched without testing, and
b) Many good tools have been developed by academia and it would be a good approach to incorporate them on the Remix
website as plug-ins.
– For Solidity researcher: Solidity interpreter needs more improvements to catch the vulnerabilities. For catching mathe-
matical errors, Solidity can incorporate a solver. This addition can also pave the way for detection of reentrancy error,
which occurs due to the misplacement of account deduction statement
– For general researcher: Improving and developing new tools and techniques is important. Some examples areas where
tools can be developed are: i) to catch new vulnerability patterns such as reported in [11], [40], ii) to make the use of
libraries safe for SC, and iii) design strategies to reduce the miner’s time to generate a block that is currently 900 seconds.
– For Security Researchers: It is also important to develop more effective security testing and adequacy criteria that are
unique for testing SC [54]. It is also important to develop algorithmic [55] and machine learning [56], and deep learning
[57] approaches for detecting vulnerabilities and security defects in SCs.
ACKNOWLEDGMENT
This research work is supported by National Science Foundation (NSF) under Grant No: 1821560.
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[53] N. Ambroladze, “Fast and scalable analysis of smart contracts,” Master’s thesis, ETH Zurich, Departement of Computer Science.
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online event, [Brno, Czech Republic], March 30 - April 3, 2020 , 2020, pp. 540–543.
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and Applications Conference, COMPSAC 2020, Madrid, Spain, July 13-17, 2020 , 2020, pp. 1332–1337.
|
{
"id": "2012.14481"
}
|
1807.08142
|
{\em Crypto-Battleships} or How to play Battleships game over the
Blockchain?
|
Battleships is a well known traditional board game for two players which
dates from World War I. Though, the game has several digital version
implementations, they are affected by similar major drawbacks such as fairness
and a trust model that relies on third party. In this paper, we demonstrate how
to implement a fair, resistant to denial-of-service, where the honest winner
earns the deposit money {\em immediately}. The game is built on a
permissionless Blockchain that supports Turing complete smart-contract
computation.
|
http://arxiv.org/pdf/1807.08142v1
|
Guy Barshap
|
cs.CR, cs.SE
|
cs.CR
|
Crypto-Battleships or How to play
Battleships game over the Blockchain?
Guy Barshap- BGU university of Israel.
Abstract
Battleships is a well known traditional board game for two players
which dates from World War I. Though, the game has several digital
version implementations, they are a ected by similar major drawbacks
such as fairness and a trust model that relies on third party. In this
paper, we demonstrate how to implement a fair, resistant to denial-of-
service, where the honest winner earns the deposit money immediately .
The game is built on a permissionless Blockchain that supports Turing
complete smart-contract computation. Furthermore, we provide a full
working game implementation1of this proposition over the Ethereum
Blockchain.
1 Introduction - Basic Battleships rules
This section describes the basic rules of the classic battleships game, which
establishes the basis for our novel contribution. Battleships is a popular
traditional board game[9] for two players, where each player is required to
hold a board game of 10 10 cell size. In each board, the players need to place
down a
eet of 5 battleships, where each battleship has di erent sizes and
occupies consecutive cells in the board. Each cell can either be a battleship's
part or an empty cell. This classic game has the following stages:
1.Placing the Battleships. To commence the game, each player needs
tosecretly arrange their
eets. The battleships can be placed in hori-
zontal or vertical arrangement with each player possessing a prede ned
equal amount and types of battleships.
E-mail: Barshag@post.bgu.ac.il
1The front-end game will be released on the website: http://www.playonchains.com .
1arXiv:1807.08142v1 [cs.CR] 21 Jul 2018
In this phase, each player also creates another board with the same size
in order to record the torpedoes shot into the opponent's
eet, as well
as their status (hit or miss).
2.Launching Torpedoes in turns. This phase operates in rounds,
where the players switch their roles in each round. In a single round,
there is one player shooting a torpedo into one of the cells of the oppo-
nent's board and on announcing the exact cell that is being targeted,
both players have to record the shot. Then, the opponent announces
whether this cell contains a part of his own battleships (i.e the oppo-
nent announces whether it is a hitormiss shot). In a situation where
all parts of the ship have been a ected, the owner of that ship must
announce "This ship was sunk".
3.Termination of the game. In the eventual outcome that a
eet of one
of the players is sunk (i.e all the battleships are sunk), the game ends
and the opponent will be announced as the winner. Pedantic players
at this stage will perform comparison of their own records against the
private opponent's board arrangement to obtain some2guarantee that
they have not been cheated.
2 Why play battleships over the Blockchain?
2.1 Limitation of the Battleships' centrelized variant
A typical battleships game is normally hosted on a third party centralized
server, however, this approach su ers from the following limitations.
Trusting the server. In a centralized server scenario, the players must
rely on the information coming from the server, since it acts as a media-
tor, unlike the case of dishonest server, which may be problematic due to
erroneous information. Furthermore, when money is involved in the games,
a server may decide whether or not to transfer money to the player who
wins the game. In addition, a potential hacker may have the opportunity to
exploit such loopholes to manipulate the performance of the server, which
inherently in
uences the outcome of the game.
2This will not give them a full guarantee, since a malicious opponent could perform
Dynamically changing battleship's location attack. We describe that attack in Section 3.1
2
12345678910
AX
BX
CXX
DXXXXX
EX
FXXX
G XX
H
I
J
Table 1: A typical Battleships board game of 10 10 cell size and 5 battleships
of sizes: 11, 12, . . . , 55.
Game suspected to a Denial of Service attack (DoS). At any point
in the game, a player who is not satis ed with the score of the game (or for
any other reason), has the privilege to launch a denial of service campaign
on the server. This is possible, since the server has a single point of failure
and there are several low cost service providers for DDoS [6]. An example of
DDoS attack that occurred in the wild, can be found in [4].
2.2 Playing the game over a Blockchain
A Blockchain architecture that allows arbitrary computation (i.e. Smart
contract [11]) o ers several advantages over a centralized variant, and can
mitigate the mentioned
aws from Section 2.1.
2.2.1 Blockchain bene ts
In this section, we describe the bene ts of executing the game over a Blockchain
instead of using centralized server.
Decentralization. A Blockchain that allows Turing-complete computa-
tion executes commands across multiple machines, which are called nodes.
This architecture enables a trust-less computation and validation over blockchain
nodes. This property is in contrast to the case of executing the game on a sin-
gle server, which makes the Blockchain resilient to denial-of-service attacks.
3
Hence, to "shut down" the computation mechanism, an attacker needs to
attack several highly maintained servers across the Internet instead of just a
few.
"The code is the law" paradigm. Once a smart-contract is uploaded
into the blockchain, it cannot be changed3. Thus, in a situation where the
game is developed with fair rules that can be audited (since the bytecodes
are publicly available once uploaded), it becomes infeasible for some entity
to interfere and change the rules during an instance of a game.
Participating in the game cannot be prevented from anyone. In
addition to the above bene ts, playing on a permissionless Blockchain cannot
be censored by a single authority, since every player can create a wallet on
their own.
Instant payment. The smart contract code has the ability to transfer
money based on certain predetermined programmed rules. Whenever such
rules occur, money transfer to a player's wallet cannot be prevented. In our
Battleships game, the winner immediately receives the deposit money of both
players (in Ether).
3 How to enforce fair play?
3.1 Survey of possible attacks
Building a game over a Blockchain can be mistakenly interpreted to be re-
silient to cyber attacks. However, such statement tends to be invalid, because
a naive implementation would su er from the following attacks.
Keeping secrets. Since the Blockchain maintains a public ledger, putting
secret values will expose it to potential cheats, in which an adversary can scan
the Blockchain and launch torpedoes on the public locations of his adversary.
This is a well known vulnerability that occurs in Blockchain's architecture,
more details on this vulnerability can be found in [5].
Dynamically changing battleships' location. In this attack, the
attacker may change the location of the ships dynamically, in his own favor,
without updating the other player. Thus, in a condition where there is no
3This claim is not 100% guaranteed, for example in the cases of fork that may arise
spontaneously, or with an occurrence of 51% attack, which is rare on a popular Blockchains
such as Bitcoin and Ethereum.
4
enforcement on the location after the rst stage of placing the Battleships ,
an attacker can attain a major advantage in the game.
Inappropriate placement of the battleships. Using this attack, an
attacker can place only a subset of the battleships or the entire battleships
such that its parts are neither consecutive or nor forming the correct shape
of the ship.
Implementation's vulnerabilities. As for any other software, every
game could have vulnerabilities and this is speci cally more prominent in the
logic game executed over blockhcain. A comprehensive survey and taxonomy
can be found in [5].
3.2 Design concepts
Herein we describe the design requirements that will mitigate the above at-
tacks.
Security requirements.
1. A player that picks a board layout have to commit the board at the
beginning of the game, which must not be changed before the game
nishes (i.e. a cheater cannot change the location of the battleships
without being caught);
2. The above commitment, must not expose any value of the location of
the battleships (i.e the locations of the battleships must remain pri-
vate).
3. The type and size of battleships of the players must obey prede ned
set of rules (i.e. there must be battleships of sizes 1 5;14, etc.).
4. In each turn, the players need to provide a proof of not cheating about
the exact value of the previous torpedo shot toward them, whether it
was a Hitor a Miss.
5. Whenever a player makes claim for a victory, he must provide proof
that he was not cheated with regard to the location of the battleships.
6. The game should have a penalty mechanism for a malicious user who
is not taking any action at a particular period of time. (i.e the game
must prevent the user from freezing the deposit of money in the smart
contract due to not continuing the game).
5
Architecture requirements.
1. The smart contract code of each move should be as light as possible.
This requirement is crucial to minimize the nance costs, as well as to
provide good user experience.
2. The code should be audited by independent researchers in order to
lower the number of implementation's bugs.
3.3 Game design overview
This section provides a brief overview of the game design, according to dif-
ferent phases of the game.
3.3.1 Registration Phase
Two di erent parties are required to register at the beginning of the game,
where both parties make a joint decision on the amount of money to com-
mit to the game. The deposited money is considered a major factor which
enforces the players to play by the rules, because any attempt to cheat in
the game will result in a punishment of giving the deposited money to the
opponent.
3.3.2 Placing the battleships
The players will then choose where to place their battleships on the board
using the game user interface (UI). Afterward, a player who is satis ed with
the layout of his own
eet, must upload the computed root of the merkle-tree
to the smart contract of the game (in a speci ed period of time).
Merkle tree of the board. A merkle tree (MT)[7] is a cryptographic
structure that allows for ecient and secure veri cation of content. This
structure helps to verify the consistency and content of the data. The struc-
ture is a binary tree, where every leaf node is labeled with the hash of a data
block that it represents and every non-leaf node is labeled with the crypto-
graphic hash of the labels of its child nodes. The topmost node is called the
root (similar to a regular binary tree).
In our design, every leaf node is labeled with a data block in the form of
xjjr, wherexdenotes whether there exist a ship with size xin the respective
6
cell, or not (i.e x= 0), andris a sucient4large random value, where the
excess number of leafs equals to 0 (those leafs completes the tree to a full
binary tree with 27= 128 cells). An illustration of concrete merkle-tree of a
simpli ed board can be seen in Figure 1.
12
AX
BX)Root
A1-A2
A1
2205A2
0813B1-B2
B1
2932B2
0417
Figure 1: Simpli ed 2 2 Battleships' board game with only one battleship
of size two, and the corresponding merkle-tree structure of that board.
Broadcasting the MT-root will enforce the player to commit the cho-
sen board and force him not to change it later on, since a cryptographic
hash function is a one-way function, which is resilient to second-preimage
attack5. Furthermore, when broadcasting this root into the blockhcain, no
single value from the underneath values will be revealed, due to the use of
random concatenation of each value.
3.3.3 Launching Torpedoes
In each turn6, a player that wants to launch a torpedo, must broadcast the
underneath value of the previous opponent's shot, along with a proof that it
is the real value.
The proof is delivered by a MT-path from the MT-root till the targeted
cell number of the previous opponent moves and this path will be veri ed on
the blockchain smart-contract. Only in a situation where the path is valid
4Common length of rcan be at least 128 bit, to make it hard to guess the value of the
cell by performing Brute-force guessing.
5The property of second-preimage resistance claims that it is computationally infeasible
to nd any second input which has the same output as that of a speci ed input
6Not include the rst move.
7
(i.e. the leaf value ts with the root of the merkle tree uploaded in the rst
phase), will the player be permitted to perform the next move. Furthermore,
we also enforce time constraint to perform valid moves, in order to avoid a
denial of service attack at a particular instance of game.
3.3.4 Final game veri cation
Finally, the smart contract will enforce the candidate winner, which is the
player that achieves a correct guess of the entire
eet of the opponent, to
reveal his own battleships' locations. Such rule is necessary to mitigate the
Inappropriate placement of battleships attack .
In case the player refuses to provide a valid MT-paths, he will be tagged
as a cheater, and the punishment is that the other player will be announced
as the winner, and thus receive the deposited money.
3.4 Software architecture of the game
We describe the software architecture of our proposed game design in this
section in order to o er a comprehensive overview of the game. The design
of the game relies on the 3-tiers architecture [10], which is very similar to a
typical decentralized application (dapps). An illustration of these layers is
depicted in Figure 3.4.
1.Presentation layer - This layer is responsible for the UI, which in-
cludes the following components:
HTML and JScript code that manages the UI of the game. It
also includes client side code which ensures game play follow the
speci ed protocol7
Web3.js [2] is the layer that connects the HTML client code to
interact with the game's smart contract.
Metamask wallet[3] enables the users to commit transaction to the
blockchain.
2.Logic layer - This layer is responsible for enforcement of the game
rules and it is placed in the smart contract code. The layer includes
the following components:
7This feature is not taken into account in the security analysis, since it is not prevented
from malicious attacker who can change the code, and bypass the mechanisms.
8
Veri cation of the boards' MT-path which relies on the solidity
library called merkle-tree-solidity [1].
Authentication and authorization of the players that participate
in the game.
Veri cation of the game rules and validity of transmitted data.
3.Data layer -This layer is responsible for storing the data that is trans-
ferred to the blockchain and includes the following values:
The MT-root of the board.
The revealed value of cells in the board introduced by previous
moves.
Presentation layer -
HTML and JScript code
Logic layer-
Battleships smart contract
Data layer -
Stored on the blockchainMT.solWeb3.js
Board
Committed values
Figure 2: Overview of the architecture's scheme
3.5 Security analysis
We give a brief security analysis by considering a semi-honest attacker whose
computational resources are polynomial bounded. We defer the formal proofs
to a full version of this article that will be published in a Journal.
3.5.1 Security
This game inherits the basic security mechanism of blockchain which in-
cludes:
Authentication of the players during the game will be performed via
private key which controls their wallet.
9
Authorization of performing moves in turns by restricting moves to
current player's turn, using smart-contract restrictions.
We now proceed to analysis of countermeasures to the types of cheats
that were introduced in Section 3.1.
Types of cheats. As discussed previously, any kind of cheat will be
punished immediately, by enforcing the rules in the smart contract code.
Table 2 describes cases of potential cheats and how the architecture monitors
such cheats in the smart contract.
Type of cheat Countermeasure mechanism
Unresponsive player Each turn is time bounded8.
Dynamically changing ships The board is committed via MT-root which
stays permanent during a game instance.
Any attempt to change the location will pro-
duce a fake proof, that the smart contract
identi es.
Inappropriate placement The winner is forced to reveal his
eet before
he receives the payment. In case the amount
or layout of the battleships does not follow
the rules, the player will be punished.
Table 2: Types of cheats and the corresponding countermeasure mechanisms
programmed into the smart contract code.
3.5.2 Privacy
The main privacy issue is how to hide the locations of players' battleships.
Since the entire data in the transactions and smart contracts' elds are public,
it must be ensured that they have not exposed parts of battleships locations
which are yet to be made public. To that end, we examine the messages in
each round of the game.
1.Commitment phase - hash function is a one-way function by de ni-
tion (i.e. given an hash output, it is hard to compute the corresponding
input). Thus, an attacker that wants to match boards of size 100 with
10
the root hash value will have to generate the entire board cells and then
compute its MT. Since, we concatenate to each block data, a random
number with a suciently large length, the whole computation com-
plexity is approximately O((2)100), where we denote as the length
ofrvalue in bits and in this experiment we use >128 bits. Hence,
the locations of the boards remain private against a computationally
bounded adversary.
2.Launching torpedoes phase - in every turn, a player must reveal his
targeted board's cell that was threatened by the previous turn. To this
end, he broadcast a MT-path from the MT-root till that cell. It is easy
to see that the publicly path does not reveal any other intermediate
values, which in order to guess them, the attacker needs to generate 2
values, as cryptographic hash function is a one-way function.
3.Termination phase - the purpose of this phase is to reveal the can-
didate's
eet location. Thus, we do not consider any privacy issues in
this phase, since the locations are not kept secret at the end of the
game.
3.6 Computational analysis
This section is concerned with the communication and computational anal-
yses, which is important to understand the complexity of executing the
game, since the computational cost of the game (in Ethereum gas units)
is proportional to the number of operations and the data transmitted to the
blockchain. However, we defer the in-depth details of these analyses to the
full publication of this article, while we provide here only theoretical analysis.
Let us denote [ H], [B], [BS] as the length in bits of the hash function's
output, the amount of the cells in the Battleships' board, and the amount of
battleships in the game, respectively.
Communication analysis.
Commitment phase -both players transmit [ H] bytes of the MT-root.
Torpedo launching phase - red in each round, the current player trans-
mits MT-path of size [ H]log([B]) and a cell number of size log([ B])
bits.
11
Computational analysis.
It is easy to see that the major costly operations derive from the MT
proof checking and the termination phase. Thus, the former computation is
bounded by 2[ B][MTP ], where [MTP ] denotes the cost of executing MT-
proof, and the latter computation is bounded by the number of battleships.
This is due to the fact that once the valid battleships cells have been received,
we only need to check that they are tied to each other.
4 Future advance mechanisms
In this section, we present ideas that describe how to extend our proposed
game, to include more sophisticated game features and advance game man-
agement mechanisms. We also defer the comprehensive description of those
features to the full publication version of this article.
4.0.1 Game variations
Multi-player case. A trivial extension is to simply increase the game
ton-multi-player game, where in each turn a player will have the priv-
ilege to choose the speci c board to launch a torpedo toward.
Additional assets. In this case, several additional features will be
included such as mines, shes, etc. Players can purchase assets and
obtain more rewards for the players. For example, an event of discov-
ering a mine will immediately release a xed amount of money to the
adversary. These assets can easily be an ERC tokens (both ERC-20 or
ERC-721 types).
4.0.2 Game management
Minimize the cost of moves. Since each move involves executing
transaction on the blockchain, it is desirable to minimize the number of
operations to reduce the costs of playing the game. One possible way is
to decrease the number of data that is pushed to the main blockchain,
by using plasma chains [8]. The latter approach will also increase user
usability, since every move will not enforce metamask transaction pop-
box.
12
Enforcing the locations of battleships in advance. To enforce
that the players' boards is containing exactly (prede ned constant) T
cells of battleships and that each battleships parts are in a consecutive
manner, we can use zero-knowledge proofs. This approach however may
increase the communication complexity overhead, which inherently in-
creases the cost of playing the game.
Catching cheaters in advance. In our proposed mechanism, despite
devising a means for discovering a cheater who tries to change the
battleships' locations or sizes and also preventing any form of money
theft from the other player at the end of the game, the cheater could
still manage to waste the time of the player and postpone the immediate
discovery of cheating until the end of the game.
This phenomena occurs since the committed MT-root does not provide
a proof of arrangement of the battleships, because the validation of the
arrangement only occurs at the end of the game, by the smart contract
code. A solution to this problem is to use the zero-knowledge schemes.
In contrast to this, we can transform this "bug" into a feature by adding
a nice "Poker" mechanism feature to the game which allows the players
the ability to blu each other. As such, a player can choose whether
he cheats in advance or not, in the rst phase of the game. At any
point in time in the game, a player can then guess whether the other
player had blu ed him or not. In case the "cheat" is con rmed, then
the player will receive an amount that is inversely proportional to the
number of rounds of game already played.
Blacklist of cheaters. After detecting cheats in the game, it is pos-
sible to take actions against the users that were involved in cheat-
ing. Such actions to those cheaters can be ban them from partici-
pating. However, this feature is in con
ict with our requirement to
non-censorship game.
5 Conclusion
In this article, we proposed the rst decentralized Battleships game,
which is composed of various cryptographic components to enforce
13
fairness, keep battleships' location secret and protect honest players
from malicious cheaters. Furthermore, playing battleships over the
blockchain provides major bene ts such as making the game DDoS re-
sistant, where the money is transferred immediately to the winner, or
to the opponent in case cheating is discovered. The logic of the game
is developed using solidity language and deploy over the Ethereum
blockchain as a smart contract.
6 Acknowledgment
I would like to thank Viki for giving the opportunity to work on this
problem. I also want to thank Oded Leiba for the valuable technical
discussions, Christiaan Verhoef and Bert Bosman from Amsterdam who
showed enthusiasm which encouraged me to re ne this game, Polina
Zilberman for helping me with proofreading and last but not least, my
supervisor Dr. Rami Puzis from the BGU university.
References
[1] https://github.com/ameensol/merkle-tree-
solidity/blob/master/src/merkleproof.sol.
[2] https://github.com/ethereum/web3.js/.
[3] https://metamask.io/.
[4] https://threatpost.com/blizzard-entertainment-hit-with-weekend-ddos-
attack/127440/.
[5] Nicola Atzei, Massimo Bartoletti, and Tiziana Cimoli. A survey of at-
tacks on ethereum smart contracts (sok). In International Conference
on Principles of Security and Trust , pages 164{186. Springer, 2017.
[6] Mohammad Karami and Damon McCoy. Understanding the emerging
threat of ddos-as-a-service. In Presented as part of the 6th USENIX
Workshop on Large-Scale Exploits and Emergent Threats , Washington,
D.C., 2013. USENIX.
14
[7] Ralph C Merkle. A digital signature based on a conventional encryption
function. In Conference on the theory and application of cryptographic
techniques , pages 369{378. Springer, 1987.
[8] Joseph Poon and Vitalik Buterin. Plasma: Scalable autonomous smart
contracts. 2017.
[9] Wikipedia contributors. Battleship (game) | Wikipedia, the free ency-
clopedia, 2018. [Online; accessed 15-June-2018].
[10] Wikipedia contributors. Multitier architecture | Wikipedia, the
free encyclopedia. https://en.wikipedia.org/w/index.php?title=
Multitier_architecture&oldid=822900859 , 2018. [Online; accessed
16-July-2018].
[11] Gavin Wood. Ethereum: A secure decentralised generalised transaction
ledger. Ethereum Project Yellow Paper , 151:1{32, 2014.
15
A Appendix
Figure 3: A screenshot from the current UI implementation.
16
|
{
"id": "1807.08142"
}
|
2305.07608
|
Torrent Driven (TD) Coin: A Crypto Coin with Built In Distributed Data
Storage System
|
In recent years decentralized currencies developed through Blockchains are
increasingly becoming popular because of their transparent nature and absence
of a central controlling authority. Though a lot of computation power, disk
space, and energy are being used to run this system, most of these resources
are dedicated to just keeping the bad actors away by using Proof of Work, Proof
of Stake, Proof of Space, etc., consensus. In this paper, we discuss a way to
combine those consensus mechanism and modify the defense system to create
actual values for the end-users by providing a solution for securely storing
their data in a decentralized manner without compromising the integrity of the
blockchain.
|
http://arxiv.org/pdf/2305.07608v1
|
Anirudha Paul
|
cs.DB
|
cs.DB
|
TO R R E N T D R I V E N ( T D ) C O I N :
A C R Y P TO C O I N W I T H B U I LT
I N D I ST R I B U T E D DATA
STO R A G E SYST E M
/a.sc/n.sc/i.sc/r.sc/u.sc/d.sc/h.sc/a.sc /p.sc/a.sc/u.sc/l.sc
/a.sc/b.sc/s.sc/t.sc/r.sc/a.sc/c.sc/t.sc
In recent years decentralized currencies developed through Blockchains are increas-
ingly becoming popular because of their transparent nature and absence of a central
controlling authority. Though a lot of computation power, disk space, and energy
are being used to run this system, most of these resources are dedicated to just keep-
ing the bad actors away by using Proof of Work, Proof of Stake, Proof of Space, etc.,
consensus. In this paper, we discuss a way to combine those consensus mechanism
and modify the defense system to create actual values for the end-users by pro-
viding a solution for securely storing their data in a decentralized manner without
compromising the integrity of the blockchain.
/one.taboldstyle /i.sc/n.sc/t.sc/r.sc/o.sc/d.sc/u.sc/c.sc/t.sc/i.sc/o.sc/n.sc
When Bitcoin [ 1] was first introduced, its Proof of Work consensus showed us a dif-
ferent way to combat adversaries. Rather than blocking the adversaries directly, it
made an attack on the network extremely expensive. Though this system is prevent-
ing any major attack on the system, the tendency of centralized mining is increasing
exponentially. Nowadays, ordinary people can not participate in bitcoin mining. As
a result, the system is not as democratized as initially thought.
Another widespread consensus is Proof of Stake, which is adapted in Ethereum
2.0[2], Cardano [ 3], etc., coins, where you stake your own money to participate.
Any bad behavior can cost the miners loosing their staked money. Though it is
power efficient, staking actual currency without doing any work can cause nothing
at stake, whale problems, etc.
To address this, we propose a consensus that uses this distributed network of the
miner to store user data. By securely storing this data and continuously proving the
proof of storage, the miners will earn seed points. Instead of staking the currency
they maintain, they will use these earned seed points to claim a spot in the mining
round. In a nutshell, miners should provide some utility to earn points that enable
them to mine further to get actual cryptocurrency. Spoofing the process of earning
these seed points is hard enough so that honest miners have no incentive to deviate
from the intended flow. And as the underlying mining technology is basically
already tested proof of stake, the integrity of the network is as good as other similar
currencies like Ethereum 2.0, Cardano, etc.
1arXiv:2305.07608v1 [cs.DB] 12 May 2023
/p.sc/r.sc/e.sc/v.sc/i.sc/o.sc/u.sc/s.sc /w.sc/h.sc/i.sc/t.sc/e.sc/p.sc/a.sc/p.sc/e.sc/r.sc /s.sc/h.sc/o.sc/r.sc/t.sc/c.sc/o.sc/m.sc/i.sc/n.sc/g.sc/s.sc 2
/two.taboldstyle /p.sc/r.sc/e.sc/v.sc/i.sc/o.sc/u.sc/s.sc /w.sc/h.sc/i.sc/t.sc/e.sc/p.sc/a.sc/p.sc/e.sc/r.sc /s.sc/h.sc/o.sc/r.sc/t.sc/c.sc/o.sc/m.sc/i.sc/n.sc/g.sc/s.sc
In the Bitcoin whitepaper [ 1], the author introduced the idea of Proof Of Work,
where miners need to continuously try different nonce to generate a hash with
the required number of zero bit. But as time goes by, this challenge of generating
the intended hash has become so complex that nowadays it is near impossible to
mine effectively without designated ASICS, which is a huge waste of money and
resources for doing calculations that have no other purpose other than showing
commitment.
To address this, Ethereum is introducing Proof of Stake in its system. But it also
introduces new problems - as now the "work" part is removed to show commitment,
miners have the opportunity to sign multiple blocks from parallel chains, making
it hard for forks to converge. Though Ethereum punishes this behavior by slashing
the coin of bad actors, it is still not completely secured from manipulation by the
whales who have the majority coin in control.
Another consensus mechanism is proof of space [ 4], where the miner fills up
its disk space with garbage information generated by mathematical hash functions,
and the verifier sends them challenges from time to time to validate whether the
miner is holding the data or not. The issue is the disk space the data is taking is not
meaningful. It is there to show commitment, just like Bitcoin’s proof of work.
We shall see later in this paper that it is possible to address some of the shortcom-
ings mentioned above.
/three.taboldstyle /a.sc/r.sc/c.sc/h.sc/i.sc/t.sc/e.sc/c.sc/t.sc/u.sc/r.sc/e.sc
The whole mechanism of this blockchain can be described with three sections.
1. Block structure
2. Consensus
3. Method of issuing the token
The block structure follows the standard Bitcoin block structure. The consensus
follows the proof of stake mechanism, but it is different in a sense it doesn’t use
the main coin as a stake but instead uses a secondary Seed Bonus Token. The main
deviation comes in the method of issuing the tokens. Though the main Torrent
Driven Coin (TD Coin) is issued with a standard block reward, the secondary coin
(Seed Bonus Token) needed for staking and mining has a different structure for
minting.
/three.taboldstyle./one.taboldstyle Block structure
Transactions are arranged in standard Bitcoin format, where every transaction is
spent from the coin, not from the account. The coin owner transfers the coin by
digitally signing the hash of the previous transaction and adding the next owner’s
public key at the end. The new owner can verify the signatures by following the
chain of ownership. And to avoid double-spending, only the oldest transaction of
any coin is considered valid. 1
In the chain, many of these transactions are arranged in a block, and their hash
value links them. The size of the blocks can be dynamically adjusted like Ethereum
based on the network congestion. 2
/a.sc/r.sc/c.sc/h.sc/i.sc/t.sc/e.sc/c.sc/t.sc/u.sc/r.sc/e.sc 3
Figure 1: Transaction structure (Standard Bitcoin) [ 1]
Figure 2: Block structure (Standard Bitcoin) [ 1]
/three.taboldstyle./two.taboldstyle Consensus
In this modified proof of stake consensus mechanism 3, miners agree to lock up the
whole amount of their secondary coin, "Seed bonus token," for getting the chance
to validate new blocks of data to be added to a blockchain.
The blockchain algorithm selects validators from the pool of queued miners based
on how much seed bonus token their accounts have. The more seed bonus token
a miner has, the better chance of being chosen to mine and earn newly minted
primary crypto - "Torrent Driven Coin" as a reward if the block gets added to the
main chain. A portion of their seed bonus token is burnt to encourage future data
seeding, and the rest is returned back to their wallet again.
If a validator is caught cheating, they could be punished by burning all their seed
bonus tokens and sending them to an unusable wallet address to which nobody has
access, making them useless forever.
/three.taboldstyle./three.taboldstyle Method of issuing the token
There are three types of token present in this system.
/one.taboldstyle. /t.sc/o.sc/r.sc/r.sc/e.sc/n.sc/t.sc /d.sc/r.sc/i.sc/v.sc/e.sc/n.sc /c.sc/o.sc/i.sc/n.sc This is the standard-issue coin that can be exchanged
between any parties present in the blockchain. It can only be minted by mining a
block in this blockchain. Other than exchanging it as money, users can also burn an
Figure 3: A high level architecture of Proof of Stake consensus [ 5]
/a.sc/r.sc/c.sc/h.sc/i.sc/t.sc/e.sc/c.sc/t.sc/u.sc/r.sc/e.sc 4
Figure 4: Minting Leech Token
Figure 5: Hosting Request
amount of this coin through a smart contract present in layer 1of this blockchain to
get the Leecher Token described below. The exchange rate between Torrent Driven
Coin and Leecher Token will be adjusted dynamically based on supply and de-
mand.
/two.taboldstyle. /l.sc/e.sc/e.sc/c.sc/h.sc/e.sc/r.sc /t.sc/o.sc/k.sc/e.sc/n.sc Leecher token grants the ability to upload your data to other
users or the ability to host other’s data in your machine and get a seed bonus.
The only way to get this token is to send an amount of Torrent Driven coin to the
predefined smart contract, and the smart contract will burn the coin and give the
sender an amount of Leecher Token in exchange. The exchange rate is controlled
algorithmically to address the supply-demand issue.
Each leech token grants access to upload or host one MB of data. If a user wants
to host 30MB of data in one additional copy in the network, the process will work
like this -
For example, Alice wants to make a copy of her data on the peer-to-peer network,
and Bob wants to host data for the seed bonus token.
First, Alice will send an amount of Torrent Driven coin to a specific smart contract
to get 30or more leecher tokens as seen in figure 4.
Then she will join the pool of hosting requests to find possible hosts with the 30
leecher tokens in place like in figure 5.
On the other side, Bob also needs to exchange Torrent Driven coins to show
commitment as a seeder. So, for example, if he has 50leecher tokens, he can request
the smart contract to match him with 50*5=250data blocks. Five is a constant
here, representing each data block will be saved by at least five seeders.
Then what the smart contract will do is match each block Alice wants to host
with five different Host addresses. Alice will see all the public keys associated with
each of her data blocks on the blockchain. The application on her side will make
five copies of data, add the public key to those blocks, make a torrent tracker and
publish the tracker on the blockchain. When the tracker gets published, the group
of hosts, including Bob, can see the tracker and use that to only download the data
attached with their individual public keys in header like in figure 6.
Not only that, each payload is also appended with the seeder’s public key and
random value before encryption which prevents seeders from swapping the pay-
load between them. Each seeder has his own unique version of the same payload
/a.sc/r.sc/c.sc/h.sc/i.sc/t.sc/e.sc/c.sc/t.sc/u.sc/r.sc/e.sc 5
Figure 6: Data Distribution Process
Figure 7: Payload Structure
that the data owner himself can only decrypt. Sample payload structure can be seen
in figure 7
The entire process will be covered with Byzantine Fault tolerance and other cross-
chain swap techniques if any involved party deviates from the intended path like
- going offline, not signing, false signing, etc. The smart contract will ensure that
other parties won’t have to consume the loss.
So this leecher token is not exchangeable between addresses. It can only be used
for hosting purposes.
/three.taboldstyle. /s.sc/e.sc/e.sc/d.sc /b.sc/o.sc/n.sc/u.sc/s.sc /t.sc/o.sc/k.sc/e.sc/n.sc The sole purpose of this token is to ensure data integrity
and facilitate staking for proof of stake.
The first issue is - what is the guarantee the host is storing the data? The standard
checksum used in torrent systems is not enough to solve this issue. Because to do
that, they need to send the data back to the validator, which is slow and unnecessary.
Instead, the original data owner or some other validator can save some part of that
data in their system, and after random intervals, they can request zero-knowledge
proof from the host. The time to response window is short to avoid data recreation.
If they can’t respond with the proof within that window, that proves the miner
is either offline or is not actually storing the data - both of these behaviors are
unacceptable.
But if they can respond to the challenge properly along with able to send a small
amount of data to show seed liveliness and passing the checksum test, the host
will be rewarded with a small amount of new token - the "Seed Bonus Token." The
download and checksum check is similar to a normal torrenting system [ 6]. And
the zero-knowledge proof is similar to this proof of space and time described in this
paper [ 7]. The whole process can be done in side chain and only publish the result
in main chain to claim reward.
/s.sc/c.sc/a.sc/l.sc/a.sc/b.sc/i.sc/l.sc/i.sc/t.sc/y.sc, /s.sc/e.sc/c.sc/u.sc/r.sc/i.sc/t.sc/y.sc, /a.sc/n.sc/d.sc /r.sc/e.sc/s.sc/o.sc/u.sc/r.sc/c.sc/e.sc-/e.sc/f.sc/f.sc/i.sc/c.sc/i.sc/e.sc/n.sc/c.sc/y.sc 6
Now there is a new token in the account for potential miners - which is really
hard to spoof and also not transferable. That’s why the reason to be a bad actor to
earn this token is very slim. So the possibility of the host honestly seeding the data
is much higher.
But now the question is, what is the incentive for them to earn this reward? The
answer is - they can use this reward to stake to get a chance to validate a block and
get the block reward in TD coin, which is actually transferable and can be used as
actual currency.
From this point on, the consensus falls back on the proof of stake. The difference
here is - instead of staking the main currency, the miners are staking their off-chain
hosting work reward earned through proof of space.
/four.taboldstyle /s.sc/c.sc/a.sc/l.sc/a.sc/b.sc/i.sc/l.sc/i.sc/t.sc/y.sc, /s.sc/e.sc/c.sc/u.sc/r.sc/i.sc/t.sc/y.sc, /a.sc/n.sc/d.sc /r.sc/e.sc/s.sc/o.sc/u.sc/r.sc/c.sc/e.sc-/e.sc/f.sc/f.sc/i.sc/c.sc/i.sc/e.sc/n.sc/c.sc/y.sc
The system is scalable in a sense underlying everything; it is still using the proof
of stake as its consensus, which is pretty scalable and secured under heavy transac-
tions. One might think the whole process of downloading and uploading data can
slow down the main blockchain - which is not true. Cause the upload, download,
and zero-knowledge prove that part of the system can be considered off-chain work
and side chain transactions. Only the result of each checkpoint is published on the
main chain. So the main chain is not slowed down by all the bottlenecks associated
with proof of space.
The main network will continue to use proof of stake, but the staked coins are
influenced by tokens earned in the independent torrenting mechanism and proof of
space consensus.
The whole system is resource-efficient because though miners have to do a lot
more work, especially allocating more disk space than the traditional proof of stake,
the silver lining is that those works are not wasted work. By using the storage, they
are providing actual value to the users. There is no unnecessary computation power
used like Proof of Work.
Overall the security is also up to par with other traditional coins. The original
main network is using proof of stake, which after many iterations and research, is
now pretty secure to be relied upon. The staked point is going through the zero-
knowledge proof of space and byzantine consensus, making it extremely hard to
profit from being a bad actor. There is no way to replicate the data as the packets are
encrypted with individual miner’s public key, and the only way to list your public
key as a valid host is by burning actual Torrent Driven coins. As the exchange rate
is controlled algorithmically, it is not easy to inflate or deflate the staking pool or
make a hostile takeover.
So by combining all those techniques, the proposed blockchain is scalable, se-
cured, and resource-efficient and provides actual value to users instead of the per-
ceived value of traditional crypto coins.
/five.taboldstyle /c.sc/o.sc/n.sc/c.sc/l.sc/u.sc/s.sc/i.sc/o.sc/n.sc
The primary goal of this white paper was to create intrinsic value for the whole
distributed mining network of blockchain. By incorporating torrenting mechanisms
in the proof of space framework, we have introduced a solution for securely and
reliably storing multiple copies of data on the internet. Moreover, as there are
monetary exchanges and interests involved in the process, the chance of abandoning
the torrent by the seeder is really low. And it is also a great way to incentify hosting
private encrypted data. In the process, it is now replacing the redundent generated
data of Proof of Space with actual meaningful information.
/c.sc/o.sc/n.sc/c.sc/l.sc/u.sc/s.sc/i.sc/o.sc/n.sc 7
By adding signatures, zero-knowledge proof, and byzantine consensus to track
the seed status and facilitate a reward mechanism, we have minimized the risk of
bad actors creating fake seed data and, in the process, attacking the staking pool.
On the other hand, removing staking coins with staking work has introduced a
toned-down version of Proof of Work in the Proof of Stake mechanism, making the
architecture secure like PoW and scalable like vanilla PoS.
Finally, introducing a utility inside the network and regulating the exchange rate
algorithmically has the possibility of reducing the current deflationary nature of the
crypto coins where no one wants to use it in the real-world other than speculative
investment. Because in this architecture, seeding more data is beneficial for the
miners, that’s why the competition among miners can reduce the cost for the storing
data for the normal users.
/r.sc/e.sc/f.sc/e.sc/r.sc/e.sc/n.sc/c.sc/e.sc/s.sc 8
/r.sc/e.sc/f.sc/e.sc/r.sc/e.sc/n.sc/c.sc/e.sc/s.sc
[1] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2009 .
[2] Vitalik Buterin. Ethereum white paper: A next generation smart contract &
decentralized application platform. 2013 .
[3] Aggelos Kiayias, Alexander Russell, Bernardo David, and Roman Oliynykov.
Ouroboros: A provably secure proof-of-stake blockchain protocol. In Annual
international cryptology conference , pages 357–388. Springer, 2017 .
[4] Stefan Dziembowski, Sebastian Faust, Vladimir Kolmogorov, and Krzysztof
Pietrzak. Proofs of space. Cryptology ePrint Archive, Report 2013 /796,2013 .
https://ia.cr/2013/796 .
[5] Christopher Natoli, Vincent Gramoli, and Paulo Veríssimo. Deconstructing
blockchains: A comprehensive survey on consensus, membership and structure,
08 2019 .
[6] Bram Cohen. Incentives build robustness in bittorrent. In Workshop on Economics
of Peer-to-Peer systems , volume 6, pages 68–72. Berkeley, CA, USA, 2003 .
[7] Tal Moran and Ilan Orlov. Simple proofs of space-time and rational proofs of
storage. In Annual International Cryptology Conference , pages 381–409. Springer,
2019 .
|
{
"id": "2305.07608"
}
|
2312.02164
|
Driver Safety Reward with Cooperative Platooning using Blockchain
|
Cooperative driving (or Platooning) focuses on improving the safety and
efficiency by connecting two or more vehicles on a road by vehicular
communication protocols. The leader is crucial as it manages the platoon,
establishes communication between cars, and perform platoon maneuvers. In this
paper, we proposed a driver incentive model which encourages platooning on
roads leading to driver safety. As, the leader of platoon have multiple
responsibilities than followers, our model rewards more incentives to leader
than followers. These incentives will be rewarded as crypto tokens. This
digital monetization method for both leaders and followers of a platoon is
accomplished by secure transactions using blockchain.
|
http://arxiv.org/pdf/2312.02164v1
|
Sruthi Rachamalla, Henry Hexmoor
|
cs.NI, cs.CR
|
cs.NI
|
Driver Safety Reward with Cooperative
Platooning using Blockchain
Sruthi Rachamalla1and Henry Hexmoor2
Southern Illinois University, Carbondale, IL 62901, USA,
sruthirachamalla@siu.edu and henry.hexmoor@cs.siu.edu
Abstract. Cooperative driving (or Platooning) focuses on improving
the safety and efficiency by connecting two or more vehicles on a road
by vehicular communication protocols. The leader is crucial as it man-
ages the platoon, establishes communication between cars, and perform
platoon maneuvers. In this paper, we proposed a driver incentive model
which encourages platooning on roads leading to driver safety. As, the
leader of platoon have multiple responsibilities than followers, our model
rewards more incentives to leader than followers. These incentives will
be rewarded as crypto tokens. This digital monetization method for both
leaders and followers of a platoon is accomplished by secure transactions
using blockchain.
Keywords: Cooperative Driving, Platooning, Blockchain Technology,
Monetization
1 Introduction
In our daily lives, we spend an average of 47 minutes on the road travelling, and
the traffic on the roads is becoming increasingly crowded. Congested roadways
are linked to longer commute, lower fuel economy, and a higher risk of motor
vehicle collisions. We could save a lot of travel time if we could reduce commuting
times by a fraction of a second. One solution to the traffic problem is cooperative
driving, also known as platooning [1]. Vehicles in a platoon communicate using
an ad-hoc network or other communication protocols. These communication
channels allow platoons to drive closer to one other while maintaining a safe
distance. A platoon of vehicles will have a leader who will interact with the
platoon followers while managing the platoon and overseeing maneuvers. The
platoon leader is in charge of speed, lane changes, braking, and so on, while the
follower vehicles are in charge of following the leader vehicle.
Cooperative driving uses vehicle-to-vehicle and infrastructure-to-vehicle wire-
less communication system. [2] emphasizes the technology aids in the interchange
of data gathered from other cars that is impossible to obtain via on-board sen-
sors. The Advanced Transportation Technology (PATH) project in California [3]
first proposed the idea of cars traveling together on the road in 1980. Cooper-
ative driving can improve the driving experience on the road by relieving the
driver from some of the driving obligations. Traditional sensor based AdaptivearXiv:2312.02164v1 [cs.NI] 24 Oct 2023
2 Sruthi Rachamalla and Henry Hexmoor
Cruise Control (ACC) isn’t enough for cooperative platooning, instead Cooper-
ative Adaptive Cruise Control (CACC) should be considered. CACC broadcasts
information such as speed, acceleration, and distance through wireless commu-
nication. By allowing CACC, the distance between vehicles can be minimized
by following closely, improving both safety and fuel efficiency . The focus on co-
operative driving or platooning has increased globally in recent years because of
the potential it holds in road transportation mainly focusing on automated and
mixed traffic. Truck platooning [4, 5], and CACC [6] were prominent examples
of cooperative driving, which focused on minimizing inter-vehicular distance by
obeying the ”Three Second Rule” safety rule [7].
Having a good leader for a platoon is really crucial in forming, maintaining,
and improving safety. There are a lot of methods in electing a platoon leader. [8]
proposed an incentive based strategy using blockchain to elect leader who is
the best for the safety of the platoon. The other way is through voting [9] to
elect the platoon leader. Some other methods are may be through scoring and
ranking the drivers based on the everyday driving and the driver with best rank
can only initiate platoon formation. Our inspiration is drawn from the ranking
method. We added an incentive or monetization factor for selecting platoon
leader based on rank. When a driver of a vehicle drives everyday there will
be a rank assigned to the driver performance and the driver with a best rank
can become a platoon leader and other drivers will be followers. To encourage
more drivers to be platoon leaders, a monetization system is required that is
fair for all members. This digital monetization is implemented by the usage of
a smart contract in blockchain technology that holds users to a higher level of
behavior, which is promising in this regard. This smart contract establishes what
constitutes acceptable behavior and prevents users from breaking that standard.
The contributions of this paper is organized as follows: Literature Survey on
Platooning and Blockchain is discussed in Section 2. The Cooperative Platoon
Earnings Methodology is described in Section 3. Algorithms for determining the
Earning setup, analysis, scoring model, and monetization heuristic are presented
in Section 4. Section 5 discusses the implementation of Driver Safety Reward
(DSR) on Rinkeby Test Network. Conclusions and future work are presented in
Section 6.
2 Literature Review
The first platooning simulator that was developed was Hestia [10]. This simula-
tor is used for simulating various scenarios using sensors but its drawback is it
do not execute the platooning maneuvers and does not simulate traffic scenarios.
In paper [11], the researchers tried to simulate the mixed traffic scenarios using
SUMO and were unsuccessful. They implemented a car following model based
on CACC to simulate inter-vehicle communication. In [12] paper, they manu-
ally simulated the mixed traffic scenarios and tested the simulator to study the
consequences of CACC on traffic.
Driver Safety Reward with Cooperative Platooning using Blockchain 3
The PLEXE [13] simulation tool is a platooning extension for SUMO [14]
which is open-source and is available to the community. This simulation tool has
different CACC car-following models to experiment. The wireless communication
protocols are available for simulating the formation and platoon management.
Mixed traffic scenarios are available to use and implement platooning maneu-
vers. The authors in paper [15], developed a simulator to implement platooning
maneuvers such as join an existing platoon and merge two platoons. In paper [9],
the authors developed a state-of-the-art simulator based on VENTOS [16] which
uses SUMO. PERMIT [17] is a tool which simulates platooning maneuvers like
join, merge, leave, and split which is built on Plexe [13].
Currently, to our knowledge only few researchers are working on effectively
combining the benefits of blockchain technology with the platooning technology
to better understand the usefulness. The authors in paper [18] used blockchain as
a medium in transportation. They achieved the communication between vehicles
in platoon and blockchain public key infrastructure by securely using hardware-
based side channels. In [19], they decreased the blockchain transaction validation
time, and verified the vehicle identity. The authors in [20], emphasised on using
blockchain with platooning to share information securely and rapidly.
In our preceding work [21], significant features are extracted from a simulated
driving dataset and driver is assigned a rank based on the behaviour on road.
Based on the rank, the driver is offered crypto tokens and these transaction
details along with driver attributes are stored on a blockchain network.
3 Cooperative Platoon Earnings Methodology
This section presents the entire overview of the proposed framework as shown
in the Fig. 1. This methodology put forward in 5 steps, namely: (1) Driving
Rank Designation Model (2) Simulation with Permit (3) Feature Extraction (4)
Digital Monetization with Platoons (5) Storing in Blockchain.
Fig. 1. Cooperative Platoon Earnings Methodology
4 Sruthi Rachamalla and Henry Hexmoor
Driving Rank Designation Model: Each Driver is assigned a rank and
based on the rank, Earning rate of the driver is determined by the heuristic as
proposed in [21].
Simulation with PERMIT: PERMIT [17], an open source platooning
simulator based on SUMO and its platooning extension PLEXE. With PERMIT,
Merge, Join, Leave and Split maneuvers can be performed. Merge is a maneuver
in which two platoons join to form one platoon. Join refers to joining one vehicle
into an existing platoon. Leave maneuver is when a car exists the current platoon.
Split refers to dissolving one single platoon into two platoons. By using the
PERMIT, we simulated all these maneuvers. This provides the data required for
the evaluating the earnings with platoon for a driver.
Feature Extraction: A platoon state represents two features: (1) number of
cars, and (2) distance travelled. For example, in Fig. 2, a platoon is represented
with five different states. First state S1has two cars. Car C3has joined the
platoon reaching to state S2. Similarly, S3is achieved. In contrast, S4is attained
by car C3leaving the platoon. Similarly, S5is also reached.
Fig. 2. States in Platoon
Digital Monetization with Platoons: With the features extracted from
the previous step, we formalized the earnings ( erd) for the driver as summation
of the earnings achieved while he drove in platoons ( erin) and earnings achieved
while driving outside the platoon ( erout)
erd=erin+erout(1)
Because different maneuvers can exist inside the platoon, we decomposed the
earning offered inside the platoon into addition of earnings during join( Per
join)
and leave( Per
leave) maneuver.
erin=Per
join+Per
leave (2)
However, the eroutis calculated as product of previous day earnings rate
(ER d−1) which determined by the Driving Rank Designation Model and distance
travelled by the driver outside the platoon( dout).
Driver Safety Reward with Cooperative Platooning using Blockchain 5
Table 1. List of Notations
Symbol Definition
erd Earnings of particular day
eroutEarnings outside of the platoon
erinEarnings inside of the platoon
ERd−1 Earning Rate of previous day
doutOut-platoon distance
n Number of cars in a platoon
j Number of followers in the platoon in join maneuver
l Number of cars left the platoon in leave maneuver
δ Balancing Factor
η Penalty Factor
S State-of-platoon
Li Length of the platoon at state i
dinIn-platoon distance at state S
w Number of platoons a driver travelled
Per
join(L) In-platoon earnings with join maneuver of leader
Per
join(F) In-platoon earnings with join maneuver of follower
Per
leave(L) In-platoon earnings with leave maneuver of leader
erout=ER d−1∗dout(3)
As mentioned earlier that a platoon leader will have a little favor by this
model due to his responsibilities, Join and Leave Maneuvers are calculated using
two different equation one representing Leader and other Follower in the platoon.
Join Maneuver For every platoon, driver joined on a particular day, and for
all the states in each platoon, calculate the product of the average of the States
of the platoon( Si) and the sum of the previous earning rate of the driver( ER d−1)
andnδ. The State( Si) is defined as the product of the Length of Platoon( Li) at
state iand distance travelled inside the platoon( din
i). Here the term jδis the
additional incentive for the leader of the platoon. It represents the summation of
the balancing factor over the number cars joined in the platoon. The balancing
factor δis used to control the amount of incentive does the drivers will provide
during the platoon. We assigned it as 0.01.
Per
join(L) =wX
p=1nX
i=1Si∗(ER d−1+jδ) (4)
The difference between Leader and Follower is there will be no additional
incentives for follower. Instead, only the balancing factor is added to the previous
day earning rate( ER d−1). Platoon Follower doesn’t require length of the platoon.
So, it just uses the distance travelled in each state.
Per
join(F) =wX
p=1nX
i=1di∗(ER d−1+δ) (5)
6 Sruthi Rachamalla and Henry Hexmoor
Leave Maneuver During the Leave Maneuver, for the leader, instead of the
number of cars joined( j), number of cars left( l) is considered. Additionally, there
will be a penalty if a car leaves the platoon before travelling ηmiles. In other
words, earnings for the followers will start only after travelling ηmiles. The
overhead incurred by changing the structure of the platoon while on the move
is the main reason for penalty. Here we considered η= 10.
Per
leave(L) =wX
p=1[nX
i=1Si∗(ER d−1−lδ) +penalty w] (6)
where,
Si=Li∗din
i (7)
penalty =(
(din−η)∗δ,ifdin< η
0, otherwise
All the notations used in the model as summarized in the table 1.
Storing in Blockchain: For secure transaction of the crypto tokens, the
extracted data from the Feature Extraction step is stored in a blockchain tech-
nology. Features stored are Driver ID, current earnings, Rank designated for the
driver, over speed limit count, distance travelled, number of sharp accelerations,
number of sharp decelerations, number of platoons he joined, platoon leader
activity, earning date.
To ensure the driver safety on road we propose following rules:
–Drivers with rank less than four cannot act as platoon leaders.
–Drivers with rank greater than three are encouraged to be platoon leaders
4 Algorithmic Approach
In this section, we present the multiple algorithms for calculating the earnings
of a day inside a platoon for a driver. In the below pseudo code snippet, total
earnings for a driver in platoon is presented.
Jer:=JoinEarnings
Ler:=LeaveEarnings
Earnings =Jer+Ler
The following snippet calculate earnings for a merge or join maneuver for
leader or follower.
Driver Safety Reward with Cooperative Platooning using Blockchain 7
Join Earnings
i f number cars joining >1:
c a l c u l a t e merge earnings
e l s e
c a l c u l a t e earnings (” l e a d e r ”)
or
c a l c u l a t e earnings (” f o l l o w e r ”)
Similar to above snippet of algorithm, leave maneuver for leader or follower
are presented.
Leave Earnings
i f number cars leaving >1:
c a l c u l a t e s p l i t e a r n i n g s
e l s e
c a l c u l a t e earnings (” l e a d e r ”)
or
c a l c u l a t e earnings (” f o l l o w e r ”)
merge earnings and split earnings refers to earnings that can be awarded in
the merge and split maneuvers respectively for a driver.
Algorithm 1 merge earnings
1:ifplatoon 1leader then
2: earnings (”leader ”)
3:end if
4:ifplatoon 2leader then
5: earnings (”follower ”)
6:end if
7:ifplatoon follower then
8: earnings (”follower ”)
9:end if
Depending on the driver type whether its leader or follower of the platoon,
earnings algorithm is used for a platoon maneuver especially for join. Similarly
penalized earnings is also defined which is used in the case of the leave maneuver.
5 Driver Safety Reward in Platoons
We implemented a simple six car platoon with Join and Leave Maneuvers in
PERMIT. With the data from the PERMIT and the above formulation we cal-
culated the earnings for leader and a follower in both the scenarios. For the
8 Sruthi Rachamalla and Henry Hexmoor
Algorithm 2 split earnings
1:ifplatoon 1leader then
2: penalized earnings (”leader ”)
3:end if
4:ifplatoon 2leader then
5: penalized earnings (”leader ”)
6:end if
7:ifplatoon follower then
8: penalized earnings (”follower ”)
9:end if
Algorithm 3 earnings(driver type)
Require: Per
join= 0, δ= 0.01
1:forwdoinnum ofplatoons :
2: ifdriver =Platoon Leader then
3: Per=ERd−1+ (joined cars∗δ)
4: forsdoinnum ofstates :
5: SP=SP+ (Ls∗ds)
6: end for
7: else
8: Per=ERd−1+δ
9: forsdoinnumber ofstates :
10: SP=SP+ (ds)
11: end for
12: end if
13: Per
join=Per
join+SP+Per
14:end for
Algorithm 4 penalized earnings(driver type)
Require: Per
leave = 0, δ= 0.01
1:forwdoinnumber ofplatoons :
2: calculate penalty
3: ifdriver =Platoon Leader then
4: Per=ERd−1−(leftcars∗δ)
5: forsdoinnum ofstates :
6: SP=SP+ (Ls∗ds)
7: end for
8: else
9: Per=ERd−1+δ
10: forsdoinnum ofstates :
11: SP=SP+ (ds)
12: end for
13: end if
14: Per
leave =Per
leave +SP+Per+penalty
15:end for
Driver Safety Reward with Cooperative Platooning using Blockchain 9
leader, the earnings resulted in 26.29 tokens and in the case of follower its 19.34
tokens.
A Rinkeby test network is used as our Ethereum network for implementation
[21]. On the rinkeby etherscan network, two smart contracts were deployed, one
for tokenization and the other for storing driver records. Tokenization contract
will authorize the driver-record contract with driver DSR test tokens and transfer
a small number of DSR test tokens to the driver-record contract at first.The
driver-record will now be able to credit DSR test tokens to allocated driver’s
wallets based on their rating. The tokenization contract, for example, created
10000000 (107) DSR test tokens and authorized the data-record to spend them.
The contract sends 10000 DSR test tokens to data-record, where they will be
assigned to the drivers.
The tokenization contract, for example, created 10000000 (107) DSR test
tokens and authorized the data-record to spend them. The contract sends 10000
DSR test tokens to data-record, where they will be assigned to the drivers. In the
Admin wallet, the MetaMask represents 9990000 (0.999*107) DSR test tokens.
6 Conclusion and Future Work
In this paper, we proposed a model to incentivize the driver behaviour in the
case of platooning. For this, we articulated a system to calculate the earnings
awarded for a driver in a platoon. This system considers the different maneu-
vers in platoon namely Join, Leave, Merge, and Split. We used PERMIT, a
platoon simulating system to mimic the aforementioned maneuvers. A Rinkeby
test network is used to award the test tokens to driver based on their earnings.
This paper assumes for the platooning in cars, but can be extended to different
homogeneous vehicles and even heterogeneous vehicles.
References
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dayuki Tsugawa. Overview of platooning systems. In Proceedings of the 19th ITS
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InProceedings of the 1st International Workshop on Communication and Comput-
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and Renate Lo Cigno. Plexe: A platooning extension for veins. In 2014 IEEE
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tion scheme for connected vehicles. In 2018 European Conference on Networks and
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proved platoon security. International Journal of Information , 7(2):1–6, 2018.
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|
{
"id": "2312.02164"
}
|
2005.14689
|
Wallet Attestations for Virtual Asset Service Providers and
Crypto-Assets Insurance
|
The emerging virtual asset service providers (VASP) industry currently faces
a number of challenges related to the Travel Rule, notably pertaining to
customer personal information, account number and cryptographic key
information. VASPs will be handling virtual assets of different forms, where
each may be bound to different private-public key pairs on the blockchain. As
such, VASPs also face the additional problem of the management of its own keys
and the management of customer keys that may reside in a customer wallet. The
use of attestation technologies as applied to wallet systems may provide VASPs
with suitable evidence relevant to the Travel Rule regarding cryptographic key
information and their operational state. Additionally, wallet attestations may
provide crypto-asset insurers with strong evidence regarding the key management
aspects of a wallet device, thereby providing the insurance industry with
measurable levels of assurance that can become the basis for insurers to
perform risk assessment on crypto-assets bound to keys in wallets, both
enterprise-grade wallets and consumer-grade wallets.
|
http://arxiv.org/pdf/2005.14689v1
|
Thomas Hardjono, Alexander Lipton, Alex Pentland
|
cs.CR
|
cs.CR
|
Wallet Attestations for Virtual Asset Service Providers
and Crypto-Assets Insurance
(Extended Abstract)
Thomas Hardjono Alexander Lipton Alex Pentland
MIT Connection Science & Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139, USA
hardjono@mit.edu alexlip@mit.edu pentland@mit.edu
1 June, 2020
Abstract
The emerging virtual asset service providers (VASP) industry currently faces a number
of challenges related to the Travel Rule, notably pertaining to customer personal informa-
tion, account number and cryptographic key information. VASPs will be handling virtual
assets of di erent forms, where each may be bound to di erent private-public key pairs on
the blockchain. As such, VASPs also face the additional problem of the management of
its own keys and the management of customer keys that may reside in a customer wallet.
The use of attestation technologies as applied to wallet systems may provide VASPs with
suitable evidence relevant to the Travel Rule regarding cryptographic key information
and their operational state. Additionally, wallet attestations may provide crypto-asset
insurers with strong evidence regarding the key management aspects of a wallet device,
thereby providing the insurance industry with measurable levels of assurance that can
become the basis for insurers to perform risk assessment on crypto-assets bound to keys
in wallets, both enterprise-grade wallets and consumer-grade wallets.
1arXiv:2005.14689v1 [cs.CR] 29 May 2020
Contents
1 Introduction 3
2 Virtual Assets, VASPs and the Travel Rule 4
2.1 FATF Recommendations No. 15 and the Travel Rule . . . . . . . . . . . . . . . . . . . 4
2.2 Key Management Con gurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Customer Accounts & Key Ownership Information . . . . . . . . . . . . . . . . . . . . 6
3 Current Challenges in the VASP Industry 8
3.1 A Secure Messaging Network for VASPs . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Synchronization of Customer Information with Transactions . . . . . . . . . . . . . . . 9
3.3 Direct Transactions from Wallets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.4 VASP Transactions to Private Wallets . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.5 On-Boarding and O -Boarding Customers . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.6 Cross-Jurisdiction Asset Transfers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 A Standard Architecture for Attestations 12
4.1 Attestations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Entities, Roles and Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 Summary of an Attestation Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Wallet Attestations to Support VASPs 17
5.1 Wallet Devices and Trusted Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.2 Basic Wallet Attestation Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.3 Types of Attestation Evidence and Relevance to VASPs . . . . . . . . . . . . . . . . . 21
6 Attestation Services within VASP Trust Networks 23
7 Areas for Innovation 26
8 Conclusions 27
2
1 Introduction
The emerging virtual asset service providers (VASP) industry currently faces a number of
challenges related to the Travel Rule, notably in connection to customer personal information,
account number and cryptographic key information. Given that VASPs will be handling
virtual assets of di erent forms, where each may be bound to a di erent private-public key
pairs on the blockchain, VASPs also face the additional problem of key management generally.
The key management aspects pertain not only to VASPs themselves { as owners of their
private-public keys { but also to customers who posses wallets (non-custodial) that hold
their private-public keys. Most end-user consumers have never had to \handle" raw keys or
own wallets [1]. From the perspective of the Travel Rule the private-public keys in customer
wallets become a concern to a VASP when the customer associates their public-key with their
account at the VASP, but then use the private-key for direct wallet-to-wallet transactions.
In cases where a VASP becomes a custodial of a customer's private-public key, then the
VASP must apply the same degree of protection as it does for its own keys. That is, the key
management lifecycle [2] must be a core part of the cyber-security strategy of the VASP. The
cyber-resilience a VASP's key management infrastructure has an impact on the business of
being a VASP, including obtaining insurance for the virtual assets in its possession. Reports
of successful hacks on cryptocurrencies [3, 4, 5] has the potential to tarnish the VASP industry
as a whole. Thus, a move towards a new decentralized economy [6, 7] brings with it new
challenges arising from the need to decentralized computing infrastructures, and the need to
understand \trust" through a decentralized lens.
The recent FATF Recommendations treats virtual assets as a digital representation of
value, and as such is covered under the existing Anti-Money Laundering (AML) regulations
and the Travel Rule. This means VASPs must be able to obtain, verify, retain and share
respective customer information (originator and bene ciary) in the same manner as existing
nancial institutions. This means, among others, personal information, account information,
and transactions information. However, as pointed out in [8], in the case of virtual assets on
a blockchain, the key-ownership information and key-operator information becomes another
aspect of the customer's account that VASPs must manage. This is because virtual assets
on blockchains are directly controlled by cryptographic keys, and therefore by the entity who
controls the private-public key pair.
In this paper, we seek to address technological means to provide the ownership information
for private-public keys that are located within a customers wallet device. More speci cally,
we explore how attestation technologies as applied to wallet systems can provide VASPs with
means to prove operational controls of keys in wallets. The capability to obtain \visibility"
into the state of keying material in a wallet device { without revealing the customer's private-
keys { provides a good basis for a VASP to perform management (remote management) of
the customer wallet. This in turn allows VASPs to address some of the regulatory compliance
requirements arising from the Travel Rule.
This paper is arranged in the following manner. We discuss the Travel Rule in Section 2,
and discuss some of the corresponding challenges to VASPs in Section 3.
3
In Section 4 we review the concept of device attestations in the context of the VASPs
and blockchains use-case, as a means to assist a VASP in obtaining better visibility into the
internal state of the wallet devices [9, 10]. We review the current e orts in industry relating
to the standardization of attestation architectures and evidence conveyance protocols. We
discuss the application of device attestations to wallets in Section 5, and explore how com-
munities of VASPs arranged in a consortium or \trust network" can use of shared attestation
services as a means for VASPs to collectively address some of the challenges arising due to
the Travel Rule.
In the current work, we seek to make the concepts around device attestations to be
more easily understandable and more accessible to the broad readership interested in VASPs,
wallets and the virtual assets industry generally. Much of the language describing attestations
come from the area of trusted computing, which has been heavily in
uenced by the two-
decades of the development of the Trusted Platform Module (TPM) chip [11, 12]. As such,
we will strive to abstract-up from the various design features of the TPM and focus on
the intent of some of these features, narrowing our interest on those features that support
attestation and its potential use for VASPs in their ecosystem. Readers interested in details
of the TPM are directed to the excellent works of [13, 14, 15].
2 Virtual Assets, VASPs and the Travel Rule
Since the advent of the Bitcoin system in 2008 [16], there has been an ever-growing interest
among the general public in the potential use of crypto-currencies (\crypto") and virtual as-
sets for decentralized unmediated nancial transaction. One of the key issues in this nascent
crypto industry is the need for virtual asset service providers to comply with the various -
nancial regulations related to Anti-Money Laundering (AML), terrorism nancing and other
banking related regulations. At the international level, the inter-governmental body estab-
lished to set the standards and promote the e ective implementation of legal, regulatory and
operational measures is the Financial Action Task Force (FATF) [17].
2.1 FATF Recommendations No. 15 and the Travel Rule
A major milestone event in the crypto-currencies industry was the publication of the FATF
Recommendation No. 15 in late 2018 which provided a comprehensive de nition the notion
ofvirtual Assets and virtual Asset Service Providers (VASP) [18]:
•Virtual Asset : A virtual asset is a digital representation of value that can be digitally
traded, or transferred, and can be used for payment or investment purposes. Virtual
assets do not include digital representations of at currencies, securities and other
nancial assets that are already covered elsewhere in the FATF Recommendations.
•Virtual Asset Service Providers (VASP): Virtual asset service provider means any nat-
ural or legal person who is not covered elsewhere under the Recommendations, and
as a business conducts one or more of the following activities or operations for or on
4
behalf of another natural or legal person: (i) exchange between virtual assets and at
currencies; (ii) exchange between one or more forms of virtual assets; (iii) transfer of
virtual assets; (iv) safekeeping and/or administration of virtual assets or instruments
enabling control over virtual assets; and (v) participation in and provision of nancial
services related to an issuer's o er and/or sale of a virtual asset.
In this context of virtual assets, transfer means to conduct a transaction on behalf of another
natural or legal person that moves a virtual asset from one virtual asset address or account
to another [18].
One of the main requirements called out in the FATF Recommendation No. 15 and its ac-
companying Guidelines document [19] is the mandated need for VASPs to retain information
regarding the originator and bene ciaries of virtual asset transfers. That is, a VASP must
posses accurate information regarding a customer before aiding that customer in conducting
transactions in virtual assets.
Another important implication of the Recommendation No. 15 is that cryptocurrency
exchanges and related VASPs must be able to share the originator and bene ciary information
for virtual asset transactions. This process { also known as the funds Travel Rule { originates
from the US Bank Secrecy Act (BSA 31 USC Secs. 5311-5330), which mandates that nancial
institutions deliver certain types of information to the next nancial institution when a funds
transmittal event involves more than one nancial institution. This rule became e ective in
May 1996 and was issued by the U.S. Treasury Department's Financial Crimes Enforcement
Network (FinCEN). Other groups of nancial institutions (e.g. Wolfsberg Group [20]) have
also tailored their AML principles based on the FATF Recommendations.
2.2 Key Management Con gurations
In contrast to traditional banking institutions { which have been operating under the same
Travel Rule for over two decades { VASPs today have the additional problem of dealing with
cryptographic keys associated with customers. This stems from the fact that virtual assets
on a blockchain is directly controlled through the private-public key associated with that
virtual asset.
For VASPs there are a number of possible models or con gurations with regards the
management and use of the private-public key pair used to sign transaction on the blockchain.
Two of the con gurations relevant to the current discussion are shown in Figure 1 (see [8]
for other variations).
In con guration (a) of Figure 1, the customer holds its private-public keys in the cus-
tomer's wallet. The Originator-VASP holds a copy of the customer's public key, possibly
enveloped within a digital certi cate (e.g. X.509 certi cate [21, 22, 23]). There are at least
two ways involving VASPs for a wallet-based key-pair to be used by the originator customer
to transfer virtual assets to a bene ciary. In the rst case, the originator creates the signed
transaction (addressed to the bene ciary) and delivers to its VASP (Originator-VASP), re-
questing the VASP to transmit the virtual asset onto the blockchain. This provides the
5
Figure 1: Two possible VASP cryptographic key management con gurations (after [8])
opportunity for the VASP to perform the relevant Travel Rule veri cations with regards to
the destination bene ciary. Thus, technically speaking the VASP acts similar to a forwarding
\gateway" that processes \ready-to-transmit" transactions signed by the private-key in the
customer wallet. In the second case, the originator customer is the entity actually transmit-
ting the transaction (i.e. direct from its wallet). However, before doing so the the originator
relies on its VASP to perform the Travel Rule veri cations regarding the destination bene -
ciary. Then, once the originator customer obtains permission (\green light") from its VASP,
the originator transmits the transaction directly from its wallet. In either of these cases, the
Travel Rule applies to the VAPSs, the originator and the bene ciary.
In con guration (b) of Figure 1 the customer does not hold any private-public keys or
own any wallet. Instead, the customer opens an account at the VASP and all the customer's
asset-transactions are dealt with by the VASP. In this approach, the VASP has at least two
options with regards key management. In the rst option, the VASP could hold a separate
private-public key pair for each customer and employ that key-pair on behalf of the customer
when transacting the customer's virtual assets. This approach is often referred to as the key
custodial con guration [24]. In the second option, the VASP employs its own private-public
key pair to sign all asset-related transactions. In this case various customer assets can be
said to be commingled . With commingled assets/accounts, the VASP can batch together
multiple transactions from its various customers, thereby reducing the overall cost (fees) of
the transaction.
2.3 Customer Accounts & Key Ownership Information
Aside from the customer personal or business information, the Travel Rule also requires
that nancial institutions and VASPs furnish the account numbers when performing asset
6
transfers. This brings a number of interesting challenges with regards to how \account
numbers" are to be realized in the case of virtual assets on the blockchain and how private-
public keys are to be associated with accounts.
With regards to account numbers, the work of OpenVASP [25] has proposed the estab-
lishment of a standardized VASP code for each VASP consisting of the last 32-bits of the
public-key of the VASP. Although the proposal is expressed in the context of the Ethereum
system [26], the notion of a 32-bit VASP code is generally speaking logical, practical and
indeed necessary from a VASP operations point of view. The 32-bit VASP code provides
sucient bit-space to uniquely identify up to millions of VASPs in the future. When used
in the context of Ethereum, VASPs also have the option of claiming a namespace within the
Ethereum Name Service (ENS), and thus allowing the VASPs public-keys (i.e. its customer's
public-keys) to be known and discoverable within the ENS context. The proposal of [25] also
includes the notion of a unique Virtual Asset Account Number (VAAN) for each customer,
where the rst 32-bit of the VAAN number is the VASP code. This proposal is logical and
reasonable, and it mimics the familiar bank ABA routing numbers and account numbers.
Given that private-public key pairs are the means to control virtual assets, we believe that
for compliance to the Travel Rule, VASPs will also need to maintain information regarding
the key-pairs of their customers. VASPs most likely will be required to share key-related
information or certi cates with other VASPs and nancial institutions involved in virtual
asset transfers. Key-related information should be considered as another attribute of the
customer account information that is required in order to comply to the Travel Rule.
Thus, in the context of the various possible key management con gurations (Section 2.2
and Figure 1), it is useful to distinguish between key-ownership information andkey-operator
information [8]. This is particularly important for VASPs from a risk management and
assets-insurance perspective [27, 28].
•Key ownership information : This is information pertaining to the legal ownership of
cryptographic public-private keys. The traditional mechanism to denote legal ownership
is through the registration of the public-key to a Certi cation Authority (CA), and for
the CA to issue a public-key certi cate for that key [29]. The certi cate itself is signed
by the CA, e ectively making the CA a notary asserting the ownership of the key-pair.
The CA itself must be a registered business operating under a service contract (known
as the Certi cate Practices Statement).
It is worth noting that ability for an entity to prove possession of a private-key { such
as exercising the private-key in a challenge-response protocol (e.g. CHAP [30]) or in
signing a transaction on the blockchain { does not imply legal ownership of the key.
•Key operator information : This is information or evidence pertaining to the legal cus-
tody by a VASP of a customer's public-private keys. This information is relevant for
VASPs which adopt a key-custodial business model, where the VASP holds and operates
the customer's public-private keys to sign transactions on behalf of the customer. Here,
the customer legally owns the public-private key-pair, but the customer-authorized legal
7
operator of the key-pair is the VASP.
In the next section we review a number of challenges faced by the emerging VASP industry.
Some of these challenges are related to the Travel Rule, while others are related to the
operational aspects of VASPs and the need for VASP industry to develop and establish new
trust infrastructures to support transaction on decentralized blockchain networks.
3 Current Challenges in the VASP Industry
Aside from crypto-asset insurance issues, there are currently a number of challenges faced
by the VASP community globally. These challenges arise not only because of existing reg-
ulations with regards to AML/FT, but also because the of the rapid changes occurring in
blockchain and DLT technologies. We brie
y discuss some of these issues as a background
to the discussion on attestations in the ensuing sections, and later to a discussion on the
bene ts of wallet attestations to VASPs and to crypto-asset insurers.
In the following we use the term regulated wallet to denote a wallet system (hardware
and software) that is in possession of a customer of a supervised (regulated) VASP [31]. The
understanding is that the customer's private-public keys are within the wallet device, and
that the wallet device is in the possession and under the control of the customer (Figure 1(a)).
From the perspective of customer information we use the term \regulated" in the sense of
FINMA [31]. This means that the customer is registered at a VASP, owns an account at the
VASP, and the VASP is able to ful ll the compliance requirements of the Travel Rule for
that customer.
We use the term private wallet to denote a wallet system belonging to an unveri ed
entity . This implies that in the extreme case the wallet-holder information is unattainable
by a VASP, despite the VASP querying other VASPs in the messaging network and querying
certi cation authorities (CA) reachable by the VASP.
3.1 A Secure Messaging Network for VASPs
The need to exchange customer account information and other private information raises
the question regarding the need for a secure messaging network for VASPs [32, 33]. Such
a messaging network may or may not be implemented using blockchain technology. How-
ever, there are a number of fundamental requirements for such a messaging network. Among
others, it must: (i) protect customer/VASP data privacy, (ii) provide secure transport of cus-
tomer/VASP information between endpoints, (iii) operate based on the strong identi cation
and authentication of VASPs in the network, and (iv) be able to operate independent of any
speci c virtual-asset blockchain network, current or future.
The need for a secure transport (e.g. SSL/TLS connection) from one VASP to another
implies that VASPs will also need to posses private-public keys designated for negotiating
the secure transport. It is good security practice to keep the SSL/TLS private-public keys
distinct from the private-public keys used by a VASP to sign transactions for the blockchain.
8
Figure 2: The VASP Messaging Network and Blockchain Network
Ideally, the SSL/TLS private-public keys should be wrapped in a digital certi cate (e.g.
X.509), possibly using Extended Validation (EV) certi cates that carry business information
regarding the VASP that owns the EV-Certi cate [34].
Such a messaging network must be able to evolve and persist over the next few decades,
independent of (but informed by) the technological advancements in blockchains. It must
allow a community of VASPs to exchange information pertinent to the Travel Rule, for any
type of virtual asset transfers (e.g. crypto-currencies, tokenized assets, etc) on current and
future blockchain systems. Figure 2 provides a high level illustration of a VASP messaging
network, shown to be logically separate from the virtual asset blockchain. Figure 3 illustrates
a possible layered architecture for the VASP messaging network.
In Figure 2 the Originator (Bene ciary) customer is assumed to have a business relation-
ship (e.g. account) with the Originator-VASP (Bene ciary-VASP). This is shown as (A) and
(B). The VASP messaging network is denoted as (C) and (D), where the Originator-VASP
can securely transmit the originator customer information to the Bene ciary-VASP in (C),
and vice versa in (D).
E orts are currently underway to develop such a messaging network and the related trust
infrastructures (e.g. certi cates, decentralized directories) to support VASPs in complying
to the various aspects of the Travel Rule (see [33, 25]). A standard customer information
model has recently been developed [35] that would allow VASPs to interoperate with each
other with semantic consistency when exchanging customer data.
3.2 Synchronization of Customer Information with Transactions
The Travel Rule requires VASPs to \synchronize" (track or correlate) between transactions
on the virtual assets blockchain with customer information. More speci cally, this means that
both the Originator-VASP and the Bene ciary-VASP must account for every customer trans-
9
Figure 3: A layered architecture for the VASP Messaging Network
action, and correlate transaction-related information to the correct originator and bene ciary
respectively.
Related to this is the question of customer data privacy [36]. Every instance of the
exchange of customer information must be driven by (pertain to) transactions from the
customers. The corollary is that VASPs are not permitted to exchange customer-related
information outside the context of a customer transaction.
This introduces a number of complications for VASPs with regards to the latency in the
completion of customer transactions. A given VASP may need to delay the transmission of
customer transaction until the relevant Travel Rule information (ie. regarding the bene ciary)
has been obtained and veri ed. This requirement is bi-directional or mutual between VASPs.
This means that the Bene ciary-VASP must also obtain and verify the originator customer
information (e.g. from the Originator-VASP) before accepting transfers of virtual assets from
the originator or its Originator-VASP. Such delays may be deemed to be \disappointing" by
customers accustomed to media crypto-hype [37, 38]. Solving this challenge is bene cial for
all VASPs around the world, but will require corporation among competing VASPs.
3.3 Direct Transactions from Wallets
One of the dilemmas faced by VASPs is the desired (demand) on the part of customers
to perform direct transactions from the customer's wallet (i.e. peer-to-peer). This is illus-
trated in Figure 2 in
ow (3a) and
ow (3b). This demand can be addressed if both wallets
(originator and bene ciary) are regulated wallets respectively. However, the dilemma arises
when only the originator wallet is regulated, and the bene ciary information is not veri able
immediately by the Originator-VASP.
One possible solution is for VASPs to permit (pre-authorize) customers with regulated-
wallets to transact up to a maximum daily limit as de ned by local regulations (e.g. $3K per
10
day). The Originator-VASP must then perform the veri cation of the bene ciary information
after the transaction has been con rmed on the blockchain. The task of post-event veri cation
would be made easier if the bene ciary is found to be associated with a Bene ciary-VASP.
This limited pre-authorized solution could be tightly integrated into user authentica-
tion/authorization Single Sign-On (SSO) protocol [39, 40, 41] via the customer's wallet.
When a customer seeks to perform a direct transaction from its wallet, the customer/user
would need to perform SSO (using the user's wallet) to the authorization service of the VASP,
which should just take a few seconds. This process essentially provides a mechanism for the
wallet to notify the VASP authorization server that the wallet will soon be transmitting a
transaction in a direct P2P fashion to a bene ciary wallet. The SSO event provides a window
of time for the VASP to verify whether the bene ciary is a known entity to the VASP (e.g.
previously transacted from the customer) and whether a Bene ciary-VASP can be located
corresponding to the bene ciary wallet holder.
More sophisticated solutions may be devised based on well-known database transaction
processing principles, such as the 2-Phase Commit (2PC) protocol [42, 43]. However, this
topic is out of scope for the current work.
3.4 VASP Transactions to Private Wallets
Another acute problem pertains to cases where an originator customer of a regulated VASP
requests asset transfers to an address (public-key) of a private wallet. This is shown as
ow (2a) and
ow (2b) in Figure 2.
The originator customer may only have informal and incomplete information regarding
the bene ciary holder of the private wallet (i.e. either a person or an organization). The
challenge, therefore, becomes one in which the Originator-VASP needs to seek information
at other VASPs regarding that destination address. Indeed, this is one of the main reasons
VASPs need to create a trust network or industry consortium operating under a legal trust
framework (see [8]). E orts such as TRISA [33] are aimed at solving this dilemma, based on
discovery protocols as well as VASP-level federated directories.
The problem also has data privacy dimension [36]. A remote VASP located in a di erent
jurisdiction may have veri ed information regarding holder of a private wallet or address.
However, that remote VASP may be prohibited (e.g. under local data privacy regulations)
from disclosing knowledge of the owner of a given address or public-key. As such, the remote
VASP maybe prohibited from even responding to the query.
3.5 On-Boarding and O -Boarding Customers
There are a number of challenges related to the on-boarding of a customer possessing a wallet.
In the case that the customer wallet is regulated (i.e. previously known to another regulated
VASP), then there are a number of practical issues that the acquiring VASP needs to face.
These include: (i) validating whether prior to on-boarding the wallet was regulated or private;
(ii) validating that the keys present within the wallet corresponds to the customer's historical
11
transactions (con rmed on the blockchain); (iii) verifying whether a backup/migration of the
wallet has occurred in the past; (iv) determining whether the customer's assets should be
moved to new keys, and if so, how the \old" keys will be archived; and so on.
In the case where the customer's wallet is private, then the VASP may choose to require
the customer to create a separate instance of the wallet application within the device, as-
sociated with the VASP. This provides a way for the VASP to be responsible only for the
\regulated partition" of the wallet while allowing the customer to retain the private portions
of the wallet. Trusted Execution Environment (TEE) technologies such as Intel SGX [44, 45]
may provide a way to achieve this partition, and at the same time provide evidence regarding
the partition on the wallet device which is under the Travel Rule responsibility of the VASP.
The case of a customer leaving a VASP also introduces a number of questions that may be
relevant under the Travel Rule. The releasing VASP may need to address various question,
including: (i) preparing evidence that the wallet was in a regulated state whilst the owner
of the wallet was a customer of the VASP; (ii) whether the customer's assets should be
moved to a temporary set of keys, denoting the end of the VASP's responsibilities for the
customer under the Travel Rule; (iii) obtaining evidence from the wallet that the \old" keys
(non-migrateable keys) have been erased from the wallet device, thereby rendering the keys
unusable in the future by the customer; and so on.
3.6 Cross-Jurisdiction Asset Transfers
Within certain jurisdictions the operational requirements may be more stringent. For ex-
ample, in Switzerland the FINMA ordinance on Anti-Money Laundering (AMLO) makes it
unambiguously clear that no exception is permitted for payments (i.e. virtual asset transfers)
involving \unregulated wallet providers" [46]. This rule, among others, is to prevent (reduce)
problems faced by supervised providers (i.e. VASPs) in cases where it has to deal with virtual
asset transfers from an unregulated wallet provider.
This brings into focus the challenge of how virtual asset transfers will occur between
two VASPs where each are operating under di erent jurisdictions with di erent levels of
stringencies. Furthermore, as long as an institution (i.e. VASP) supervised by FINMA is not
able to send and receive the customer information required in payment transactions, such
transactions are only permitted between wallets of the institution's own customers.
Relevant to the current work on attestations is the requirement in FINMA that the
ownership of a wallet be proven using \suitable technical means" [31].
4 A Standard Architecture for Attestations
Recently, the notion of attestations has have garnered interest within di erent technical
standards organizations and industry consortiums, beyond the TCG alliance (e.g. FIDO
Alliance [47], Global-Platform [48], IETF [49]). Despite the notion of device attestations
nearing two-decades in age [50], the concepts around attestations { such as endorsements,
validations and freshness { are just recently coming into wider attention in the broader
12
Figure 4: Overview of the concept of the Attester and Attesting Environment
industry. The hope is that a canonical attestation architecture will allow standards to be
developed that implement the various protocols and
ows for relevant sectors and products
(routers and network equipment [51, 52], mobile devices [48], cloud stacks [53], etc). By having
a common reference architecture, di erent e orts can share common terminologies, concepts
and implementations and therefore a ect a reduction in costs of developing and deploying
the infrastructures supporting cyber-resilience and trustworthy computing generally.
4.1 Attestations
The fundamental idea of attestations of a \thing" (e.g. a computing device) is that of the
conveyance of truthful information regarding the (internal) state of the thing being attested
to. In the related literature on trustworthy computing the term \measurement" is used to
mean the act of collecting (introspecting) claims or assertions about the internal state, and
delivering these claims as evidence to an external party or entity for automated review and
security assessment.
However, as we know today computing environments can be structurally complex and may
consist of multiple elements (e.g. memory, CPU, storage, networking, rmware, software),
and computational elements can be linked and composed to form computational pipelines,
arrays and networks. Thus, the dilemma is that not every computational element can be
expected to be capable of attestation. Furthermore, attestation-capable elements may not
be capable of attesting every computing element with which it interacts. The attestation
capability could in fact be a computing environment itself (Figure 4). The act of monitoring
trustworthiness attributes, collecting them into an interoperable format, integrity protecting,
authenticating and conveying them requires a computing environment { one that should be
separate from the one being attested. Figure 4 illustrates the recognition of this distinc-
tion, namely of the target environment being attested to, and the attesting environment that
13
Figure 5: Canonical architecture for attestations (after [55, 56, 57])
performs the work stated above1.
The complexity of the problem has led to a number of e orts in industry to de ne an
attestation architecture that incorporates some of these key concepts { such as the concept
of the root-of-trust { and to develop standards that implement attestation concepts [54, 49].
The roles and functions of the attestation architecture is shown in Figure 4. In a nutshell, in
Figure 4 an attester conveys evidence of trustworthiness (of the attested target environment)
to a veri er entity. The veri er operates based on policies that are supplied by the owner of
the veri er.
4.2 Entities, Roles and Actors
The attestation architecture of [55] de nes of a set of roles that implement attestation
ows.
Roles are hosted by actors , where actors are deployment entities. Di erent deployment models
may coalesce or separate various actor components and may call for di ering attestation
conveyance mechanisms. However, di erent deployment models do not fundamentally modify
attestation roles, the responsibilities of each role, nor the information that
ows between
them. In the following sections, we may use the actor and role terminology interchangeably
when appropriate in order to simplify discussion (see Figure 5).
•Attester : The Attester (e.g. target device) provides attestation Evidence to a Veri er.
The Attester must have an attestation identity that is used to authenticate the conveyed
Evidence and establishes an attestation endpoint context. The attestation identity is
often established as part of a manufacturing process that embeds identity credentials
in the entity that implements an Attester.
1An example of an attesting environment is the Quoting Enclave within Intel SGX [44, 45].
14
•Veri er : The Veri er accepts Endorsements (from Endorsers) and Evidence (from the
Attester) then conveys Attestation Results to one or more Relying Parties. The Veri er
must evaluate the received Endorsements and Evidences against the internal appraisal
policies chosen or con gured by the owner of the Veri er [57]. The Attestation Service
Provider (ASP) is typically the actor which implements the Veri er role. An example
of the ASP role is described in [58].
•Relying Party : The Relying Party (RP) role is implemented by a resource manager that
accepts Attestation Results from a Veri er. The Relying Party trusts the Veri er to
correctly evaluate attestation Evidence and Policies, and to produce a correct Attesta-
tion Result . Thus, we assume that the RP and the Veri er has a business relationship
or some other basis for trusting one another. The Relying Party may further evaluate
Attestation Results according to Policies it may receive from an Owner. The Relying
Party may take actions based on the evaluation of the Attestation Results.
•Endorser : An Endorser role is typically implemented by a supply chain entity that cre-
ates reference Endorsements (i.e., claims, values or measurements that are known to be
authentic). Endorsements contain assertions about the device's intrinsic trustworthi-
ness and correctness properties. Endorsers implement manufacturing, productization,
or other techniques that establish the trustworthiness properties of the Attesting Envi-
ronment. This is shown as
ows (a) and (b) in Figure 5.
•Owner of Veri er : The Veri er Owner role has policy oversight for the Veri er. It
generates Appraisal Policy for Evidence and conveys the policy to the Veri er. The
Veri er Owner sets policy for acceptable (or unacceptable) Evidence and Endorsements
that may be supplied by Attesters and Endorsers respectively.
The policies determine the trustworthiness state of the Attester and how best to rep-
resent the state to Relying Parties in the form of Attestation Results. The Veri er
Owner manages Endorsements supplied by Endorsers and may maintain a database of
acceptable and/or unacceptable Endorsements. The Veri er Owner authenticates Ver-
i ers and maintains lists of trustworthy Endorsers, peer Veri ers and Relying Parties
with which the Veri er might interact.
•Owner of Relying Party :
The Relying Party (RP) Owner role has policy oversight for the Relying Party (RP).
The RP-Owner sets appraisal policy regarding acceptable (or unacceptable) Attesta-
tion Results about an Attester that was produced by a Veri er. The RP-Owner sets
appraisal policies on the Relying Party that authorizes use of Attestation Results in
the context of the relevant services, management consoles, network equipment, an en-
forcement policies used by the Relying Party. The Relying Party Owner authenticates
the Relying Party and maintains lists of trustworthy Veri ers and peer Relying Parties
with which the Relying Party might interact.
15
•Evidence : The Attestation Evidence is a role message containing assertions from the
Attester role. Evidence should have freshness and recentness claims that help establish
Evidence relevance. For example, a Veri er supplies a nonce that can be included with
the Evidence supplied by the Attester. Evidence typically describes the state of the
device or entity. Normally, Evidence is collected in response to a request (e.g. challenge
from Veri er).
Evidence may also describe historical device states (e.g. the state of the Attester during
initial boot). It may also describe operational states that are dynamic and likely to
change from one request to the next. Attestation protocols may be helpful in providing
timing context for correct evaluation of Evidence that is highly dynamic.
•Endorsements : Endorsement structures contain reference Claims that are signed by an
entity performing the Endorser role (e.g. supply-chain entity or manufacturer of the
target device). Endorsements are reference values that may be used by Owners to form
attestation Policies.
Some endorsements may be considered \intrinsic" in that they convey static trust-
worthiness properties relating to a given actor (e.g., device, environment, component,
TCB, layer, RoT, or entity). These may exist as part of the design, implementation,
validation and manufacture of that actor implementation.
An Endorser (e.g. manufacturer) may assert immutable and intrinsic claims in its
Endorsements, which then allows the Veri er to carry-out appraisal of the Attester
(e.g. device) without requiring Attester reporting beyond simple authentication.
4.3 Summary of an Attestation Event
Figure 5 illustrates the canonical attestation model. When an Attester (e.g. target device)
seeks to perform an action at the Relying Party (e.g. access resources or services controlled
by the Relying Party) the Attester must rst be evaluated by the Veri er. Among its inputs,
the Veri er obtains endorsements from the Endorser (e.g. device manufacturer) in
ow (a)
of Figure 5. Prior to allowing any entity to be evaluated by the Veri er, the Owner of the
Veri er must rst con gure a number of appraisal policies into the Veri er for evaluating
Evidences. The policies are use-case speci c but may require other information about the
Attester (or User) to be furnished to the Veri er. This is shown in Step 1 of Figure 5.
Similarly, in Step 2 the owner of the Relying Party (e.g. resource or service) must con gure
a number of Appraisal Policies for Attestation Results into the Relying Party.
When the Attester requests access to the resources at the Relying Party (Step 3), it will
be redirected to the Veri er (Step 4) { the understanding being that the Attester must deliver
attestation Evidence to the Veri er. Included here are the endorsement(s) that the Attester
obtained previously from the Endorser (
ow(b) of Figure 5). The
ow represented by Step 3
may be multi-round and may include a nonce challenge that the Attester must include in its
computation of the Evidence as a means to establish freshness.
16
After veri cation and appraisal of the Attester completes, the Veri er delivers the Attes-
tation Result to the Relying Party in Step 5. The Relying Party in its turn must evaluate the
Result against its own policies (set previously in Step 2). If the Relying Party is satis ed with
its evaluation of the Attestation Result regarding the Attester, it will provide the Attester
with permission to complete the action it seeks to perform (e.g. access resources at the RP).
5 Wallet Attestations to Support VASPs
Following from the previous discussion regarding the emerging standard architecture for
attestations, we explore the application of the attestation architecture for the case of wallet
devices. We consider the potential bene ts of wallet attestations for VASPs, notably in the
context of regulated wallets. Readers interested in the application of attestations to nodes
(i.e. mining nodes [16], validator nodes [26]) are directed the work of [59].
In the current work we use the generic term wallet device to encompass both the hardware
and software of the wallet system (see the NIST de nition of wallets in [60]). Furthermore,
we use the term broadly to mean wallet systems located within consumer electronic devices
(e.g. mobile devices, smartphones, PC computers, etc.) as well as Enterprise-grade key
management systems deployed within organizations [2]. Thus, as will be seen, attestation
capabilities should be used by wallets regardless of their portability factor (i.e. smartphones)
or legal ownership (i.e. individuals or organizations).
The need to protect keys has been a requirement since the emergence of digital cryptog-
raphy. The need for key protection capabilities expanded with the adoption of public-key
cryptography [61] into the mainstream computing and networking industry. In the late 1990s
the demand for key protection capabilities in industry emerged with the rise of the Certi ca-
tion Authorities (CA) business model. The high-cost of Hardware Security Module (HSM)
cards in the late 1990s meant that only CAs and corporate buyers could a ord these HSM
cards.
The e ort to produce a low-cost trusted hardware chip commenced in 1999 with the
formation of the Trusted Computing Platform Alliance (TCPA), which was subsequently
renamed the Trusted Computing Group (TCG) [62]. The goal of the TCG was to develop a
trusted hardware speci cations that permitted the hardware to be manufactured at very low
cost (e.g. less than a couple of dollars). The cost had to be extremely low { compared with
HSM cards, that could cost several hundred dollars per card { because the initial targeted
market segment was the PC computer market (i.e. PC OEMs), which is a very cost-sensitive
segment of the market. At the same time, Smart Cards [63] were under development and
was targeted primarily for the newly emerging mobile phone market. Thus the TCG trusted
hardware must also be below the cost of smart cards.
The speci cations for trusted hardware from the TCG alliance was called the Trusted
Platform Module (TPM), with the hardware version 1.2 becoming available in the 2004-
2005 timeframe. Wide deployment of the TPMv1.2 begun in 2006, notably with the new
purchase requirements from the U.S. Army. More speci cally, in February 2006 the U.S.
17
Figure 6: High-level illustration of device key hierarchy
Army Small Computer Program published a new Consolidated Buy-2 (CB2) Desktop and
Notebook minimum speci cations for Army customers. The Army's new speci cation requires
desktop and laptop personal computers be equipped with the new TPM (v1.2) hardware. This
event represented a milestone in the adoption of trusted computing standards.
It is fair to say that the notion of \attestations" and device \measurements" originated
from the TCPA/TCG alliance, whose members in the early 2000s were grappling with the
very hard problem of de ning technical trust (i.e. trust derived from technical means) [50, 13].
Being able to truthfully prove or attest as to the system state was considered core to the
de nition and value-proposition of trusted computing. Unlike HSM cards and Smart Cards,
one of the main driving use-cases for the TPM hardware was to protect the PC computer,
such as protecting the PC computer through its boot-up sequence (e.g. secure boot). Thus,
the idea is that with the aid of a low-cost well-designed hardware (i.e. the TPM) that was
soldered to the motherboard, the computer should be able to report (or attest) about its
boot-up sequence. Other applications in the PC context included encrypting les/folders
and self-encrypting disk-drives [64, 65].
5.1 Wallet Devices and Trusted Hardware
There are several features of trusted hardware that make it attractive for use in the virtual
assets industry [66]:
•Cryptographic engine, protected storage and tamper-resistance : Current trusted hard-
ware typically contains a cryptographic processor which implements a number of rudi-
mentary functions related to cryptography. Examples include encryption (symmetric
key), digital signatures, hash functions and key-generation. Trusted hardware typically
possess protected storage for securing keys during system use, and when shutdown.
18
Tamper-resistance in trusted hardware provides protection against forced exportation
of cryptographic keys (up to a point). A number of trusted hardware implementation
may provide an auto-erasure of keys should physical tampering occur to sensitive parts
in the interior of the hardware. As such, the value of the asset being protected by the
keys should be measured against the approximate cost of attacking the hardware.
•Hardware-bound and non-migratable keys : A core feature of trusted hardware such as
the TPM is the ability of certain types of cryptographic keys to be generated inside
the hardware, and for internal key hierarchies to be established. Using the example of
the TPM, certain types of keys can be designated as non-migrateable at creation time,
meaning that the key is bound to that single TPM and that it can not be migrated or
exported from the TPM (see Figure 6 (a) and (b)). The use of non-migratable keys are
advantageous when addressing the need to prevent the copying of keys.
It is important to note that non-migrateable private-public key pairs can be used to
uniquely identify the device (i.e. using the public key) [11, 67]. Mechanism to provide
privacy to these keys/hardwares have also been created (see [68, 69]).
•Application-level keys linked to non-migratable keys : Certain types of keys generated
inside the trusted hardware can be designated to be accessible to application softwares.
Thus, for the use-case of virtual assets transfers, one or more key-pairs maybe generated
and stored inside the trusted hardware and be invoked to sign transactions for the
blockchain. The public-key of such key-pairs can be copied to locations external to the
hardware, allowing certi cates to be created for that public-key.
A non-migratable key can be used internally to \certify" the application-level keys
(Figure 6 (c) and (d)), thereby providing a provenance link to the non-migratable key
(and therefore to trusted hardware). This feature maybe useful in attestation cases
where the user has to prove the origins of an application-level key-pair. Typically,
application-level keys can be designated to be migrateable at creation time, allowing
the key-pair to me migrated (or backed-up) to a new compatible trusted hardware
using a secure key migration protocol [70].
•Hardware-based attestations : Certain types of trusted hardware support the truthful
reporting of one or more of its internal state variables, signed using a reporting-key
that is derived from a non-migratable key. This capability permits an external entity
to query the trusted hardware regarding attributes, including internal possession of
keys (i.e. private-keys), without revealing the keys.
Common examples of trusted hardware that posses some or all of the above features include
the Trusted Platform Module (TPM) hardware (version 1.2 [11] and version 2.0 [12]), and
the ARM TrustZone [71].
19
Figure 7: Wallet attestation
ows
5.2 Basic Wallet Attestation Flows
Following from Figure 5, the roles/entities within the wallet attestation
ows are as follows
(see Figure 7):
•Appraisal policies de ned by VASP : The wallet's attributes of interest to the VASP
can be determined by the VASP con guring the relevant appraisal policies at the ASP
(Step 1). This drives the ASP to request evidences from the wallet device as appropriate
to the con gured policies.
In the case that the VASP belongs to a consortium of VASPs (Section 6) then additional
consortium-level policies may also be con gured at the ASP (Step 2).
•Wallet as Attester : The target device being evaluated in this case is the wallet hardware,
as a result of the customer seeking to perform a transaction (Step 3). The wallet is
expected to provide evidence (Step 4), among others, regarding its installed hardware,
software and rmware.
•The ASP as Veri er : The function of the veri er is represented as the ASP, which
could be a service owned and operated by the VASP, by a VASP consortium, or by a
trusted third party.
20
•The VASP as the Relying Party : The relying party in Figure 7 is the VASP itself. In
the regulated-wallet scenario where the VASPs customers posses wallet devices with
trusted hardware, one goal of the VASP is to obtain attestation-evidence from these
devices. Step 5 illustrates the ASP yielding the attestations results to the VASP as the
relying party.
5.3 Types of Attestation Evidence and Relevance to VASPs
The evidence reportable by trusted hardware depends largely on the capabilities of the trusted
hardware and the surrounding system implementing the crypto-wallet functionality. In gen-
eral there are a number of system attributes reportable from a wallet that may complement
the customer information in the context of the Travel Rule. With regards to key-ownership
information and key-operator information (Section 2.3), attestations technologies allows a
VASP to obtain truthful (unforgeable) information from the wallet, such as: (i) howa key-
pair was created (e.g. generated onboard, or injected from outside), (ii) where it was created
(e.g. under shielded storage), and (iii) the current location of the key-pair (e.g. geolocation
of wallet).
The following is a non-exhaustive list of some of the possible wallet and key information
that can be obtained using attestations:
•Key creation provenance : Most (if not all) current generation crypto-processor trusted
hardware have the capability to create/generate a new private-public key pairs inside
the protected/shielded location of the hardware, and to maintain keys inside its long-
term non-volatile protected storage. Furthermore, evidence regarding this process can
be yielded by the trusted hardware, allowing the provenance of such keys to be asserted.
Key-provenance evidence is useful for VASPs in many use-case scenarios. For example,
in the case of a newly on-boarded customer with a wallet, the VASP may wish to
ascertain the provenance of the existing customer transaction signing key-pair found in
the wallet. If the provenance of the existing key-pair in the wallet is unveri able, then
the VASP may require the customer (i.e. wallet) to generate a new key-pair inside the
wallet.
This, in turn, provides the VASP with a clear line of responsibility and accountability
under the Travel Rule with regards to customer-originated transactions. The VASP
has exculpatory evidence regarding the on-boarding of the new customer and the start
of use of the new key-pair.
•Key-type evidence and key loss recovery : As mentioned previously, some crypto-processor
trusted hardware (e.g. TPMv1.2 and TPM2.0) support the creation of non-migratable
keys (Figure 6). A VASP may request periodic attestation-evidences from its cus-
tomers' wallets regarding the type of the transaction signing key-pair(s) currently in
use (e.g. whether private-key non-migratable). The VASP may also require these
non-migratable keys to be backed-up to a secure storage location at the VASP, using
21
a secure backup/migration protocol appropriate for the trusted hardware in the wal-
let [70]. This migration \blob" is typically cryptographically sealed in that it can only
be installed onto a new equivalent trusted hardware under the customer's authoriza-
tion (e.g. migration password). The combined use of key-type evidence and key backup
procedure allows VASPs to more e ectively handle emergency cases (e.g. perceived loss
of private-key, actual loss of wallet device, etc.).
For example, if a wallet device is lost/stolen and the VASP has recent attestation-
evidence that the transaction key is non-migratable, then this gives the VASP some
time to carry out emergency measures (i.e. assuming it will take some time for the
thieves to crack the the tamper-resistance of trusted hardware). Such emergency
procedures, for example, could mean: (i) recovering the sealed migration keys (i.e.
migration \blob") from the VASP backup storage into a new wallet device with the
same/equivalent trusted hardware; (ii) activation of trusted hardware and the a ected
certi ed transaction signing key-pair; (iii) moving all asset on the blockchain from that
a ected public-key (address) to a new temporary private-public key-pair (e.g. owned
by the VASP).
Aside from providing crucial customer service in times of emergency, the VASP will also
obtain sucient technical evidence to justify this customer emergency asset transfer
should the VASP be queried under the Travel Rule (e.g. why large amounts of assets
moved from a customer address to the VASP address).
•Evidence of signature-origin of transactions on the blockchain : Related to the key cre-
ation provenance and key-type, the use of a hardware-bound private-key to sign trans-
actions permits the device-origin of that transaction to be ascertained.
This kind of evidence may be important in scenarios in which the VASP needs proof
that a set of con rmed transactions on the blockchain originated from the speci c device
belonging to one of its customers.
•Evidence of geolocation of wallet : VASPs can obtain evidence regarding the geolocation
of a wallet device, and therefore evidence regarding the geolocation of the hardware-
bound keys in the wallet. This may provide a means for VASPs to enforce geolocation-
related policies for customers to ensure that the VASPs customers are operating within
the permissible jurisdiction (e.g. customer wallet must be in-country to sign transac-
tions).
For example, the work of [72] includes the ability to report location coordinates (lati-
tude, longitude and altitude) of the attester device. In turn, this can be reinforced with
geo-fence policies relevant to the speci c deployment scenario.
•Key usage sequence : VASPs can also make use of a number built-in features of trusted
hardware via the application software (e.g. mobile app) on the wallet. For example, the
application can use the underlying trusted hardware to maintain a sequential history of
the objects (transactions) signed using the private key inside the trusted hardware (e.g.
22
in the TPM using the hash-extend operation with the PCR registers and monotonic
counter [11, 12]).
For a VASP, this feature allows the VASP to perform an accurate accounting as to
which order transactions were signed by the wallet system, as compared to the order in
which the transactions were processed (i.e. con rmed) on the blockchain, and whether
any transactions were lost (e.g. transmitted from the wallet, but never reaching the
unprocessed-pool (UTXO model [16]), etc.).
•Evidence of wallet system con gurations : Attestation technologies allows a VASP to
obtain visibility into the components (hardware, software and rmware) of the wallets
of its customers.
Although this may appear to be intrusive, this has the advantage of allowing the VASP
to advise (or require) customers to replace a weak wallet system with a stronger system.
More broadly, this allows VASPs to o er remote device-manageability services to its
customers, including continuous monitoring of the system health of the wallets.
System health monitoring and reporting has been deployed in the Enterprise networking
industry for sometime now [73]. Examples include Microsoft's NAP [74], NAC from
Cisco [75] and the TNC from the TCG [76, 77].
Figure 6 provides a high level illustration of a simple key-hierarchy inside the trusted hard-
ware of a wallet system. A given device platform may ship with one or more of manufacturer
installed keys. This is shown as the Device Identity Key in Figure 6 (a). Examples include the
manufacturer Endorsement Key (EK) in the TPM [78], the DeviceID key in routers [67, 52]
and the Secret Device Key in server chassis/hardware [79, 80]. A corresponding certi cate
may be issued by the manufacturer (Figure 6 (e)).
The customer's transaction signing key-pair (d) may be derived from the non-migrateable
device identity key (b). This generational-link between the key (d) to key (b) and to key (a)
in Figure 6 allows the attestation process to discover this link and report it as evidence to the
VASP. Finally, in Figure 6 (f) the VASP could issue an X.509 certi cate for the customer's
transaction public-key. The VASP can be the issuing CA, or the VASP can outsource this
function to a commercial CA (see [8] for a discussion of VASPs and CAs). The resulting
customer X.509 certi cate could bear some markings (i.e. speci c elds or tags) indicating
that the provenance of the public-key is known to the issuing VASP. This allows other VASPs
and their customers (bene ciaries) to obtain some degree of con dence regarding the signing
key and the wallet system employed by the originator customer.
6 Attestation Services within VASP Trust Networks
In order to begin solving the various issues around the Travel Rule and the challenges in
obtaining originator/bene ciary customer information, the nascent VASP industry should
establish VASP trust networks or interconnected communities in a manner similar to the
23
Figure 8: Attestation network for VASP consortiums
ISP communities on the Internet [8]. The term \trust network" is used here to denote a
community of VASPs (e.g. regional level or national level) that has come together in a
consortium arrangement to collaborate under a common legal trust framework (system rules
de nition) for all its membership. As an industry consortium, the members of the VASP
trust network would then de ne the technical speci cations and pro les that would need
to be adopted by all members of the consortium. This ensures a high degree of functional
interoperability within the community of VASPs.
As part of the service de nition of the VASP trust network, the following services maybe
useful to consider in the context of wallet attestations (Figure 8):
•Common baseline appraisal policies : The VASP community should develop a set of
baseline policies for the appraisal of wallet systems used in the community. This may
include appraisal policies speci c for customer wallets (consumer-grade), and also ap-
praisal policies for a VASPs key management system, which could be an enterprise-
grade system built also using trusted hardware. Figure 8(a) illustrates the notion of a
consortium-level appraisal policies.
•Common device con guration manifests : The community of VASPs should de ne a
number of approved wallet device con gurations (hardware, software, rmware) in order
to allow their customer to obtain one of the approved con gurations. These approved
con guration as de ned by their manufacturer is also known as reference manifests [81,
82]. For each device con guration, a matching set of appraisal policies should be created
by the consortium and be made accessible to all VASP members.
•Shared attestation veri cation service : The consortium as a community should provide
attestation veri cation services. This is illustrated in Figure 8(b). These are also
referred to as Attestation Service Providers (ASP) for the community (see Section 4.2).
The work of [58] points to an example of an ASP service in the cloud.
24
Figure 9: Cross-VASP attestation appraisals
•Shared integration model for customer single sign-on : All VASPs should seek to ensure
good customer experiences, with minimal friction across a variety of wallet application
softwares (e.g. mobile app, dekstop app, browsers, etc).
To this end, the VASP community should harmonize the various single sign-on (SSO)
protocols and
ows, across the various approved types of wallet devices and applications.
Several identity management and SSO protocols have been standardized over the past
two decades (e.g. SAML2.0 [39, 83], OAuth2.0 [84], OpenID-Connect [85]).
•Cross-VASP attestation veri cations : A given VASP should posses its own attestation
veri cation system distinct from the consortium's shared attestation veri cation service.
This is illustrated in Figure 8(d)-(e).
The goal is to allow a VASP to perform appraisals of evidences conveyed by the wallet
trusted hardware belonging to a customer of a di erent VASP (within the consortium).
Having this capability, the VASP should then be open to appraising evidences from
regulated wallets belonging to customers of non-member VASPs, and even private-
wallets of holders seeking to be on-boarded as new customers.
The notion of Cross-VASP attestation appraisals is summarized in Figure 9. Here, an orig-
inator customer of VASP1 holding a regulated wallet with trusted hardware seeks to perform
a direct transaction with a bene ciary entity who is customer of VASP2. Both VASP1 and
VASP2 are members of the trust network consortium. This is shown as Step (1) of Figure 9.
The wallet of the originator is referred to as Attester A, while the wallet of the bene ciary is
referred to as Attester B. Upon request from VASP1, the bene ciary (Attester B) generates
attestation-evidence in Step (2a) and conveys it to the Veri er (ASP) within VASP1. If
both the wallets of the originator and the bene ciary complies to the appraisal policies of
VASP1, the originator is then given authorization from VASP1 to transact directly with the
25
bene ciary (Step (3)). Alternatively, the VASP1 may require the bene ciary (Attester B) to
convey its attestation-evidence to the consortium's ASP, as shown in Step (2b).
7 Areas for Innovation
There are a number of potential areas of innovation for the newly emerging VASP industry.
Related to the topic of device attestations and trusted hardware for wallets, Some of these
are as follows
•Wallet Levels of Assurance : Similar to the notion of Levels of Assurance (LOA) of
authentication events de ned by NIST [86, 87, 88], a VASP trust network consortium
could de ne a number of levels assurance as a function of the wallet condition and
other key management aspects. Today the highest level (Level 4) achievable in the
NIST scheme [86] is one in which a remote network authentication event employs cryp-
tographic hardware.
For example, a VASP consortium could recognize a number of di erent types of wallets
(e.g. client software, browser plugins, etc.), the types of acceptable trusted hardware,
user biometric authentication to the wallet device, and so on, and use these as input
into the wallet LOA matrix.
Using attestations, wallets could convey evidence to the ASP Service of the VASP
consortium in order to obtain a wallet LOA assignment. In turn, this provides some
basis for the insurance industry to begin risk assessment of crypto-asset, wallets and
VASPs.
•Wallet LOA for Crypto-Asset Insurance : There is interest in the insurance industry to
provide insurance services to crypto-funds [28]. However, the insurance industry will
need some technically measurable representation of \trust" in the VASP management
of cryptographic keys. We believe it is insucient for VASPs to describe (e.g. in a
document) the type of trusted hardware employed by a VASP and employed by the
VASP's customer-wallets. Instead, attestations evidences should be yielded directly by
the wallets (both enterprise-grade and consumer-grade wallets).
Wallet attestations maybe able to provide insurers with strong evidence regarding the
internal state of the trusted hardware, the keying material protected by the trusted
hardware, and other aspects of the wallet system. Crypto-asset insurers may choose to
operate its own evidence veri cation service (e.g. ASP service) in order to be able to
obtain an independent attestation-result evaluation.
•VASP Consortium Repository of Approved Software and Firmware : Related to the
attestation of wallet devices, the VASP trust network consortium could maintain a
repository of software, rmware and patches for the various approved wallet devices in
it ecosystem. This could be done in collaboration with the various software vendors
and hardware manufacturers in the wallet space.
26
Such a repository would assist VASPs in the on-boarding of new customers, by (i) re-
quiring new customers to obtain one of the devices and con gurations approved by the
VASP trust network consortium, and (ii) by ensuring that these customer devices sub-
sequently install only known good software, rmware and patches from the consortium's
repository.
•VASP certi cate pro le for wallet non-migrateable keys : The VASP trust network con-
sortium could develop a certi cate pro le for transaction signing public-keys that are
provably non-migrateable.
The X.509 certi cate for a transaction public-key (non-migrateable private-key) could
bear some markings that conveys assurance to the recipient that the corresponding
private-key was bound to a trusted hardware. In turn, this may increase con dence in
the counter-party in dealing with the customer wilding the wallet associated with the
X.509 certi cate.
8 Conclusions
Today there is a great opportunity for VASPs to shape and in
uence the development of future
wallets systems with features, such as attestations, that would aid VASPs in complying to
the the Travel Rule and help in the manageability of customer wallets. The ability for a
VASP to obtain unforgeable evidence from a wallet system regarding the provenance of keys,
as well as their usage and location, provides the VASP with additional means to address
the problem of the synchronization between transaction on the blockchains and the account
information required by the Travel Rule.
Wallet attestations maybe able to provide crypto-funds insurers with strong evidence
regarding the key management aspects of a wallet device, thereby providing the insurance
industry with measurable levels of assurance that can become the basis for insurers to perform
risk management about a crypto-asset.
Looking more broadly, if virtual assets and blockchain technology are going to become a
fundamental building block of the new economy then infrastructures that support the estab-
lishment of trust between counter-parties will need to be developed and deployed. Because
VASPs are the primary touch-points with users seeking to transact in virtual assets, this
means that deploying trust-infrastructures will be largely the task of VASPs and the VASP
communities globally.
27
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35
|
{
"id": "2005.14689"
}
|
2210.06139
|
Zero-Knowledge Optimal Monetary Policy under Stochastic Dominance
|
Optimal simple rules for the monetary policy of the first stochastically
dominant crypto-currency are derived in a Dynamic Stochastic General
Equilibrium (DSGE) model, in order to provide optimal responses to changes in
inflation, output, and other sources of uncertainty. The optimal monetary
policy stochastically dominates all the previous crypto-currencies, thus the
efficient portfolio is to go long on the stochastically dominant
crypto-currency: a strategy-proof arbitrage featuring a higher Omega ratio with
higher expected returns, inducing an investment-efficient Nash equilibrium over
the crypto-market. Zero-knowledge proofs of the monetary policy are committed
on the blockchain: an implementation is provided.
|
http://arxiv.org/pdf/2210.06139v1
|
David Cerezo Sánchez
|
cs.CR, cs.CE, econ.GN, q-fin.EC
|
cs.CR
|
Zero-Knowledge Optimal Monetary Policy
under Stochastic Dominance
David Cerezo Sánchez
david@calctopia.com
13th October 2022
Abstract
Optimal simple rules for the monetary policy of the first stochastically
dominant crypto-currency are derived in a Dynamic Stochastic General
Equilibrium (DSGE) model, in order to provide optimal responses to
changes in inflation, output, and other sources of uncertainty.
The optimal monetary policy1stochastically dominates all the pre-
vious crypto-currencies, thus the efficient portfolio is to go long on the
stochastically dominant crypto-currency: a strategy-proof arbitrage fea-
turing a higher Omega ratio with higher expected returns, inducing an
investment-efficient Nash equilibrium over the crypto-market.
Zero-knowledge proofs of the monetary policy are committed on the
blockchain: an implementation is provided.
Keywords : optimal monetary policy, optimal simple rules, stochastic
dominance, stochastic calculus, DSGE model, strategy-proof, Nash equi-
librium, zero-knowledge, crypto-currency
JEL classification : C11, C54, D58, D81, E42, E47, E52, E61, G11
1STATEMENT ON MONETARY POLICY GOALS AND STRATEGY :
The primary mandate is (stochastic) dominance.
The primary means of adjusting the policy stance is through changes in money growth.
The monetary policy is implemented with pre-committed policy rules, only to be revised in
case of technology shocks or in the event of a financial crisis: the stance of monetary policy
will adjust as appropriate if risks emerge that could impede the attainment of its goals, and
this document will be reviewed and updated with any changes.
Unlike other crypto-currencies, this monetary policy synchronises with macro-economic
observables, other fiat currencies and CBDCs: its primary goal is to follow cooperative
equilibria, falling back to non-cooperative equilibria as last resort.
1arXiv:2210.06139v1 [cs.CR] 12 Oct 2022
Contents
1 Introduction 3
1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Related Literature 3
2.1 Comparison with prior work . . . . . . . . . . . . . . . . . . . . . 5
2.2 Survey of the Monetary Policy Impact on Crypto-currencies . . . 6
3 Environment, Framework and Efficient Portfolio 8
3.1 Economic Environment . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Decision Framework . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.1 Welfare Loss Function . . . . . . . . . . . . . . . . . . . . 11
3.2.2 Ranking Simple Policy Rules . . . . . . . . . . . . . . . . 12
3.3 Efficient Portfolio . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.1 Pricing Stochastic Dominance . . . . . . . . . . . . . . . . 14
3.3.2 Efficient Stochastically Dominant Portfolio . . . . . . . . 17
3.3.3 Omega ratio of Stochastically Dominant Crypto-currencies 21
3.3.4 Arbitraging with Stochastic Dominance . . . . . . . . . . 21
4 Model and Policies 22
4.1 Monetary Policy Rules . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.1 Central Bank . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.2 Bitcoin’s Monetary Policy . . . . . . . . . . . . . . . . . . 23
4.1.3 Ethereum 2.0’s Monetary Policy . . . . . . . . . . . . . . 25
4.1.4 McCallum’s Policy Rule . . . . . . . . . . . . . . . . . . . 26
4.1.5 A Reconsideration of Money Growth Rules . . . . . . . . 26
4.2 Ranking of Policy Rules . . . . . . . . . . . . . . . . . . . . . . . 27
5 Implementation Details 27
5.1 Global Implementation . . . . . . . . . . . . . . . . . . . . . . . . 27
5.2 Zero-Knowledge Monetary Policy . . . . . . . . . . . . . . . . . . 28
5.2.1 Security Model . . . . . . . . . . . . . . . . . . . . . . . . 29
5.2.2 Protocol Description and Implementation . . . . . . . . . 30
6 Conclusion 33
I Appendix 38
2
1 Introduction
One of the notorious deficiencies of crypto-currencies is their lack of monetary
policy, as currently defined and studied in the field of macroeconomics: non-
etheless, monetary crypto-policymakers must act in an optimal manner. In this
paper, we initiate the study of optimal monetary policies for crypto-currencies in
order to derive optimal simple rules that stochastically dominate the monetary
policy of other previous crypto-currencies, and ultimately, prove that the efficient
portfolio is to go long on the stochastically dominant crypto-currency.
1.1 Contributions
In summary, we make the following contributions:
•pioneer the introduction of the first optimal monetary policy for crypto-
currencies
•devise the first stochastically dominant crypto-currency, its dominance
arising from its optimal monetary policy
•derive optimal simple rules for a crypto-currency in a Dynamic Stochastic
General Equilibria model
•prove that the efficient portfolio is to go long on stochastically dominant
crypto-currencies: in fact, it’s a strategy-proof arbitrage featuring a higher
Omega ratio with a higher expected return, inducing a Nash equilibrium
over the crypto-currency market
•describe how zero-knowledge proofs for the implemented monetary policy
are committed on the blockchain
Inanutshell, wecontributeanewmethodologyforanalysingandderivingoptimal
simple rules for the monetary policy of stochastically dominant crypto-currencies,
in order to create efficient portfolios of stochastically dominant crypto-currencies.
This paper intends to be a self-contained guide covering all the necessary theory
and practical aspects.
In section 2, we discuss related literature and prior work. In section 3, we
introduce our economic environment, analysis framework, and efficient portfolio.
In section 4, we describe our economic model and optimal monetary policies.
Finally, we detail some features of the technical implementation in section 5,
including how to commit the implemented zero-knowledge policy, and then we
conclude in section 6.
The reader interested in less theoretic and most empirical analysis may skip
to subsection 4.2,
2 Related Literature
The seminal contribution of this paper is to start the study of the first optimal
monetary policy for crypto-currencies: until now, all the study of the field
3
was concentrated on the monetary policies for stablecoins [ MIOT19 ,Cer19a] or
the models of the interaction between crypto-currencies, fiat currencies and/or
CBDCs with a view of understanding their shocks to the economy (starting from
the seminal [BK16]).
Figure 2.1: Relative supply of crypto-currencies[Gal19]
Figure 2.2: China’s simulated quantity rule and actual M2 growth [LZ09]
4
However, the study of the monetary policy of crypto-currencies has always
been relegated to matters related to their supply [ Gal19], as shown in the previous
Figure 2.1, in stark contrast with the quantity rules of money used in the real
world (e.g., China’s policy rule as shown in the previous Figure 2.2). In fact, the
equational expression of both policy rules couldn’t be more different: Bitcoin’s
supply equation Stin periodtcan be given by
SBTC
t = 21107
1 t
where is the growth rate ( 0:825for yearly periods and 0:953for
quarterly periods, see 4.5), and China’s quantity rule of money [ LZ09] for the
previous Figure 2.2 can be given by
Mt= M
t+1Mt 1 2^Yt 3(t
t)
with Mdenoting the nominal money growth, M
tthe log of equilibrium
money growth, 1= 0:88the lag of nominal money growth, ^Ytthe output gap,
2= 0:16the coefficient of response to changes of the output gap, tthe inflation
rate in period t,
tthe target inflation rate, and 3= 0:06the coefficient of
response to changes in inflation.
There is no related literature about what should be the optimal monetary
policy of a crypto-currency according to the methods of modern macro-economics,
as customarily practised on central banks: in fact, current crypto-currencies
are designed for anarcho-autarkic settings on which they don’t have to keep
track of inflation, GDP, or money growth, not even the exchange rate of other
crypto-currencies.
Moreover, previous sources of dominance in the crypto-currency market
were the first mover advantage of Bitcoin, or the network effects inherent to
payment networks [ HG16] : as a novel contribution, this paper introduces optimal
monetary policies as a source of dominance in the crypto-currency market.
Furthermore, simple rules are preferred over complex models [ FV20]: in the
foreseeable future, simple rules will still dominate the design of markets over
complex models due to their many strengths and few weaknesses [Tay20].
2.1 Comparison with prior work
Previous work from the same author [ Cer19a] described methods to conduct the
monetary policy in a decentralised fashion, but with the following differences:
1.Previous work [ Cer19a] focused on a stablecoin, but this paper targets the
volatile crypto-currency market.
(a)However, this paper introduces the novelty of stochastic dominance of
monetary policy rules and its usefulness to dominate other previous
crypto-currencies.
2.The technical implementation of this paper is significantly simpler than
[Cer19a], without compromising the security model: that is, it provides
similar cryptographic guarantees on a decentralised blockchain.
5
Additionally, the results of this paper are also valid for the setting of [ Cer19a] :
the optimal simple policy rules obtained in this paper (3.2.2 and 4.2) could be
directly incorporated into the “Economic Model for a Central-Banked Currency”
(Section 4.3 of [Cer19a]).
2.2 Survey of the Monetary Policy Impact on Crypto-
currencies
Although crypto-currencies such as Bitcoin were designed to replace the discre-
tionary decisions of monetary policymakers from central banks, even to insulate
them from macro-economic shocks, in reality their decisions continue impacting
their price and volatility. In this subsection, a survey of recent research about
this topic is presented, which shall inform the design of monetary policy rules in
the next subsection (4.2):
6
Paper Period Results
[Hsi21] 2010-2020 Unanticipated 1 bp on 2-year Treasury yield is
about a 0.25% decrease in Bitcoin price and 1.23%
three days later (stronger at high and low quantiles)
[Kar21] 2014-2021 Disinflationary ECB policy shocks (2-year interest
rates of 10 basis points) lead to a persistent decrease
in Bitcoin price (-20%), whereas inflationary ECB
information shocks lead to price increases;
conversely, contractionary US policy shocks (2-year
interest rates of 10 basis point) increase Bitcoin
prices (+7%) but fall during expansionary US
information shocks (due to flows to foreign
exchanges with emerging market currencies)
[PF21] 2016-2021 Cointegration between Bitcoin prices and M2,
deeper with time delays
[CS20] 2010-2018 SVAR model shows no response of Bitcoin prices to
shocks to nominal interest rate (1-year US treasury
rate), only to stocks, VIX; but increase after a
positive shock to the price level (Billion prices
index)
[CLL+17]2013-2017 Mineable crypto-currencies show US volatility
spillovers during FOMC announcement period, but
not dApp or protocols
[PL19] 2010-2018 Bitcoin price increases 0.26% at no FOMC
announcement, 0.96% on the day before and
decreases 1% on the announcement day. Bitcoin
price doesn’t change on CPI, PPI or employment
rate announcements
[CMC21] 2010-2021 Positive link between cryptocurrencies and forward
inflation rates is identified only during COVID-19
[CS21] 2010-2020 Bitcoin prices appreciate against inflation (or
inflation expectation) shocks, but do not decrease
after policy (1-year US treasury rate) uncertainty
shocks (i.e., only when excluding ZLB constraint)
[BGW21] 2019-2020 Daily changes in Bitcoin prices Granger cause
changes in the forward inflation rate in a significant
and persistent way, but not vice-versa
[BGW14] 2010-2014 In the short term, Bitcoin price adjusts to changes
in money supply, GDP, inflation, and interest rate
Table 1: Survey of Monetary Policy Impact on Crypto-currencies
7
A practical example of the effects of inflation on Bitcoin price can be found
below, shedding $1K on 13/9/2022 in just 3 minutes (10% of market
capitalization) as US CPI inflation for August overshoots at 8.3% year-on–year
(expected 8.1%):
Figure 2.3: Effect of inflation on BTC/USD
3 Environment, Framework and Efficient Portfo-
lio
We consider pre-commitment rules in rational expectations models by a Bayesian
risk-averse policymaker that is given the task to choose a policy feedback coef-
ficient function mapping the parameter space into the set of policy feedback
coefficients interpreted as random variables with probability distributions given
from the posterior distributions of the model parameters, in order to minimise
the expected disutilities of welfare loss for all disutility functions by ranking the
policy rules according to a stochastic dominance criterion that is robust against
all of the parameter uncertainty about the structure of the economic model.
3.1 Economic Environment
The setting of this paper is the general form of linear rational expectations
models with uncertainty as set out in [ And08]: many Dynamic Stochastic
General Equilibrium models can be approximated by linear rational expectation
(LRE) equations,
F1(1;2)Etxt+1+F1(1;2)Etxt+1+F2(1;2)Etut+1
+F3(1;2)xt+F4(1;2)ut+F(1;2)vt= 0;(3.1)
8
G2( )Etxt+1+G3( )xt+G( )vt=G1( )ut; (3.2)
M1(m)xt+M2(m)ut+M(m)vm;t=yt;t= 0;1;2;::: (3.3)
with the equation 3.1 describing the dynamics of the private sector around the
deterministicsteady-state, 3.2beingthepolicyequation, and3.3themeasurement
equations, all the above using the following notation:
•xtis a vector of nnon-policy endogenous variables
•uta vector of kpolicy variables
•yta vector of mn+kobservable variables
•Etis the operator of conditional expectation with respect to an information
set in period t
•Fi;F;Gi;G;Mi;andMare matrices depending on the parameters
•vm;tandvtare vectors of independent and identically distributed innova-
tions with zero mean and identity variance-covariance matrix I
•1is a vector of structural non-policy random parameters
•2is a vector of structural non-policy calibrated parameters
•mis a vector of measurement parameters
• is a vector of random policy parameters (feedback or response coefficients)
The solution to the system of linear rational expectation equations 3.1 - 3.2 is
given by a state equation of the form
zt=A(s; )zt 1+B(s; )vt; t= 1;2;:::; (3.4)
for the initial vector of states variables z0=h
x0
0;u0i0
and for unknown matrices
A(s; ),B(s; )withs=h
0
1;0
2i0
.
The parameter uncertainty in model 3.3 - 3.4 is measured by the posterior
probability distribution function according to the following Bayes rule:
p(; jYt) =p(; )p(Ytj; )
p(Yt); (3.5)
where=h
s;0
mi0
,Yt=h
y0
1;y0
2;:::;y0
ti0
is a sequence of observable vectors
at timet,p(Ytj; )is a likelihood function, and p(; )=p()p( )is a
prior posterior probability distribution function. The elements of A(s; ),
andB(s; )are usually non-linear functions of the vectors sand , and the
9
posteriors are not analytically available so we use the likelihood principle to treat
posteriors as a measure of uncertainty about the parameters; thus, simulations
are used to find approximations to the marginal posterior distributions and .
With this approach, optimal policy coefficients are assumed to be random
variables with probability distributions inherited from the posterior distributions
ofthestructural modelparametersobservedwithparameteruncertainty, avoiding
treating optimal feedback coefficients as fixed numbers much like policymakers
usually do.
3.2 Decision Framework
In this subsection, we use a decision procedure to evaluate and rank simple
policy rules in rational expectation models. A Bayesian policymaker formulates a
statistical decision problem to choose a policy rule under parameter uncertainty
with the following tuple
(Yt;p();p( l);;D;M;Lt)
in which each term defines:
•Ytdenotes the history of the observable variables over tperiods
•subjective prior distributions p()for the structural parameters =h
0
s;0
mi0
2=sm
•subjective prior distributions p( l)for the policy parameters l2lfor
l= 1;2;:::;N
•a setDof actions
•a setM=
Pzjs;d:s2s;d2D
of linear rational expectations models
under consideration, differing in the values of the structural parameters
s2sand the values of the policymaker’s action d2D
•loss function Lt(;d)that quantifies the policymaker’s choice of applying
a given policy rule when a particular model holds
Considering how to rank a set of policy rules in the model of 3.2 with N2
different functional forms
G1l( l)ul;t=G2l( l)Etxt+1+G3l( l)xt+Gl( l)vt; t= 0;1;2;:::(3.6)
withl= 1;2;:::;N, the vector l2lcollecting the policy feedback coefficients,
withGilandGlbeing matrices that depend on the policy feedback parameters.
The decision space Dis of the form
D=f(l;fl) :l= 1;2;:::;N (3.7)
fl:!lg (3.8)
in which the following conditions hold:
10
•the admissible policies d= (l;fl)is a rule from 3.6
•fl:!lis a policy feedback coefficient from a given class of measurable
functionsFlsuch that the system of linear rational expectation equations
3.1 and 3.6, with l=fl()for all2has a solution which is given by
the state equation
zl;t=Al(s; l)zl;t 1+Bl(s; l)vt; t1 (3.9)
withz0beinganinitialstate, zl;t=h
x0
t;u0
l;ti0
,zl;0=z0,andAl(s; l);Bl(s; l)
are unknown matrices with s=h
0
1;0
2i0
•every setFlincludes all constant functions fl() =const
•the parameters space lconsists of all vectors of policy parameters lfor
everyl= 1;2;:::such that for all s2s, the system of linear rational
expectation equations 3.1 and 3.6 has a unique solution
The procedure of the Bayesian policymaker is to first observe the history of
the observable variables Ytovertperiods, and for every l= 1;2;:::;Nsets the
subjective prior distributions p()of the structural parameters and p( l)of the
policy parameters l2l. Then, it analyses the following set of linear rational
expectation models,
M=
Pzjs;d:s2s;d2D
for endogenous non-policy xtdescribed by 3.1 and policy variables ul;tdescribed
by 3.6. The predictive probability distribution Pzjs;dof the future state variables
z= (zl;t+s)s=0;1;2;:::2Zevolves according to 3.9.
3.2.1 Welfare Loss Function
The welfare loss function of the Bayesian decision maker’s objective at time tis
defined by
Lt:ZtVD![0;1)
receiving the following parameters:
•a vector of current state variables zt2Zt
•all future shocks v=f(vt+s;vm;t+s)gs=1;2;:::2V
•all vectors of structural parameters from the parameter space
•all admissible decisions dfrom the decision space D
11
In order to evaluate the objective function, the Bayesian policymaker could take
the unconditional average of the welfare losses over the current state and all
possible future shocks:
Lt(;d) =Z
ZtZ
VLt(zt;v;;d )dPv(v)dPzt(zt) (3.10)
or the conditional expected value of the welfare loss given zt:
Lt(;djzt) =Z
VLt(zt;v;;d )dPv(v) (3.11)
with=h
0
s;0
mi0
2and the policymaker decision d2Dthat is able to
modify the model structure Pzjs;d, the posterior distribution of the structural
parameters PjYt;d, and the value of the expected welfare loss Lt(;d).
In this paper, we will use a quadratic welfare loss function given by:
Lt(;d) =tr
WX
zl(s;d)!
=vars;d(^t) +wyvars;d(^yt)(3.12)
for alld=(l;fl)2Dands2swherevar;d(zl;t;i)is the unconditional
variance of the state variable zl;t;iin thel-th specification of the DSGE model,
whilewyis the diagonal weight of Wfor the output gap. These diagonal weights
reflect the monetary policy preferences of the central bank over the objectives:
specifically, we set wy= 0:05andw= 1.
3.2.2 Ranking Simple Policy Rules
Now we can start formulating robust optimality criteria to generate rankings of
simple policy rules 3.6 based on the optimal Bayesian policymaker’s objective
functionLt: first, a fixed vector of structural parameters ^2is chosen from
the parameter space , and then the expected welfare loss Lt
^;d
is minimised
subject to the recursive state equations 3.9 under criteria of k-degree stochastic
dominance (SD k) [Lev16]. Stochastic dominance is a useful concept for analysing
risky decision-making under uncertainty when only partial information about
the decision maker’s risk preferences is available.
Definition 1. (SDkordering) . Defined by the indefinitely many inequalities
ZL
0u(x)dFL1(x)ZL
0u(x)dFL2(x) (3.13)
between the expected disutilities of non-negative valued random losses L1SDk
L2with the cumulative distribution functions L1FL2andL2FL2, for
all functions u2Ukwith strict inequality for some u, whereUkis the set of
all disutility functions with the i-th derivative of usuch thatu00,u00
0;:::;u(k)0. Note that SD kimplies SDlfor allk > l, and that the SD 1
12
dominance of L2overL1implies that L1SDkL2holds for some finite k.
Additionally, we denote with SDkthe inequality between random welfare losses
defined by the SDkordering for k= 1;2;:::;1. Recall that SD1 ordering
assumes all non-decreasing disutility functions (non-satiable); SD2 is for risk-
averse policymakers towards welfare losses, restricting the disutility functions to
convex and non-decreasing; SD3 additionally prefers negatively skewed welfare
loss distributions (prudence); and SD4 requires that u40(temperance).
Accordingly, simple policy rules can be analysed using the SD kordering from
the previous Definition 1.
Definition 2. (SDk-optimal policy) . Finding the best SD k-optimal simple
policy under parameter uncertainty is solved by searching for the SD k-optimal
decisiondSDk
1=
lSDk
1;flSDk
1
2Dfrom the set of all admissible decisions D
such that the corresponding distribution of welfare loss Lt
;dSDk
1
satisfies
Lt
;dSDk
1
SDkLt(;d) (3.14)
for alld2Dand subject to 3.9: dSDk
1generates the distribution of minimised
welfare loss, the smallest in terms of the SD kordering.
Assume that the Bayesian policymaker solves a parameterised optimisation
problem to find the value of the optimal policy feedback coefficient function
fmin
l():
Lmin
l;t() =min
l2lLt(;(l; l)) =Lt
;
l;fmin()
(3.15)
foreachvalueofthestructuralparameters 2andforeverypolicyspecification
l=f1;2;:::;Nggiven in 3.6. Note that fmin
l()is a selection from the optimal
choice correspondence set:
fmin
l()2min
l() =
min
l2l:Lmin
l;t() =Lt
;
l; min
l
We define min
l=fmin
l()to be the vector of optimal policy feedback coefficient
of rulelcalculated for the vector of structural parameters : thus, the optimal
policy feedback coefficient function fmin
l:!lis measurable and the pair
l;fmin
l
belongs toD.
The uncertainty of the structural parameters is considered in order to find
the probability distribution of the optimal policy response coefficients
min
lp
fmin
l 1jYt;l
(3.16)
and the minimised welfare loss is given by
Lmin
l;tp
Ltfmin
l 1jYt;l
(3.17)
where the inverse image of A2B()under!fmin
l()is
fmin
l 1(A) =
2:fmin
l()2A
13
and the inverse image of B2B()under!Lt
;fmin
l()
is
Ltfmin
l 1(B) =
2:Lt
;fmin
l()
2B
Next theorem 3.18 gives sufficient conditions for the optimal solution to 3.14
and shows how the SD k-optimal decision can be found.
Theorem 3. Assume that the decision sets lforl= 1;2;:::;Nare non-empty
compact subsets of Rr, the parameter space is an open subset of Rp, and
the policymaker’s welfare loss Ltis a Carathéodory-type integrable function. If
d= (l;fl)2Dis a policymaker decision such that lis defined by
Lmin
l;tSDkLmin
l;t;8l2f1;2;:::;Ng (3.18)
whereLmin
l;t;l2f1;2;:::;Ngare random minimised welfare losses as defined in
3.15, 3.16 and 3.17; and fl=fmin
lis the optimal policy feedback coefficient
function that solves 3.15, as denoted by
min
l2lLt(;(l; l)) =Lt(;(l;fl())) =Lmin
l;t();82(3.19)
thend=dSDk
1is the SDk-optimal decision.
3.3 Efficient Portfolio
In this subsection, we derive an efficient portfolio for stochastically dominant
crypto-currencies providing the best expected returns in comparison with the
other crypto-currencies. Instead of using the mean-variance framework, we prefer
to use marginal conditional stochastic dominance [ SY84]: all risk-averse investors
prefer a portfolio Aover a portfolio Bif the portfolio return of Ais stochastically
dominant over that of B, moving out all dominated assets. Furthermore, almost
marginal conditional stochastic dominance [ MJYW14 ] could be used to prevent
extreme utility functions in the set of risk-averse investors.
3.3.1 Pricing Stochastic Dominance
In order to ease exposition, suppose there are only two crypto-currencies in two
separate, segmented markets: a stochastically dominant crypto-currency, D, and
Bitcoin,B(resp. any other PoW/PoS crypto-currency). Consumption in the
dominant market at time tis denoted by cD
t, andcB
tin the Bitcoin denominated
market (resp. any other PoW/PoS crypto-currency). Consumers can transact
in one market but not both simultaneously, that is, utility uj(Cj;tmsj;t)at
timetin marketj2fD;Bg, whereCj;tdenotes complete-market consumption
that is distorted by the non-hedgeable monetary policy shock msj;t, rendering
incomplete the system of markets: moreover, we assume that msj;tandCj;tare
statistically independent for any j2fD;Bg.
14
Lemma 4. Suppose the utility function u(x)is of the form Constant Relat-
ive Risk-Aversion (CRRA), then the Stochastic Discount Factor (SDF) in the
dominant market is given by
MD
t+1=msD;t+1CD;t+1
msD;tCD;t
=MCD
t+1MmsD
t+1 (3.20)
and in the Bitcoin market (resp. any other PoW/PoS crypto-currency) is given
by
MB
t+1=msB;t+1CB;t+1
msB;tCB;t
=MCB
t+1MmsB
t+1 (3.21)
where
is the coefficient of relative risk aversion, and
MCj
t+1=Cj;t+1
Cj;t
;Mmsj
t+1=msj;t+1
msj;t
Lemma 5. (Fundamental Pricing Equation). The Euler equation for hold-
ers of the stochastically dominant crypto-currency is given by:
Et
MB
t+1Qt+1
Qt
=Et
MD
t+1MmsB
t+1
MmsD
t+1
=1
RD
t+1(3.22)
and the Euler equation for the Bitcoin holder (resp. any other PoW/PoS crypto-
currency) is given by:
Et
MD
t+1Qt+1
Qt
=Et
MB
t+1MmsD
t+1
MmsB
t+1
=1
RB
t+1(3.23)
whereQtis the real exchange rate, and RD
t+1andRB
t+1are the risk-free rate in
the dominant and Bitcoin market, respectively.
We assume that the logarithm of the Stochastic Discount Factors (SDFs) in
the two markets are normally distributed: mD,mB,mmsD,mmsB,mCDand
mCB(i.e., we denote logarithms of capitalised variables with their lowercase
variant).
Lemma 6. The arbitrage-free expected return on the stochastically-dominant
crypto-currency is given by:
Et(qt+1) =rB
t rD
t+1
2
Vart
mB
t+1
Vart
mD
t+1
+EtmmsB
t+1 EtmmsD
t+1
(3.24)
thus a relative rise of EtmmsB
t+1overEtmmsD
t+1leads to the appreciation of the
stochastically dominant crypto-currency.
Definition 7. (Logarithmic utility function). The utility function is
u(x) = log (x)
15
thus we have the following additively separable representation for the two shocks,
consumption and monetary (resp. csandms):
u(csms) = log (csms) =u1(cs)u2(ms) = log (cs) + log (ms)
with values for D1andB1foru1andD2andB2foru2:D1andD2are marginals
of the joint probability distribution D(cs;ms )in the dominant market, while
B1andB2are marginals of the joint probability distribution B(cs;ms )in the
Bitcoin market (resp. any other PoW/PoS crypto-currency).
Lemma 8. (First-order Stochastic Dominance). A necessary and suffi-
cient condition for the first-order stochastic dominance of the stochastically dom-
inant crypto-currency over Bitcoin (resp. any other PoW/PoS crypto-currency)
is
B1(cs)D1(cs)
and
B2(cs)D2(cs)
with strong inequality for at least some values in csor inms.
Given the definitions 7, the stochastically dominant crypto-currency in the
dominant market with a joint probability distribution D(cs;ms ), is preferred to
the Bitcoin market with B(cs;ms )if:
EDu(cs;ms ) EBu(cs;ms ) (3.25)
=Z
(B1(t) D1(t))du1(t) +Z
(B2(s) D2(s))du2(s) (3.26)
=Z
(B1(t) D1(t))1
tdu(t) +Z
(B2(s) D2(s))1
sdu(s)0(3.27)
Lemma 9. (Second-order Stochastic Dominance). A necessary and suffi-
cient condition for the second-order stochastic dominance of the stochastically
dominant crypto-currency over Bitcoin (resp. any other PoW/PoS crypto-
currency)Z1
t(B1(s) D1(s))ds0
and Z1
t(B2(s) D2(s))ds0
for alls>twith at least one strict inequality.
We assume that the consumption shocks in the two markets, dominant and
Bitcoin, are roughly the same,
D1B1
as both crypto-currencies are part of the same general economy (i.e., csis
renderedCas in its initial definition), thus monetary shocks play a pivotal role:
users prefer the stochastically dominant crypto-currency given the projected
expected utility of its stochastically dominant monetary policy, D2, over Bitcoin’s
B2(resp. any other PoW/PoS crypto-currency).
16
Theorem 10. (Dominant Expected Returns). A dominance relation-
ship between the distribution of the monetary policy shock of the stochastically
dominant crypto-currency, msD(D2), over Bitcoin, msB(B2), implies a rise
Et(qt+1)6 of the price of the stochastically dominant crypto-currency Din
terms of Bitcoin (resp. any other PoW/PoS crypto-currency).
Proof.WhenD2dominatesB2in the first-order sense 8, then
EtmmsB
t+1 EtmmsD
t+1>0
and vice versa. Thus,
EtmmsB
t+1 EtmmsD
t+1=msB;tEt1
msB;t+1
msD;tEt1
msD;t+1
=Z1
tdB2(t) Z1
tdD2(t)
=Z1
td(B2 D2)
=Z
(D2 B2)d1
t
= Z
(D2 B2)1
t²dt> 0
and the consequent rise of Et(qt+1)as given by 6.
3.3.2 Efficient Stochastically Dominant Portfolio
The stochastic dominance between two crypto-currencies of 8 and 9 further
extends into a stochastically dominant portfolio of crypto-currencies. As in
previous sections, we can distinguish between different orders of portfolio domin-
ance: first (Definition 11), second (Definition 13), ..., SD k-orders (Definition 15)
of portfolio dominance. Note that recent empirical research corroborates that
the inclusion of crypto-currencies in portfolios is itself stochastically dominant
[HNP+21, Rah20, MBN20, Coh21, TT18, AAT21].
GivenNalternatives and a random vector of their outcomes %, a decision
maker can combine them into portfolios and all portfolio possibilities are denoted
by
=
2RNj10= 1; n0; n= 1;2;:::;N
Definition 11. Portfolio2dominates portfolio 2by the first-order
stochastic dominance ( %0SD1%0 ) if
F%0(x)SD1F%0 (x);8x2R
with strict inequality for at least one x2Rand withF%0(x)denoting the
cumulative probability distribution of returns of portfolio . Necessary and
sufficient conditions for the first-order stochastic dominance ( %0SD1%0 ) if:
17
•Eu(%0)Eu(%0 )for all expected utility ( Eu) functions and strict
inequality holds for at least some utility function
•F 1
%0(y)F 1
%0 (y)for ally2[0;1]with strict inequality for at least one
y2[0;1]
•VaR ( %0)VaR( %0 )for all 2[0;1]with strict inequality for at
least one 2[0;1]
Definition 12. A given portfolio 2is first-order stochastic dominant
(Definition 11) inefficient if there exists portfolio 2such that%’SD1%’.
Otherwise, portfolio is first-order stochastic dominant efficient.
Second-order stochastic dominance can be similarly defined as first-order:
Definition 13. Portfolio2dominates portfolio 2by the second-order
stochastic dominance ( %0SD2%0 ) if and only if
F(2)
%0(y)SD2F(2)
%0 (y);8y2R
with strict inequality for at least one y2Rand withF(2)
%0(y)denoting the twice
cumulative probability distribution of returns of portfolio . Necessary and
sufficient conditions for the second-order stochastic dominance ( %0SD1%0 )
if:
•Eu(%0)Eu(%0 )for all expected concave utility functions and strict
inequality holds for at least some concave utility function
•Non-satiable and risk-averse decision maker prefers portfolio to portfolio
and at least one prefers to
•F 2
%0(y)F 2
%0 (y)for ally2[0;1]with strict inequality for at least one
y2[0;1], whereF 2
%0(y)is a cumulated quantile function
•CVaR ( %0)CVaR( %0 )for all 2[0;1]with strict inequality for
at least one 2[0;1], where
CVaR ( r0) =min
v2R;zt2R+v+1
1 SX
t=1ptzt
such thatzt xt v; t = 1;2;:::;S
Definition 14. A given portfolio 2is second-order stochastic dominant
(Definition 11) inefficient if there exists portfolio 2such that%’SD2%’.
Otherwise, portfolio is second-order stochastic dominant efficient.
The previous two definitions, first-order and second-order, can be generalised
to thek-order:
18
Definition 15. Portfoliodominates portfolio with respect to the k-order
stochastic dominance ( SDk ) ifEu(%0)Eu(%0 )for all utility functions
u2Unwith strict inequality for at least one such utility function, with UN
being the set of Ntimes differentiable utility functions such that: ( 1)iu(i)0
for alli= 1;2;:::;N.
Definition 16. A given portfolio is SDk-efficient (k2) if there exists at
least one utility function u2UNsuch thatEu(%0 ) Eu(%0)0for all2
with strict inequality for at least one 2.
And the previous one period stochastic dominance can be generalised to the
multi-period setting:
Theorem 17. ( [Lev73] ). LetFn(x)andGn(x)be the cumulative distributions
of twon-period risks where nis the number of periods and xis the product of
the returns corresponding to each period ( x=x1;x2;:::;xn). Then, a sufficient
condition for Fndominance over Gnby first-order stochastic dominance for
every non-decreasing utility function is that such dominance exists in each period,
namely:
Fi(xi)Gi(xi);8i;(i= 1;2;:::;n )
and there is at least one strict inequality, namely:
Fi(xi0)<Gi(xi0)
for somexi0.
Theorem 18. ( [Lev73] ). A sufficient condition for Fndominance over Gnby
second-order stochastic dominance for all non-decreasing concave utility functions
is that such dominance exists in each period, namely:
Zxi
0[Gi(ti) Fi(ti)]dti0;8i;(i= 1;2;:::;n )
and there is at least one strict inequality.
Finally, notethattheconceptofstochasticdominancealsoextendstostrategy-
proof allocation rules and game strategies:
Definition 19. . A strategy sisstochastic dominance strategy-proof if, for all
investorsi2I, all security ranking profiles (Ri;R i)2RI, and all misreports
R0
i2R, investori’s assignment xi;j2si(Ri;R i)stochastically dominates
yi;j2si
R0
i;R i
atRi(i.e., independent of the other investors’ ranking
reports), that is,X
si(Ri;R i)xi;jX
si(R0
i;R i)yi;j
19
Alternatively, stochastic dominance strategy-proof can also be defined in terms
of expected utility if, for all utility functions ui2URi, we have that
Eusi(Ri;R i)(ui)Eusi(R0
i;R i)(ui)
All the previous definitions naturally lead to the following theorem regarding
the stochastically dominant crypto-currency:
Theorem 20. The efficient portfolio is to go long on the stochastically domin-
ant crypto-currency: thus, the stochastically dominant strategy-proof allocation
rule for any investor is to hold this efficient portfolio with the stochastically
dominant crypto-currency. Furthermore, a higher return can be expected from
the stochastically-dominant crypto-currency.
Proof.Given a stochastic ranking of policy rules 3.14 of a stochastic ordering
(Definition 1) of monetary policy rules 3.6 that generates a stochastically dom-
inant crypto-currency by first-order 8 or second-order 9 dominance: then, the
SDk-efficient portfolio 16 (also, first-order 12 or second-order 14 efficient) that
dominates with respect to the k-order stochastic dominance 15 (also, first-order
11 or second-order 13 dominant) is the portfolio containing the stochastically
dominant crypto-currency as, by definition, this the one crypto-currency with
the SDk-optimal policy rule 3.14 that SD kdominates (Definition 1) all the other
policy rules. The proof extends trivially to the multi-period case by Theorems
17 and 18.
Moreover, for all investors i2I, the stochastically dominant strategy-proof
allocationrule19istoholdtheSD k-efficientportfolio16withthecrypto-currency
with the SD k-optimal policy rule 3.14.
Furthermore, a higher return can be expected from the stochastically-
dominant crypto-currency by the iterated deletion of strictly dominated strategies
when extending Theorem 10 to the market portfolio setting.
Finally, the efficient and strategy-proof portfolio of Theorem 20 induces an
investment-efficient Nash equilibrium:
Definition21. A mechanism Minduces efficient investment within by investor
i2Iif, for all valuation functions vIrfig2VIrfig, if
^vi2arg max
~vi2Vin
E(vi;vIrfig)h
ui
M
vi;vIrfig
;vii
ci
~vio
then we have
E(^vi;vIrfig)h
V
M
vi;vIrfig
;
vi;vIrfigi
ci
^vi
+
sup
vi2Vin
E(~vi;vIrfig)h
V
M
vi;vIrfig
;
vi;vIrfigi
ci
~vio
for all cost functions ci. In other words, a mechanism induces efficient in-
vestment by iwithinif, assuming agents report truthfully, every expected
utility-maximising investment choice by imaximises expected social welfare
within.
20
Theorem22. For any stochastic uncertainties 0and0, if the portfolio is
approximately strategy-proof 19 within for investor iand approximately efficient
within(i.e., first-order 12, second-order 14 or SD k-efficient 16 ), then it induces
an approximately efficient investment within (+)(#Crypto-currencies )toi,
independent of the other investors’ investments. Furthermore, the stochastically
dominant crypto-currency induces a Nash equilibrium over the crypto-currency
market that maximises ex-ante social welfare.
Proof.Follows trivially from Theorem 5 and Corollary 2 of [ HKK19]. It also
holds in expectations for any given investment choice profile of the other agents
using Theorem 7 from [HKK19].
Remark23.On the immovable commitment of crypto-currency monet-
ary policy rules and the lack of discretion : most crypto-currencies follow
the example of Bitcoin, where the monetary policy was fixed since its launch and
it was pre-announced that it will never change. This is in stark contrast with
the monetary policy of fiat currencies, where discretion is preferred in case of a
financial crisis. In other words, the widely accepted monetary policy stance of
crypto-currencies to fix their monetary policies not only leaves them vulnerable
to a financial crisis, but also turns them into dominated crypto-currencies by
stochastically dominant crypto-currencies.
3.3.3 Omega ratio of Stochastically Dominant Crypto-currencies
TheOmegaratio[ KS02]isarisk-returnperformancemeasureofanasset, portfolio,
or strategy which takes into account all the higher moment information in the
returns distribution and also incorporates sensitivity to return levels, unlike the
Sharpe ratio. It is defined as the probability-weighted ratio of gains versus losses
for some threshold return target ,
() =R1
[1 F(r)]dr
R
1F(r)dr=wTE(r)
E
( wTr)++ 1
Note that first-order stochastic dominance 11 implies Omega ratio dominance:
Theorem24. (Theorem 2, [ GJW17]). For any two returns XandYwith means
XandYand Omega ratios
X()and
Y(), respectively, if XSD1Y,
then
X()
Y()for any2R.
Corollary 25. The efficient portfolio long on the stochastically dominant crypto-
currency of Theorem 20 has a higher Omega ratio for any return threshold.
3.3.4 Arbitraging with Stochastic Dominance
If there exists a First-order Stochastic Dominance between two assets 8, under
certain conditions, arbitrage opportunities will also exist: thus investors will
increase not only their expected utilities, but also their wealth if they shift
their holdings to the dominant asset from the dominated one (i.e., a risk-free
investment opportunity with positive returns).
21
Theorem 26. (Arbitrage versus Stochastic Dominance - [ Jar86]). Given a
complete market M, there exists an arbitrage opportunity if and only if there
exists assets xandy2Msuch that:
•xSD1y
•[Pi(y ) Pi(x )]0for all 2Rand for some investor i2I,
wherePi()is theith investor’s subjective probability belief over the finite
number of states of nature
In other words, arbitrage implies First-order Stochastic Dominance but the
inverse is not necessarily true: it’s only true when the cumulative distribution
functions of the assets are perfectly correlated or the risky asset is a monotone
function of the asset even in the absence of perfect correlation. Note that
crypto-currency markets are unusually highly correlated compared to other asset
markets.
In practice, empirical studies may statistically detect First-order Stochastic
Dominance, but arbitrage opportunities may not exist: nonetheless, investors
can increase their expected utilities, as well as their expected wealth, if they
shift their holdings to the dominant asset from the dominated one [WPL08].
4 Model and Policies
DSGE models constitute the modern workhorse of monetary policy analysis,
with a recent survey finding 84 models used by 58 institutions [ Yag20]. In this
section, a Dynamic Stochastic General Equilibrium (DSGE) parsimonious model
is introduced to an economy featuring a Central-Bank Digital Currency (CBDC)
and a crypto-currency, calibrated and estimated for the United States.
The model is further simplified by opting for a closed-economy instead of a
small open-economy, justified by previous results showing that optimal policies
under parameteruncertainty lackexchangerate responses [ JP10] andthatwelfare
loss functions for small open economies do not include foreign variables when
the calibration is imposed [GM05].
4.1 Monetary Policy Rules
The following monetary policy rules are implemented in this model.
4.1.1 Central Bank
Taylor’s rule for the monetary policy:
it= (2 1) (i) +1it 1+3
1 Mt 1
Yt 1
+ (2 1) ( (t ) + y(log(Yt) log(Yt 1))) +it(4.1)
whereiare smoothing parameters and is the inflation feedback coefficient.
22
4.1.2 Bitcoin’s Monetary Policy
Although Bitcoin’s monetary policy is not described in its paper, its implementa-
tion appears in the source code [ Nak09a]: the initial reward of 50 BTC is halved
every 210,000 blocks (4 years), and each block is mined approximately every 10
minutes. The supply formula in block time is given by,
B(t) =min(t;T)X
t=150
2H(t)(4.2)
H(t) =t
210000
(4.3)
wheretis the block height, H(t)is the number of reward halvings up to block t,
andT= 33x210000. Bitcoin supply is limited beyond block T, given by
Btotal=32X
i=050
2i2100002:1107(4.4)
Equation 4.2 can be fitted as an exponential curve, given by:
SBTC
t = 2:1107
1 t
(4.5)
whereStis the supply in period t, and is the growth rate with 0:825for
yearly periods and 0:953for quarterly periods. The previous exponential
curve can be rewritten in recursive form as:
f0= 2:1107(4.6)
ft= 0:825ft 1 (4.7)
SBTC
t = 2:1107 ft (4.8)
or equivalently:
SBTC
t+1=SBTC
t + (1 )
2:1107 SBTC
t
(4.9)
= SBTC
t + (1 )2:1107(4.10)
The following figure displays the evolution of bitcoin supply assuming exact
10-minute confirmation times.
23
Figure 4.1: Bitcoin’s controlled supply[Wik22]
As previously pointed out in Figure 2.1 comparing the relative supply of
crypto-currencies, most crypto-currencies follow similar supply curves but use
different parameters: therefore, without loss of generality we will only consider
Bitcoin in this paper in representation of all the other crypto-currencies.
Note that Bitcoin’s monetary policy is independent of any observable variable
(e.g., inflation, output, ....) and Satoshi Nakamoto pre-committed not to ever
modify it: in macro-economics, this monetary policy can be interpreted as a
deflationary version of Friedman’s k-percent rule[ FS63] (i.e., constant money
growth). Following Poole’s classical Keynesian analysis [ Poo70] in a stochastic IS-
LMmodel, monetarypoliciestargetingonlythemoneystockallowmoneydemand
shocks to contribute to macroeconomic volatility: indeed, recent analysis in
modern New Keynesian models [ Ire00,CD05,Gal15] demonstrate that constant
money growth rules lead to excess volatility in both output and inflation when
the economy faces money demand shocks, or other disturbances that require
output and inflation to adjust. This situation is further aggravated by an inelastic
supply curve in both the short and the long term: as the following comparative
chart shows 4.2, supply inelasticities imply dramatic price changes with even
minor changes in demand, thus contributing to Bitcoin’s volatility.
24
Figure 4.2: Elastic v. Inelastic Supply Charts
However, money growth rules perform much better when they are able to
adjust to movements in the output gap and inflation as exemplified by the two
following rules 4.11 and 4.12: advantageously, these money growth rules are
able to stabilise inflation by pre-committing to an average rate of money growth
and focusing directly on stabilising the output gap over shorter time horizons,
instead of the aggressive responses to inflation needed by interest rate rules (i.e.,
Taylor’s rule).
4.1.3 Ethereum 2.0’s Monetary Policy
Other crypto-currencies feature a much more complex monetary policy than
Bitcoin’s monetary policy 4.1.2, although in essence they all suffer from the same
shortcoming: they fail to react to changes in inflation, output gap, or any other
macro-economic aggregate (unlike the monetary policy presented in this paper).
For the particular case of Ethereum 2.0 after transitioning to Proof-of-Stake
(a.k.a., “the Merge”), the monetary policy will be described by the following
features:
•almost deflationary by default: issuance reduced from 2 Ether/block to a
variable number depending on the total amount of Ether at stake (currently
around 13.3MM ETH), which will be around 600K ETH/year, implying a
90% reduction
•deflationary burning of transaction fees (EIP-1559)
•double use as store of value and gas for smart contracts
25
Accounting in a monetary policy rule for all the previous features will only
make it more deflationary, thus less reactive to changes in the macro-economic
environment (i.e., a narrower path of policy responses) and therefore much more
stochastically dominated even than Bitcoin’s monetary policy 4.1.2.
4.1.4 McCallum’s Policy Rule
A classical monetarist policy, McCallum’s rule [MN99] is specified by:
bt= x (xt 1 bt 1 xt 17+bt 17)
16+
x
t 1 xt 1
(4.11)
where the previous variables are defined as:
•btis the logarithm of the adjusted monetary base
•xtis the logarithm of the adjusted nominal GDP
•x
tis the target value of xtfor quarter t(growing smoothly at the rate
x).
The second term provides a velocity growth adjustment intended to reflect long-
lasting institutional changes, while the third term features feedback adjustment
inbtin response to cyclical departures of xtfrom the target path x
t, with
0chosen to balance the speed of eliminating x
t xtgaps against the danger
of instrument instability.
4.1.5 A Reconsideration of Money Growth Rules
If the Federal Reserve would have used a money rule targeting money growth
instead of the interest rate during the 2007-2009 recession, the US economy would
have recovered more quickly, and during the 2009-2015 period of zero nominal
interest rates, it would have stabilised output and inflation with comparable
performance [ BI22]. While the recent consensus was that policy rules using
constant rates of money growth would have performed poorly in comparison
to Taylor rules, recent work [ BI22] shows that money growth rules augmented
to adjust to movements in the output gap and inflation in a manner similar to
the Taylor rule will perform significantly better, on par with more conventional
Taylor rules for the interest rate. Thus, the reconsidered money growth rule is
given by
ln (t=) =mmln (t 1=) +mln (t=) +mxln (xt=x)(4.12)
where the previous variables are defined as:
•t=Mt=Mt 1denotes the growth rate of nominal money
•denotes the steady-state rate of money growth
•denotes the steady-state rate of inflation
26
•xdenotes the steady-state values of the output gap
Depending on the values of the parameters, the following cases can be considered:
•m=m=mx= 0is theconstant money growth rule as advocated by
Friedman [FS63]
•m<0andmx<0allow to stabilise inflation and the output gap in
response to shocks
•m<0,mx<0andmm>0prescribe a gradual response of money
growth to movements in inflation and the output gap, much like the Taylor
rule with interest rate smoothing
4.2 Ranking of Policy Rules
5 Implementation Details
The calculation of the stochastically-dominant optimal monetary policy is imple-
mented using Dynare [ ABJ+22] with an additional 225.000 MATLAB/Octave
LOCs.
5.1 Global Implementation
Different countries feature different macro-economic indicators (inflation, interest
rate, output, GDP growth, exchange rates...), thus it is very important for the
consensus protocol to be aware of the different nationalities of its participants
(nodes and/or users): Pravuil [ Cer21] is specifically designed for an international
setting as it integrates national identity cards and biometric passports in layer 1,
making it ideal to implement different monetary policies in different countries.
Furthermore, the combination of Zero-Knowledge Proof of Identity[ Cer19b]
with the Zero-Knowledge Stochastically Dominant crypto-currency induces the
following pincer manoeuvre:
27
Figure 5.1: Pincer manoeuvre inducing a downward spiral on PoW/PoS crypto-
currencies (in red) and a virtuous cycle for the Zero-Knowledge Stochastically
Dominant crypto-currency (in green)
5.2 Zero-Knowledge Monetary Policy
To understand the reason behind the lack of advanced monetary policies in
crypto-currencies as the ones described in this paper in subsections 4.1, one
has to look back to a reply by Satoshi Nakamoto [ Nak09b] on its original post
announcing the first implementation of Bitcoin:
Indeed there is nobody to act as central bank or federal reserve
to adjust the money supply as the population of users grows. That
would have required a trusted party to determine the value, because I
don’t know a way for software to know the real world value of things.
If there was some clever way, or if we wanted to trust someone to
actively manage the money supply to peg it to something, the rules
could have been programmed for that.
Fortunately, the author of this paper is more knowledgeable: this subsection
describes a zero-knowledge protocol to securely compute monetary policies using
authenticated economic series and commit their resulting zero-knowledge proofs
on the blockchain.
28
Figure 5.2: Committing zero-knowledge monetary policies on a blockchain
As pictured in the Figure 5.2 above, there are 3 parties to the protocol:
•Miner: commits transactions to the blockchain and gets rewarded accord-
ing to a monetary policy rule using data from providers of authenticated
economic series, and optionally its own private data.
•Providers of Authenticated Economic Series : take economic series
from public providers (e.g., FRED, db.nomics, ...) and authenticate their
data on the blockchain by signing with their private keys skecon, so it can
later be verified by everyone with the public key pkecon.
•BlockchainValidators : blockchainnodesthatverifytransactions, blocks,
and proofs. As previously discussed 5.1, they should be running the Pravuil
[Cer21] consensus protocol.
5.2.1 Security Model
The security model is defined with an ideal functionality FzkMonetaryPolicy that
rigorously sets the security requirements of the zero-knowledge protocol:
•Initialisation : the blockchain is initialised with public input data pand
a computation circuit C
•AuthenticateEconomicData : the provider of Authenticated Economic
Series sends a data authentication request to obtain the digital signature
seconover (datapublic ).
•zk-CommitMonetaryPolicy : miners request with authenticated data
containing inputpublic;inputprivate,thehashhofinputs,andthe outputminer.
29
Ideal Functionality FzkMonetaryPolicy
FzkMonetaryPolicy interacts with the adversary A, the miner, the providers of
authenticated economic series, the ideal functionality Fsigand the ideal
blockchain ledger functionality Lwith the following queries:
•Initialisation : upon receiving (init;C;p )on initialisation:
–store the circuit Cand the public input data p
–send (init;C;p )toA
•AuthenticateEconomicData : upon receiving
(authenticate; datapublic )from a provider of authenticated economic
series:
–send (sign;provider; datapublic )toFsigand receives signature secon
–send (sign;provider; datapublic )toA
•Validate : uponreceiving
validate; outputminer;inputpublic;inputprivate;h;secon
from a miner:
–send (verify;provider;h;s econ)toFsigand check that it’s correct
–check that
p;outputminer;inputpublic;inputprivate;h
satisfies the
circuitC
–send
validate; outputminer;inputpublic;h;secon
toA
The ideal functionality FzkMonetaryPolicy captures the following design goals:
•Authenticity : blockchain validators execute only on resulted computa-
tions from providers of authenticated economic series, rejecting otherwise.
•Privacy : the private data of the miner is never exposed to anyone, and
the blockchain validators are executed correctly without the private data
using the zero-knowledge proof.
5.2.2 Protocol Description and Implementation
Using a zero-knowledge SNARK scheme , the steps of the proposed scheme
would be as follows:
•Initialisation : A security parameter 1is picked in accordance with the
security requirements, and a circuit Cis constructed for the computation
over the authenticated data. Then, a trusted generator or a MPC protocol
setups the zk-SNARK with
1;C
to create the Common Reference String
for proof generation and verification.
Concurrently, the provider of authenticated economic series chooses a
public/private key pair (pkecon;skecon). Only then, (CRS;pkecon)are
published on the blockchain for everyone to check their validity.
30
•AuthenticateEconomicData : providersofauthenticatedeconomicseries
obtain signatures seconwith parameters (skecon;(h;datapublic )).
•zk-CommitMonetaryPolicy : miner uses circuit Cof the monetary
policytoobtaintheresult outputminerandahashhof
inputpublic;inputprivate
;
then, the miner executes the zk-SNARK for proving with parameters
CRS;p;inputpublic;inputprivate;outputminer;h
obtainingthezero-knowledge
proof. Then, the miner sends a transaction to the blockchain validators
as follows:
txskminer =
validate;; inputpublic;outputminer;h
•Validation : blockchain validators verify the zk-SNARK with parameters
CRS;pkecon;;p;inputpublic;outputminer;h
: onlyincaseit’sfoundvalid,
then the block from the miner is accepted with the computed monetary
policy.
31
zk MonetaryPolicy Protocol
Miner:
•zk-CommitMonetaryPolicy : on input
commit;p; inputpublic;inputprivate;outputminer;h
–prove with zk-SNARK:
=Prove
CRS;p;inputpublic;inputprivate;outputminer;h
–sendtxskminer =
validate;; inputpublic;outputminer;h
to the
blockchain validator
Providers of Authenticated Economic Series :
•Initialisation :
–(pkecon;skecon) =KeyGeneration
1
•Commit Authenticated Economic Data :
–compute h =Hash (datapublic )andsecon =
Sign (skecon;(h;datapublic ))
–send (h;secon)to the blockchain
Blockchain Validators:
•Initialisation : upon receiving (init;C;p;CRS;pk econ)
–Store the public input data pforC
–Store the common reference string CRS and pkecon
•Validation : upon receiving
validate;; inputpublic;outputminer;h
–Check that his stored on the blockchain
–Checkthatzk-SNARK
CRS;pkecon;;p;inputpublic;outputminer;h
is valid
–If valid, proceed to store the transactions, block, and associated
zk-proof
The following theorem formalises the security and privacy of the above
scheme:
Theorem 27. Ifis a simulation-extractable zk-SNARK with data authentica-
tion scheme, then the above scheme is a privacy-preserving scheme under the
universally composable framework.
Proof.See I.
Corollary 28. In the implementation, a simulation-extractable zk-SNARK such
32
as Plonk must be used [ GKK+21], even if it has larger proofs than other more
succinct zk-SNARKs.
An implementation in Go using gnark[ BPH+22] is available at https://
github.com/Calctopia-OpenSource/cothority/tree/zkmonpolicy
6 Conclusion
The present paper has tackled and successfully solved the problem of optimal
monetary policies specifically tailored for crypto-currencies, stochastically domin-
ating all the other previous crypto-currencies. Furthermore, the efficient portfolio
is to hold the stochastically dominant crypto-currency implementing the optimal
monetary policy, a strategy-proof arbitrage featuring a higher Omega ratio with
a higher expected return, inducing a Nash equilibrium over the crypto-currency
market.
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37
Part I
Appendix
Proof.(Theorem 27 ) . The protocol 5.2.2 securely realises the ideal function-
alityFzkMonetaryPolicy 5.2.1: by using the universal composability framework,
we first show an ideal-world simulator for the dummy adversary Aautomatically
passing messages to and from the actual adversary, the environment E; then, we
show the indistinguishability of the ideal and the Fsig-Hybrid worlds.
Ideal-world simulator . For conciseness, we only focus on the simulator S
and not on the blockchain functionality.
-Initialisation : simulatorSobtains[CRSand a trapdoor by running a
simulated setup algorithm of the zk-SNARK scheme . Then, simulator Skeeps
and sends[CRStoE.
-Simulatinghonestparties (notethatonly zk-CommitMonetaryPolicy
needstobesimulated): Esends
validate; outputminer;inputpublic;inputprivate;h
toanhonestminerandsimulator Sreceives
validate; outputminer;inputpublic;h
from the ideal functionality FzkMonetaryPolicy ; then, simulator Sgenerates an
indistinguishable proof using trapdoor (i.e., without knowing inputprivate).
Finally,Ssends
validate;; inputpublic;outputminer;h
to the blockchain val-
idators.
-Simulating corrupted parties :Erequests to the simulator Son be-
half of corrupted parties; then Sprocesses as follows: Sreceives (validate;
outputminer;inputpublic;inputprivate;h)and extracts inputprivatefrom the proof
using the trapdoor , then sends
validate;; inputpublic;outputminer;h
to
FzkMonetaryPolicy .
Indistinguishability between the ideal and the Fsig-Hybrid worlds :
a series of games from the Fsig-Hybrid protocol execution until the ideal world.
-Fsig-Hybrid model : a dummy adversary passes messages for the environ-
mentE, the actual adversary.
-HybridH1: adds to theFsig-Hybrid world calls to the simulated setup
that generates (kept by the simulator) and [CRS, sent toE.H1replaces the
real proofs with the simulated proofs using [CRSand : due to the computational
zero-knowledge property, H1is computationally indistinguishable from the Fsig-
Hybrid world.
-HybridH2: adds the simulation of the blockchain to the H1world. From
the adversaryE’s point of view, H2is indistinguishable from H1because the
blockchain functionality is public.
-HybridH3: adds to theH2world, the extraction of the private witness
from a zero-knowledge proof bySif it is a valid proof, otherwise aborts. H3
is indistinguishable from H2because the abort probability is negligible due to
the simulation extractability property of the zk-SNARK.
Finally, the ideal and the Fsig-Hybrid world are computationally indistin-
guishable because H3is computationally indistinguishable for Efrom the ideal
38
simulation. Note that any universal-composable signature scheme can implement
Fsigdue to the universal composition theorem.
39
|
{
"id": "2210.06139"
}
|
1712.02027
|
Evolutionary Game for Mining Pool Selection in Blockchain Networks
|
In blockchain networks adopting the proof-of-work schemes, the monetary
incentive is introduced by the Nakamoto consensus protocol to guide the
behaviors of the full nodes (i.e., block miners) in the process of maintaining
the consensus about the blockchain state. The block miners have to devote their
computation power measured in hash rate in a crypto-puzzle solving competition
to win the reward of publishing (a.k.a., mining) new blocks. Due to the
exponentially increasing difficulty of the crypto-puzzle, individual block
miners tends to join mining pools, i.e., the coalitions of miners, in order to
reduce the income variance and earn stable profits. In this paper, we study the
dynamics of mining pool selection in a blockchain network, where mining pools
may choose arbitrary block mining strategies. We identify the hash rate and the
block propagation delay as two major factors determining the outcomes of mining
competition, and then model the strategy evolution of the individual miners as
an evolutionary game. We provide the theoretical analysis of the evolutionary
stability for the pool selection dynamics in a case study of two mining pools.
The numerical simulations provide the evidence to support our theoretical
discoveries as well as demonstrating the stability in the evolution of miners'
strategies in a general case.
|
http://arxiv.org/pdf/1712.02027v4
|
Xiaojun Liu, Wenbo Wang, Dusit Niyato, Narisa Zhao, Ping Wang
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cs.GT
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cs.GT
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arXiv:1712.02027v4 [cs.GT] 29 Dec 20171
Evolutionary Game for Mining Pool Selection in
Blockchain Networks
Xiaojun Liu∗†, Wenbo Wang†, Dusit Niyato†, Narisa Zhao∗and Ping Wang†
∗Institute of Systems Engineering, Dalian University of Tec hnology, Dalian, China, 116024
†School of Computer Engineering, Nanyang Technological Uni versity, Singapore, 639798
Abstract —In blockchain networks adopting the proof-of-work
schemes, the monetary incentive is introduced by the Nakamo to
consensus protocol to guide the behaviors of the full nodes ( i.e.,
block miners) in the process of maintaining the consensus ab out
the blockchain state. The block miners have to devote their
computation power measured in hash rate in a crypto-puzzle
solving competition to win the reward of publishing (a.k.a. ,
mining) new blocks. Due to the exponentially increasing dif ficulty
of the crypto-puzzle, individual block miners tends to join mining
pools, i.e., the coalitions of miners, in order to reduce the income
variance and earn stable profits. In this paper, we study the
dynamics of mining pool selection in a blockchain network, w here
mining pools may choose arbitrary block mining strategies. We
identify the hash rate and the block propagation delay as two
major factors determining the outcomes of mining competiti on,
and then model the strategy evolution of the individual mine rs as
an evolutionary game. We provide the theoretical analysis o f the
evolutionary stability for the pool selection dynamics in a case
study of two mining pools. The numerical simulations provid e
the evidence to support our theoretical discoveries as well as
demonstrating the stability in the evolution of miners’ str ategies
in a general case.
Index Terms —Blockchain, proof-of-work, mining pool, evolu-
tionary game
I. I NTRODUCTION
Since its introduction in the grassroot online project “Bit -
coin” [1], the technology of blockchain has attracted signi f-
icant attentions across the academia, the industry and the
public. A blockchain network is built upon a virtual overlay
Peer-to-Peer (P2P) network as a decentralized temper-proo f
system for transactional record logging [2]. For permissio nless
blockchains, the Nakamoto consensus protocol [1] is widely
adopted to financially incentivize the full nodes (block min -
ers) to abide by the longest-chain rule in maintaining the
blockchain state. In a blockchain network, the blockchain u sers
issue the digitally signed transactions between their cryp to-
graphic addresses (a.k.a., wallets). Following the Nakamo to
protocol, the block miners pack an arbitrary number of such
verified transactions into a data structure, known as a candi date
block, and broadcast it to the rest of the network. A blockcha in
is thus organized as a hash-linked list of such blocks and
stored distributively as the local replica on each block min er.
Following the “longest-chain rule”, at a given time instanc e,
the longest one among the proposed blockchain views will be
ultimately recognized by the network as the current state of the
blockchain [2]. Both the engineering practices and theoret icalstudies [3] have shown that the Nakamoto protocol is able
to guarantee the persistence and liveness of a blockchain in
a Byzantine environment. In other words, when a majority
of the mining nodes honestly follow the Nakamoto protocol,
the transactional data on the blockchain are guaranteed to b e
immutable once they are recorded.
In the Nakamoto protocol, the financial incentive mecha-
nism consists of two parts: (a) a computation-intensive cry pto-
puzzle solving process to make Sybil attacks financially una f-
fordable, and (b) a reward generation process to award the
miners when their published blocks are recognized by the
entire network. The crypto-puzzle solving process is imple -
mented through a Proof-of-Work (PoW) process [3], where
the miners exhaustively query a trusted random oracle (e.g. , a
SHA-256 hash function) to find a string satisfying a preimage
condition based on their individual block proposals. In the
block awarding process, the first miner whose candidate bloc k
gets accepted by the majority of the network will receive a
monetary reward for its contribution in approving transact ions
and extending the blockchain by one block. Except for the
transaction fees named by the transaction issuers, the winn er
in a round of the block mining competitions may also receive
anex-nihilo , fixed-amount of award according to the token-
generation mechanism of the blockchain [1], [2].
The probability of winning a puzzle-solving competition
depends on the ratio between a miner’s hash rate (i.e., numbe r
of queries to the random oracle per unit time) and the total
hash rate of the entire network [3]. In addition, the block
propagation time in the P2P network determines the final
result of block confirmation within one consensus round, sin ce
only the first block propagated to the majority of the network
can be confirmed as the new head of the blockchain [4],
[5]. In practical scenarios, the chance for individual mine rs
to win in one round of the mining competitions has been
negligible due to the exponential growth of hash rate in the
network. As a result, the real-world blockchain networks ar e
dominated by the proxy nodes that represent the coalitions
of miners known as mining pools [2]. A mining pool works
as a task scheduler by dividing a puzzle-solution task into
smaller sub-tasks and assigning them to the miners accordin g
to their devoted hash rate. By aggregating the hash rate of
many miners, the probability for a mining pool to win a block
reward becomes significantly large. Then, an individual min er
can secure its small but stable share of reward according to
2
the ratio between its hash rate and the hash rate of the pool.
In this paper, we study the problem of mining pool selection
in a PoW-based blockchain network, where each mining pool
may adopt a different arbitrary block mining strategy [6]. B y
assuming that the individual miners are rational and profit-
driven, we propose a model based on the evolutionary game
to mathematically describe the dynamic mining-pool select ion
process in a large population of individual miners. Conside ring
the computation power and propagation delay as the two major
factors to determine the results of mining competitions, we
focus on how these two factors as well as the cost of the com-
putation resource (mainly in electricity) impact the strat egies
of the individual miners for pool selection. Based on a case
study of two mining pools, we provide the theoretical analys is
of evolutionary stability for the pool-selection dynamics . Our
numerical simulation results provide the evidences that su pport
our theoretical discoveries and further present the experi mental
insight into the impact of the arbitrary strategies on the re ward
outcomes of different mining pools.
II. P ROBLEM FORMULATION
A. Financially Incentivized Block Mining with Proof-of-Wo rk
We consider a blockchain network adopting the Nakamoto
consensus based on Proof-of-Work (PoW) [1]. Assume that
the network is composed of a large population of Nindividual
miners. For each miner, the chance of mining a new block is
in proportion to the ratio between its individual hash rate f or
solving the crypto-puzzles in PoW and the total hash rate in
the network. According to the Nakamoto consensus protocol,
the miner of each confirmed block receives a fixed amount of
blockchain tokens from the new block’s coinbase and a flexibl e
amount of transaction fees as the reward for maintaining the
blockchain’s consensus and approving the transactions [2] .
We consider that the individual miners organize themselves
into a set of Mmining pools, namely, M={1,2,...,M}.
We further consider that each mining pool may set different
requirement on the hash rate contributed by an individual
miner trying to join the pool. Let ωidenote the individual hash
rate required by pool i(i∈M ), andxidenote the miners’
population fraction in pool i. Then, the probability for pool i
to mine a block in one consensus round can be expressed as:
Prmine
i(x,ωωω) =ωixi/summationtextM
j=1ωjxj, (1)
whereωωω= [ω1,...,ω M]⊤,x= [x1,...,x M]⊤,/summationtext
i∈Mxi=
1and∀i,xi≥0.
After successfully mining a block, pool ibroadcasts the
mined block to its neighbors in the hope that it will be
propagated to the entire network and confirmed as the new
head block of the blockchain. However, in the situation wher e
more than one mining pool discover a new block at the
same time, only the block that is first disseminated to the
network will be confirmed by the network. All of the rest
candidate blocks will be discarded (orphaned). According t o
the empirical studies in [4], [5], the block propagation tim eis mainly determined by two factors, namely, the transmissi on
delay over each link and the transaction verification time at
each relaying node. For a block of size s, the transmission
delay can be modeled as τp(s)=s
γc[4], where γis a network
scale-related parameter, and cis the average effective channel
capacity of each link. On the other hand, since verifying a
transaction requires roughly the same amount of computatio n,
the block verification time can be modeled as a linear functio n
τv(s) =bs, wherebis a parameter determined by both the
network scale and the average verification speed of each node .
Then, the average propagation time for a block of size scan
be expressed as:
τ(s) =τp(s)+τv(s) =s
γc+bs. (2)
Based on (2), the incidence of abandoned (i.e., orphaning)
a valid block due to the propagation delay can be modeled
as a Poisson process with mean 1/T, whereTis maintained
by the network as a fixed average mining time (e.g., 600s
in Bitcoin) [4]. Then, the probability of orphaning a valid
candidate block of size sin one consensus round is:
Prorphan(s) = 1−e−τ(s)/T= 1−e−(s
γc+bs)/T. (3)
From (1) and (3), the probability for pool ito ultimately win
a block mining race with a block of size sican be derived as:
Prwin
i(x,ωωω,si) =ωixi/summationtextM
j=1ωjxje−(si
γc+bsi)/T. (4)
We assume that the transactions in the blockchain network
are issued with an invariant rate of transaction fees. When
the transactions are of fixed size, pool i’s mining reward
from transaction fee collection can also be modeled as a
linear function of the block size si. Letρdenote the price
of transaction in a unit block size [5]. Then, the reward of
poolifrom transaction fees can be written as ρsi. LetR
denote the fixed reward from the new block’s coinbase. Then,
the expected reward for pool ican be expressed as follows:
E{ri(x,ωωω,si)}= (R+ρsi)ωixi/summationtextM
j=1ωjxje−(si
γc+bsi)/T.(5)
Since the process of crypto-puzzle solving in PoW is com-
putationally intensive, the rational miners also have to co nsider
the cost of power consumption due to hash computation in
the block mining process. Noting that the new blocks are
discovered with a roughly fixed time interval, we denote the
energy price for generating a unit hash query rate during tha t
time interval by p. Then, we can obtain the expected payoff
for an individual miner in pool ias follows:
yi(x,ωωω,si) =R+ρsi
Nxiωixi/summationtextM
j=1ωjxje−(si
γc+bsi)/T−pωi.(6)
B. Mining Pool Selection as an Evolutionary Game
Consider that the individual miners are rational and aim to
maximize their net payoff given in (6). Then, it is nature to
model the process of mining pool selection in the population
3
of individual miners as an evolutionary game. Mathematical ly,
we can define the evolutionary game for mining pool selection
as a 4-tuple:G=/an}bracketle{tN,M,x,{yi(x;ωωω,si)}i∈M/an}bracketri}ht, where
•Nis the population of individual miners, |N|=N.
•M={1,2,...,M}is the set of mining pools, and
(wi,si)is the mining strategy preference of each pool
i∈M .
•x= [x1,...,x M]⊤∈X is the vector of the population
states, where xirepresents the fraction of population that
choose mining pool i.X={x∈RM
+:/summationtext
i∈Mxi= 1}.
•{yi(x;ωωω,si)}i∈M is the set of individual miner’s payoff
in each mining pool. yi(x;ωωω,si)is given by (6).
We note that ωiandsiform the predetermined mining
strategy of pool i. Given a population state x∈X , we can
derive the average payoff of the individual miner in Nbased
on (6) as follows:
y(x) =M/summationdisplay
i=1yi(x;ωωω,si)xi. (7)
Then, by the pairwise proportional imitation protocol [7],
the replicator dynamics for the evolution of the population
states can be expressed by the following system of Ordinary
Differential Equations (ODEs) ∀i∈M [7]:
˙xi(t) =fi(x(t);ωωω,si) =xi(t)(yi(x(t);ωωω,si)−y(x(t))),(8)
where˙xi(t)represents the growth rate of the size of pool i
with respect to time t.
We are interested in the Nash Equilibria (NE) of game G
described by (8). Let Y(x)denote the vector of individual
payoffs for all the mining pools, Y(x)=[y1(x),...,y M(x)]⊤
and letE(Y)denote the set of NE in game G. Then,E(Y)
can be defined as follows [8]:
Definition 1 (NE) .A population state x∗∈X is an NE of
the evolutionary game G, i.e.,x∗∈E(Y), if for all feasible
population state x∈X the following inequality holds
(x−x∗)⊤Y(x∗)≤0. (9)
It is straightforward that an NE is a fixed point of the repli-
cator dynamics given by (8), namely, ∀i∈M,fi(x(t);ωωω,si) =
0[7]. Then, we need to further investigate the stability of an
NE state x∗∈E(Y)for pool selection. Suppose that there
exists another population state x′trying to invade state x∗by
attracting a small share ǫ∈(0,1)in the population of miners
to switch to x′. Then,x′is an Evolutionary Stable Strategy
(ESS) if the following condition holds for all ǫ∈(0,ǫ):
/summationdisplay
i∈Mx∗
iyi((1−ǫ)x∗+ǫx′)≥/summationdisplay
i∈Mx′
iyi((1−ǫ)x∗+ǫx′).(10)
Based on (10), we can formally define the ESS as follows.
Definition 2 (ESS [8]) .A population state x∗is an ESS of
gameG, if there exists a neighborhood B ∈ X , such that
∀x∈B−x∗, the condition (x−x∗)⊤Y(x∗) = 0 implies that
(x∗−x)⊤Y(x)≥0. (11)Algorithm 1 Mining Pool Selection Following the Pairwise
Proportional Imitation Protocol.
1:Initialization :∀i∈N , minerirandomly selects a mining
pool to start with.
2:t←1
3:whilexhas not converged andt <MAX COUNT do
4: fori∈N do
5:j←Rand(1,M){Randomly selects a mining pool
j∈M}
6: Determine whether to switch to pool jaccording to
the following probability of pool switching ρi,j:
ρi,j=xjmax(yj(x;ωωω,sj)−yi(x;ωωω,si),0).(12)
7: end for
8:t←t+1
9:end while
In Algorithm 1, we describe the strategy evolution of the N
individual miners following the revision protocol of pairw ise
proportional imitation [9]. When receiving a signal for str ategy
revision of choosing a new pool, an individual miner switche s
from it current pool to the new pool probabilistically accor ding
to (12). As the population size increases, the pairwise pro-
portional imitation will asymptotically lead to the replic ator
dynamics described by the ODEs in (8).
C. A Case Study of Two Mining Pools
In this section, we consider a special case of a blockchain
network with two mining pools, i.e., M= 2. Let the pop-
ulation fraction of each pool be x1=x, andx2= 1−x.
From Definition 1 and by solving ˙xi(t) = 0,i∈[1,2], we can
obtain Theorem 1 as follows.
THEOREM 1.Based on the replicator dynamics in (8), a
blockchain network of two mining pools has three rest points
in the form of (x∗,1−x∗)with
x∗∈/braceleftbigg
0,1,a−b
Np(ω1−ω2)2−ω2
ω1−ω2/bracerightbigg
, (13)
wherea= (R+ρs1)ω1e−(s1
γc+bs1)/T,b= (R+
ρs2)ω2e−(s2
γc+bs2)/Tand0<a−b
Np(ω1−ω2)2−ω2
ω1−ω2<1.
Proof. Fromfi(x(t)) = 0,∀i∈{1,2}, we have
fi(x(t)) =xi(t)(yi(x(t),ai)−y(x(t)))
=xi(t)(1−xi(t))/parenleftbigga−b
N(ω1xi(t)+ω2(1−xi(t)))−p(ω1−ω2)/parenrightbigg
.
(14)
Then, by solving fi(x(t)) = 0 , we can obtain the three rest
points for the case of two mining pools as (x∗,1−x∗), where
x∗∈/braceleftBig
0,1,a−b
Np(ω1−ω2)2−ω2
ω1−ω2/bracerightBig
. Since from any initial
statex(0)∈X, the rest point of (8) should stay in the interior
ofX, we have the following condition:
0<a−b
Np(ω1−ω2)2−ω2
ω1−ω2<1. (15)
4
Then, the proof of Theorem 1 is completed.
Now, we are ready to investigate the evolutionary stability
of the three fixed points. In the case of x∗= 0 andx∗= 1,
the population state is (0,1)and(1,0), respectively. We know
that the two fixed points are of the similar form, since the
individual payoff functions are similar for each mining poo l.
Therefore, we only need to check the case with x1=x∗= 0.
Lemma 1. For gameGwith two mining pools, 1) The rest
point with x∗= 0 is an ESS, if the conditions given by (16)
and (17) hold:
a−b
Nω2−p(ω1−ω2)<0, (16)
/parenleftbigga−b
Nω2−p(ω1−ω2)/parenrightbigg/parenleftbigg
pω2−b
Nω2/parenrightbigg
>0. (17)
2) If the conditions given by (18) and (19) hold, the rest poin t
withx∗=a−b
Np(ω1−ω2)2−ω2
ω1−ω2is an ESS.
c(a(ω1+ω2)+ω1(−2b+Npω1(ω2−ω1)))
(a−b)<0,(18)
pc(−bω1+aω2)(a−b+Npω1(ω2−ω1))
(ω1−ω2)>0, (19)
wherec=a−b+Npω2(ω2−ω1).
Proof. According to Definition 2.6 in [9], the asymptotically
stable state of the ODE system given in (8) is guaranteed to be
an ESS. When the replicator dynamics is continuous-time, it
is asymptotically stable if the Jacobian matrix of the dynam ic
system at the equilibrium is negative definite, or equivalen tly,
if all the eigenvalues of the Jacobian matrix have negative r eal
parts [10]. For the replicate dynamic system given in (8), th e
Jacobian matrix of the replicator dynamics in a two-mining-
pool network is
J=
∂f1(x)
∂x1∂f1(x)
∂x2∂f2(x)
∂x1∂f2(x)
∂x2
/vextendsingle/vextendsingle/vextendsingle/vextendsingle/vextendsingle
(x1=x∗,x2=1−x∗). (20)
Further, the elements in (20) can be derived as follows:
∂f1(x)
∂x1=(1−2x1)/parenleftBigg
a
N(ω1x1+ω2x2)−pω1/parenrightBigg
−aω1(x1−x2
1)
N(ω1x1+ω2x2)2−bω2x2
2
N(ω1x1+ω2x2)2+pω2x2,(21)
∂f1(x)
∂x2=x1/parenleftBigg
pω2−aω2(1−x1)
N(ω1x1+ω2x2)2
+bω2x2
N(ω1x1+ω2x2)2−b
N(ω1x1+ω2x2)/parenrightBigg
, (22)∂f2(x)
∂x1=x2/parenleftBigg
pω1+aω1x1
N(ω1x1+ω2x2)2
−bω1(1−x2)
N(ω1x1+ω2x2)2−a
N(ω1x1+ω2x2)/parenrightBigg
, (23)
∂f2(x)
∂x2=(1−2x2)/parenleftBigg
b
N(ω1x1+ω2x2)−pω2/parenrightBigg
−bω2(x2−x2
2)
N(ω1x1+ω2x2)2−aω1x2
1
N(ω1x1+ω2x2)2+pω1x1.(24)
Based on (21)-(24), we have
1) After some tedious mathematical manipulations, the de-
terminants of the principle minors of Jatx∗= 0 should
satisfy the following conditions to guarantee the negative
definiteness of J:
det(J11)=a−b
Nω2−p(ω1−ω2)<0, (25)
det(J)=/parenleftbigga−b
Nω2−p(ω1−ω2)/parenrightbigg
(pω2−b
Nω2)>0.(26)
2) Similarly, at x∗=a−b
Np(ω1−ω2)2−ω2
ω1−ω2, the following
conditions can be obtained for the negative definiteness
ofJafter some mathematical manipulations:
det(J11)=c(a(ω1+ω2)+ω1(−2b+Npω1(ω2−ω1)))
N(a−b)(ω1−ω2)2
<0, (27)
det(J)=pc(−bω1+aω2)(a−b+Npω1(ω2−ω1))
N(a−b)2(ω1−ω2)>0.
(28)
Then, the proof to Lemma 1 is completed.
We note that the blockchain network is comprised by a large
population of individual miners in the real-world scenario s.
Then, from Lemma 1, we can employ the asymptotic analysis
and obtain the following theorem on evolutionary stability of
the rest points.
THEOREM 2.Assume that the population size Nis suffi-
ciently large. Then, neither of the rest points with x∗∈{0,1}
is evolutionary stable. The rest point with x∗=a−b
Np(ω1−ω2)2−
ω2
ω1−ω2is an ESS if the following conditions are satisfied:
/braceleftbigga−b <0,
(bω1−aω2)(ω2−ω1)>0.(29)
Proof. First, at the rest point with x∗= 0, by Lemma 1,
we can obtain the following conditions for the Jacobian if
ω1≤ω2,
lim
N→+∞det(J11)= lim
N→+∞a−b
Nω2−p(ω1−ω2)≥0.(30)
Then, the Jacobian matrix is not negative definite. Alterna-
tively, ifω1> ω2, we have
lim
N→+∞det(J11)= lim
N→+∞a−b
Nω2−p(ω1−ω2)<0,(31)
5
and
lim
N→+∞det(J)= lim
N→+∞(a−b
Nω2−p(ω1−ω2))(pω2−b
Nω2)<0.
(32)
Again, the Jacobian matrix cannot be negative definite. Then ,
the rest point with x∗= 0 is not an ESS. Following the same
procedure, we can show that the rest point with x∗= 1is not
evolutionary stable, either.
By [10], we know that any rest point in the interior of X
is an NE. Then, for the NE with x∗=a−b
Np(ω1−ω2)2−ω2
ω1−ω2,
following Lemma 1, we obtain
lim
N→+∞det(J11)
= lim
N→+∞(a−b+Npω2(ω2−ω1))a(ω1+ω2)
N(a−b)(ω1−ω2)2+
(a−b+Npω2(ω2−ω1))ω1(−2b+Npω1(ω2−ω1))
N(a−b)(ω1−ω2)2
= lim
N→+∞Np2ω1ω2
a−b, (33)
and
lim
N→+∞det(J)= lim
N→+∞p(a−b+Npω2(ω2−ω1))
N(a−b)2(ω1−ω2)·
(−bω1+aω2)(a−b+Npω1(ω2−ω1))
N(a−b)2(ω1−ω2)
= lim
N→+∞Np3ω1ω2(bω1−aω2)(ω2−ω1)
(a−b)2. (34)
By (33) and (34), the Jacobian matrix is negative definite if t he
conditions given in (29) are satisfied, hence the NE (x∗,1−x∗)
is an ESS. Then, the proof to Theorem 2 is completed.
III. E VOLUTION ANALYSIS
In this section, we conduct several numerical simulations
and provide the performance evaluation of the individual
miners’ pool-selection strategies in different situation s. We
first consider a blockchain network with N= 5000 individual
miners, which evolve to form two mining pools (i.e., M= 2).
For the purpose of demonstration, we set the block generatio n
parameters as λ= 1/600,1
γc+b= 0.005,R= 1000 ,ρ= 2
andp= 0.01. We also set the initial population state as
x= [0.75,0.25]. We first consider that the two pools adopt
their mining strategies with the same block size, s1=s2=
100, and different computation power contribution, ω1= 30
andω2= 20 . By Theorem 2, we know that such strategy
adaptation satisfies the condition for an ESS in the interior of
the simplexX. Figure 1(a) demonstrates the evolution of the
miners’ pool-selection strategies. According to Figure 1( b), the
strategies converge to a global ESS of (0.4,0.6), which is in
accordance with our theoretical prediction. We also observ e
that relatively fewer miners choose to join the pool requiri ng
a higher hash rate (i.e., pool 1) at the ESS. This is because a
higher computation power requirement will lead to an increa se
in the mining cost, which exceeds the profit improvement that
the miner can obtain in that pool.0 100 200 300 400 500 600 700 800 900 100000.10.20.30.40.50.60.70.80.91
x1
x2
(a)0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 100.10.20.30.40.50.60.70.80.91
(b)
Fig. 1. (a) Evolution of the miners’ population states over t ime with two
mining pools. (b) Replicator dynamics of the pool-selectio n strategies and the
evolution trajectory starting from x(0)=(0.75,0.25).
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 100.10.20.30.40.50.60.70.80.91
x1
x2
(a)0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.1-0.08-0.06-0.04-0.0200.020.040.060.080.1
(b)
Fig. 2. (a) Population state at the ESS vs. varying delay coef ficient1
γc+b.
(b) Average payoff of the miners at the ESS vs. varying delay c oefficient
1
γc+b.
Further, we analyze the influence of the network condition
on the pool-selection strategies of the individual miners. In
Figure 2, we show the evolution of the stable population
states and the corresponding average payoff of the individu al
miners with respect to varied delay coefficient1
γc+b. In the
simulation, we adopt the same mining strategies as in Figure 1.
Figure 2(a) shows that as the propagation delay coefficient
increases, more miners will tend to join the pool with a
smaller hash rate requirement ( ω2=20 ). Jointly considering
the payoffs at NE shown in Figure 2(b), we know that a larger
delay coefficient leads to a higher probability of orphaning
blocks of the same size. As a result, the miners prefer to join
the pool that induces lower mining cost. We can also observe
in Figure 2(b) that the payoffs of the mining pool remain
unchanged at zero. This phenomenon can be interpreted as
a situation of market equilibrium where the demand for the
hash rate exactly meets the supply with the current settings of
reward parameters.
Finally, we consider a more general situation with four
mining pools, where each pool adopts in their mining strateg y
the same block size as si= 100 (1≤i≤4) and different
requirement on the hash rate contribution as ω1=10 ,ω2=20 ,
ω3=30 andω4=40 . The evolution of the miner population
states is presented in Figure 3(a). In the considered case,
we observe that when the miners’ pool-selection strategies
converge to the equilibrium, selecting pool 1 becomes a
dominating strategy since by contributing a higher hash rat e,
the profit gain is unable to cover the power consumption cost
6
0 100 200 300 400 500 600 700 800 900 100000.10.20.30.40.50.60.70.80.9
x1
x2
x3
x4
(a)0 100 200 300 400 500 600 700 800 900 1000-0.2-0.15-0.1-0.050
x1
x2
x3
x4
(b)
Fig. 3. (a) The population states evolution with respect to d ifferent delay
coefficient1
γc+b, where mining strategy variables are s1= 100 ,s2= 120
andω1= 20 ,ω2= 20 . (b) Payoff evolution with respect to different delay
coefficient1
γc+b.
for each miner. Then, the individual miners prefer to decrea se
their dedicated hash rate since they are sensitive to the min ing
cost. Figure 3(b) show that the payoffs of joining a pool
evolves from negative value to zero. Again, this indicates a
situation where the block mining business becomes a perfect
competition market with an NE payoff of zero, and no miner
can switch its pool selection without undermining some othe r
miner’s payoff at the equilibrium.
IV. C ONCLUSION
In this paper, we have investigated the dynamic mining pool-
selection problem in a blockchain network using Nakamoto
consensus protocol. We model the dynamics of the individual
miner’s pool-selection strategies as an evolutionary game . In
particular, we have considered the computation power and
propagation delay as two major factors that determine the
outcome of the block mining competition. Furthermore, we
have theoretically analyzed the evolutionary stability of the
pool selection dynamics based on a case study of two mining
pools. For the case of two mining pools, we have shown
that the blockchain network conditionally admits a unique
evolutionary stable state. Our simulation results have pro vided
the numerical evidence for our theoretical discoveries.
REFERENCES
[1] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash s ystem,” Self-
published Paper , May 2008.
[2] F. Tschorsch and B. Scheuermann, “Bitcoin and beyond: A t echnical
survey on decentralized digital currencies,” IEEE Communications Sur-
veys Tutorials , vol. 18, no. 3, pp. 2084–2123, third quarter 2016.
[3] J. Garay, A. Kiayias, and N. Leonardos, “The bitcoin back bone protocol:
Analysis and applications,” in 34th Annual International Conference
on the Theory and Applications of Cryptographic Techniques , Sofia,
Bulgaria, Apr. 2015, pp. 281–310.
[4] P. R. Rizun, “A transaction fee market exists without a bl ock size limit,”
Self-published Paper , Aug. 2015.
[5] N. Houy, “The bitcoin mining game,” Ledger Journal , vol. 1, no. 13,
pp. 53 – 68, 2016.
[6] B. A. Fisch, R. Pass, and A. Shelat, “Socially optimal min ing pools,”
arXiv preprint arXiv:1703.03846 , 2017.
[7] J. Hofbauer and K. Sigmund, “Evolutionary game dynamics ,”Bulletin of
the American Mathematical Society , vol. 40, no. 4, pp. 479–519, 2003.
[8] J. Hofbauer and W. H. Sandholm, “Stable games and their dy namics,”
Journal of Economic Theory , vol. 144, no. 4, pp. 1665 – 1693.e4, 2009.
[9] J. W. Weibull, Evolutionary game theory . MIT press, 1997.[10] R. Cressman, Evolutionary dynamics and extensive form games . MIT
Press, 2003, vol. 5.
|
{
"id": "1712.02027"
}
|
1805.04698
|
Bitcoin Risk Modeling with Blockchain Graphs
|
A key challenge for Bitcoin cryptocurrency holders, such as startups using
ICOs to raise funding, is managing their FX risk. Specifically, a misinformed
decision to convert Bitcoin to fiat currency could, by itself, cost USD
millions.
In contrast to financial exchanges, Blockchain based crypto-currencies expose
the entire transaction history to the public. By processing all transactions,
we model the network with a high fidelity graph so that it is possible to
characterize how the flow of information in the network evolves over time. We
demonstrate how this data representation permits a new form of microstructure
modeling - with the emphasis on the topological network structures to study the
role of users, entities and their interactions in formation and dynamics of
crypto-currency investment risk. In particular, we identify certain sub-graphs
('chainlets') that exhibit predictive influence on Bitcoin price and
volatility, and characterize the types of chainlets that signify extreme
losses.
|
http://arxiv.org/pdf/1805.04698v1
|
Cuneyt Akcora, Matthew Dixon, Yulia Gel, Murat Kantarcioglu
|
q-fin.RM
|
q-fin.RM
|
Bitcoin Risk Modeling with Blockchain Graphs
Cuneyt Gurcan Akcora∗, Matthew F. Dixon†, Yulia R. Gel‡and Murat Kantarcioglu§
Abstract
A key challenge for Bitcoin cryptocurrency holders, such as
startups using ICOs to raise funding, is managing their FX
risk. Specifically, a mis-informed decision to convert Bitcoin
to fiat currency could, by itself, cost USD millions.
In contrast to financial exchanges, Blockchain based
crypto-currencies expose the entire transaction history to
the public. By processing all transactions, we model the net-
work with a high fidelity graph so that it is possible to char-
acterize how the flow of information in the network evolves
over time. We demonstrate how this data representation
permits a new form of microstructure modeling — with the
emphasis on the local topological network structure to study
the role of users, entities and their interactions in formation
and dynamics of crypto-currency investment risk. In partic-
ular, we identify certain sub-graphs (chainlets) that exhibit
predictive influence on Bitcoin price and volatility and char-
acterize the types of chainlets that signify extreme losses.
keywords :Cryptocurrencies ,Graph analysis ,forecast-
ing,financial risk ,ICOs .
JEL codes : C58, C63, G18
1 Introduction
Nascent empirical research suggests that short-run Bitcoin
price behavior is prone to bubbles and busts and somewhat
detached from asset pricing theory [9, 10, 5]. Since Bitcoin
derives its economic value from a “network effect” – the more
individuals who use Bitcoin, the more valuable the entire
Bitcoin ecosystem becomes- it is expected that transaction
activity is strongly linked with Bitcoin price changes [9, 10].
In contrast to existing financial networks, Blockchain based
crypto-currencies expose the entire transaction graph to the
public. Bitcoin transactions are listed for all participants
and the most significant agents can be immediately located
on the network.
In contrast to closed financial systems, the largest ac-
counts in a crypto-currency exchange are listed and can be
tracked over time and have been popularly referred to as
∗Postdoctoral Fellow, Data Security and Privacy Lab, University of
Texas at Dallas.
†Assistant Professor of Finance & Statistics, Stuart School of Busi-
ness, Illinois Institute of Technology.
‡Professor of Mathematics, Department of Mathematical Sciences,
University of Texas at Dallas.
§Professor of Computer Science, Director of the Data Security and
Privacy Lab, University of Texas at Dallas.“whales”. The econometrics of Bitcoin seeks new interdis-
ciplinary research to demonstrate how full disclosure of an
agent’s actions in a crypto-currency market inform price dis-
covery and ultimately serve as an early-warning indicator for
excess market volatility or even a crash.
With the goal of building a predictive model, we therefore
depart from classical times-series cross sectional models that
leverage standard macro economic variables such as GDP
and inflation. Instead, we use Bitcoin’s microstructure.
By processing all financial interactions, our objective is
to model the network with a high fidelity graph so that it
is possible to characterize how the flow of information in
the network evolves over time. This novel data representa-
tion permits an entirely new form of financial econometrics
— with the emphasis on the topological network structures
rather than covariance of historical time series of prices. The
role of users, entities and their interactions in formation and
dynamics of crypto-currency risk investment, financial pre-
dictive analytics and, more generally, in re-shaping the mod-
ern financial world is a novel area of research [11, 6, 7, 8,
12].
2 Method: Chainlets and Data
Processing
As shown in Figure 1, a Bitcoin graph consists of three main
components: addresses, transactions and blocks (see [1] for a
primer on Blockchain graphs). One approach to understand
how transactions relate to market price is to introduce the
novel concept of k-chainlets [2].
Ak-chainlet is a Bitcoin sub-graph of k≥1 transactions
and their corresponding input and output addresses corre-
sponding to different accounts, not necessarily unique to a
user. In the simplest case, a single transaction creates a 1-
chainlet with one or more inputs and a single output. For
example, in Figure 1, transaction t2results in the transfer
of Bitcoin from addresses a3,a4,a5to address a8. Such a
transaction creates a 1-chainlet that has three inputs and
one output. We denote this subgraph as a chainlet of type
C3→1, where 3 and 1 are the number of input and output
addresses, respectively.
A 1-chainlet is the smallest building block of the Bitcoin
graph; inputs and outputs of the chainlet are determined
at once, and the transaction is digitally signed. This signed
information cannot be modified, but multiple 1-chainlets can
be combined to extend the graph. For simplicity, in the rest
of this work, we use the term chainlet to refer to 1-chainlets.
1
arXiv:1805.04698v1 [q-fin.RM] 12 May 2018
a6t1
t2 t4t3
a2a1
a7
a4a3
a5a8a9
a10
a11
a12
a13
TimeFigure 1: A transaction-address graph representation of the
Bitcoin network. Addresses are represented by circles, trans-
actions with rectangles and edges indicate a transfer of coins.
Blocks order transactions in time, whereas each transaction
with its input and output nodes represents an immutable de-
cision that is encoded as a subgraph on the Bitcoin network.
Some addresses, such as a6in the figure, may contain un-
spent bitcoins.
Graph analysis allows us to evaluate the local topological
structure of the Bitcoin graph over time and assess the role
of chainlets on Bitcoin price formation and dynamics.
Figure 2 illustrates how the activity of the network can be
represented by a chainlet matrix. On a given day, we count
the occurrences of each Ci→jand store it in a chainlet ma-
trix. The maximum number of inputs or outputs of a chain-
let can be large, however, sometimes exceeding 1000. When
the number of inputs and/or outputs exceeds a threshold N,
we refer to these chainlets as ”extreme chainlets”. In our his-
torical analysis of daily snapshots, we choose N= 20, which
corresponds to the 97.5 percentile of all chainlet occurrences.
Figure 2: This figure illustrates how the network is repre-
sented in time with a 20×20chainlet matrix. Each matrix
is formed by taking snapshots of the Bitcoin graph and count-
ing the occurrences of Ci→j,∀i,jon a given day. The color
scale denotes the frequency of chainlet occurrences. The left
and right extreme chainlets are shown by the bottom row and
far right column respectively.
It is instructive to distinguish between ’left extreme chain-
lets’ and ’right extreme chainlets’. Left extreme chainlets
are the subsetCl:={Ci→j|i=N, j∈{1,...,N}}, ashighlighted in the bottom row in Figure 2. They represent
transactions from of a large number of accounts to fewer
addresses. As a general rule, left extreme chainlets indicate
bitcoin investment.
Right extreme chainlets are the subset Cr:={Ci→j|i∈
{1,...,N−1}, j=N}, as highlighted in the far right col-
umn in Figure 2. They represent the sale of a large sum of
Bitcoins across the market - the seller divides the balance
and sends them to potentially hundreds of Bitcoin addresses.
We denote the USD amount of Satoshis transferred on date
tby left and right extreme chainlets as Al
tandAr
t, and the
total occurrences as Ol
tandOr
trespectively.
Figure 3 shows the ratio of extreme chainlets to total oc-
currences at time t, denoted as Ox
t. We also measure ex-
treme chainlet activity with the ratio of Bitcoins transacted
by extreme chainlets, Ax
t. For example, if the volume to-
day was 2M Satoshis and 200K Satoshis were transacted by
using extreme chainlets, then Ax
t= 0.1.
0.050.100.150.20
1 40 154 251 360
Day of 2015Ratio
Figure 3: Ratio of extreme chainlets by daily occurrence
over 2015. On June 3 (day 154), New York State finan-
cial services superintendent announced BitLicense: a set of
rules that would govern virtual-currency businesses. BitLi-
cense came into effect on September 8th (day 251). Rather
than complying with these rules, cryptocurrency exchanges
demanded their customers to leave their platforms. Many
customers left by selling their Bitcoins, as evidenced by high
extreme chainlet activity.
3 Forecasting Bitcoin
The extent to which we can build predictive models from the
chainlets has already led to some promising results [2] (see
[2] for specification of the types and groups of chainlets that
exhibit predictive influence on Bitcoin price and volatility).
3.1 Risk modeling
We characterize the uncertainty of a ’loss’ and, in particular,
estimate the probability of extreme losses occurring over a
future horizon. The loss is defined as the negative of the log
2
returns,Lt=−rt, wherert:=ln(Pt+1/Pt) andPtis the
Bitcoin price on day t.
Bitcoin prices are sourced from Coinbase over the period
1/1/2012 to 10/7/2017 (2107 observations)1and the corre-
sponding chainlet matrices are available through the website
2.
Table 1 shows the results from regressing the square of
the log returns r2
t, a proxy for volatility, against xt– the
vector of daily extreme chainlet activity.
Estimate Std. Error t-value Pr(>|t|)
(Intercept) 0.9995 0.0807 12.385<2e-16 ***
Al
t 0.7248 0.1063 6.818 1.21e-11 ***
Ar
t 0.2959 0.1278 2.316 0.02068 *
Ax
t -0.5348 0.1313 -4.073 4.82e-05 ***
Ol
t -0.5699 0.1074 -5.304 1.25e-07 ***
Or
t -0.4541 0.1644 -2.762 0.00579 **
Ox
t 0.5043 0.1832 2.753 0.00595 **
Table 1: This table shows the statistical significance of the
extreme chainlet regressors on r2
t, a proxy for volatility.
Note that both the response and the regressors have been
standardized.
0.00.20.40.6
−2 0 2
Lossesdensity
0.00.20.40.6
−2 0 2
Lossesdensity
Figure 4: The empirical densities of the standardized daily
losses conditioned on the lower (red) and upper (green)
α= 0.05percentiles of extreme chainlet activity by (top)
amount (Ax) and (bottom) occurrences ( Ox). The standard-
ized unconditional loss density is shown by the black line.
Results establishing Granger causality between prices and
1Our analysis (see Figure 3 in [2]) showed that the Bitcoin network
did not stabilize until late 2011.
2https://github.com/cakcora/CoinWorkschainlets are shown in [2]. We emphasize that the purpose of
our analysis here is to augment these results with the distri-
butional properties of losses given extreme chainlet activity.
Figure 4 and Table 2 show the unconditional loss densities,
φ(Lt) (black) and conditional densities of the standardized
daily Bitcoin losses over the same period from 1-1-2012 to
10-7-2017. The mean of the loss density is observed to shift
to the right (indicating higher losses) when conditioned on
the top fifth percentile (green) of extreme chainlet activity
measuresAx
t(top) andOx
t(bottom). Conversely, we ob-
serve that the mean of the loss density shifts to the left
when conditioned on the lower fifth percentile (red) of ex-
treme chainlet activity measures Ax
t(top) andOx
t(bottom).
The skew and kurtosis of the conditional loss distributions
are also observed to differ significantly from the uncondi-
tional loss distribution.
pdf mean std.dev. skewness kurtosis
φ(Lt) 0 1 0.518 12.082
φ(Lt|Ax
t<Φ−1
Ax
t(0.05)) -0.047 1.107 3.283 31.618
φ(Lt|Ax
t>Φ−1
Ax
t(0.95)) 0.0861 0.843 1.590 6.046
φ(Lt|Ox
t<Φ−1
Ox
t(0.05)) -0.081 0.633 1.296 8.114
φ(Lt|Ox
t>Φ−1
Ox
t(0.95)) 0.118 0.930 2.025 10.457
Table 2: This table show the moments of the conditional and
unconditional empirical density functions corresponding to
those shown in Figure 4.
3.2 GARCH
The application of GARCH models to forecast Bitcoin has
been extensively investigated in [4, 3]. We supplement these
findings, by demonstrating the importance of including ex-
treme chainlet activity, xtin the GARCH model. We choose
anARMA (p,q)−GARCHX (1,1) model:
yt=µ+p/summationdisplay
i=1yt−i+q/summationdisplay
i=1ut−1+σtut, (1)
σ2
t=α0+α1u2
t−1+βσ2
t−1+βT
xxt, (2)
whereytare the observed daily returns, utare standard
skewed Student’s t-innovations and σtis the volatility. Ad-
ditional diagnostics, provided on request, show a positive
ARCH effect and that a GARCH(1,1) model has the lowest
AIC with a ARMA(2,2) model for the mean equation. Both
models pass a Box-Ljung and Lagrange Multiplier test at
the 99% confidence level for the residuals and square of the
residuals. Separate sign-bias tests show that asymmetry is
not significant.
Table 3 compares the 99% daily Value-at-Risk (VaR)
backtest of the ARMA(2,2)-GARCHX(1,1) model with the
3
Aug 15 Sep 01 Sep 15 Oct 010.00 0.05 0.10 0.15 0.20Forecast Rolling Sigma vs |Series|
Time/HorizonSigma
GARCH model : sGARCHHorizon: 30
Actual
Forecast
|Series|
Aug 15 Sep 01 Sep 15 Oct 010.00 0.05 0.10 0.15 0.20Forecast Rolling Sigma vs |Series|
Time/HorizonSigma
GARCH model : sGARCHHorizon: 30
Actual
Forecast
|Series|Figure 5: Next day volatilities estimated using a
(top) ARMA(2,2)-GARCH(1,1) model and a (bottom)
ARMA(2,2)-GARCHX(1,1) model. The forecasting horizon
is rolled over a 30 day out-of-sample period (red). In sam-
ple volatility estimates are shown in blue and observed daily
returns are shown in gray.
ARMA(2,2)-GARCH(1,1) model over an historical period
of 1857 days. The backtest is performed over an in-sample
rolling horizon, with model refitting every 7 days. The
ARMA(2,2)-GARCHX(1,1) model is observed to pass the
Kupiec unconditional and the Christoffersen conditional
coverage tests at the 95% confidence level whereas the
ARMA(2,2)-GARCH(1,1) underestimates the VAR and fails
both tests.ARMA(2,2)-GARCH(1,1) ARMA(2,2)-GARCHX(1,1)
alpha 1%
Expected Breaches 18.6
Actual VaR Breaches 33 15
Unconditional Coverage (Kupiec)
H0: Correct Breaches
LR.uc Statistic 9.201 0.742
LR.uc Critical 3.841
LR.uc p-value 0.002 0.389
Reject Null YES NO
Conditional Coverage (Christoffersen)
H0: Correct Breaches and Independence of Failures
LR.cc Statistic 9.451 0.986
LR.cc Critical 5.991
LR.cc p-value 0.009 0.611
Reject Null YES NO
Table 3: This table compares coverage test results of VaR
backtests using the GARCH and GARCHX model.
Figure 5 compares the day ahead forecasted volatility,
over a 30 day out-of-sample rolling horizon, (top) without
and (bottom) with the chainlet regressors. The GARCHX
model is observed to predict higher volatility than the
GARCH model and is found to be preferable for modeling
risk. Under a quadratic loss function, we reject H0in the
Diebold-Mariano test (DM=-2.2728) and conclude that the
differences in the model residuals are significant at the 95%
level (p= 0.023).
4 Summary
We model the blockchain transaction history of Bitcoin with
high fidelity graphs. Extreme chainlet activity, character-
ized by transaction amounts and occurrences are shown em-
pirically to result in increased probability of losses and to
significant changes in the volatility. With the inclusion of
these chainlet activities as external regressors in the variance
equation, we show a significant improvement in the GARCH
model for predicting extreme next day losses. Orders in the
mean and variance equation being equal, the inclusion of
extreme chainlet regressors results in nearly 90% reduction
in the number of false daily 99% VaR breaches or under-
breaches over an approximately 5 year backtesting horizon.
5 Acknowledgements
This material is based upon work supported by the National
Science Foundation under Grant No. IIS 1633331.
4
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Kancs. “The economics of BitCoin price formation”.
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[8] P. Gomber, J.-A. Koch, and M. Siering. “Digital Fi-
nance and FinTech: current research and future re-
search directions”. In: Journal of Business Economics
7.5 (2017), pp. 537–580.
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activity”. In: Economics Letters 167 (2018), pp. 81
–85. issn: 0165-1765. doi:https : / / doi . org / 10 .
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S0165176518301125 .
[10] L. Kristoufek. “What are the main drivers of the Bit-
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5
|
{
"id": "1805.04698"
}
|
1808.00811
|
Digging into Browser-based Crypto Mining
|
Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin
rewarding the miner for finding blocks for new transactions. The Monero
currency enables mining with standard hardware in contrast to special hardware
(ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new
revenue model for website operators. In this work, we study the prevalence of
this new phenomenon. We identify and classify mining websites in 138M domains
and present a new fingerprinting method which finds up to a factor of 5.7 more
miners than publicly available block lists. Our work identifies and dissects
Coinhive as the major browser-mining stakeholder. Further, we present a new
method to associate mined blocks in the Monero blockchain to mining pools and
uncover that Coinhive currently contributes 1.18% of mined blocks having turned
over 1293 Moneros in June 2018.
|
http://arxiv.org/pdf/1808.00811v2
|
Jan Rüth, Torsten Zimmermann, Konrad Wolsing, Oliver Hohlfeld
|
cs.CR, cs.NI
|
cs.CR
|
Digging into Browser-based Crypto Mining
Jan Rüth, Torsten Zimmermann, Konrad Wolsing, Oliver Hohlfeld
Communication and Distributed Systems, RWTH Aachen University, Germany
{lastname}@comsys.rwth-aachen.de
ABSTRACT
Mining is the foundation of blockchain-based cryptocurrencies
such as Bitcoin rewarding the miner for finding blocks for new
transactions. The Monero currency enables mining with standard
hardware in contrast to special hardware (ASICs) as often used in
Bitcoin, paving the way for in-browser mining as a new revenue
model for website operators. In this work, we study the prevalence
of this new phenomenon. We identify and classify mining websites
in 138M domains and present a new fingerprinting method which
finds up to a factor of 5.7 more miners than publicly available
block lists. Our work identifies and dissects Coinhive as the major
browser-mining stakeholder. Further, we present a new method to
associate mined blocks in the Monero blockchain to mining pools
and uncover that Coinhive currently contributes 1.18% of mined
blocks having turned over 1293 Moneros in June 2018.
CCS CONCEPTS
•Security and privacy →Malware and its mitigation ;•Net-
works →Network measurement ;
KEYWORDS
Mining, Cryptocurrency, Monero, Blockchain, Webassembly, Wasm,
Malware, Cryptojacking
ACM Reference Format:
Jan Rüth, Torsten Zimmermann, Konrad Wolsing, and Oliver Hohlfeld. 2018.
Digging into Browser-based Crypto Mining. In IMC ’18: Internet Measure-
ment Conference, October 31–November 2, 2018, Boston, MA, USA. ACM, New
York, NY, USA, 7 pages. https://doi.org/10.1145/3278532.3278539
1 INTRODUCTION
The web economy has traditionally used advertisements as means
to monetize services that are offered free of charge. This business
model relies on the implicit agreement between content providers
and users where viewing ads is the price for the “free” content.
This traditional approach has very recently been complemented
by a new monetizing model in which the computational resources
of website visitors are used to mine cryptocurrencies to generate
revenue for the website operators (browser-based mining).
Mining is the method of producing new blocks in blockchain
systems, most prominently cryptocurrencies such as Bitcoin. It
requires miners to solve a computationally expensive puzzle to
cryptographically link a new block to the previous block in the
IMC ’18, October 31–November 2, 2018, Boston, MA, USA
©2018 Association for Computing Machinery.
This is the author’s version of the work. It is posted here for your personal use.
Not for redistribution. The definitive Version of Record was published in IMC ’18:
Internet Measurement Conference, October 31–November 2, 2018, Boston, MA, USA ,
https://doi.org/10.1145/3278532.3278539.blockchain. The difficulty to solve this puzzle depends on the com-
bined computing power of all users—depending on the difficulty, an
individual requires powerful machines to increase the probability of
mining a block (e.g., GPUs, FPGAs, or even ASICs). To provide an in-
centive for contributing computational power, miners are awarded
currency for every mined block. This monetary reward has ren-
dered crypto mining a business—browser-based mining extends
this business to monetize the web.
Not all cryptocurrencies are equally suited for browser-based
mining. The hardware imbalance and the consequential high diffi-
culty to mine Bitcoin renders its in-browser mining inefficient and
motivates the use of, e.g., Monero as an alternative currency that can
be efficiently mined on CPUs and thus browsers. Given its design,
Monero has been adopted by websites (e.g., The Piratebay or a video
streaming service with subsequent media exposure [ 9,28]) and even
among botnets to mine on millions of compromised hosts [ 18]. To
ease browser mining, APIs [ 5,6] exist, e.g., for in-game financ-
ing [7], link forwarding [ 16], captchas, during video streaming [ 10]
or even as an entry fee for parties [ 15]. Our work identifies Coin-
hive [ 5] as a widely used service which provides a framework for
embedding a Monero miner into a website. While these frameworks
enable mining without the users’ knowledge (cryptojacking), other
services (Authedmine) actively ask users for their consent to do so
as an alternative to displaying ads. Besides media reports, little is
known about the ubiquity and use of browser-based mining.
Given these new possibilities, we provide a first in-depth study
of the prevalence andeconomics of browser-based mining as a new
web business model. We base this perspective on crawls of 137M
.com/.net/.org domains and the Alexa Top 1M list to first identify
sites using browser-based mining enabling to create a new finger-
printing method to identify mining code. Second, we dissect the
short link service of the largest web-mining stakeholder Coinhive
and screen their market power and profits. Our contributions are:
•We investigate the prevalence of browser-mining in the three
largest TLDs and the Alexa Top 1M, i.e., at over 138M domains.
•We present a new Wasm-based fingerprinting method showing
the inadequate capabilities of block lists to detect mining.
•Moreover, we identify the largest browser-based mining provider
Coinhive and dissect their link-forwarding service.
•We present a novel methodology enabling us to associate blocks
in a privacy-preserving blockchain to a mining pool.
•By applying our methodology, we screen Coinhive and show that
they contribute 1.18% of the blocks in the Monero blockchain
mining Moneros worth 150,000 USD per month (as of writing).
Structure. Section 2 establishes the basics of mining. Section 3
measures the prevalence of browser-mining. Section 4 studies the
practices, userbase, and economics of Coinhive. Section 5 discusses
related work and Section 6 concludes the paper.arXiv:1808.00811v2 [cs.CR] 21 Sep 2018
IMC ’18, October 31–November 2, 2018, Boston, MA, USA Rüth et al.
Miner’sCoinbase TXmaj: 7min: 7ts: time.now()prev:nonce: ???merkle_root:num_tx: 4…BlockchainMerkle TreeP2P NetworkPending TXNetworkStateDifficultyBlock Reward…PoWInputBlock HeaderTX1TX2TX3TX4TX5TX..?
Figure 1: Monero blockchain and PoW mining input.
2 BROWSER-BASED MINING 101
Blockchain-based cryptocurrencies build on the principle of embed-
ding financial transactions in a public, tamper-proof series of blocks.
To evolve the system, new blocks must constantly be appended to
store pending transactions; their generation is called mining . Miners
solve a crypto puzzle as a proof of work (PoW) whose difficulty is
dynamically adjusted to produce new blocks at a constant block rate
guaranteeing predictability and tamper resistance. Consequently,
when more miners compete for finding blocks, the difficulty rises
such that the block rate is met. When the PoW meets the difficulty,
it links the newly mined block (containing new transactions) to the
previous one rewarding the miner with currency in exchange for
the contributed computing power.
The recent hype around cryptocurrencies has led to substantial
increases in difficulty resulting in the need for faster hardware
to mine blocks profitably w.r.t. the energy costs. To increase the
chance of earning currency, miners seek to increase their available
computational power. This quest for speed is currently served by
GPUs, FPGAs, or even specialized ASICs. One can host substantial
amounts of mining hardware in dedicated data centers. Another
way is to bundle the computing power of multiple miners in mining
pools that share the earned revenue for the newly mined block.
Browser-based Mining. Utilizing the computation power of web-
site visitors provides yet another mean of increasing the mining
power. By embedding mining code into websites, a miner can make
use of the visitor’s CPU resources during the visit. The website oper-
ator thereby saves energy costs and mining hardware investments.
Thus, web-based mining is an alternative revenue generating model
to monetize websites and services. However, hidden mining or with-
out user consent (i.e., cryptojacking) poses a significant challenge
and it is a known attack vector (Section 5). While browser miners
for Bitcoin exist (e.g., jsMiner from 2011 [ 30]), the performance im-
balance between CPUs, GPUs, and ASICs poses an insurmountable
challenge for Bitcoin browser mining. Consequently, browser-based
mining requires cryptocurrencies with PoW functions that are only
efficiently computable on CPUs.
Monero. Launched in 2014, Monero [ 25] (see Figure 1) is a privacy-
preserving cryptocurrency whose PoW is designed to be ASIC
resistant (memory intensive and periodically redesigned) enabling
CPUs and thus browser-based mining. Specifically, it uses the Cryp-
tonight hash function [ 23] in its PoW to mine a new block with an
average block rate of two minutes. Figure 1 shows the PoW inputs;
in Monero, a miner constructs a Merkle tree of the transactions that
are to be included in the new block, requiring at least the Coinbase
11.01.18 11.03.18 02.03.18 11.05.18 27.02.18 08.05.18 28.02.18 09.05.18
Scan Date0.000.250.500.751.00NoCoin Detection Share
Alexa
.com
.net
.org
coinhive
authedminewp-monero
cryptolootcpmstar
other710 621 6676 5744 618 553 473 399# Potential Mining DomainsFigure 2: NoCoin detected miners on the Alexa Top 1M and
.com/.net/.org domains.
transaction paying the block reward to the miner. A node in this
tree is the hash of its two children with the hash of the transactions
as the leaves of the tree. Including the tree’s root links the trans-
actions to the PoW and the final block. Now the miner’s goal is to
find a nonce such that the PoW output (a hash) meets the global
difficulty (here, literally the product of the hash multiplied by the
difficulty must be smaller than 2256). Thus, a miner needs to draw a
new nonce and recompute the hash until it satisfies this goal. The
network can easily verify that the proof holds through a single
round of hashing, and by including the block in the blockchain,
rewards the miner with the block reward expressed through the
Coinbase transaction. When using mining pools, the pool pushes
jobs (containing the PoW input) asking the miners participating in
the pool to find a nonce that satisfies a lower difficulty than that
of the total network. When this lower difficulty is met, the miner
is awarded a share of the final block reward and if by chance the
actual difficulty is also met, the pool mined a block.
3 PREVALENCE OF BROWSER MINING
We start our analysis of browser-based mining by investigating its
prevalence in the web. Thus, we visit landing pages of a large body
of domains and identify the presence of mining code using two
approaches. Initially, we use a light-weight approach to download
website landing pages via TLS across several datasets, i.e., .com
(∼116M), .net ( ∼12M), .org ( ∼9M), and Alexa Top 1M ( ∼950K), and
match their HTML body against a public filter list (Section 3.1).
Subsequently, we instruct a Chrome browser to visit a subset of
these domains to execute the webpage code and thereby monitor
Websocket interactions and WebAssembly (Wasm) code as preva-
lent techniques for browser-based mining (Section 3.2). We obtain
our datasets through DNS resolutions [ 11] from zone files available
at Verisign [27] (.net/.com) and PIR [19] (.org).
3.1 NoCoin List
We visit every domain, prefixed with www. , via TLS and download
the first 256 kB of the domains’ landing pages using zgrab . 256 kB
offers a good tradeoff between capturing most content (i.e., scripts
are often located in the head of the document) and having a point
where to stop downloading when pages do not stop sending data.
We then extract all javascript tags using lxml to apply the NoCoin
filter list [ 12]. This list contains regular expressions to detect and
subsequently block mining code using common ad blockers. Fig-
ure 2 shows the number of domains with hits to NoCoin filter rules
Digging into Browser-based Crypto Mining IMC ’18, October 31–November 2, 2018, Boston, MA, USA
Alexa .orgClass. Count Class. Count
1 coinhive 311 coinhive 711
2 skencituer 123 cryptoloot 183
3 cryptoloot 103 web.stati.bid 120
4 UnknownWSS 56 freecontent.date 108
5 notgiven688 46 notgiven688 92
Total WebAssembly 796 WebAssembly 1491
Table 1: Top 5 ( ∼80%) WebAssembly signatures. Most Web-
Assembly are miners ( ∼96%), dominated by Coinhive.
on the top x-axis. Relative to the number of domains, the bars on the
y-axis show the relative share of the top 5 mining scripts (multiple
per website possible). We find the prevalence of mining websites to
be rather low. Yet in comparison, (popular) Alexa-listed domains
have the largest share (up to 0.07%). This seems likely since mining
is most profitable with websites having many visitors. Looking at
the miners, we find Coinhive, a Monero-based miner to be most
prevalent (used by >75% of the mining sites). Notably, Authedmine,
a variant of Coinhive asking for explicit user consent to mine and
wp-monero a WordPress plugin follows but at much lower shares.
We find other miners with smaller shares, e.g., Cryptoloot a Coin-
hive clone. By manually inspecting a random subset, we find false
positives, e.g., cpmstar is a gaming ad-network that we could not
verify to contain mining code. For the once popular jsMiner [ 30],
we find only 31 instances in all datasets combined.
Takeaway. We observe a low prevalence of mining in landing pages
according to the NoCoin list. Most miners are Monero-based with
Coinhive having the largest share ( >75%).
3.2 Chrome
We complement the NoCoin analysis by broadly investigating pat-
terns of mining behavior when actually executing the pages. This
enables to find mining domains beyond NoCoin-listed pattern.
Through manual miner code inspection, we find that the major-
ity of javascript miners utilizes WebAssembly (Wasm) for efficient
PoW calculation. WebAssembly [ 29] is a binary instruction format—
featured in recent browsers—that enables to compile e.g., C-code to
Wasm for efficient execution within the browser. Further, the com-
munication to the backend servers providing the PoW input often
uses Websockets. To detect these, we instrument a stock Chrome
web browser using the Chrome Dev Protocol [ 3] to capture all
Websocket communication and to dump all detected Wasm code.
To decide when a page is fully loaded, we wait for the page’s load
event and set a 2 s timer on every DOM change but wait no longer
than additional 5 s before we mark the page as loaded completely.
In case of no load event, we wait no longer than 15 s to mark the
website as timed out. We further save the first 65 kB of the final
HTML to enable comparison with the NoCoin list used previously.
Measurements. As this measurement is more time consuming,
we restrict our scope to the .org zone and the Alexa 1M. We pre-
fix every domain with http://www. allowing Chrome to follow
redirects to the secured variant if necessary. Thus in contrast to
our previous TLS-only measurement, we also analyze non-HTTPS
websites. We build signatures from the Wasm code by combining
(in a strict order) and then hashing the contained functions withNoCoin
Hitshaving Wasm
MinerWasm
Hitsblocked
by NoCoinmissed
by NoCoin
Alexa 993 129 737 129 608 (82%)
.org 978 450 1372 450 922 (67%)
Table 2: Miners on Chrome data (incl. non-TLS) found
through NoCoin and by our WebAssembly signatures.
Alexa .orgNoCoin Signature NoCoin Signature
1Gaming 19% Pornogr. 19% Gaming 29% Religion 9%
2Edu. Site 9% Tech. 8% Business 8% Business 8%
3Shopping 8% Filesharing 8% Edu. Site 6% Edu. Site 8%
4Pornogr. 7% Edu. Site 5% Pornogr. 5% Health Site 7%
5Tech. 6% Ent. & Music 5% Shopping 4% Tech. 6%
Categorized 79% 74% 54% 42%
Table 3: Top 5 categories according to Symantec RuleSpace.
SHA256. Through manual inspection of the Wasm, we build up a
database of ∼160 different assemblies (often versions of the con-
ceptually same Miner) that we found and categorized them, e.g.,
through their Websocket communication backend or by some other
distinguishing feature that we found in the code. Such features e.g.,
comprises the number of XOR, shift or load operations which we
found to be quite distinctive or function name hinting at the hash
function itself.
Table 1 summarizes our findings for the Alexa 1M and the .org
TLD from measurements in the first two weeks of May 2018. We
observe most Wasm code to contain mining functionality and most
miners are again Coinhive. To put the Chrome-based approach
in perspective to the NoCoin list, we apply the NoCoin block list
to HTML saved by Chrome, i.e., after the execution of javascripts.
Table 2 shows the number of miners detected by the NoCoin list and
the fraction of mining Wasm on this part as well as the total number
of websites classified through our Miner Wasm signature database
and the subset of websites that were detected by the NoCoin list. We
observe that NoCoin classifies many websites as miners, of which
only a fraction actually embeds mining Wasm code. This indicates
false positives which we verified through random inspections. If
we take a look at the websites for which we found Wasm mining
signatures, again, the NoCoin list only classifies a fraction of these
as having a miner—false negatives.
Classification. We use the Symantec RuleSpace1[24] engine to
categorize the mining websites. Table 3 shows the top 5 categories to
which RuleSpace assigned the websites for the NoCoin list matches
as well as our signature-based approach. We observe a diverse set
of categories and that RuleSpace can classify a larger body of Alexa
domains than .org domains. Interestingly, the categories for NoCoin
and our approach differ, especially the top category shows a large
mismatch, i.e., Gaming vs. Pornography and Gaming vs. Religion.
This could be caused by the aforementioned gaming ad-network.
Takeaway. Miners are already embedded on websites today. Simple
block lists are ineffective to detect them all and our signature-based
approach can detect sites beyond the NoCoin block list. Still, Coinhive
is the most used mining service.
1Used in Symantec products to classify websites.
IMC ’18, October 31–November 2, 2018, Boston, MA, USA Rüth et al.
100101102103104
Indexed token sorted by # links100101102103104105106Links per tokenAbsolute
CDF
0.000.170.330.500.670.831.00
CDF
Figure 3: The number of links per token (users) is heavily
biased towards a small number of users.
4 THE COINHIVE SERVICE
Coinhive provides a mining service advertised with the slogan
“Monetize Your Business With Your Users’ CPU Power” [5], we ob-
served Coinhive to have the most widespread use (see Section 3).
Their services are built on providing a highly optimized Monero
javascript miner to be embedded in websites. In turn, Coinhive
keeps 30% of the mined reward. Apart from offering this API, Coin-
hive offers e.g., a Captcha service and a short link forwarding service
which is the subject of our first analysis. Our tools on which the
following analysis is based are available at [20].
Regardless of the actual service, the process works as follows: i)
A Coinhive user (e.g., a website owner) is assigned a unique token
that is included in the API calls which is used to associate the mined
shares. ii)Upon a website visit, the miner is loaded and connects to
the Coinhive pool and authorizes with the user’s token to receive
input for hashing. iii)Once a valid hash is found, it is committed to
the Coinhive pool. iv)Eventually, Coinhive pays their users 70% of
the block reward and keeps the remaining 30%.
4.1 Short Link Forwarding Service
To begin analyzing Coinhive, we focus on its short link forwarding
service, which is similar to a common short link service (e.g., bit.ly)
but additionally requires to compute a configurable number of
hashes before resolving the link. When a user visits a link, she
only sees a progress bar indicating the share of hashes that have
been computed, when all locally computed hashes have been sent
to the service (i.e., the progress bar is full), the service will return
the original link and will instruct the browser to redirect the user
to it. This link redirection monetization is comparable to short
link services delaying the redirection while serving advertisements
and paying the link creator a commission [ 14]. With Coinhive, the
creator of the short link receives a share of the block reward that is
mined by the users visiting the short links.
Their short links follow a simple structure, identified by an al-
phanumeric ID: https://cnhv.co/[a-z0-9] . We observed that
new links are assigned increasing IDs which enables one to enumer-
ate the link address space. As of February 2018, up to 4 characters
are used, resulting in a total of 1,709,203 active short links. We
visit all links and gather the Coinhive redirection HTML document
to collect i)the link creator’s token—used to associate performed
hashes to the link creator—as well as ii)the number of hash compu-
tations required by the link creator to resolve the link. Even though
a single user could own multiple tokens, we will regard users and
tokens as synonymous in this paper.
2829210211212213214215216
# Hashes required100101102103104105106# Links
10510121019
0.000.170.330.500.670.831.00
CDF13s 26s 51s 2m 3m 7m 14m 27m 55mDuration @20H/s
1.4h 16Gyr
All links
User bias removedFigure 4: Required number of hashes and their frequency
of occurrence as well as the time it takes to compute these
hashes. Please note the skewed x-axis.
Without actually computing hashes, we can already look at i)
the distribution of short links per Coinhive users as well as ii)the
required number of hashes to resolve the links. Figure 3 shows the
distribution of short links per user. We observe a power-law which
highlights the existence of a few heavy users that created a large
number of links. In fact, 1/3 of all links are contributed by a single
user only and roughly 85% of all links are created by only 10 users.
Of course, a single user could use multiple tokens, however, this
would only emphasize our current observations.
To actually resolve the link, the user needs to compute the re-
quired number of hashes set by the link creator. Figure 4 shows
the distribution of this link resolution difficulty in the number of
required hash computations. The blue (dark) portion of the CDF
depicts all observed links, while the red (light) portion removes
the previously observed bias by heavy user by counting a required
# hashes only once per user; i.e., 1000 links from the same user with
the same number of required hash computations are only counted
once instead of 1000 times as in the blue (dark) dataset. To provide
a perspective on the time it takes to resolve a short link, the top
x-axis shows the duration to compute the required # of Cryptonight
hashes in a Chrome browser with a commodity laptop2. We observe
that the majority of links can be resolved in less than 51 sec (1024
hashes). The heavy user bias is most prominent at 512 hashes, still,
when removing the user-bias over 2/3 of the links of all users can
be solved with at most 1024 hashes in below one minute. To our
surprise, many links require a longer time to resolve; we find many
different users and over hundreds of short links that set the max-
imum of 1019hashes which takes several billion years to resolve.
While the user’s willingness to wait certainly depends on the con-
tent that is supposed to be behind a short link, high values suggest
either no desire to have them ever resolved or misconfigurations
(e.g., short link creators are not aware of the actual duration).
Link Destinations. To understand the kinds of links that the short
link service is used for, we resolve all links which require less
than 10K hashes from the unbiased dataset (covering 85% of this
dataset see red (light) CDF in Figure 4). Additionally, we resolve
a random sample of 1000 links for each of the top ten Coinhive
users. To efficiently resolve the short links without a web browser,
we replicate the working principle of the web miner in a non-web
implementation that can resolve multiple short links in parallel
making use of the official optimized Monero hash code. We found
22013 Macbook Pro 2.8 GHz Intel Core i7: 20 h/s with 4 threads.
Digging into Browser-based Crypto Mining IMC ’18, October 31–November 2, 2018, Boston, MA, USA
Domain Category Freq. Domain Category Freq.
youtu.be Ent. & Music 20% ftbucket.info Msg. Board 9.9%
zippyshare.com Filesharing 10% getcoinfree.com Finance 9.2%
icerbox.com Filesharing 10% ul.to Filesharing 4.2%
hq-mirror.de Ent. & Music 10% share-online.biz Filesharing 2.9%
andyspeed
racing.comAutomotive 10%oboom.com Filesharing 2.8%
Table 4: Top 10 domains in 89% of all samples from the top
10 short link creators.
that Coinhive alters the block header contained in the PoW inputs
before sending them to the users which the web miner reverts deep
within its WebAssembly3. This appears to be a countermeasure
to prevent using the Coinhive web miner outside of the Coinhive
environment, e.g., in custom mining pools. We had to roughly
compute 61.5M hashes which we were able to do in little less than
two days on a capable server machine.
Top 10 User. We first investigate a random sample from all short
links of the top 10 users (1000 links each) representing 80% of the
link targets. Table 4 shows a classification for the top 10 domains
(accounting for roughly 89% of all sampled URLs) that we extracted
from the destination URL. We again utilize the RuleSpace categories
to manually classify those 10 domains. As the table shows, most
links point to streaming and filesharing services.
Top Categories. We employ the RuleSpace engine to further clas-
sify the unbiased dataset into categories. One URL can have multiple
categories, therefore, a single URL can contribute to different cate-
gories. For roughly 1/3 of the URLs RuleSpace has no classification,
for the remainder, Table 5 lists the top 10 categories and how often
a URL falls into each category. We observe that sites to fall into a di-
verse set of categories, unlike the top 10 users for which filesharing
and streaming were the dominant categories (Table 4).
Takeaway. Coinhive’s link forwarding service is dominated by links
from only 10 users. They mostly redirect to streaming videos and
filesharing sites. We find that most short links can be resolved within
minutes, however, some links require millions of hashes to be computed
which is infeasible.
4.2 Estimating the Network Size
While we find many websites to use Coinhive (see Section 3), it
remains unclear how many users visit these sites. Thus, the mining
power and the achievable payouts are unknown. To understand the
available mining power and thereby the users of Coinhive, we need
to identify which blocks in the Monero blockchain were mined
through Coinhive.
Methodology. When a block is mined by the Coinhive network,
one of the clients must have found a nonce that satisfies the PoW
difficulty. Then, a new block can be mounted into the blockchain
which contains the block header that is also part of the PoW input,
as well as all the transactions that have implicitly been included in
the PoW input through the Merkle tree root (see Figure 1). Thus, if
we find the PoW input for which a suitable nonce was found, we
can investigate the blockchain and look at the block that succeeds
the block referenced in the PoW. If the transactions in that block
form a Merkle tree whose root is equal to that in the PoW input, we
3A simple XOR with a fixed value at a fixed offsetCategory Count Category Count
Tech. & Telecomm. 1,522 Shopping 572
Gaming 737 Finance and Investing 502
Dynamic Site 727 Ent. & Music 313
Business 578 Educational Site 305
Pornography 577 Hosting 298
Table 5: Top 10 categories of the unbiased dataset <10K
hashes.
010
% Blocks
010305070911131517192123
Hour of Day (UTC)26.04.1803.05.1810.05.1817.05.1824.05.18Day
0 10
# Blocksmedian
Figure 5: Mined blocks over time from the Coinhive net-
work. Black parts mark outages of ourinfrastructure.
can be sure that the PoW input was the one that was used to mine
the block. This uniquely identifies the origin as each block contains
the Coinbase transaction (first leaf of the Merkle tree) which is
used to pay the block rewards to the miner (i.e., Coinhive). Thus
we could never by accident see a Merkle tree root of another miner
in the PoW input.
We investigate the PoW inputs that are delegated by Coinhive
to its users by connecting to one of their mining pools and request
a new PoW input every 500 ms. As the network finds a new block
on average every two minutes, we cluster the PoW inputs by the
pointer to the previous (at time of reception, most recent) block.
We found that we never obtain more than 8 different PoW inputs
(even though more exist theoretically). Coinhive currently operates
32 mining endpoints (which can be gathered from the javascript
or by enumerating the domain name), when we connect to all of
them and repeat the process, we observe at most 128 different PoW
inputs per block. While this suggests that there are two endpoints
per backend system, it also puts us into the position to actually
investigate each of the 128 PoW inputs and check the Merkle tree
root against the Merkle tree root of the transactions in the block
that was actually mined after that referenced in the PoW input.
Measurements. We have been requesting new PoW inputs for
four weeks and we are thus able to confidently estimate a lower
bound on the blocks mined through Coinhive. Figure 5 shows a blue
block for every Coinhive-mined block as well as the total number
of blocks on that day. As finding blocks correlate with users mining
through Coinhive, we were interested to see if blocks are found
at certain times which could hint at the geolocation of the users.
However, the figure (upper subplot) shows that blocks are found
throughout the whole day which might be an indicator of the global
reach of Coinhive. We find multiple days with significantly more
blocks than on average, e.g., the 30th of April, 10th and 22nd of
May 2018. The 30th of April precedes Labor Day, a public holiday in
IMC ’18, October 31–November 2, 2018, Boston, MA, USA Rüth et al.
Med. Avg. Hashrate Currency
[blocks/day] [MH/s] [XMR]
May 9.0 8.8 5.5 1231
June 10.0 9.7 5.5 1293
July 9.0 9.1 5.8 1215
Table 6: Coinhive mining statistics for three month in 2018.
over 80 countries, time zone shifts to UTC or holidays could explain
increased Internet usage resulting in more mined blocks. Similarly,
the latter two were Ascension Day and the day after Pentecost,
both public holiday in many (mostly) European countries.
In the median (average), we find 8.5 (9.0) blocks per day, we
noticed a disruption of Coinhive’ service on the 6th and 7th of
May which resulted in only a few to no announced PoW inputs.
We can estimate the combined hash rate of Coinhive by taking
the network’s difficulty into account. The difficulty denotes the ex-
pected number of hashes that are required to find a block which is
adjusted after each block such that the block rate of two minutes is
met. Over the course of our observations, the median difficulty was
55.4G hashes, which translates to a network hash rate of 462M h/s.
As Coinhive mines roughly 8.5 blocks per day, they produce 1.18%
of all 720 blocks/day which translates to 5.5M h/s. If we assume
that a web client performs between 20 to 100 h/s, then Coinhive
requires between 292K and 58K constantly mining users. Compare
our findings with numbers reported by Coinhive [ 4] from Septem-
ber 2017 is difficult. Coinhive wrote that their hash rate peaked
at 13.5M h/s (then 5% of the network’s hash rate). However, our
results are averages over long periods of time and derived from
the statistical properties of the network, while those published are
momentary peak rates, thus a direct comparison is not possible.
If we sum up the block rewards of the actually mined blocks
over the observation period of four weeks, we find that Coinhive
earned 1,271 XMR. Table 6 complements our four-week analysis
with three full months in 2018 showing its continuity. Similar to
other cryptocurrencies, Monero’s exchange-rate fluctuates heavily,
at the time of writing one XMR is worth 120 USD, having peaked at
400 USD at the beginning of 2018. Thus, assuming 120 USD, Coin-
hive mines Moneros worth around 150,000 USD per month of which
they say, they give 70% to their users. Still, the operational costs
seem manageable, making it potentially profitable for Coinhive.
Takeaway. Coinhive currently contributes ∼1.18% of the mining
power of the Monero network. While probably profitable for Coinhive,
it remains questionable whether mining is a feasible ad alternative.
5 RELATED WORK
Browser-based mining has been subject to substantial media cover-
age, e.g., reports on Pirate Bay [ 9] mining, about hacked websites
for mining [ 28], miners injected into the Google’s DoubleClick ad-
platform [ 26] or drive-by Monero mining on Android [ 22]. Many
blog posts exist that report on Alexa-listed websites to include min-
ing code [ 2,17], however, without detailing a methodology. A list
published on Github [ 21] provides an overview of potential mining
domains. However, this list also includes entries such as google.com
which is unlikely to be mining. To the best of our knowledge, [ 8]
are the first to academically investigate browser-based mining par-
allel to our work. While they also find Coinhive to be the most
prominent service, their analysis is based on a string search overa large set of HTTP bodies, thus not accounting for actual HTML
structure which we did in our first analysis (see Section 3.1) which
showed to already produce skewed results, e.g., the categories of
websites significantly differs. We thus complement their results by
incorporating WebAssembly fingerprinting and further shed light
on the inner workings of Coinhive. In concurrent work, Konoth
et al. [ 13] estimate revenue for websites on the Alexa Top 1M in-
cluding mining scripts. Further, they also analyze the nature of
Wasm mining for detection using a very similar methodology to
what was the basis for our fingerprinting such as counting certain
instruction or memory access pattern. Based on monitoring DNS,
[1] also observes Coinhive as the dominant player. They report
that most crypto miners are present on adult websites in the Alexa
Top 1K/3K. Similar with regards to link-forwarders, [14] analyzed
ad-based link forwarding services and their revenue model which
relates to that of Coinhive, thus we believe their results to also
apply here.
6 CONCLUSION
This paper analyzes the prevalence of browser-based mining, a new
revenue generating model to monetize websites and an alternative
to ad-based financing that is enabled by ASIC resistant cryptocur-
rencies such as Monero. By inspecting 137M .com/.net/.org and
Alexa Top 1M domains for mining code, we indeed find websites
that utilize browser-mining. Yet, the prevalence of browser mining
iscurrently low at <0.08%of the probed sites. For its detection,
we find the public NoCoin filter list to be insufficient to broadly
detect browser mining. We thus present a new technique based
on WebAssembly fingerprinting to identify miners, up to 82% of
thereby identified mining websites are not detected by block lists.
We identify Coinhive as the largest web-based mining provider
used by 75% of the mining sites. Given its popularity, we further
dissect Coinhives’ link-forwarding service. We find that 10 heavy
users contribute over 80% of all short links mostly targeting stream-
ing and filesharing services. The remaining short links target a
diverse set of websites. We continue by dissecting the economics of
Coinhive, we devise a new method that allows associating mined
blocks with a mining pool and we find that Coinhive mines 1.18%
of all Monero blocks and their visitors have a combined median
hash rate of 5.5M h/s. While we find that Coinhive turns around
Moneros worth 150,000 USD per month, the current value stability
of cryptocurrencies requires further investigations if browser-based
mining can be an alternative revenue model to ad-based financ-
ing. Further, the impact of the CPU intensive miner on a website’s
performance, a mobile device’s battery lifetime or a visitor’s en-
ergy bill is yet to be quantified but it could be a huge hurdle to be
competitive to ad-based financing on a larger scale.
ACKNOWLEDGMENTS
Funded by the Excellence Initiative of the German federal and state
governments, as well as by the German Research Foundation (DFG)
as part of project B1 within the Collaborative Research Center (CRC)
1053 – MAKI. Further, we thank Martin Coughlan from Symantec
for granting us access to the RuleSpace classification engine as well
as the network operators at RWTH Aachen University, especially
Jens Hektor and Bernd Kohler.
Digging into Browser-based Crypto Mining IMC ’18, October 31–November 2, 2018, Boston, MA, USA
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|
{
"id": "1808.00811"
}
|
2308.06829
|
When Web 3.0 Meets Reality: A Hyperdimensional Fractal Polytope P2P
Ecosystems
|
Web 3.0 opens the world of new existence of the crypto-network-entity, which
is independently defined by the public key pairs for entities and the
connection to the Web 3.0 cyberspace. In this paper, we first discover a
spacetime coordinate system based on fractal polytope in any dimensions with
discrete time offered by blockchain and consensus. Second, the novel network
entities and functions are defined to make use of hyperdimensional
deterministic switching and routing protocols and blockchain-enabled mutual
authentication. In addition to spacetime network architecture, we also define a
multi-tier identity scheme which extends the native Web 3.0
crypto-network-entity to outer cyber and physical world, offering
legal-compliant anonymity and linkability to all derived identifiers of
entities. In this way, we unify the holistic Web 3.0 network based on
persistent spacetime and its entity extension to our cyber and physical world.
|
http://arxiv.org/pdf/2308.06829v1
|
Hao Xu, Yunqing Sun, Xiaoshuai Zhang, Erwu Liu, Chih-Lin I
|
cs.NI, cs.AR, cs.CR, cs.DC
|
cs.NI
|
1
When Web 3.0 Meets Reality: A Hyperdimensional Fractal
Polytope P2P Ecosystems
Hao Xu, Yunqing Sun, Xiaoshuai Zhang, Erwu Liu and Chih-Lin I Fellow, IEEE
Abstract —Web 3.0 opens the world of new existence of the
crypto-network-entity, which is independently defined by the
public key pairs for entities and the connection to the Web
3.0 cyberspace. In this paper, we first discover a spacetime
coordinate system based on fractal polytope in any dimensions
with discrete time offered by blockchain and consensus. Second,
the novel network entities and functions are defined to make
use of hyperdimensional deterministic switching and routing
protocols and blockchain-enabled mutual authentication. In ad-
dition to spacetime network architecture, we also define a multi-
tier identity scheme which extends the native Web 3.0 crypto-
network-entity to outer cyber and physical world, offering legal-
compliant anonymity and linkability to all derived identifiers of
entities. In this way, we unify the holistic Web 3.0 network based
on persistent spacetime and its entity extension to our cyber and
physical world.
Index Terms —Web 3.0, fractal network, decentralized infras-
tructure and identity, blockchain, polytope
I. I NTRODUCTION
Web 3.0 represents the next stage in the development of
the World Wide Web by encouraging peer-to-peer networks
among all users who enjoy equal ownership of their digital
assets without relying on centralized servers or third-party
intermediaries. By leveraging the Web 3.0 ready network,
entities are both reached locally and globally in a unified
access scheme. However, existing computer networking pro-
tocols, e.g., IPv4 and IPv6, treat the address with local and
global routing based on routing tables within the domain. On
the other hand, the current network cannot resolve addresses
and identifiers without the records from name servers, which
are eventually aggregated as the root Domain Name Servers
(DNS) held by central internet agencies and hinders decentral-
ization in the global scale [1]. With the existing infrastructure,
peer-to-peer networks are easily suffocated by tycoons and
experiencing interruptions of services, as P2P networks are
merely extensions of physical networks, where all devices are
connected to central network infrastructures.
Distributed Ledger Technology (DLT) or Blockchain is
considered as the backbone of Web 3.0, which enables infor-
mation distribution, and protects data integrity and credibility
while maintaining consistency using consensus mechanisms
Hao Xu is with Huawei Technologies (UK), Cambridge, CB4 0WG, UK, E-
mail: hao.xu@ieee.org. Yunqing Sun is with Department of Computer Science,
McCormick School of Engineering and Applied Science, Northwestern Uni-
versity, Evanston, IL, US, E-mail: yunqing.sun@northwestern.edu. Xiaoshuai
Zhang is with University of Glasgow, Glasgow, G12 8QQ, UK, E-mail:
Xiaoshuai.Zhang@glasgow.ac.uk. Erwu Liu is with College of Electronics
and Information Engineering, Tongji University, Shanghai, China; E-mail:
erwuliu@tongji.edu.cn. Chih-Lin I is with China Mobile Research Institute,
Beijing, China; E-mail: icl@chinamobile.com.[2]. And when it comes to the existence of objects and
value in Web 3.0 due to anonymity and its independence
from physical networks, there is always a debate about the
metaphysical significance of everything on the blockchain
and Web 3.0. People often wonder if their assets are real in
Web 3.0, or if their identities are indiscernible in this digital
realm. Can our identities truly be located in cyberspace with
precise spacetime, even though they may not have any physical
presence?
A. Motivations of Web 3.0 network and identity management
Web 3.0 offers a viewpoint of decentralization for recon-
structing WWW and the internet services in controversial to
big giants and centralized infrastructures with the power of
blockchain [3]. Blockchain has only been considered as an
one-dimensional data structure and has a little reflection of the
actual/logical world. In fact, linear and repetitive data blocks
resemble the idea of discrete time in Web 3.0. As the pillar of
decentralized ecosystems, the blockchain is not only a chained
undeniable crypto vessel but a spatio-temporal description of
everything connected and mapped into the topology.
1) Web 3.0 network: Based on persistent decentralized
medium and data, the Web 3.0 network is not only the
extension of reality, but a new “reality” is happening within.
However, to define an existence, the network needs to provide
the persistent and independent coordinates of the existent,
which are inherently true when they are connected in any
space where the coordinate of the existent can be twofold: the
space and the time. Thanks to blockchain-backed network, the
network state database, e.g., connectivity, temporal topology
and aliveness status, are subject to continuous refresh based
on concurrent connections at any given time. An example
of network identity mapping into a network address with
blockchain timestamp can be found in Fig. 1, where the
network is defined as a regular triangle with Koch fractal,
more iterations are illustrated in Fig. 2. Compared with the
current centralized network paradigm, such a fractal shaped
architecture is native globally peer-to-peer and deterministic
with shortest paths to better facilitate the future encrypted Web
3.0 network.
Furthermore, the issue of network scalability needs to be
addressed to construct a truly global and interplanetary Web
3.0 network. Unlike existing networks, the global Web 3.0
network will be a massive entity involving countless users and
stakeholders, offering a vast array of services and applications,
and processing enormous transactions. This amplifies the need
for high network throughput and highlights the negative impact
of network latency. Novel networks are yet to concern thearXiv:2308.06829v1 [cs.NI] 13 Aug 2023
2
Local routing table
maintained by home routerWide area routing table
maintained by lower-tier
routerRegional routing table
maintained by mid-tier router
SnowedNet SnowedNet
New blocks commit
Home (Higher-tier) Router
Mid-tier Router
BC ADD 7
BC ADD 8
BC ADD 9
....IP 7
IP 8
IP 9
...BC Address IP Address
Interface 7
Interface 8
Interface 9
....SnowedNet Routing T able
Blockchain T ransactions
InterfacesBC ADD 4
BC ADD 5
BC ADD 6
....IP 4
IP 5
IP 6
...BC Address IP Address
Interface 4
Interface 5
Interface 6
....SnowedNet Routing T able
Blockchain T ransactions
Interfacesz
BC ADD 1
BC ADD 2
BC ADD 3
....IP 1
IP 2
IP 3
...BC Address IP Address
Interface 1
Interface 2
Interface 3
....SnowedNet Routing T able
Blockchain T ransactions
InterfacesBCADD 0
....IP 0
...BC Address IP Address
Interface 0
....SnowedNet Routing T able
Blockchain T ransactions
Interfaces
BC ADD 0
|
BC ADD 9
....IP 0
|
IP 9
...BC Address IP Address
Interface 0
|
Interface 9
....SnowedNet Routing T able
Blockchain T ransactions
InterfacesLocal device is learned
by its Home Router ,
which commits to the
lower-tier router Local branch
fork
Local branch
merge
Global Ledger
Local Ledger
Global Ledger
Local Ledger
New blocks commit
......
Tier 2 Tier 2Tier 1Tier 1Tier 2
Mid branch
Lower-tier Router
Higher-tier Router
Lower-tier Router
Lower-tier RouterLower branch
Lower-tier Router
Higher-tier Router
Mid-tier RouterLower branch
Lower branchOroboros
Tier 0Tier 1
Tier 2Local branch
Mid-tier Router
Higher-tier Router
Tier 2
Tier 2Mid branch
Fig. 1. Architecture of the hyperdimensional fractal network for Web 3.0 ( D= 2).
consistent global state with high regional throughput, as seen
in Fig. 1, where the network shards are committed into the
upper network nodes.
2) Identity management: When it comes to existence, iden-
tity matters. The whole network needs to recognize a user’s
identity and its derivatives in a consistent manner i.e., no
matter how the identifiers are processed in cryptography, they
can always be linked back to a real identity and the existent
in the network. Ghost identities, which are pure cryptographic
products not linked to any real entities, shall not be considered
a real identity, as there is no spatio-temporal existence of them.
Users’ identities are managed and verified by third-party
applications and service providers i.e., they possess the identity
information of their clients. Compared with Web 1.0/2.0,
identity management in Web 3.0 needs to be more universal
because no centralized entity can harness users’ identities [4].
Furthermore, since all users are equivalent and their identities
are kept anonymous in the decentralized Web 3.0 network, a
real user identity should only be possessed and controlled by
the user itself and the sensitive information used in identity
verification needs to be regulated by the user.
Web 3.0 seeks to eliminate privacy and data governance
issues in today’s Internet. With the features of decentraliza-
tion and anonymization, users have more control over their
personal data and privacy where they own their autonomy
to make choices. However, Web 3.0 may bring some new
potential challenges in the perspective of legislation such as
data jurisdiction and self-sovereign identity, which should be
further analyzed in line with the objectives of General Data
Protection Regulation (GDPR). In this paper, a novel schemefor the universal identity with their juridical compliance is
proposed based on their spatio-temporal existence in Web 3.0.
B. Contribution
In this paper, we propose a fundamental spacetime co-
ordinate system with the skeleton of a network built on
blockchain. This coordinate system combines a novel network-
spatial blockchain in a hyperdimensional manner with the state
description of the whole network and the identities mapped
to the spatial ontological entries [5]. It impacts the existing
network by enabling deterministic geometrical mapping of
every connected entity to the spatial description of logical
space which is agnostic to the real world. Hence, they are
defined only by the topological relations and identity while
being timestamped by blockchain.
Our contributions to Web 3.0 lay in two aspects:
•A hyperdimensional simplex fractal Web 3.0 network
based on blockchain, coined as SnowedNet, offering a
discrete spatio-temporal coordinate, which is mapped to
the physical world, to connect existent with considering
blockchain consensus process and routing strategies;
•A universal identity management scheme that extends the
use of anonymous and consistent Web 3.0 identity in the
cyber world and physical world.
II. H YPERDIMENSIONAL FRACTAL WEB3.0 NETWORK
The first mathematical introduction of fractal shape was first
discovered in measuring the length of the coast of Britain.
A fractal shape is usually defined as a rough or fragmented
3
geometric shape that can be divided into several parts, and
each part is at least approximately a reduced shape of the
whole, i.e., it has the property of self-similarity as shown in
Fig. 2. A fractal shape is an abstract object in mathematics
used to describe things that exist in nature. Inspired by fractal
shape and Hausdorff dimension [6], the fractal dimension of
the network is proposed to analyze complex networks. As seen
in Fig. 1, the network can grow in terms of tiers, which follows
the same pattern of the previous step, and the network can have
infinite tiers, allowing infinite connections and infinite shards
of the network.
Koch fractalHyper-dimensional n-simplex fractal
2-simplex
2-simplex
3-simplex
3-simplex
1-simplex
1-simplex
3-simplex
5-simplex
4-simplex
2-simplex
3-simplex
4-simplex
5-simplexEdge
Face
1-simplex
1-simplex Iteration 1
Iteration 2
2-simplex
Fig. 2. Hyperdimensional n-simplex fractal edge and face stereophonic
projection and Koch fractal iterations (tiers) 1 and 2, note that 4-simplex
and 5-simplex cannot be viewed as a whole in 3D, and Koch fractal iterations
of n-simplex ( n≥4) are beyond authors’ imagination.
A. Fractal network overview
The blockchain has a timely synchronized mechanism that
ensures the information is distributed across nodes with the
ability to verify the data integrity efficiently. In the fractal
network, the blockchain brings an opportunity to enable the
dynamics of label-based routing/switching [7], in the way
of updating the routing/switching records in a timely and
accountable manner with the reinforced security for trans-
portation and authentication because of encrypted identities.
With the given amount of records, if the blockchain records
are set to be only kept at the given length and refresh the
chain and blocks over time, and old blocks are considered
in the last epoch, which would not be synchronized to the
networking devices but stored in the archive. New records will
replace the old records block by block on a periodical basis.
The behavior of such blockchain is like an Ouroboros , the
snake has its tail in its mouth, rolling over and over again.
The record which points to an entity in the real world will be
set to refresh every time a block is generated, hence becominga dynamic network client. The blockchain is also considered
a global public network, which keeps all records on every
participating node. Meanwhile, each fraction or segment of
the longest chain can be regarded as a segment of records of
what happened on the chain, and verifiable with little effort
by distributed nodes regardless of where records are kept on
the chain.
B. Hyperdimensional fractal network architecture for Web 3.0
1) Hyperdimensional fractals: Network is naturally visible
as a flat surface with every connection from end to end.
However, the use of overlay in the network has created a new
mind-blowing topological challenge, with the state-of-the-art
software-defined networks (SDN). The SDN controller builds
multiple bridges among the peer-to-peer connected entities, for
example, VPN and tunneling between servers. The underlying
network is responsible for forwarding the network traffic based
on distance vectors or link states. Blockchain full nodes are
considered as peers in the network, and they are required to
be a full replica of each other.
The network is initially established on multi-port switches,
by building an equivalent relation between switches with a
given number of ports so a node can have direct connections
according to quantity of ports, D. If all nodes are connected
directly, they will form a topological relation where each node
hasDarms, and each arm has identical jumps among all
nodes represented by a unit length in the topology. In the case
D= 1, we have a segment line with two vertices, known as
1-simplex [8], shown in Fig. 2, and 2-simplex, an equilateral
triangle in the case D= 2, and a tetrahedron with D= 3, 4-
simplex, a.k.a., pentachoron, when D= 4. However, we can
have switches with ports as many as 24/48/96 ports, which
leads to D= 24 /48/96to create a 24/48/96-simplex and
forms a near hypersphere in the dimension of 24/48/96. The
dimension of the network as a whole is largely decided by
the switch with the most ports D, and all sub-network can
be seen as a Ddimensional space reduced to its actual port
quantity. It is always true to have a full topological description
of the lower dimension structure in a higher dimension. Note
that, blockchain consensus and the network throughput might
become the bottleneck of achievable dimensions. Consensus,
in particular, voting-based consensus, suffers heavy commu-
nication overhead and a significant computation complexity
challenge. As for the network throughput, the traffic amount
on the tier 0 routers is effectively the summit of the whole
network.
Meanwhile, when we attach a sub-network to any nodes,
e.g., shards in Fig. 1, it establishes a tiered topology with
its hierarchy similar to the parent network. In that case,
the network starts growing to higher tiers, no matter which
dimension it belongs to. The topology is fractal in both
dimensional and geometrical.
2) Design overview - entities and functions: To sculpt the
Web 3.0 network with fractal shapes abstracted from reality,
the computer network is mapped into a new network architec-
ture we propose, named SnowedNet with characteristics from
blockchain, fractal shapes, security, routing and switching. The
entities in SnowedNet are listed as follows.
4
•Blockchain address ( BCADD )is any encoded hashed
public key that fits the requirements of blockchain plat-
forms, acting as the encrypted identity, for instance, ERC-
20 compatible address or W3C/ISO DID (decentralized
identity) compliant if required in the future. The BCADD
is a prerequisite for anything connected to the network.
•SnowedNet nodes , are an umbrella term for both
SnowedNet routers or end devices. The node of
SnowedNet is marked with BCADD . When the node is
an end device, it can be routed directly and determined
without further lookup of blockchain addresses, as it is a
vertices of the topology.
•SnowedNet records consist of two basic types of trans-
actions, self-claims and routing requests. They are basic
units in both SnowedNet blockchain and SnowedNet
routers and switches. For every record, there is an as-
signed label by the end-point routing node, and the label
contains the information of current label-based routing
information.
•SnowedNet blocks , are ordinary blockchain blocks,
linked by hash and timestamps.
•SnowedNet shards , are the composition unit of a given
height of segments, the node within the shard keeps the
identical records as other peer nodes under the same
segments. And the shard represented by interconnected
nodes within the same tier is considered as a contention
group.
•SnowedNet segments , the length unit of SnowedNet
blockchain, are consisted of blocks with varied lengths.
A segment is defined with the number of blocks, known
as the capacity of the segment. Moreover, the lower tier
always keeps more segments of blocks.
•SnowedNet addresses , are hybrid addresses of
SnowedNet topology labels and entities’ BCADD and
their other (optional) addresses. A complete SnowedNet
address is both globally routable and switchable.
•Block height is represented by tier number and the
indexes of labels. The tier is counted from 0, as shown in
Fig. 1. The block height is limited by a maximum value
defined by the tier number and the segment capacity.
•Routing path is the outcome of the calculated shortest
paths between the requested client and the origin node.
The path is dynamic and real-time based on the latest
routing information shared by its upper-tier routers. The
path can always be obtained by the upper-tier router, as
it knows the whole composition of current sub-nets with
all labels under its hierarchy.
•SnowedNet epoch , is a preset time period for a complete
refresh in the direction marked by arrows in Fig. 1.
SnowedNet refresh makes the original blockchain folded
as a coil. All records are reeled with the latest records
always on the surface, resembling the blockchain into a
block ring.
3) Consensus process: The consensus for maintaining the
blockchain network is proof-of-routes (length). The router with
most routes should have a greater chance to mine. Meanwhile,
the winning nodes are under continuous testing of the routing(or switching) speed, by evaluating searching algorithms to
validate its performance. A threshold can be set up for each
router’s minimum searching performance in each tier, as
the consensus should maintain its minimum commitment to
deterministic network and end-to-end latency.
The nodes in vertices or the routing node are set to be
re-elected during the mining activities in each tier, and the
winner node can replace its lower-tier node in a competitive
manner. In addition, the unsuccessful node will be set back to
the higher tier.
To obtain the maximum live routes, the following steps of
sending heartbeat packet can be used:
1) The node unicasts to all neighboring nodes, as one of
the requirements for neighbor discovery and verification.
The maximum hop for neighbor discovery is 1.
2) The node multicasts to all routing nodes (except neigh-
bor nodes) stored in the current routing node, i.e.,
vertices of all records on the Koch curve. The maximum
hop for the remote router is 2×t, t̸= 0, the tier number.
The transmissions of packets are designated to the router
vertices to avoid flooding in the network and remove the
necessity of pinging every router along the way to the
destination.
3) The node broadcasts to all local entities’ records stored
in the current routing node. The maximum hop for
reaching the entity is 1.
For each successful transmission with a response received
by the current routing node, the length of routes is calculated
in terms of the total segment length, s, which is the minimum
unit of SnowedNet length defined as the shortest side length.
In practice, the segment length is equivalent to 1 hop between
two highest-tier neighboring nodes, i.e., the shortest segment
in the fractal shape.
4) Routing strategy: Having the segment and vertices
defined as the number of records and routing nodes, the
SnowedNet is illustrated in Fig. 1. By comparing the snowflake
shape with the network characteristics of SnowedNet, we can
conclude the following attributes: tier number t; node numbers
N; the length of segments kept by the highest tier node noted
ass; segment length S; the number of blocks per segment
b; block numbers B; the number of records per block r; and
record numbers R.
The number of nodes Ncan be defined with the given
number of tiers t,
N= 3×4t. (1)
The length of total segments Sfor a given number of tiers
tis,
S= 3×((4
3)t). (2)
The total number of blocks Bin the above SnowedNet for
one SnowedNet epoch and the total records Rkept in the given
SnowedNet with rrecords in each block are
B=b×S, R =r×B. (3)
The blockchain records contain the information needed for
routing and switching, which are essential self-claimed identi-
ties from clients of their current network address bindings. In
5
the case of switching, the local record utilizes the binding of
the entity’s encrypted address, i.e., BCADD , and the network
interface, e.g., the port number of a switch. By having the
BCADD as the header, the endpoint switch/router is able
to steer the traffic between any entities tagged within the
switch/routing table records and the attached interfaces.
As a part of blockchain-integrated functionality in Web 3.0,
smart contracts can be directly involved in making Service
Level Agreement (SLA) deals between two endpoint users.
Such SLA information is readable to all routers, and acknowl-
edgments of resource reservation can be collected by replying
to the request hosted by smart contracts.
III. V ERIFIABLE IDENTITIES BETWEEN REALITY AND
WEB3.0
As aforementioned, crypto-network-entity, a network avatar
of the user’s credential, is native to Web 3.0, existed in the
fractal polytope shape of a decentralized network, and its
identifiers are used by dApp, DeFi, Decentralized Autonomous
Organizations (DAOs) and decentralized infrastructures. On
the other hand, identities should be consistent in both decen-
tralized and centralized infrastructures. Therefore, there is a
requirement of consistent verifiable identities between Web 3.0
and reality, satisfying the legal anonymity and the linkability
of decentralized identifiers from network to applications.
A. Overview
To ensure user anonymity in the Web 3.0 network, a three-
tier identity mechanism was introduced in deController [4]
to connect entities in reality with anonymous identifiers in
the network and services for security and privacy. According
to the identity generation procedure, the authorities can au-
dit anonymous identities in Web 3.0 network. However, the
public should have no information about the identity linked
to the anonymous identifiers.Moreover, once the identifiers
generation procedure involves more parameters from networks
or services, auditing from authority becomes a challenge.
Here, we follow the design of the three-tier identity, refine
the parameters in identity derivation, and enable the public
verifiable authenticity of the privacy-preserving links between
identities in Web 3.0 and reality as illustrated in Fig. 3 and
Fig. 4.
B. Identity and Operations
All the identifiers should not reveal any sensitive identity in-
formation. As shown in Fig. 3, when the user moves from one
jurisdiction (J1) to another (J2), the identifiers BCADD and
APPID are generated from the registered RealID and privacy-
preserving invisible links are built among them. BCADD and
APPID can be verified by the public as seen in Fig. 4 to
allow people to confirm the BCADD andAPPID provided by
the user are valid when the user uses them in the Web 3.0
cyberspace, e.g., network, applications, services, etc.
•Identity credential: noted by RealID is the master key
of the user in the Web 3.0 identity layer implicitly
linked to the identity IDof the entity in reality, which is
Applications
Network layer
Service providerAuthorityJ1
Fractal networkUser
J3 J2
Own
RegisterApplication layer
Used inDeployUsed byUsed by
RealIDBCADD
Generate/UpdateAPPID 1 APPID 2
Derive Derive
Identity layerFig. 3. Three-tier identity scheme for the Web 3.0 fractal network.
unchangeable in the Web 3.0 cyberspace and should be
issued, updated, and verified by the authorities from its
country of origin or other permitted authorities in reality.
RealID is the root of all the identifier derivation in the
Web 3.0 cyberspace.
•Web 3.0 identity: noted by BCADD is the identity of the
user in the network layer. BCADD can be generated and
updated by the user itself to be derived with optional
parameters para auth from authorities based on RealID .
The BCADD and its link to the corresponding RealID
can be verified by the public.
•Virtual identifiers: noted by APPID , represents users’
identities in the application layer for different services
and applications. Generally, APPID is derived from
BCADD together with optional parameters para′
authfrom
authorities and the corresponding service/application.
Furthermore, it can contain essential user information for
specific usage such as balance checks for services and
age confirmation for applications.
There are three distinct requirements that should be con-
sidered to implement the proposed three-tier identity scheme
including identity anonymity, linkability between two tiers
and public verification. However, conventional certificates
and other public-key methods are difficult to meet all three
requirements in a fully decentralized infrastructure.
To achieve the required identity operations, we have two
research questions: 1) How to verify the users’ ownership of
anonymous identifiers? 2) How to verify anonymous identi-
fiers are generated using authority-provided parameters, hence
approved by authorities?
C. Technical route: Non-Interactive Zero-Knowledge Proof
(NIZK)
One promising technical route could be zero-knowledge
proof (ZKP), first proposed by Goldwasser, Micali, and Rack-
off in 1985 [9]. The highlight of ZKP is one party proves
to another party using cryptographic methods that it possesses
knowledge without revealing any information about the knowl-
edge.
6
There are two well-established categories of ZKP: interac-
tive and non-interactive. The interactive ones enable the prover
to show its enough knowledge of the statement to the verifier
via interactive procedures. However, such a design requires
both the prover and the verifier keeping online and probably
incurs heavy communication overhead.
To mitigate the communication complexity of interactive
ZKP, non-interactive zero-knowledge (NIZK) [10] is pro-
posed to prune the interactive rounds to one round, which
is more desirable than interactive ZKP in the Web 3.0 sce-
nario. A general NIZK system is sculpted by three algo-
rithms: (Setup ,Prove ,Verify )with a statement xand wit-
nessw. (1) Setup (1κ)→σ, where σis a public random
string; (2) Prove (σ, x, w )→π, where πis the proof; (3)
Verify (σ, x, π )→0/1, where 1 means accepts the proof and
0 means rejects.
To apply the NIZK scheme to our refined identity scheme,
the algorithm generating BCADD from RealID /para auth or
the algorithm generating APPID from BCADD /para′
auth
is regarded as the statement xin NIZK. Meanwhile,
RealID /para auth andBCADD /para′
auth are regarded as wit-
nesses in different statements. To implement the Setup pro-
cedure, a smart contract or the authorities can be regarded as
trusted parties to generate the public random string. Then, the
user performs Prove and includes the proof πinto auxiliary
values auxBCADD /auxAPPID . The verification procedure can be
implemented as smart contracts in Web 3.0 so all BCADD and
APPID can be verified by the public.
D. Three-tier identity based on NIZK
Though BCADD can be derived directly from RealID , it
needs to meet the legal requirement as the authority may need
to attain the user’s identity. Therefore, the authority needs to
first bind RealID toIDand urge the use of BCADD derivation
with the provided parameters. We present an example of how
public verifiable anonymous identities are generated, used, and
verified in Web 3.0 as demonstrated in Fig. 4.
1) From RealID toBCADD :First, user registers RealID
to authority as shown in Fig. 4, step 1. Then, the user
receives parameter para authwith auxiliary value auxauthfrom
an authority, as shown in Fig. 4, step 2, where auxauthcannot
be forged by other parties. auxauthcan be regarded as signature
ofpara authandRealID to endorse their authenticity. In step 3,
user generates BCADD from RealID and para auth. Then,
instep 4, user generates auxBCADD from auxauth,para auth,
andRealID . Note that both BCADD andauxBCADD perfectly
hide RealID .auxBCADD can be used to verify the validity of
the parameters in generating BCADD . Each time user uses
BCADD in the network, it attaches auxBCADD , as shown in
step 5, which enables the public to verify that BCADD is
generated from a real RealID that is already registered to
authority.
2) From BCADD toAPPID :When a user access an
application in Web 3.0 and this application needs to verify the
private data held by the user, e.g., the age of the user, it sends
a verifying request with para appand auxiliary value auxappto
the user, as shown in Fig. 4, step 6. Since the age information
auxauthVerification contract
BCADD
APPIDRealID
Generate
ProofVerification
Verification contract
Generate
ProofVerification
Generateparaauth
VerifyPublicPublic
Verify
Authorities
Generate
auxBCADD
Web 3.0
applications/servicesauxapp
paraappRegister
(optional)
aux'auth
para'auth
auxAPPIDUser
UserAccessReq
uest1
2
34
5
67
89
10Fig. 4. Workflow of the three-tier identity scheme based on NIZK.
is personal information should be held by authorities, the user
should receive para′
authwith aux′
auth, as shown in step7, where
para′
auth contains user age and other optional parameters.
Then, the user derives APPID from BCADD ,para app,para′
auth
instep 8, and generates auxAPPID from auxBCADD ,auxapp,
aux′
auth,para app,para′
authandBCADD instep 9. Noted that
both APPID andauxAPPID hides BCADD ,RealID and user
age. If user attaches auxAPPID with APPID , the public could
verify that APPID is linked to a valid BCADD orIDby
checking auxAPPID instep 10.
In this way, we can establish a holistic identity scheme,
extending RealID toBCADD in the network layer and
BCADD toAPPID in the application layer so a joint au-
thentication can be achieved by the network for applica-
tions via decentralization-ready physical infrastructures, e.g.,
blockchain network routers and mobile networks [11].
IV. C HALLENGES AND OPPORTUNITIES
A. Questioning the existence of crypto-network-entity
The definition of new “existence” brings rigorous discussion
on the juridical aspect. In the discovered network, the existence
comes after the crypto-loaded physical entity is plugged into
the network, hence leaving the record of crypto-network-
entity on the blockchain ledger. Therefore, the challenges
to new world legal governance become an extension of the
cyber or physical world, depending on where in the cyber
or physical world the crypto-network-entity is plugged in.
The physical location of vertices nodes in the fractal simplex
network is also subject to the local jurisdiction, and subjects
to the real physical world entity. In this way, people may
wonder whether the network is independent or not, and the
answer can be straightforward by examining the existence
7
when physical entities are removed. The status of the network
is independent from any single networking device but subjects
to the decentralized infrastructure as a whole. However, the
status of the network does not undermine the existence of
crypto-network-entity as long as the decentralized information
medium stays true.
B. Questioning on network calculus and pathfinding
In the fractal regular polytope network, we have the shortest
path known as 2t, but the stub of the network has entered
an extremely complex space where routes need to be found
in hyperdimension. More advanced manifold-based geometric
solutions to the shortest route on the fractal polytope network
are critical challenges and future work for the comprehensive
simplex fractal network. Furthermore, more basic properties
such as network mass, density, volume, acceleration, power,
etc., can be mapped into the fractal polytope network for a
better understanding of network characteristics.
C. Questioning the privacy and legal compliance of anonymity
Due to the nature of user anonymity in Web 3.0, it does not
set any restriction to the real identity behind the user which
may result in abuse and forgery of user identities. Therefore,
the supervision of RealID is a key point to be considered to
align the proposed three-tier identity scheme to legislation in
building the global Web 3.0 cyberspace. As shown in Fig. 4,
an optional design is to utilize authorities to issue and update
a user’s IDto avoid the user forging its identity information or
using identity information of others without permission. When
users try to access certain Web 3.0 resources, in which real-
name registration is required, such a design can ensure the
generated BCADD from RealID and the derived APPID from
BCADD contain authorized identity information to represent
the real identity of the user.
V. C ONCLUSION AND FUTURE WORK
In this paper, we propose SnowedNet, reforming the spatial
information of everything that is connected to Web 3.0 in
a hyperdimensional simplex topology, timestamp everything
with blockchain consensus, and extending the Web 3.0 ex-
istence to cyberspace and physical space with ZKP on their
identities derivatives. The fractal-driven topology is illustrated
with a 2-simplex, a regular triangle with Koch iterations,
showing not only the juridical broader mapped into shards but
also enormous capacity and simplicity in building the future
generation decentralized network.
However, there are limited findings on the shortest path on
higher dimensional fractal topology. In future work, a compre-
hensive study on hyperdimensional routing protocol with state-
of-the-art polytope theories is plan to help find the shortest
path in any dimension of fractal simplices. Furthermore,
NIZK verifiable identities and identifier derivation need to be
explored to enhance the efficiency of identifier generation to
crystallize a comprehensive cryptographic scheme. In addition,
NIZK identity statement should be compatible with various
structures of identity information from different jurisdictional
authorities.REFERENCES
[1] L. Jin, S. Hao, Y . Huang, H. Wang, and C. Cotton, “Dnsonchain:
Delegating privacy-preserved dns resolution to blockchain,” in 2021
IEEE 29th International Conference onNetwork Protocols (ICNP),
2021, pp. 1–11.
[2] X. Li, P. Russell, C. Mladin, and C. Wang, “Blockchain-enabled applica-
tions in next-generation wireless systems: Challenges and opportunities,”
IEEE Wireless Communications, vol. 28, no. 2, pp. 86–95, 2021.
[3] J. Bambacht and J. Pouwelse, “Web3: A decentralized societal infras-
tructure for identity, trust, money, and data,” 2022.
[4] H. Xu, Y . Sun, Z. Li, Y . Sun, X. Zhang, and L. Zhang, “deCon-
troller: A Web3 Native Cyberspace Infrastructure Perspective,” IEEE
Communications Magazine, 2023.
[5] O. Eriksson and P. J. ˚Agerfalk, “Speaking things into existence:
Ontological foundations of identity representation and management,”
Information Systems Journal, vol. 32, no. 1, pp. 33–60, jan
2022. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1111/
isj.12330
[6] K. J. Falconer, The Geometry of Fractal Sets, ser. Cambridge Tracts in
Mathematics. Cambridge University Press, 1985.
[7] A. Viswanathan, E. C. Rosen, and R. Callon, “Multiprotocol Label
Switching Architecture,” RFC 3031, Jan. 2001. [Online]. Available:
https://www.rfc-editor.org/info/rfc3031
[8] P. McMullen and E. Schulte, Abstract Regular Polytopes. Cambridge
University Press, dec 2002.
[9] S. Goldwasser, S. Micali, and C. Rackoff, “The knowledge complexity
of interactive proof-systems,” in Proceedings oftheSeventeenth Annual
ACM Symposium onTheory ofComputing, ser. STOC ’85. New
York, NY , USA: Association for Computing Machinery, 1985, p.
291–304. [Online]. Available: https://doi.org/10.1145/22145.22178
[10] A. Fiat and A. Shamir, “How to prove yourself: Practical solutions to
identification and signature problems,” in Conference onthetheory and
application ofcryptographic techniques. Springer, 1986, pp. 186–194.
[11] H. Xu, X. Liu, Q. Zeng, Q. Li, S. Ge, G. Zhou, and R. Forbes, “De-
centRAN: Decentralized Radio Access Network for 5.5G and beyond,”
inIEEE ICC Workshops, 2023.
|
{
"id": "2308.06829"
}
|
2403.15074
|
Tax Policy Handbook for Crypto Assets
|
The Financial system has witnessed rapid technological changes. The rise of
Bitcoin and other crypto assets based on Distributed Ledger Technology mark a
fundamental change in the way people transact and transmit value over a
decentralized network, spread across geographies. This has created regulatory
and tax policy blind spots, as governments and tax administrations take time to
understand and provide policy responses to this innovative, revolutionary, and
fast-paced technology. Due to the breakneck speed of innovation in blockchain
technology and advent of Decentralized Finance, Decentralized Autonomous
Organizations and the Metaverse, it is unlikely that the policy interventions
and guidance by regulatory authorities or tax administrations would be ahead or
in sync with the pace of innovation. This paper tries to explain the principles
on which crypto assets function, their underlying technology and relates them
to the tax issues and taxable events which arise within this ecosystem. It also
provides instances of tax and regulatory policy responses already in effect in
various jurisdictions, including the recent changes in reporting standards by
the FATF and the OECD. This paper tries to explain the rationale behind
existing laws and policies and the challenges in their implementation. It also
attempts to present a ballpark estimate of tax potential of this asset class
and suggests creation of global public digital infrastructure that can address
issues related to pseudonymity and extra-territoriality. The paper analyses
both direct and indirect taxation issues related to crypto assets and discusses
more recent aspects like proof-of-stake and maximal extractable value in
greater detail.
|
http://arxiv.org/pdf/2403.15074v3
|
Arindam Misra
|
q-fin.GN, cs.CR
|
q-fin.GN
|
1
Arindam Misra
Abstract
The Financial system has witnessed rapid technological change s. The ri se of Bitcoin and other crypto
assets based on Distributed Ledger Technology mark a fundamental change in the way people transact
and transmit value over a decentralized network , spread across geographies. This has created
regulatory and tax policy blind spots, as governments and tax administrations take time to understand
and provide policy responses to this innovative, revolutionary, and fast -paced technology.
Due to the breakneck speed of innovation in blockchain technology and advent of Decentralized
Finance , Decentralized Autonomous Organizations and the Metaverse , it is unlikely that the policy
interventions and guidance by regulatory authorities or tax administrations would be ahead or in sync
with the pace of innovation. This paper tries to explain the principles on which crypto assets function ,
their underlying technology and relates them to the tax issues and taxable events which arise within
this ecosystem.
It also provides instances of tax and regulatory policy responses already in effect in various
jurisdictions , including the recent changes i n reporting standards by the FATF and the OECD . This paper
tries to explain the rationale behind existing laws and policies and the challenges in their
implementation . It also attempts to present a ballpark estimate of tax potential of this asset class and
suggests creation of global public digital infrastructure that can address issues related to
pseudonymity and extra -territoriality . The paper analyses both direct and indirect tax ation issues
related to crypto assets and discusses more recent aspects like proof -of-stake and maximal extrac table
value in greater detail . The overall objective of the paper is to enable a tax policymaker, an auditor, or
an investigator to obtain a reasonable understanding of this technologically challenging realm of
economic activity , and formulate tax polic y and laws according to the specific requirements of their
individual jurisdictions.
Keywords: Blockchain, Crypto Assets, NFT, Bitcoin, Ethereum, DeFi, DAO , Crypto Tax
TAX POLICY HANDBOOK FOR CRYPTO ASSETS
2
Index
Page No .
1. Introduction 4
2. Bitcoin and the Blockchain 6
2.1 The Origins 6
2.2 Bitcoin in a nutshell 6
2.3 Basic Concepts 7
2.3.1 Hash Functions 7
2.3.2 SHA256 security 8
2.3.3 Digital Signatures 9
2.3.4 ECDSA and Bitcoin addresses 11
2.3.5 Bitcoin Transactions 16
2.3.5.1 Tax Implications of UTXO Based Transactions 18
2.3.6 The Bitcoin Blockchain 19
2.3.7 Bitcoin Mining 21
2.3.7.1 Mining Pools 26
2.3.7.2 Estimating the extent of Mining 26
2.3.7.3 Bitcoin Mining and Game Theory 28
2.3.8 Taxation of Mining Activity and Rewards 29
2.3.8.1 Direct Taxes on Mining 30
2.3.8.1.1 Taxation of crypto asset acquired as mining rewards 30
2.3.8.1.2 Disposal/transfer of crypto asset s acquired as mining
rewards 31
2.3.8. 2 Indirect Taxes on Mining 31
2.3.8.2.1 Indirect Taxes on Supply of crypto assets 36
2.3.8.2.2 Indirect Taxes on use of crypto assets for payments for
goods and services 37
2.3.8.2.3 Taxing the externalities of mining using the proof -of-
work mechanism 37
2.3.9 Proof -of-Stake and Forging 37
2.3.10 The Bitcoin Network 39
2.3.11 Forking 40
2.3.11.1 Hard Fork 40
2.3.11.2 Soft Fork 42
2.3.11.3 Taxation of forking events 43
2.3.12 Bitcoin Wallets 43
3. Crypto Exchanges 45
4. Source of Value of crypto assets and Bootstrapping 47
5. Initial Coin Offerings 48
6. Airdrops 49
7. Ethereum 49
7.1 Proof -of-Stake based consensus in Ethereum 51
7.1.1 Slots and Epochs 53
7.1.2 Rewards and Penalties 55
7.1.2.1 Rewards 55
7.1.2.2 Penalties and Slashing 57
7.1.3 Staking Pools 57
7.1.4 Taxation of Proof -of-Stake based consensus in Ethereum 58
7.1.4.1 Direct Taxes related to Proof -of-Stake based consensus in
Ethereum 58
3
7.1.4.2 Indirect Taxes related to Proof -of-Stake based consensus
in Ethereum 59
7.1.4.3 Taxes on MEV rewards on Ethereum 60
7.2 Smart Contracts 60
7.2.1 Issue with code -only smart contracts 62
7.3 Tokens 63
7.3.1 ERC-20 Token Standard 64
7.4 Non-Fungible Tokens 64
7.4.1 Legal Status of NFTs 68
7.4.2 ERC 1155 Token Standard 70
7.4.3 Taxation of NFTs 71
8. Decentralized Finance 72
8.1 MakerDAO 73
8.2 Uniswap 75
8.3 Taxable events in DeFi ecosystem 80
8.3.1 Direct Taxes in DeFi ecosystem 80
8.3.2 Indirect Taxes in DeFi ecosystem 82
8.4 Maximal Extractable Value (MEV) on Ethereum 82
8.4.1 MEV Supply Chain and its taxation 85
9. Decentralized Autonomous Organizations – DAOs 87
9.1 Legal Entity Status of DAOs 91
9.1.1 Unregistered DAOs 92
9.1.2 DAOs registered as a legal entity 93
9.2 Taxation issues of DAOs 93
10. International Cooperation and Exchange of Information 94
10.1 FATF Standards on VAs and VASPs 95
10.1.1 Challenges in Travel Rule implementation 96
10.2 Crypto -Asset Reporting Framework 97
10.3 Need for Global Public Digital Infrastructure 99
10.4 The Challenge of Anonymity Enhancing Crypto Assets 101
11. Conclusion 104
References 105
4
1. Introduction
On 3rd January 2009, Satoshi Nakamoto mined the first block of Bitcoin (the genesis block) and created
a revolutionary system of storing and transmitting value in a trustless1 manner over an open network.
The genesis block also insinuated at the philosophy behind the newly created network. The
transaction which generated the first 50 Bitcoins contained the phrase “The Times 03/Jan/2009
Chancellor on brink of second bailout for banks.” which alluded to the problems of the contemporary
monetary and financial system. However, Bitcoin was and remains largely a network for storing and
transmitting value through the blockchain .
Few years later , on 30th July 2015 Vitalik Buterin started Ethereum, which had more features as
compared to Bitcoin and worked like a global general purpose computing virtual machine. The ability
to run customized code on blockchain started an entire ecosystem of applications which provide
innovative financial products from lending a nd borrowing , to insurance through smart contracts2.
With blockchain networks like Solana the transaction speeds can outnumber traditional networks like
Visa3. Entities like Decentralized Autonomous Organizations , Decentralised Applications and the
Metaverse challenge the traditional notions of source and residence -based taxation and pose new
challenges to taxing this form of digital economy.
This has given rise to a new asset class which has unique features like openness, decentralization,
pseudonymity , and transparency. This asset class also aims at improving the efficiencies of the current
financial and monetary system where transfer of securities can take a couple of days, or
sending/receiving huge sums of money across nations can take a few days. It enables creating a
financial system where users with lower capital do not get differentially treated than those with huge
capital4. Although there are studies that indicate that entities with large capital can get certain
advantages in the crypto asset ecosystem (Aramonte et. al 2021) .
This has also created new challenges for tax policy and administration, which is more accustomed to
function in a centralized banking ecosystem where identities of the participants are known or possible
to be known. The beneficial owners hip of entities can be found and the source and origins of
transactions can be traced in the centralized system. However, with crypto assets, especially those
held and transacted using non-custodial wallets5 it is difficult , if not impossible, to identify the natural
or juridical person behind the transaction. The nature of these assets also blurs regulatory boundaries
as they possess the characteristics of money, equity, commodity, financial instruments, and property
1 A trustless system is one in which the participants do not need to trust a third party or each other for the
system to function as intended.
*The author belongs to the Indian Revenue Service and is currently working as a Joint Commissioner at the Tax
Policy Research Unit, Department of Revenue, Government of India . Email: misra.arindam@gov.in
**This paper is not a legal or investment guide or advice and is purely academic in nature. Readers should
draw their own legal conclusions and investment decisions regarding Crypto Assets and their Taxation .
*** The findings, interpretations, and conclusions expressed herein are those of the author and do not
necessarily reflect the views of the Government of India . This paper represents the personal views of the author
only, not the views of the Government of India . The author accept s sole responsibility for any errors.
**** Readers are advised not to send any Crypto Assets to any public addresses given in the paper , the author
is not respon sible for loss of any Crypto Assets due to any such actions.
2 Smart Contracts are analogous to automated agreements stored on the Blockchain which can accept or
release crypto assets or perform certain actions when certain conditions specified in the form of code are met
3 Solana: Better Than Your Credit Card?
4 https://hbr.org/2022/05/how -digital -currencies -can-help -small -businesses
5 Non -Custodial wallets allow users to have full ownership of their crypto assets without relying on any central
custodian of the secret keys corresponding to the crypto assets
5
(Auer, R., & Claessens, S. (2018) ). Consequently, many countries have no regulatory or taxation
framework for this asset class, and there is wide variety in regulation and taxation of this asset class
across jurisdictions which have formulated policies and guidance for crypto assets .
The increasing volumes of transactions in crypto assets make it a sizeable tax base, which if not taxed
appropriately , can lead to potential revenue loss. Thus, it becomes important for policymakers to have
a conceptual understanding of this asset class and its financial ecosystem, to effectively formulate and
administer realistic tax policies and laws regarding crypto assets, or to use the existing legal and policy
framework to collect due taxes. It is also imperative for investigators and law enforcement agencies
to acquaint and train themselves with this technology , as they are likely to have more frequent
encounters with it while performing audit and investigating tax fraud cases .
There is abundant literature and guidance available to the tax authorities, practitioners, and taxpayers
regarding the regulatory and taxation regime s for crypto assets in different jurisdictions. However, it
is imperative to have a grasp o f the underlying technology and dynamics of these assets and not just
the legal provisions , to be able to effectively regulate and tax them. For example, notions like the site
of storage of Bitcoins and crypto assets , and the services provided by various members of this
ecosystem and their place of supply have a bearing on multiple issues related to direct and indirect
taxes. It is important to understand the mining process using proof -of-work and proof -of-stake
mechanisms , and the nexus between a miner or validator and crypto asset owner whose transaction
is included on the blockchain by the miner or validator ( service provider) for the purpose of indirect
taxation.
Also, questions like tax treatment of Non-Fungible Token s (NFTs) and what exactly the owner of an
NFT gets legally , are central to their taxation. This paper tries to relate the technological underpinning s
of this asset class to the tax events they trigger and the source of economic value creation in this
ecosystem, which can enable the tax administrations to tax them intuitively based on the existing tax
provisions or any other specific provisions or guidance for crypto assets. It also tries to quantify th eir
tax potential from publicly available data on crypto asset transactions .
This paper first explains the basic concepts of blockchain technology using Bitcoin as a classic example.
It also explains the vulnerabilities of the Bitcoin Blockchain to emerging technologies like quantum
computing. The later sections deal with Ethereum and its recent change to the proof -of-stake
consensus mechanism . The discussion tries to link the underlying technolog y to the regulatory and tax
implications. It also tries to make the reader understand why a specific regulatory or enforcement
action which is effective in the current centralized system , may not be practical in case of crypto
assets.
The paper also includes the regulatory and tax policy responses by multiple jurisdictions to specific
areas in crypto assets to enable the tax practitioner s to understand the various approaches used to
tax this asset class. For example, a law enforcement agency’s request to a Decentralized Application
to identify the beneficial owner of a particular crypto asset address might be impossible for the
application to process for purely technological reasons. The understanding of underlying technology
is also important to formulate practical and realistic policies to ring fence this technology from its
potential misuse for tax evasion, money laundering , terror financing , proliferation financing and other
illegal activities and at the sam e time , not to stifle the innovative spirits of the torch bearers of this
technology , which also has huge potential benefits (Marian, 2013 ), (Kapsis, 2023) , (Shin & Rice, 2022) .
6
2. Bitcoin and the Blockchain
2.1 The Origins
The Oxford English dictionary defines Bitcoin as “a system of electronic money, used for buying and
selling online and without the need for a central bank” and “a unit of the bitcoin electronic system of
money” which essentially captures the notion we attribute to Bitcoin , when we refer to the Bitcoin
network for storing and transmitting value , whose unit of accounting is unsurprisingly known as ,
Bitcoin. It also refers to the protocol or software that various nodes in the Bitcoin network run to
transmit and include the trans actions on the blockchain in the form of blocks .
At a very basic level , the Bitcoin Blockchain records transactions between entities on a public ledger
structured in the form of blocks by using cryptographic techniques without involving any central
authority like a bank. However, to prevent fraud and double -spend transactions6 the blockchain relies
on a network of nodes which maintain the integrity of the chain of transaction s cryptographically and
are in -turn rewarded for that.
2.2 Bitcoin in a nutshell
Bitcoin has an open permissionless blockchain where anyone who generates a correct cryptography -
based pseudonym (Bitcoin address) and its corresponding private key , can start transact ing. Also,
anyone with appropriate hardware and network capabilities can join the network and get rewarded
(depending on their computational power) for adding transactions bundled into blocks on the
blockchain (mining) , securing and maintaining the blockchain . Unlike a banking system where users
must undergo a Know Your Customer ( KYC) procedure and risk profiling before opening an account,
one can instantaneously generate Bitcoin pseudonym(s) and start transacting on the blockchain .
In a common banking transaction between Bob and Alice the account balance of Bob is reduced by a
certain amount and that of Alice is increased by that amount by a central authority . Bob may also pay
some transaction fee to the bank for facilitating the transaction . The central authority essentially plays
the role of minimizing the counter party risk involved in the transaction.
Similarly in the Bitcoin Blockchain , users transmit amount denominated in Bitcoins to each other.
However, instead of relying on the bank for processing the transaction they use the Bitcoin Blockchain
which approves this transaction with the help of various nodes (miners) which cryptographically record
the set of transactions called a block in a public blockchain using a process called mining. Instead of
paying the bank fee the users pay fees to the miners who maintain the integrity of the Bitcoin
Blockchain and get this fee along with an in -built economic incentive in the Bitcoin Blockchain as a
reward.
Transmitting value through the Bitcoin Blockchain involves no settlement intermediaries and the
blockchain is oblivious and agnostic to the geographical location of the owner of the Bitcoin
pseudonym ous address . This creates and maintains an immutable global public ledger in which all the
BTC value ever exchanged by a Bitcoin pseudonym ous address is readily and publicly available, and is
used to verify and validate future transactions.
6 A double -spend transaction is one in which a Bitcoin owner in the ecosystem , Alice, owns a certain BTC value
and transmits it to Bob for buying a good or a service. However, the flaw in the network does not prevent her
from resending the same BTC value to Carol, a third us er. This allows Alice to spend the same Bitcoins twice.
7
2.3 Basic Concepts
The underlying technology of Bitcoin tries to solve the problem of transmitting value from one user to
another in a trustless manner, using a distributed public ledger without involving any central authority.
This problem requires technological solutions w hich ensure trust in a trustless environment. Just as a
banking system needs to develop mechanisms to verify identity and maintain the security of the bank
accounts , the Bitcoin system also solves these problems by combining cryptography, game -theory,
comp uter networking and economics.
Cryptography is extensively used in the Bitcoin ecosystem . Hash functions and digital signatures form
the bedrock on top of which the entire technological stack of Bitcoin and other crypto assets is built .
To understand the mechanics of any transactions in this ecosystem , it is imperative to understand how
these fundamental building blocks like hash functions and digital signatures along with economic
incentive mechanisms make this system work effectively in a fault tolerant manner. The next section
explains the basic concepts of hash function s and digital signatures which will be used repeatedly in
the subsequent sections.
2.3.1 Hash Functions
In a blockchain like Bitcoin the copy of the public ledger is stored by many participants of the Bitcoin
Blockchain called nodes. As each individual node can store, transmit, and validate transactions, it is
important that the transactions are verifiable and temper -evident , as in a n open and permissionless
system like Bitcoin it is impossible to prevent tempering by malicious nodes, but possible to make the
tempering evident through cryptograph y. In solving this problem in the blockchain , hash functions
have a critical role to play. Many of us might have come across hash of a particular file while
downloading it from a website . The purpose of the hash is to enable the user downloading the file to
confirm that the file has not been tempered and is same as present on the website.
Hash functions are mathematical functions which map a string of any length to a string of fixed length
based on certain algorithm that scrambles the input string to generate a seemingly unrelated output
string of fixed length . It is like a unique fingerprint of given dat a string . It can be represented as shown
in Fig.1
Fig.1 Hash Functions
For any hash function to be cryptographically secure it must have the following properties:
a) Computational Efficiency: For an input string of certain size, the time taken by the algorithm
to calculate the hash or digest of the input should be a linear function of the size of the input
string.
Input String (arbitrary length)
Hash Value (fixed length)
Hashing Algorithm
8
b) Collision Resistance: The hash value function maps the infinite set of strings of variable length
to a finite set of hash values of fixed length. From the pigeonhole principle7 it can be inferred
that there exists a string 𝑦 for a string 𝑥 and hash function 𝐻(), such that 𝐻(𝑥)=𝐻(𝑦)=𝑧
when 𝑥≠𝑦 . Such a case is called a collision, when two non -equal input strings have the same
hash value. However, for cryptographically secure hash functions, even though the collisions
exist , it should be computationally infeasible to find two strings that have the same hash.
c) Hiding: It should be computationally infeasible for an attacker to guess the input string
correctly by looking at the hash. And even if a slight change is made in the input string, the
cryptographic hash changes completely .
This can be seen with the SHA256 hash of the string “Crypto Asset taxation is interesting”
concatenated with various special characters as shown in Table 1 .
String SHA256 hash
Crypto Asset taxation is interesting~ 94c68e0e2fe8e816b7290352dd689c54cca15d4c18717586c84e37c6a81c1356
Crypto Asset taxation is interesting@ a233387a164f7ef4d4c630e5f1c73cdc1c87a3e68e437a917d3cdc971b0d603c
Crypto Asset taxation is interesting# 5e47cbeb3a3926059ab42cef858386caa05ac6776fc8bfeba0294bd2b8a4266d
Crypto Asset taxation is interesting$ b2a85c2e59a7e480950c885b8e28730addbe40fc713e5268d7e46a1d2f5c9397
Crypto Asset taxation is interesting% 9050a1d69e3d4cff17cbcfd73166d78f827d125e040641341e36f9a8d5bd6aa2
Crypto Asset taxation is interesting& cc7375d79bab0cac25f831dfb9fa05458423d37cfeaedfbdb61f577f94a6a601
Crypto Asset taxation is interesting* 7bd72477ad02701a977019bba8d1c919b2775358f9c25817cb442bc83bed459d
Table 1. Changing hash Values with changes in input string
From the table , it can be inferred that chang ing a single character generates a totally different hash
value. This characteristic of the SHA256 Cryptographic hash function is important for making the
blockchain temper -evident, which means that every participant in the blockchain would be able to
identify any tempering by a malicious actor with transactions or other block fields in the blocks that
are already a part of the blockchain .
2.3.2 SHA256 security
Bitcoin uses the SHA256 algorithm for hashing. It is a secure hashing algorithm designed by the United
States National Security Agency8. It is widely believed to be collision resistant as for a given string 𝑥 an
attacker trying to find an input string 𝑦 for a hash function 𝐻() such that 𝐻(𝑥)=𝐻(𝑦)=𝑧 would
have to calculate at least 2128 hashes, which is an extremely large number. The highest hash rate
generated by the Bitcoin network till March 2024 is ~600 Exa9 hashes per second . Even at that hash
rate it would take around 17,983 ,805,11710 years to find a collision by brute force calculation , which
is more than the estimated age of our universe. However, there are certain hash functions which do
have efficient collision detection methods.
In case of SHA256 we do not have knowledge of any such efficient collision detection methods as of
now , and it is largely believed to be collision resistant , as despite best efforts , collisions could not be
found11. However, there is no way to prove that a hash function is collision resistant. In the past , hash
functions have been broken and phased out of security systems . A famous case is that of MD5 hashing
algorithm which was widely used but eventually phased out for use as a cryptographic hash function.
7 If n+1 pigeons occupy n holes, then some hole must have at least 2 pigeons
8 National Institute of Standards and Technology (NIST). Secure Hash Standard (SHS). Federal Information
Processing Standards Publication 180 -4.
9 1018
10 ((2^128) ÷ ( 600 × (10^18) × 365×86400))
11 https://steviecellis.medium.com/the -beautiful -hash -algorithm -f18d9d2b84fb
9
Wang & Yu (2005) found and published ways to attack MD5 hash ing algorithm12 and Magnus & Stefan
(2005) produced two Post Script files which had the same MD5 hash13.
Hash functions are so critical for blockchain s as they are the primary mechanisms of creating trust
about the integrity of information being exchanged between the nodes of the network. If an
underlying algorithm like SHA256 breaks down, the trust in the Bitcoin blockchain would also crumble .
Quantum computers can also significantly affect the cryptography used in Bitcoin. It is estimated that
in the 2030s quantum computers would be able to crack SHA25614 unless Bitcoin Blockchain migrates
to quantum safe cryptography. As the blocks and transactions in the Bitcoin Blockchain are identified
using the hash value of their header, a well -orchestrated attack by an attacker having a reasonably
sized quantum computer can potentially cause significant damage to trust in the Bitcoin Blockchain .
However, just as the traditional banks continuously improve the security of their credit cards and
internet banking solutions, Bitcoin Blockchain can also find potential solutions to circumvent the likely
threat f rom quantum computers .
2.3.3 Digital Signatures
The Bitcoin Blockchain needs to identify the originators of transactions and owners of funds who can
spend their Bitcoins on the Bitcoin Blockchain using valid transactions. This is analogous to a banking
system where the bank assigns a customer identification number to each customer and each customer
has their respective username and passwords for online banking. It is a mechanism to establish the
identity , to verify the originator and authorizer of the transactions. However, in centralized banking
system s, additional checks are possible to verify the identity of the originator of the transaction in
circumstances of suspicion and there is a rigorous process of onboarding which includes mapping the
customer identification number to a natural person using Social Security Number, Passport etc. The
beneficial ownership of the account is also captured while opening the bank account. As the Bitcoin
Blockchain does not have any centralized authority to verify identity, it relies on cryptographic
techniques like digital signatu res to validate the identity of the participants in the system.
A digital signature is like a stamp of authentication of the origin of a message, transaction or document
which establishes that the information did indeed originate from the person who has signed the
message, transaction or document and it has not been altered. An excellent overview of how digital
signatures work can be found in security tip (ST04 -018) of the US Cybersecurity and Infrastructure
Security Agency15. We often come across digital signatures in our daily lives while receiving digital
documents from the government . Various PDF documents contain digital signatures which are verified
as soon as the PDF file is opened. One such example is the AADHAAR document (Indian social security
number) downloaded in PDF format.
The user downloading the document can ensure that the document has indeed been issued by the
issuing authority i.e., the Unique Identification Authority of India (UIDAI) and has not been tempered
with. Fig. 2 and Fig. 3 show how a user can verify the identity of the issuer and absence of any
tempering in the document.
12 Wang, X., & Yu, H. (2005, May). How to break MD5 and other hash functions. In Annual international
conference on the theory and applications of cryptographic techniques (pp. 19 -35). Springer, Berlin,
Heidelberg.
13 Daum, Magnus & Lucks, Stefan. (2005). Attacking Hash Functions by Poisoned Messages "The Story of Alice
and her Boss.
14 In the 2030s, quantum computers will be able to crack the SHA -256 algorithm used by Bitcoin
15 https://www.cisa.gov/uscert/ncas/tips/ST04 -018
10
Fig. 2 Digital Signature being used to ensure that the document has not been modified
Fig. 3 Properties of digital signature
From the above figure it is clear that the said digital signature is issued by a Certifying Authority which
contains the public key and the identity of the Unique Identification Authority of India (UIDAI) . It is a
kind of authorised directory of public keys which maps them to a natural person or an organization.
As shown in Fig. 3 the digital signature can also be used for non-repudiation .
The signature of the Unique Identification Authority of India (UIDAI) on the AADHAAR document
makes it impossible for the UIDAI to claim that it has not issued and signed the said document. The
understanding of non -repudiation property of digital signatures is crucial for an investigator or tax
auditor to prove that the funds in a crypto asset wallet are indeed controlled and beneficially owned
by a natural or juridical person.
Digital signatures are based on public key cryptography . A public key cryptographic system uses a pair
of keys with each pair consisting of a public and a private key. The public key is publicly known and
can be shared with various users and the private key is kept secret . To prove identity, the transaction
is signed (encrypted) using the private key and the signed as well as the original transaction is
transmitted to the receiver by the sender . The signed( encrypted ) message is then decrypted by the
receiver using the public key of the sender , which is known to the receiver, and is matched with the
original (unencrypted ) transaction . If the two match , then it can be inferred that the message did
indeed originate from the owner of the private key corresponding to the public key used to decrypt
the signed message . For example ,
Consider the private key
1ee283aa3024205e69c14b11b65685ac367213aba131e654d97020cfe5aa5818
11
and its corresponding public key
fc1a62a566ce576431cc2c96dac6766fb8fcea90b8da28307b1af1647a361f874c9604c3c98497a11538
7b0382df141d4cdda97bc8502a7cf7af267ce91d3f45
Alice has to send the message “Crypto Taxation is interesting” to Bob , and Bob needs to verify that
this message is indeed from Alice , and that it has not been tempered. Thus, Alice uses her private key
to encrypt the message “Crypto Taxation is interesting” and generates the signed (encrypted)
message . She transmits the signed message along with the original message to Bob . Cryptographically
the signed message would be
02df346b572d370d5bb19cd5a6de115335284d4c6de7683078eef9d6df11e14c236af35e817a303d3d
ed6b3e63531e5ad26f6db006fbbf97ca6c96de60db3326.
Bob receives the signed message as well as the original message “Crypto Taxation is interesting”. He
decrypts the signed message using the known public key of Alice and compares it with the original
message “Crypto Taxation is interesting” . If the two messages match, then Bob can say with certainty
that the message is indeed sent by Alice and has not been tempered . If a malicious actor tempers even
a single bit of the original or signed message sent by Alice, Bob would come to know that the message
has been tempered or was not sent by Alice . This use of digital signatures in blockchains makes them
temper -evident. This can be seen pictorially in Fig. 4 .
Public key cryptography is central to the addresses used by Bitcoin to send and receive Bitcoins. The
public key is analogous to the bank account number in a banking system and private keys are
analogous to the passwords used to authorize transactions. The public key is derived from the private
key using a one -way function16, which has the nature of making the computation possible only from
the private key to the public key. These one -way functions are based on properties of large prime
numbers. For example, it is very easy to find the product of two very large prime numbers, but it is
extremely difficult, given the product , to factorize it into the two large prime numbers.
The public key can be easily derived from the private key using the one -way function, but it is
practically impossible to derive the private key from a public key. Bitcoin uses the Elliptic Curve Digital
Signature Algorithm (ECDSA) which has 256-bit private keys. This means that a Bitcoin private key is a
random 256-bit number. 256-bit keys are highly secure and currently it is practically impossible for an
attacker to derive the private key from a public key using brute force.
In the best case the attacker would have to guess 2128 keys , and even if the attacker uses the fastest
supercomputer in the world with capacity of 1.1x1018 floating point calculations per second , with 10
floating point operations to be carried to try a key, it would take around 1 trillion years to derive the
private key. However, with the advent of quantum computers it is believed that algorithms like the
Shor’s algorithm can break public key cryptography in reasonable time by adopting more efficient
method s of factorizing large numbers (Fahmy , 1997) . This can pose a significant threat to public key
cryptography in general and crypto assets in particular.
2.3.4 ECDSA and Bitcoin addresses
Bitcoin uses the Elliptic Curve Digital Signature Algorithm (ECDSA) for its digital signature scheme.
There are multiple elliptic curves in ECDSA. Bitcoin uses the Secp256k1 elliptic curve to derive the
public keys from private keys. However, even though p ublic and private keys are required for bitcoin
16 In computer science, a one -way function is a function which is easy to compute for every input, but given a
computed output, it is very hard to invert.
12
transactions, in order to reduce the size of transactions, instead of using the public key, Bitcoin uses
the public address which is a modified version of the public key.
This transformation creates various types of addresses through the algorithm depicted in Fig. 5.
Besides allowing for a greater number of transactions in a block due to lower transaction size, this
transformation has some positive security implications, as it provides quantum resistance to existing
public keys17 which hold Bitcoin and reduces the possibility of errors that users might make in
specifying the public addresses while sending the Bitcoins, due to the presence of a checksum.
Fig.4 Signature verification in public key cryptography
17 As discussed in 2.3.3 using Shor’s algorithm on quantum computers it would be possible to obtain the private
key corresponding to a given public key efficiently. However, if the attacker has a hashed version of the public
key and not the public key itself, he/she need to find the public keys first using Grover’s Algorithm
ALICE
Crypto Taxation is interesting
02df346b572d370d5bb19cd5a6de11
5335284d4 c6de7683078eef9d6df11e
14c236af35e817a303d3ded6b3e6353
1e5ad26f6db006fbbf97ca6c96de60d
b3326
Signed message
Message en crypted using Alice ’s
private key
1ee283aa3024205e69c14b11b65685
ac367213aba131e654d97020cfe5aa5
818
BOB receives the signed message a nd
the origi nal message , decrypts th e
signed message using Alice ’s public
key
fc1a62a566ce5 76431cc2c96dac6766f
b8fcea90b8da28307b1af1647a361f8
74c9604c3c98497a115387b0382df14
1d4cdda97bc8502a7cf7af267ce91d3f
45
and compare s to the original
mess age
Crypto Taxation is
interesting
Crypto Taxation is
interesting
As both the messages are
identical, the mess age
was indeed sent by Alice
and has not been
tempere d
13
Fig. 5 Deriving a Bech32 Bitcoin address from a public key
For getting an identity on the Bitcoin Blockchain , the user must generate a random number between
0 and 2256, which becomes the private key of the user. The user derives the corresponding public key
of this private key using the Secp256k1 elliptic curve of ECDSA and it is transformed into a Bitcoin
address using the process shown in Fig. 5 . In this process it is critical to have a good source of
randomness as the key generation algorithm uses a random seed to generate the key pair.
There are multiple websites which help users to generate such key pairs, they use various methods to
generate a truly random number by using techniques like moving a mouse pointer or entering random
text as input. One such website is www.bitaddress.org . Fig. 6 is a screenshot from the website, which
shows how it uses the mouse movement to generate a truly random number which is used to generate
Bitcoin key pairs.
Fig. 6 Random number generation using mouse pointer for generating Bitcoin private key
The generated Bitcoin address is shown in Fig. 7. It is important to note that in this process of
generating a Bitcoin address and its corresponding private or secret key, there is no requirement to
provide any identity related information. No Know Your Customer (KYC) procedure is required to
generate t his valid Bitcoin address which can be used for transaction on the Bitcoin Blockchain . The
validity of the bitcoin address can be easily verified on the Bitcoin Blockchain Explorer as given in Fig.
8.
As we have freshly created this address there are no transactions on this address. Similarly, a user can
create any number of addresses and make transactions on the Bitcoin Blockchain without undergoing
any Identity verification or KYC. Unlike the digital signatures which had their public keys verified by a
central authority like in case of UIDAI, the Bitcoin Blockchain does not require attaching any real -world
identity to the digital signature .
This has implications for tax enforcement and other law enforcement agencies , as this identity on the
Bitcoin Blockchain is not mapped to a real -world identity. Unlike details like Swift Code and a name or
possibly a social security number attached with a bank account, there is no such personal or Public Key SHA256 RIPEMD160 Checksum BTC address
14
geographical information associated with a Bitcoin address. However, there are some mechanisms to
do so on a probabilistic basis. This makes it pseudonymous.
Fig. 7 Bitcoin address generated using the Random Seed
Fig. 8. The newly created Bitcoin address displaying zero BTC amount on Bitcoin Blockchain
In this case, methods like subpoena and summons cannot be issued to a central authority like a bank
or a stock exchange to obtain information about the beneficial owner of this Bitcoin address. Methods
to map a real -world identity to this Bitcoin address are usually based on blockchain analytics and
inference. In case of a centralized exchanges where users do not generate their own key pairs and the
exchange does this for them, the KYC information is captured before on -boarding a customer and can
be obtaine d after satisfying the legal requirements, through their law enforcement portal. However,
such centralized exchanges might primarily rely on the self -declaration of tax residency by the user.
The Bitcoin address is basically a 160 -bit number. Therefore, a maximum of 2160 unique addresses can
be generated. This might raise concerns for some users about collisions in Bitcoin address generation,
i.e., two users independently generating same Bitcoin addresses and their corresponding private keys .
However, as the number of addresses is too large, the probability of a collision is almost non -existent.
For a detailed mathematical analysis of this problem, you may refer to an article by andytoshi on The
Bitcoin Birthday Paradox18. It concludes that there would be a 0.1% chance of a collision if 5.4x1022
addresses are in existence. Still there is no reason to worry about collisions as:
18 https://download.wpsoftware.net/bitcoin -birthday.pdf
15
i) The probability of collision is extremely low (1 in 1048) for each independent collision
ii) It would take trillions of years to reach a stage where so many Bitcoin addresses are
generated that the newly generated addresses have a significant chance of colliding with
the old ones.
iii) Since only 21 million Bitcoins can ever be generated and the smallest possible unit of
Bitcoin is 1 Satoshi or 10-8 Bitcoin, only 2.1x1014 addresses can possibly have any amount
of Bitcoin in them and an attacker would have to generate trillions of Bitcoin addresses
before he/she can find a collision address which has Bitcoin balance .
Thus, it is practically impossible for two Bitcoin users to generate the same address independently
unless they have used the same or predictable source of randomness. So, if a taxpayer presents the
argument of his/her Bitcoin being stolen by someone who maliciously generated their public and
private key pair, the taxman should know that it is practically impossible for anyone to do so, and the
private key might have been compromised in some othe r way. In the next section we would see how
the addresses and their corresponding private keys are used to transact on the Bitcoin Blockchain .
From the point of view of tax investigation and enforcement as well as for other law enforcement
agencies it is important to have a general understanding about the characteristics of the public and
private keys as well as the public addresses of Bitcoin and other crypto assets. For investigators
involved in enforcement actions as well as the forensic analysis of evidence , it is important to be able
to identify crypto assets address and keys to find the quantum and flow of funds.
It would also be important to use regular expressions to identify certain crypto asset addresses and
keys located in mobile devices or hard drives. Although, nowadays various forensic investigation tools
provide this functionality. However, knowledge about the various types of addresses of crypto assets
might help the investigators and the taxman to prima facie identify the crypto assets involved in the
investigation.
The algorithm shown in Fig. 5 generates 3 types of Bitcoin addresses which can enable an investigator
to identify/suspect a string of characters representing a Bitcoin address. Awareness about Bitcoin
address types can also aid an investigator in tracking the flow of funds in cases of theft, fraud or
establishing the ownership or control of a Bitcoin address. The three types of Bitcoin addresses
generally used in transactions are:
i) P2PKH address es: These addresses are the first version of Bitcoin addresses and are also
known as legacy addresses. They start with the number ‘1’ and have 26 to 36 characters.
The transactions using these addresses are larger in size and hance require more
transaction fee making this address type inefficient. For example,
1KfVFNtxkknugXhA9uYkohMWxG8f78nyax
ii) P2SH address es: These are like P2PKH addresses and provide more complex functionality
like multiple signatures for spending the Bitcoins. They start with the number ‘3’. For
example, 3FMFtFAJxKmvpumn3sGjA6xjXw1M4Bw97v
iii) P2WPKH or Bech32 : These transactions are advance transactions which help to reduce
transaction size and make them faster and more efficient. These are used in SegWit
transactions, a new format for Bitcoin transactions in which to accommodate more
transactions in a block , certain information (regarding witnesses) was removed from the
input field of the block. These addresses start with ‘bc1’. For example,
bc1q9jayxqvah5gynukddmmvs7jc9xwjc0c6emulpp
Similarly the Bitcoin private keys start with ‘K’,‘L’ or ‘5’ like
Kx4cBkAHgD9CrYNhTM12P5cNgVfwTeG5nN2R4KxcZjPPLx7DfrEr
L5WNtTRjhqisXpnkCbr2mRu6oR7k34axzggh4nyXKuQU6aozLnkQ
5J5PZqvCe1uThJ3FZeUUFLCh2FuK9pZhtEK4MzhNmugqTmxCdwE
16
These patterns can help investigators to identify the use of Bitcoin blockchain by the taxpayer or
suspect. The regular expressions19 for Bitcoin and other crypto assets’ addresses and keys can be used
to look for use of such assets by the taxpayer or suspect on a computer or disk using specialized
software .
In Bitcoin, mnemonics -based keys, often referred to as mnemonic seeds or recovery phrases , as shown
in Fig. 8A , are a human -readable and memorable representation of the private key or seed used to
generate a deterministic wallet. These phrases typically consist of a sequence of 12, 18, or 24 words
chosen from a predefined list of words. Mnemonics serve as a conven ient way for users to back up
and restore their cryptocurrency wallets without needing to store or remember the raw cryptographic
keys themselves. They are crucial for wallet recovery in case of loss, theft, or hardware failure, as users
can simply input their mnemonic seed into a compatible wallet software to regain access to their
funds. Mnemonics -based keys adhere to the BIP -39 standard20, ensuring compatibility across different
Bitcoin wallets and implementations. Thus, whenever a n auditor or investigator comes across such
mnemonics it should be identified as a seed phrase for a crypto asset wallet.
Fig. 8A mnemonics -based keys
2.3.5 Bitcoin Transactions
Bitcoin is primarily a means for transmitting value from one user to another through an open,
permissionless and trustless network. Bitcoin Blockchain has multiple mechanisms to ensure the
validity of transactions which make sure that it is usually impossible to carry out fraudulent
transactions. A transaction is usually of the form of Alice paying Bob a certain Bitcoin value . The various
aspects of ensuring security and integrity of transactions are:
i) The users must own the Bitcoin value that they wish to transfer and should be able to
prove the ownership unambiguously on the network. For example , if Alice has 2.5 BTC the
Bitcoin Blockchain based on a consensus, should agree that Alice owns 2.5 BTC
ii) Appropriate funds should be available with the user. It should be impossible to transfer 3
BTC when a user owns only 2.5 BTC21.
iii) The originator and authorizer of the transaction must be verifiable as Alice
iv) The transaction should not have been tempered
One of the most important ideas in a transaction on Bitcoin Blockchain is that of an Unspent
Transaction Output (UTXO). UTXOs are quantities of Bitcoin sent to various public addresses which
have not yet been spent by them. They represent the units of Bitcoin which can be referenced by the
19 Regular Expressions are patterns which are used for matching strings by computers
20 https://en.bitcoin.it/wiki/BIP_0039
21 There is no concept of loans or any overdrafts in Bitcoin transactions.
17
owners of the Bitcoin addresses to which the UTXOs are assigned , and can be spent on the Bitcoin
Blockchain .
For every valid transaction Alice must reference a UTXO which is a result of previous transaction by
Alice herself or some other address in the Bitcoin Blockchain which transferred Bitcoins to Alice’s
Bitcoin address. The only exception to this is the first transaction in every block , called the Coinbase
transaction, which results in newly created bitcoins being assigned to the miner of the block as reward.
Coinbase transactions do not require any input UTXO, but only the pay -out Bitcoin address.
UTXOs are analogous to cheques which are written in the name of a specific Bitcoin address owner
and can be spent only by them. It also important to note that while Bitcoins are fungible, UTXOs are
not fungible. A user must use the entire value of the input UTXO either by transferring Bitcoin value
to other Bitcoin addresses , their own address or as transaction fee for miners . The difference in the
value of the input and output UTXOs is deemed to be the transaction fee for miners.
UTXOs can be imagined as piggybanks assigned to specific users and every time the user intend s to
spend the amounts kept in the piggybank , they must break the entire piggybank assigned in their
name , and create new piggybank(s) in the name of the intended transferees or the transferor
himself/herself which can only be spent by them. In the transaction depicted in Fig. 9 these new
output UTXOs (piggybanks) are for the Bitcoin addresses of Bob, Carol, and Alice. Any change that is
not assigned to either Bob and other transferees or Alice’s new piggybank , is by default the miner fee
given by Alice for adding her transaction on the Bitcoin Blockchain .
Fig. 9 A Bitcoin Transaction
In the above transaction Alice sends 2.5 BTC to Bob, 0.2 BTC to Carol and 0.25 BTC to herself (to a new
Bitcoin address controlled by her or her existing Bitcoin address) the balance amount of 0. 05 BTC is
the miner fee. The UTXOs are in the form of a locked script which can only be spent by a user who has
a public address , same as that specified in the locking script. It is a ki nd of cheque written for the user
whose public address is same as that specified in the script.
The input UTXOs of 1.2 and 1.8 BTC have been locke d using a script by the sender for Alice’s Bitcoin
address . So, Alice refers to the input UTXOs in the input side of her transaction and specifies the output
Bitcoin addresses of Bob, Carol, and herself along with locking scripts and signs the transaction using
her private key. She then broadcasts the transaction signature along with her public key which allows
1.2 BTC
Alice
1.8 BTC
Alice
Unlock
Alice’s
UTXOs
Lock Output in new UTXOs
for Bob, Carol and Alice.
Balance for miner fee and
sign using Alice’s pvt key
2.5 BTC
Bob
0.2 BTC
Carol
0.25 BTC
Alice
0.05 BTC
Miner
18
the nodes of the Bitcoin Blockchain to make sure that the transaction has indeed originated from Alice
and that she is the one entitled to spend the input UTXOs of 1.2 and 1.8 BTC.
The nodes compare Bitcoin address corresponding to the public key of Alice with the Bitcoin address
specified in the input UTXO, if they match , then they infer that indeed Alice is entitled to spend the
Bitcoin amount contained in the input UTXO. Then the nodes verify the signature of the transaction
broadcasted by Alice . If the signatures are verified, then it can be inferred that indeed Alice has
authorized the spending of the input UTXOs.
This ensures that only Alice can spend the input UTXOs owned by her Bitcoin address. Suppose an
eavesdropper Eve knows Alice’s public key somehow and tries to spend her UTXOs. She cannot do it
as Eve does not know the private key of Alice and would have to sign the transaction using her own
private key or any other private key . The nodes of the Bitcoin network would fail to verify the
signatures and would come to know that it is not Alice but someone else who has signed the
transaction or the transaction ha s been tempered .
In case Eve uses her own private and public key pair to create a transaction having UTXOs meant for
Alice as input, the Bitcoin address corresponding to the public key of Eve will not be same as that of
Alice’s public key and the nodes will be unable to successfully run the unlocking script of UTXOs meant
for Bitcoin address of Alice, making Eve unable to spend Alice’s UTXOs. This combination of locking
and unlocking scripts along with digital signatures makes sure that UTXOs are not spent fraudulently
by malicious actors.
While broadcasting the transaction , Alice transmits the signature of the transaction along with her
public key. This has potential implications for security threats from quantum computer s, as until only
the Bitcoin address is known to the attacker with a reasonabl y resourceful quantum computer, he/she
will have to first find out the public key itself using Grover’s Algorithm and then use Shor’s Algorithm
to find out Alice’s private key in reasonable time. However, with the disclosure of public key while
spending the UTXOs, it might become easier for an attacker to find the private key of Alice before the
transaction is included in the next few blocks , allowing him /her to spend Alice’s UTXOs fraudulently.
It can also be seen that a Bitcoin transaction22 on the blockchain has no narration, purpose code or
identity or IP address information of any device and natural or juridical person attached with it. Thus,
by merely looking at the transaction it is not possible to know the device or IP address that originated
the transaction as well as the real natural or juridical persons behind it or the purpose of the
transaction. However, some blockchain analytics and inferences can help in such identification.
2.3.5.1 Tax Implications of UTXO Based Transactions
The UTXO based design of Bitcoin and other similar crypto assets like Bitcoin Cash and Monero can
have implications for determining the cost basis for calculation of the capital gains liability. As the
UTXOs are not fungible, each UTXO(s) belonging to a public key(s) of a taxpayer can be uniquely
identified . While transacting on the Bitcoin Blockchain, the taxpayer might choose an optimal
combination of the input UTXOs which minimizes the transaction fee, but this might give rise to
different tax liabilitie s due to possibly different cost basis of each UTXO . This can lead to significant
complexity for owners of crypto assets while reporting the basis of transactions and calculating their
tax liability from crypto asset transactions.
22
https://www.blockchain.com/explorer/transactions/btc/e9412cfda50be55c0a1989fb0ac7a2514a2f47852804d
4d7cfdebb33f5e780f3
19
To simplify the calculation of cost basis and the tax liability many tax administrations allow various
accounting methods to determine the cost basis of crypto assets. The methods are as follows:
i) First In First Out (FIFO): The earliest brought crypto asset is sold first
ii) Last In First Out (LIFO): The most recently brought crypto asset is sold first
iii) Highest In First Out (HIFO): The most expensive crypto asset is sold first
iv) Specific Identification (Spec ID): The specifically identified asset in record is sold. This is
the method most consistent with the UTXO based design of Bitcoin and other similar
crypto assets .
v) Average Cost Basis (Known as Share Pooling in the UK): A method wherein identical assets
are pooled and the average cost basis is taken for each of these pools. Depending on the
average basis being taken for the entire financial year or for each disposal t he methods
are also known as the total average and moving average methods respectively.
vi) Periodic Basis: The cost basis in some jurisdictions is determined by the value of the crypto
asset at the beginning of each tax year. This method is used by the Belastingdienst
vii) PVCT (Plus-value à court terme ) method: While dispos ing a crypto asset, the cost basis is
calculated by the fraction of the acquisition cost of the entire crypto holdings relative to
the sales proceeds. This method is used by the Direction Générale des Finances Publiques
As one cannot transact with oneself, the transfer of crypto assets from on wallet address, hosted or
un-hosted , owned by the same person or entity is not taxed. However, such transfers would require
maintaining records of the original incoming crypto asset for an accurate determination of the cost
basis for determining capital gains or losses at the time of disposal of the crypto asset .
Choice of different methods can have an impact on the tax liabilities of individuals as well as the
revenue collected through taxation on crypto asset s. As crypto asset s resemble securities in many
aspects and some have even been classified as securities by regulators like the SEC, they are also
susceptible to “wash sales”. However, as in some jurisdictions, wash sale rules apply only to securities,
and crypto assets are classified into different asset classes in multiple jurisdictions, investors can use
the regulatory arbitrage to lower their ta x liability . There is also evidence of Tax Loss Harvesting (Cong,
Landsman, Maydew, & Rabetti, 2023) in jurisdictions like the US to lower the tax liability.
2.3.6 The Bitcoin Blockchain
Wikipedia23 provides a comprehensive definition of a blockchain as “A blockchain is a decentralized,
distributed, and often public, digital ledger consisting of records called blocks that are used to record
transactions across many computers so that any involved block cannot be altered retroactively,
without the alteration of all subsequent blocks. They are authenticated by mass collaboration powered
by collective self -interests. This allows the parti cipants to verify and audit transactions independently
and relatively inexpensively. A blockchain database is managed autonomously using a peer -to-peer
network ”
The distributed public ledger of Bitcoin is based on the blockchain . The transactions in a blockchain
are grouped together in the form of blocks and the subsequent blocks are cryptographically linked to
the previous blocks, thus forming a chain. A complex interplay of cryptography, distributed consensus
regarding the state of the blockchain and a system of economic incentives keep the Bitcoin Blockchain
secure. It leads to only valid blocks being added to the blockchain and prevents malicious actors from
compromising the integrity of the blockchain . Blockchain is not the only way to structure data and
blocks for crypto assets . Some crypto assets like IOTA, Nano and Obyte use Directed Acyclic Graph
framework to store data24.
23 https://en.wikipedia.org/wiki/Blockchain
24 https://finance.yahoo.com/news/cryptocurrencies -dag-based -framework -why -081019399.html
20
Unlike the banking system, the copies of ledgers in a public blockchain are maintained in the form of
a distributed database which can be openly accessed by anyone. The transactions broadcasted by the
users of the blockchain are verified by the nodes of the Bitcoin network and are included into a block
only when they are found to be valid . Addition of each block to the top of the existing blockchain
requires solving a rigorous mathematical puzzle by nodes called miners which add a new block to the
blockchain . The promptest miner to solve the puzzle gets to add the block create d by it on top of the
existing blockchain and extend it. It also earns him/her the mining rewards in the form of newly
created Bitcoins through the Coinbase transaction .
This difficulty in adding another block to the existing blockchain makes it practically impossible for any
malicious actor to temper the earlier blocks or transactions. Any effort at tempering becomes evident
to the participating nodes and due to the enormous computational resource s required for mining, it
is extremely difficult for a malicious actor to create a blockchain which is a few blocks different from
the original blockchain . A system of economic incentives promotes honest behaviour by participants ,
as they own or hope to own Bitcoin in the form of miner’s reward , and have an incentive to keep the
trust in the blockchain by maintaining its integrity .
The structure of the blockchain is shown in Fig 10. Each block contains a header and several valid
transactions. The header of the block consists of the following:
i) Merkle Root: It is the hash of the merkle tree25 which contains all the transactions in the
block. Its main objective is to ensure that no transaction has been modified in the block.
It is also used to quickly verify if a transaction is a part of a block.
ii) Nonce: It is a value which is the solution of the mathematical problem that the miner must
find to mine the block. A nonce value once found by a miner can be easily verified by
anyone.
iii) Previous Block Hash: Every Bitcoin block contains the hash of the previous bloc k. This
makes the blocks connect in the form of a chain as shown in Fig. 10, hence the name
blockchain . This property makes the blockchain temper evident as any change in a block
changes the block hash and breaks the blockchain as shown in Fig. 11
Fig. 10 Blockchain Structure (representative)
Fig. 1 1 Maliciously modified blockchain
25 In Computer Science, A merkle tree is a data structure which is used for verification of data in a large
distributed dataset. It allows huge amount of data to be efficiently mapped in the form of a tree. If it is required
to check if any changes have been made to the data in the merkle tree it can be done by checking if the data is
consistent with the root hash instead of traversing the entire merkle tree .
21
If a malicious actor tries to alter the transaction on Block 651 where Ron sends 3 BTC to Alice instead
of 2 BTC (Assuming that the added transaction is indeed authorized by Alice). As one of the
transactions in the merkle tree has been changed, the merkle root will change and consequently the
block hash would change. This would make the actual hash of the Block 651 inconsistent with the hash
of the Block 651 recorded in the header of Block 652. The nodes on the blockchain would take
cognizance of this and rely on the longest consistent chain instead and continue adding new blocks on
top of that chain.
Bitcoin blocks are identified using the unique hash of their header. This relies on the collision
resistance of SHA256 hashing algorithm. This is the reason why if an attacker , equipped with a
quantum computer can find an input 𝑥 such that 𝐻(𝑥)=𝐻(𝐻𝑒𝑎𝑑𝑒𝑟 𝑜𝑓 𝐵𝑙𝑜𝑐𝑘 651 )=00064 𝑏 then
the integrity of the blockchain can be seriously compromised.
2.3.7 Bitcoin Mining
The Bitcoin Blockchain needs to achieve a distributed consensus in which the participants of the
network agree on which blocks form a part of the blockchain . As various nodes broadcast new
transactions to be included in blocks and added to the existing blockchain , there must be a mechanism
to decide which transactions will be added to the blockchain and which node adds the new block . The
new blocks added to the blockchain must be verified and the nodes must be in sync about the current
state of the blockcha in. This common view of the blockchain is called a consensus.
We come across the term miners and mining very often in the context of crypto assets. To effectively
tax mining it is important to understand what exactly does it mean to mine Bitcoin . This facilitates the
understanding of the direct and indirect tax implications of this service of adding blocks to the
blockchain which creates economic value in the Bitcoin ecosystem. Many tax administrators might
want to have an idea about the trends and extent of mining in their jurisdiction. It can be estimated
using trends in network traffic and some services like Bitnodes26. However, many Bitcoin nodes use
the ToR network and are very hard to trace using their IP addresses.
Also, as the probability of earing mining rewards is based on the percentage of total hashing rate
(computational power) available with the miner, most individual miners join a mining pool. A mining
pool significantly reduces the variance of rewards received by a miner. For a fee, the mining pool
makes the rewards almost certain if the individual miner has a hashing rate above a certain threshold.
Mining activities can result in direct as well as indirect tax events which are discussed later.
The Bitcoin Blockchain needs to have a robust mechanism to ensure that only valid transactions are
included in the blockchain and a system of incentives is in place to encourage honest actors and
disincentivize malicious actors in the system. It is important to note that not all nodes of the Bitcoin
network are involved in mining , as mining is a very resource intensive activity . Some nodes only relay
the transactions received by them to broadcast th ose transactions to the entire network effectively.
Only nodes involved in mining can add another block to the blockchain .
One of the important problems to be solved by a miner node is to select the transactions out of the
broadcasted transactions to be included in the block which the miner would try to mine. When a
Bitcoin user submits a transaction to the Bitcoin network, it is broadcast ed using a flooding algorithm
to the entire Bitcoin network and the miner nodes come to know about its existence. The broadcasted
transactions form what is known as a mempool, which is a pool of unverified transactions being
26 https://bitnodes.io/nodes/live -map/
22
broadcast on the Bitcoin network27. The transaction fee and the block reward are the incentives for
the miner nodes to verify transactions and mine new blocks to be added to the blockchain .
To gain maximum reward in Bitcoins , the miners solve an optimization problem like the knapsack
problem28 in Computer Science (Fig. 1 2) while picking up the transactions from the mempool for
inclusion in Blocks to maximize their block rewards . The miners check if the transactions are valid and
the senders have the funds and the authorization to spend the UTXOs and construct the merkle tree
of the transactions to form a block. The miner also puts its own Bitcoin address in the Coinbase
transaction for receiving the block reward. It is usually believed that miners pick up the transaction
with the highest transaction fees, but it is not entirely correct. Bitcoin transactions have multiple
inputs and outputs which ar e measured in bytes. However, for the purpose of inclusion in the blocks ,
another criterion called weight units is used , as all bytes in a Bitcoin transaction do not have the same
weight.
The miner uses the criterion of “fee per weight unit ” for inclusion in the mining block and maximizes
his/her payoff. For example, if a miner must choose between a transaction which has 4 input UTXOs
and 4 output addresses and the other transaction having 2 input UTXOs and 1 output address , and
both transactions have the same transaction fee. Then the miners would pick up the second
transaction over the first one as it is smaller and has more transaction fee per weight unit. This would
enable the miner to fit more transactions into the block as the limit for Bitcoin transactions in a block
is 4 million weight units29.
Fig. 1 2 The Knapsack Problem30
The next problem in mining is to select the node which would add a valid block to the blockchain . One
possible solution for it could be to randomly select a miner node and allow it to add a valid block to
the blockchain . This solution will rely on the premise that most of the times an honest miner node will
be selected and it would add a valid block to extend the blockchain . However, this solution has a
potential problem of the network getting flooded by numerous malicious miner nodes largely as the
margina l cos t of adding another malicious miner node would not be much. Thus, there needs to be
some barriers to entry for participation in the validation process of transactions, adding new blocks
and gaining from the consequent rewards. Computational intensity of mining acts as such a barrier.
Bitcoin solves this problem using proof -of-work mechanism . In the proof -of-work mechanism , each
node involved in mining must perform intensive computation to find the solution to a mathematical
problem to ‘mine ’ a block and add it to the blockchain . Once the solution has been found, a node can
27 This pool of transactions getting arranged into blocks and being mined , along with other parameters of
Bitcoin network can be visualized at https://mempool.space/ and https://mempool.space/mining
28 https://en.wikipedia.org/wiki/Knapsack_problem
29 https://en.bitcoin.it/wiki/Weight_units
30 Source: https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Knapsack.svg/375px -
Knapsack.svg.png
23
broadcast the solved block to the entire Bitcoin network and claim the reward for validation. When a
node involved in mining solves the mathematical puzzle and adds a block to the blockchain , it is said
to have mined a block.
To understand the mathematical puzzle that the miners solve to mine a Bitcoin block, it is pertinent
to look the block hash of some of the actual Bitcoin blocks mined recently and added to the blockchain .
One can go to any Blockchain Explorer31 and find out the block hash of the last 10 recently mined
blocks of Bitcoin Blockchain. They are shown in Table 2.
Block Height32 Block Hash
832072 00000000000000000000fd8369848fa6bcb3bef188d76fc7a1d38992a5e881d1
832071 000000000000000000023f411f7ebea61731f0d7990a4da1f7106375501d100e
832070 00000000000000000002a7473666215bd65b39e74ffbd7b9d2eccb8336430f4a
832069 00000000000000000001907e66f40f2bd56e267d73dd37a7541161efea891808
832068 000000000000000000004df71f0b4419e9c3355dd2c399b19bd56f65ad58c113
832067 000000000000000000004ff4ea42aa1a467363d22e524913e6913eaf5d24e8fb
832066 000000000000000000009de6eee5f331dfdbb1ef59a15ea095552a00983bdbc0
832065 00000000000000000002782005e1e33e276d47763cc86822983cd258e04d1614
832064 000000000000000000021a6cf69f6deabb2641d4afb5aede607584f9c02e6566
832063 0000000000000000000211c4647e2610ae16307851e38b7953c399b501b788e5
Table 2 Block Hash of 10 Bitcoin Blocks
A striking feature of the block hash values of these blocks are that the first 19 digits of the hexadecimal
hash are 0s. This is not a coincidence , and is related to the mining puzzle which each miner tries to
solve to ‘ mine ’ a block and receive the block reward. The Bitcoin block header is 80 bytes in size and
consists of multiple fields as shown in Fig. 13
To add a block to the blockchain the miner must find a nonce, which is a number when entered in the
block header and subsequently the SHA256 hash of the block header is calculated , the resulting hash
value is less than the target hash. Or mathematically:
𝐻(𝑣𝑒𝑟𝑠𝑖𝑜𝑛 ||𝑃𝑟𝑒𝑣𝐵𝑙𝑜𝑐𝑘𝐻𝑎𝑠 ℎ||𝑀𝑒𝑟𝑘𝑙𝑒𝑅𝑜𝑜𝑡𝐻𝑎𝑠 ℎ||𝑡𝑖𝑚𝑒 ||𝑛𝐵𝑖𝑡𝑠 ||𝑛𝑜𝑛𝑐𝑒 )<𝑡𝑎𝑟𝑔𝑒𝑡 ℎ𝑎𝑠ℎ
This is the reason why in Table 2 the hash values of blocks have their first 19 digits as zero. Finding
such a nonce is an extremely challenging computational problem and this is the reason why massive
hardware is deployed in various mining farms to try as many nonces as possible in the least possible
time, so that the miner can find the right nonce and broa dcast the ‘ mined ’ block to the rest of the
network and get the miner’s fee as well as the block reward. Once a valid nonce is found, the other
nodes can very easily verify that the hash value in the proposed block header is indeed lower than the
hash value target.
To ensure that the mining difficulty keeps pace with the rapidly evolving hashing capacity of the
network, it is in -built in the Bitcoin Core software33 to recalibrate the difficulty level of the problem
after every 2016 Blocks (~ two weeks) such that a Bitcoin block is mined every 10 minutes on an
average. This can be practically seen in the hash values of blocks mined at various points of time in
31 A Blockchain Explorer is a website that enables anyone to search any information regarding a Transaction,
Address or Block in the Blockchain. Different Crypto Assets have different Blockchain Explorer websites.
32 Block Height is the number of blocks mined since the Genesis block of Bitcoin was mined in January 2009
33 https://github.com/bitcoin/bitcoin/blob/master/src/pow.cpp
24
Fig. 1 3 A Bitcoin Header34
Block
Height
Date Block Hash
50 2009 -01-11 0000000026f34d197f653c5e80cb805e40612eadb0f45d00d7ea4164a20faa33
75000 2010 -08-18 00000000000ace2adaabf1baf9dc0ec54434db11e9fd63c1819d8d77df40afda
150000 2011 -10-20 0000000000000a3290f20e75860d505ce0e948a1d1d846bec7e39015d242884b
225000 2013 -03-09 000000000000013d8781110987bf0e9f230e3cc85127d1ee752d5dd014f8a8e1
300000 2014 -05-10 000000000000000082ccf8f1557c5d40b21edabb18d2d691cfbf87118bac7254
375000 2015 -09-18 000000000000000009733ff8f11fbb9575af7412df3fae97f382376709c965dc
450000 2017 -01-26 0000000000000000014083723ed311a461c648068af8cef8a19dcd620c07a20b
525000 2018 -05-30 0000000000000000002ffaa108b110ff7fd64475841b6ab65abd9bb43f8ede1d
600000 2019 -10-19 00000000000000000007316856900e76b4f7a9139cfbfba89842c8d196cd5f91
675000 2021 -03-17 000000000000000000057b6df3f61f96fbdd4b9c4d76fcb975cb0e8a56577d51
750000 2022 -08-18 0000000000000000000592a974b1b9f087cb77628bb4a097d5c2c11b3476a58e
825000 2024 -01-09 00000000000000000001432b1ea8b3b710c3fb7e628d605cf4a42c25d7822431
Table 3. Block Hash values after every 75000 blocks
the past. Table 3 shows the block hash values recorded after every 75000 mined blocks. The number
of zeros in the hash is has been increasing, indicating lower and lower target hash values. This can also
be seen with the graphs of difficulty and hash rates of the Bitcoin network given in Fig. 14.
The Bitcoin mining difficulty and Bitcoin network’s hash rate follow a close trajectory. This is also a
mechanism through which the Bitcoin protocol ensures that there are always enough participants in
the Bitcoin network which validate the transactions and keep the blockchain secure. When the
34 Source: https://developer.bitcoin.org/reference/block_chain.html#block -headers
25
number of miners goes down, the hash rate of the Bitcoin network goes down and it takes longer to
mine new Blocks at the existing difficulty level. Consequently, the mining difficulty is lowered and this
attracts more miners till a new equilibrium is achieved. The sharp fall in the hash rate around mid -
2021 can be attributed to the crypto ban by China. Subsequently the difficulty of mining reduced and
hash rate started having an upward trend with mining activity increasing in USA and Kazakhstan ,
consequentl y increasing the mining difficulty again . This can be seen in Fig. 1 5 showing the country -
wise share of hash rate.
Fig. 1 4 Bitcoin mining difficulty and Bitcoin network hash rate35
Fig. 1 5 Country -wise share of Bitcoin hash rate36
Mining is a highly resource intensive activity and miners deploy significant computational capacity in
the form of Application -Specific Integrated Circuits (ASICs) which are optimized for producing trillions
of SHA256 hashes per second. This also results in consumption of massive amounts of electricity in
operating the hardware as well as cooling it. It is estimated that if Bitcoin mining consumes more
electricity than 167 countries37.
The probability of a miner successfully mining a block is directly proportional to the percentage of the
total hash rate of the Bitcoin network that the miner can produce. Thus, i f a miner produces a hash
35 Source: https://mempool.space/graphs/mining/hashrate -difficulty#3y
36 Source: https://ccaf.io/cbeci/mining_map
37 https://www.aa.com.tr/en/economy/bitcoin -mining -consumes -as-much -energy -as-167-countries/3109915
26
rate which is 0.001% of the total hash rate of the Bitcoin network, the miner can expect to mine one
in 100000 blocks. As we know that approximately 2016 blocks are mined in two weeks, this means
that it would take around 100 weeks or two years on an average for a miner to successfully mine a
block. Till then he/she would have to make the capital investment and incur the costs of depreciation
of hardware, the Internet and electricity.
2.3.7.1 Mining Pools
As discussed above, the possibility of a standalone miner successfully mining a block is miniscule and
it involves high degree of risk , as even after spending substantial amount on electricity and IT
hardware , it is possible that a standalone miner might not successfully mine a block for several years.
Thus, to hedge this risk, miners join mining pools . A mining pool is a group of crypto asset miners who
pool their computational resources over a network to increase the probability of success in mining
crypto assets, the rewards are divided amongst the participants of the pool rather than a winner -
takes -all case in standalone mining. The mining pools are operated by a centralized entity which
divides the task of computation between the pool members – the ‘worker’ miners . A comparison of
the leading mining pools can be found at Bitcoin Wiki38.
Joining a mining pool significantly reduces the risk and uncertainty associated with mining. The mining
pools have pool managers which manage the pay outs of mining rewards to the members of the pool
depending on the computation done by them. In Bitcoin mining , the mining pool puts its own Bitcoin
address in the Coinbase transaction and shares a work template with the ‘worker’ miner who returns
‘shares’ to the mining pool. A share is not exactly a solved block , but one in which the block hash is
only a few times higher than the target hash. For example, a mining pool taking share which are nearly
10 times of the target hash can be mathematically expressed as:
𝐻(𝑣𝑒𝑟𝑠𝑖𝑜𝑛 ||𝑃𝑟𝑒𝑣𝐵𝑙𝑜𝑐𝑘𝐻𝑎𝑠 ℎ||𝑀𝑒𝑟𝑘𝑙𝑒𝑅𝑜𝑜𝑡𝐻𝑎𝑠 ℎ||𝑡𝑖𝑚𝑒 ||𝑛𝑏𝑖𝑡𝑠 ||𝑛𝑜𝑛𝑐𝑒 𝑠ℎ𝑎𝑟𝑒)<10×𝑡𝑎𝑟𝑔𝑒𝑡 ℎ𝑎𝑠ℎ
This is a proof that the ‘worker’ miner has indeed put in a lot of computational effort as getting a hash
which is close to the target hash is also computation ally intensive. This results in a pay out from the
mining pool depending on the pay-out policy. Some pools pay ‘worker’ miners for every share whereas
some pools pay out only when the pool finds a block. When a ‘worker’ miner joins a mining pool even
though he/she finds solves a block, he/she cannot take the entire block reward as the Coinbase
transaction has the Bitcoin address of the mining pool. This is an important aspect from the
perspective of taxation . The shares made to a mining pool in lieu of pay -outs by the mining pools can
have direct and indirect tax consequences which have been discussed later. There are also multiple
issues related to the identification of miners and determining their tax residen cy for the purpose of
direct and indirect taxation. These issues will also be discussed subsequently.
2.3.7.2 Estimating the extent of Mining
Mining is an important part of any blockchain which ensures trust and security in the blockchain . It
produces economic value in the blockchain ecosystem and leads to generation of income which might
be liable to tax. However, for mining Bitcoin or any other crypto asset mined using the proof -of-work
mechanism, only hardware that can produce high hash rates, the Internet and electricity are required.
Usually, a ‘worker’ miner joins a mining pool based on self-certification , the pool usually does not carry
out any Know Your Customer verification or Anti -Money Laundering measures to link the
pseudonymous ‘worker’ miner to a natural or juridical person.
Tax administrations would also be interested to know the incidence of mining in their jurisdiction. As
mining activity is mostly pseudonymous and only estimates and trends can be drawn from data either
38 https://en.bitcoin.it/wiki/Comparison_of_mining_pools
27
obtained from the mining pools or through open -source network analytics. (Sun et al., 2022) studies
the spatial distribution of Bitcoin mining through bottom -up tracking and geospatial statistics. The
Cambridge Centre for Alternative Finance has developed a mining map39 which shows the
geographical distribution of the total hash rate of Bitcoin over a period . This is based on the data
obtained from various mining pools and extrapolating it to provide the monthly percentage share of
the country in providing the global hash rate of the Bitcoin network. It provides a snapshot of the
extent of mining in each country from September 2019 to January 2022. The methodology adopted
for this estimation has some important assumptions like inability to specify the geolocation of miners
using VPN or proxy services40. Which indicates that it can be very difficult to map various miners with
certainty .
Mining pool networks have specific characteristics which can be used to detect the miners who are
members of the mining pool. Using public network analysis tools like Shodan it is possible to identify
the IP addresses of some miners who use specific port n umbers to connect to the pools. Shodan41 is a
search engine for devices connected across the Internet. It can help to find IP addresses and number
of users using a specific port number in a country or region. By having a look at the number of ports
and port numbers used by a particular IP it can be inferred if it is a miner or a general computer. For
example, some popular pools use port number 3333 for Transmission Control Protocol (TCP)
connection with the participant miners. The result of such a Shodan query for finding IP addresses
who are using port 3333 is given in Fig. 1 6
Fig. 1 6 Query for port number 3333 on Shodan
Various IP addresses which have port 3333 open are displayed. The information about two such IP
address from South Korea and Poland is shown in Fig. 1 7 and Fig. 1 8 respectively . This potential miner
has only few ports open as compared to a general computer which might need to have various other
open ports. This makes it highly probable that these computers/ASICs are involved in mining. Due to
the imperfection of this method in finding an accurate number of miners, it may only enable users to
see the trends in mining in a country, region, or city.
39 https://ccaf.io/cbeci/mining_map
40 https://ccaf.io/cbeci/mining_map/methodology
41 https://www.shodan.io
28
Fig. 1 7 Open ports of a potential miner in South Korea as seen on Shodan
Fig. 1 8 Open ports of a potential miner in Poland as seen on Shodan
2.3.7.3 Bitcoin Mining and Game Theory
The participants in the Bitcoin Blockchain are rational and they try to maximize payoffs for their actions
in the Bitcoin ecosystem. As Bitcoin is based on a decentralized network, malicious nodes/actors
cannot be prevented from joining the network. Bitcoin solves this problem effectively by using
incentives and cryptographic checks which can detect and counter the actions of malicious actors. The
behaviour of the participants in the Bitcoin ecosystem can be understood and predicted using game
theory. The application of game theory to crypto asset mining is an active area of research.
Various participants try to maximize their payoff s and adopt strategies accordingly. Mining can be
considered a repeated zero -sum game between various rational miners who do not know each other’s
strategy. Thus, to gain maximum out of the game, the miner s tend to follow the strategy of honesty
and submit only verified blocks to the blockchain . The miners have a disincentive in duping the system
as the validity of the fraudulent transaction can be verified and the other honest nodes would discard
the propose d block. As mining is a resource intensive activity, any such efforts would cost the miner
dearly even if he/she has a substantial part of the total hash rate of the network.
Some mining pools can adopt a strategy to infiltrate other mining pools and make their infiltrator
workers send the shares to the pool but withhold the shares whenever they ‘solve’ a block. This can
enable other pools to bleed profits out of their competitors. This strategy can harm the profits of rival
pools but the workers cannot div ert the profits to the original pool as the work templates shared by
the victim pools would have their Bitcoin address in the Coinbase transaction. A detailed study on such
pool attacks has been done by Li et al. , (2020) .
As discussed earlier, the percentage of hash -power of miners/mining pools determines their
probability of mining the next block. The hash -rate share of various mining pools in the Bitcoin network
in the past one year is shown in Fig. 1 942
42 Source: https://mempool.space/graphs/mining/pools#1y
29
Fig. 1 9 hash -rate share of various mining pools in the Bitcoin network in the past one year43
There can be a scenario in which a single pool has more than 51% of the hash -power of the network.
This can enable the pool to delay the transactions of some Bitcoin addresses and carry out double
spends. The delay in service can be caused if the pool excludes the transactions received from a set of
Bitcoin addresses which it wants to exclude from the n etwork. However, since all nodes would not
have this discriminatory behaviour, the pool can at best delay the transactions of a subset of Bitcoin
addresses. Double spending can be done by such a pool as it can simultaneously send two transactions
in the network that spend the same Bitcoins . The first transaction would pay the provider of the good
or service purchased with some UTXOs and the second transaction would use the same UTXOs for
payment to an address controlled by the pool. As the pool controls more than 51% of hash -rate of the
network it can get the second transaction included in the longest blockchain resulting in orphaning of
the first transaction a nd loss to the supplier. This would significantly reduce the confidence in the
Bitcoin network and adversely affect its price.
As the pools also have an incentive to maintain or enhance the value of Bitcoin which they get as
reward, it disincentivizes any pool to grow to such a large size that it affects Bitcoin and they cause
losses to everyone including themselves. However, such an attack can be launched by an entity which
does not have much stake in the Bitcoin ecosystem or which is facing a larger loss in value terms in
real world .
It is sometimes misunderstood that a mining pool with 51% of hash rate can steal Bitcoins out of the
addresses of users. Due to the digital signature -based mechanism of transactions it is impossible for a
mining pool to maliciously steal the Bitcoins out of any Bitcoin addresses. The pool can at best delay
certain transactions or carry out double spends as described above.
2.3.8 Taxation of Mining Activity and Rewards
As discussed above, mining is an important source of value creation in the blockchain as it adds new
transactions to the blockchain and provides trust and security. It creates an incentive for the miners
due to which they participate in validating transactions and add blocks to the blockchain . The miner
essentially provides a service to the Bitcoin owners by verifying the validity of transactions, arranging
them in the form of a block and solving for the nonce to mine the block, in lieu of the Bitcoin bloc k
43 https://mempool.space/graphs/mining/pools#1y
30
rewards and user fees. Mining activity can have direct and indirect tax consequences for miners as
well as mining pool operators. The following sections first discuss the direct tax issues in mining related
to the acquisition and ‘disposal’ of crypto assets obtained through mining. The subsequent section
discusses the indirect tax issues related to mining. In the discussion to follow, mining mainly refers to
mining using the proof -of-work mechanism. The proof -of-stake mechanism and its taxation is
discussed subsequently.
2.3.8.1 Direct Taxes on Mining
Mining provides economic value to the miners in lieu of the services provided by them to blockchain
users as well as the blockchain protocol. The income of miners arises either from the protocol rewards
for ‘mining’ a new block, the user fee paid by the users transacting on the blockchain or pay -outs from
a mining pool for ‘shares’ by the ‘worker ’ miners. As this constitutes income or accrual of assets/value,
a tax liability may arise, depending on the tax jurisdiction of the miner, as different tax adminis trations
tax mining activity differently.
Moreover, the disposal/sale or any further transactions with the mined crypto assets are also taxable
events in various jurisdictions. The tax treatment of the income or accrual of assets/value and
disposal/sale or any further transactions of mined crypto assets depends on the nature and scale of
the activity carried out by miners. The two potential taxable events in the mining process are:
i) Acquisition of crypto assets as mining rewards
ii) Disposal/transfer of crypto assets acquired as mining rewards
2.3.8.1.1 Taxation of crypto asset acquired as mining rewards
A miner carries out mining activity using IT hardware that generates trillions/ billions of hashes per
second. As soon as a valid block is found by the miner, it is transmitted to other nodes in the Bitcoin
network. If the ‘worker ’ miner is a part of a mining pool, it sends ‘shares’ to the mining pool over the
Internet and the pool, in turn, transmits the ‘mined’ block over the Bitcoin network. The rewards are
received by the miner when it successfully mines a block or when the ‘worker ’ miner in a mining poo l
cashes out his/her accrued rewards.
The acquisition of crypto assets as mining rewards is subject to income tax in most jurisdictions. The
miners are supposed to report the mining incomes separately as ‘miscellaneous income’ in many
jurisdictions and the tax liability arises on the fair market value of the acquired crypto assets at the
time of acquisition, with allowable deductions for the costs of mining. Many tax jurisdictions carve out
an exception for ‘hobby miners’ who undertake mining activities out of interest or as a pastime on a
smal l scale, with an intention to accumulate the mined crypto assets instead of trading them to earn
a profit.
In some jurisdictions, hobby miners are not liable to pay income tax on the acquired crypto assets at
the time of acquisition of such assets. However, the expenses incurred by the hobby miners are
treated as the basis of acquisition . On the other hand , many jurisdictions take the basis of acquisition
of crypto asset s of hobby miners as zero and allow no deductions for expenses.
Besides this, in some jurisdictions like the US, crypto mining is considered as a self -employment
activity and miners are subject to payment of employment taxes as well as social security
contributions for their income from mining. However, as highlighted above, as it is difficult to find the
tax jurisdictions o f miners, the payment of taxes is largely subject to self-compliance by the individual
miners. Since the mining of first block by Satoshi Nakamoto in 2009 till February 2024 mining rewards
31
and user fee of approximately 60 billion USD has accrued to miners44. Similarly, the mining and
transaction rewards accrued to Ethereum miners till 15th September 2022 , when Ethereum switched
to proof-of-stake based consensus mechanism are approximately 35 billion USD45. These are
significant amounts for tax administrations as the direct and indirect taxes on it might run into billions
of dollars after deducting the relevant expenses. Moreover, as the market capitalization of Bitcoin
reaches an all -time high of ~1.3 trillion USD46 and that of Ethereum nears 450 billion USD47, the amount
of taxes due on these mined crypto assets might be in hundreds of billions of dollars .
2.3.8.1.2 Disposal/transfer of crypto asset s acquired as mining rewards
In most jurisdictions, the tax treatment of disposal of the crypto assets acquired as mining rewards
depends upon the nature and scale of activity by the miners. The income/gain from the disposal of
crypto asset s acquired as mining rewards is classified as business income or capital gains. If the miners
deploy large number of equipment to carry out mining on a commercial scale or frequently engage in
trading of crypto asset s acquired as mining rewards instead of holding and accumulating them, they
are clas sified as commercial miners and the profits from such activities are taxed as business
income/ commercial profits, after deducting allowable expenses.
In other cases, the disposal of crypto asset s acquired as mining rewards is considered to give rise to
capital gains which are taxed at the time of ‘disposal’ of the crypto asset s. In some jurisdictions which
do not have a capital gains tax, such disposals may not result in a capital gains tax liability. However,
for calculating capital gains, issues related to determination of the basis, as discussed earlier , might
arise, and may require clear guidance by the tax administrations. Also , unlike crypto asset s acquired
as mining rewards which can be clearly ascertained on the blockchain through the Coinbase
transactions, the accrual of capital gains cannot be easily ascertained through blockchain analysis
alone as many of the apparent ‘disposals’ of the crypto asset s acquired as mining rewards might either
be payouts by the mining pool to the ‘worker ’ miners or transfers to other addresses controlled by the
same taxable entity/person , resulting in no tax liability for the transaction.
2.3.8. 2 Indirect Taxes on Mining
As described above, Bitcoin mining activity involves various actors like:
i) Bitcoin users who broadcast transactions
ii) Individual miners who combine the transactions in the mempool into a block and mine
the blocks to include in the Bitcoin blockchain.
iii) Mining pools which create blocks of transactions and divide the computation task
between various ‘worker’ miners
iv) ‘Worker’ miners which try to find the nonce for a block given by a mining pool and get
paid based on ‘shares’ sent to the mining pool
The Bitcoin network and the users provide economic incentive to the miners to perform mining and
record their transactions on the blockchain . These activities are akin to providing a digital service to
the users who transmit transactions and pay the transaction fee and the Bitcoin protocol which
provides the block reward in lieu of this service. However, the high degree of uncertainty associat ed
with mining rewards leads to many miners joining mining pools as ‘worker’ miners , this complicates
the indirect t ax treatment of the services provided. This gives rise to two category of service providers
namely:
44 https://explorer.btc.com/btc/blocks
45 https://etherscan.io/chart/blockreward
46 https://coinmarketcap.com/currencies/bitcoin/
47 https://coinmarketcap.com/currencies/ethereum/
32
A) Individual miners and mining pools which create blocks of transactions broadcasted by the
users of Bitcoin network and add new blocks to the blockchain through mining.
B) ‘Worker’ miners which get a pre formed block from a mining pool, which contains the payout
address of the mining pool in the Coinbase transaction. They provide the mining pool with
‘shares’ and get rewarded for the same.
There are also the mining pools which provi de services of reducing the variance of payout rewards of
‘worker’ miners which join the mining pools instead of mining Bitcoin as individual miners. These
service providers in the Bitcoin ecosystem are depicted in Fig. 20
Fig. 20 Mining service s by various service providers
The tax treatment of the services offered by these two categories of miners are based on various
questions directly related to the service offered by the two categories of miners to Bitcoin users (which
is rewarded in the form of transaction fee ) and the Bitcoin protocol (which is rewarded in the form of
new Bitcoins issued through the Coinbase transaction ). Some of the important questions that are
critical in determining the indirect tax treatment of mining activities are:
i) Does there exist a direct nexus or a legal synallagmatic relationship between the user
broadcasting a transaction and the miner adding the transaction on the blockchain?
ii) Does the service offered by miners to the blockchain network , for which they are
rewarded with the block reward , constitute a taxable service for a consideration?
iii) Does the service provided by the ‘worker’ miners to the mining pool have a bearing on
the nature of service provided and its tax treatment?
iv) What is the place of supply of such services?
These questions have a critical role in determining the levy of indirect taxes on mining activities .
Multiple jurisdictions have different legal provisions for taxability and exemptions to services like
Bitcoin mining , with most jurisdictions treating mining activity as exempt f or indirect tax purposes .
Two important issues related to the questions raised above are those of existence of a
nexus/link/contract between the miner and the users transacting on the blockchain , which also
determines the place of su pply of the services, and if such nexus is established or deemed to exist, the
Bitcoin Owners
Bitcoin Owners
Individual Miner
Bitcoin Network
Computation
Hedging
Mining Pool
Computation
Hedging
Mining Pool
Country A
Country B
Country C
Country D
Country E
Country F
‘Worker’ miner
‘Worker’ miner
33
issue of exemption of services of miners as financial services , which is contingent upon the treatment
of Bitcoin and other crypto asset s as a negotiable instrument or any other financial instrument which
is exempt . However, the broad principles and considerations while determining the levy of GST/VAT
on mining activities remain common and provide the answers to the questions above.
As laws and jurisprudence in this area seem to be most developed in the EU the following discussion
analyses these issues from the perspective of the EU Law, although the broad principles and
considerations would apply to almost all jurisdictions.
The primary consideration for levying an indirect tax on a miner is that he/she should be engaged in
an ‘economic activity.’ The miner should be performing his/her activities for an income that is linked
to its activity. The Article 9 of the EU Directive 2006/112/EC of 28 November 2006 on the common
system of value -added tax defines a ‘taxable person’ and ‘economic activity’ as:
‘Taxable person’ shall mean any person who, independently, carries out in any place any economic
activity, whatever the purpose or results of that activity.
Any activity of producers, traders or persons supplying services, including mining and agricultural
activities and activities of the professions, shall be regarded as economic activity. The exploitation of
tangible or intangible property for the purposes o f obtaining income therefrom on a continuing basis
shall in particular be regarded as an economic activity.
As per the Council Implementing Regulation (EU) No 282/2011
“Electronically supplied services as referred to in Directive 2006/112/EC shall include services which
are delivered over the Internet or an electronic network and the nature of which renders their supply
essentially automated and involving minimal human intervention, and impossible to ensure in the
absence of information technology”
Also, mining activities could also be seen as falling within the definition of an electronically supplied
service as per section 3.1.5 of the Working paper No 854 of the EU VAT Committee which states:
"When the platform is run in an automated manner with minimal human intervention and the
provision of the service is impossible without information technology, then the access to such platform
supplied for consideration should be seen as covered by the def inition of electronically supplied service"
Also, in a recent case before the Dutch Court of The Hague the claimant performed mining activities
and raised a question that does the incentive/remuneration received by her pertain to her mining
activities and would the mining activity be seen as an ‘economic activity’?
The court adjudicated that the transaction fee as well as the block reward were considerations for
validating transactions on the Bitcoin Blockchain and that the fact that the petitioner does not always
receive the transaction fee was irrelevant48. The court compared this to a home buyer reaching out to
multiple realtors to buy a home but choosing only one of them to buy the house and pay commission.
The other realtors would still be considered to be engaged in economic activity with only one being
remunerated.
Further the court argued that validating the transactions was important for blocks to be created and
added to the blockchain and this made the validation, verification and mining of coins inseparably
48 https://www.lexology.com/library/detail.aspx?g=e4cfac02 -9134 -4629 -85cb -cffaa9859dc0
34
intertwined. However, the mining activity was exempted from VAT by the court by virtue of Article
135(d) of the EU VAT Directive.
From a theoretical standpoint also, the presence of incentives to the miners is an existential factor for
crypto assets like Bitcoin , as the fundamental problem in the Bitcoin Blockchain of making multiple
nodes agree on transactions added to the blockchain , when it is known that some of the nodes can be
faulty or malicious, is theoretically impossible to achieve without incentives. This is a widely studied
problem known as the Byzantine Agreement problem in Computer Science, which is named after the
Byzantine Generals’ Problem, in which generals of the Byzantine army must agree on a coordinated
attack or retreat . There are no incentive mechanisms in this problem and it is proven that the problem
cannot be solved if more than 1/3rd of the generals are traitors49. Also, another theoretical result
related to distributed systems - the FLP ( Fischer -Lynch -Paterson ) impossibility theorem states that no
deterministic protocol solves the byzantine agreement problem in the asynchronous model, with even
a single faulty node (Fischer, Lynch, & Paterson, 1985) .
However, in the Bitcoin Blockchain we observe that with the presence of incentives in the form of
block rewards and transaction fees for the miners, the Bitcoin distributed consensus protocol for the
ledger has worked well for years. This makes incentives the cornerstone of Bitcoin as a ne twork and a
crypto asset . It leads to the alignment of the interest of the miners with the holders of Bitcoin
transmitting transactions on the Bitcoin Blockchain to be included in the public ledger. This makes the
activiti es performed by the miner in expectation of incentives an economic activity by design .
As discussed earlier , the probability of an individual miner mining a Bitcoin block is directly
proportional to the fraction of the hash rate of the miner to the total hash rate of all the miners
combined. This results in high variance and uncertainty in rewards received by the solo miners who
deploy resources for mining. To reduce the uncertainty in mining rewards, solo miners often join a
mining pool.
The ‘worker’ miners share hashes with the pool and based on different criteria like the number of
hashes shared below a target range (not the same as the Bitcoin hash target range), or some other
pay-out agreement , pay-outs are made by the mining pool. This provides the ‘worker’ miners frequent
pay-outs depending on the number of ‘shares’ made with the mining pool. The mining pools charge a
fee for this service (usually 1 -3%)50 and deduct the same from the pay -outs given to the ‘worker’
miners.
The mining pool creates a block of transactions and distributes the mining computation between the
various ‘worker’ miners which join the mining pool. The template shared by the mining pool with the
‘worker’ miners contains the transactions picked by the mining pool and the pay -out address in the
Coinbase transaction is that of the mining pool . In case a valid nonce is found by a ‘worker’ miner
which mines the current block, the entire block reward goes to the mining pool and the ‘worker’ miner
cannot earn the full block reward , but is entitled to the reward as per the pay -out scheme agreed
between the ‘worker’ miner and the mining pool.
This makes the operation of the mining pool an ‘economic activity’ which provides a service of hedging
the risks by significantly lowering the variance of reward payments for mining activity. The
consideration received for this service (usually 1 -3%) is in the form of crypto asset s being mined, which
is deducted from the pay -out due to the ‘worker’ miner. This is an important aspect from the
perspective of indirect taxation as the ‘worker miners’ and the mining pools provide two services to
each other in a symbiotic relationship. The mining pools help to reduce the uncertainty and variance
49 https://en.wikipedia.org/wiki/Byzantine_fault
50 https://fastercapital.com/topics/understanding -pool -rewards -and-payouts.html
35
of the mining rewards of the ‘worker miners ,’ who in turn, provide computational power in the form
of billions/ trillions of hashes per second (shares) to the mining pool, which may or may not result in
‘min ing’ a block by the mining pool and can never result in direct block rewards to the ‘worker’ miner .
There are multiple case laws like C -2/95 SDC51 and C -350/10 Nordea52 which provide that for a service
to be VAT exempt , the service must be an exempt service itself . The argument that it is an input service
to an exempt service does not suffice for VAT exemption to a service. Considering the nature of these
services , neither of these services may classify as a financial service which can exempt the service
providers from VAT/GST. However, the service provided by the mining pool or individual miners to the
blockchain and its users might be exempt from the perspective of VAT/GST as financial services ,
depending on the classification of the crypto asset s involved as negotia ble instrument or some other
instrument which qualify the service as financial services in the domestic law of a jurisdiction .
Now that we have a case where VAT/GST might be leviable on the services provided by the mining
pool and the ‘worker’ miners to each other, it is important to determine the place of supply of such
services as it may be zero rated in most jurisdictions , if the services are supplied to a non -resident.
The taxability will also be affected by whether an individual or enterprise is providing and/or availing
the services and whether such entities have obligations for VAT/GST registration . It may also lead to
impli cations for providing input tax credit and refunds for suppliers of zero -rated mining related
services.
This problem is like the problem of taxation of cross -border services, using crypto assets -based
payments. Brondolo, (2021) provides a detailed discussion on potential solutions to this issue . Under
the vendor colle ction model, the mining pools , if they are non -resident suppliers of hedging services
to the ‘worker’ miners , would be required to register for VAT/GST in the jurisdictions of the ‘worker’
miners and charge and collect VAT/GST and pay the collected amounts to the respective jurisdictions.
For exa mple, i n India th ese hedging services would qualify as an Online Information Data Base Access
and Retrieval (OIDAR) service and the mining pool is obligated to pay 18% GST on it , if the service is
availed by an Indian ‘worker’ miner , especially if it is not registered under GST . In case of Business -to-
Business supplies the recipient may be obligated to collect VAT/GST on a reverse charge basis.
Also, the services exported by the mining pool to the worker miner can be zero -rated and entitle the
mining pool for a refund of the input tax credit in its country of registration , with the ‘worker’ miner
paying taxes for import of services in its own jurisdiction .
This might be a humongous compliance burden for mining pools which might be required to collect
the tax residency and VAT/GST information from ‘worker’ miners. However, this may be simplified if
the tax jurisdictions create crypto asset wallets for depositing the aggregated VAT/GST collections
along with filing of returns by the mining pools to the respective jurisdictions . A similar arrangement
may also be required for the computational services of ‘worker’ miners used by the mining pools in
return of pay -out for ‘shares’ by the m. Alternatively, the mining pool may also collec t and pay VAT/GST
net of the fee charged to ‘worker’ miners in its own jurisdiction on a reverse charge basis.
In various tax jurisdictions the transaction fee by the users in lieu of the servic e of including the user’s
transaction in the blockchain through ‘mining’ might also be taxable as a service53. In that case, in line
with the destination principle of VAT/GST the blockchain user/ mining pool ( in a vendor collection
model ) might be liable to pay VAT/GST in the jurisdiction of the user, depending on turnover threshold
51 https://eur -lex.europa.eu/legal -content/EN/ALL/?uri=CELEX%3A61995CJ0002
52
https://curia.europa.eu/juris/document/document.jsf;jsessionid=CD1328EA651A7F81A11CBDAB3C8E3AC9?te
xt=&docid=108324&pageIndex=0&doclang=EN&mode=lst&dir=&occ=first&part=1&cid=1203786
53 The service provided by the mining pool to the users might not be exempt from the perspective of VAT/GST
as financial service, depending on the classification of the Crypto Assets in the domestic law of a jurisdiction
36
and other requirements for VAT/GST collection as per the domestic laws of the user’s jurisdiction.
However, the determination of tax jurisdiction of crypto asset users who transmit transactions on the
blockchain without any information about the tax residency of the use r, which has a bearing on the
place of supply of the service , remains the biggest challenge for this . A more complicated issue will be
the taxation of services provided by the mining pools or individual miners to the blockchain itself , for
which they receive the fixed block reward programmed in the blockchain . The place of supply of such
service cannot be determined and the payment for the service may amount to billions of dollars each
year.
If we consider the scenario depicted in Fig. 20, A mining pool registered in countr y D might be liable
to pay VAT/GST on the services provided to the ‘worker ’ miners of countr y A, B and C as per the
domestic laws of countr ies A, B and C, and might be required to register for VAT/GST in countr ies A, B
and C especially if the ‘worker’ miners are not registered under GST . The GST/VAT amounts would be
paid by the mining pool to the countries A, B and C for providing hedging services to minimize the
variance and uncertainty of block rewards in mining. Also, the individual ‘worker’ miners in the
countries A, B and C would also have an indirect tax liability for provid ing computational services for
Bitcoin mining to the mining pool registered in count ry D. Alternatively, as discussed earlier, the
mining pool registered in country D may also collect and pay VAT/GST net of the fee charged to
‘worker’ miners in countr ies A, B and C, in its own jurisdiction countr y D on a reverse charge basis. The
mining pool might also have a VAT/GST liability for providing transaction validation services to a user
in countr y E, where the final consumption of the service of the ‘worker’ miners in countries A, B and
C takes place , as the Bitcoin user in country E pays a tra nsaction fee to the mining pool for mining
his/her transaction.
The individual miner in countr y F might also be liable to pay VAT/GST in countr y E on the transaction
fee paid by the Bitcoin user if it validates the transaction of the Bitcoin user in country E and includes
it in a block mined by it. Depending upon the turnover thresholds of various jurisdictions, the
individual miner might not be liable to pay VAT/GST on the transaction fee.
Some mining pools like Terra Pool54 implement KYC and provide real -time AML measures . Other top
mining pools also specify in their privacy policy that they collect information like username, email
addresses, wallet addresses, IP addresses, unique device IDs, information related to amount of
computing power provided to the pool, the rewards, and processed payouts. The privacy policy of
many mining pools makes the users aware that their personal data can be used by the mining pool to
comply with domestic and international legal obligations . This can be used by the mining pool s to
determine residency of ‘worker’ miners and enable tax administrations or other law enforcement
agencies to seek information regarding their tax residents using the services of such pools for both
direct and indirect tax purposes. However, the determination of p lace of supply for the ordinary
Bitcoin or other crypto asset users will remain a challenge.
2.3.8. 2.1 Indirect Taxes on Supply of crypto asset s
In various jurisdictions there are specific provisions in the indirect tax law which exempt certain
financial transactions , loans deposits etc. from VAT/GST. For example , in India the Notification No.
12/2017 - Central Tax (Rate) exempts loans, deposits purchase of foreign currency etc. from GST.
However, in many jurisdictions there are no such explicit exemptions for crypto asset s. Thus, it is
possible to interpret the indirect tax law to consider the crypto asset transactions as a taxable supply
and levy GST on such supplies. However, many jurisdictions do not treat crypto asset transactions as
taxable supply , but one such exception is Singapore where prior to 1st January 2020, the supply of
54 https://terrapool.io/
37
virtual currencies (including cryptocurrencies such as Bitcoin) was treated as a taxable supply of
services.
2.3.8. 2.2 Indirect Taxes on use of crypto asset s for payments for goods and service s
In most jurisdictions, the ‘disposal’ of crypto asset s in lieu of any goods or services attracts GST/VAT
like fiat currency and the ‘disposal’ event might also attract income tax or capital gain s liability based
on the domestic provisions for taxation of crypto asset s which are mainly based upon the nature and
scale of engagement of the individual or entity in crypto asset s transactions. Baer et al., (2023)
highlight the profound risks that crypto asset s might pose for collection of VAT/Sales Tax on final sales
of goods and services.
2.3.8. 2.3 Taxing the externalities of mining using the proof -of-work mechanism
As highlighted in earlier sections, crypto asset mining using proof -of-work consensus mechanism is a
resource intensive activity with a huge carbon footprint of both the electricity consumption and the
specialized IT hardware that is used to find nonces to ‘mine’ a block of Bitcoin. This produces
externalities which calls for imposition of taxes like the excise tax which discourage mining or make
up for its associated costs on the environment.
The Biden administration has proposed a 30% excise tax on electricity used for Bitcoin mining. It is
called the Digital Asset Mining Energy (DAME) tax. Similarly other options like a graded tax on
electricity consumption by miners as being used by other jurisdictions like Kazakhstan can be used.
Moreover , measures to incentivise miners to switch from proof -of-work based mining to staking and
forging , which is based on the proof -of-stake protocol (as discussed below) and has a much less carbon
footprint , can be considered. Alternatively, i f the excise taxes are found difficult to administer or
impose, miners can be denied to claim deductions for electricity consumption and depreciation for IT
hardware from their revenues to calculate taxable profits.
2.3.9 Proof -of-Stake and Forging
Proof -of-work and proof -of-stake are two different consensus mechanisms in blockchain technology.
A consensus mechanism is a method for maintaining the integrity of the blockchain in which nodes of
the blockchain network develop a consensus about the blocks and length of a blockchain. The proof -
of-stake consensus mechanism requires much less computation and resources to add blocks to the
blockchain than proof -of-work. In this method, nodes do not compete against each other to mine the
block, and have to sta ke their own minimum amount of crypto asset to be a part of the consensus
mechanism55 Nguyen et al. (2019) . These assets act as a collateral and are locked and cannot be moved
before the locked period. If a node wants to stop being a forger , its staked crypto asset s are released
after a certain period, once it has been verified that it has not been involved in any malicious behaviour
in adding blocks and verifying transactions. By staking crypto asset s in a wallet the validator nodes
make themselves available to propose new blocks and validate the blocks proposed by other forgers .
The proof -of-stake mechanism is depicted in Fig 21.
A node is selected by the blockchain to be the proposer of the next block using a pseudo -random
process . The proposed block is then verified by other nodes and if it is verified by more than 2/3rd of
the nodes, the block is added to the blockchain . The proposer node gets the reward in the form of
55 A detailed analysis of proof -of-stake consensus mechanism has been done by Nguyen et al. (2019)
Nguyen, C. T., Hoang, D. T., Nguyen, D. N., Niyato, D., Nguyen, H. T., & Dutkiewicz, E. (2019). Proof -of-stake
consensus mechanisms for future blockchain networks: fundamentals, applications and opportunities. IEEE
Access , 7, 85727 -85745.
38
transaction fee. The proof -of-stake method usually lead s to generation of new crypto asset s and the
process of adding new blocks to the blockchain is called forging instead of mining.
Fig.21 proof -of-stake consensus mechanism
Crypto assets frequently begin by selling pre -mined coins or they begin with the proof -of-work
consensus mechanism and then move to the proof -of-stake consensus mechanism (like in case of
Ethereum) . Various blockchains use different methods to choose the node for proposing the next
block in proof -of-stake . The two most popular methods are randomized block selection and coin age
selection. In randomized block selection , the node with the lowest hash value and the highest stake is
chosen. As stake sizes are public , the next forger can typical ly be predicted by other nodes . The coin
age selection method selects nodes depending on the length of time that their crypto asset s have
been staked, the age of a coin is determined by multiplying the number of days the coins have been
staked by the number of coins staked. After a forging block a node's currency age is reset to zero and
they must wait a specific amount of time before forging another block , this prevents large stake nodes
from controlling the consensus mechanism .
The proof -of-stake consensus also has a penalty mechanism which discourages forger misbehaviour
which discourages malicious activity and promotes honest nodes. If a node fails to propose a block on
its turn or proposes more than one block then a slashing penalty is imposed on the node by taking
away a portion or entire staked crypto asset . The malicious node can even be banned for the current
epoch or permanently. A part of the slashed funds is usually given to the node which reports the
malicious behaviour and the rest are b urnt56. On one hand this provides the blockchains with the best
and most effective forgers, on the other hand the inability of a node to propose a block due to factors
like server downtime, buggy algorithms and network attacks can penalize forgers heavily .
Although the proof -of-stake consensus described above is broadly the same as one used by the
Ethereum blockchain, however there are marked differences between the two. The proof -of-stake
mechanism currently used by the Ethereum Blockchain i s described in detail in the section on
Ethereum. The taxable events in this consensus mechanisms with the Ethereum Blockchain as the
example are also discussed in the subsequent sections .
56 Crypto Assets are burnt by removing them from circulation permanently by sending them to a special
address, called a burn address, that cannot send or receive any Crypto Assets , making them inaccessible and
effectively destroy ing them.
1. Algorithm assigns a
proposer
2. Selected node
proposes a block
3. Other validators (2/3rd) verify
and approve the block
4. Proposed block is
added to the
Blockchain
39
2.3.10 The Bitcoin Network
The Bitcoin nodes are connected to each other through a decentralized network where there is no
central server. It is essentially a peer -to-peer network which relays and validates Bitcoin transactions
and secures and maintains the Bitcoin Blockchain. Anyon e is free to join the network if they have
access to the Internet. The collection of such peer -to-peer nodes can be called as the Bitcoin network.
All the nodes in the network perform basic functions like discovering peer nodes and maintaining a
connection with them , as well as propagation and validation of transactions and blocks.
The Bitcoin network consists of various types of nodes as listed below:
a) Mining nodes: These nodes run full copy of the blockchain and produce valid blocks for being
added to the blockchain . They get rewards in the form of newly created Bitcoins.
b) Full Nodes: These nodes also maintain a full copy of the blockchain and can independently
verify transactions.
c) Super Nodes: These nodes have many connections with the full nodes and act as redistribution
relays to ensure that every node in the network has the latest copy of the blockchain .
d) Light/Simple Payment Verification (SPV) Nodes: These nodes do not maintain a full copy of
the Bitcoin Blockchain and operate on small devices like smartphones or tablets. They verify
transactions by querying their peers to retrieve the subsets of the blockchain they require to
validate a transaction.
The Bitcoin network scheme is shown in Fig. 22
Fig. 22 The Bitcoin network
When a node broadcasts a transaction to its peers, they further broadcast it and flood the network.
Thus, the propagation of transactions in the network is not instantaneous and every node does not
have the same view of the mempool. When a transaction is b roadcasted over the network, as
discussed earlier , there is no IP address related information associated with the transaction. Thus, it
is not usually feasible to find out the IP address of the node that broadcasted a specific transaction.
This makes it di fficult to attach IP addresses to Bitcoin addresses by listening to transactions on the
Bitcoin network.
Juhász, Stéger, Kondor, & Vattay, (2018) have tried a Bayesian approach to identify Bitcoin users
where they installed more than 100 Bitcoin clients and analysed the propagation of these messages
in the Bitcoin network . They claim to have identified several thousand Bitcoin clients and bind their
SPV Node
Full Node
Miner Node
Full Node
Mining Pool
Worker ASICs
40
transactions to geographical locations. However, such an approach would provide a n estimate which
might not enable a tax administration to assign an IP address and subsequently an IP address
owner/user to a Bitcoin transaction beyond reasonable doubt .
As the Simple Payment Verification /Light nodes depend on other nodes for verification of
transactions, they can be used to identify Bitcoin addresses in their wallet, and enable associating IP
addresses to Bitcoin addresses. To prevent this , the SPV Nodes use bloom filters57. However, Gervais,
Capkun, Karame, & Gruber, (2014) found that SPV clients with bloom filters also leak considerable
information regarding Bitcoin addresses in the ir wallet and make associating IP addresses to Bitcoin
addresses possible .
Another feature of the Bitcoin network is that the participating nodes usually connect to each other
using the port number 833358. This can create a potential problem for the network in case of a
widespread disruption in traffic on this port, which can seriously affect the mining and validation of
blocks and consequently, the price of Bitcoin significantly. However, this issue has been addressed in
Bitcoin Core 23.059 .
2.3.11 Forking
Just like a fork in a road, forking in a blockchain refers to a situation where the blockchain diverges
into two potential paths forward. This can arise due to a temporary lack of consensus in the network
regarding the block(s) to be added to the main blockchain , resulting in a temporary fork. This can also
be due to changes in the software run by various nodes which validate transactions and add blocks to
the blockchain. If the blockchain software undergoes a major change, for example due to a major
security threat or a necessary improvement, and the new blockchain is incompatible with the previous
version, such a change is called a Hard Fork . If the software updates are backward compatible it results
in a Soft Fork .
As the Bitcoin Blockchain is decentralized with each node maintaining its own copy of the blockchain ,
it is possible that at some instance divergence might arise between the nodes about the blockchain .
Temporary forks can emerge in cases like when two blocks are mined within a short interval by two
different miners , and they reach various nodes almost simultaneously . This results in some nodes
adding the first block and some nodes adding the second block to the blockchain , resulting in the
nodes having two different perspectives of the blockchain at that moment . The blockchain eventually
reconverges as more blocks are added to one of the forks. The blocks added to the other fork get
orphaned and are discarded, the transactions in orphaned blocks remain unconfirmed. This is the main
reason why it is recommended to wait for 6 confirmations of the transaction i.e., waiting for six
subsequent blocks to be added to the blockchain after the transaction for confirming the validity of
the transaction. This is graphically depicted in Fig. 2 3
2.3.11.1 Hard Fork
A hard fork occurs in a blockchain when a major change happens in the blockchain software/protocol
and these are incompatible with the previous version of the blockchain . This is a way through which
new features can be incorporated in the blockchain protocol or security loopholes in the software can
be plugged. However, all nodes of the Bitcoin network might not accept the change and upgrade. As
the changes introduced make the nodes that upgrade incompatible with the old protocol, a split
occurs in t he bloc kchain . The blocks created by the nodes running the old version of the protocol
57 https://bitcoinops.org/en/topics/transaction -bloom -filtering/
58 https://bitcoin.org/en/full -node#network -configuration
59 https://github.com/bitcoin/bitcoin/blob/e88a52e9a2fda971d34425bb80e42ad2d6623d68/doc/release -
notes/release -notes -23.0.md#p2p -and-network -changes
41
Fig. 2 3 Temporary Blockchain Fork
would be invalid for the upgraded nodes. As a result, two crypto asset s are created and the owners of
the crypto asset s prior to the point of hard forking get equal number of crypto asset s in the both the
blockchains right after the hard forking.
A prominent example of hard forking is the creation of Bitcoin Cash Blockchain from the Bitcoin
Blockchain . To increase the number of transactions that can be included in a block in Bitcoin , it was
proposed to segregate the signatures from the main transaction data to accommodate more
transactions in the 1MB block size limit. To avoid the update, some Bitcoin developers and users
decided to initiate a hard fork and created the Bitcoin Cash which has a block size of 8MB and does
not segregate signatures. Hard fork s are important events from tax perspective as it results in the
ownership of crypto asset s in both the forks of the blockchain after the fork has taken effect, for those
who owned any Bitcoin amount right before the fork . For example , the hard fork in Bitcoin took effect
from block number 478558 of Bitcoin blockchain . If we look at the address
3FGs7JfaoAZTT6Sda73XrJ6i5Gwsuw9GUC which received 8.0 BTC in a transaction included in the no.
47855760 right before the hard fork happened and query the Blockchain Explorer61 at present , we find
a result as shown in Fig 24.
This shows that this Bitcoin address also became a Bitcoin Cash address and the owner of this address
received 8.0 Bitcoin Cash by virtue of the hard fork. This is analogous to splitting of shares in equity
market and is an important taxable event.
60
https://www.blockchain.com/btc/tx/98ecc4189d3f33eee96afbff948219ba0ba2342c263241602b95437050c11
30b
61 https://www.blockchain.com/search?search=3FGs7JfaoAZTT6Sda73XrJ6i5Gwsuw9GUC
42
Fig. 24 Receipt of Bitcoin Cash by owner of Bitcoin due to a hard fork
2.3.11.2 Soft Fork
Soft Forks are minor changes in the blockchain protocol/software that are backward compatible. Thus,
a new blockchain is not created and the blocks created by nodes running the updated software are
treated as valid by the nodes running the old version of the software. However, the blocks created by
old nodes would be treated as invalid by the nodes running the upgraded software. If a significant
number of nodes upgrade their software, this results in no disruption in the functioning of the
blockchain . Soft forks usually do not have any tax implications unless they trigger a hard fork like in
case of Bitcoin.
43
2.3.11.3 Taxation of forking events
A) Hard Forks: As explained earlier for those who own any crypto asset amount right before the fork ,
hard forks result in the ownership of crypto asset s in both the forks of the blockchain after the
fork has taken effect. This results in acquisition of crypto asset s by the users of one blockchain on
another blockchain without any consideration. The two taxable events that arise out of a hard
fork are:
i) Acquisition of crypto asset s in the other blockchain
ii) Disposal of the crypto asset s acquired as result of the hard fork
The tax treatment of a cquisition of crypto asset s in the other blockchain due to a hard fork differs
across jurisdictions. While some jurisdictions like the US and Japan consider crypto asset s received
because of hard fork s as taxable income , with the value of the newly acquired crypto asset s taken
as the fair market value of the crypto asset s at the time of the hard fork .
Other jurisdictions like the UK, Finland , Sweden etc. do not tax the crypto asset s received because
of hard fork s at the time of their receipt , and their cost of acquisition is taken as zero. However,
upon the disposal of the crypto asset s received because of hard fork s, a capital gain or income tax
may be charged depending upon the local law (presence or absence of capital gain s) with the tax
liability depending upon the basis/cost of acquisition. In Australia, the Australian Tax Office has
issued guidance for hard fork s where individuals/entities running crypto asset businesses need to
apply trading stock tax rules instead of Crypto Tax Rules in hard fork events.
B) Soft Forks: As soft fork s are minor software changes and do not result in a new blockchain , they
are not taxable events and result in no tax liability.
2.3.12 Bitcoin Wallets
Bitcoin Wallets are software that store the address (es) of a Bitcoin user (Fig. 25). A wallet is a software
that keeps a track of the crypto asset s owned by various addresses in the wallet, generates and
manages new addresses for new transactions (as it is recommended to generate new addresses for
every Bitcoin transaction ). The wallet software also provides a consolidated interface enabling the
owner to find the aggregate sum of all UTXOs owned by him/her in the form of a Bitcoin balance.
Recommendations of transaction fees are also provided by wallet software after analysing the
network congestion and transaction fee trends .
Fig. 2 5 A Bitcoin wallet (representation)
There are mainly two types of wallets used by Bitcoin and other crypto asset s. Cold wallet s are the
wallets stored on a paper or some specialized hardware wallets that are not connected to any network
or the Interne t. A collection of Bitcoin addresses as shown in Fig. 7 is known as a paper wallet. There
are multiple hardware wallets like the Trezor hardware wallet and the Ledger Nano hardware wallet.
44
These USB drive like wallets do not communicate with any other device unless physically plugged while
accessing the private keys. It is like the digital signature USB drives which need to be plugged -in to
sign a document.
Hot wallets are connected to the Internet and the blockchain network. They are often in the form of
smartphone apps like the Trust wallet or computer applications and browser extensions like the
Coinbase wallet and Jaxx Liberty wallet respectively. Since, hot wallets are connected to the Internet,
they are usually recommended to be used only for small transactions. For privacy, the wallets create
a new address for every Bitcoin transaction. Many wallets also use a mnemonic seed phrase along
with a password and using this seed key they can generate many public and private key pairs and use
a new key for every transaction . Such wallets are known as Hierarchical Deterministic wallets. One
such wallet is shown in Fig. 2 6.
The practice of generating a new Bitcoin address for every transaction also protects Bitcoin users from
risk of quantum computer enabled attacks. As explained earlier, while broadcasting a signed
transaction, a user Alice needs to send her public key along with the signature. Thus, even if a quantum
computer enabled attacker Qua finds Alice’s private key using Shor’s algorithm, he can derive no
benefit , as Alice’s address would have no UTXOs to be spent once the transaction is recorded on the
blockchain .
Such wallets make it difficult to track the transactions on the blockchain , as a wallet can have
numerous Bitcoin addresses and it can be difficult to associate them to a single user by analysing
transactions on the blockchain . Also, the Bitcoin balance shown by the wallet on the dashboard is an
aggregate of all the UTXOs that can be spent by all the Bitcoin addresses in the wallet. Thus , there is
nothing like One Bitcoin which resides in a wallet , a balance of 1.2 BTC on the wallet means that the
Bitcoin Blockchain agrees that the Bitcoin addresses in the wallet have ownership of UTXOs which
aggregate to 1.2 BTC.
Fig. 2 6. A Hierarchical Deterministic wallet that generates a new address for every transaction.
These wallet services generate and manage addresses using ECDSA , as described above and provide
services like creating transactions with optimum number and amounts of UTXOs, signing them and
broadcasting on the Bitcoin network. Most wallets do not charge any fees for it. However, if the wallet
45
charges a fee, it might be subject to service tax/GST in various jurisdictions. In many jurisdictions the
wallet services charging fee might not be exempted from VAT/GST as for a service to be VAT Exempt
the service must itself be an exempt service, and the argument that it is an input service to an exempt
service does not suffice for VAT exemption to a service (C -2/95 SDC62 and C -350/10 Nordea63)
It is also important to note that many such hot and cold wallets do not capture any identity information
regarding the user or carry out any KYC compliance procedures. Anyone with access to the Internet
and a smartphone or PC can create an account or buy a hardware wallet and start transacting in crypto
asset s. This kind of wallets where the keys are owned and directly controlled by the user of the wallet
are known as non -custodial wallets.
3. Crypto Exchanges
The Bitcoin Blockchain allows users to transact 24x7 using wallet software which facilitate signing and
broadcast ing transactions to be included in blocks that get added to the Bitcoin Blockchain. However ,
Bitcoin owners also have two other fundamental requirements of being able to convert fiat currency
into crypto asset s and vice versa, as well as exchanging one crypto asset into another. The crypto
exchanges provide these facilities and many more to the owners of crypto asset s. In this ecosystem
the excha nges can be divided into two categories , centralized and decentralized exchanges. The major
differences between them are:
Centralized Exchanges Decentralized Exchanges
Exchange is the custodian of private key s Owner is the custodian of private key s
KYC is required Usually, No KYC is required
Easy to use Relatively difficult to use
Products like derivatives available Offer only basic products
Higher liquidity Lower liquidity
Can convert fiat currency to Crypto assets Cannot convert fiat to crypto asset s directly
The wallets provided by crypto asset exch anges are known as custodial wallets as the keys contained
the wallet are in the custody of the crypto exchanges. The users access their accounts and authorize
transactions using a PIN or a password which the exchange executes locally or on the blockchain , on
behalf of the user, charging a fee.
Some of the other facilities provided by the exchanges are:
i) Liquidity: Due to the participation of market makers, the crypto exchanges can provide
liquidity to the markets. It enables facilitation of trades as enough number of
buyers/sellers are present at any given point through facilities like margin trading .
ii) Derivatives: Products like Futures and Options based on the underlying crypto asset s are
offered by exchanges.
iii) Passive Income: By providing services like liquidity to exchanges, staking crypto asset s in
proof -of-stake consensus mechanism, loans, and yield farming64, many crypto exchange s
allow users to earn passive income on their crypto asset s. Many centralized and
decentralized exchanges offer such services and in the latter case they form a part of
62 https://eur -lex.europa.eu/legal -content/EN/ALL/?uri=CELEX%3A61995CJ0002
63
https://curia.europa.eu/juris/document/document.jsf;jsessionid=CD1328EA651A7F81A11CBDAB3C8E3AC9?te
xt=&docid=108324&pageIndex=0&doclang=EN&mode=lst&dir=&occ=first&part=1&cid=1203786
64 A form of platform arbitrage where users staking or lending their Crypto Assets move their assets between
platforms that give the highest yield/return.
46
Decentralized Finance (DeFi) which is an emerging area in crypto asset ecosystem and
warrants an in -depth discussion as a part of a separate paper.
Besides centralized and decentralized exchanges there are also some peer -to-peer trading platforms
like LocalBitcoins and Paxful which list various individual suppliers of crypto asset s who provide quotes
and acceptable volumes of trade. The suppliers specify methods like credit card payments, bank
transfers, gift cards etc. for paying for crypto asset s like Tether, Bitcoin or Ethereum. These platforms
provide escrow service which minimizes the counter party risk and the seller transfers the crypto
assets to the buyer once the receipt of the payment by the seller is confirmed.
As the sellers on such platforms are persons who might be neither registered as crypto asset traders
nor regulated, they can be potentially used by taxpayers to evade their due taxes on their crypto asset
transactions. However, to comply to AML/ CFT laws and regulations such services have verification
levels depending on the types of transactions being undertaken by the user. For example ,
LocalBitcoins has 4 verification tiers in which the most basic level captures information regarding Full
Name, Country of Residence, Email address and Phone number65 of the user . This can enable tax
administrations and other law enforcement agencies to gather information regarding such
transactions in their jurisdiction.
Many centralized crypto exchanges execute trades based on order matching and get their liquidity
from market makers and margin trading facility. As the transaction costs of recording the transactions
on the blockchain are high, the centralized exchanges execute transactions by passing accounting
entries in their own accounts of crypto asset s or accounts of other users maintained with them. This
off-chain nature of transactions enables faster processing times and greater scalability compared to
on-chain tran sactions, which must be validated and confirmed by the blockchain network. For
example , if Alice buys 0.1 BTC from a centralized exchange, the exchange might debit its own BTC
account by 0.1 and credit 0.1 BTC to Alice’s account in the form of an accounting entry . The exchange
may also match the sell order of Bob against the buy order of Alice and debit Bob’s BTC account by
0.1 BTC and credit 0.1 BTC to Alice’s account. The exchange would also credit the fiat currency value
of 0.1 BTC to Bob’s fiat currency funds and debit the equivalent amount from Alice’s fiat currency
funds kept with the exchange.
Usually, the transactions undertaken on a centralized exchange cannot be traced on the blockchain
except when the funds are transferred in the form of crypto asset s from one exchange to another by
the user, or to/from any other non -custodial wallet addresses outside the exchange. Similarly, some
exchanges may utilize on -chain settlement mechanisms for certain types of trades or transactions to
provide greater transp arency and security. However, most trading activities on centralized exchanges
are conducted off -chain to optimize performance and scalability, albeit at the expense of
decentralization and trustless nature inherent to blockchain technology.
The management of user keys also differs between centralized exchanges and decentralized
exchanges . In centralized exchanges, users typically do not have direct control over their private keys,
which are used to sign transactions and authorize the transfer of funds. Instead, users rely on the
exchange to manage their keys on their behalf, with the exch ange holding custody of the keys
associated with user accounts. This centralized custody model introduces counterparty risk, as users
must trust the exchange to safeguard their assets and manage their keys securely. However, some
exchanges offer additional security features such as two -factor authentication and cold storage to
mitigate this risk.
65 https://localbitcoins.com/guides/verification -guide
47
The exchange charges a service fee for this transaction and is liable to pay VAT/ Service Tax/GST on it.
It is also the case that the service fee can be paid in the form of another crypto asset (which is native
to the exchange or freely tradable) at a discount. This makes the transaction equivalent of buying a
service in exchange of crypto asset s and may be taxed accordingly .
4. Source of Value of crypto asset s and Bootstrapping
There are multiple examples of assets which have a value without an underlying asset. For example ,
the valuation of shares of many start -ups are based on the perception of the company in the minds of
the shareholders and the expectation of higher profits in the future. Tulip Mania, the Dutch
speculative bubble in the 17th century which led to prices of some Tulip bulbs reaching unprecedented
levels, is another such case where the Tulip bulbs did not have any inherent value or utility , but derived
their value from the value buyers ascribed to them .
Bitcoin and other crypto asset s have some characteristics of other conventional assets which ascribe
value to them. Scarcity is one feature which makes it rare, just as Gold and Diamond derive their value
from scarcity, the fact that only 21 million Bitcoins can ever be issued, makes i t scarce. There is also
some evidence to suggest that the lack of centralized control over Bitcoin makes it trustworthy and a
hedge against inflation , as no central bank can ever impose an ‘inflation tax’ on Bitcoin owners.
Various instances have come to light where citizens of a country who have reduced faith in their
Central Banks use Bitcoin as a safe store of value66. Also, t he secure nature of immutable blockchain
which is almost impossible to modify along with cryptographic safeguards guarantee the owners of
Bitcoin the ownership and the right to transfer the assets securely. If the users believe d that an
attacker can maliciously steal Bitcoins from them, Bitcoin would not have much value as an asset. The
fact that the users can transfer a part or whole of their Bitcoins to another user who is willing to
transact, irrespective of his /her location establishes the acceptance and portability of Bitcoin.
This trust in the value of Bitcoin also has positive feedback on the nodes which add blocks to the
blockchain (miners). It creates an incentive for them to secure the blockchain and maintain its
integrity . The incentive mechanism promotes honest behaviour by nodes and miners which maintains
the integrity and trust in the Bitcoin Blockchain which has not suffered outages. As seen earlier, if the
number of miners decreases resulting in lower hash rate, the mining difficulty decreases resulting in
an increase in the number of miners. The value ascribed to Bitcoin is a complex interplay between the
factors depicted in Fi g. 27
Fig. 27 Source of Bitcoin Value
66 https://www.mariblock.com/africans -should -embrace -stablecoins -safeguard -savings -against -inflation -
currency -devaluation/ Future
Value of
Bitcoin
Number of
Mining
NodesSecurity of
Bitcoin
Blockchain
48
In the above figure, the perceived future value of Bitcoin is an incentive for a sizeable number of nodes
to validate the transactions and mine blocks, which enhances or maintains the security of the
blockchain and thus, in turn keeps Bitcoin valuable.
Other crypto asset s like Ethereum, Tether and Solana also derive their value from properties and
mechanisms of Bitcoin with a few modifications. Ethereum with is smart -contract67 ecosystem
provides a wide platform on top which various services like lending and borrowing can be offered.
Another token, Solana claims highest transaction speed of 65,000 transactions per second with
average transaction fees of $0.0002568. It is also growing its stack of services which can offered on top
of Solana Blockchain. Such USPs and Network effects also contribute to the value of crypto asset s.
The security provided by high hash rates and large number of miners can explain the value ascribed
to crypto asset s in stable blockchain s like Bitcoin. However, to start and establish a blockchain or a
crypto asset on top of an existing blockchain like Ethereum, the developers need to bootstrap it to
create a virtuous cycle and enhance its pros pects and investment. This is done through various
incentives like higher initial block rewards , initial coin offering s and airdrop s along with aggressive
marketing . In case of Bitcoin when Satoshi Nakamoto mined the first block of Bitcoin the Bitcoin
Blockchain was not secured by many nodes producing trillions of hashes per second. To incentivise
more nodes and miners to join the Bitcoin network , the block reward was 50 BTC per block. The idea
of a new decentralized system of exchanging value in a trustless manner attracted users and miners
who also believed that the price of Bitcoin would increase once it gains wider acceptance, which was
indeed the case. This system of incentives has also resulted in honest behaviour being the dominant
strategy for miners and users if bitcoin mining is considered a repeated zero -sum game.
5. Initial Coin Offerings
Many blockchain developers undertake projects to launch their own blockchain or another token on
top of an existing blockchain like Ethereum. This is somewhat like a start -up launching its product. For
this purpose, developers need funds for development as well as a user base for the token or blockchain
who participate in the network and contribute in terms of nodes, value in tokens and transactions
over the blockch ain. Thus, many developers come out with initial coin offering and sell /promise to sell
the new tokens/ crypto asset s to investors at a price, just like in the IPO of a c ompany , to raise capital
for the project . To enable investors/buyers to understand the proposed token/blockchain the
developers usually come out with a whitepaper which contains:
- The long terms goals of the developers for the token/blockchain
- The detailed architecture and code of the token/blockchain
- Plan for marketing and advertisement
- The USP of the token/blockchain
- Details like developer fee, the initial distribution of assets and rights and permissions on the
blockchain /token for user and developers
- Details of reward and incentive mechanism in mining and transactions, if any
This makes the participants and investors stakeholders in the success of the project and spreads the
risk involved in launching a new token/blockchain. However, ICOs are not regulated in many
jurisdictions or their regulation is not clear. This results in investors not having rights and safeguards
similar to the equity market s. As many crypto asset s are very similar to securities in nature there is a
possibility of intervention of securities regulators which aim at enforcing investor protection measures
67 A self-executing contract with the terms of the agreement between buyer and seller being directly written
into lines of code
68 Digital Assets Primer: Only the first inning - Bank of America
49
similar to securities market s. There are numerous examples of various successful ICOs. Ethereum was
funded through an ICO in 2014 where buyers exchanged Bitcoin for Ether. In the Ethereum ICO some
amount was set aside for the developers and the Ethereum Foundation. Nowadays Ethereum through
its smart contract execution enables the creation o f new tokens and platforms which are interoperable
on the Ethereum Blockchain.
ICOs are important taxable events as the fair market value of new crypto asset s at the time of their
acquisition would be the basis for calculating the capital gain s on the ‘disposal’ of such assets in lieu
of fiat currency or another crypto asset (subject to domestic tax laws). Also, as most crypto asset s are
highly centralized in the initial phases and its only after the crypto asset gains some traction that their
control is transferred to a completely decentralized organization, such crypto asset s may be classified
as securities and subject to taxes due on securities and regulations of the securities regulator.
6. Airdrops
Another method used by developers of new tokens or blockchain s is to give out crypto tokens for free
to incentivise its usage and enhance market presence and exposure . These free tokens are known as
airdrop s. They are also a kind of reward for the early adopters of the new crypto asset . For example,
Uniswap, a decentralized exchange gave 400 Uniswap token to all the Ethereum accounts that had
interacted with its smart contract prior to September 1, 2020 . This was aimed at rewarding users for
using the Uniswap exchange and make them stakeholders in the success of Uniswap .
To receive airdrop s users are required to perform actions like using the platform or service, signing up
for newsletters and mailing lists, hold tokens for a specified period , contribute to the development of
the project etc. Non-Fungible Tokens or NFTs can also be airdropped for using an NFT platform or
buying/sell ing NFTs.
Airdrops are also important taxable events and in most jurisdictions an income tax is chargeable upon
the receipt of an airdrop . The receipt of an airdrop is not taxed but is considered acquisition of a crypto
asset with zero basis in some jurisdictions . Also , the subsequent sale, swap, spend or gift transactions
are also subject to capital gain s. In some jurisdictions, depending upon the domestic tax laws and
guidance, gifts might not be taxed.
7. Ethereum
Bitcoin is mainly used to transfer value from one address to another in a secure and efficient manner.
However, it does not offer many opportunities to build applications on top of the Bitcoin Blockchain
as the Bitcoin script is not Turing complete69 and does not allow user defined logic and customized
functions to execute in transactions. As seen earlier, Bitcoin transactions also require the entire UTXO
amount to be spent and do not provide control over the amount that can be withdrawn from a UTXO .
Ethereum is different from Bitcoin as it provides above and beyond the capacity to transfer value from
one address to another. Ethereum is mistakenly considered a crypto asset whereas it is a decentralized
platform that is designed to run smart contract s, with Ether as its native asset which gives basis to i ts
value.
As Ethereum’s language is Turing complete, it can act like a giant decentralized general -purpose
computer which is censorship resistant and minimizes third party risks in transactions. To understand
Ethereum it is important to imagine a blockchain as a state -machine where transactions change the
state of the blockchain . A blockchain is essentially a "cryptographically secure transactional singleton
69 In Computer Science a Turing Complete system is one that can mimic a Turing Machine. A Turing Machine is
a theoretical machine with a memory tape of infinite length which can calculate or compute anything for
which an algorithm exists.
50
machine with shared -state."70. This is depicted pictorially in Fig. 28 71 . The consensus mechanism
essentially aims to make all the nodes /validators of the Ethereum network agree on the current state
of the blockchain .
Fig. 28 Blockchain as a State Machine72
Ethereum is an account based blockchain unlike Bitcoin which is UTXO based. For a Bitcoin user the
‘Balance’ of his/her account is the sum of all the UTXOs for which he/she owns the private keys. In
Ethereum, the accounts keep a track of the balance automatically. There are two types of accounts in
Ethereum a) Externally Owned Accounts which have private keys and no code associated with them
and b) Contract Accounts which do not have private keys and have code associated with them . Both
these types of accounts have a Balance field associated with them , which shows the amount of Ether
owned by these accounts. The state of the Ethereum Blockchain consists of the balance, data, code,
and all other fields of all the accounts. The fields in a transaction on the Ethereum Blockchain are given
in Table 4.
A contract account contains the code of the smart contract and it is controlled by the smart contract
code. In an Ethereum transaction when the destination address is a contract address , the smart
contract code is executed on the Ethereum Virtual Machin e. The smart contract function specified in
the data payload is called, if no function is specified, a fallback function is called.
From Address of externally owned account initiating the transaction
To The address of the receiver. 0x00000 in case of creation of a smart contract
Value Amount in Wei (10-18Ether)
Data Bytecode sent as input for contract creation or execution
Gas Limit The maximum amount of gas user is willing to pay for the transaction
Gas Price The amount of Ether the user is willing to pay for each unit of gas. In gwei(=10-9 Ether)
Nonce Sequence number of transaction for the EOA
Signature Proves the origin of the transaction, not required in transactions where one contract
invokes a function in another contract.
Table. 4 fields in a transaction on the Ethereum Blockchain
An Ethereum block consist s of transactions which i) change the balance associated with EOA and smart
contract accounts ii) change data stored in a smart contract or iii) create, destroy (deprecated) or
change the code of a smart contract. Each transaction affects the data stored in contracts in every
single full node on the Ethereum network. Thus, Ethereum can be imagined as a giant decentralized
computer where users can run their code and store data after paying the fee for the transaction.
However, these computations are mainly use d for basic functions like changing the balance and
70 https://docs.ethhub.io/ethereum -roadmap/ethereum -2.0/stateless -clients/
71https://www.preethikasireddy.com/post/how -does -ethereum -work -anyway
72Source: https://uploads -ssl.webflow.com/5ddd80927946cdaa0e71d607/5ddd80927946cdd1dd71d6f1_how -
does -ethereum -work -anyway -2.png
51
ownership of tokens held by the smart contract and validating signatures instead of storing large files
or executing complex programs .
Ethereum also has a much smaller block time of 12 seconds as opposed to ~10 Minutes in Bitcoin. It
also has much lower fees as compared to the Bitcoin Blockchain . The fee to be paid for any transaction
in Ethereum is called gas. To complete a transaction, the user needs to provide appropriate amount
of gas depending on the size and nature of the transaction. Simple transactions to transfer Ether from
one account to another do not cost as much gas as breeding CryptoKitties73 using a smart contract .
The user must provide adequate gas for the entire transaction to go through, if the entire gas is not
used for the transaction, the excess gas is refunded. However, if the gas amount is not enough for the
entire transaction to take place, the entire gas amount is consumed and forfeited.
Gas also protects the Ethereum Blockchain from infinite loops as even if a malicious user tries to
execute an infinite loop on the Ethereum EVM it would stop after the transaction runs out of gas. Any
contract execution in Ethereum must be triggered by an externally owned account. The triggered
transaction can in -turn trigger further transactions on other contract accounts . The triggered
transactions are executed atomically, i.e., either the full transaction involving all the intended smart
contract s is exe cuted or all the changes made by the incomplete transaction are reversed. Ethereum
also uses digital signature and hash functions like Bitcoin. However, Ethereum uses the KECCAK -256
algorithm and an Ethereum address is a 42 -character hexadecimal address derived from the last 20
bytes of the public key . The signed transactions are sent to a mempool, thereafter the nodes verify
and propagate the transactions using flooding.
7.1 Proof -of-Stake based consensus in Ethereum
Ethereum initially started as a blockchain based on the proof -of-work consensus mechanism but in
order to reduce the negative externalities of proof -of-work , it switched to a proof -of-stake based
consensus mechanism similar to one descri bed in the section 2.3.9 . The Ethereum proof -of-stake
mechanism is based on each validator staking 32 ETH to be a part of the set of validators which
propose and attest blocks. Besides this the validators have various other duties assigned to them, for
which they get paid in the form of rewards at regular intervals . Any malicious behaviour by a validator
is penal ized through slashing – a mechanism in which the staked Ether is slashed for violating the
proof -of-stake rules , it also result s in removal of the validator from the network.
The validators deposit 32 ETH in a smart contract on Ethereum and must wait for certain period in the
activation queue before they can take part in proposing and validating blocks. The 32 ETH deposit is
made to a smart contract that keeps track of all the staking validators. The validators specify a
withdrawal address in the smart contract to which the payouts are made , as shown in Fig . 29 The
depositor send s 32 ETH along with its public address and a withdrawal address to the smart contract.
It is important to note that the staked ETH becomes a part of the consensus layer and is accounted for
separately from Ether in execution layer Ethereum accounts and contracts. No Ethereum execution
layer transactions can be run on staked ETH and no transfers can be made between validator accounts.
The validators can top -up their ETH balance on the smart contract later if it goes below 32 ETH. The
validators are rewarded for proposing as well as validating proposed blocks with a part of the user fee
and newly issued ETH by the Ethereum consensus laye r known as ‘Issuance.’ The validators are also
penalized if they do not complete their assigned duties of block proposal and validation. However,
these penalties are much less severe than the slashing penalties which are levied for malicious
behaviour. The base fee in each block is burnt in accordance with EIP 155974 which may be smaller or
larger than Issuance for the block.
73 https://www.cryptokitties.co/about
74 https://eips.ethereum.org/EIPS/eip -1559
52
Fig. 29 Ethereum deposit smart contract
Validators are paid rewards at regular intervals of time . At any point i f a validator is found violating
proof -of-stake rules , its deposit is slashed and it exits the system. The remaining deposit of a slashed
validator can only be withdrawn after certain time has elapsed. If a validator voluntarily decides to
withdraw its stake and cease to be a part of the Ethereum consensus mechanism , it can initiate a
withdrawal and if no breaches of the proof -of-stake rules are found , the entire staked ETH is returned
to the validator which can be withdrawn after a certain interval . No gas is required for this transaction.
The flowchart depicting deposits and withdrawals in the Ethereum proof -of-stake smart contract is
given in Fig. 30 75 The system of entry and exit of validators is not instantaneous to maintain a stable
pool of validators over a certain period .
The proof -of-stake based consensus mechanism in Ethereum is like a voting -based mechanism in
which the validators carry out the functions proposing the next blocks of the blockchain as well as
attesting the blocks proposed by others. The attestation of a b lock is essentially a vote on the validity
of the block and its inclusion in the blockchain. The validators maintain the integrity of the blockchain
by honestly carrying out the duties assigned to them, with the other validators keeping an eye over
their c onduct. The mechanism is designed such that it incentivises honest behaviour by the validators
and places heavy penalties on dishonest behaviour by the validators.
In the proof -of-work consensus mechanism, the high capital required for establishing a mining farm
along with its high running costs creates barriers to entry in the mining ecosystem and plays a major
role in incentivising honest behaviour by the validator s. Similarly, in the proof -of-stake based
consensus mechanism of Ethereum, staking of 32 ETH by each validator creates a high capital
requirement. Ethereum tries to achieve consensus amongst the set of validators through a
combination of two different cons ensus mechanisms – the Latest Message Driven Greedy Heaviest -
Observed Sub -Tree (LMD GHOST) and the Casper Friendly Finality Gadget (Casper FFG). The LMD
GHOST is different from the longest chain rule in the Bitcoin Blockchain and the proof -of-work based
Ethereum as it counts every block in a fork as a vote, even if they are conflict ing blocks and not a part
of the longest chain. This means that the fork with the highest number of validator votes is the one to
which new blocks are added.
75 https://notes.ethereum.org/@hww/lifecycle
53
Fig. 30 D eposits and withdrawals in the Ethereum proof -of-stake smart contract76
7.1.1 Slots and Epochs
Time periods on the Ethereum Blockchain are divided into slots and epochs. One slot which results
into a block proposal and attestations is 12 seconds long and 32 slots together form an ‘epoch’ as
shown in Fig .31. By design, in Ethereum every 12 seconds a random validator is chosen to propose a
block to be added to the Ethereum Blockchain. The block proposed by the validator needs to be
verified by other validators in the validator set through an ‘attestation .’ These validators, other than
the proposer have a responsibility for verifying the proposed block if it is correct. Attestations include
information about the current block the validators are attesting to, as well as the previous block they
are building upon . However, not all the validators are required to attest every block and, in each
epoch, the set of validators is distributed into 32 randomly chosen committees, with each committee
being responsible for one slot which is 12 seconds long.
At the start of each slot, the first validator of the committee proposes a block to be added to the
Ethereum Blockchain. The rest of the members of the committee are supposed to attest the proposed
block. Validators attest to the validity of proposed blocks by signing and broadcasting attestations . A
validator can only attest one block per slot in an epoch and any violation of this results into slashing.
Each proposer validator is required to propose a block in the first 4 seconds of the slot, failing which
the validators in the committee are required to attest the previous block.
76 https://ethos.dev/beacon -chain#
54
Fig. 31 Slots and epoch in Ethereum consensus mechanism77
Considering the current number of validators is >800,000 each committee is likely to consist of >2500 0
validators. It is an uphill task to collect signatures of so many validators for each slot. Consequently,
validators in each committee are further divided into 128 subnets , as shown in Fig. 32, and validators
produce a n aggregate BLS signature in each subnet. Validators in the committee collect all the
individual BLS signatures they receive and aggregate them into a single BLS signature using a process
called "signature aggregation." This aggregation is possible because of the unique additive property
of BLS si gnatures, allowing multiple signatures to be combined into one without losing any security
properties . By collecting attestations from an ample number of validators, the network can finalize
blocks, guaranteeing their inclusion in the Ethereum Blockchain and making them resistant to reversal
unless a substantial portion of the validator set collaborates to do so.
The choice of the block proposal and assignment of validators into committees and subnets is
dependent on an Ethereum Blockchain process that generates a pseudo -random number . The random
number is generated on Ethereum using an algorithm called RANDAO78. The validator selection is fixed
two epochs in advance . This implies that validators know a few minutes in advance about their
upcoming proposer or attestation duties . This process pre determines a small set of 64 validators who
are entrusted to propose blocks for each slot in the two epochs (12 mins 48 seconds) .
Fig. 32 Committees and Subnets in Ethereum consensus mechanism
77 https://ethos.dev/assets/images/posts/beacon -chain/Beacon -Chain -Slots -and-Epochs.png.webp
78 https://ethereum.org/gu/developers/docs/consensus -mechanisms/pos/block -proposal/
55
Besides the LMD GHOST which provides the fork choice rule79, the Ethereum Blockchain also aims to
achieve finality in the blockchain through the Casper FFG which is a Practical Byzantine Fault Tolerance
(PBFT) inspired and improved consensus protocol. Finality refers to the guarantee that a block cannot
be altered or removed from the blockchain without burning at least 33% of the total staked E ther .
Finality in Casper FFG is achieved through “checkpoint” blocks, which are always the first block s in an
epoch.
Validators agree on the state of a block at checkpoint blocks, and if two -thirds of the validators agree,
the block is finalized . It usually takes two epochs for the Ethereum Blockchain to attain finality as by
the end on two epochs >2/3rd of the validators likely vote for two ‘checkpoint’ blocks. If a block is not
able to exceed the two-thirds threshold , finality is not achieved , the fork choice rule would kick -in to
determine which chain to follow and finality will be achieved when the 2/3rd majority is m et. If finality
is not achieved for more than four epochs, ‘inactivity leak’ mechanism gets activated.
The inactivity leak is a feature of the Ethereum proof -of-stake consensus mechanism that is activated
when the network fails to finalize a checkpoint for more than four epochs (25.6 minutes). The inactivity
leak is designed to restore finality in the event of the permanent failure of large numbers of validators.
It does so by gradually reducing the stakes of validators who are not making attestations until the
remaining validators control two -thirds of the stake and can resume finalizing checkpoints. The
inactivity leak also prevents validators from receiving attestation rewards during this period, to
discourage attacks that might deliberately cause the network to lose finality .
7.1.2 Rewards and Penalties
To incentivise the validators, the Ethereum Blockchain offers rewards to the participants in the
network. Also, to penalize behaviour that is detrimental or outrightly malicious , the Ethereum
Blockchain also has a system of penalties and slashing which act as a deterrence against such
behaviour.
7.1.2.1 Rewards
The main rewards which validators receive in the Ethereum ecosystem are the n ewly issued Ether
created by the protocol and the t ransaction fee paid by the users transacting on the Ethereum
Blockchain. The validators are assigned various duties like proposing and attesting blocks in a slot as
well as participation in sync committees and they receive rewards fo r correct and timely performance
of these duties . ‘Correct ’ attestation implies that the attestation of the block by the validator agrees
with the fork choice of the block proposer. This ensures that only participants in the winning fork (in
case of an available fork choice ) receive the reward s. Failure in performing duties in the specified
timeframe results in missed rewards. The block proposers are also rewarded for reporting any
violations of the slashing rules, which may not happen so often. In a block , majority of rewards of the
validators come from attestations. An attestation contains three votes and each vote is eligible for a
reward if it satisfies the conditions given in Table 5:
S. No. Validity Timeliness
1 Correct source Within 5 slots
2 Correct source and target Within 32 slots
3 Correct source, target and head Within 1 slot
Table. 5 Timelines for attestation reward eligibility80
79 The Fork Choice Rule (FCR) is a crucial mechanism in blockchain networks, determining which branch of a
forked chain to accept as the canonical or main chain.
80 https://eth2book.info/capella/part2/incentives/rewards/
56
Even if the above duties are performed in a timely manner by the validator, it may not receive the
rewards due to various extraneous factors like the block proposer missing to propose a block within 4
seconds in a slot, or the block proposer proposing a block on a minority fork which is discarded after
a few blocks. Thus, the rewards accrued to the validator due to randomness in allocation of duties as
well as other extraneous factors stated above have a variance and vary over time. The breakdown of
expecte d rewards for proposers and validators is given in Fig. 33
Fig. 33 B reakdown of expected rewards for proposers and validators81
Sync committees allow light Ethereum clients to keep track of the chain of beacon block headers. Light
clients are nodes that do not download the entire blockchain , but only rely on the block headers and
some cryptographic proofs to verify the state of the blockchain . Sync committees are groups of 512
validators that are randomly selected every 256 epochs (about 27 hours) . They are responsible for
signing the block headers that are the new head of the chain at each slot. The signatures of the sync
committee members are broadcast to the network and are used by light clients to authenticate the
block headers without downloading the full blocks. Validators are rewarded for participating in sync
committees.
The maximum amount of newly issued Ether per year – Annual issuance, is proportional to the square
root of the number of validators in the network. However, the annual returns of the validators are
inversely proportional to the square root of the number of validators. This results in a design like the
proof -of-work based consensus where the difficulty level of the target hash is related to the overall
hashing power of the network. Similarly, in Ethereum as the number of validators increases the annual
retur n on staked Ether goes down and vice versa, resulting in an optimal number of validators being
present in the Ethereum c onsensus protocol. Mathematically
𝐴𝑛𝑛𝑢𝑎𝑙 𝐼𝑠𝑠𝑢𝑎𝑛𝑐𝑒 ∝ √𝑁
𝐴𝑛𝑛𝑢𝑎𝑙 𝑅𝑒𝑡𝑢𝑟𝑛 ∝ 1
√𝑁
Here 𝑁 is the number of validator s on the Ethereum consensus layer . This mechanism helps the
consensus layer in reaching the equilibrium number of validators at a given time.
81 https://eth2book.info/capella/part2/incentives/rewards/
57
7.1.2.2 Penalties and Slashing
To create negative incentives for validators who fail to contribute in a desired manner to the Ethereum
Blockchain , penalties are levied. The validators are penalized for incorrect, late, or missing
attestations (votes) . The validators are penalized for incorrect , late, or missing source82 and target
votes83 but there is no penalty for a missed head vote84. Besides this, validators who fail to participate
in sync committees receive a penalty equal to the reward they would have earned had they
participated in the committee correctly. These penalties act as ‘sticks’ in the consensus protocol to
motivate the validators to perform their duties diligently. However, these are not penalties for
malicious behaviour or potential attacks on the protocol.
The mechanism to deal with m alicious activity on the Ethereum consensus layer is Slashing . Upon
detection of violation of rules or any dishonest behaviour by a validator the Stake of such validator is
slashed and it is removed from the network. Slashing protects the protocol against any attacks . For
example , it prevents a validator from voting for two blocks in the same slot. Also, the incentive to the
block proposers for reporting any violations of slashing rules acts as a protection against such
malicious behaviour.
The penalties and slashing mechanisms in the Ethereum consensus layer have important tax
implications which are related to the admissibility of the such penalties and slashing to be allowed as
an admissible expense . While penalties can be allowed , as they might be caused due to bona fide
reasons , the stake of a validator slashed due to malicious behaviour may not be allowed as an expense
for the purpose of taxation.
7.1.3 Staking Pools
When an individual/entity runs an Ethereum validator node on its own and makes the entire 32 ETH
deposit it is said to be involved in ‘Solo’ staking . Solo staking also involves a reasonable degree of
technical knowledge and the validators retain the full control of the keys of their deposited Ether. This
form of staking also involves incurring the hardware and operational costs only, without paying any
service fee to staking pool s, resulting in higher profits. It also helps to prevent the accumulation of
majority stake with one central entity on the consensus layer .
For the validators who have access to the required capital but do not possess the required knowhow
or do not want to run a validator node , usuall y delegate the technically difficult tasks to a service
provider for a fee. The validators retain the control of the keys of their deposited Ether and do not
need to purchase any hardware or software to run validator nodes. However, this method of staking
might involve increased third party risks due to potential downtime or bugs with the software of the
service provider.
82 Source vote: This is a vote for the lower checkpoint of a link between two checkpoints from different
heights. A checkpoint is a block that is divisible by 50, and a link is a connection between two checkpoints that
represents a validator’s attestation. A source vote is used to determine the justified and finalized checkpoints,
which are the blocks that have received enough votes from validators to be considered final and irreversible
83 Target vote: This is a vote for the higher checkpoint of a link between two checkpoints from different
heights. A target vote is used to measure the participation rate of validators and to reward them for voting on
the correct chain. A target vote also con tributes to the finality of checkpoints, as a checkpoint can only be
finalized if its previous checkpoint is justified
84 Head vote: This is a vote for the most recent block that the validator sees as the head of the chain. A head
vote is used to implement the LMD -GHOST fork choice rule, which selects the chain with the most votes from
validators as the canonical chain. A hea d vote also helps to prevent stale blocks from being proposed and
included in the chain
58
The 32 ETHs required to be deposited for being a validator on the Ethereum consensus layer is a
significant barrier to entry for individuals/entities which want to participate in the consensus
mechanism and earn rewards. Those inclined to participate in staking who do not possess the required
capital or those who want to earn rewards over and above the staking rewards collaborate and stake
Ether through a staking pool . Various stake rs who possess smaller amounts of ETH collaborate and
pool their assets to p articipate in staking on the Ethereum consensus layer and earn rewards.
In many staking pool s the depositors are issued tokens that represent a claim on the staked ETH
amount and its associated rewards. For example , on depositing ETH on a popular staking platform
LIDO, the depositor gets and equivalent number of stETH tokens which are pegged 1:1 with Ether and
can be traded or used like any other ERC token on Ethereum to earn additional income, over and
above the staking rewards by transacting on various DeFi platforms as shown in Fig . 34 Consequently,
more than 3 0% of the ETH currently deposited on the Ethereum consensus layer Deposit Smart
Contract is staked through LIDO85. The stakers on such platforms have a choice to run a pool node and
get additional rewards for it. The stakers pay a commission /fee (For example LIDO charges 10%) which
is split between the pool node operators and the Decentralized Autonomous Organization (DAO) of
LIDO .
Fig. 34 Staking pool token issuance and trading
7.1.4 Taxation of Proof -of-Stake based consensus in Ethereum
The activities of the validators in the proof -of-stake based consensus in Ethereum and the rewards
received by them can have direct and indirect tax implication s as they result in accrual of income and
involve providing certain services to the users of the Ethereum Blockchain. The main issues concerning
the direct tax treatment of rewards accrued by staking revolve around the treatment of the reward as
an active or passive income for the validato r. For the levy of indirect taxes, the classification of nature
of service provided by the validator , the classification of Ethereum as a negotiable instrument,
property, asset etc. and the tax residency of the validation service recipient for ascertaining the place
of supply would be important to determine the incidence of taxes.
7.1.4.1 Direct Taxes related to Proof -of-Stake based consensus in Ethereum
As discussed above, t he validators in the Ethereum consensus layer receive rewards for block
proposals and attestation along with participation in sync committees and reporting any violation of
the proof -of-stake rules . The rewards received by the validator above 32 ETH does not increase the
weight of the validator in the consensus layer and is withdrawn automatically every few days as
reward payment. The rewards are credited to the validator’s payout address at regular intervals and
as these are initiated at the consensus layer , no gas is required for such payout transactions .
The receipt of a staking reward by a validator would be a taxable event in most tax jurisdictions as it
results in accession of wealth over which the validators have complete dominion. However, their tax
treatment depends largely upon whether a jurisdiction considers the reward as a passive income for
85 https://dune.com/hildobby/eth2 -staking
4 ETH
LIDO Deposit
4 stETH
DeFi
59
the stake deposited in the deposit contract by the validators or as an active self -employment income .
The treatment would also be contingent upon the rewards being accrued because of solo staking or
staking through a staking pool .
To classify the income from staking as active or passive income it is important to understand the
nature of relationship between:
i) The Ethereum consensus layer and the Solo staking validator
ii) The Ethereum consensus layer and the staking pool validator
iii) The staking pool and person/entity contributing ETH to the staking pool and/or running a
pool node.
The solo and staking pool validators are assigned with specific duties and tasks of proposing and
attesting blocks on the blockchain . They agree to the rules of the consensus layer protocol and receive
regular rewards for performance of assigned duties. Moreover, they are also subject to penalties for
downtime and malicious behaviour. Also, the rewards received cannot be considered a compensation
for the use or forbearance of money. Thus, the solo validators can be subject to self -employment taxes
in some jurisdictions and the associated social security con tributions . Some jurisdictions may consider
the fact that as staking involves little or no effort and can be earned without the active involvement
of the person or entity, and tax it as a passive income akin to interes t. The staking pool validators,
being involved in the business of staking would be treated correspondingly.
The taxpayers earning income by contributing ETH to a staking pool cannot be considered to be self -
employed as instead of the contributors , the staking pool /pool node operator is assigned with specific
duties and tasks of proposing and attesting blocks on the blockchain and is also subject to penalties
for downtime and malicious behaviour. The rewards accrue to them on the Ethereum execution layer
through the staking pool instead of the consensus layer . Thus , they may not be subject to self -
employment taxes and the corresponding social security contributions.
The accrual and/or ‘disposal’ of the rewards would be a taxable event in most tax jurisdictions and
attract income tax or a capital gain s tax. If the validators are engaged in the business of providing
validation services on a commercial scale, the income might be taxed as business income. The fair
market value of the rewards at the time of accrual would be the basis for calculation of the capital
gain s tax . In this ecosystem, the staking pool s are also likely to accrue income as an entity, which
usually exists as a Decentralized Autonomous Organization. For example , LIDO which is the leading
staking pool on Ethereum operates as a Decentralized A utonomous Organization. The tax issues
related to such entities are discussed in subsequent sections .
7.1.4.2 Indirect Taxes related to Proof -of-Stake based consensus in Ethereum
The validators, staking pool s, the individuals/entities that contribute to the staking pool s and
Ethereum users involved in providing or receiving services may be subject to indirect taxes depending
upon:
i) The nature of service provided
ii) The place of supply of services
iii) The recipient of the services
The solo validators and staking pool s are involved in providing validation services to the users carrying
out transactions on the Ethereum Blockchain and may be considered to be self -employed. The y
essentially provide two kinds of services, those to the users carrying out transactions on the Ethereum
Blockchain (for which they get rewarded in the form of transaction fee) and to the Ethereum
consensus layer for finalizing blocks (for which they get rewarded in the form of newly issued ETH) .
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For the transaction validation services to the user, the validators might be subject to GST if the
validator is a resident of the jurisdiction same as that of the validator. Services provided by the
validator to users in other jurisdictions would constitute an export of services and would be zero rated
in most jurisdictions. In case of Ethereum, taxation of services provided by the staking pools or solo
stakers to the Ethereum Blockchain itself, for which they receive the issuance reward programmed in
the Ethereum Blockchain , would be a complicated issue . In this case also, t he place of supply of such
service cannot be determined and the payment for the service may amount to billions of dollars each
year. Mechanism like those described in section 2.3.8.1 for collecting indirect taxes on miners and
mining pools in Bitcoin can be applied for services of staking pools and solo stakers.
In case of other models of staking like using Staking -as-a-Service the services provided by the staking
service provider might also be subject to GST depending upon the place of supply and the residency
of the staking service provider and the individual/entity staking the ETH. In the case of staking pool s,
the commission charged by the pools would also be subject to GST as it is in lieu of a service provided
to the depositors. As discussed in the case of mining rewards , only the direct validation services
provided to the Ethereum users and the consensus protocol may be considered exempt services as
other services would largely qualify as input to validation services. Similar to the treatment of miners
providing services to users in other jurisdictions, the validators providing zero -rated services might be
entitled to refunds for the input taxes paid for exporting the service.
7.1.4.3 Taxes on MEV rewards on Ethereum
As described above the users broadcast their transactions on the Ethereum Blockchain into a
‘mempool’ and the validators which are supposed to propose blocks in a slot bundle the transactions
into a ‘Block’. As all the mempool transactions are visible to everyone , the validators can also come to
know about potential arbitrage opportunities on various DeFi applications. This can enable the
validators to ‘front run ’ such transactions to extract a profit by altering the order of transactions. This
is known as M aximal Extractable Value on the Ethereum Blockchain and acts as an ‘invisible tax’ on
Ethereum users and lead s to imperfect markets. The role and taxation of MEV Ecosystem on the
Ethereum Blockchain is described subsequent to the section on DeFi as the readers can better
appreciate the mechanism through which value is extracted by validators and other actors by
reordering DeFi transactions.
7.2 Smart Contract s
Smart contracts are agreements written in code which are automatically executed by the Ethereum
Blockchain when certain conditions are met. For example , few individuals might pool Ether in a smart
contract for organizing a fair lottery and making a pay -out to the winner without relying on any central
authority for trust and fairness. Another analogy is that of a vending machine which delivers the
desired product and the residual amount, if any. It follows a specific algorithm for delivering the item
automatically.
These contracts try to eliminate third party risk as once crypto asset s are kept in the custody of the
smart contract, they can only be withdrawn or released when the contract conditions are met. The
smart contracts contain code which has well defined functions which execute the contract. Invocation
of functions can alter th e balance of the contract by depositing or releasing Ether to or from the
contract , change the internal contract state like assign a token like Tether (USDT) from one account to
another and alter the data stored in the contract. Smart contracts are used to perform important
functions in the Ethereum Blockchain like
i) Authenticating the identity of the user invoking the transaction with the smart contract
ii) Store and update the data intended to be stored by users. For example , ownership
information of fungible and non -fungible tokens, maximum supply of a token etc.
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iii) Provide trust as a third party governed solely by terms of an agreement subjected to public
scrutiny by users .
iv) Expose functions to be invoked by other contracts
The transaction that creates a smart contract is special transaction as it has a special destination
address called a zero address . The smart contract s once created cannot be altered ever in the
Ethereum Blockchain. Only if a smart contract has a self -destruction function, it could be destroyed
earlier , leaving a blank Ethereum account and destruction of the storage and state of that smart
contract , but this function has been deprecated in the later releases of Ethereum .
From the discussion above it can be seen that a smart contract is essentially code running on the
Ethereum Virtual Machine which executes when certain conditions are met. There are multiple
viewpoints on the legality and enforceability of a smart contract and many argue that smart contract s
are neither smart nor contracts (Durovic, 2021) . Levi & Lipton (2018) argue that smart contract s can
be categorized into code -only smart contracts and ancillary smart contract s which are essentially
vehicles to effectuate the provisions of the traditional text-based contracts. In commo n law , a contract
is a “ an agreement between private parties creating mutual obligations enforceable by law. The basic
elements required for the agreement to be a legally enforceable contract are: mutual assent, expressed
by a valid offer and acceptance; adequate consideration; capacity; and legality ”86. This definition can
be satisfied by ancillary smart contracts . As agreements need not always be in writing to be enforced87,
code -only smart contracts might also be enforceable in some jurisdictions. Just like a user using the
vending machine sees the price displayed for each item in the display and acts according to the
instructions written on the vending machine, he/she gains certain implied rights and a contract is
formed without any written terms, conditions and obligations.
Levi & Lipton (2018 ) argue that the Uniform Electronic Transactions Act (UETA) in the USA provides
for records created by computer programs, and electronic signatures the same legal effect as written
documents. UETA states that an Electronic Agent is “capable within the parameters of its
programming, of initiating, responding or interacting with other parties or their electronic agents once
it has been activated by a party, without further attention of that party,” . The federal Electronic
Signatures Recordi ng Act (E -Sign Act) in the US provides that a contract or other record relating to a
transaction “may not be denied legal effect, validity, or enforceability solely because its formation,
creation, or delivery involved the action of one or more electronic agents so long as the action of any
such electronic agent is legally attributable to the person to be bound.”88.
Article 9 of the EU Directive on Electronic Commerce 2000 states that “ Member States shall ensure
that their legal system allows contracts to be concluded by electronic means. Member States shall in
particular ensure that the legal requirements applicable to the contractual process neither create
obstacles for the use of elec tronic contracts nor result in such contracts being deprived of legal
effectiveness and validity on account of their having been made by electronic means ”. Sates like
Arizona89 and Nevada90 have amended their UETA to include blockchain and smart contract s which
can have far reaching implications on their legal status and enforceability. However, most of the users
who interact with a smart contract would have no means to verify what the code actually does and
pre-emptively find out the vulnerabilities if any, this may be treated as a case where the user was
unaware of the terms of agreemen t of the contract and the smart contract might not be enforceable.
86 https://www.law.cornell.edu/wex/contract
87 Lumhoo v. Home Depot USA, Inc ., 229 F. Supp. 2d 121, 160 (E.D.N.Y. 2002)
88 15 U.S.C. § 7001(h).
89 https://www.azleg.gov/legtext/53leg/1r/bills/hb2417p.pdf
90 https://www.leg.state.nv.us/nrs/nrs -719.html#NRS719Sec245
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7.2.1 Issue with code -only smart contracts
Code -only smart contracts might also face challenges due to a fundamental issue in Computer Science
called the ‘halting problem .’ The halting problem refers to the challenge of determining whether a
given computer program, when provided with input, will eventually terminate, or run indefinitely. It
is proven to be undecidable, indicating that there is no universal algorithm capable of solving the
halting problem for all combinations of programs and inputs. For code -only smart contracts the
undecidability of the halting problem poses challenges , as the user of the smart contract has no means
to verify the deterministic execution of the smart contract code . If a party engages with a smart
contract without understanding its code or implications, it may call into question whether true
consent was given . The legality and other aspects of smart contract s are an active area of research
and more judicial amendments and court rulings are expected to bring more clarity on the legality and
enforceability of smart contract s. This may also have profound implications for tax treatment of
various transactions involving smart contracts .
To prove that the halting problem is undecidable, we can use a proof by contradiction. We assume
that there exists a Turing machine 𝐻 that can decide the halting problem, and then we derive a
contradiction.
The Halting Problem
Let 𝐻 be a Turing machine that decides the halting problem. This means that for any pair of input
strings 〈𝑀,𝑤〉 , where 𝑀 is a description of a Turing machine and 𝑤 is an input string for 𝑀, 𝐻 halts
and outputs "Accept" if 𝑀 halts on input 𝑤, and it halts and outputs "Reject" otherwise.
Now, we construct a new Turing machine 𝐷 with the following behaviour:
Given an input string 𝑀 (a description of a Turing machine):
1. Run 𝐻 with input 〈𝑀,𝑀〉.
2. If 𝐻 outputs "Accept," 𝐷 enters an infinite loop.
3. If 𝐻 outputs "Reject," 𝐷 halts.
Now, let us consider what happens when we feed 𝐷 its own description 〈𝐷〉:
1. If 𝐷 halts on input 〈𝐷〉, according to its construction, it should enter an infinite loop (step
2).
2. If 𝐷 enters an infinite loop on input 〈𝐷〉, according to its construction, it should halt (step
3).
This creates a contradiction. If 𝐷 halts on input 〈𝐷〉, then it should enter an infinite loop according
to its construction. If 𝐷 enters an infinite loop on input 〈𝐷〉, then it should halt according to its
construction .
Thus, our assumption that there exists a Turing machine 𝐻 that decides the halting problem
leads to a contradiction. Therefore, the halting problem is undecidable.
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7.3 Tokens
The Collins dictionary defines token as a countable noun. It is defined as “ A token is a piece of paper
or card that can be exchanged for goods, either in a particular shop or as part of a special offer ”91. In
the context of a blockchain a token represents something similar. But, instead of a piece of paper or
card , it is an abstraction created on the blockchain , often through smart contract s to represent assets,
equity, collectible, currency etc. It is often confused and used interchangeably with the term coin in
the parlance of crypto asset s. Coins are the native assets of blockchain s whereas tokens are
abstractions created on top of blockchain s which may or may not represent any underlying asset. For
example , Ether and Bitcoin are the native assets of the Ethereum and Bitcoin Blockchain respectively
whereas Tether (USDT) is a token created through a smart contract which regulates its supply and
pegs it to the US Dollar . In order to in teract with the USDT smart contract the user still has to pay gas
in Ether denominated in gwei(10-18 Ether).
Smart contracts enable creation of tokens which may represent a wide variety of tangibles and
intangibles . There can be different types of tokens like:
i) Payment Tokens: These are native assets of the blockchain , also known as coins, which
are used for making payments. For example , Ether, Bitcoin and Monero
ii) Utility Tokens: These tokens provide access to a specific product or service on the
blockchain . For example , the WRX or the WazirX token is used to make payments for
transaction fee on the WazirX crypto exchange and sometimes enables users to get a
discount on the transaction fee .
iii) Security Token: These tokens derive their value from external assets like stocks and bonds.
For example , the tZero token which represents tokenized assets like stocks and bonds.
iv) Governance Token: These tokens provide voting rights to the holders regarding various
proposals for changes in a Decentralized Application or a Decentralized Autonomous
Organization. For example , The Maker token (MKR) allows holders to vote on decisions
regarding the decentralized lending platform Maker.
v) Non -Fungible Tokens: These tokens represent a collectible or something unique which is
not interchangeable with any other token. For example , the Bored Ape Yacht Club92 NFTs
with each ape being unique.
The tokens mentioned in examples above cannot be classified strictly into the categories . There are
tokens which have multiple characteristics and can be categorized into more than one category. For
example , the MKR token is a utility token in the Maker ecosystem as well as its governance token
which can be used for initiating and voting on proposals. It can also be exchanged for other crypto
asset s and purchased on crypto exchanges. This makes the regulation of these tokens complicated . As
security tokens are essentially tokenized assets like securities and bonds, they are regulated by
securities market regulators like the SEC. In some instances, like in case of Ripple ( XRP), the regulators
may treat the issued/minted tokens as securities, giving rise to litigation93.
From the discussion above it can be observed that there are certain tokens which purportedly
represent real underlying assets like real estate, securities, US Dollars and Gold. The transfer of these
assets on the blockchain may not mitigate the counterparty risk in a real -world environment. For
example , a user on Ethereum Blockchain may have no means to authenticate the ownership of a
corporation’s equity for a particular Ethereum address without involving the real -world custodian of
listed securities, posing a counter -party risk to the transaction. On the other hand , ownership of
tokens like WRX, MKR can be cryptographically proven on the blockchain and their transfer on the
blockchain does not involve any counterparty risk.
91 https://www.collinsdictionary.com/dictionary/english/token
92 https://etherscan.io/address/0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d
93 https://www.sec.gov/news/press -release/2020 -338
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7.3.1 ERC-20 Token Standard
The ERC -20 is an Ethereum Blockchain standard for fungible tokens. Many tokens are based on the
ERC-20 standard. An ERC -20 token smart contract should have some mandatory functions like those
for defining the total supply of tokens, transfer of tokens from one account to another, functions
authorizing certain accounts to transfer tokens from authorizing accounts etc . Some examples of
popular ERC -20 tokens are USDT, DAI and MKR which are discussed in detail in later sections . Anyone
can create their own ERC -20 token . However , to make the token popular and widely acceptable ,
bootstrapping through incentives like ICOs and airdrop s is required , which may be taxable events for
the users as well as creators.
The USDT is a so -called stablecoin that is told to be pegged to the US Dollar. It is an asset backed ERC -
20 token which enables the owner to exchange 1 USDT for 1 USD. The total supply of the USDT tokens
on Ethereum is controlled using the USDT smart contract on the Ethereum Blockchain (Fig. 35) .
Whenever a request for exchanging a Dollar with USDT is made, the smart contract issues new USDT
tokens to the Ethereum account with corresponding addition on the asset side. On the other hand,
when a user converts USDT to USD or an equivalent asset, USDT tokens are burnt and equivalent
asset s held are released. As the so -called stablecoin is pegged to USD it can be used to buy or sell
other crypto asset s using a dollar -pegged stablecoin.
Fig. 35 Issuance and burning of token s
7.4 Non -Fungible Tokens
Non -Fungible Tokens represent unique and non -interchangeable assets on a blockchain . The
ownership of an NFT is usually established on the blockchain through a smart contract . The purported
underlying asset in an N FT can be anything ranging from a piece of art, an antique piece, real -estate,
music etc. Some of the famous NFT sales like the NFT of the first tweet by Jack Dorsey and the
“Everydays - The First 5000 Days ” NFT by Beeple created lot of buzz and increased the interest in NFTs.
NFTs are enabling content creators and sellers of unique non -fungible items or assets to connect
directly with the users. NF Ts are an integral component of various metaverse s where a lot of e -
Commerce takes place through NFTs. For example , unique land parcels in metaverse s like
Decentraland are essentially NFTs which can be bought or sold in the metaverse using crypto asset s.
The non-fungible nature of NFTs gives them value , in case of crypto asset s like Ether , every Ether
denomination is same and completely fungible. On the other hand, NFTs like the NFT of the first tweet
by Jack Dorsey are unique .
ERC-721 is the token standard for Non -Fungible Tokens on Ethereum. Although, there are other
blockchain s like Solana which also have a rapidly developing NFT and smart contract ecosystem. In an
ERC-721 smart contract the unique NFTs are assigned Token IDs . The process of establishing the
ownership of representation of a unique item on the blockchain is known as Minting. The unique
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attributes of the item are recorded on the blockchain in the smart contract , which assigns a unique
tokenID to the item. As the collectibles/artworks are often very large files, the metadata describing
the unique collectible and the URL of the actual artwork is provided in the URI (Uniform Resource
Identifier) field in the smart contract. This highlights an important fact regarding many NFTs which
represent digital artwork that the NFTs usually only store a pointer URI to the URL of the artwork and
no art itself on the blockchain . Users can write their own smart contract to mint NFTs, however, many
platforms like OpenSea and Rarible provide services to create smart contracts and mint NFTs for a fee.
Let us try to understand the ERC -721 framework using the famous Bored Ape Yacht Club collection on
the Ethereum Blockchain. On visiting the website of Bored Ape Yacht Club on OpenSea94 we find many
unique images of bored apes. To understand how the ownership of these NFTs is recorded and
transferred on the blockchain we can click any NFT to find out its current owner’s account address,
the address of the smart contract and the TokenID of the NFT .
The smart contract holds the information regarding the NFTs and maintains the record of its
ownership for each token ID (Unique collectible). The smart contract of Bored Ape Yacht Club on
EtherScan can be used to find owner account addresses of each NFT, their URIs as well as ownership
history. The transaction history of the smart contract can be seen and if a user goes to the first few
transactions, he/she can see the transactions that minted the BAYC NFTs . The transaction for minting
a TokenID associates the TokenID and other information like the URI (containing the description of the
artwork along with the URL of the artwork ), with an Ethereum account in the BAYC smart contract .
To find where the metadata of the NFT image is stored on the Ethereum Blockchain one can query the
smart contract on Etherscan by using the Read Contract Tab on the Contract stub95. One can find the
following important functions in the smart contract which can be queried to read data without paying
any gas on the Ethereum Blockchain. They are listed as below:
A) MAX_APES: Defines the maximum number of token s (Unique Apes) that can be minted in this
contract. 10000 for BAYC.
B) balanceOf: Returns the number of BAYC NFTs owned by an Ethereum address
C) ownerOf: It returns the Ethereum address that owns a specific BAYC TokenID
D) tokenURI: It takes token ID as the input and returns the Uniform Resource Indicator which is
usually a link containing the metadata of the NFT
Thus , an ERC721 smart contract essentially keeps a track of the minted NFTs and the accounts
that own them . Every time a new NFT is minted, gas must be paid to record the URI, ownership,
and other data on the blockchain . While changing the ownership also, gas must be paid to record
the change in the smart contract , along with some consideration if it is a sale or without
consideration if it is merely a transfer.
As discussed earlier, the token URI usually contains the metadata regarding the NFT which might
in-turn contain the URL where the actual digital image is stor ed. Querying the tokenURI field for
any token ID provides the address:
ipfs://QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/ 17XX
as shown in Fig. 36 which is address of data stored in Inter Planetary File System - a peer -ot-peer
decentralized storage which stores data based on the hash identifier of the item96. Accessing the
metadat a present at the given URI we find a json document which contains the metadata of the
NFT as shown in Fig . 37. This is the core of the NFT which lists its characteristics as well as provides
the link to the URL of the digital image of the artwork . This URL is a source of potential risk for the
94 https://opensea.io/collection/boredapeyachtclub
95 https://etherscan.io/address/0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d#readContract
96 https://ipfs.tech/
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NFT as it makes it essentially a pointer to a URL. If the image URL is changed from the unique
artwork in a token ID to any random picture, the NFT and the ownership of the image can be
altered by a third party hosting the artwork . To mitigate that risk , BAYC uses the IPFS to store the
image and metadata. As IPFS is a hash identifier -based peer -to-peer network which ensures that
the URI would not change or go offline.
Fig. 36 tokenURI of an NFT
{"image":"ipfs://QmW2uHxzxDfxGDUroqYkDQRaNuXSb2Vc9zDpCb53J9u1Qa","attributes":[{"trait_ty
pe":"Clothes","value":"Leather Jacket"},{"trait_type":"Fur","value":"Golden Brown"},{"trait_type":"H
at","value":"Sea Captain's Hat"},{"trait_type":"Eyes","value":"Coins"},{"trait_type":"Mouth","value":
"Bored Unshaven Cigarette"},{"trait_type":"Background","value":"Yellow"}]} Angry"},{"trait_type":"B
ackground","value":"Yellow"}]}
Fig. 37 NFT Metadata
Blockchain explorers also provide the transaction history of a particular TokenID since it was minted.
For any NFT Token ID the sale and transfer transactions can be seen on the blockchain . The
consideration paid in various NFT transactions is also given. However, a relook at the smart contract
functions reveals that NFT smart contract s usually have no field for recording the price at which the
sale takes place. NFT smart contracts typically do not directly record the price of sale of the NFT within
the contract itself. However, they often include functionalities to facilitate the transfer of ownership
and may emit events or logs that contain information about the sale, including the price. Also, the
owners and intermediate holders of the NFT are merely Ethereum Account addresses which cannot
be easily mapped to a real -world identity.
When purchasing an NFT, the transaction typically involves two separate components:
1. Transfer of the NFT : This is the actual transfer of ownership of the NFT from the seller's address
to the buyer's address . This transfer is recorded on the blockchain and is immutable. Once the
transfer is complete, the buyer becomes the new owner of the NFT.
2. Consideration or Payment : This is the payment made by the buyer to the seller in exchange for
the NFT. While the transfer of the NFT itself happens on the blockchain , the payment for the NFT
usually occurs off -chain. Buyers and sellers typically arrange payment separately from the
blockchain transaction using traditional payment methods such as credit cards, bank transfers, or
crypto asset transfers .
This hinders a taxman to find out the income and capital gains accrued to various holders of the NFT
(Fig. 38) . However, NFT marketplaces like OpenSea in their privacy policy97 state that they capture the
email address, first name, last name as well as the blockchain address and IP address along with mobile
device IDs. These can be used by tax administrations and law enforcement to map NFT owner account
97 https://opensea.io/privacy
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addresses to natural or juridical persons in tax fraud and money laundering cases . The valuation of
NFTs just like any other piece of art can be complex and subjective. NFTs can be wash traded to
artificially enhance their prices and laundering money.
Various popular NFT marketplaces presently do not capture any KYC information for trading and
minting NFTs. This enhances the risk of tax evasion and money laundering in NFTs. The Royal United
Services Institute (RUSI) in the UK has published a report98 that assesses the money laundering risk in
NFT marketplaces. The U.S. Department of the Treasury has also published a study99 on the facilitation
of money laundering and the financing of terrorism through the trade in works of high -value art . It
finds that emerging digital art forms like NFTs present new money laundering and terror financing
risks. The blockchain analytics firm Chainalysis identified 262 users who ha d sold an NFT to a self -
financed address more than 25 times100.
Another important feature of NFTs is that the creators can earn royalty income from the subsequent
sale of the NFT. While minting the NFTs on platforms like OpenSea, the creators can specify the
percentage of royalty to be paid for every subsequent sale of the NFT. However, as can be seen in the
most NFT smart contract s, there is no field for specifying the royalty percentage. Thus, this
specification of royalty is enforceable only when the sale of the NFT takes place on the specific
platform. If the NFT is sold on some other platform or through a direct smart contract interaction, the
NFT may be transferred without any royalty payment as the royalty specification and payment is not
in-built into the NFT smart contract .
As multiple marketplaces have multiple royalty payment implementations which may not be
compatible, developers have proposed a n NFT Royalty Standard - EIP 2981101 which enables all
marketplaces to retrieve royalty payment information for a given NFT and enables marketplace
agnostic royalty payments for secondary sales. However, the proposal is not yet implemented and if
a marketplace chooses not to implement the proposal, no royalty would be paid automatically for
secondary sales.
The increase in trading volumes of NFTs has created an ecosystem of Decentralized Finance
applications which involve NFT related commercial activity. They enable users to earn passive income
besides the royalty income on sales . As NFTs are often unique collectibles , they can be rented out for
use by others. For example , a popular and expensive sword or a weapon can be rented by users in an
online game or a metaverse . Platforms like reNFT102 provide NFT rental infrastructure for the
metaverse . It allows NFT owners to rent their NFTs for a set amount and time after taking certain
amount as collateral. There are also protocols which do not transfer the original NFT but issues an
expiring wrapped version of the NFT to the borrower. NFTs can also be staked or deposited into smart
contracts in lieu of tokens issued by the platform , which can lend the NFT to generate passive income
which is shared with the owners of the NFT. The users can also deposit NFTs to get tokens that are
composable and fungible at a ratio of 1:1 and can be used to acquire other NFTs or their fractions , on
the platform or staked further. Many services pool multiple NFTs and provide a liquid market for their
sale and purchase with a floor price for each NFT.
98 https://rusi.org/explore -our-research/publications/commentary/nfts -new -frontier -money -laundering
99 https://home.treasury.gov/system/files/136/Treasury_Study_WoA.pdf
100 https://www.financialexpress.com/digital -currency/wash -trading -and-money -laundering -observed -in-nfts-
report/2497965/
101 https://eips.ethereum.org/EIPS/eip -2981
102 https://www.renft.io/
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Fig. 38 NFT transaction history on Ethereum Blockchain explorer
7.4.1 Legal Status of NFTs
The basic idea behind NFTs purportedly representing art work, collectibles, real estate etc. is that by
owning the NFT on the blockchain one can have property rights in another underlying asset like art or
real estate. But what property rights of the underlying asset does the ownership of the NFT
automatically transfer to the owner is a matter of debate. The fundamental question is about the
property rights that the process of tokenization of a tangible or intangible asset on the blockchain
brings with it. Moringiello & Odinet (2022) analysed the legal aspects of tokenization and examined
how the token is connected to the underlying asset, if at all , and what does the current legal
framework say about it. They argue that NFTs do not have attributes of other token s like negotiable
instruments and bills of lading . They also survey dataset of terms of service from the most prominent
NFT platforms to explore their legal effects as compared to the claims.
token ization is not a new concept in law and economics. Various negotiable instruments, securities
and deeds are based on the principle that the tokens represent the ownership rights of some other
assets. When a user buys an NFT and the NFT is assigned to the blockchain address controlled by the
user on the blockchain , it is believed by many buyers that they acquire the ownership and other
associated rights in the underlying artwork or collectible just by virtue of acquiring the NFT. The large
amounts invol ved in NFT purchases might prompt a connoisseur of art to assume that the digital work
itself has been transferred, whereas the NFT bought by the users essentially contain s metadata and
not the image itself.
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Moreover, anything that can be digitized can be minted into an NFT as the process of minting
essentially creates a unique TokenID for that digital work of art and records it into a smart contract in
the blockchain . Thus, it is technologically possible to mint a copyrighted item for which the user does
not have ownership or the copyright to be minted into an NFT purportedly “owned” by the user. The
ownership , reproduction and use of an artwork or collectible in most jurisdictions is govern ed by the
copyright law, which may or may not recognize NFTs as a valid deed of transferring the copyright or
other rights in the artwork. Thus, it is argued that from a copyright perspective that minting an NFT
describing an artwork without authorization might not be clearly a copyright infringement as it can be
considered generation of a string of numbers in relation to a work but not reproduction or adaptation
of the work itself103.
Guadamuz104 (2022) argues that most NFTs do not involve a transfer of rights and in some instances
where a user must check a box for transferring rights while minting an NFT , it might not fulfil the
requirements of the copyright law of the jurisdiction of the creator of the NFT. He gives the example
of Copyright Designs and Patents Act 1988 of UK which requires the copyright agreement “in writing
signed by or on behalf of the assignor” . For example , in case of real estate in India the transfer of
ownership is governed and effectuated through the Transfer of Property Act. The sale or purchase of
any real-world real-estate merely through the transfer of an NFT representing the real-estate would
not be recognized as a transfer under the Transfer of Property Act.
Moringiello & Odinet (2022) survey ed the terms of service of various leading NFT platforms and found
that none of the terms of service provided any clear link between ownership of an NFT and rights in
the underlying creative work. One of the terms stated that buyer obtains ownership of “a
cryptographic token representing the artist’s creative Work as a piece of property, but” obtains no
ownership of the “creative Work itself.” They argue that the only right the owner of the NFT gets by
making the purchase is the right to display the NFT.
Moringiello & Odinet (2022) reviewed the terms of service of a site SuperWorld which maps the Earth
to create virtual land assets which participants can “buy”. The users can buy or sell real estate tokens .
The terms of service state that “purchase ” of tokens conveys no rights in the underlying art. The users
can monetize the property in SuperWorld. Marinotti105 (2022) argues that a lthough the NFTs used in
metaverse s are on the blockchain but the constituents that make the metaverse like land and goods
exist on private servers which are stored on secured databases running proprietary code. He argues
that the metaverse platforms effectively grant the users only access to the digital assets . As the
metaverse NFTs have functional use in the metaverse , blocking access or deleting the NFT from the
metaverse for violation of terms of service can significantly affect the utility and financial worth of the
NFT. A more detailed and comprehensive discussion about the copyright issues of NFTs ca n be found
in an article by James Grimmelmann, Yan Ji, and Tyler Kell106 which also mentions the case of Spice
DAO which involved a dispute over ownership and control of a digital art collection on the Ethereum
blockchain .
Various tax administrations in recent rulings have also clarified the nature of NFTs . The Spanish tax
administration in its ruling V0486 -22 on 10th March 2022107 stated the fact that “ the object of the sale
is not the illustrations themselves but NFTs, that is, non -fungible tokens that grant the buyer rights of
use but in no case the underlying rights to ownership of the work. ”. The Norwegian tax administration
103 https://www.wipo.int/wipo_magazine/en/2021/04/article_0007.html
104 https://www.weforum.org/agenda/2022/02/non -fungible -tokens -nfts-and-copyright/
105 https://theconversation.com/can -you-truly -own -anything -in-the-metaverse -a-law-professor -explains -how -
blockchains -and-nfts-dont -protect -virtual -property -179067
106 https://www.theverge.com/23139793/nft -crypto -copyright -ownership -primer -cornell -ic3
107 https://petete.tributos.hacienda.gob.es/consultas/?num_consulta=V0486 -22
70
in its guidance108 also states that “ An NFT will not normally be considered home contents/movable
property in this connection, as the NFT in itself is a digital code and not the actual object it is linked
to.” These rulings and clarifications can have consequences for direct and indirect tax treatment of
NFTs.
One interesting perspective on NFTs is to consider them as vouchers, which can be either single -
purpose or multipurpose in nature109. In the context of NFTs, single -purpose vouchers refer to tokens
that are designed for a specific utility or use case. Once redeemed or utilized, their purpose is fulfilled,
and they may lose their value or become non -transferrable. For example, an NFT representing a ticket
to a virtual event provides access to that specific event only. Single -purpose NFTs provide a direct link
between the digital asset and its owner, facilitating transparent ownership and provenance tracking.
On the other hand, multipurpose vouchers in the realm of NFTs refer to tokens that possess versatile
utility across various contexts or platforms. T hese NFTs retain their value and transferability even after
redemption, allowing owners to utilize them in different ways or trade them in secondary markets.
Multipurpose NFTs may represent a range of assets or rights whose value often depends on factors
such as scarcity, demand, and interoperability with different ecosystems. For instance, an NFT
representing a virtual land parcel in a metaverse could be utilized for various purposes, such as
building virtual structures, hosting events, or generating revenue through in -world activities. Similarly,
an NFT representing membershi p in a decentralized governance platform might grant voting rights,
access to exclusiv e content, or dividends from platform activities. Treating NFTs as single -purpose or
multi -purpose vouchers can have consequences for taxation of NFTs from the perspective of VAT/GST.
7.4.2 ERC 1155 Token Standard
A closer look at the ERC -721 smart contract s reveals that an ERC -721 smart contract can be used only
to map unique TokenIDs to Ethereum addresses. I t cannot hold ERC -20 tokens along with NFTs (ERC -
721 token s). Also, there are no functions to transfer the NFTs in bulk. All these shortcomings result in
payment of huge amount in gas fees while creating and transferring NFTs. Moreover, there are use
cases where more than one copy of a collectible like a weapon or outfit in an online game might be
required along with the native ERC -20 token of the game.
The ERC 1155 token standard solves this problem and enables the smart contract to hold fungible as
well non -fungible tokens. ERC-1155 solves th ese problems by assigning TokenIDs to each kind of
token(s) held by the smart contract , which also keeps a track of their ownership by mapping them to
Ethereum addresses. In case of Non -Fungible Tokens, the respective TokenID is mapped to only one
Ethereum address , whereas for an ERC -20 token the TokenID can have many tokens issued whose
balances are maintained in th e smart contract. It also enables the creation of multiple copies of a
single artwork or collectible which can be minted in limited quantities and assigned to different users
(Ethereum Accounts). For example , an online game can have an ERC 1155 smart contract which can
hold the native ERC -20 token of the game as well as collectibles like weapons or armour which are
assigned different TokenIDs and available in one or more than one quantity. This can be pictorially
represented in Fig. 39
108 https://www.skatteetaten.no/en/person/taxes/get -the-taxes -right/shares -and-securities/about -shares -
and-securities/digital -
currency/nft/#:~:text=The%20taxable%20value%20of%20NFTs,the%20value%20assessment%20upon%20requ
est.
109 VALUE ADDED TAX COMMITTEE (ARTICLE 398 OF DIRECTIVE 2006/112/EC) WORKING PAPER NO 1060
71
Fig. 39 ERC 1155 Token Standard (illustration)
7.4.3 Taxation of NFTs
NFTs are collectibles and can be considered as blockchain representation of various art forms like
paintings, music, videos etc. They may also be tokens created purely for acting as tokens for a specific
purpose like entry into a concert or represent the position of a liquidity provider in a liquidity pool .
The first potential taxable event is the creation or ‘Minting’ of the NFT. The creation of the NFT usually
involves recording the metadata and URI of the artwork on the blockchain . As this does not lead to
accrual of any income and is merely a process for recording information about the artwork on the
blockchain it might not be taxed in most jurisdictions. However, only a few jurisdictions have issued
guidance related to taxation of NFTs. The Norwegian tax administration in its guideline has clarified
that creating an NFT does not trigger taxation110. Also, the transfer of NFT from one account to the
other owned by the same person/entity would also not be taxable.
The tax treatment of the transactions related to the NFT after minting depend upon the Intellectual
Property Rights that get transferred to the purchaser. As discussed above , as buying an NFT does not
necessarily transfer all the Intellectual Property Rights associated with the artwork , the first sale of
the primary transfer of the NFT may be treated as a license for the purpose of taxation. The secondary
and subsequent transactions of the NFT may be treated as a sale as the recipient of the primary
transfe r would sell all its rights to the recipient of the secondary or subsequent transfer. However , the
secondary sale might also require payment of royalty to the owner of the underlying artwork and
might also involve issues related to amortization of the basis by the taxpayer.
For the seller making the secondary sale it might attract income tax or capital gains tax depending
upon the classification of NFTs by the jurisdictional tax administration and the prescribed rates as well
as the nature of activity and the period of holding . For example , in the US NFTs are treated as
collectibles and might be subject to higher tax rates. However, the tax treatment would depend on
the facts and circumstances of each transfer and the rights relinquished and acquired by the transferor
and the transferee respectively. The royalty payments on secondary sales might be subject to
withholding taxes in many jurisdictions , depending upon the tax residency of the transfero r. The lack
of sale consideration data on the blockchain as well as the limited ability to map Ethereum addresses
to Tax Identification Numbers can make it difficult for tax administrations to collect due taxes.
Howe ver, as many NFT owners publicly mention their social media handles /IDs it might be possible
for tax administrations to o btain the ownership information from such platforms.
110 https://www.skatteetaten.no/en/person/taxes/get -the-taxes -right/shares -and-securities/about -shares -
and-securities/digital -currency/nft/
TokenID 1
Total Supply 10000
TokenID 2
Total Supply 1
TokenID 3
Total Supply 10
Address 1: 5000
Address 2: 3000
Address 3: 2000
Address 1
Address 1: 3
Address 2: 2
Address 3: 5
ERC 1155
72
NFT sales can also be subject to GST/VAT in many jurisdictions as it may be considered a taxable supply
of services . For example, NFTs would be considered as digital services as per Council Implementing
Regulation (EU) No 282/2011 . The individuals or entities involved in trade of NFTs can might be
considered ‘taxable person’ for GST/VAT as they carry out an ‘economic activity ’ with direct nexus
between the supply of service and the consideration. Even NFTs minted for functional purposes like
for entry into concerts and restaurants might also be subject to GST/VAT in many jurisdictions
depending upon their classification as a voucher, with possible exemptions up to a certain limit. The
services offered by various NFT marketplaces for minting and sale of NFTs may also be subject to
GST/VAT depending upon the tax residency of the clients. The royalty payments related to NFTs may
also be subject to GST /VAT in various jurisdictions as royalties are considered as licensing services in
many jurisdictions. GST/VAT liability on royalties received may depend on whether they pertain to the
right o f using the NFT or the right to resell the NFT. The issues and tax treatment related to place of
supply, claiming input tax credit and exemption of supplies made to residents in foreign jurisdictions
would be similar to the issues related to taxation of Bitcoin Mining activities.
Some tax administrations have already issued rulings and guidance that clarify the tax treatment of
NFTs. The Spanish tax administration has clarified that the NFT sales are electronically supplied
services and will be subject to VAT , depending on the place of supply111. The Belgian tax administration
has also clarified that the NFT sale is to be regarded as a service provided by electronic means. If the
supply of NFTs is deemed to take place in Belgium standard VAT rates would apply112. A striking feature
of such rulings and guidance is the presumption of place of supply to exist somewhere in the real
world, where a destination tax administration can collect the VAT/GST due. However, the supply of
such services in virtual worlds like a metaverse raise fundamental questions regarding the taxability
and tax treatment of such transactions. One such example is the judgement of the German Federal
Finance Court in a case involving the online platform ‘Second Life’ where the court overruled the
judgement of the Col ogne Finance Court and concluded that such transactions in virtual world do not
constitute participation in economic activity in the real world and are outside the scope of VAT/GST113.
Despite these uncertainties and issues, i t is worth mentioning that t he market cap of NFTs is
approximately 4 billion USD with all time sales volumes of around 137 billion USD114 which makes it
difficult for tax administrations to overlook the revenues associated with NFTs
8. Decentralized Finance
The financial system performs a critical job of financial intermediation and enables the flow of capital
from lenders to borrowers . Similarly, in the domain of crypto asset s using platforms like centralized
crypto exchange s or decentralized platforms using smart contract s, such financial intermediation is
possible. The smart contract ecosystem enables the automation of deposit, disbursal , lending and
repayment of funds. This has created an entire ecosystem of financial services which operate on the
blockchain , called DeFi or Decentralized Finance. DeFi enables enormous opportunities of financial
innovation using crypto asset s and smart contract s. At the same time, it also poses new challenges to
tax administrations and law enforc ement agencies as lack of KYC norms and regulatory oversight can
enable tax evasion and money laundering. The wide variety of applications and products offered by
DeFi warrant a separate paper to describe the mechanics as well as the taxation aspects of this
ecosystem, which are not very clear and well understood today. However, this paper discusses two
promin ent DeFi platforms MakerDAO and UniSwap which account for a significant portion of assets
locked up in DeFi applications.
111 https://petete.tributos.hacienda.gob.es/consultas/?num_consulta=V0486 -22
112 https://www.dekamer.be/QRVA/pdf/55/55K0073.pdf - page 11 4
113 https://www.justiz.nrw.de/nrwe/fgs/koeln/j2019/8_K_1565_18_Urteil_20190813.html
114 https://coinmarketcap.com/nft/
73
8.1 MakerDAO
MakerDAO is one of the most popular DeFi projects. It is a Decentralized Autonomous Organization
that issues and manages two ERC -20 tokens. The first token is known as DAI, which is a crypto -
collateralized so called stablecoin soft pegged to the US Dollar , the second token is Maker (MKR) which
is used as a governance token of the Maker protocol as well as a utility token for auctioning DAI for
MKR and vice versa. In unforeseen situation s it can also be used as a resource for recapitalization and
repayment of Maker protocol debt . The MakerDAO is essentially a system of smart contracts running
on the Ethereum Blockchain that manage the supply of DAI and MKR . A system of incentives and other
mechanisms enable creation and destruction of DAI as well as its backing with appropriate collateral
to maintain its soft peg to the US Dollar using smart contract s. Unlike the so called centralized
stablecoins, the collateralization and assets in the MakerDAO ecosystem are publicly available and do
not requi re any proof of reserve or audit. A detailed overview of the MakerDAO is available in the
whitepaper115.
The MakerDAO can be considered a digital vault system which gives a DAI denominated loan to users
which lock -up their E ther or other authorized crypto assets into a smart contract , which is called a
Collateralized Debt Position (CDP). This is the mechanism that generates DAI as shown in Fig. 40. The
DAI loan accrues interest denominated in DAI which is called the ‘stability fees’ in the Maker protocol.
When the DAI loan is returned along with the stability fee by the borrower, the retuned DAI is burnt ,
the stability fees go to the surplus smart contract account and the collateralized assets are released
(Fig. 41) .
Fig. 40 Collateralized Debt Position
Fig. 41 Repayment of DAI loan
This system is analogous to a mortgage in the traditional financial system. One of the key differences
is that the collateralized crypto asset s like E TH and Basic Attention Token (BAT) are much more volatile
than the assets mortgaged in traditional finance. Thus, MakerDAO users need to overcollateralize the
DAI loans by at least 150% for E TH which can be higher (>175%) for some other crypto assets like BAT.
This facilitates the stability of the protocol as well as the peg of DAI to the USD , as even if the value of
CDP reduces, the Maker protocol would still be able to discharge the liability of issued DAI. If the CDP
value falls below the threshold, t he protocol enables three scenarios:
i) The borrower deposits more collateral to the CDP.
ii) The borrower repays some of the DAI debt.
115 https://makerdao.com/en/whitepaper/
4 ETH
4 ETH= 6000 DAI
4000 DAI
Stability Fee
4 ETH
4 ETH= 6000 DAI
4000 DAI
Stability Fee 90 DAI
4000 DAI
90 DAI
Surplus
74
iii) The protocol opens the CDP for auction by keepers wh ich liquidate the CDP.
The MKR token is the governance token of the Maker protocol which enables the holders to vote on
proposals as well as initiate proposals for changes in the protocol. The parameters like stability fee,
the crypto assets to be taken as collateral , as well as their collateralization ratios are decided through
voting, with each MKR token holder entitled to one vote. If due to an extreme event the protocol
accrues a debt, the governance mechanism decides through voting to issue new MKR tokens for
recapitalizatio n. This is very similar to a situation in traditional finance where new equity is issued to
infuse capital in a sick enterprise .
It is also noteworthy that apart from the Maker protocol DAI and MKR can also be bought and sold on
various crypto exchange s. In order to incentivise people to hold DAI rather than selling it and to
stabilize its value in the open market, t he Maker protocol also enables the DAI borrowers to deposit
the borrowed DAI into smart contracts that yield returns at the rate of the DAI savings rate . The DAI
savings rate is also determined by the governance ecosystem of MakerDAO. Just like in ordinary
finance where the lending rate is higher than the savings rate, the stability fee is always higher than
the DAI savings rate. Besides this, the borrowers of DAI can use it to earn passive income on other
DeFi applications or create further leveraged positions. The DAI stability fee, savings rate and the
collateralization ratio are like instruments in the armour of a central bank which can affect the demand
and supply of DAI resulting in movement of price of DAI in the desired direction as compared to the
US Dollar .
Various external actors also play an important role in the Maker protocol operations. They play a
critical role in maintaining the peg against the US Dollar. The main external actors in the Maker
protocol are:
i) Keepers: They are independent actors which take advantage of arbitrage opportunities
which help DAI to move towards the target price of 1 USD. They participate in Surplus
Auctions, Debt Auctions, and Collateral Auctions which help to maintain sufficient
reserves against the issued DAI in the protocol. Keeper s are usually bots operated by
natural or legal persons.
ii) Oracles: As the blockchain s cannot access any off-chain information like asset prices of
assets locked in CDPs or the price of DAI, oracles deliver price information about the assets
locked in CDPs in order to enable the protocol to know when to trigger a liquidation.
iii) Emergency Oracles: They are the oracle s selected by Maker governance as last line of
defence against Maker governance or oracle s. They are authorized by Maker governance
to freeze oracle s and trigger an emergency shutdown in an unforeseen situation.
iv) DAO Teams : These are individuals and service providers who are authorized by Maker
governance to provide services to MakerDAO.
Keepers play an important role in maintain ing the peg of DAI to the US Dollar as they participate in
various auctions like the Surplus Auctions, Debt Auctions, and Collateral Auctions . The various auctions
in the MakerDAO ecosystem and their role in the protocol is as follows:
i) Surplus Auctions : The DAI loans with accrued stability fee denominated in DAI is repaid
back to the CDP smart contract . Once the DAI principal along with the stability fee is
repaid, the collateralized assets are released. The excess DAI obtained from the stability
fee goes to a smart contract which accumulates it to a level determined by the Maker
governance , after which an auction for the surplus DAI to be converted to MKR can be
triggered by a keeper and the MKR tokens obtained consequent to the auction are burnt.
75
ii) Debt Auctions : It is possible in certain cases that in situations where the value of the
collateralized asset collapses rapidly, the liquidations might not be able to repay the entire
collateralized assets and the protocol may incur debt. When this debt crosses a certain
threshold determined by the Maker governance , an auction can be triggered by a keeper
to recapitalize the protocol by auctioning off MKR for a fixed number of DAI.
iii) Collateral Auctions : These auctions are triggered to recover debt in liquidated vaults .
When the collateral position in a CDP vault drops below the specified liquidation ratio the
vault becomes available for liquidation . The keeper bots liquidate the vaults by sending
transactions which trigger an auction of the assets locked in the vault . It is important to
note that as the liquidation transactions can only originate from an externally owned
account on Ethereum , the Maker protocol cannot automatically trigger the collateral
auctions and relies on keepers for the same. The amount of DAI recovered by the auction
is used to repay the debt and the remaining collateral is returned to the user after
deduction of a penalty fee. The keeper gets a fee for the transaction as an incentive to
keep a watch on vault s that can be liquidated.
The MakerDAO users and owners of MKR and DAI do not undergo any KYC procedure to determine
their tax residency or for identification of the beneficial owner ship of the assets, this gives rise to a
complex scenario where a number of individuals or entities come together to transact on a
decentralized lending platform which also issues a so -called stable coin and maintains its peg to the
US Dollar. The individuals or entities also participate in the governance of the Maker Decentralized
Autonomous Organizatio n and take decisions which have financial implications for the protocol and
the holders of MKR token themselves.
The tax implications of transactions in such ecosystems can be very complex and due to unforeseen
and trans -national nature of such organizations there is virtually no guidance available for taxation of
the truly Decentralized Autonomous Organization s which do not have a well -defined legal identity .
Although, the individual transactions are still governed by the tax guidelines and laws of the respective
jurisdictions of the members and users of the Decentralized Autonomous Organization ’s
platforms/proto cols. The taxation of such Decentralized Autonomous Organizations is discussed in the
subsequent sections.
8.2 Uniswap
Uniswap is one of the most popular Decentralized Finance projects and one of the biggest
decentralized exchanges built on the Ethereum Blockchain which is censorship resistant, non -custodial
and trustless. It enables users to swap one ERC -20 token for another for a fee, without presence of
market makers116 who are individuals or entities . Unlike the traditional order -book based exchanges
there is no single authority like WazirX and Binance which matches buy and sell orders on Uniswap .
The trades are executed against on -chain liquidity pools which pool the crypto asset s provided by all
the liquidity providers, unlike individual orders by individual market makers in a traditional centralized
exchange.
In Uniswap, unlike a traditional centralized crypto or securit ies exchange , a counter -party is not
required to execute a trade. Instead, an automated market maker facilitates every trade according to
the supply and demand of the ERC -20 token s. This can be considered to be analogous to barter trad ing
in crypto tokens . There are two possible models in crypto exchanges, the first one is a centralized
model which enables users to buy and sell their crypto asset s for fiat currency and vice versa after
undergoing a KYC process. The second model is that of an entirely decentralized exchange which can
116 https://en.wikipedia.org/wiki/Market_maker
76
deal only in crypto asset s and facilitates the exchange of one crypto asset for another purely on the
basis of barter, without the involvement of any fiat currency or KYC procedures .
Analogous to the first case if an individual who has only Rubies wants to have some Emeralds which
he/she does not possess , he/she would either have to find someone who is willing to exchange his/her
Rubies for Emerald (s) which would be extremely difficult . It would also be difficult to find out how
many Rubies are a fair deal for one Emerald and vice versa. The individual would instead try to find an
exchange where he/she can exchange Rubies for a common medium of exchange like the Indian Rupee
or the US Dollar and then exchange it for Emeralds , as depicted in Fig. 42(a) . However, exchanging the
Ruby for money would require t he individual to have a bank account and identification proofs for KYC
before the individual can undertake the transaction . Also, the exchange would charge the individual a
fee for providing this intermediation and liquidity facility both when converting Rubies to fiat currency
and subsequently using the fiat currency for buying Emeralds . This is analogous to the mechanism of
trading in centralized exchanges with the added disadvantage of crypto asset s being in the custody of
the exchange rather than the users.
Fig. 42 (a) Exchange based on fiat currency Fig. 42(b) Barter based on Automated Market Maker
Analogous to the second case, there can be an automated store like Amazon Go named Unistore which
does not deal in fiat currency at all and carries out purely automated barter trades of precious stone
pairs like Ruby /Emerald , Emerald /Sapphire , Sapphire /Diamond etc. as depicted in Fig. 42 (b). If we
consider the Ruby/Emerald pair in the store, the store has a pool of individuals who own Rubies and
Emeralds and wish to earn passive income on their assets . The owners of the Rubies and Emeralds are
given an offer by Unistore to deposit equal values of Rubies and Emeralds into the baskets in t he store
in order to earn a fixed fee on every Ruby/Emerald swap executed in Unistore . The owners of the
Rubies and Emeralds can withdraw their precious stones at their will along with the accumulated fee.
The liquidity providers are required to deposit Rubies and Emeralds of equal value (not equal number)
in order to maintain the stability of the protocol which is based on an automated market maker
(AMM) .
Unistore facilitate s its users to anonymously exchange Rubies for Emeralds and vice versa without
converting them to fiat currency , in lieu of a small fee which is used to pay the owners of Rubies and
Emeralds who decide to pool them into Unistore to provide the liquidity . This enables a user to
anonymously walk into the store and exchange Rubies for Emeralds and vice versa through an
Automated Market Maker, without any human intervention and questions about identity and
jurisdiction. This is achieved through smart contracts which run an Automated Market Maker that
decide s the price for each Emerald exchanged in lieu of a Ruby and vice versa.
77
Uniswap uses a constant product market maker which can be mathematically represented as (1)
where 𝑄𝑅𝑢𝑏𝑦 is the quantity of Rubies in the pool and 𝑄𝐸𝑚𝑒𝑟𝑎𝑙𝑑 is the quantity of Emeralds in the pool
and K is a constant.
𝑄𝑅𝑢𝑏𝑦 ×𝑄𝐸𝑚𝑒𝑟𝑎𝑙𝑑 =𝐾 (1)
When K=1600 this would result in a curve shown in Fig. 43
Fig. 43 Constant product market maker of Unistore where K=1600
For example , if a user swaps their Ruby for an Emerald when there are 40 Rub ies and 40 Emeralds in
the pool already, the supply of Ruby would increase and that of Emerald would decrease , thus the
price of Ruby would go down and the user would get a smaller number of Emeralds for every
subsequent Ruby swapped for an Emerald. Such fluctuations give rise to arbitrage opportunities which
are used by rational actors in the DeFi ecosystem to carry out trades which move the exchange rate
towards equilibrium levels . This also gives rise to opportunities for validators to earn income over and
above the transaction fee and issuance and is known as Maximal Extractible Value (MEV).
The operation of the Automated Market Maker is purely algorithmic without the need for any counter -
party’s presence to facilitate individual transaction s. Theoretically it can provide infinite liquidity with
exchange rates of tokens rising asymptotically. A transaction would be available even for the last
available Emerald for an infinite number of Rubies . Uniswap is identical to a Unistore trading in ERC -
20 token pairs instead of precious stone pairs.
However, in Uniswap V3 the liquidity providers can choose the range on the curve for which they wish
to provide liquidity , resulting in much greater market depth. This results in each liquidity provider
providing a unique position on the liquidity curve. Therefore, these positions cannot be represented
by ERC -20 LP tokens and are instead minted in the form of LP NFTs which specify the ownership of the
corresponding liquidity position on the bonding curve of the Automated Market Maker as shown in
Fig. 44.
It can also be seen from the curve in Fig. 43 that every trade would result in a change in price for every
unit of the ERC -20 token bought, this is known as slippage. Hence, large trades executed in pools with
low liquidity can result in significant price fluctuations when the trade is actually executed on chain .
This is one of the main reasons why validators who have visibility of such transactions might reorder
such transactions to benefit from such variations . The value derived by such reordering is called
Maximal Extractible Value (MEV) and provides additional revenue to the validators. The t axable events
triggered in the MEV ecosystem are discussed in the subsequent section s.
𝑄𝑅𝑢𝑏𝑦 =𝑥; 𝑄𝐸𝑚𝑒𝑟𝑎𝑙𝑑 =𝑦
78
Fig. 44 A Uniswap LP NFT
In order to maintain and incentivize liquidity and pooling of tokens, Uniswap provides the users who
pool their tokens (known as liquidity provider s) with LP tokens which can be used by them to earn
passive income in the form of share of the transaction fee of charge by Uniswap . Uniswap V2 charged
a flat fee of 0.3% for all the swap transactions117. However, Uniswap V3 has a fee structure of 0.05%,
0.3% and 1% depending on the token being exchanged118. The users pooling their assets are given a
percentage of LP tokens or LP NFTs that represent the liquidity reserve which accumulates the liquidity
fee, when the users decide to stop providing their assets for liquidity, they redeem the LP tokens or
LP NFTs to get their share of the liquidity reserve. The LP tokens or NFTs are burnt consequently. This
mechanism is the main source of liquidity for decentralized exchanges like Uniswap. However, the
conversion of the crypto asset pair into LP tokens/LP NFTs and vice versa might be considered a
‘disposal’ in many tax jurisdictions.
In this process, the project itself does not receive any income , as all the fee is given to the liquidity
provider s. It is noteworthy that LP tokens are also ERC -20 tokens which can also be used by liquidity
provider s to earn passive income on other DeFi platforms. However, providing liquidity has its own
risks with a possibility of divergence loss which is caused due to the change in price of assets locked
in the Liquidity Pool smart contract. A divergence loss is also known as an impermanent loss, it is s aid
to have occurred when the current price of the tokens pooled into the liquidity pool changes relative
to the price when the tokens were deposited. A detailed explanation can be found here119. Both
upward and downward movement of relative price of assets causes divergence loss. Mathematically
deriving from (1) it can be expressed in terms of the price ratio 𝑝 as given below and the plot of (2) is
shown in Fig. 45.
𝐷𝑖𝑣𝑒𝑟𝑔𝑒𝑛𝑐𝑒 𝐿𝑜𝑠𝑠 =2√𝑝
1+𝑝−1 (2)
117 https://docs.uniswap.org/contracts/v2/concepts/advanced -topics/fees
118 https://docs.uniswap.org/concepts/protocol/fees
119 https://pintail.medium.com/uniswap -a-good -deal -for-liquidity -providers -104c0b6816f2
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Where 𝑝 is the ratio of the price of assets currently to the price at the time of depositing. However,
despite the possibility of a divergence loss users pool their assets in liquidity pools as fees from
liquidity mining offsets the divergence losses.
Fig. 45 Divergence loss
The users can list any ERC -20 token pair for swapping on the exchange, As on March 2024 there were
2056 token pairs listed on Uniswap120. The percentage of transaction fee to be charged is determined
using the governance mechanism of Uniswap wherein each holder of the governance token UNI can
initiate proposals for change and cast their votes on proposed changes. This makes Uniswap a
Decentralized Autonomous Organization like the MakerDAO where no single ‘person’ owns the entity
and it exists in a truly decentralized manner . Although, Uniswap has recently establish ed an NPO – the
Uniswap Foundation121, a real -world legal entity which support s decentralized growth and long -term
sustainability of Uniswap. Such Decentralized Autonomous Organizations pose various unique tax and
legal challenges which are discussed in the subsequent section s.
The market ‘ depth ’ in decentralized exchanges like Uniswap is obtained by having large pools of asset
pairs which are liquidity pools and this facilitation of swap transactions in lie u of fee is known as
liquidity mining . In Uniswap V 2.0 the liquidity providers had to deposit equal value of both the ERC -
20 tokens in the liquidity pool , a condition which has been relaxed in Uniswap V3 . For example , if the
value of 3 BAT is same as 1 DAI then the liquidity miner would have to deposit 300 BAT and 100 DAI
in the BAT/DAI liquid ity pool. It is worth noting that the crypto asset s being swapped in Uniswap are
also available for open market trading on other platforms. Just as it would be possible for users of
Unistore to utilize an arbitrage opportunity where the swapping ratio of the Ruby/Emerald pair is
higher or lower than the ir open market price ratio , the crypto asset pairs like DAI/BAT or ETH/DAI
having different price ratios than those prevailing in other exchanges would give arbitrageur s an
opportunity to earn profit . This facilitates the maintenance of liquidity and price ratios close to the
market price ratios in Uniswap liquidity pools.
120
https://www.coingecko.com/en/exchanges/uniswap#:~:text=Uniswap%20(v3)%20is%20a%20decentralized,pa
irs%20available%20on%20the%20exchange .
121 https://www.uniswapfoundation.org/
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8.3 Taxable events in DeFi ecosystem
The market cap of DeFi crypto market is ~ 138 billion USD122. The number of DeFi users ha d increased
to ~7.5 million in late 2021 and has declined since123. Many DeFi transactions result in accrual or
realization of income to the depositors, borrowers , DeFi protocols and other actors and service
providers in the DeFi ecosystem. As the income received or accrued along with the associated services
might be taxable , it is important to unde rstand the tax implications of various DeFi transactions . Some
of the direct and indirect tax events in the DeFi ecosystem and their potential tax treatments are given
below. This list is not exhaustive and the treatment of the events below mentioned might be
significantly different in different jurisdictions124.
8.3.1 Direct Taxes in DeFi ecosystem
The tax treatment of deposit ing crypto asset s into a DeFi protocol smart contract and their locking up
into a Collateralized Debt Position (CDP) would largely depend on the treatment of locking up of crypto
asset s into a Collateralized Debt Position (CDP) as ‘disposal .’ As the protocol can allow the CDP to be
auction ed by keepers to liquidate the CDP, some tax administrations might take a view that this is
tantamount to the transfer of beneficial ownership , thus making the creation of a CDP a ‘disposal’ of
the underlying crypto asset . Some tax admini strations might treat the CDP as a kind of escrow account
which does not lead to transfer of beneficial ownership and hence may not be considered a taxable
event. To understand the instance of transfer of beneficial ownership of the crypto asset s locked in
the CDP it would be worthwhile to delve deeper into the mechanism of a Collateral Auction in the
MakerDAO protocol. A closer look at the documentation of the liquidation module of MakerDAO125
reveals that a liquidation is triggered when a keeper detects a CDP that is below the liquidation ratio
and triggers a liquidation by calling the Dog.bark function126 . Thus, it can be inferred that the beneficial
ownership of the CDP remains with the borrower till the value of assets in the CDP falls below the
liquidation ratio and a liquidation is triggered by a keeper.
The issuance and disbursal of the loan amount in any other crypto asset in lieu of a CDP might also be
taxable for the DeFi platform, but without any tax implications for the DeFi platform user . This might
lead to issues of tax neutrality a nd taxation of such activities being inconsistent with their economic
rationale and may also increase the administrative and compliance burden on users . Such transactions
are very similar to repo transactions as they do not lead to transfer of all economic rights . Thus , some
jurisdictions might consider including such transactions in the repo rules or create new rules for
treating such sale and repurchase transactions as loans. This makes their tax treatment in sync with
their economic rationale.
Some liquidity pools issue tokens to liquidity providers which give them a right to exchange the issued
tokens for the original crypto asset pair deposited by the liquidity providers at the time of repayment.
This arrangement may be considered as disposal by some tax administrations and subject to capital
gain s. However, such transactions are also like repo transactions and some jurisdictions might
consider including such transactions in the repo rules or create new rules for treating such sale and
repurchase transactions in line with their economic rationale . Only t he HMRC has issued guidance on
122 Top DeFi Tokens by Market Capitalization | CoinMarketCap
123 https://www.statista.com/statistics/1297745/defi -user -number/
124 Currently as there are no generally accepted definitions of DeFi which are used or accepted by tax
administrations, the terms used below might not have the same meaning as used in regulatory or
statutory parlance.
125 https://docs.makerdao.com/smart -contract -modules/dog -and-clipper -detailed -documentation
126 https://github.com/makerdao/dss/blob/liq -2.0/src/dog.sol
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such liquidity pools and has also done a public consultation to try to sync the tax treatment of such
transactions with their economic rationale.
The interest paid by the borrower to the DeFi application, like the stability fee in case of MakerDAO
may constitute taxable income in the form of interest for the application (after deducting the interest
paid by the application to the borrowers) and might require withholding taxes by the borrower
depending upon the legal residency of the DeFi application. The stability fee might be deductible as
an expense for the borrower if the DAI is used as an investment or for trading. Actions like auctioning
of the accumulated stability fee might also give rise to income for the DeFi application due to changes
in the value of DAI with respect to MKR, even though the MKR acquired because of the surplus auction
is eventually burnt.
The deposits made by borrowers in the MakerDAO smart contract for earning income at the DAI
savings rate might also constitute ordinary income and taxable at the fair market value of the return
in DAI when it is received. In this case, the DeFi application might be required to withhold taxes based
on the tax residency of the borrower. B esides this, any further leveraging of the borrowed DAI or MKR
and any income therefrom or any capital gains arising on disposal may also be taxable as income and
capital gai ns respectively and may also have associated withholding requirements. However, some
jurisdictions to reduce the administrative and compliance burden might consider taxing the net
income or capital gains accruing due to such leveraging.
In case the governance of the application decides to recapitalize the protocol through a mechanism
like Debt Auction in MakerDAO, the conversion of the issued MKR into DAI might also be taxable for
the application. A CDP liquidation by a keeper might also be a taxable event for the borrower as the
CDP of the borrower would be ‘disposed’ to repay the DAI loan and the excess ETH will be returned to
the borrower after deduction of a penalty. The borrower might be subject to capital gain s tax , the loss
or gains would be determined by the basis of the liquidated crypto asset s. The penalty might be
allowed by some jurisdictions as a deductible expense if the borrowed DAI was used as an investment
or for trading .
Any governance tokens issued to the participants of the DeFi application in the form of airdrop s or
other wise would be taxed in most jurisdictions as income at the fair market value of the governance
token at the time of receipt. Spending, trading or selling the acquired governance tokens might attract
capital gains or taxed as business profit depending upon the nature and scale of the activity. The fee
charged by decentralized exchange s (DEXs) for swap transactions might also constitute their income.
Also, the returns paid by the DEXs to the liquidity providers might also constitute the income of
liquidity providers and allowed as an expense for the DeFi application. Any income or capital gains
obtained by further leveraging or selling tokens like UNI , LP or NFTs of a DEXs Liquidity Pool might also
attract taxes on income and Capital gains respectively. In most of the liquidity pools like Uniswap, the
liquidity providers get a fixed number of LP tokens or an LP NFT which represents their share in the
liquidity pool which increases in val ue over time. The LP tokens or the NFT can be exchanged for the
original tokens al ong with the financial return on the liquidity provided. T his realized gain at the time
of withdrawal or sale might be subject to capital gains in most jurisdictions.
DeFi applications might also hire teams/individuals to develop certain functionalities or provide
services to the DAO like assistance in establishing a foundation for the protocol or developing or fixing
software of the application . Such income might be characterized as self -employment income in some
jurisdictions and may require deduction of social security contributions and other taxes. Depending
upon the tax residency of the individuals hired by the DAO taxes might be required to be withheld.
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Also, the fee charged or incentives given to various other actors or entities like keeper s and oracle s
providing services to the DeFi application , might also be chargeable as income .
8.3.2 Indirect Taxes in DeFi ecosystem
Various stakeholders in DeFi ecosystem provide services to the users and other entities for a
consideration. For example , Uniswap users need to pay a fee for swapping one crypto asset for
another. Keepers also provide the service s of monitoring the collateralization ratios of the CDPs and
initiate Dutch auctions when they fall below the liquidation ratio. The keeper triggering the liquidation
receives an incentive in the form of a percentage of the collateral auctioned . Oracles perform the
service of providing external data to the smart contract s of DeFi platforms and charge a fee for the
same. The fee charged by various ent ities in the DeFi ecosystem may also be subject to GST/VAT
depending upon the tax residency, registration requirements, thresholds, and place of supply. The LP
NFTs minted on UniswapV3 may also be subject to VAT/GST in many jurisdictions.
8.4 Maximal Extractable Value (MEV) on Ethereum
Maximal Extractable Value refers to “ the maximum value that can be extracted from block production
in excess of the standard block reward and gas fees by including, excluding, and changing the order of
transactions in a block. ”127. The nodes on Ethereum have visibility of the transactions in the mempool
as transactions are not encrypted . This gives various actors on the Ethereum Blockchain the ability to
front -run some transactions and capture lucrative arbitrage and other opportunities like liquidation s.
For example, at a certain price ratio in a liquidity pool , if such actors are aware of a transaction that is
likely to cause the relative prices of tokens in that liquidity pool to move significantly , they might try
to place their own transaction (s) before and/or after the transaction , taking advantage of the prior
knowledge about the likely change in relative prices of the tokens in the li quidity pool. In one such
transaction128 due to difference in prices of the Ether and DAI between Uniswap and Sushiswap , a user
was able to make a profit of 45 ETH in a single transaction.
As it is known in advance that a large transaction might cause prices on the decentralized exchange s
to move , the user trying to extract MEV can pay much higher gas fee to place the buy and sell
transactions before and after the target transaction. Such strategies can cause a ‘tax’ to be levied on
the blockchain users due to slippage caused by sandwich trading. As users who want to extract MEV
are ready to pay very high gas fees to include transactions extracting MEV , common users have to pay
much higher ga s fee with increased network congestion and gas prices. However, not all MEV on
blockchain has such undesirable effects. MEV acts as an incentive for actors on the blockchain to keep
the prices in various DeFi applications at the general equilibrium price and makes the markets more
efficient.
As validators decide which transactions are included in the block and in what order, one might
conclude that the entire MEV would accrue to the validators. However, exploiting arbitrage and
liquidation opportunities requires running specialized algorithms and gaining faster access to the
transactions transmitted on the Ethereum network . This requirement is fulfilled by certain specialized
actors like builders, searchers, and specialized infrastructure like relays in the Ethereum ecosystem ,
which play a significant role in MEV extraction. Thus, unlike the traditional method of transactions
being sent by users to the mempool over the E thereum network , which are then subsequently
included in blocks proposed by validators , is hardly executed in practice. A cursory look at the most
recent blocks of the Ethereum blockchain129 highlights that in most of the blocks the fee recipient is
not the validator but a ‘builder .’ As the block proposer specifies the address which receives the fee , it
can be inferred that the blocks were created by the ‘builders’ which received the fees.
127 Maximal extractable value (MEV) | ethereum.org
128 https://etherscan.io/tx/0x5e1657ef0e9be9bc72efefe59a2528d0d730d478cfc9e6cdd09af9f997bb3ef4
129 https://etherscan.io/blocks
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To prevent the ir transactions from being front run , the actors in the MEV ecosystem join a mechanism
which communicates the transaction bundles prepared by a set of specialized bots called searchers
which continuously look out for MEV opportunities on the Ethereum Blockchain. The searchers submit
transaction bundles to specialized actors called ‘builders’ wh ich construct most profitable blocks from
these transaction bundles and transmit these blocks to validators through secure communication
channels called relays. Relays rece ive blocks from builders and forward them to validators, however
they can reorder or censor bundles based on their own policies . They also provide an available,
reliable , efficient, and fast channel of communication between builders and validators and provides a
layer of abstraction and anonymity between them.
Flashbots, a popular block space auction platform used by many searchers uses the first-price sealed -
bid auction or blind auction mechanism , wherein users can privately communicate their bid
transaction order preference without paying for failed bids130. The transactions bundle flow between
a searcher and builder is shown in Fig . 46.
Fig. 46 T ransactions bundle flow between a searcher and builder131
In the above scenario , the transaction bundles will be sent to the private transaction pool of Flashbots
instead of the common mempool of Ethereum nodes , which protects the transactions from front -
running. The searchers need to trust block builders as they have full visibility of the transaction s in the
bundle and can maliciously front -run the searchers as the builders can also operate searchers
themselves . As shown in Fig. 47 a searcher can submit transaction bundles to multiple builders and
the builders i n-turn can submit the blocks to various relays which connect them to validators.
Fig. 47 Searchers submitting bundles to builders who submit full blocks to relays132
The mechanism of creation of blocks by builders and their transmission securely to the validators
through relays creates centraliz ation within the Ethereum ecosystem which can be used for censoring
certain transactions by builders and relays . For example , on Nov 21 202 2 it can be seen in Fig. 48 that
130 https://docs.flashbots.net/flashbots -auction/overview
131 https://docs.flashbots.net/assets/images/searcher -architecture -d9a0bd137035304fc54067ce243c32ce.png
132 https://docs.flashbots.net/assets/images/block -builder -flow -0c01103143daeac8b79cc377ff248630.png
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79% of all Ethereum Blocks were OFAC compliant as MEV Boost, the then dominant off-chain
marketplace used by validators for selling block space to builders is OFAC compliant.
Fig. 48 Daily OFAC Compliant Blocks on Ethereum (source: https://www.mevwatch.info )
As relays also have full visibility of the transactions they need to be trusted by the builders. Relays do
not provide the full block to the validators on request. Instead, the header of the most profitable block
is provided to the validator which receives the full block from the relay only upon committing to
propose the block by sending the signed block header to the relay , this is depicted in Fig. 49.
Fig. 49 Relay selecting the most profitable block and providing validator with the block header
followed by the full block133
The validator can be connected to multiple relays and upon receipt of multiple block headers from
multiple relays, proposes the most profitable block on the Ethereum Blockchain as shown in the Fig.
50
133 https://docs.flashbots.net/assets/images/relay -flow -8f9aca183eaf4b8213220bc5bd71eb3a.png
85
Fig. 50 Validator proposing the most profitable block on the consensus layer134
The overall communication and auction architecture for block space is shown in Fig. 51
Fig. 51 Overall communication and auction architecture for Ethereum block space135
8.4.1 MEV Supply Chain and its taxation
The MEV ecosystem described above facilitates the extraction of MEV on the Ethereum Blockchain
with the help of various specialized actors and service providers which play crucial role in the entire
process. The income accruing to multiple actors in this ecosystem would be subject to tax in most
jurisdictions with most jurisdictions classifying it as a business activity , allowing deduction of allowable
expenses . However , as on date , there does not exist any specific guidance on fee or rewards received
through MEV . The MEV re lated services might also be subject to indirect taxes as actors like builders ,
validators get paid for their service of creating and proposing blocks containing speci fic transaction
bundles through the mechanism described above.
To understand the tax events in the process of MEV extraction starting from the creation of bundles
by the searcher to the final proposal of the block by the validator, it is important to track the payments
made to/by searchers , builders, and validators in a block on the Ethereum Blockchain. For example , in
the Ethereum Block No. 18840841 it can be observed that the block reward ( transaction fee – base
fee burn t) was transferred to the address 0x1f9090aaE28b8a3dCeaDf281B0F12828e676c326 (rsync -
builder.eth ) as shown in Fig 52.
134 https://docs.flashbots.net/assets/images/validator -flow -f3a8249b600db2b2b8d0a0344f336f95.png
135 https://docs.flashbots.net/assets/images/mevboost -searcher -bundle -flow -
bae4ba67a9d8d928efe337f36defa14a.png
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Fig. 52 Reward in Ethereum Block No. 18840841 accrued to rsync -builder.eth
A look at the MEV Info of the same block as shown in Fig. 53 indicates that the recipient of the Proposer
fee for the block is the Lido: Execution Layer Rewards Vault (validator) with the smart contract address
0x388C818CA8B9251b393131C08a736A67ccB19297
Fig. 53 MEV Info of the Block No. 18840841 of Ethereum
This transaction can also be seen in the list of transactions in the block 18840841 . As shown inf Fig. 54
this is the reward received by the validator (Lido) over and above the issuance and other rewards
received on the consensus layer . This is also the fee paid by the builder to the block proposer for
proposing the block sent by the builder through the relay as described above.
Fig. 54 list of transactions in the block 18840841
This might lead to a conclusion that for this block the builder paid more fee to the validator than the
reward received by it, thereby incurring a loss. However, a closer look at the internal transactions in
this block (Fig. 55) highlights two transactions where amounts of 0.013180 ETH and 0.032166 ETH
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were sent to the builder. This is the fee paid by the searchers or other users to the builder for inclusion
of their transaction bundle in the block. Thus, the net fee received by the builder for the block is
0.031971 + 0.013180 + 0.032166 - 0.063398 = 0.0139 19 ETH
Fig. 55 Internal transactions in block number 18840841
It can be observed that the searcher paid the builder through a transaction on the blockchain rather
than high gas fee for inclusion in the block, this method conditions the searcher’s bid on the inclusion
of their transaction in the block and obviates the need to pay for unsuccessful bids. However, to gain
reputation and remain competitive some builders might discount some blocks and incur a short -term
loss.
The payments made by the searchers and other actors to the builder and by the builder to the
validator constitute payments in lieu of services of including the transaction bundles in the blocks
formed by the builder and proposing the blocks formed by builder on the Ethereum Blockchain by the
validator respectively. Thus, depending upon the tax residency of the builders and validators the
services provided by them to searchers and builders may be subject to VAT/GST. As relays are not
currently monetized, they would not be subject to VAT/GST . The total value of MEV rewards extracted
before ‘the Merge’ of Ethereum is $675,623,114136. After ‘the Merge ’ of Ethereum the total MEV
extracted is 464,201 ETH137 which is ~800 million USD. Although these figures are not huge , but with
increasing transaction volumes it might not be possible for tax administrations to ignore this aspect of
the crypto asset s ecosystem.
9. Decentralized Autonomous Organizations - DAOs
Both the DeFi applications discussed above rely on smart contracts for their execution without the
need for any centralized entity or agency. The parameters like savings rate , token pairs to be
exchanged on the platform, collateralization ratio for various tokens etc. are decided by members
holding voting rights through governance tokens of the DeFi applications. Such DeFi applications are
classic examples of Decentralized Autonomous Organizations , which run on permissionless public
blockchain s without any centralized institutional structures , usually not having offices or addresses
where operations are carried out or decisions are made. The rules that govern such organizations are
encoded into and enforced through smart contracts on the blockchain , unlike conventional
organizations where board of directors and personnel with clearly defined powers and responsibilities
take decisions on behalf of the shareholders or participants .
DAOs attempt to address the ‘agency problem’ faced by many traditional organizations by making
decision -making more participative . Each member of the DAO’s governance contributes virtual assets
136 https://explore.flashbots.net/
137 https://transparency.flashbots.net/
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to the DAO and obtains rights to vote through governance tokens . Holders of governance tokens can
make proposals and vote on proposals through various voting models like Token -Based Quorum
Voting , Quadratic Voting etc.138. The wide variety of activities carried out by participants is based on
foundations of Cryptography and properties of the transactions undertaken on the blockchain . This
provides pseudonymity to the DAO ’s participants who can use the applications to carry out
transactions without any KYC requirements or disclosure of tax residency or beneficial ownership
information. Also, this renders the DAO technologically unable to gather the required information for
filing and compliance purposes, if any. As the application is essentially smart contract code running on
a public blockchain , it is possible to create a DAO without any formal registration with a State , any
government entity, or a regulator. This makes it possible for a DAO to have an organizational form
which does not formally associate with any legal entity with the location of its operations and users
not being known with certainty.
The World Economic Forum’s report Decentralized Autonomous Organization Toolkit defines DAO s as
organizational structures that use blockchains, digital assets and related technologies to allocate
resources, coordinate activities and make decisions . In simple terms, Decentralized Autonomous
Organizations (DAOs) are like digital clubs or teams where people work together, but there is no boss
or leader. Instead, decisions are made by everyone involved. Imagine a school club where all members
vote on what activities to do, what snacks to bring, and how to spend the club’s money. That is
analogous to how DAOs work, but they use blockchains and smart contracts instead . DAOs are not
just confined to financial applications like decentralized exchange s but can have a wide gamut of for -
profit and non -profit objectives. Some examples of different kind of DAOs are:
i. Protocol DAOs : These are like the “rule -makers” for online applications . They decide how the
application works, what features to add, and when to update it.
example : Uniswap is a popular decentralized exchange (DEX) protocol. Its governance
decisions are made by UNI token holders .
ii. Grant DAOs : These are akin to charity clubs. People pool their money to help others, like
donating to a cause they care about.
example : Gitcoin Grants allows contributors to fund open -source projects and public goods in
the crypto space .
iii. Social DAOs : These are like online communities. Members collaborate on projects, share
ideas, and organize events , undertaking tasks that require a big team effort .
example : Friends With Benefits (FWB) is a social DAO where members participate in events,
discussions, and projects .
iv. Collector DAOs : It is like a group of people who collect rare collectibles . They decide which
collectibles to buy and how to take care of them.
example : Flamingo DAO acquires rare NFTs (non -fungible tokens) and digital art.
v. Investment DAOs : These are like investment clubs. People put their money together to invest
stocks , new technologies , startups etc .
example : MetaCartel Ventures invests in early -stage blockchain projects.
vi. Media DAOs : Akin to a group of content creators (like YouTubers or bloggers) working
together. They decide what videos to make or articles to write.
example : Forefront is a media DAO where creators share revenue and collaborate on content.
Many of the different kinds of DAOs listed above have profit as one of their primary motives . Many
DAOs may also involve contributions from its members as well as other economic transactions like
acquisition of assets by the DAO, changes in value of assets or distribution of profits to the members .
The DAO’s governance tokens might also be tradable on secondary markets . All such transactions are
likely to involve transfer of economic value and may be taxable events , resulting in tax liability for the
138 DAO Voting Mechanisms Explained - LimeChain
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DAO at the entity level or for the participant. However, in the absence of a legal entity structure and
limited or no information about the location of its operations and users , the DAOs are likely to suffer
from uncertainty about their legal structure, tax liability , residency , and jurisdiction as well as the
applicable laws, rules , and regulations. It is likely that due to this uncertainty , till date no tax
administration has issued any guidance for taxing DAOs.
Despite the uncertainties associated with DAOs , the total value of assets locked in DAO treasuries as
of February 2024 is around 33 billion USD with around 10 million governance token holders , out of
which around 3 million are active voters and proposal makers139 . These statistics as shown in Fig. 56
are showing an increasing trend and are difficult for tax administrations across the world to ignore.
Fig. 56 DAO treasury values and number of active voters
Some pertinent questions related to DAOs which regulators in general and tax administrations in
particular would want to seek answers to are:
a) How do DAOs classify under existing laws? Are existing legal structures and frameworks
sufficient to provide legal personhood to DAOs or new legal technology is required for DAOs
which pose challenges to any known classification?
b) How does the place of operation of DAO get determined and which laws, rules and regulations
apply to it ?
c) How do the organizational structure and nature of the token s/crypto asset s determine the
regulatory framework of the DAO?
d) Is the DAO required to identify or collect information about its members ? If yes, then how it
does so to fulfil its legal and regulatory obligations ?
e) Who has the power to act legally on behalf of the DAO and how does the DAO confer such
power s?
f) How does a DAO carry out its legal affairs like filing of taxes and other compliances like social
security contributions , withholding taxes etc. ?
g) How the profits of DAOs from thei r multiple revenue streams are determined and who is
obligated to pay the corresponding taxes?
h) How do investors/participants and the DAO are prevented from No Taxation as well as Double
Taxation?
i) Do DAO participants have an unlimited or limited liability for debt s, judgements , obligations,
and liabilities of the DAO?
Though this is not an exhaustive list of issues being faced by stakeholders in the DAO ecosystem, but
the issues highlight the unique challenges posed by DAOs. Most of these questions arise because of
Blockchain based operations of DAOs and the pseudonymity of their members/users. These questions
might be difficult or impossible to answer due to the technological bottom -lines of crypto asset s. To
139 https://deepdao.io/organizations
90
answer these questions standardized technological solutions which address these issues on a global
scale would be required. It would also require global policy action to formulate bespoke laws, rules
and regulations that address the technological framework of DAOs, the blockchain s on which they
operate and their underlying crypto asset s.
Due to such uncertainties, there have been multiple instances where DAOs have faced actions from
regulators and other stakeholder s, some of which are given below:
a) CFTC action on Ooki DAO140: Ooki DAO faced regulatory action by the U.S. Commodity Futures
Trading Commission (CFTC ) for allegedly operat ing an illegal trading platform without proper
registration as a futures commission merchant. The CFTC filed a legal action against Ooki DAO,
alleging violations of the Commodity Exchange Act. Ooki DAO failed to respond, leading to a
default judgment. The court held that ‘Ooki DAO is a ‘person’ under the Commodity Exchange
Act and thus can be held liable for violations of the law .’ The court also ordered Ooki DAO to
pay a penalty and permanently cease operations.
b) SEC action on The DAO141: In 2017, the SEC investigated digital assets associated with The
DAO, a virtual organization which was created in 2016 with more than 11,000142 anonymous
investors who collectively raised more than 150 million USD143. The SEC applied the Howey
test, which is a legal framework used to determine whether an arrangement constitutes an
investment contract (and thus a security) and found that The DAO tokens met the criteria for
investment contracts. The SEC concluded that t hese tokens were securities, subject to federal
securities laws as The DAO did not meet the criteria for Regulation Crowdfunding exemption.
It issued a warning in the report that the digital assets offered by virtual organizations like The
DAO could be sub ject to federal securities laws.
c) MakerDAO arbitration144: On 12th March 2020, the crypto asset s market witnessed a crash
and the event later came to be known as Black Thursday. On the Black Thursday , the value of
Ether plummeted dramatically. As explain ed earlier, this led to under -collateralization of the
DAI loans in the Collateralized Debt Position vaults, triggering liquidations. Due to multiple
issues related to the crash in price of Ether, several CDP vaults could be liquidated at a bid
price of $ 0 resulting in heavy losses to the borrowers. Subsequently, a class -action lawsuit
was filed against the Maker Foundation which governed the Maker protocol and DAI , claiming
loss and damages for misleading investors about the risks associated with MakerDAO . The suit
culminated in an arbitration and subsequent dissolution of the Maker Foundation .
There is a general lack of certainty about the tax and regulatory treatment of DAOs which results in
instances of actions as given above. Many legal experts are of the opinion that DAOs would be
classified as general partnerships (Shenk, Van Kerckhoven, & Weinberger, 2023) . Some experts also
argue that DAOs have certain characteristics of corporations and they should be given the ability to
choose their legal entity status for tax and other regulatory purposes (Brunson, 2022) . However, the
truly Decentralized Autonomous Organization is an entity which lives and operates on the blockchain
and suffers from real impediments in its interaction wi th the physical world . Such interaction requires
agents who are natural persons acting on behalf of the entity for important tasks like regulatory
compliances, filing and payment of taxes and compliance and cooperation with law enforcement .
140 https://www.cftc.gov/media/8736/enfookidaoorder060923/download
141 https://www.sec.gov/news/press -release/2017 -131
142 The DAO Attack: Understanding What Happened – CoinDesk
143 https://www.bitstamp.net/learn/crypto -101/ethereum -dao-hack/
144 https://www.coindesk.com/policy/2020/09/29/28m -makerdao -black -thursday -lawsuit -moves -to-
arbitration/
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Thus, it would be important for a DAO to have a real-world legal identity to avail benefits of legal
personhood like access to banking , more certain tax and regulatory treatment and limited liability .
This would have to be augmented by technological solutions which help to ameliorate issues related
to pseudonymity on the blockchain .
9.1 Legal Entity Status of DAOs
DAOs can be defined as “ organizational structures that use blockchains, digital assets and related
technologies to allocate resources, coordinate activities and make decisions .145” As discussed above,
such organizations can commence their operations without any registration due to the pseudonymous
nature of its members and permissionless nature of the public blockchain s on which DAOs function.
Although this might have advantages of enabling decentralized decision making without a
management structure with clearly defined roles, it makes it difficult for DAOs to avail benefits of legal
personhood like being able to sue as an entity, having a bank account and being able to enter into
contracts , limited liability of its members , and simplified compliances. The legal entity statu s of a DAO
has profound consequences related to applicability of tax laws , AML/CFT/CPF and other regulatory
obligations of the DAO and its me mbers . This makes the issue of a DAOs legal entity status crucial.
Classification of DAOs as domestic or foreign general partnerships, unincorporated associations,
domestic or foreign corporations, foundations , or limited liability corporations has bearing on various
important issues like:
A) Liability of the members for debts, judgements, obligations, and liabilities of the DAO
B) Collection and filing of information relating to taxes and other regulations in multiple
jurisdictions
C) Determination of tax liability of the DAO and its members and identification of taxable events
It is also possible that various sub organizations within the DAO may have different classifications as
legal entities. For example , components of the DAO which manage the treasury and the component
managing voting on the protocol might have different classifications.
Many countries have enacted provisions to recognize DAOs as a legal entity. The Innovative
Technology Arrangements and Services (ITAS) Act of Malta146 enables DAOs to register by applying to
the Malta Digital Innovation Authority . In Switzerlan d, DAOs can be structured as ownerless
foundations and jurisdictions like Cayman Islands enable creation of an LLC structure that acts as real-
world interface of the DAO147.
Many states like Wyoming and Vermont in the US have also enabled DAOs to register as LLCs148.
However, many of these legal entities require filing of physical forms along with the relevant
information or establishing and maintain a registered agent in the jurisdiction , which might be
operationally difficult for truly decentralized DAOs and against their foundational principles. The
following discussion about DAOs and their legal aspects is from the perspective of the laws in the US,
other jurisdictions may have similar or different legal treatment and classification depending on their
domestic law.
145 Decentralized Autonomous Organization Toolkit
146 https://legislation.mt/eli/cap/592/eng/pdf
147 https://www.forbes.com/sites/irinaheaver/2023/08/14/the -ultimate -crypto -legal -guide -to-structuring -
your -dao/?sh=777f38957b81
148 https://sos.wyo.gov/Business/Docs/DAOs_FAQs.pdf
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9.1.1 Unregistered DAOs
One of the most important issues concerning the legal entity status of DAOs is the legal identity of an
unregistered Decentralized Autonomous Organization which operates on the blockchain with
pseudonymous members without registering with any State, government entity, or a regulator . As
mentioned earlier, in an important case related to the Ooki DAO in the US, the US District Court for
Northern District of California has adjudicated that Ooki DAO was an unincorporated association with
token holders as its members, who joined it voluntarily. It was observed that Ooki DAO was a ‘person’
under the Commodity Exchange Act and it could be sued as it was an unincorporated association . The
notice was served by Commodity Futures Trading Commission (CFTC ) through the online community
forum of the DAO , which was upheld by the court. A civil monetary penalty of $643,542 was levied
and its operations were shut down.
In California, an unincorporated association refers to a gathering of individuals united by a shared
purpose or goal, but lacking formal incorporation status with the state. Despite this absence of
incorporation, such associations maintain legal standing a nd can undertake activities such as
contractual agreements, property ownership, and litigation. While possessing fewer formalities and
regulatory obligations compared to corporations, they still hold recognition under the law. Example s
encompass social clu bs, community organizations, and informal business partnerships. There are no
State filing requirements for unincorporated associations , however, if the association is operating a
business , it may need to obtain relevant licenses or permits , which the Ooki DAO purportedly failed
to obtain from the CFTC. This case is important as it highlighted that for unregistered DAO s, members
of unincorporated association may have unlimited personal liability for the debts, obligations, and
liabilities of the DAO.
It may also be possible to classify many DAOs as general partnerships. General partnerships are a type
of business structure where two or more individuals (or entities) come together to operate a business
for profit. In a general partnership, each partner shares in the management, profits, and liabilities of
the business. They may be formed through a written or oral agreement and the members have
unlimited personal liability for the debts, obligations, and liabilities of the business. Thus, it might be
poss ible that all members holding the governance tokens of DAOs which have not otherwise been
incorporated as a legal entity form a general partnership inadvertently , as the intention to form a
partnership is not a pre -requisite for forming a general partnership. However, the pseudonymity of
the members of the general partnership and lack of the concept of a ‘principal office’ is a major
roadblock in determination of the jurisdiction that governs the DAO as a general partnership.
The classification as a general partnership might obligate a DAO to reporting requirements in
jurisdictions like the US. Even if none of the members of the DAO happen to be US tax residents , but
if the income of the DAO is attributable to the US, it might be required to report the same to the IRS.
Partnerships may also be required to submit an information return, Form 1065, to the IRS, detailing
the partnership's financial information. Additionally, each member receives a Schedule K -1, which
outlines their share of income, deductions, and other tax items, to be reported on their personal tax
returns. Due to pseudonymous nature of membership of the DAO, such information might be
extremely difficult to gather and furnish. Also, c onsidering the nature of DAOs operations it is not clear
that w ho would have the power to act l egally on behalf of the DAO to make such filings and how does
the DAO confer such powers ?
As many of the DAOs may not be domestic i.e. ‘Organized in the US’ it would be possible to classify
them as foreign partnerships. If the DAOs governance tokens are tradable on the secondary market,
it may be classified as a Foreign Publicly Traded Partnership. Foreign partnerships that conduct certain
activities within the US are subject to filing obligations with the IRS. This typically includes filing Form
1065, "U.S. Return of Partnership Income," if the partnership earns income effectively connected wit h
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a U.S. trade or business, or if it receives U.S. source income subject to withholding. Additionally, the
partnership may need to issue Schedule K -1 to each partner to report their share of income,
deductions, and other tax items
9.1.2 DAOs registered as a legal entity
Some DAOs are already registered in various jurisdictions under the existing legal framework s. This
provides the DAOs with the benefits of legal personhood, but might not eliminate the legal and
regulatory uncertainty completely. DAOs choose multiple legal wrappers based on legal and
regulatory considerations as well as tax implications. Some DAOs are constituted as foundations under
the laws of jurisdictions like the Cayman Islands and Switzerland . Such structures have benefit of
limited liability to identify members and beneficial owners , with limited liability of members of the
foundation for its debts and other obligations .
The LLC structure provides members of the DAO with benefit of limited liability and the flexibility to
choose how it is taxed based on specific circumstances . Brunson (2022 ) argue s that just like check -
the-box regulations led to more widespread adoption of LLCs as a legal entity, DAOs can also be given
a choice to opt their entity status for tax purposes. However, as Decentralized Autonomous
Organization live and operates on the blockchain and the Internet, and due to the pseudonymous
nature of their member s, DAOs registered as entities might find it technologically infeasible to collect
and report information related to tax residency, beneficial ownership etc. of its members.
Technological solutions that address the pseudonymity problem can help to resolve many legal,
regulatory and tax related issues related to both registered and unregistered DAOs.
9.2 Taxation issues of DAOs
One of the fundamental aspects related to taxation of DAOs and their members is the classification of
the tokens of the DAO. Some tokens might be classified as securities on application of the Howey Test
or any other similar test for securities in a jurisdiction. In such a scenario the tokens will be taxed as
securities. However, the tokens might serve dual purpose as utility tokens o f the DAO’s
protocol/application . Unless jurisdictions prescribe specific tax treatment for such tokens , it is likely
that ac tions like those taken by SEC in case of The DAO and by the CFTC in case of Ooki DAO may
continue.
The individuals or entities may undertake many transactions with the DAO which might be taxable.
The members may either receive governance tokens of the DAO or NFTs in the form of airdrop s or
they may pay a consideration for buying the tokens. In case of airdrop s, in most jurisdictions , an
income tax is chargeable upon the receipt of the airdrop . However, in some jurisdictions the receipt
of an airdrop is not taxed but is considered acquisition of a crypto asset with zero basis. Also, the
subsequent sale, swap, spend or gift transactions are also subject to capital gain s. However, in some
jurisdictions, depending upon the domestic tax laws and guidance, gifts might not be taxed.
In case the member pays a consideration for buying the governance tokens of a DAO which qualifies
as a general partnership , in some jurisdictions like the US , no gain or loss would be recognizable ( 26
U.S. Code § 721149), making the initial contribution non -taxable . For DAOs w hich may be treated by
default as general partnerships unless they register as any other entity, they might subject to pass -
through taxation where the DAO does not pay taxes as a business entity and the individual partners
pay taxes on their share of profits. The portfolios of the DAOs in the form of investments or NFTs etc .
may appreciate and taxed when realized , DAOs might also have income from staking on DeFi platforms
149 Section 721 of the Internal Revenue Code (IRC) of the United States deals with the tax treatment of
contributions to partnerships in exchange for partnership interests. Specifically, it states that no gain or loss
shall be recognized to a partnership or to any of its partners in the case of a contribution of property to the
partnership in exchange for an interest in the partnership.
94
and the Ethereum Beacon Chain . DAO’s sale of governance tokens for other crypto asset s or fiat
currency for funding the treasury, software development or any other purpose might also be a taxable
event. The individual partners of the DAO would have to account for profits from all the activities of
the DAO to pay the ir due taxes, which might be a challenge.
The partners in a jurisdiction might pay taxes in their jurisdiction of residence as well as taxes in foreign
jurisdictions for the DAOs profits attributable to those jurisdictions. This multi -jurisdictional liability
might make profit attributions and tax liability ascertainment extremely co mplex and possibly lead to
double or no taxation. If the DAO is registered as a corporation or is treated as a corporation for tax
purposes , its profits will be taxed at the level of corporation as well as at the time o f distribution of
profits. The distribution of profits by the DAOs or burning of governance tokens might be taxed as
dividends or share buyback in some jurisdictions.
DAOs also engage in the procurement of goods and services from multiple individuals and entities
which would be taxable for the recipients of the consideration. These transactions might also give rise
to VAT/GST liability and if the consideration paid is in the form of crypto asset s it might be considered
disposal for the purpose of capital gain s. For example , a DAO may assign responsibility for some of
their operations to a specialized entity or another DAO which might be subject to VAT/GST. DAOs that
employ people might have obligations to pay employer taxes, social security contributions etc.
Revenu e sharing arrangements or royalty payments might also be subject to taxes. Many of the
transactions and taxable events mentioned above might have withholding tax obligations. However,
in the absence of tax residency information, DAOs might be have to withhold taxes at a higher rate.
The mechanism for payment and seeking credit for withheld taxes is also a practical challenge.
10. International Cooperation and Exchange of Information
Ever since the first Bitcoin was mined in 2009 the landscape and scale of crypto asset s has changed
significantly. Even with recent dips in value of crypto asset s this asset class remains , and is likely to
remain a challenge for tax administrations across the world due to its pseudonymous and extra -
territorial nature. Many tax administrations have formulated laws and policies to tax this asset class ,
whereas some jurisdictions have put an outright ban or a partial ban on crypto asset s, whose efficacy
has not been very high (Chen & Liu, 2022) . However, many jurisdictions do not have any tax or
regulatory policies regarding crypto assets which creates a regulatory arbitrage .
Due to the unique characteristics of crypto asset s, in absence of clearly defined tax treatment and
guidance, lack of third -party -reporting and regulatory arbitrage , the collection of taxes is primarily
based on voluntary compliance , which might result in significant tax gaps . The UNCTAD has observed
that crypto asset s may enable tax evasion or avoidance through offshore flows whose ownership is
not easily identifiable, undermine capital controls , erode the tax base, and consequently hinder
Domestic Resource Mobilization for achieving the Sustainable Development Goals150.
The IMF Executive Board also discussed a board paper on key elements of an appropriate policy
response to crypto assets151 and considered that “the growing adoption of crypto assets in some
countries, the extra -territorial nature of crypto assets and its providers, as well as the increasing
interlinkages with the financial system, motivate the need for a comprehensive, consistent, and
coordinated response ” and it also agreed that the applicability of tax regimes should be clarified.
The UNCTAD also issued a policy brief that “ highlights the importance and urgency of international
cooperation regarding cryptocurrency tax treatments, regulation and information sharing as well as
150 https://unctad.org/system/files/official -document/presspb2022d10_en.pdf
151 https://www.imf.org/en/News/Articles/2023/02/23/pr2351 -imf-executive -board -discusses -elements -of-
effective -policies -for-crypto -assets
95
of redesigning capital controls to take account of the decentralized, borderless and pseudonymous
features of cryptocurrencies. ”152
The G20 Roadmap on crypto asset s also highlighted the significance of international collaboration in
tackling regulatory hurdles associated with crypto assets153. It underscored the necessity to share
information and coordinate actions to establish uniform regulatory frameworks across different
jurisdictions. This concerted effort aims to effectively address the challenges arising from the global
nature of crypto activities and promote regulatory consistency to ensure a cohesive approach
worldwide.
Presently, the FATF standards on virtual asset s and virtual asset service provider s and the Crypto -Asset
Reporting Framework proposed by the Organisation for Economic Co -operation and Development
(OECD) aim at achieving the comprehensive, consistent, and coordinated approach to Tax and
AML/CFT related aspects of crypto asset s. They are discussed in the following sections
10.1 FATF Standards on VAs and VASPs
The FATF standards pertaining to virtual asset s (VAs) establish a comprehensive framework aimed at
regulating virtual asset service provider s (VASPs) to mitigate the risks associated with money
laundering, terrorist financing, proliferation finan cing and other illicit activities. These standards
mandate that countries enforce licensing and registration protocols for VASPs, conduct thorough
customer due diligence (CDD), maintain transaction records, and promptly report any suspicious
activities to rele vant au thorities154. Furthermore, the FATF guidelines stress the importance of robust
supervision, enforcement mechanisms, international collaboration, and the adoption of risk -based
approaches to safeguard the integrity of the financial system while fostering innovation in the virtual
asset realm.
The FATF recommendations obligate VASPs to implement the Travel Rule , which requires VASPs to
share the information related to a transaction's sender and recipient with other VASPs . It mandates
that VASPs must obtain, hold, and transmit required originator and beneficiary information,
immediately and securely, when conducting virtual asset transfers , particularly for those surpassing a
designated threshold. This regulation aims to boost transparency and curb the potential for money
laundering, terrorist financing, and other unlawful activities within the virtual asset s ecosystem by
enabling authorities to track and monitor transac tions more efficiently, mirroring the obligations
imposed on conventional financial institutions. It characterizes some products like so called
Stablecoins and P2P transactions as high risk and guides the jurisdictions to take measures limiting the
ability of users to transact anonymously. However, the guidance does not quantify the size of the P2P
transactions and its associated money laundering /terror financing /proliferation financing risks.
The FATF defines virtual asset s as a “ digital representation of value that can be digitally traded or
transferred and can be used for payment or investment purposes. Virtual assets do not include digital
representations of fiat currencies, securities, and other financial assets that are alread y covered
elsewhere in the FATF Recommendations .”155 This definition covers almost all types of virtual assets
except few like NFTs. A VASPs is defined as:
152 https://unctad.org/publication/cost -doing -too-little -too-late-how -cryptocurrencies -can-undermine -
domestic -resource
153 https://www.mea.gov.in/Images/CPV/G20 -New -Delhi -Leaders -Declaration.pdf
154 https://www.fatf -gafi.org/en/publications/Fatfrecommendations/Fatf -recommendations.html
155 https://www.fatf -gafi.org/en/publications/Fatfrecommendations/Guidance -rba-virtual -assets -2021.html
96
any natural or legal person who is not covered elsewhere under the Recommendations and as a
business conducts one or more of the following activities or operations for or on behalf of another
natural or legal person:
i) Exchange between virtual assets and fiat currencies;
ii) Exchange between one or more forms of virtual assets;
iii) Transfer of virtual assets; and
iv) Safekeeping and/or administration of virtual assets or instruments enabling control over
virtual assets;
v) Participation in and provision of financial services related to an issuer’s offer and/or sale of
a virtual asset.
This definition does not cover m iners , validators and ancillary service providers like hardware wallet
manufacturers, cloud service providers, software developers etc. DeFi Applications are not VASPs as
per this definition. However, DeFi applications which have strong centralized operations, colloquially
known as DeFi in Name Only (DINO) , as well as not yet decentralized nascent DAOs may qualify as
VASPs as per the definition.
The Travel Rule requires the following information to be obtained and transferred before the VA
transfer takes place
i) originator’s name
ii) originator’s account number (wallet address)
iii) originator’s physical (geographical) address, or national identity number, or customer
identification number, or date and place of birth
iv) beneficiary’s name
v) beneficiary’s account number (wallet address)
The information is not required to be a part of the blockchain transaction . In case of transfer to an
unhosted wallet the VASPs are required to obtain the counterparty information from the customer.
Moreover, VASPs are required to perform CDD for customers transacting above a threshold amount
of virtual asset s. The ordering VASP is required to perform due diligence on the beneficiary VASP about
the Data Protection and Privacy ( DPP) measures related to security of travel rule information. I t also
encourages VASPs to collect additional information like:
a. the purpose of transaction or payment;
b. details about the nature, end use or end user of the item;
c. proof of funds ownership;
d. parties to the transaction and the relationship between parties;
e. sources of wealth and/or funds;
f. the identity and the beneficial ownership of the counterparty; and
g. export control information, such as copies of export -control or other licenses issued by the
national export control authorities, and end -user certification.
10.1.1 Challenges in Travel Rule implementation
The FATF Travel Rule requires VASPs to obtain, hold, and transmit required originator and beneficiary
information, immediately and securely, when conducting virtual asset transfers . However, due to the
pseudonymous nature of crypto asset s it might not be easy to ascertain the iden tity of the beneficiary
VASP and might require reliance on blockchain analytics and specialized networks /protocols for travel
rule solution s like TRISA156, OpenVASP157 and Notabene158. The FATF acknowledges that “ To date, the
FATF is not aware of any technically proven means of identifying the VASP that manages the
156 https://trisa.io/
157 https://www.openvasp.org/
158 https://notabene.id/
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beneficiary wallet exhaustively, precisely, and accurately in all circumstances and from the VA address
alone ”159. The technology neutral stand of FATF recommendations makes them agnostic to the
products, services, solutions, or technologies provided by various vendors if they comply with the
AML/CFT obligations .
There are many commercial solutions which provide travel rule services, but some of suffer from
various shortcomings like:
a) Lack of interoperability – this remains the most important challenge in Travel Rule
implementation
b) Lack of consensus on counterparty VASP due diligence
c) Inability to transfer information of both originator and beneficiary VASP before the
transaction on blockchain
d) Issues in recordkeeping and retrieval of Travel Rule information
e) Transmission of transaction IDs instead of wallet addresses by some Travel Rule solution
providers
Besides these, there are jurisdic tion specific issues like different approaches to transaction thresholds,
data privacy laws and approach on transactions to unhosted wallets. There are also issues related to
jurisdictions where the FATF recommendations related to VASPs have not been applied yet – The
Sunrise Issue. Moreover, as the FATF recommendations might not be complied by some jurisdictions
and tax administrations might find it difficult to access information that is mainly intended for AML
use, they may face challenges related to real-time use of such information for tax administration
purposes . Also, there are many technological challenges in compliance to the Travel Rule. In the
Targeted Update on Implementation of the FATF Standards on virtual asset s and virtual asset service
provider s, FATF has called on industry to accelerate efforts to strengthen solutions that are global,
and can accommodate nuances in requirements across jurisdictions, in line with the expectations of
the FATF Standards .
10.2 Crypto -Asset Reporting Framework
The Crypto Assets Reporting Framework introduced by the OECD addresses the challenges posed by
crypto asset s in the realm of taxation , specifically addressing the lack of taxpayer information . This
framework establishes guidelines and standards for the reporting and automatic exchange of
information regarding crypto asset transactions among tax authorities globally. With the rapid
proliferation of crypto assets and increase in their market capitalization , the OECD recognize d the
imperative to ensure transparency and compliance within this evolving landscape.
The CARF seeks to enable tax authorities to effectively monitor and regulate crypto asset transactions,
to help them combat tax evasion. By establishing clear reporting requirements for crypto asset
transactions, the framework aims to enhance tax compliance. Moreover, by facilitating the exchange
of information among tax authorities across jurisdictions, it fosters internation al cooperation in
addressing the unique tax challenges posed by crypto assets. Besides providing a framework for
multilateral and bil ateral agreements for exchange of information, it also provides a n XML schema for
informatio n exchange.
The CARF160 is similar to the Common Reporting Standard (CRS) developed by the OECD which help s
jurisdictions to obtain offshore accounts information through annual automatic exchange of
information. It defines the term “Relevant Crypto -Asset ” which means “ any Crypto -Asset that is not a
Central Bank Digital Currency, a Specified Electronic Money Product or any Crypto -Asset for which the
159 https://www.fatf -gafi.org/content/dam/fatf -gafi/guidance/Updated -Guidance -VA-VASP.pdf page 39
160 https://www.oecd.org/tax/exchange -of-tax-information/crypto -asset -reporting -framework -and-
amendments -to-the-common -reporting -standard.pdf
98
Reporting Crypto Asset Service Provider has adequately determined that it cannot be used for payment
or investment purposes .” This definition includes crypto asset s like NFTs and excludes specific
categories of crypto asset s like CBDCs . It makes the following three types of transactions reportable
to tax authorities:
• exchanges between Relevant Crypto -Assets and Fiat Currencies;
• exchanges between one or more forms of Relevant Crypto -Assets; and
• Transfers (including Reportable Retail Payment Transactions) of Relevant Crypto -Assets.
The amount and details of units of crypto asset s transferred to unhosted wallets are also require d to
be collected and retained for 5 years . The individuals or entities carrying out transactions related to
crypto asset s are known as “Reporting Crypto -Asset Service Provider s. A Reporting Crypto -Asset
Service Provider is defined as ‘any individual or Entity that, as a business, provides a service
effectuating Exchange Transactions for or on behalf of customers, including by acting as a
counterparty, or as an intermediary, to such Exchange Transactions, or by making available a trading
platform .’
The commentary to the CARF clarifies the applicability of the definition to individuals and entities like
software developers and decentrali sed exchanges. It states “ An individual or Entity that is making
available a platform that solely includes a bulletin board functionality for posting buy, sell or conversion
prices of Relevant Crypto -Assets would not be a Reporting Crypto -Asset Service Provider as it would
not provide a service allowing users to effectuate Exchange Transactions. For the same reason, an
individual or Entity that solely creates or sells software or an application is not a Reporting Crypto -
Asset Service Provider, as long as it is not using such software or application for the provision of a
service effectuating Exchange Transactions for or on behalf of customers. ”
The Reporting Crypto -Asset Service Provider are required to submit reports on Reportable Users161
and also undertake due diligence on the customers, both individuals and entities. They are required
to report the following information regarding Crypto -Asset Users that are Reportable Users or that
have Controlling Persons that are Reportable Persons:
- Name
- Address
- Jurisdiction(s) of Residence
- Tax Identification Number (s)
- Date and Place of Birth
The name, address and identifying information of the Reporting Crypto -Asset Service Provider along
with details of ‘Relevant Transactions’ need to be provided. Tax Identification Number ( TIN) and Place
of B irth may not be required to be reported if the domestic law does not require collecting the
information or TIN has not been issued by the reportable jurisdiction. The information is to be
obtained on a self -certification basis and its reasonableness needs to be confirmed relying on other
information collected by the Reporting Crypto -Asset Service Provider , like AML /KYC related
information.
The CARF is an amendment to CRS and based on a separate legal framework from the CRS, which
makes it possible for countries to implement CARF without signing up for CRS (Falcao & Michel, 2023) .
The definitions of Relevant Crypto -Asset s and Reporting Crypto -Asset Service Provider are wider than
the corresponding FATF definitions. This can enable tax authorities to obtain tax related information
for a wider gamut of crypto asset s and from a larger set of exchanges/ intermediaries. However, the
161 D Section IV: Defined Terms
99
determination of place of residence of a taxpayer might be a challenge in case of decentralized
exchange s which would likely qualify as Reporting Crypto -Asset Service Provider .
Although the XML Scheme standardizes the information exchange but absence “ of any technically
proven means of identifying the VASP that manages the beneficiary wallet exhaustively, precisely, and
accurately in all circumstances and from the VA address alone ” as acknowledged by FATF , would also
be a challenge for CARF as self -declaration based due diligence and blockchain analytics based
attribution of crypto asset addresses to natural and legal persons might not provide reliable and
accurate information for levy of taxes. Also, the information is provided annually which might lead to
flight of assets in tax fraud cases.
10.3 Need for Global Public Digital Infrastructure
Pseudonymity and extra-territoriality are two key aspects of crypto asset s which are a major hurdle
for tax administrations across the world to realize the full potential of taxes due on crypto asset s. Most
crypto asset service provider s and law enforcement agencies rely on blockchain analytics solutions
which attribute crypto asset addresses to a natural or legal person. Such attribution would not be
required for off -chain transactions undertaken by an individual or entity with a Reporting Crypto -Asset
Service Provider as the information can be obtained through CRS . The accurate determination of such
information for on -chain transactions i.e. P2P transactions is essential for levying taxes as the
ownership of private keys corresponding to the wallet would be required to be established before the
levy of tax. As the blockchain analytics -based attribution is mainly obtained through clustering
techniques and transaction behaviours, it might not establish legal ownership or control of the private
keys beyond doubt162.
One example of such attribute of a crypto asset address is its jurisdictional tax administration.
Accurate information about the originator and beneficiary tax jurisdiction is essential for addressing
multiple issues related to taxation in a transaction , like:
a) Percentage of amount to be withheld, if at all
b) Attribution of profits to a jurisdiction
c) Preparation and submission of information returns of an entity for a jurisdiction
d) Application of Double Taxation Avoidance Agreements
e) Determining tax treatment of a DAO and its members
The information related to off -chain transactions would be collected and retained for the prescribed
periods by the Reporting Crypto -Asset Service Provider . Information related to the transfer of crypto
asset s from a Reporting Crypto -Asset Service Provider needs to be obtained from the user , which
might require a cryptographic process similar to the Address Ownership Proof Protocol163. As the
transaction information is already stored on the blockchain it might be more prudent for tax
administrations to attribute crypto asset addresses to TINs using cryptographic methods and then use
the blockchain information about P2P transactions to determine tax liability of the taxpayer.
Presently the CRS information is exchanged through secure networks established by the participating
countries' tax authorities or the Common Transmission System (CTS) provided by the OECD which are
designed to ensure the confidentiality, integrity, and privacy of the exchanged data . However, this
data is exchanged annually after a lag and is not designed to provide real -time information which
might be the requirement for many tax jurisdictions to attribute the crypto asset address to a tax
jurisdiction and TIN . To meet this requirement tax administratio ns across the globe would require a
global public digital infrastructure for obtaining, maintaining , and facilitating the ac cess to such
attribution data ensur ing the confidentiality, integrity, and privacy of the data , where exchange of
162 https://finance.yahoo.com/news/crypto -tracing -revolutionizing -crime -fighting -124050120.html
163 https://www.21analytics.ch/what -is-aopp/
100
data can take place in accordance to policies based on Exchange of Information Agreements between
tax jurisdictions . The tax administration might use measures like prescribing higher withholding tax
rates for transactions with wallets for which such attribution information could not be obtained from
any ta x jurisdiction in real time .
One probable design of such a system may use digital signature certificates issued to every TIN holder
from its respective tax authority . Using the digital signature certificate issued by the tax authority and
digital signature s from their wallet addresses the tax jurisdiction of the TIN can obtain a cryptographic
proof of the ownership of the crypto asset private keys by the TIN holder. The tax jurisdiction would
be required to maintain a record of all public addresses of its TIN holders for which it holds the
cryptographic proof , and in real time, respond to queries from other jurisdictions about the ownership
of a Crypto address by a TIN holder in the jurisdiction . As the number of unique Ethereum address es
which have been used for transactions till date is in tens of Millions and the number of unique Bitcoin
address es that have been used for transactions till date is in few billions, using appropriate data
structures and search methods, the tax administrations can respond to requests from other
jurisdicti ons about a TIN holder in real -time.
Whenever a new transaction is to be carried out, the wallet software can broadcast a message signed
with the digital signature issued by the originating jurisdiction enquiring about the tax jurisdiction of
the beneficiary crypto address as depicted in Fig. 57 . Depending upon the EOI agreements some or no
tax jurisdiction may respond to the broadcast about the presence of the TIN of the beneficiary crypto
asset address owner in the affirmative. In case a response is received the transaction can be carried
out as usual on the blockchain , as the jurisdictions, by virtue of processing requests based on
cryptographic proofs, would automatically obtain the visibility of the transactions, and may also
exchange the personal information related to the TINs . In case no response is received broadcast
about the presence of the TIN of the beneficiary crypto asset address from any tax jurisdiction, the
originator may be obligated by its tax jurisd iction to withhold taxes at a higher rate to safeguard the
interest of the tax administration of the transaction originator. This design may also enable tax
administrations to perform their own analytics on on-chain transaction data and the benefici al
ownership and controlled entity information captured in the tax returns filed by their taxpayers .
Another such secure information sharing mechanism that can be used by virtual asset service
provider s can be XMTP Protocol164 based messaging. This messaging protocol provides a wallet -to-
wallet messaging functionality provided both the wallets on -board to an XMTP protocol -based
application. This service can provide a VASP with technically proven means of identifying the VASP
that manages the beneficiary wallet exhaustively, precisely, and accurately in all circumstances and
from the VA address alone , if the beneficiary VASP also uses an XMTP protocol -based application. The
onward and backward communication between two wallets is depicted in Fig. 58 and 59. Multiple
applications built on the protocol would be interoperable . An application can be developed as a global
public digital infrastructure like the SWIFT network for exchanging Travel Rule information between
VASPs , ensuring the adherence to Data Privacy and Protection . As most VASPs follow the interVASP
Messaging Standard 101 (IVMS 101)165, which is a data format for exchanging travel rule information,
like the XML scheme in CARF, a standard protocol for communication can solve the problem of
interoperability. Just as the protocols approved by the Internet Engineering Task Force like the
Hypertext Transfer Protocol166 (HTTP ) and Transport Layer Security167 (TLS) Protocol provide the broad
framework which needs to be adhered to by developers, similar adoption of standards would be
required to overcome the problem of interoperability. The SWIFT network used by financial
164 https://xmtp.org/ .
165 https://www.intervasp.org/
166 https://www.ietf.org/rfc/rfc2616.txt
167 https://www.ietf.org/rfc/rfc5246.txt
101
institutions for the rapid, precise, and secure transmission of transaction information is another such
example .
Fig. 57 A Digital Signature based solution to find tax jurisdiction of a crypto asset address
10.4 The Challenge of Anonymity Enhancing Crypto Assets
Tracing anonymity -enhancing crypto assets, particularly those like Monero, presents multifaceted
challenges due to the sophisticated technological features embedded within these cryptocurrencies.
Monero leverages cutting -edge cryptographic techniques such as ring signatures, stealth addresses,
and confidential transactions to provide users with unparalleled privacy and fungibility. One of the
primary hurdles in tracing Monero transactions lies in its use of ring signatures, which obfuscate the
sender's iden tity by mixing the sender's transaction with those of other users, making it virtually
impossible to determine the true source of a transaction.
Country A
Country B
Country C
Country D
Country E
broadcca
Country F
Country G
Who has 0xf9efa2?
I have 0xf9efa2 !
102
Fig. 58 XMTP: Onward communication between two wallets168
168 https://xmtp.org/ Portions of this page are modifications based on work created and shared by XMTP and
used according to terms described in the Creative Commons 4.0 Attribution License
103
Fig. 59 XMTP: Backward communication between two wallets169
Unlike Bitcoin, where transactions can be traced through a transparent ledger, Monero's blockchain
obscures transaction details, including sender, recipient, and transaction amount, thereby thwarting
traditional tracing methods. Additionally, Monero employs stealth addresses to enhance privacy by
generating unique, one -time addresses for each transaction, making it challenging for external
observers to link transactions to specific recipients. Furthermore, Monero's integration of confidential
transactions encrypts transaction amounts, adding another layer of complexity to tracing efforts.
Thes e technological innovations collectively render Monero transactions highly opaque and resistant
to external scrutiny, posing significant challenges for tax authorities and law enforcement agencies
seeking to enforce taxation laws and tracing illicit activities . Blockchain mixers and tumblers also pose
similar challenges.
Addressing these challenges necessitates the development of novel investigative techniques and
regulatory frameworks tailored to the unique characteristics of privacy -centric cryptocurrencies like
Monero, as well as collaboration between public and private sectors as well as governments on a
global scale. Various blockchain intelligence and analytics solution providers claim the ability to trace
Monero . However, it remains extremely difficult, if not impossible , to trace Monero purely through
conventional methods used for other crypto asset s like Bitcoin and some other conventional
investigative inputs might be required.
169 https://xmtp.org/ Portions of this page are modifications based on work created and shared by XMTP and
used according to terms described in the Creative Commons 4.0 Attribution License
104
11. Conclusion
Crypto assets have posed unique challenges to tax administrations throughout the world . On one hand
they need to appropriately classify crypto assets as property/asset or means of payment /negotiable
instrument and on the other hand address challenges due to their pseudonymous and extra -territorial
nature. Tax administrations are innately designed to tax conventional means of holding and
transmitting value as well as economic activity. This technologically challenging realm of economic
activity creates tax design issues related to neutrality, efficiency and equity as well as their practical
implementation. Policymakers would need to address the complex issues related to direct and indirect
taxation of crypto assets , including issues related to the environmental costs of crypto assets, to
provide clarity about their tax treatment without impeding progress or inhibiting innovation in this
domain.
With ‘Crypto Winter’ slowly turning into a ‘Crypto Spring’ the revenues involved would be significant ,
especially for developing countries due to the large policy and compliance gaps that exist. The
significant amounts involved in GST/VAT related to sales of goods and services in lieu of crypto assets
as well as GST/VAT on cross border services like mining services , MEV and NFTs need urgent attention
of tax administrations. This would require a policy response which is coherent and compatible with
the technological bottom -lines of crypto assets and provides certainty to facilitate compliance . At the
same time, they need to be conscious about the tax policy arbitrage opportunities that exist in this
ecosystem, which may result in non -compliance and revenue loss. The swift pace of innovation in this
field presents a formidable challenge for policymakers to effectively stay abreast to formulate prudent
and practical policies . The migration of commercial activity from digital commerce in real world to
virtual worlds like metaverses can fundamentally challenge the ideas on which our present tax systems
are designed. In near future, tax admini strations might be compelled to have a presence on various
blockchain s to collect the due taxes.
Capacity development, especially in the context of developing countries, must be at the core of the
strategy of tax administrations to deal with this asset class. Having policymakers well versed with the
technolog y related aspects of crypto assets , which have profound tax policy implications, can
formulate appropriate policies to help realize the revenues associated with asset class. Each auditor
who can accurately determine the tax liability of crypto asset owners in his/her jurisdiction can collect
the fa ir share of taxes as well as prevent the misuse of this technology for tax evasion, bypassing
capital controls and money laundering . Just as issues related to international taxation and transfer
pricing remain focus areas for capacity development in many jurisdictions, similar focus needs to be
adopted for the taxation related issues of crypto assets.
The inherent nature of crypto assets transcends national borders, rendering traditional tax
frameworks inadequate in capturing and regulating these assets effectively. The decentralized and
pseudonymous nature of blockchain technology poses formidable challenges in tracking and reporting
taxable transactions. Effective exchange of taxpayer information among countries and
implementation of the travel rule are indispensable for combating tax evasion and money laundering
as well as ensuring compliance within the crypto ecosystem . It would also help to avoid non -taxation
and double taxation of crypto assets. Without attribution of transactions to natural and legal persons
even high -capacity jurisdictions might be unbale to effectively tax this asset class. As this ecosystem
continues to evolve, collaborative efforts are essential to address emerging issues such as DeFi, NFTs
and the metaverse . The creation of global public digital infrastructure for exchange of such
information in real time like the SWIFT network , based on standardized protocols can help to mitigate
many practical challenges associated with pseudonymity. Also, the issues related to anonymity
enhancing crypto ass ets as well as services like tumblers and mixers need to be addressed as those
intending to evade taxes may resort to them as avenues for tax regulatory arbitrage shrink.
105
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|
{
"id": "2403.15074"
}
|
2405.00051
|
Arbitrage impact on the relationship between XRP price and correlation
tensor spectra of transaction networks
|
The increasing use of cryptoassets for international remittances has proven
to be faster and more cost-effective, particularly for migrants without access
to traditional banking. However, the inherent volatility of cryptoasset prices,
independent of blockchain-based remittance mechanisms, introduces potential
risks during periods of high volatility. This study investigates the intricate
dynamics between XRP price fluctuations across diverse crypto exchanges and the
correlation of the largest singular values of the correlation tensor of XRP
transaction networks. Particularly, we show the impact of arbitrage
opportunities across different crypto exchanges on the relationship between XRP
price and correlation tensor spectra of transaction networks. Distinct periods,
non-bubble and bubble, showcase different characteristics in XRP price
fluctuations. Establishing a connection between XRP price and transaction
networks, we compute correlation tensors and singular values, emphasizing the
significance of the largest singular value. Comparisons with reshuffled and
Gaussian random correlation tensors validate the uniqueness of the empirical
tensor. A set of simulated weekly XRP prices, resembling arbitrage
opportunities across various crypto exchanges, further confirms the robustness
of our findings. It reveals a pronounced anti-correlation during bubble periods
and a non-significant correlation during non-bubble periods with the largest
singular value, irrespective of price fluctuations across different crypto
exchanges.
|
http://arxiv.org/pdf/2405.00051v1
|
Abhijit Chakraborty, Yuichi Ikeda
|
physics.soc-ph, q-fin.GN, q-fin.ST
|
physics.soc-ph
|
Arbitrage impact on the relationship between XRP price
and correlation tensor spectra of transaction networks
Abhijit C hakraborty1,2,∔*and Yuichi I keda1,†
1Kyoto University, Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto,
606-8306, Japan
2RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Saitama, 351-0198,
Japan
E-mail: ∔abhijit@iisertirupati.ac.in
†ikeda.yuichi.2w@kyoto-u.ac.jp
The increasing use of cryptoassets for international remittances has proven to be faster and more
cost-e ffective, particularly for migrants without access to traditional banking. However, the inher-
ent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, intro-
duces potential risks during periods of high volatility. This study investigates the intricate dynamics
between XRP price fluctuations across diverse crypto exchanges and the correlation of the largest
singular values of the correlation tensor of XRP transaction networks. Particularly, we show the im-
pact of arbitrage opportunities across di fferent crypto exchanges on the relationship between XRP
price and correlation tensor spectra of transaction networks. Distinct periods, non-bubble and bubble,
showcase di fferent characteristics in XRP price fluctuations. Establishing a connection between XRP
price and transaction networks, we compute correlation tensors and singular values, emphasizing the
significance of the largest singular value. Comparisons with reshu ffled and Gaussian random corre-
lation tensors validate the uniqueness of the empirical tensor. A set of simulated weekly XRP prices,
resembling arbitrage opportunities across various crypto exchanges, further confirms the robustness
of our findings. It reveals a pronounced anti-correlation during bubble periods and a non-significant
correlation during non-bubble periods with the largest singular value, irrespective of price fluctua-
tions across di fferent crypto exchanges.
KEYWORDS: cryptoassets, price fluctuation, correlation tensor, singular values
1. Introduction
In recent years, cryptoassets have become widely used as an essential means of international
remittances [1, 2]. For example, when migrants transfer money earned from working in the destina-
tion country to family members in the country of origin, international remittances using cryptoassets
are faster and cheaper than the traditional method of transferring money via banks. In particular, for
migrants who do not have bank accounts in their economically developing countries of origin, inter-
national remittances using cryptoassets are the only practical way to send money. However, the prices
of cryptoassets are more volatile than fiat currency at times, and international remittances during pe-
riods of high price volatility are said to be risky [3]. The price of cryptoassets is independent of the
blockchain-based remittance mechanism for cryptoassets. The price is determined when fiat currency
and cryptoassets are exchanged on cryptoasset exchanges.
The price of a cryptoasset generally varies from exchange to exchange, as well as the point
*Currently at the Department of Humanities and Social Sciences, Indian Institute of Science Education and Research
Tirupati, Tirupati 517619, India.
1arXiv:2405.00051v1 [physics.soc-ph] 15 Apr 2024
in time when the cryptoasset is exchanged for fiat currency. Concerning di fferences in the price of
cryptoassets at di fferent points in time, if cryptoassets are purchased and remitted abroad to family
members in the country of origin when the price of cryptoassets is low, and if cryptoassets can be
sold when the price of cryptoassets is high, the family members in the country of origin can obtain
more economic value. On the other hand, concerning the price di fference between exchanges, if
cryptoassets are purchased on an exchange where the price of cryptoassets is low and remitted abroad
to family members in the country of origin, and cryptoassets are sold on an exchange where the price
of cryptoassets is high, the family members in the country of origin can obtain more economic value.
This is referred as an arbitrage.
The application of Random Matrix Theory (RMT) [4–6] has become a prevalent approach in the
analysis of financial time series data, encompassing domains like stock prices [7–9], foreign exchange
rate [10, 11], and other macroeconomic indicators. Recently, we utilized random matrix theory to
study crypto transaction networks [12–16]. Specifically, our method [12, 13] involving correlation
tensor spectra provides a crucial understanding of the relationship between XRP price and the largest
singular value. Our previous work [12, 13] found that the largest singular value of the correlation
tensor for the transaction networks and the price of cryptoassets (on a particular exchange) shows
a significant negative correlation. Using this negative correlation may, therefore, reduce the risk of
price volatility of cryptoassets at di fferent points in time. However, no attention has so far been paid
to the impact of exchanges on the price di fference of cryptoassets. If the price di fference by exchange
is more significant than the di fference by point in time, then the negative correlation described above
would have no practical significance.
In this paper, we first examine the time series of the price of the cryptoasset XRP on representative
exchanges to determine the magnitude of the price di fferences by the exchange. It then examines the
correlation between the first singular value of the correlation tensor of the transaction network and
the price, considering the exchange-specific price di fferences obtained from the actual price time
series. Statistical tests test the significance of the correlation. This study aims to answer whether the
negative correlations between the largest singular value of the correlation tensor for the transaction
networks and the prices of cryptoassets obtained in our previous studies are statistically significant
when considering the di fferences in prices between exchanges.
The paper is organized as follows. We describe our data in Section 2. In Section 3 we provide a
brief description of our methods. Following that, Section 4 presents a comprehensive discussion of
the results obtained from our investigation. Finally, in Section 5, we share concluding observations.
2. Data
We collect XRP daily close price data from nine di fferent crypto exchanges. The typical 24-hour
trade volume on these crypto exchanges is tabulated in Table I. It is important to note that among the
nine exchanges, Bittrex and FTX are currently not operational. We additionally present the monthly
volumes across three leading cryptoasset exchanges in Fig. A·1.
XRP transaction data is collected from the Ripple API. We construct weekly networks of users’
wallets from the transactional data by aggregating the XRP transaction volume for a week between
pairs of wallets. It is noteworthy that the weekly transaction networks are weighted and directed
in nature. XRP wallets serve as the nodes, and a weighted directed link represents the total XRP
transferred from a source wallet to a destination wallet in a week.
3. Methods
We provide a concise overview of our methodology in the following subsections.
2
Table I. Crypto exchanges in terms of 24 hour volume on February7, 2024, according to analytics website
Coinranking.
Exchange 24h V olume ( billion USD)
Binance 11.20
OKX 1.39
Huboi 1.20
Kraken 0.85
Bitstamp 0.19
Bitfinex 0.18
Exmo 0.03
Bittrex -
FTX -
3.1 Network embedding
Network embedding is a technique in machine learning that transforms nodes in a network into
low-dimensional vectors, capturing the structural and relational information of the graph. We em-
ployed the node2vec algorithm [17] to embed each weekly weighted directed network into a D-
dimensional space. Setting the return parameter pand in-out parameter qboth to 1 in the algorithm
signifies the use of unbiased random walks. Consequently, every node in the networks is represented
by a D-dimensional vector, denoted as Vα
i, where iandjare node indices, and αandβare compo-
nents in the D-dimensional space. This algorithm encodes both local community structure and global
network topology in the learned node embeddings.
3.2 Correlation tensor
Utilizing the correlation tensor method and its diagonalization through double Singular Value
Decomposition (SVD) o ffers valuable insights into crypto transaction systems. [12–14]. In the weekly
XRP transaction networks, we consider Nnodes conducting at least one weekly transaction during
the investigated period, categorizing them as regular nodes. Each regular node within the embedding
space is presented as a time series of D-dimensional vectors, indicated as Vα
i(t). Here, ivaries from
1 toN,tspans from 1 to T, andαranges from 1 to D. The correlation tensor among components of
regular nodes is expressed as:
Mαβ
i j(t)=1
2∆Tt+∆TX
t′=t−∆T[Vα
i(t′)−Vα
i][Vβ
j(t′)−Vβ
j]
σVα
iσVβ
j, (1)
In this equation, we compute the sum across five weekly networks at times t′={t−2,t−1,t,t+1,t+2}
within a time window of (2 ∆T+1), where ∆T=2 for our analysis. The terms Vα
iandσVα
idenote the
mean and standard deviation of Vα
iover a time window of (2 ∆T+1)=5 weekly networks at times
{t−2,t−1,t,t+1,t+2}. It’s crucial to acknowledge that a smaller ∆Tintroduces more noise into
the correlation tensor. However, opting for a larger ∆Tis not feasible due to the detailed temporal
evolution of the networks. In this analysis, we adopt a dimensionality of D=32. The influence of the
correlation tensor on window size (2 ∆T+1) and dimension Dis elaborated in [12].
3.3 Double singular value decomposition
To get the spectrum of the correlation tensor, we employ a double SVD approach as outlined
below:
We consecutively diagonalize Mαβ
i jthrough a bi-unitary transformation, or SVD, first in terms
of the ( i j)-index and subsequently the ( αβ)-index. In the initial step, we represent Mαβ
i jas a sum of
3
matrices using the SVD technique.
Mαβ
i j=NX
k=1Lαβ
ikσαβ
kRαβ
k j. (2)
In the subsequent stage, we proceed to decompose each singular value σαβ
kinto a sum of matrices,
employing the SVD method:
σαβ
k=DX
γ=1Lαγ
kργ
kRγβ
k. (3)
Finally, substituting Eq. 3 into Eq. 2, we get the following expression for Mαβ
i j:
Mαβ
i j=NX
k=1DX
γ=1ργ
k(Lαβ
ikRαβ
k j)(Lαγ
kRγβ
k). (4)
whereργ
kdenotes the N×Dgeneralized singular values, characterized by being both real and positive,
due to all elements of the correlation tensor, Mis real.
Fig. 1. The daily XRP prices at Huboi (HTX) crypto exchange. The four vertical lines represent the following
dates: A) January 6, 2020; B) November 1, 2020; C) February 1, 2021; and D) August 1, 2021.
4. Results
We show the daily XRP prices from January 18, 2018, to January 22, 2024, at the Huobi exchange
in Fig. 1. The graph illustrates a considerable fluctuation in XRP prices, ranging from 0.136 USD to
4
1.838 USD during this timeframe. Vertical lines on the Fig. mark specific dates: A) January 6, 2020;
B) November 1, 2020; C) February 1, 2021; and D) August 1, 2021. In this study, our primary focus
lies on the periods AB and CD. These two phases exhibit distinct characteristics regarding XRP price
fluctuations. Notably, the XRP price remained relatively stable during period AB. In contrast, period
CD showcases a notable surge and decline in XRP prices. We label CD as the ”bubble period” and
AB as the ”non-bubble period” for XRP prices.
We examine how the XRP price varies among di fferent crypto exchanges in Fig. 2. We have
considered daily XRP prices from nine crypto exchanges, which are listed in Table 1. Considering
these nine crypto exchanges, we calculated the daily mean XRP price, ⟨XRP/USD⟩(daily), shown
in Fig. 2 (a) for the period AD. The daily fluctuation in XRP price among the exchanges is measured
by the standard deviation σ. The fluctuation in the daily XRP price among the crypto exchanges is
illustrated in Fig. 2 (b). The fluctuation in the daily XRP price among the crypto exchanges was very
low during the first ten months of 2020. However, we observed a high fluctuation, even up to 5%, be-
yond the first ten months of 2020. We calculated the weekly XRP price, ⟨XRP/USD⟩(weekly), for a
particular crypto exchange by averaging the daily XRP price over seven days. The mean ⟨XRP/USD⟩
(weekly) and standard deviation σ(weekly) in the weekly XRP price among the crypto exchanges are
shown in Fig. 3 (a) and (b), respectively. The fluctuations are relatively low for weekly XRP prices,
as expected.
Fig. 2. Mean daily XRP prices, ⟨XRP/USD⟩(daily) and fluctuation σ(daily) over nine di fferent crypto
exchanges.
To establish a connection between the XRP price and the XRP transaction network, we compute
the correlation tensor among the components of the regular nodes, denoted as N. This process is
detailed in the Method section. The number of regular nodes is determined to be 465 and 753 for the
time periods AB and CD, respectively. Focusing on the regular nodes during period CD, we compute
the correlation tensor Mαβ
i j(t) for the week t=April 5-11, 2021. The correlation tensor contains a
total of N×N×D×Delements. Extracting essential insights from this tensor involves diagonalizing
it through a technique known as double SVD, as detailed in Method section. The double SVD is
an extension of the standard SVD, typically employed for matrices. By applying double SVD to the
5
Fig. 3. Mean weekly XRP prices, ⟨XRP/USD⟩(weekly) and fluctuation σ(weekly) over nine di fferent
crypto exchanges.
weekly correlation tensor, Mαβ
i j(t), we derive the singular values denoted as ργ
k(t).
Determining the significance of the empirical correlation tensor requires comparing it with the
reshu ffled correlation tensor. To construct the reshu ffled correlation tensor, we perform a reshu ffling
of the components within the time window (2 ∆T+1) for the embedded regular node vector Vα
i.
Utilizing these reshu ffled embedded regular node vectors, we compute the reshu ffled correlation ten-
sor according to Eq. 1. Furthermore, we simulate singular values of a Gaussian random correlation
tensor utilizing random matrix theory [6, 18–21]. The elements of the Gaussian correlation tensor,
denoted as Gαβ
i j, are drawn from a Gaussian distribution with a mean of zero and a standard deviation
ofσG=0.5, where ( i,j=1,..., N) and (α,β=1,..., D). We opt for σG=0.5 to align with the
standard deviation of our empirical correlation tensor.
The probability distribution function for the largest singular values of the Gaussian random cor-
relation tensor, denoted as (˜ ρ1
k) for all k, is expressed as [13]:
P(˜ρ1
k)=1
πσ2
Gq
(˜ρ1
1)2−(˜ρ1
k)2. (5)
Here, the largest singular value is given by ˜ ρ1
1=2σGD√
Nfork=1.
Fig.4 (a) displays the singular values ργ
kfor all k∈[1,N] andγ=1, along with the singular values
for the reshu ffled correlation tensor. Notably, only the largest singular value ρ1
1for the empirical
correlation tensor surpasses the largest singular value of the reshu ffled correlation tensor.
We extend this comparison to other singular values ργ
kfor all k∈[1,2,3,..., N] andγ∈
[2,3,4,..., D] in Fig.4 (b). Here, we observe that several singular values of the empirical corre-
lation tensor surpass the largest singular values of the reshu ffled correlation tensor, although these
singular values are notably smaller than ρ1
1. Consequently, their contribution to the correlation tensor
is relatively minor.
Additionally, we compare the empirical singular values with those of the Gaussian random cor-
relation tensor, where the elements are drawn from a normal distribution. The singular values ˜ ρ1
kof
the Gaussian random correlation tensor follows Eq. 5. In Fig.4(c), the simulated singular values, ˜ ρ1
k,
6
Fig. 4. Singular value examination of correlation tensors from April 5 to 11, 2021. (a) Comparative analysis
of singular values, ρ1
k, for the empirical correlation tensor (depicted by black filled circles) and the reshu ffled
correlation tensor (represented by red filled triangles) across all kvalues. (b) Exploration of singular values,
ργ
k, for the empirical correlation tensor and the reshu ffled correlation tensor, encompassing all kvalues with
γ >1. (c) Simulated singular values, ρ1
k, for the Gaussian random correlation tensor (illustrated as green open
squares), accompanied by the corresponding analytic curve from Eq. 5 (solid purple line). (d) Examination
of simulated singular values, ργ
k, for the Gaussian random correlation tensor, considering all values of kwith
γ>1.
of the Gaussian correlation tensor exhibit a smooth fit with the analytical curve provided by Eq.5.
Fig.4 (d) presents the spectrum, ˜ ργ
k, of the Gaussian random correlation tensor across all kvalues,
whereγranges from 2 to D. It is evident that the singular values of the Gaussian correlation tensors
are much smaller than those of the empirical and reshu ffled correlation tensors. The singular values
of the reshu ffled correlation tensor tend to approach those of the Gaussian correlation tensor when
the time window ∆Tbecomes significantly larger than N[12]. While we present results specifically
for the week April 5-11, 2021, it is worth noting that these findings remain qualitatively consistent
for any other week.
We generate a set of 1000 time series representing weekly XRP prices, denoted as XRP /USD( t)
(simulated). These time series are simulated from Gaussian distributions characterized by the mean
7
Fig. 5. Comparison of the mean simulated weekly XRP prices, ⟨XRP/USD(t)⟩(simulated) with the mean
correlation coe fficient,⟨r(t)⟩, depicting the relationship between the mean simulated weekly XRP prices,
⟨XRP/USD(t)⟩(simulated) and the largest singular value ρ1
1(t−1). A moving window of 9 weeks is applied
for two distinct periods: (a) AB, covering January 6, 2020, to November 1, 2020, and (b) CD, ranging from
February 1, 2021, to August 1, 2021. The black curves illustrate the mean simulated weekly XRP prices,
⟨XRP/USD(t)⟩(simulated). The blue curves, marked with green and red triangles, represent the mean correla-
tion coe fficient,⟨r(t)⟩, with green triangles denoting significant correlations (p-value <0.05) and red triangles
indicating no significant correlations (p-value >0.05). The two lower panels display the corresponding p-values
for Pearson correlations. Dotted grey vertical lines delineate the weekly windows.
⟨XRP/USD⟩(weekly) and the standard deviation σ(t) (weekly). To assess the relationship between
the simulated weekly XRP prices and the largest singular value ρ1
1(t−1), we employ the Pearson
correlation coe fficient. Specifically, we calculate the correlation within a moving time window of
length 9 weeks. The Pearson’s correlation coe fficient, denoted as r(t), is defined as follows:
r(t)=1
2∆τt+∆τX
t′=t−∆τ[y(t′)−⟨y⟩][ρ1
1(t′−1)−⟨ρ1
1⟩]
σyσρ1
1, (6)
where the variable ycorresponds to XRP /USD( t) (simulated), and we have chosen ∆τ=4. Herein,
the symbols σand⟨·⟩denote the standard deviation and mean, respectively, of the quantities within
the temporal span of (2 ∆τ+1) weeks.
We calculate mean correlation coe fficient⟨r(t)⟩over 1000 simulated set of weekly XRP prices,
XRP/USD( t) (simulated). The mean simulated weekly XRP prices are represented as ⟨XRP/USD( t)⟩
(simulated). We investigate the temporal correlation separately for two di fferent periods, AB and CD.
The temporal variation of the mean correlation coe fficient⟨r(t)⟩between the simulated weekly XRP
prices, XRP /USD( t) (simulated), and the largest singular values ρ1
1(t−1) of the weekly correlation
tensors is shown along with the mean simulated weekly XRP prices, ⟨XRP/USD( t)⟩(simulated), in
Fig. 5. The anti correlation is high and significant during the period CD. The anti correlation mostly
8
Fig. 6. (a) Distribution of correlation coe fficients between the largest singular value ρ1
1(t−1) and simulated
1000 weekly XRP Price times series XRP/US D (t) for the week, t=April 12−18,2021. (b)Distribution of
associated p-values.
non-significant during the period AB. This reflects the fact that the formation of a large bubble in
the XRP price is indicated by a strong anti correlation with the largest singular value. The findings
underscore the impact of XRP price fluctuations across various exchanges. Specifically, the largest
singular value, ρ1
1, demonstrates a pronounced and meaningful average negative correlation during
the bubble period. Conversely, during non-bubble periods, the average correlation is observed to be
non-significant.
Our analysis extends to a more detailed exploration of the correlation coe fficients with each sim-
ulated weekly XRP price time series, XRP /USD( t) (simulated). Specifically, we focus on two distinct
weeks, namely t1andt2, each chosen from distinct periods - t1from April 12-18, 2021, falling within
the bubble period CD, and t2from April 27- May 3, 2020, situated within the non-bubble period AB.
In Fig. 6, we present the distributions of correlation coe fficients, r(t), along with associated p-values
between the largest singular value ρ1
1(t−1) and a simulated set of 1000 weekly XRP Price time series,
XRP/USD( t) (simulated), for the week t=t1. Notably, all correlation coe fficients r(t) exhibit a highly
negative and statistically significant pattern. Conversely, in Fig. 7, we illustrate the distributions of
correlation coe fficients r(t) and corresponding p-values for the same analysis conducted for the week
t=t2. Remarkably, all correlation coe fficients r(t) in this case are observed to be non-significant.
These findings provide additional a ffirmation that our results remain consistent across each simulated
time series. This underscores the notion that price fluctuations across various crypto exchanges do not
exert any significant influence on our conclusions. This phenomenon can be attributed to the minimal
price variations of XRP across various trading exchanges, leading to limited arbitrage opportunities.
Notably, the p-values associated with the correlation between the largest singular value of the corre-
lation tensor and the price reveal that this correlation remains significant even when accounting for
the price di fferentials of XRP across diverse exchanges, particularly during the bubble period.
9
Fig. 7. (a) Distribution of correlation coe fficients between the largest singular value ρ1
1(t−1) and simulated
1000 weekly XRP Price times series XRP/US D (t) for the week, t=April 27 - May 3 ,2020. (b)Distribution of
associated p-values.
5. Conclusions
We have examined the impact of XRP price fluctuations across di fferent crypto exchanges on the
correlation between the largest singular values of the correlation tensor of XRP transaction networks
and the XRP price. For this purpose, we have collected XRP price data from nine di fferent crypto
exchanges. We have displayed the daily and weekly prices and their fluctuations over a period of
approximately 1.5 years. To uncover the correlation between price and XRP transaction networks,
we have calculated the singular values of correlation tensor spectra. Comparing this with a reshu ffled
correlation tensor, we found that only the largest singular value is significant.
To capture the price impact of fluctuations, we have generated a set of 1000 simulated weekly
XRP price time series from a Gaussian distribution with an identical mean and standard deviation as
empirical XRP prices. We have found that irrespective of XRP price fluctuations at crypto exchanges,
the largest singular value shows a strong, significant anti-correlation during the bubble period and a
non-significant correlation during the non-bubble period with the XRP price. Our simulated weekly
XRP price mirrors the potential arbitrage opportunities present across various cryptocurrency ex-
changes. Upon examination of correlation coe fficients and p-values between the largest singular value
and these time series, it becomes evident that arbitrage exerts a non-significant impact. This can be
attributed to the relatively low arbitrage opportunities across diverse cryptocurrency exchanges.
Appendix A: Cryptoassets monthly exchange volume
We illustrate the monthly spot market volume across three prominent cryptoasset exchanges from
January 2020 to August 2020 in Fig. A·1. It visually demonstrates the evolution of Binance as it
ascends to become the largest crypto exchange during this period.
10
Fig. A·1. Monthly spot market volumes across three major cryptoasset exchanges from https://www.
theblock.co/data/crypto-markets/spot .
Appendix B: Data sources:
•Binance: https://www.investing.com/crypto/xrp/xrp-btc-historical-data
•OKX: https://www.investing.com/crypto/xrp/xrp-usd-historical-data
•Huboi: https://www.investing.com/crypto/xrp/xrp-usd-historical-data?cid=1058260
•Kraken: https://www.marketwatch.com/investing/cryptocurrency/xrpusd
•Bitstamp: https://www.CryptoDataDownload.com
•Bitfinex: https://www.CryptoDataDownload.com
•Exmo: https://www.CryptoDataDownload.com
•Bittrex: https://www.CryptoDataDownload.com
•FTX: https://www.CryptoDataDownload.com
•Transaction networks: https://xrpl.org/data-api.html#payment-objects .
Acknowledgements
We express our gratitude to Tetsuo Hatsuda for his valuable comments on our manuscript. YI
acknowledges the financial support received from the Ripple Impact Fund (Grant Number: 2022-
247584) for partial support to this work.
References
[1] D.C. Mills, K. Wang, B. Malone, A. Ravi, J. Marquardt, A.I. Badev, T. Brezinski, L. Fahy, K. Liao, V .
Kargenian, and M. Ellithorpe, FEDS Working Paper (2016).
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[2] L. Rella, Frontiers in Blockchain, 2, 14 (2019).
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[4] M. L. Mehta, Random matrices , Elsevier (2004).
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[6] A. M. Sengupta, and P. P. Mitra, Physical Review E 60(3), 3389 (1999).
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[8] V . Plerou, P. Gopikrishnan, B. Rosenow, L. A. N. Amaral, and H. E. Stanley, PRL 83, 1471 (1999).
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[12] A. Chakraborty, T. Hatsuda, and Y . Ikeda Scientific Reports 13, 4718 (2023).
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[14] A. Chakraborty, T. Hatsuda, and Y . Ikeda JPS Conf. Proc. 40, 011003 (2023).
[15] Y . Ikeda, In Digital Designs for Money,Markets, and Social Dilemmas, 203–220 (Springer, 2022).
[16] Y . Ikeda and A Chakraborty, JPS Conf. Proc. 40, 011004 (2023).
[17] A. Grover, and J. Leskovec, Proc. ACM SIGKDD int. conf. on Know. dis. and data mining 22855 (2016).
[18] A Edelman, and N.R. Rao, Acta numerica 14233 (2005).
[19] J.-P. Bouchaud, M. Potters, Financial applications of random matrix theory: a short review , inThe Oxford
Handbook of Random Matrix Theory , Eds. G. Akemann, J. Baik, P. Di Francesco, Oxford University Press,
Oxford (2015), Chapter 40, 824-850.
[20] M. Rudelson, R. Vershynin, Non-asymptotic theory of random matrices: extreme singular values , inPro-
ceedings of the International Congress of Mathematicians 2010 (ICM 2010) (In 4 Volumes) Vol. I: Plenary
Lectures and Ceremonies Vols. II–IV: Invited Lectures , 1576-1602 (2010), World Scientific.
[21] W. Bryc, J. W. Silverstein, Random Matrices: Theory and Applications 9(04), 2050012 (2020),
12
|
{
"id": "2405.00051"
}
|
1707.07442
|
Intelligent Vehicle-Trust Point: Reward based Intelligent Vehicle
Communication using Blockchain
|
The Intelligent vehicle (IV) is experiencing revolutionary growth in research
and industry, but it still suffers from many security vulnerabilities.
Traditional security methods are incapable to provide secure IV communication.
The major issues in IV communication, are trust, data accuracy and reliability
of communication data in the communication channel. Blockchain technology works
for the crypto currency, Bit-coin, which is recently used to build trust and
reliability in peer-to-peer networks having similar topologies as IV
Communication. In this paper, we are proposing, Intelligent Vehicle-Trust Point
(IV-TP) mechanism for IV communication among IVs using Blockchain technology.
The IVs communicated data provides security and reliability using our proposed
IV-TP. Our IV-TP mechanism provides trustworthiness for vehicles behavior, and
vehicles legal and illegal action. Our proposal presents a reward based system,
an exchange of some IV-TP among IVs, during successful communication. For the
data management of the IV-TP, we are using blockchain technology in the
intelligent transportation system (ITS), which stores all IV-TP details of
every vehicle and is accessed ubiquitously by IVs. In this paper, we evaluate
our proposal with the help of intersection use case scenario for intelligent
vehicles communication.
|
http://arxiv.org/pdf/1707.07442v2
|
Madhusudan Singh, Shiho Kim
|
cs.CR
|
cs.CR
|
Intelligent Vehicle -Trust Point: Reward based
Intelligent Vehicle Communication using Blockchain
Madhusudan Singh
Yonsei Institute of Convergence Technology
Yonsei University
Seoul, South Korea
msingh@yonsei.ac.kr
Shiho Kim
School of Integrated Technology,
Yonsei University
Seoul, South Korea
shiho@yonsei.ac.kr
Abstract—The Intelligent vehicle (IV) is experiencing
revolutionary growth in research and industry, but it still suffers
from many security vulnerabilities. Traditional security methods
are incapable to provide secure IV communication. The major
issues in IV communication, are trust, data accuracy and
reliability of communication data in the communication channel.
Blockchain technology works for the crypto currency, Bit-coin,
which is recently used to build trust and reliability in peer-to-
peer networks having similar topologies as IV Communication.
In this paper, we are proposing, Intelligent Vehicle -Trust Point
(IV-TP) mechanism for IV communication among IVs using
Blockchain technology. The IVs communicated data provides
security and reliability using our proposed IV-TP. Our IV-TP
mechanism provides trustworthiness for vehicles behavior, and
vehicles legal and illegal action. Our proposal presents a reward
based system, an exchange of some IV-TP among IVs, during
successful communication. For the data management of the IV-
TP, we are using blockchain technology in the intelligent
transportation system (ITS), which stores all IV-TP details of
every vehicle and is accessed ubiquitously by IVs. In this paper,
we evaluate our proposal with the help of intersection use case
scenario for intelligent vehicles communication.
Keywords — Blockchain, intelligent vehicles, security,
component; ITS
I. INTRODUCTION (Heading 1)
Current ITS system uses ad-hoc networks for Vehicle
communication such as DSRC, WAVE, Cellular Network,
which does not guarantee secure data transmission. Currently,
vehicle communication application security protocols are based
on cellular and IT standard security mechanism which are not
up-to-date and suitable for ITS applications. Still many
researchers are working to provide standard security
mechanism for ITS. Our proposed mechanism is advantages as
it is easy to implement, it’s a peer -to -peer communication, it
provides a secure and trust environment for Vehicle
communication with immutable database and ubiquitous data
access in a secure way. Our proposal is based on a very simple
concept of using Blockchain based trust environment for data
sharing among Intelligent Vehicles using the IV-TP (Intelligent
Vehicle -Trust Point). We are exploiting the features of
Blockchain i.e. distributed and open ledger which is encrypted
with Merkel tree and Hash function (SHA -256) and are based
on Consensus Mechanism (Proof of Work Algorithm). We
have not mentioned the details of the Blockchain mechanism
for our application Intelligent Vehicle data sharing due to the
limitation of space.
Previously, some researchers combined automotive and
blockchain technology but most of them considered
applications based on services and smart contracts. However,
our proposal concentrates on secure and fast communication
between intelligent vehicles (self-driving cars) [5]. We have
proposed a unique crypto Intelligent Vehicle -Trust Point (IV-
TP) based on blockchain technology. Our proposal explains the
management of IV-TP for intelligent vehicles communication
and we elicited the benefits of IV-TP and evaluated our
proposal with intersection use case scenario for intelligent
vehicles communication.
We organize our articles as follows; Section II presents the
motivation of using Blockchain based trust environment for
data sharing among Intelligent Vehicles using the IV-TP
(Intelligent Vehicle -Trust Point) over traditional security
methods. Section III presents the introduction of blockchain
technology and existing work of blockchain technology for
Intelligent Vehicles communication. Section IV, describes, our
proposed reward based intelligent vehicles communication
mechanism based on blockchain technology, Section V,
discusses the generation of the intelligent vehicle trust point
(IV-TP), Section VI evaluates our proposal with intersection
based use case scenarios; Section VII concludes our paper, and
discuss our future work for our proposed mechanism
II. RELATED WORK
A. Blockchain Technology
Blockchain technology is distributed, open ledger, saved by
each node in the network, which is self-maintained by each
node. It provides peer-to-peer network without the interference
of the third party. The blockchain integrity is based on strong
cryptography that validates and chain blocks together on
transactions, making it nearly impossible to tamper with any
individual transaction without being detected [6].
Fig.4 shows the Blockchain technology features such as
shared ledger, Cryptography, Signed blocks of transactions,
and digital signatures [6].
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea go vernment(MSIP) (No.2017 -0-
00560, Development of a Blockchain based Secure Decentralized Trust network for intelligent vehicles)
B. Previous work: Blockchain technology for Intelligent
Transportation System.
Yong yuan, et.al [7] has proposed the blockchain
technology for ITS for establishment of secured, trusted and
decentralized autonomous ecosystem and proposed a seven -
layer conceptual model for the blockchain.
Benjaminet.al [8], have also proposed the blockchain
technology for vehicular ad-hoc network (VANET). They have
combined Ethereum’ blockchain based smart contracts system
with vehicle ad -hoc network. They have proposed combination
of two applications, mandatory applications (traffic regulation,
vehicle tax, vehicle insurance) and optional applications
(applications which provides information and updates on traffic
jams and weather forecasts) of vehicles. They have tried to
connect the blockchain with VANET services. Blockchain can
use multiple other functionalities such as communication
between vehicles, provide security, provide peer-to-peer
communication without disclosing personal information etc.
Ali dorri et.al. [9] have proposed the blockchain technology
mechanism without disclosing any private information of
vehicles user to provide and update the wireless remote
software and other emerging vehicles services. Sean Rowen
et.al. [10] have described the blockchain technology for
securing intelligent vehicles communication through the visible
light and acoustic side channels. They have verified their
proposed mechanism through a new session cryptographic key,
leveraging both side-channels and blockchain public key
infrastructure.
We define our blockchain mechanism for the intelligent
vehicles communication environment. We propose the secure
environment peer-to-peer communication between intelligent
vehicles without interfering/disturbing other intelligent
vehicles. We also evaluate our proposed mechanism with
intersection road scenario based use case. Fig. 2. Proposed blockchain Intelligent Vehicle Communication
A. Network enabled Connected device
It is an internet -enabled device, which can organize,
communicate in VANET such as Smartphone, PDA, Intelligent
Vehicles, etc.
B. Vehicular Cloud Computing
VCC is a hybrid technology that has a remarkable impact
on traffic management and road safety by instantly using
vehicle resources, such as computing, data storage, and internet
decision -making.
C. Blockchain supported intelligent vehicles
Blockchain consists of a technically unlimited number of
blocks which are chained together cryptographically in
chronological order. In this, each block consists of transactions,
which are the actual data to be stored in the chain.
IV. VEHICLES -TRUST POINT GENERATION
We propose an Intelligent Vehicles -Trust Point (IV-TP)
crypto unique ID that is issued by vehicle seller/authorized
dealers. This IV-TP is developed by blockchain crypto
mechanism and is similar to bitcoin. This IV-TP is issued to
every intelligent vehicle. During communication, vehicles
provide IV-TP to build trust in the communication network.
The Vehicular networks having blockchain enabled
service/user data providers, manages the IV-TP.
IV-TP is an encrypted unique number, which uniquely
issued to every IV and called as IV-TP ID. Every IV has its
own IV-TP ID, generated by the authorized authority. The IV-
TP is earned by calculating some computation in the group
communication. Greater the IV-TP attained by an IV, higher
will be its respect and honor. With the help of IV-TP, one can
get the complete history of vehicles (accident history, condition
of IV, crime history, etc.). The IV-TP access method is show in
figure 6.
III. INTELLIGENT VEHICLE -TRUST POINT : REWARD -BASED
INTELLIGENT VEHICLES COMMUNICATION USING BLOCKCHAIN
We propose a reward based intelligent vehicles
communication using blockchain technology. Our proposed
mechanism has three basics technologies including
communication network enabled connected device, Vehicular
Cloud Computing (VCC) and blockchain technology (BT). Authorized Dealer
Fig. 3. IV-TP access methods
Fig. 1. Blockchain technology
Blockchain technology based intelligent vehicles
communicate with each other following the steps shown below:
We have explained the process of IV-TP sharing and
verification in figure 7.
A. Key generation
Firstly, each IV will generate its private and public key.
The blockchain will maintain the public key of all IVs in
network and when an IV want to communicate another IV then
it will access the public of another IV from the blockchain.
B. Digital Signature:
Secondly, each vehicle shall digitally sign the message to
check integrity and non-repudiation of the message. With
digitally signed message, receiver can easily find, that the
message is not tampered, and the sender of the message is a
valid IV in the network.
C. Verification
Lastly, the receiver after receiving message identifies the
sender by verifying the digitally signed encrypted message.
After verification, receiver decrypts the message with the
Fig. 4. Message process between two IV
Example Setting of IV network
Consider, five intelligent vehicles having IV-TP as IV-
1, IV-2, IV-3, IV-4, & IV-5, respectively. Message is
broadcasted from IV-1 to IV-5 as shown in figure 8.
Each vehicle broadcasts the message in the network.
The blockchain maintains a table shown in Table 1
showing the information of “who communicates with
whom”. It does not require any personal information
of any IV. It only needs IV-TP of IVs. TABLE 1. COMMUNICATION INFORMATION OF IVS ON BLOCKCHAIN
Each IV broadcast the message in the network, using the
message framework shown in fig. 9.
Fig. 6. Message Framework example
In fig 9, IV-1 and IV-5 are IV-TP of intelligent vehicles 1
and 5 respectively. TF is time flag of message broadcasted
from intelligent vehicles.
D. Consensus Protocols
Broadcasted message will be validated only after
verification by more than 50% of the network vehicles. This
validation process is based on Proof of Driving (PoD)
algorithm. PoD provides evidence that the vehicles are legal
and are running in the same network shared by the approved
vehicles at the communication time. All vehicles
communication data will be managed on the vehicular cloud
with the IV-TP ID. If in future, IV owners want to sell or
change their IVs, then they can access their complete data
history via the vehicular cloud.
V. USE CASE EVALUATION OF BLOCKCHAIN ENABLED IV
COMMUNICATION
We evaluate our proposed method with the help of use
case. We randomly select the intersection scenario as a use case
example for the explanation of our proposal.
A. IV communication on Intersection scenario
Consider, four IVs (IV-1, IV-2, IV-3, and IV-4) reach the
intersection, almost at the same time, as shown in figure 10. In
this condition, how our proposed system will help to overcome
this situation. Before coming to the intersection, each IV will
broadcast its status (a message that they want to cross the
intersection) on the network. Every IV near the intersection
will receive the broadcasted message in the network. They will
first verify the IV-TP ID from VC, and then each vehicle will
calculate the received time of the broadcasted message of the
vehicles. The vehicle, which first calculates the time, will
broadcast the vehicles IV-TP ID, which first arrived at the
intersection. Other vehicles will also calculate and verify the
first arrived vehicle at the intersection. If everyone agrees, they
will give way to the first arrived vehicle to go first or cross the
Send public
key of IV-1
Blockchain
(Store Public
key of IV’s in Access public
Public ledger) key of IV-1
Decryption process
Encryption process
Fig. 5. Intelligent Vehicular networks
intersection first. In table 2, we show the IV-TP ID of each
vehicle and their message broadcasted time, and received time
from other vehicles.
Fig. 7. Intersection scenario for IV communication
According to the table 2 shown below, IV-1 comes first on
the Intersection region and IV-3 calculates first so, IV-3 will
broadcast the message that IV-1 will move first based on first
come first serve approach and IV-3 will also propose move
sequences for other vehicles ( IV-1then next IV-2, then next
IV-3 and last IV-4). Other IVs (IV-1, IV-2, and IV-4) will
calculate the time and agree with the schedule given by IV-3.
IV-1 will get some reward in terms of IV-TP with its own IV-
TP ID. All this information of the vehicles will be stored on the
vehicular cloud with their IV-TP IDs.
TABLE 2. TIME SCHEDULE OF MESSAGE BROADCASTED AND RECEIVED BY
IV’S
IV-
TP Message
Transmissio
n Time
(sec.) Message
Received
by IV-TP Receiver
Time
(sec.) Reward
Note: our proposed mechanism use case is for general
vehicles and not specified for special vehicles such as
ambulance, police, VIP’s vehicles, etc.
In our use case, the vehicles are an intelligent machine
(internet connected self-driving vehicles) so, they have enough
computational power to calculate time. VI. CONCLUSION
In this paper, we have presented a reward based intelligent
vehicle communication based on blockchain technology and
not for specific services as previously proposed by other
researchers. We have proposed crypto IV-TP that will help to
improve the privacy of IVs. IV-TP provide fast and secure
communication between IVs. It also helps to detect the detailed
history of IVs communication. IV communication data will be
stored on the VC, as long as the user wants. During any
accident, the IVs communication history and their reputations
are ubiquitously available to authorized organizations (hospital,
insurance company, police etc.) and home via VC.
In future, we will simulate our proposed mechanism for
multiple vehicle communication scenarios as well as analyze
different use cases (suspicious actions by IVs and managing
IV-TP etc.) with a solution.
ACKNOWLEDGMENT
This work was supported by Institute for Information &
communications Technology Promotion (IITP) grant funded by
the Korea government (MSIT) (No.2017 -0-00560,
Development of a Blockchain based Secure Decentralized Trust
network for intelligent vehicles) .
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|
{
"id": "1707.07442"
}
|
1712.07564
|
Transaction Propagation on Permissionless Blockchains: Incentive and
Routing Mechanisms
|
Existing permissionless blockchain solutions rely on peer-to-peer propagation
mechanisms, where nodes in a network transfer transaction they received to
their neighbors. Unfortunately, there is no explicit incentive for such
transaction propagation. Therefore, existing propagation mechanisms will not be
sustainable in a fully decentralized blockchain with rational nodes. In this
work, we formally define the problem of incentivizing nodes for transaction
propagation. We propose an incentive mechanism where each node involved in the
propagation of a transaction receives a share of the transaction fee. We also
show that our proposal is Sybil-proof. Furthermore, we combine the incentive
mechanism with smart routing to reduce the communication and storage costs at
the same time. The proposed routing mechanism reduces the redundant transaction
propagation from the size of the network to a factor of average shortest path
length. The routing mechanism is built upon a specific type of consensus
protocol where the round leader who creates the transaction block is known in
advance. Note that our routing mechanism is a generic one and can be adopted
independently from the incentive mechanism.
|
http://arxiv.org/pdf/1712.07564v2
|
Oguzhan Ersoy, Zhijie Ren, Zekeriya Erkin, Reginald L. Lagendijk
|
cs.CR
|
cs.CR
|
Transaction Propagation on Permissionless
Blockchains: Incentive and Routing Mechanisms
O˘guzhan Ersoy, Zhijie Ren, Zekeriya Erkin and Reginald L. Lagendijk
Cyber Security Group, Department of Intelligent Systems,
Delft University of Technology
Delft, The Netherlands
Email: o.ersoy@tudelft.nl, z.ren@tudelft.nl, z.erkin@tudelft.nl, r.l.lagendijk@tudelft.nl
Abstract —Existing permissionless blockchain solutions rely
on peer-to-peer propagation mechanisms, where nodes in a
network transfer transaction they received to their neighbors.
Unfortunately, there is no explicit incentive for such transaction
propagation. Therefore, existing propagation mechanisms will
not be sustainable in a fully decentralized blockchain with
rational nodes. In this work, we formally define the problem
of incentivizing nodes for transaction propagation. We propose
an incentive mechanism where each node involved in the prop-
agation of a transaction receives a share of the transaction fee.
We also show that our proposal is Sybil-proof. Furthermore, we
combine the incentive mechanism with smart routing to reduce
the communication and storage costs at the same time. The
proposed routing mechanism reduces the redundant transaction
propagation from the size of the network to a factor of average
shortest path length. The routing mechanism is built upon a
specific type of consensus protocol where the round leader who
creates the transaction block is known in advance. Note that
our routing mechanism is a generic one and can be adopted
independently from the incentive mechanism.
Index Terms —Blockchain, transaction propagation, incentive,
routing.
I. I NTRODUCTION
In this work, we investigate transaction propagation on per-
missionless blockchains with respect to incentive compatibility
and bandwidth efficiency. The former, incentive compatibility,
is an essential component of permissionless blockchain to
maintain its functionality with rational participants [1], [2].
The latter, bandwidth efficiency, is an important factor for
efficient use of limited resources available in the network.
Although a number of works have studied incentive com-
patibility problem of blockchains, they are limited to min-
ing mechanism, e.g. investigating selfish mining attacks [3]–
[6], and block withholding attacks [7]–[10]. The existing
blockchain solutions such as Bitcoin [11] and Ethereum [12]
do not pay attention to incentives for transaction propagation in
the network. This is due to the fact that the mining networks
in those solutions are centralized in practice [13]–[15] and
thus, they do not exhibit a fully decentralized structure. There
are only two works that address incentive compatibility of
transaction propagation in blockchain by Babaioff et al. [16]
and Abraham et al. [17]. Unfortunately, both works suggest
a specific solution for the incentive compatibility but do
not provide a formal definition of the problem. Furthermore,the proposed solutions are also designed for certain network
topologies.
In terms of bandwidth inefficiency, existing solutions suffer
from multiple broadcasting of the same transaction over the
network. For example, in Bitcoin, each transaction is received
by the nodes (miners) in the network twice: once during
the advertisement, i.e. broadcasting of the transaction at the
beginning, and once after the validation, i.e. broadcasting
of the block including the transaction. While validation is
essential since each node in the network stores every validated
transaction, the advertisement does not need to be received by
all nodes. However, redundancy for advertisement is inevitable
in such cases where the round leader who creates the validated
block is unknown in advance since the transaction needs to be
broadcast to all potential round leaders. In recent blockchain
proposals where the round leader is known in advance, what
we call first-leader-then-block (FLTB) type of consensus pro-
tocols [18]–[21], it is possible to improve bandwidth efficiency
by reducing the communication cost by directly routing the
transaction to the round leader. To the best of our knowledge,
there is no prior work on optimizing bandwidth efficiency for
fully decentralized blockchain.
In this work, our contribution is three-fold: 1) Sybil-proof
incentive compatible propagation mechanism, 2) bandwidth-
efficient routing mechanism, and 3) bandwidth and storage
efficient transaction propagation mechanism which combines
the first two mechanisms.
We formally define incentive compatibility of propagation
mechanisms in fully decentralized blockchain networks. We
show that there is no Sybil-proof and incentive compati-
ble propagation mechanism for poorly connected networks
(specifically for 1-connected networks). For other network
topologies, we find the following incentive compatible and
Sybil-proof formula, which distributes the transaction fee
among propagating nodes:
fk
[i]=(
FC(1 C)i 1fori<k;
F(1 C)k 1fori=k;
whereFis the fee,kis the length of the propagation path, fk
[i]
is the share of the ithnode in that path, and Cis a parameter
related to the network topology. The incentive mechanism isarXiv:1712.07564v2 [cs.CR] 14 Jun 2018
independent of the choice of consensus protocol and works
with any consensus protocol.
We propose a routing mechanism compatible with FLTB-
type consensus protocols. Our proposal reduces the commu-
nication cost of the transaction propagation from the size of
the network to the scale of average shortest path length. In a
random network topology of more than 500 nodes, we achieve
over97% communication cost reduction compared to de facto
propagation mechanism for the advertisement. Furthermore,
we also present a propagation mechanism which combines our
incentive and routing mechanisms in a storage and bandwidth
efficient way. For incentive mechanism, our combined protocol
requires storing only a single signature to provide the integrity
of the path, unlike the existing works, which use a signature
chain including signatures of each node in the path.
The rest of the paper is organized as follows: Section II
presents the related work. Our blockchain model and notations
are defined in Section III. Section IV formulates requirements
of the incentive problem and computes the generic solution.
Smart routing mechanism is presented in Section V and
combined with incentive mechanism in Section VI.
II. R ELATED WORK
The lack of incentive for information propagation in a
peer-to-peer network has been known and studied in different
settings [22]–[25]. Kleinberg and Raghavan [24] proposed an
incentive scheme for finding the answer for a given query in
a tree-structured network topology. Li et al. [25] focused on
node discovery in a homogeneous network where each node
has the same probability of having an answer for the query.
In [22], [23], the authors analyzed the incentive problem for
multi-level marketing which rewards referrals if the advertise-
ment produces a purchase. In these marketing models, the
reward is shared among all nodes in the tree including the
propagation path.
The proposed solutions for peer-to-peer networks [22]–[25]
are not applicable for the permissionless blockchains. In peer-
to-peer solutions, nodes are asked to provide a specific datum
like the position of a peer or the answer to a query. In
blockchains, however, transaction propagation is requested to
advertise the transactions and eventually place them into a
valid block. Alternatively, finding an answer to a query is
equivalent to validation of a transaction by round leader in the
blockchain. Query propagation in a peer-to-peer network has
two main differences compared to a blockchain transaction
propagation: nodes do not compete against the ones who
forwarded the message to them and nodes cannot generate a
response to a query that they do not have the answer, i.e. either
they have the right answer or not. Whereas in a blockchain,
a block is generated by the round leader and every node is a
potential round leader. Essentially, nodes in a blockchain are
competitors that have an incentive not to propagate whereas
other peer-to-peer nodes do not have the incentive since they
cannot generate the answer to the query by themselves.
Recently, blockchain oriented propagation mechanisms have
been proposed [16], [17]. In [16], Babaioff et al. uncoveredthe incentive problem in the Bitcoin system where a rational
node (miner) has no incentive to propagate a transaction. They
focused on a specific type of network, namely regular d-ary
directed tree with a height H, and assumed that each node
has the same processing power. In that setting, the authors
proposed a hybrid incentive (rewarding) scheme and proved
that it is also Sybil-proof. In [17], Abraham et al. proposed
a consensus mechanism, Solidus, offering an incentive to
propagate transactions and validated blocks (puzzles). In their
incentive mechanism, the amount of processing fee passed to
the next node is determined by the sender. Both works rely on
a signature chain to prevent any manipulation over the path
and thereby, to secure the shares of propagating nodes.
[16] and [17] provided analyses of their proposals based on
game theory. For the analysis, [16] assumes a tree-structured
network which eliminates the case of competition against com-
mon neighbors and it is not realistic for blockchain network
topology. Whereas, the analysis in [17] is limited to the case
of competition between nodes that have common neighbors.
For bandwidth efficiency, to the best of our knowledge,
there is no prior work for fully decentralized blockchain
without dedicated miners (round leaders). Nevertheless, Li et
al. [25] presented a distributed routing scheme for peer-to-peer
networks. The authors focused on one-to-one routing which is
dedicated to a single target node. Whereas in blockchain it
needs to be one-to-all routing, which connects the complete
network to the round leader. In addition, [25] does not take
into account the possibility of a failing routing caused by a
failing or malicious node in the routing path.
III. O URBLOCKCHAIN MODEL AND NOTATIONS
In this section, we describe our blockchain model and the
notation used in the paper.
Network. It is a dynamic peer-to-peer network means that
there are nodes joining and leaving constantly. Unlike to the
existing works [16], [17], we do not have a restriction on the
network topology.
Participants. Each participant is denoted by a node in the net-
work. We assume a permissionless blockchain where anyone
can participate and contribute to the ledger directly. Moreover,
there is no discrimination between nodes (participants), i.e.,
they can all be the owner of a transaction and propose a block
as a miner (round leader). For identification, each node has a
public and private key pair and can be validated by his public
key.
Consensus and leader election. Incentive mechanism defined
in Section IV works regardless of the consensus structure.
Whereas, the routing mechanism requires special treatment,
which we call first-leader-then-block (FLTB ) type consensus
protocols.
FLTB protocols can be defined as the consensus model
where the round leader is validated before he proposes the
block. Any leader election mechanism which is independent
of the prospective block of that leader can be converted
into FLTB type. Examples of the FLTB consensus protocols
are Proof-of-Work (PoW) based Bitcoin-NG [18] and several
Proof-of-Stake (PoS) based ones [19]–[21].
The rest of the definitions and notations are listed below:
Neighbor nodes : Directly connected nodes in the network,
adjacency in the graph.
Client : The source or the sender of a transaction. Client
of a transaction T, denoted by cT.
Round Leader : The legitimate node (participant) who
constructs the current block.
Intermediary Node : A node on the transmission path
between the round leader and a client.
Lr: The credential of round leader which validates the
round leader of round rand can be verified by all nodes
in the network. For example, it could be a special hash
value in a PoW protocol or the proof of possessing the
chosen coin in a PoS protocol. In general, regardless
of the consensus mechanism, credentials are linked to
the public key of the leader and can be verified by a
corresponding signature.
(ni): The probability of node nibeing the round leader,
also referred as the capacity of node ni. It corresponds
to the mining power in PoW or the stake size in PoS
protocols and is assumed to be greater than zero for
every node in the network. (S)corresponds to the total
capacity of the all nodes in set S.
NT
K: The set of nodes who know (received) the transac-
tionT.Nn;T
Kpresents the set from the point of view of
noden(includingnitself).
NT
NK: The set of nodes who do not know (received)
transactionTyet.Nn;T
NK denotes the set from the point
of view of node nand includes only the neighbors of n.
IV. I NCENTIVE MECHANISM
We now describe our incentive mechanism. For the sus-
tainable functioning of a fully decentralized blockchain where
the nodes (participants) are able to create new identities and
behave according to their incentives, propagation mechanism
needs to be Sybil-proof and incentive compatible [1].
Conventional incentive instrument, namely transaction fee,
almost always refers to the reward of the round leader.
Here, we refer transaction fee as it consists of the reward
to propagate and to validate transactions. Thereby, rational
nodes are encouraged not only to validate transactions but
also to propagate them. How to determine the fee is out of
the scope of this paper but we assume that each transaction
fee is predefined by either the client or a known function. We
focus on how to automatically allocate the fee among all the
contributors of the process.
Fee sharing function (rewarding mechanism). The fee
sharing function distributes the transaction fee among the
propagating nodes and the round leader. Note that it is highly
probable that the same transaction is received more than once
by the round leader (and intermediary nodes) because of the
propagation mechanism. A rational round leader would choose
the one which maximizes his profit. Like existing works [16],
[17], the fee sharing function described here deals with thepath which is included in the block. For a transaction (added
to the block) with fee Fand propagation path P, the function
Fdetermines the shares of each node involved:
F:fF;Pg !ffjPj
[i]gjPj
i=1wherejPjX
i=1fjPj
[i]=F:
jPjdenotes the number of nodes involved in the processing
of a transaction with fee F, wherejPj 1of the nodes
are in the propagation path between the client and the round
leader. LetjPj=k, i.e., length of the propagation path of the
transaction is k. Then,fjPj
[i]denotes the share of ithnode in
the propagation path, fk
[k]is the share of the round leader andPk
i=1fk
[i]=F.
In the rest of the section, we formulate the necessities of the
fee sharing function to incentivize propagation of an arbitrary
transactionTwith feeF. An ideal incentive compatible prop-
agation mechanism should satisfy the following properties:
1)Sybil-proofness : An intermediary node, as well as the
round leader, should not benefit from introducing Sybil
nodes to the network.
2)Game theoretically soundness : A transaction should not
be kept among a subset of the network. There should
be adequate incentive for rational nodes willing to
propagate, thence it will eventually reach to the whole
network.
By formulating these conditions, we achieve the following
theorem (where Cis a constant which can be chosen according
to the network connectivity):
Theorem 1. In a 2- or more connected blockchain network,
each rational node n2 NT
Kwith(n)< C(Nn;T
K)
propagates transaction Twithout introducing Sybil nodes, if
the transaction fee Fis shared by the following method:
fk
[i]=(
FC(1 C)i 1for1i<k;
F(1 C)k 1fori=k:
Proof of the theorem is divided into the following sections.
The requirements are formulated in Sections IV-A and IV-B,
and the fee sharing function satisfying them is computed in
Section IV-C.
A. Sybil-Proofness
Here, we use the same definition of Sybil nodes in [16]: fake
identities sharing the same neighbors with the original node
that do not increase the connectivity of the network. Because
of the Sybil-proof consensus algorithm, Sybil nodes do not
increase the capacity of their owner, i.e., the probability of
being the round leader.
We investigate the problem in two different settings: 1-
connected networks and the rest. k-connected network means
that removal of any k 1nodes does not disconnect the
network. In 1-connected networks, there exists a bridge which
is the only connection between two distinct subnetworks.
Though 1-connected network model seems to be unrealistic
topology for permissionless blockchains, it is important to see
the intuition behind the non-competition effect.
1-connected networks. In 1-connected networks, there are
critical nodes which have special positions in the propagation
paths between some node pairs. A critical node for a node
pair appears in all possible paths between these two nodes.
The following lemma shows that non-competing advantage
of critical nodes makes it impossible to have a Sybil-proof
incentive mechanism for 1-connected networks.
Lemma 2 (Impossibility Lemma) .For 1-connected networks,
there is no Sybil-proof and incentive compatible propagation
mechanism which rewards every node in the propagation path.
Proof. Assume that, because of 1-connectedness of the net-
work, a node nihave a critical position for a transaction
T, meaning that it is certain he will be included in the
propagation path of that transaction. If niis one side of the
bridge combining two distinct subnetworks, nican be sure that
each transaction coming from its subnetwork and validated in
the other one has to pass through ni. In Figure 1, we illustrate
the two possible paths of a transaction passing through ni.
Since the round leader and also intermediary nodes after ni
will receive one of the paths, they do not have any choice but
accept the path sent by ni.
cT
fk
[1]
fk
[i 1]
fk
[i]
fk
[i+1]
fk
[k 1]
fk
[k]
n1
ni 1
ni
ni+1
nk 1
nk
fk+1
[1]
fk+1
[i 1]
fk+1
[i]
fk+1
[i+2]
fk+1
[k]
fk+1
[k+1]
n0
i
fk+1
[i+1]
Fig. 1. The fee sharing before and after a Sybil node ni0added by ni
Now, we investigate the share of a node niwith and without
a Sybil node. As given in Figure 1, niis theithnode in the
original propagation path and his corresponding fee shares are
fk
[i]andfk+1
[i]+fk+1
[i+1]. In order to demotivate ni,fk
[i]should
be greater than or equal to fk+1
[i]+fk+1
[i+1]. Since the position of
the node would change for different transactions and rounds,
the condition should hold for all positions:
8i2f1;:::;kg; fk
[i]fk+1
[i]+fk+1
[i+1]
(summing for all i’s)=)kX
i=1fk
[i]kX
i=1fk+1
[i]+kX
i=1fk+1
[i+1]
(Definition ofF)=)FF fk+1
[k+1]+F fk+1
[1]
=)fk+1
[k+1]+fk+1
[1]F
(Definition ofF)=)fk+1
[k+1]+fk+1
[1]=F:
Therefore, other than the first propagating node and the round
leader, there is no reward for the rest of the propagating nodes
which contradicts with rational behavior.Eclipse and partitioning. Note that this monopolized behav-
ior is similar to the eclipse and partitioning attacks where the
adversary separates the network into two distinct group and
controls all the connections between them [26], [27]. Indeed,
Lemma 2 can be generalized to the case where the adversary is
able to control all the outgoing connections of a client. In that
case, there is no way to deviate the adversary from creating
Sybil nodes for that specific transaction. We assume that client
nodes are able to defend against the eclipse attacks using the
countermeasures defined in [26].
In a 2- or more connected network, there are multiple paths
between any two nodes. Therefore, we can immediately focus
on the multiple paths case where there are competing paths
for the same transaction and the round leader includes one of
them to the block.
A node can profit from a fee by either being an interme-
diary node who propagates it or being the round leader who
creates the block. We investigate the Sybil-proof conditions of
intermediary nodes and the round leader separately.
a) Intermediary nodes: An intermediary node can be de-
viated by the actions of the nodes who receive the transaction
afterwards. Since there are multiple paths, the round leader
will receive the same transaction from at least two different
paths. In other words, the round leader would decline all but
one of the paths (for each transaction). An intermediary node
will be demotivated if introducing a Sybil node would increase
the chance of rejection of his path.
If the share of the round leader decreases as the propagation
path length increases, then he will choose the shortest path
for each transaction. In that case, introducing Sybil nodes will
decrease his chance to be included in the block. Therefore,
providing larger gain to the leader for choosing the shortest
path is sufficient and can be formulated as fk
[k]>fk+1
[k+1].
b) Round leader: In some cases, round leader is de-
termined before the block is created or even several rounds
earlier [18]–[20]. Since the round leader is guaranteed to be
in the propagation path, it is needed to be taken into account
separately. In addition, an intermediary node can propagate
righteously to his neighbors and then add Sybil nodes for his
own mining process. Therefore, in any case (predefined leader
or not), it is necessary to make an additional policy for the
round leader.
In the case of sSybil nodes, share of the round leader will
change from fk
[k]toPs
i=0fk+s
[k+i]for somek. In order to deviate
the round leader, fk
[k]Ps
i=0fk+s
[k+i]is required.
Since the latter condition includes the former one (as
fk+1
[k]>0), Sybil proofness condition can be formulated as:
8k1;8s1; fk
[k]sX
i=0fk+s
[k+i]: (1)
B. Incentive Compatibility
The decision of the propagation of a transaction can be
analyzed as a simultaneous move game where each party takes
action without knowing strategies of the others. All players
(nodes in our case) are assumed to be rational and they decide
their actions deducing that the others will also act rationally.
Some nodes may cooperate with each other. We assume that
colluding neighboring nodes already share every transaction
with each other and take actions as one. In other words, they
act as a single combined node in the network which can be
seen as Sybil nodes.
Here, we investigate the propagation decision by comparing
the change in the expected rewards for a transaction T. In the
beginning, each transaction is shared with some nodes, at least
with the neighbors of the client. We will find the required
condition to propagate through the whole network. We first
investigate the propagation decision by comparing the change
in the expected rewards immediately after the action. Then,
we extend our analysis with a permanence condition which
guarantees that the ones who propagate will not suffer from
any future actions.
We show that the sharing decision of a node is independent
of the probability of his neighboring nodes being the round
leader. Instead, it depends on his own probability against the
rest who knows the transaction.
Lemma 3 (Equity Lemma) .Propagation decision of a node
is independent from the neighbors’ capacities. A rational node
would propagate to either all of its neighbors or none of them.
Proof. Let a transaction Twith feeFis known by a node n,
and its distance to the cTisk. The expected reward of node
ncan be defined as a function R()whose input corresponds
to the capacities of the nodes who received Tfromn, then
R(X) =fk
[k](n) +fk+1
[k]X
(Nn;T
K) +X:
We show that R()is a monotone function. In order to show
that a function is a monotone, it is enough to show that the
sign of its derivative does not change in the domain range.
For our case, it can be seen that the sign is independent of the
input:
R0(X)=fk+1
[k]
(Nn;T
K) +X
fk
[k](n) +fk+1
[k]X
(Nn;T
K) +X2
=fk+1
[k](Nn;T
K) fk
[k](n)
(Nn;T
K) +X2:
SinceR()is a monotone function, then it achieves the
maximum value at one of the boundary values. In our case, the
boundary values are X= 0 where no neighbors received the
transaction and X=
Nn;T
NK
where all neighbors received
it. Here, we omit the fact that ()is also a monotone function.
Thus, we can say that a rational node maximizes his profit by
propagating to either all of its neighbors or none of them.
Lemma 3 simplifies to evaluate interfering multiple node
decisions which is discussed in the following Lemma.Lemma 4 (Propagation Lemma) .Let a node n2 NT
K,
Nn;T
NK6=;where the distance between nandcTisk. All
neighbors of nwill be aware of Tif
fk+1
[k]
fk
[k]>(n)
(Nn;T
K):
Proof. Assume that some of the neighbors of nare not aware
ofT, i.e.,Nn;T
NK6=;. From Lemma 3, we know that ndid not
propagate the transaction to any of his neighbors. Therefore,
at the moment, the only way that nprofits from Tis being
the round leader with a reward fk
[k].
Table I presents expected reward of nwith respect to each
possible action of nandNn;T
K. The propagation decision of
Nn;T
K may not include all its members, thereby all possible
decisions are taken into account. Here, CN corresponds to the
common neighbors of nandNn;T
K,NCN 1distinct neighbors
ofnandNCN 2distinct neighbors of Nn;T
K (who decide
to propagate), i.e., CNSNCN 1=Nn;T
NK. SinceCN is
received the transaction from both nand the rest of the Nn;T
K,
represents the percentage of the ones in CN decided to
continue with the one including n.
If all nodes ofNn;T
K decide not to propagate with their
neighbors, then nwill benefit from propagating Tin the case
of
fk
[k](n)+fk+1
[k](Nn;T
NK)
(Nn;T
K)+(Nn;T
NK)>fk
[k](n)
(Nn;T
K)()fk+1
[k]
fk
[k]>(n)
(Nn;T
K):
If (some) nodes in Nn;T
Kdecide to propagate T, thennwill
benefit from propagating Tin the case of
fk
[k](n)+fk+1
[k](NCN 1)+ fk+1
[k](CN)
(Nn;T
K)+(Nn;T
NK)+(NCN 2)>fk
[k](n)
(Nn;T
K)+(CN)+(NCN 2)
(=fk+1
[k]
fk
[k]>(n)
(Nn;T
K)+(CN)+(NCN 2)andNCN 16=;:
Note thatNCN 1=;means that all the neighbors of nare
also neighbors ofNn;T
Kwho decide to propagate. In addition,
the sufficiency condition is independent of . Therefore, in
any case, iffk+1
[k]
fk
[k]>(n)
(Nn;T
K)is satisfied, then all neighbors of
nwill be aware of the transaction.
Corollary 5. Letfk+1
[k]Cfk
[k]for some constant C2
(0;1).NT
Kwill continue to expand until there is no more node
n2NT
Khaving neighbors in NT
NK and satisfying (n)<
C(Nn;T
K).
Remark I. Here, it is possible to define different Ckvalues for
each distance k, i.e.,fk+1
[k]Ckfk
[k]. One might argue that,
as the distance increases, it could be possible to find nodes
satisfying(n)
(Nn;T
K)<Ckfor smallerCkvalues. However, as
seen in Section VI, this is not always the case. In addition,
the intermediate node may not know the exact distance, thus
using the same Cvalue would make the decision simpler.
Remark II. Note that the propagation decision is based on
Nn;T
Kinstead ofNT
Ksince the latter one may not be available.
This could lead to better consequences for propagation because
nodes may predict NT
Kgreater than its actual size and decide
TABLE I
THE EXPECTED REWARD OF nFROM TREGARDING POSSIBLE DECISIONS OF nAND THE REST OF Nn;T
K.
Nn;T
K(excluding n)
Decision Not Propagate (some) Propagate
nNot Propagatefk
[k](n)
(Nn;T
K)fk
[k](n)
(Nn;T
K)+(CN)+(NCN 2)
Propagatefk
[k](n)+fk+1
[k](Nn;T
NK)
(Nn;T
K)+(Nn;T
NK)fk
[k](n)+fk+1
[k](NCN 1)+ fk+1
[k](CN)
(Nn;T
K)+(Nn;T
NK)+(NCN 2)
accordingly. Nonetheless, a carefully chosen Cvalue will lead
the nodes to share it with an overwhelming probability.
Remark III. Being the round leader should be more appealing
than being an intermediary node, thus the round leader would
try to fulfill the round block capacity to maximize his profit.
The system may not work at full capacity if the nodes gain
the same reward from propagating instead of validating (as
the round leader) transactions. In Corollary 5, the propagation
condition is given as fk+1
[k]Cfk
[k]. We fix the condition in
favor of the round leader:
8k; fk+1
[k]=Cfk
[k]: (2)
Permanence condition. In the simultaneous move analysis,
we investigated one step at a time, i.e., what will happen
immediately after the decision of propagation. However, all
possible future actions should be taken into account. For
example, the sender of a transaction should consider the pos-
sibility of the further propagation done by the receiver. From
Lemma 3, capacities of the neighboring nodes do not have
any influence on the sharing decision. Unless the processing
fee share decreases, which is caused by some possible future
actions like increased path length, the same lemma will be
satisfied. If the share of a propagating node is non-decreasing
with respect to the path length, then the ones who propagate
will not suffer from any future actions. This can be formulated
as
8i<k; fk
[i]fk+1
[i]: (3)
C. Fee Sharing Function
With the equations obtained from the required conditions,
we can uniquely determine the fee sharing function and
conclude Theorem 1. First, using permanence condition (3),
Sybil-proofness condition (1), can be reduced to fk
[k]
fk+1
[k+1]+fk+1
[k]:
8k1; fk
[k]fk+1
[k+1]+fk+1
[k]fk+2
[k+2]+fk+2
[k+1]+fk+1
[k]
fk+3
[k+3]+fk+3
[k+2]+fk+2
[k+1]+fk+1
[k]
8s1;fk+s
[k+s]+s 1X
i=0fk+i+1
[k+i]fk+s
[k+s]+s 1X
i=0fk+s
[k+i]:
Therefore, we can update the Sybil-proofness condition as:
8k1; fk
[k]fk+1
[k+1]+fk+1
[k]: (4)Then, we can obtain the following equations:
Using (4)kX
i=1fi
[i]kX
i=1fi+1
[i+1]+kX
i=1fi+1
[i]
=)F=f1
[1]fk+1
[k+1]+kX
i=1fi+1
[i]
Using (3) =)Ffk+1
[k+1]+kX
i=1fk+1
[i]=F
=)fk
[i]=fk+1
[i]andfk
[k]=fk+1
[k+1]+fk+1
[k]:(5)
After all, we can finalize the fee sharing function which
corresponds to Theorem 1. Using (2) and (5), the share of
the round leader can be computed:
fk
[k]=fk 1
[k 1](1 C) ==F(1 C)k 1: (6)
Using (5) and (6), the share of an intermediary node can be
computed:
8i<k; fk
[i]=fi+1
[i]=FC(1 C)i 1:
D. Discussion
Integration. Implementation of the incentive mechanism
should take into account the security and efficiency concerns.
The propagation path should be immutable in a way that an
adversary cannot add or subtract any node neither in the propa-
gation process nor after the block generation. At the same time,
storage efficiency is also essential since these path logs are
needed to be stored in the ledger by every node. Both existing
incentive-compatible blockchain solutions [16], [17] adopted a
signature chaining mechanism where each propagated message
includes the public key of the receiver and signature of the
sender. This protocol prevents any manipulation over the path
and thereby secures the shares of each contributor. It requires
additional storage which is the signatures of the contributors.
Although signature chaining solution requires the knowledge
of the public key of the receiver and stores signatures of each
sender, it is generic and can be applied to any blockchain. In
Section VI, we present a novel and storage-efficient solution
which is feasible for FLTB blockchains. It is embedded into
routing mechanism and does not require the knowledge of the
public keys of the neighboring nodes.
Determining Cparameter. Cvalue plays an important role
to make sure that there will be incentive to propagate a transac-
tion for some nodes until it reaches to the whole network. On
the one hand, as the choice for the Cvalue increases, it will
be easier to satisfy the propagation condition since there will
be more chance to find nodes satisfying (n)< C(NT
K).
On the other hand, the higher Cvalue, the lower fee remains
for the rest of the propagation path. It significantly reduces
the fee of the round leader, thereby the incentive. For these
reasons, it is required to choose a moderate Cvalue, e.g., a
reasonable choice would be C=2
NconwhereNcondenotes
default number of connections of a node. For example, in
Bitcoin network where Ncon= 8, nodes will propagate unless
they assume that their mining power is greater than 25% of
the ones having the transaction. Even at the very beginning,
at leastNconnodes have the transaction, C=2
Nconsetting
would provide overwhelming probability to have nodes willing
to propagate according to Corollary 5.
Client ( 0 capacity) nodes. The main goal of the propagation
incentive mechanism is to make sure that the transactions
are received by the nodes who are capable of validating
transactions as well as creating blocks. For that reason, we
mainly focused on the nodes having a capacity greater than
zero, i.e.,()>0. Nevertheless, a client node can be seen as
a potential capacity node because of the possible propagation
of the client. Regarding Lemma 3 and permanence condition
(3), a rational node, who decided to propagate, would benefit
from propagating to the client nodes as well. At the same
time, a client node will always benefit from propagating any
transaction since otherwise it will not have any chance to gain
a fee.
Decentralization effect. In the conventional permissionless
blockchains, all rewards including block reward and trans-
action fees are given to the block owner. In other words,
nodes have only one incentive to participate in the network:
being round leader. The less chance individual nodes have
to be the round leader, the more they are motivated to
join into centralized forms (e.g. mining pools) [13], [28].
Conversely, the transaction fee is shared with all propagators
nodes. In addition, since many transactions are included in a
single block, aiming processing fees of (some) transactions
has significantly more chance than being the round leader.
Thereby, it is reasonable to conclude that incentive mechanism
would have a positive impact on the decentralization of the
permissionless blockchains.
V. R OUTING MECHANISM
As a non-hierarchical peer-to-peer network, the blockchain
ledger is validated by all nodes (miners) individually. This
requires broadcasting every data and blocks over the network
since every node needs to keep a record of the chain to validate
new blocks. In existing permissionless blockchains, every
transaction is broadcast throughout the network by the client,
then the new block including (some of) these is constructed
and broadcast by the round leader. Hence, each transaction is
broadcast at least twice. Even more ( inv) messages are sent
to check the awareness of the neighbors on the transaction.
In Nakamoto-like consensus protocols, the round leader is
validated simultaneously with his proposed block where theredundant propagation of the client is inevitable. In FLTB
protocols, on the other hand, it is possible to validate the round
leader before the block is proposed. It enables to determine
a direct route between each client and the round leader. Our
routing mechanism in Algorithm 1 finds the shortest paths
between clients and the round leader for each round. Instead
of sending each transaction to all nodes in the network, it is
relayed over the shortest path between the client and the leader.
The distance between (almost) any two nodes in a connected
graph is dramatically smaller than the size of the network [29].
This is equivalent to cost reduction from O(N)toO(lnN)in
a random network of size N[30], [31].
The treat model of routing mechanism we present in this
section considers a malicious adversary rather than a rational
one. In the routing mechanism, a malicious adversary may try
to block or censor some of the transaction propagations.
Our protocol can be divided into two parts: Recognition
Phase where the routes are determined and Transaction Phase
where the transactions are propagated (see Figure 2). First,
in the recognition phase, the round leader is recognized
throughout the network and his credential is propagated with
a standard gossip protocol. Each node nilearns his closest
node towards the round leader, gradient node (gni), who is
the first node forwarding the credential. In the transaction
phase, each client forwards his transaction to (some of) his
neighbors. Then, each node, receiving a transaction for the
first time, directly transmits to his gradient node. Here, the
reason for clients to broadcast to more than one neighbor is
that one path could yield a single point of failure. It could
be caused by the nodes who fail or maliciously censor some
of the transactions. As presented in the experimental results,
forwarding transaction to a few of the neighbors (precisely
Ncon) is sufficient. Note that, the routing mechanism works
under asynchronous network assumptions since a client does
not have to wait for all nodes but Nconof his neighbors. Sim-
ilarly, for an intermediary node, waiting for the first credential
message is enough to propagate received transactions.
Lr
Lr
Lr
Lr
Lr
Lr
Lr
Lr
Fig. 2. The Routing Mechanism. The left one illustrates the Recognition
Phase and connections to the gradient nodes are shown with bold solid lines.
On the right, three clients and their transaction paths are presented.
Locational privacy. There have been several papers investi-
gating anonymity in the permissionless blockchain networks,
especially for the Bitcoin network [32]–[34]. It is found out
that matching public keys and IP addresses can be done by
eavesdropping. In this manner, FLTB -based blockchains may
expose to DoS (denial-of-service) attacks against to the round
Algorithm 1 The Routing Algorithm
Recognition Phase
Leader provides his credential Lrto his neighbors.
forNoden1tonNdo
ifFirst time receiving Lrthen
Store ID of the sender (gradient) node nj, i.e.,
gni nj
PropagateLrto neighbors.
end if
end for
Transaction Phase
Client provides transaction Tto his neighbors.
forEach nodenireceivingTdo
ifFirst time receiving Tthen
Send it to the gni
end if
end for
leader. We want to stress that our routing mechanism does
not leak any more locational information about the position
of the leader other than the original FLTB protocols do. It just
takes advantage of the announcement of the leader which is
done exactly in the same manner with the FLTB protocols.
Therefore, our routing mechanism does not cause any addi-
tional vulnerabilities for DoS-like attacks against the round
leader. Yet, it is possible to improve the locational privacy via
anonymity phase where the message is first forwarded in a
line of nodes, then diffused from there [35]. The extra cost
of anonymity would be a few nodes on the line which is still
proportional to the logarithmic size of the network.
A. Experimental Results
In this experiment, we use Barab ´asi-Albert (BA) graph
model [30] which simulates peer discovery in a peer-to-peer
network. It starts with a well-connected small graph and each
new node is connected to some of the previous nodes with a
probability proportional to their degrees.
Barab ´asi-Albert (BA) [30] and Erd ˝os-R ´enyi (ER) [31] graph
models have been used to simulate permissionless blockchains
[36], [37]. In our setting, we combine both models where the
network starts with a small ER graph and grows according to
BA model. We start with 50 nodes in ER model [31] with edge
probability of 1=2, meaning that on average each node has 25
connections. Then, each new node is added by connecting with
Nconnodes in the network. For each (N;Ncon)pair analyzed,
we generated various graphs using Python graph library [38].
Bandwidth gain. In [39], the average shortest path length
between any two nodes, i.e., the average path length, of a
BA graph is shown to be in the order oflnN
ln lnN. Hence,
our routing protocol reduces the communication cost of a
message transaction from O(N)toO(NconlnN
ln lnN). The
communication gain is up to 99% for scaled networks (see
Figure 3), which can be verified by counting the average
number of nodes visited per transaction. Here, we assume thatthe first arriving credential is coming from the node which is
closest to the leader with respect to the number of nodes in
between. In other words, the delay between any two nodes is
computed by the node-distance.
In Figure 3, we count only one redundant communication
for each transaction. Even more redundancy is caused by the
flooding of each transaction because the same transaction is
received from different neighboring nodes. In other words, the
total redundancy is not N, but on average NconN. In the
existing blockchains, this additional redundancy is reduced by
the sending the hash of the transaction to check whether the
neighbor has it or not. If storage size of the transaction is
relative to the size of the hash, then the total number of relays
of a transaction would be significantly more than double of the
network size. For example, Statoshi info [40], a block explorer
of Bitcoin, shows that average incoming bandwidth usage for
the transactions ( tx) is,2:87KBps, less than for the checking
messages ( inv),4:12KBps (measurements taken between
02:00 AM and 14:00 PM in 13 of Feb. 2018). To conclude,
since our mechanism does not suffer from the flooding effect,
the actual communication gain would be much higher than the
result in Figure 3.
102103104
Network Size (# of nodes)101102103104Bandwidth Usage (# of visited nodes)Our routing mech. with Ncon=8
Our routing mech. with Ncon=16
Standard routing mech.
Fig. 3. Communication cost for advertisement of a transaction.
Failing transmissions. Since each transaction is propagated
among a small set of nodes, we need to take into account
the possibility of propagation failure which can be caused
by the nodes who fail or censor the transaction. The fail-
ure probability of a transaction can be approximated by
1 (1 h)lnN
ln lnN 1Ncon
wherehdenotes the probability
of a node in the network who fails or censors the transaction.
These failing nodes are the ones who were present at the
recognition phase and failed just afterwards. Long-term offline
nodes can be ignored since they will not be chosen as gradient
nodes. Thus, Figure 4 demonstrates that our routing is robust
against instant network fluctuations. For a blockchain network
withN= 10000 andNcon= 8, similar to Bitcoin network, if
30% of the active nodes fail after the recognition phase, only
9%of the transactions will be affected.
VI. C OMBINED PROPAGATION MECHANISM
In this section, we show how to deploy both of the incentive
and routing mechanisms for any blockchain having a FLTB
consensus protocol. At first glance, they seem to conflict
102103104
Network size (# of nodes)10-510-410-310-210-1100Failure probability of a propagationh=0.3, Ncon=8
h=0.4, Ncon=8
h=0.3, Ncon=16
h=0.4, Ncon=16Fig. 4. Probability of a transaction failing to be received by the round leader
where his the probability of an intermediary node being a failing or censoring
node.
with each other because the incentive mechanism is used to
encourage propagation while the routing mechanism helps
to reduce redundant propagation. We combine them in a
way that rational nodes are encouraged to propagate only
the transactions which are coming from the predefined paths
of the routing mechanism. As demonstrated in Algorithm 2,
we use the same infrastructure with the routing mechanism,
and we include proofs of the intermediary nodes such that
their contributions cannot be denied. Each transaction path
is defined and secured by a path identifier which includes
the public keys of the propagating nodes. Blocks consist of
transactions as well as their path identifiers used to claim
processing fee shares.
In the recognition phase, each intermediary node conveys
the leader credential and the path identifier. Incoming and
outgoing path identifiers of a node nare denoted by INn
andOUTn, which are used to validate and secure the propa-
gation path. The round leader `produces the initial identifier,
OUT`=H(Lr;PK`), and propagates to his neighbors. Each
nodenupdates the identifier coming from the gradient node by
OUTn=H(INn;PKn). This operation is done just for the
gradient node (first one sending Lr), then updated identifier
and the credential are forwarded to the neighbors. Nodes may
ignore the subsequent identifiers except a client who stores the
firstNconones for the transaction phase.
After the routing paths are determined, each client delivers
the signed transaction and the incoming identifier to his Ncon
neighbors. The first receiving nodes, check the signature, then
add their public keys to the transaction and forward it to their
gradient nodes. From that point, each intermediary node in the
path first checks the validity of the path via the public keys
included and his own identifier, then forwards the transaction
including his public key to the gradient node.
Once transactions are received by the round leader, he
includes the valid ones into the block. The block consists of the
credential, hash of the previous block and valid transactions
with their paths. Then, the block is propagated throughout the
network.
Incentive for block propagation. As a consequence of the in-Algorithm 2 The Combined Propagation Algorithm
Recognition Phase
LeaderlpropagatesLr
forEach nodenido
ifFirst time receiving LrandINn0then
ifLris valid then
AssignINni INn0and gradient node as n0
ComputeOUTni=H(INni;PKni)
PropagateLrandOUTnito neighbors.
end if
end if
end for
Transaction Phase
ClientcTprovidesSigned (T;INcT)(andPK=;) to the
firstNcongradient nodes.
forEach nodenireceivingSigned (T;INcT)andPK do
ifFirst time receiving Tthen
ifSignature path holds then
UpdatePK PKSfPKnig
SendSigned (T;INcT)andPK to the gradient
node.
end if
end if
end for
centive and routing mechanisms, intermediary nodes also have
incentives to propagate the block since they share processing
fees. Even more, the ones who are closer to the leader would
have higher motivation since they probably gain from more
transactions.
Storage efficiency. Any propagation incentive mechanism
requires additional data storage than the data itself to keep
track of the propagation path. Previous works having incentive
[16], [17] utilize signature chains where each node signs the
transaction and the public key of the receiver. Therefore,
additional to the transaction, the signature package of each
propagating node is included. On the other hand, our solution
with the path identification benefits from the recognition phase
of the routing protocol, and its additional storage requirement
is only the public keys of propagating nodes and a signature
of the client. Since the ability to claim propagation reward and
the validation of the path need to be available, our propagation
mechanism demands minimal storage components.
Privacy of the intermediary nodes. Signature chains and
the proposed path identifier yield a direct connection between
nodes network ID and their public keys. Unlike signature
chains, our solution consists of two phases and the propagating
nodes validate it by checking whether their input is preserved
or not. This enables us to tackle the privacy issue by replac-
ing plain public keys with commitments. Instead of directly
including a public key, each node can obscure it in a simple
commitment with a random number ( CTi=H(PKi;Ri)).
All verifications can be handled with the commitments while
claiming propagation reward requires to reveal it. The com-
mitment version uses the same network structure without
compromising the identities of the nodes except clients and
the round leader. The location of the round leader and clients
will be known to their neighbors. They may need to update
their key pairs or replace their connections for the next rounds.
VII. C ONCLUSION
In this work, we investigated two transaction propagation
related problems of blockchains: incentive and bandwidth
efficiency. We presented an incentive mechanism encourag-
ing nodes to propagate messages, and a routing mechanism
reducing the redundant communication cost.
We analyzed the necessary and sufficient conditions provid-
ing an incentive to propagate messages as well as to deviate
participants (nodes) from introducing Sybil nodes. We studied
different types of network topologies and we showed the
impossibility result of the Sybil-proofness for the 1-connected
model. We formulated the incentive-compatible propagation
mechanism and proved that it obeys the rational behavior.
We presented a new aspect of the consensus algorithms,
namely first-leader-then-block protocols. We proposed a smart
routing mechanism for these protocols, which reduces the
redundant transaction propagation from the size of the network
to the scale of average shortest path length. Finally, we
combined incentive and routing mechanisms in a compatible
and memory-efficient way.
Future work and open questions. In Section IV-D, we
mentioned the parameter choice and possible outcomes of
the incentive mechanism. Detailed effect of incentive model
and parameter choice are left as a future work. Another open
question is the effect of the incentive mechanism on the
topology of the network. Nodes would benefit from increasing
their connection to contribute more transaction propagations,
i.e., it would increase the connectivity of the network. Using
that result, a rigorous analysis on the choice of the Cparameter
can be done. Finally, there are open problems regarding the
economics of the transaction fee: analyzing the accuracy of the
de facto formulas in the existing cryptocurrencies with respect
to the cost of the propagation and validation and investigating
the possible impacts of the sharing fee like decentralization
effect.
VIII. A CKNOWLEDGMENT
This work was supported by NWO Grant 439.16.614
Blockchain and Logistics Innovation.
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|
{
"id": "1712.07564"
}
|
1709.07790
|
A Petri Nets Model for Blockchain Analysis
|
A Blockchain is a global shared infrastructure where cryptocurrency
transactions among addresses are recorded, validated and made publicly
available in a peer- to-peer network. To date the best known and important
cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency
and in particular on the modeling of the Bitcoin Blockchain by using the Petri
Nets formalism. The proposed model allows us to quickly collect information
about identities owning Bitcoin addresses and to recover measures and
statistics on the Bitcoin network. By exploiting algebraic formalism, we
reconstructed an Entities network associated to Blockchain transactions
gathering together Bitcoin addresses into the single entity holding permits to
manage Bitcoins held by those addresses. The model allows also to identify a
set of behaviours typical of Bitcoin owners, like that of using an address only
once, and to reconstruct chains for this behaviour together with the rate of
firing. Our model is highly flexible and can easily be adapted to include
different features of the Bitcoin crypto-currency system.
|
http://arxiv.org/pdf/1709.07790v2
|
Andrea Pinna, Roberto Tonelli, Matteo Orrú, Michele Marchesi
|
cs.CR, cs.DC, cs.SE
|
cs.CR
|
A Petri Nets Model for Blockchain
Analysis
Andrea Pinna1, Roberto Tonelli1, Matteo Orr u2and
Michele Marchesi3
1Department of Electrical and Electronic Engineering (DIEE),
University of Cagliari,
Piazza D'Armi, 09100 Cagliari, Italy.
2Computer Science Dept. | Technion - IIT
Technion City, CS Taub Building,
3200003 Haifa, Israel.
3Department of Mathematics and Computer Science,
University of Cagliari,
Via Ospedale 72, 09124 Cagliari, Italy.
Email: a.pinna@diee.unica.it, roberto.tonelli@dsf.unica.it, matteo.orru@cs.technion.ac.il,
marchesi@unica.it
A Blockchain is a global shared infrastructure where cryptocurrency transactions
among addresses are recorded, validated and made publicly available in a peer-
to-peer network. To date the best known and important cryptocurrency is
the bitcoin. In this paper we focus on this cryptocurrency and in particular
on the modeling of the Bitcoin Blockchain by using the Petri Nets formalism.
The proposed model allows us to quickly collect information about identities
owning Bitcoin addresses and to recover measures and statistics on the Bitcoin
network. By exploiting algebraic formalism, we reconstructed an Entities network
associated to Blockchain transactions gathering together Bitcoin addresses into the
single entity holding permits to manage Bitcoins held by those addresses. The
model allows also to identify a set of behaviours typical of Bitcoin owners, like
that of using an address only once, and to reconstruct chains for this behaviour
together with the rate of ring. Our model is highly
exible and can easily be
adapted to include di erent features of the Bitcoin crypto-currency system.
Keywords: Blockchain; Petri Nets; Bitcoin; Cryptocurrency
Received 00 January 2009; revised 00 Month 2009
1. INTRODUCTION
The Bitcoin electronic cash system was conceived in the
2008 by the scientist Satoshi Nakamoto [1] with the aim
of producing digital coins whose control is distributed
across the Internet, rather than owned by a central
issuing authority, such as a government or a bank. It
became fully operational on January 2009, when the
rst mining operation was completed, and since then it
has constantly seen an increase in the number of users
and miners.
At the beginning, the interest in the bitcoin digital
currency was purely academic, and the exchanges in
bitcoins were limited to a restricted elite of people more
interested in the cryptography properties than in the
real bitcoin value. Nowadays bitcoins are exchanged to
buy and sell real goods and services as happens with
traditional currencies.
The main distinctive feature introduced by the
Bitcoin system is the Blockchain, that is a sharedinfrastructure where all bitcoins transfer are recorded.
Value transfer is called transaction and is an operation
between users. To send and receive bitcoins, a
user needs an alphanumeric code, called address .
Address represents the users' account and to each
address a private key is associated. No personal
information is usually recorded in a Blockchain and for
this reason Bitcoin protocol o ers pseudo-anonymity.
Di erent blockchains have been implemented so far
and the technology often seems to work properly,
even if most of them su er from a lack of software
engineering principles application in their development
and deployment [3].
To date blockchain is the technology underlying
Bitcoin, but is also the technology underlying other
cryptocurrencies, such as Ethereum, Litecoin and
MaidSafeCoin. By analyzing this technology we can
obtain many statistical properties of its associated
cryptocurrency network, as well as the typical behavior
The Computer Journal , Vol. ??, No. ??, ????arXiv:1709.07790v2 [cs.CR] 26 Sep 2017
2 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
of users, for example how users move bitcoins between
their various accounts in order to preserve and reinforce
their privacy.
In this paper, we introduce a novel approach, based
on a Petri Net (PN) model to analyze the Blockchain.
Using Petri Net we de ne a single useful model, a unique
data structure, by which not only all main information
about transactions and addresses are represented, as
can be done using other approaches, but also the overall
architecture and scheme of blockchain transactions are
fully and natively implemented through a well known
and powerful formalism.
We assume that each address corresponds to a place
and each Bitcoin transaction corresponds to a transition
in a Petri Net (also known as Place/Transition Net
or P/T Net). The proposed model, called \Addresses
Petri net", allows to quickly collect information on
the identities owning Bitcoin addresses and to recover
measures and statistics on the Bitcoin network. We
reconstruct an Entities network associated to Block
Chain transactions gathering together Bitcoin addresses
into the single entity holding permits to manage
Bitcoins held by those addresses. In other words, the
use of PN formalism easily allows us to construct rst
the\Addresses Petri Net", and then the \Entities Petri
Net". Even if we analyzed only a few features of the
bitcoin blockchain, our model perfectly ts blockchain
behavior and features and can potentially be used to
exploit the full behavior of this new technology and to
preform statistical simulations on it.
There a number or advantages in using PN as a model
to investigate the Bitcoin transactions.
First of all, the well-de ned algebraic model allows to
manage straightforward algorithms to perform several
structural analysis.
Second, it allows to represent natively the Blockchain
transactions, providing an alternative graphical repre-
sentation of the Blockchain scheme.
Finally, it opens up the possibility to perform dy-
namic simulations to forecast the future properties
of the Bitcoin network. In fact the model allows the
creation of higher level representations of the Bitcoin
ledger, by grouping addresses in speci c places and
obtaining transition ring statistics.
The remaining of the paper is organized as follows.
In Section 2 an overview of related works is reported, in
Section 3 we illustrate the Bitcoin payment system, in
Section 4 we describe our model. Speci cally, Section
4.2 illustrates the Addresses Petri Net associated to
Bitcoin addresses. Section 4.3 describes Entities Petri
Net and the proposed algorithm used to infer from the
Entites Petri Net, the Addresses Petri Net.
In Section 5 we illustrate the application of our
model to the Bitcoin system and present the results.
Finally, Section 6 presents the discussion and Section 7
concludes.2. RELATED WORKS
In these last years, the unique features of Blockchain
have attracted more and more researchers and several
are the works that examined this shared data collection.
Even if several papers focused on heuristics and
algorithms in order to analyze and cluster Bitcoin
addresses identifying network of users, no researcher
focused on the analysis of the blockchain by modeling
it within the framework of Petri Nets. This idea was
carried on as the topic of the Ph.D. program of A.
Pinna [2] and some preliminary results are reported
in [4]. Consequently this section on related works will
mainly focus on the works in literature which investigate
blockchain technology, structure and properties from
the point of view of dynamical networks.
Ron and Shamir [5] analyzed and measured the
Blockchain up to the block number 180,000, from
January 03th, 2009 to May 13th, 2012, by using
a model called transaction graph . They analyzed
the distribution of the number of transaction per
address and introduced the concept of entity as a
group of addresses of the same owner. They ran
a variant of a Union-Find graph algorithm in order
to nd sets of addresses belonging to the same user.
First, they constructed the transaction graph, the
address graph, and then constructed the contracted
transaction graph and the entity graph. Thanks to this
entity graph, the authors determined various statistical
properties of each entity, such as the distribution of
the accumulated incoming bitcoins, the balance of
bitcoins updated to May, 13th 2012, and the balance
of the number of transactions per entity and per
address. The authors obtained, for both the original
and the clustered network (the entities network), some
statistical properties which are typically encountered
in complex networks [6, 7, 8, 9]. In addition they
investigated the most active entities in the system.
In [5], the users' common practice to move bitcoins
between their various accounts (addresses) is tracked
as a good practice to preserve and reinforce user's
anonymity.
As a preliminary result , the potentiality of the Petri
Nets formalism for investigating users behavior has been
discussed in [4]. That work focused on the identi cation
of \disposable addresses" (addresses used just once).
Many other strategies adopted in order to preserve
and reinforce users' anonymity have been analyzed
in literature. Some of these strategies improve the
privacy and anonymity including mixing protocols, are
discussed in CoinShue [10]. CoinJoin and CoinParty
[11] investigated the use of anonymity networks
obtained by using software like TOR. Biryukov et
al. in [12] found countermeasures to block users who
access in the Bitcoin network using Tor or other similar
protocols. Reid and Harrigan [13] studied how an
attacker could make a map of users' coins movement
tracing their addresses and gathering information from
The Computer Journal , Vol. ??, No. ??, ????
A Petri Nets Model for Blockchain Analysis 3
others sources. They also focused in the topology of
addresses network and transaction network, showing
their properties of complex networks. These results
can be compared to those reported in [14] for clustering
other software networks. Androulaki et al [15] analyzed
how users try to reinforce theirs anonymity in the
Bitcoin system. In particular, they studied the
technique of changing address and how this makes more
complex the network.
Meiklejom et al. [16] proposed an heuristic to
recognize the changing addresses method, and to keep
track of potential criminal users, thanks to information
extracted from the Blockchain and from other sources,
such as forums. They also tried to give a name to each
address. Kondor et al. [17] focus on retrieving the
Blockchain transaction network, studying its features
over the time.
Recently, Lishke and Fabian [18] proposed an
exploratory analysis of the Blockchain and of Bitcoin
users. They studied the economy and main features
of the Bitcoin cash system, but did not focus neither
on the concept of "entity", nor on disposal addresses,
as we do in this work. Their analysis revealed the
major bitcoin businesses and markets, giving insights
on the degree distribution (probability density function
and complementary cumulative distribution function)
of bitcoin transactions for several aggregations of time,
businesses categories and country. These distributions
revealed the existence of a scale-free network, and hence
that Bitcoin network follows a power law distribution
although not over the entire period. These results
can be compared to those reported in [9] about the
mechanism of power law distribution generation in
similar technological networks and have also been
replicated in this paper, where we found that the
distributions of several investigated quantities follow a
power-law very closely (see section 5 for details).
The surge of interest regarding Bitcoin led scientists
to face several other topics, in addition to the
Blockchain analysis, Cocco et al. in [19] presented an
agent-based arti cial cryptocurrency market in which
heterogeneous agents buy or sell cryptocurrencies, in
particular Bitcoins. The model proposed is able to
reproduce some of the real statistical properties of the
price returns observed in the Bitcoin real market. In
[20] the same authors proposed an agent-based arti cial
cryptocurrency market in order to model the economy
of the mining process. Starting from GPU's generation
they reproduce some "stylized facts" found in real-
time price series and some core aspects of the mining
business.
Other works focus on security and privacy issues
[21], cryptographic problems [22], social aspects of the
Bitcoin users behavior [23, 24, 25] and on and economic
aspects and the implication of the cryptocurrency
phenomenon, see for instance works [26].
FIGURE 1. Simpli ed transaction schema.
3. THE BITCOIN CASH SYSTEM: AN
OVERVIEW.
The Blockchain is a distributed and global database
where all information about bitcoins' transactions are
stored, but the term can also be used to denote the
technology behind. It works as a public ledger which is
composed of an ordered sequence of blocks. Blocks are
validated and inserted into the chain and each block
contains data about a variable number of validated
transactions.
Bitcoin transactions originally represented value
transfer of a cryptocurrency but they can be used to
transfer any kind of information. Each transaction is
composed by an input section and an output section,
which report a list of addresses4and their associated
values meaning bitcoins.
The information associated to each transaction in the
Blockchain are characterized by:
A list of inputs, each one containing one previous
transaction;
A non empty list of outputs (possibly coinciding
with some inputs);
The associated amounts to each output.
Users can own one or more addresses, and address
creation is costless. Users' anonymity is preserved since
the Blockchain stores only addresses, and neither user
names nor other identity information are required to
create an address.
Bitcoin clients (software which allow users to interact
with the Bitcoin network) manage the addresses in
digital wallets . Wallets store both public and private
keys which are used to receive and to send payments.
Fig. 1 shows a simpli ed scheme of the interaction
among transactions (called i) and addresses (called
j). In the gure seven transactions and six addresses
are involved in the chains. The balance of bitcoins
owned by users is associated to their own address,
4An alphanumeric string of 32 elements which can begin only
with "1" or "4", e.g.
1JQfV fzfxtfUb 9kexSt 7mHhcHxX 6fyBJ 5A:
;
The Computer Journal , Vol. ??, No. ??, ????
4 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
and it is equivalent to the total value of the unspent
transaction outputs (i.e., UTXO s) that the address has
received and not spent yet. Each square in the input
section represents a spent transaction. Each square
in the output section that is not connected to the
input of another transaction, represents an UTXO. For
example, the addresses 1; 2; 4and 5have one or
more UTXOs, so their balance is not null.
Each transfer of bitcoins among users implies changes
on the balances associated to the respective addresses,
similarly to what happens with a traditional bank
account. Transaction requests wait in a \pending"
status in the peer-to-peer network until they are
validated by miners, in order both to prevent frauds
and to avoid double spending.
Technical details about the network implementation
can be found in [1]. Brie
y, users interact with
the Bitcoin network through clients which establish a
Internet connection with some other client.
Each client become a node of the peer-to-peer
network and, potentially, each node of the Bitcoin
system has the same importance of any other one.
Nodes listen for transaction requests arriving from
other nodes. A transaction between addresses can be
accepted only if it satis es the following constraints:
The transaction's inputs must correspond to the
outputs of previous unspent transactions (UTXO)
with same address and values;
The transaction's output total value must be less
or equal to the total value of the inputs, with a
possible di erence being the transaction fee.
The validation procedure, called mining , is carried
out by miners and consists in solving the (computation-
ally hard) problem of determining an hash key starting
with a given number of zeros ( nonce ) starting from a
set of transactions requests as input. This hash key will
be associated to the new validated block. In addition
to transaction data, each new block contains several in-
formation such as the hash code of the previous block
in the Blockchain, its height (its associated progressive
number), and the IP address of the miner.
Mining the blocks is a competitive task which involves
all the miners in the peer-to-peer network, which try to
be the rst to validate the next block. The rst miner
who is able to validate a new block5receives a reward
in bitcoins (presently 12.5 BTC).
A special kind of nodes in the peer-to-peer network,
called full nodes , check the new blocks, speci cally
their validity, also verifying that they respect the
Bitcoin's core consensus rules6. The diculty of this
computational problem is automatically adjusted by
the network, from time to time, in order to maintain
constant, on a statistical base, the release rate of the
5who become a part of the main branch of the Blockchain,
after eliminating the forks using a consensus rule.
6https://en.bitcoin.it/wiki/Full-nodenew blocks (about a new one every ten minutes) and
the consequent release of new Bitcoins.
In Fig. 1, we can identify the mining transactions.
They are the transactions 1;2;4and6, which
are the transactions having their input section empty.
Nowadays, miners are gathered in pools to optimize the
computational e ort and to make constant the incoming
of pool members. The whole Bitcoin system can be
seen as a special typology of nancial system in which,
according with its technical speci cation, everyone can
be a trader. Real time nancial instruments, made
possible by cloud and grid computing[27] could aid users
in that operations.
4. THE MODEL: THE BLOCKCHAIN
PETRI NET
The proposed model is based on the Petri Net
formalism. Using the Petri Net formalism obtained a
lightweight but useful representation of the Blockchain
that we call the Addresses Petri Net. Petri Net is an
oriented graph, made of two types of nodes, place and
transitions, where each node can be connected only with
a node of the other type. Also the Bitcoin Blockchain
can be modeled as an oriented graph, made of two types
of nodes, addresses and transactions, where the latter
activate transfers of tokens between the former, and
thus can be natively modeled by using the Petri Net
formalism for places and transitions, respectively.
4.1. Petri Nets: A brief introduction
A Petri Net [28] is a formalism to describe systems
based on a bipartite graph with two kind of nodes
called places and transitions . For this reason, Petri
nets are also called Place Transition nets (P/T nets ).
Connections between nodes are made by directed arcs.
Each node can be only connected to nodes of the other
type and there are two types of arcs: arcs ingoing into
a transition, called pre-arc , and arcs outgoing from a
transition, called post-arc .
One of the advantages of using Petri Nets is that
they are also well described by an algebraic formalism.
The formalism provides sets to de ne the nodes, and
matrices to describe the arcs. A Petri Net Nis a
quadruple de ned as described below.
Definition 4.1.
N= (P;T;Pre;Post ) (1)
where
P=fp1;p2;:::;pmgis the set of mplaces,
T=ft1;t2;:::tngis the set of ntransitions,
Pre:PT!Nis the Pre-incidence function
Post :PT!Nis the Post-incidence function.
The Computer Journal , Vol. ??, No. ??, ????
A Petri Nets Model for Blockchain Analysis 5
PreandPost incidence functions are usually de ned
by mean of matrices with dimension equal to mn.
Each element of these matrices contains the number of
arcs which connect places with transitions. The Pre
matrix contains the numbers of ingoing (to transitions)
arcs for each place-transition pair. Vice versa, each
element ofPost matrix is the number of outgoing arcs
for each place-transition pair.
Petri nets are also a powerful formalism to describe
discrete event systems, as is the case of blocks
generation in the Blockchain. To model the state of
a system, a marking M(i.e., a vector which de nes the
distribution of tokens in places) is needed. Transitions
are aimed at modifying the marking of the system.
Transitions absorb tokens from places connected with
Pre-arcs and produce tokens for the places connected
with Post-arcs, an operation called ring of a transition.
Petri net and the associated initial marking form the
Network system de ned as hN;M0i, where M0is the
initial marking. In this work we do not describe a
speci c state of the Blockchain so we do not need to
de ne a marking.
4.2. Addresses Petri Net
In order to obtain the Petri net algebraic representation
for the Blockchain we provide a set theory description
of the two Blockchain elements involved, e.g., addresses
and transactions.
We denoteA=f 1; 2;:::; mgthe nite set of m
addresses registered either as inputs or outputs in
the Blockchain, and with = f1;2;:::;ngthe set of
ntransactions validated by the Blockchain.
LetN = (P ;T;PreA;PostA ) be the network of
addresses, where:
P =fp 1;p 2;:::;p mgis the set of mplaces
with each place p associated to one and only one
address 2A;
T=ft1;t2;:::tngis the set of ntransitions where
each transition tis associated to one and only one
transaction 2;
PreA : is the pre-incidence matrix;
PostA : is the post-incidence matrix.
The setsP andTcan be recovered by browsing
all the addresses and transactions validated in the
Blockchain, which are publicly available, and inserting
a new place every time a new address is found, and a
new transition every time a new bitcoin transaction is
encountered.
In order to build the matrices PreA andPostA let
us consider one transaction in the Blockchain and the
associated transition t. In the Blockchain, a transaction
consists in a set of input and output addresses with the
associated amounts in bitcoin. We denote by In()A
FIGURE 2. Addresses Petri Net equivalent to the
simpli ed transaction chains in Fig. 1
PreA =2
66666640 0 1 0 0 0 0
0 0 0 0 1 0 1
0 0 0 0 1 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 13
7777775p 1
p 2
p 3
p 4
p 5
p 6
t1t2t3t4t5t6t7
FIGURE 3. Pre-incidence matrix of the Petri net for the
example in Fig. 1.
the set of input addresses, and by Out() A the
outputs set. For each address 2In() we consider
its associated place p and we add a pre-arc leaving
fromp and arriving to the transition tassociated to
. At the same time, for each address 2Out() we
add a post-arc leaving from transition tassociated to
and arriving to the place p associated to . For each
couple (p ;t) to which a pre-arc has been added we set
PreA (p ;t) = 1, while for each couple ( p ;t) to which
apost-arc has been added we set PostA (p ;t) = 1.
This model does not carry all the information
available in the Blockchain (e.g. transactions amounts)
and so it cannot completely represent Blockchain's
behavior and properties. However, in contrast with
the methodologies used in other works, in which
di erent models were applied in order to analyze
the Blockchain overloading the analysis, our approach
natively represents the Blockchain structure and
dynamics and includes into one single model and
into one single data structure di erent features and
properties of the Blockchain.
Consider for instance the simpli ed transaction
chains in Fig. 1. There are seven transaction and six
places. The equivalent Address Petri Net is composed
by six places and seven transitions. The graphical
representation is shown in Fig. 2. This Net is de ned
by a set of places P =fp 1;p 2;:::;p 6g, a set of
transactions T=ft1;t2;:::t 7gand by the pre-andpost-
incidence matrices PreA andPostA , shown in Fig. 3
and 4.
These matrices can be straightforwardly used to
perform several analysis of the network. For example,
we can compute the di erence between post and pre-
The Computer Journal , Vol. ??, No. ??, ????
6 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
PostA =2
66666641 1 0 0 0 0 0
0 0 1 1 0 1 0
0 0 1 0 0 0 0
0 0 1 0 0 0 0
0 0 0 0 1 0 1
0 0 0 0 1 0 03
7777775p 1
p 2
p 3
p 4
p 5
p 6
t1t2t3t4t5t6t7
FIGURE 4. Post-incidence matrix of the Petri Net for the
example in Fig. 1.
incidence matrices and consider one of its row. The
number of not null elements in such row is equal to the
number of UTXO contained in the address related to
the place corresponding to the row. This number must
be greater than or equal to zero, and if it is equal to
zero the balance of the associated address is null.
In addition, we can easily compute the number of
times that an address appears as input in a transaction.
In fact, all the not-zero elements of the row iof the
matrix PreA provide the number of times the address
corresponding to the place p =ihas been the input
of a transaction.
As other example we consider the case of di erent
transactions occurring in di erent moments which share
the same input set and the same output set. Using our
model, these transactions can be represented with only
one transition, which is characterized by a ring clock.
This feature, along with the creation of the entities
net, can be useful to enable a dynamical and high level
analysis of the Bitcoin system. We will show in the
following that our model allows to easily detect such
sets of transactions.
4.3. Entities Petri Net and algorithm to
manage them
It is quite common for Bitcoin users to hold more than
one address in order to manage bitcoin exchanges and
anonymity more easily. As in [5] we de ne an entity
as the person, the organization, the group of people, or
the rm that hold the control of the bitcoins associated
to a set of addresses. All addresses appearing in an
input section of a single transaction must be owned by
the same entity. This is because, in order to activate
the bitcoin transfers from those addresses, the same
entity must hold all the private keys of all corresponding
wallets
In order to build the Entities Petri Net Nwe
associated each entity to a collection of addresses,
associating places p2PinNto a set of places p of
N . We denote by E=f1;2;:::;kgthe set of entities
where each entity2Eis a nite set of addresses such
thatA.
The matrix PreA hasmrows, one for each place, and
ncolumns, one for each transition. Given a transition t
we consider the array PreA (;t) which is the column ofLet beT=Tthe set of unexplored transitions and
E=;the set of entities.
whileT6=;
1. take at:t2Tand remove this form T
2. lete=;
3. for alli:PreA (pi;t)6= 0 doe=e[fpig
4. lete=ethe set of unexplored places
5. whilee6=;
(a) take a place p2e
(b) letT0=;
(c) for all j:PreA (p;tj)6= 0 doT0=
T0[ft0g
(d) for allt02T0
i. letenew=;
ii. for allh:PreA (ph;t0)6= 0 doenew=
enew[fphgendfor
iii.e=e[enewande=e[enew
iv.e=enp
v.T=Tnt0
endfor
endwhile
6.E=E[e
endwhile
FIGURE 5. Algorithm used to compute the set Eof
entities.
PreA with index t. Its non zero elements correspond
to placesp with PreA (p ;t) = 1, namely places
with outgoing arcs pre-arc towards transition t. These
placesp correspond to input addresses 2In(), for
the transaction corresponding to transition t. As a
consequence, all these places belong to one single entity
2E.
It is also possible that a given address appears in two
or more input sections, together with other addresses.
In this case, the entity must be composed by all the
addresses in these input sections.
To build the Entities Petri Net, E, we applied the
following algorithm.
We denote unexplored place , every place which is an
element of the current entity, but is not yet processed.
In fact, in order to nd other places to be inserted
into the current entity e, each unexplored place must
be processed as in step 5. In this step, all the other
placesphelement ofeare found.
Eache2Eis a set of places of the Addresses Petri
Net or, equivalently, is the representation of a set of
addresses that compose an entity.
The algorithm creates the set Eof entities. The
correctness of the algorithm can be discussed analyzing
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A Petri Nets Model for Blockchain Analysis 7
Entity inE Places
e1fp 1g
e2fp 2;p 3;p 6g
e3fp 4g
e4fp 5g
TABLE 1: Entity in the Entities Petri Net of the
simpli ed transaction chains in Fig. 1.
PreE =2
6640 0 1 0 0 0 0
0 0 0 0 2 0 2
0 0 0 0 0 0 0
0 0 0 0 0 0 03
775p1
p2
p3
p4
t1t2t3t4t5t6t7
FIGURE 6. Pre-incidence matrix of the Entities Petri Net
for the simpli ed transaction chains in Fig. 1.
the two requirements: the nite number of iterations
and the correctness of the solution. Firstly, the number
of iterations is limited by the number of transitions. In
fact, the set of unexplored transitions will be emptied
every time a transition will be examined. In particular,
both in step 1 and in step 5.d.v. a transition is
removed from T. Regarding the second point, because
place determination occurs by evaluating the pre-arcs
connected to each transition, entities are correctly
created and populated. Furthermore, it is possible to
check that the resulting entities form mutually disjoint
sets and that the result of the entities' union contains
all the places of the Addresses Petri net.
We can de ne N, the Entity Petri Net, as N=
(P;T;PreE;PostE ), wherePis the set of places that
are associated one to one with elements of the entities
setE.
The de nition includes the set Tof transitions. This
is the same that we have in the Addresses Petri Net.
In order to compute PreE andPostE rows, we take
every entity e2E. Given an entity e, we rst extract
fromPreA and then from PostE the rows correspond-
ing to every place p 2e. Then, for each matrix, we
sum these rows together. In this way, we obtain one
new row for both PreE andPostE , corresponding to
the entitye.
For instance, looking at the Address Petri Net in Fig.
2 and at the PreA matrix, we recognize that places
p 2;p 3andp 6can be joined to an entity, and that
hence their related addresses 2; 3; 6are owned by
the same person. In total four entities are recognized
as described in Table 1.
To each entity, a place p2Pis then associated. In
the following tables, PreE andPostE of the example
resulting Entities Petri Net are shown in Fig. 6 and 7.
In Fig 8, the graphic representation of the Entities Petri
net is shown.PostE =2
6641 1 0 0 0 0 0
0 0 2 1 1 1 0
0 0 1 0 0 0 0
0 0 0 0 1 0 13
775p1
p2
p3
p4
t1t2t3t4t5t6t7
FIGURE 7. Post-incidence matrix of the Entities Petri
Net for the simpli ed transaction chains in Fig. 1.
FIGURE 8. The Entities Petri Net of the simpli ed
transaction chains in Fig.1
5. RESULTS
Blockchain can be explored mainly using two ap-
proaches. The rst consists in downloading all binary
data from the peer-to-peer network, and in identifying
transactions, addresses and other information by using
protocol instructions. The second one consists in ex-
ploring speci c websites where the decoded Blockchain
is shown, and application interfaces or other utilities,
are provided to explore it. We followed the second ap-
proach and downloaded blocks as formatted JSON les
from the website blockchain.info .
We parsed the rst 180,000 blocks in the Blockchain,
corresponding to a period of about three and half years,
from January 2009 to March 2012, in order to compare
our results with those in [5].
The data processing performed in this work is carried
on in steps as shown in Fig. 9. All implementations are
made with R language and RStudio IDE.
The analyzed portion of Blockchain was processed
without speci c hardware resources and processing time
to elaborate the rst 180,000 blocks has been about 250
hours long. The average time required to compute a
block is 5 seconds. The single block computation time
depends on the number of addresses contained in it,
considering every address in input or in output of a
transaction. The procedure requires about ten seconds
to elaborate eight addresses and does not increase
signi cantly even when matrices become larger.
The situation has quite changed for blocks validated
in subsequent periods. Currently a block contains
about three thousand addresses and the time to com-
pute it using our Petri Nets modeling is about six
minutes long. Generally the time to elaborate an ad-
dresses is larger if the address has not yet been found
and the search algorithm must add it into the matrices.
The Computer Journal , Vol. ??, No. ??, ????
8 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
Json
processing Transactions
processingPT net
computing $tx[[105]]$out[[16]]$type[1] 0$tx[[105]]$out[[16]]$addr[1] "1GzHUt4mC2KSH5HHapbB4M49SFGMLBBqbs"$tx[[105]]$out[[16]]$value[1] 10000$tx[[105]]$out[[16]]$n[1] 15$tx[[105]]$out0f5a9de425ede9d278f4e13519231d0bb648e188ac"$tx[[105]]$out[[17]]$tx[[105]]$out[[17]]$spent[1] TRUE
Files of
blocksA transaction
Chains
computingEntities net
computingUsers
computationTransaction data request
Net dataEntity data
Chains dataBlockchain.info
API
FIGURE 9. Diagram of the data processing path for the study of Blockchain
The downloaded JSON les, elaborated and saved in
a R structure, occupies 2.8GB and after the elabora-
tion the addresses Petri net occupies about 800MB in
RAM. Saving corresponding data in a Rdata le, it
occupies about 40MB.
5.1. Results of the Addresses Petri net
We found 3,730,480 di erent addresses and 3,142,019
transactions, which in our model correspond to the
number of rows and columns of matrices PreA
orPostA . We associated the addresses to the
corresponding places in the set P in the Petri Net
N . From the analysis of the matrices PreA and
PostA , simply counting the non zero elements, we
found 4.575.888 pre-arcs and 7.352.494 post-arcs in
total. The number of non zero elements L(i) on
the corresponding row of PreA (p i;) represents the
number of transitions occurring from the place p i
through a pre-arc . The number of non zero elements
L(i) in the row PostA (p i;) represents the number
of transitions connected to the place p ithrough a
post-arc . Using this formalism our model easily takes
into account the total number of bitcoin transactions in
input and output of each address.
Figures 10 and 11 report the Complementary Cumu-
lative Distribution Functions (CCDF) de ned as the
probability PthatP(L)> x, whereLis de ned as
the number of non-zero elements in the matrices PreA
andPostA respectively.
The gures show an uneven distribution of inand
outtransactions among addresses so that there are
many addresses with few transactions and relatively
few addresses with many transactions, displaying a
typical power-law distribution. Such distribution has
been straightforwardly recovered using the Petri Nets
formalism.
In table 2 we report the ten most used addresses,
found summing up the number of non zero elements in
PreA rows to that of non zero elements in PostA rows.
L= number of non zero elements in a PreA row ‒P(L)>xFIGURE 10. CCDF of the length L for PreA .
L= number of non zero elements in a PostA row ‒P(L)>x
FIGURE 11. CCDF of the length L for PostA .
Our analysis identi es also 609,295 addresses with
only zeros in PreA rows, and at least one non null
element in PostA rows, namely 609,295 addresses never
used to spend (until the 180,000 block), but only to
accumulate. Part of them are still unused up to today.
Table 3 reports the rst ten ranked by the number of
post-arcs , namely the number of incoming transactions,
and shows their current balances, as checked from
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A Petri Nets Model for Blockchain Analysis 9
blockchain.info: four of these (row 1,7,8 and 9 in the
tab) are never used again, and can be called dormant .
Their balance can be quite high, since they've been used
like sort of bitcoin deposits.
In order to analyze users practices for preserving
anonymity, we focused on recognizing chains of
transaction where disposable addresses are involved.
As already mentioned, a disposable address is an
address used only two times: one time to receive
bitcoins and one time to give away all these bitcoins.
Transactions which involve disposable addresses have
only a disposable address in the input section and one
disposable address in the output section, together with
other addresses. Usually, in the output section only
two addresses in total are present. This practice is
commonly used and can be performed automatically.
Users who adopt it, usually give rise a long chain of
transactions without waiting for con rmation.
With our model, disposable addresses and their
chains can be easily traced analyzing PreA andPostA
matrices. To identify the involved transactions we
identi ed the correspondent transitions having only a
pre-arc and two post-arc in the Addresses Petri Net.
These are transitions that correspond to columns of
PreA having only one non zero element and to columns
ofPostA having two non zero elements but in di erent
rows.
Disposable addresses are likewise identi ed through
the correspondents places.
Give a place, the correspondent row of the Pre and
Post matrices must have one and only one non zero
element.
Under these conditions, it is possible that an Ad-
dress Petri net transition becomes a cyclic transition
in the Entities Petri net. In fact, in this case, output
addresses and input addresses of the associated trans-
action are ascribed to the same entity. This indicated
that the owner of the transaction wanted to move coins
between addresses in his possession.
The algorithm developed to build the chains of this
kind of transitions is described in detail in Appendix A.
Applying this algorithm we found 122,155 disposable
address chains, involving 1,350,010 di erent addresses
and transactions. Figure 12 reports the CCDF of
the chains lengths, showing that these are unevenly
distributed, with the longest chain counting 3,658
transactions.
We also counted how many times users repeat the
same transaction in terms of the same set of addresses
in input section and the same set of addresses in output
section. In our model identifying these repetitions is
trivial. When two or more transactions involve identical
sets of addresses in input and output, the corresponding
transitions are connected with the same places both in
pre and in post matrices.
In fact, taking the matrix PA= (PreA;PostA ), in
which the two matrices are concatenated in column,
L= length of a transaction chainP(L)>xFIGURE 12. CCDF of the distribution of the length of
the chains.
N= number of transaction in a setP(N)>x
FIGURE 13. CCDF of the size L of grouped transaction
set for the address net
for each column tjit is possible to check the existence
of other identical columns.
We found that about 11% of transactions are a
repetition of another one. These represent repeated
transfer of bitcoins from one group of addresses to
another group of addresses where the two groups are
always the same, revealing steady
uxes of bitcoins.
Figure 13 reports the CCDF for the sizes of these groups
of repeated transactions.
5.2. Results of the Entities Petri Net
The reducing algorithm discussed in section 4.3 is
applied to the Addresses Petri Net in order to recover
the corresponding Entities Petri Net. Among the
owners, we found that 2,461,010 entities hold all the
3,730,480 addresses, and the distribution of addresses
among entities is highly not uniform. Figure 14 shows
that also such distribution follows a power-law very
closely. This means that there are many entities holding
The Computer Journal , Vol. ??, No. ??, ????
10 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
Address L pre L post tags
1VayNert3x1KzbpzMGt2qdqrAThiRovi8 270,204 275,398 deepbit.net
1dice8EMZmqKvrGE4Qc9bUFf9PX3xaYDp 14,606 14,605 SatoshiDICE 48%
1dice97ECuByXAvqXpaYzSaQuPVvrtmz6 13,137 13,124 SatoshiDICE 50%
159FTr7Gjs2Qbj4Q5q29cvmchhqymQA7of 8,016 8,425 - spammer ? -
1CDysWzQ5Z4hMLhsj4AKAEFwrgXRC8DqRN 6,382 9,501 Instawallet
1E29AKE7Lh1xW4ujHotoT4JVDaDdRPJnWu 7,761 8,079 - unknow -
15VjRaDX9zpbA8LVnbrCAFzrVzN7ixHNsC 6,999 7,888 faucet donation
15ArtCgi3wmpQAAfYx4riaFmo4prJA4VsK 6,578 6,622 faucet donation
1dice9wcMu5hLF4g81u8nioL5mmSHTApw 6,318 6,306 SatoshiDICE 73%
1Bw1hpkUrTKRmrwJBGdZTenoFeX63zrq33 5,498 5,498 - unknow -
TABLE 2: Summary of rst 10 most used addresses
Address L post current balance BTC
15S1TFTosxrgZxkqJR2n1AFJ22ZJE2rTCk 3,853 120.85215349
1PtnGiNvhAKbuUQ6nZ7nF3CDKCKGfeMsCX 1,199 0
129FTwWoi5H5ujasMZ6M6VjJzBJfsXVQGw 1,138 0.78425567
1FN9kKsZA9XttrAwuDDgsXjs6CXUR2fzmt 1,111 0
1DYvtKtZ2Ay9vTjzjb9BiRauMgXdjRDaD 973 14.5601
1STRonGxnFTeJiA7pgyneKknR29AwBM77 949 1.79274504
1Q3nqtUzBp6jw7opi674Pyfgu4MUmVRdrk 861 16.31551365
1Hh3eNNqR8MajEtDfvUF3hoxgf8CuUXVwY 819 257.32881319
14sx4sFdUE9YDpJ9XbD6xAUEKPKvc8QHq2 811 59.56546509
17igtzSD39ZAapsut2DQTTKFyqSp7CToMq 809 0
TABLE 3: Summary of rst 10 most imbalanced addresses
S= size of a set of addressesP(S)>x
FIGURE 14. CCDF of the distribution of addresses across
entities.
a single address but also a few entities controlling
very many addresses, and thus able to control a great
fraction of the bitcoins
ux transactions.
There are only 246,660 entities containing two or
more addresses and these contains 1,516,130 addresses.
The number of non null elements in the rows of matrices
PreE andPostE for the Entities Petri Net is reported
in Figure 15 and 16 respectively. This corresponds
the number of transactions where the entities areinvolved. They clearly show a power-law distribution
for transactions among the entities.
In Tab. 4 we report the ten most used entities, found
summing up the number of non zero elements in PreE
rows to that of non zero elements in PostE rows. Their
balances can be computed summing the balances of all
addresses belonging to the corresponding entity and are
owned by a single user.
Like for the Addresses Petri Net, we computed groups
of repeated transitions for the Entities Petri Net. We
found that about 22 :6% of transactions are a repetition
of another one occurred among the same entities in
input and in output. This information allows to identify
steady
uxes of bitcoins at the owners level. Figure
17 reports the CCDF for the sizes of these groups of
repeated transactions.
6. DISCUSSION
The Petri Net formalism can be natively used to infer
many information and features about Bitcoin users and
the Blockchain. We used the Petri Net model to gather
together group of addresses (as entities or groups of
disposable addresses) trying to associate an identity to
each group. We also estimated how many users have
been actually involved in the rst three years and half
of Bitcoin activity.
Analyzing the entities we found that 1,516,130
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A Petri Nets Model for Blockchain Analysis 11
Entity number L pre L post size tags
95237 270,204 275,398 2 deepbit.net
2 102,186 283,973 156,725 ilovethebtc
37 51,228 147,712 78,251 jmm5699
11 49,959 97,732 10,37 - unknow -
130 20,857 58,350 23,649 Instawallet
66437 14,219 60,868 13,289 Rai, Dread88
42 9,268 31,147 10,561 Quip, iosp and other
37598 8,923 31,004 12,520 generalfault, safetyvest.com
220 11,133 27,487 9,093 zephram
1503 9,044 29,400 10,116 folk.uio.no/vegardno
TABLE 4: Summary of rst 10 most active entities
L= number of non zero elements in a PreE row ‒P(L)>x
FIGURE 15. CCDF of the lenght L for PreE .
L= number of non zero elements in a PostE row ‒P(L)>x
FIGURE 16. CCDF of the lenght L for PostE .
addresses are controlled by 246,660 owners at most.
With our model we were able to trace transactions
chains whenever disposable addresses are involved.
Each chain holds addresses belonging to one owner,
but one owner may control more than one chain.
So, according to our results, the 1,350,010 addresses
involved are owned at most by 122,155 owners. Using
preandpost matrices we found that 609,295 addresses
are used only as output of bitcoin transactions and are
N= number of transaction in a setP(N)>xFIGURE 17. CCDF of the size L of grouped transaction
set for the Entity Petri Net net
not involved in entities or chains. These three facts,
enable us to estimate a threshold for the number of
di erent Owners or users in the Bitcoin system. We
compute that there were 368,815 engaged (or expert)
owners that adopted disposable addresses practice
or used two or more addresses in their operations.
The addresses of such owners are involved in the
72;6% of transactions. We suppose that the 609,295
addresses that appear only in output are used by some
engaged owners for the purpose of bitcoin depositing.
Finally, the 255,045 remaining addresses are owned by
occasional users. Furthermore we tried to associate an
identity to addresses showed in Tab. 2 and to entities
showed in Tab. 4.
Information about these addresses can be found
on the Internet, in particular on blockchain explorer
websites like blockchain.info , or specialized forums like
bitcointalk.org . Some of theme are made more easily
recognizable by attributing a tag. Take the case of the
most used address we found, which appears 270,204
times as the input of a transaction. We were able to
recognize that it belongs to a (now closed) Bitcoin pool
which was called DeepBit .
Another example regards the most used entity in
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12 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
output that includes 156,725 addresses and regroup the
4:2% of the total number of addresses. Searching on
the Internet some of its addresses, we found out that
who manages this entity has used varied tags, such as
ilovethebtc ,mikeo ,FredericBastiat ,edgeworth , etc.7
Several addresses in that entity have no tag.
From all the reported CCDFs it is clear that all the
distributions are characterized by a strongly uneven
amount of transactions across the addresses, either for
pre and post transactions. This means that there
are many addresses where the bitcoins are hardly
exchanged, and few addresses where the rate of bitcoin
exchange is particularly high. This analysis can be
helpful for identifying addresses which are used by pool
of miners. In fact, when miners join together in a pool
to share computational facilities for mining operations,
they need to de ne a common address where the mining
rewards is accounted to. Then they need to redistribute
the amount of gained bitcoins among all the pool users.
As a consequence the address will be a ected by a
number of transitions in the corresponding Petri Net
as large as the pool's size.
Finally, since we analyzed a limited window of
180,000 blocks, the amount of transitions found in
the matrices are also a signature of the average
rate of Bitcoin transferred between di erent entities
and such rate can be used to infer information on
the organizations which can manage massive Bitcoin
transfers.
6.1. Advantages of the PT modeling
In this section we discuss the motivations for
preferring the PT formalism in modeling the bitcoin
transactions on the blockchain (as well as other possible
transactions) and describe the intrinsic advantages
carried by this formalism. Part of this discussion will
include proposals for further research.
First, PT formalism allows for the "non determinism
criteria" in the system's dynamics. Such criteria
accounts for respecting the locality principle in
the system's evolution. In other words, PT nets
formalism natively includes independence between
events generated by enabled transactions so that one
enabled transaction can occur regardless the occurrence
of other transactions for any given marking. Once
an (or a set of) enabled transaction occurs he new
marking has to be evaluated in order to understand
which transactions are enabled in the new marking.
Such formalism perfectly ts into the blockchain
transactions system where only transactions with
non null UTXO are "enabled" and can occur,
and their occurrence is independent from other
7It is possible to nd a portion of the addresses included
in that entity, in the input section of a Bitcoin transaction,
available on blockchain.info and reachable from this short link
http://tinyurl.com/ilovethebtc . Some of theme have a tag.transactions occurrence. A transaction occurrence is
not deterministic and depends not only by the owner
decision of sending bitcoin to another address, but also
on the winning miners and on the probability that
such transaction is included into the block validated by
the hashing mechanism, which in turn has a di erent
probability depending on the fee the owner accepts
to pay. In such model enabled transactions natively
correspond to UTXO and the marking corresponds to
the set of all UTXO determined by the last block
validated. The validation of a new block, where
transactions are included in an independent fashion,
determines a new "marking" of the bitcoin PT net with
a renewed set of UTXO. This enabling mechanism is
hardly accounted for using a simple bipartite graph or
a matrix representation for the bitcoin network and its
transactions, even if many properties illustrated in this
paper can be recovered by using such representations.
The advantage of the PT nets formalism is that it
natively includes such features.
The second aspect we discuss is related to simulation
modeling which allows to analyze systems dynamics
and which is a typical advantage provided by the
PT nets formalism. Di erently from bipartite
graphs, which account for a static analysis, PT nets
formalism includes systems dynamics and allows for
non deterministic system's dynamics modeling. In fact
in PT simultaneous transactions can occur provided
they are not in con
ict. Again this is a characterizing
feature of bitcoin transactions dynamics where many
non con
icting transfers of bitcoin between addresses
can be included into the same validated block in the
blockchain. Con
icting transactions, like for example
double spending, are controlled and not allowed.
Furthermore PT nets can include into the dynamics
modeling priorities between transactions and this can
be used in a statistical modeling of the di erent
probabilities the bitcoin transactions have to occur
depending on the fee the owner accepts to pay.
A third feature natively included into the PT
nets formalism is the sequence of transactions: two
transactions t1 and t2 are in a sequence if t1 precedes
t2 with t1 enabled and t2 not enabled for a given
marking, and when the occurrence of t1 enables t2 in
the new marking. The bitcoin transactions network
natively contains sequences of transactions, like for
example the sequences of UTXO generated into a single
chain of disposable addresses monitored in our work
and used for preserving bitcoin anonymity. Once again
sequences of transactions can hardly accounted for
using di erent representations, like bipartite graphs or
matrices, without inserting ad hoc constraints into such
representations.
Another important advantage is that PT nets
formalism includes the possibility to set state equations
for the evolution dynamics. Given an initial marking
the state equation allows the determination of the
new marking according to the rules xed for choosing
The Computer Journal , Vol. ??, No. ??, ????
A Petri Nets Model for Blockchain Analysis 13
the enabled transaction that e ectively occur. The
rules can be chosen with great freedom (respecting
network constraints) and in particular a stochastic or
probabilistic approach can be used in order to simulate
the evolution of the blockchain from a statistic point
of view. For example, one of the future improvements
the authors are presently working on is to collect
statistics on the bitcoin
uxes between addresses paying
attention to address clustered in entities, to addresses
corresponding to exchanges and to addresses owned by
miners pools, in order to assign transitions probabilities
for bitcoin
ux exchanges between such addresses
to be used for choosing the enabled transactions to
choose into the corresponding PT nets to make evolve
its marking using a statistical approach. This will
provide a set of possible future marking, each with its
own probability of occurrence, which will correspond
to future states of the blockchain. Such statistical
modeling can provides hints on which addresses are
going to get richer with a given probability, which pools
of miners are going to exploit the future mining and at
which rate and so on. The possibility of performing such
a statistical simulation for the blockchain dynamics is
straightforward within the PT nets model whilst is
hardly accounted for using di erent approaches which
are mainly static.
Last but not least, the formalism, through the use of
pre and post matrices, allows to recover many di erent
and independent results following straightforwardly
from standard computations over the pre and post
matrices associated to the blockchain transaction
network. For example, counting the number of
rows and columns of matrices PreA orPostA it is
straightforward to nd the number of addresses and
transactions, or we can nd bitcoin addresses never
used to spend looking at addresses with only zeros in
PreA rows and at least one non null element in PostA
rows, or we can recover disposableaddresses looking at
transitions that correspond to columns of PreA having
only one non zero element and to columns of PostA
having two non zero elements but in di erent rows.
7. CONCLUSIONS
In this paper we introduced a novel approach, based
on a Petri Net model to parse the Blockchain. Our
purpose was to de ne a single useful model in which all
main information about transactions and addresses are
represented. Collecting the rst 180 thousand blocks,
we were able to associate a place for each address and
a transition for each bitcoin transaction. Our Petri net
includes preandpost-incidence matrices where all links
between addresses and transactions are modeled.
We are aware about the limitation of computational
problem of a matrix approach. The portion of
Blockchain which we chosen was processed without
speci c hardware resources. Anyway, the current size
of the Blockchain (over 480,000 blocks and the totalnumber of transaction is over 240 million) could not
allow us to handle easily all the blocks information.
However, by using this model, we were able to pick
out signi cant and original results. This formalism
has proven powerful methodology for performing many
kinds of measurements and analysis. Analyzing the
number of pre and post arcs, we had proof of the
presence of power-law like distributions. We made
use of both incidence matrices for determining all
transactions chains, identifying a typical disposable
addresses usage by Bitcoin users. By measuring
the chains' lengths, we found again power-law like
distributions. We were also able to determine that some
transactions involve the same group of address in input
and in output. We gathered these transaction in sets
and the size of such sets follow again a power-law like
distribution. By reading information of pre-incidence
matrix, we were able to identify the entities and we
built the Entities Petri Net, repeating on such Petri net
all the measures done for the Addresses Petri Net.
Furthermore, despite the current Blockchain size is
about two order of magnitude greater than the size of
the portion that we have studied, our approach can be
adopted to study a speci c portion of the Blockchain.
For example starting from a speci c set of addresses
which we want to investigate and analyze. In fact,
it is always possible to build the addresses Petri Net
containing only the part of blockchain of interest.
On the basis of all the obtained results, we believe
that our model can be used for studying a large
set of other issues related to other systems based
on Blockchain technology, such as Ethereum. Today,
Ethereum attracts increasing attention and will be one
of our future research topic.
The Computer Journal , Vol. ??, No. ??, ????
14 A. Pinna, R. Tonelli, M. Orr u, M. Marchesi
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APPENDIX A. CHAINS OF DISPOSABLE
ADDRESSES
We model the Blockchain as a Petri net, a bipartite
oriented graph N, de ned as N= (P ;T;Pre;Post ),
whereP is the set of the places (addresses) ,Tis
the set of the transitions (transactions) ,Pre is the
Pre-incidence matrix and Post is the Post-incidence
matrix. The element ijin the Pre matrix de nes how
many times the address iis in the input section of the
transaction j, instead, in the Post matrix, it de nes
how many times the address iis in the output section
of the transaction j. After having built of the Petri net
N, we focus our attention on the chains of disposable
addresses, and hence on the transactions having only
one address, a, in the input section and only two
addresses, band c, in the output section. In more
detail, the address ain the input section is used by
a useru1to send bitcoins to one of the addresses in
the output section, b, belonging to a user u2. The
other address, c, in the output section is created by
the useru1to collect the change. We created the set of
potentially disposable addresses Ad, starting from the
setAof the addresses and from the set of thetransactions in the Blockchain.
Let dbe the set of transaction dsuch that:
d =fd:jIN(d)j= 1;jOUT (d)j= 2;IN(d)2Ad;
9 2OUT (d) : 2Ad;8d2dg:
In order to build a chain, for each dwe need to know
the previous transactions dp=PREV (d). Using
Pre andPost matrices, it is very easy to look for
these previous transactions. We call dsdthe
set of transaction dsthat could be considered the
starting point of a chain because it does not have a
previous transaction inside d. We denote with dsthe
address in input to a transaction ds. Finally, we call
NEXT (d) the transaction d0which has, in the input
section, the disposable address that is contained in the
output section of the transaction d. To nd the chains
cof disposable addresses, we de ned and implemented
the following algorithm:
1. LetC=;be a set of empty chains, c,
2. for each ds2ds:
(a) take an empty chain, c,
(b) insertdsinc
endfor
3. for each c2C
(a) take the last element inserted in c,d,
(b) while9d0=NEXT (d)
i. insertd0in c
endwhile
endfor
The algorithm returns a set Cof chainsc. Each chain
ccontains the transactions ordered by execution order.
The Computer Journal , Vol. ??, No. ??, ????
|
{
"id": "1709.07790"
}
|
1708.09721
|
Blockchain Based Intelligent Vehicle Data sharing Framework
|
The Intelligent vehicle (IV) is experiencing revolutionary growth in research
and industry, but it still suffers from many security vulnerabilities.
Traditional security methods are incapable to provide secure IV data sharing.
The major issues in IV data sharing are trust, data accuracy and reliability of
data sharing data in the communication channel. Blockchain technology works for
the crypto currency, Bit-coin, which is recently used to build trust and
reliability in peer-to-peer networks having similar topologies as IV Data
sharing. In this paper, we have proposed Intelligent Vehicle data sharing we
are proposing a trust environment based Intelligent Vehicle framework. In
proposed framework, we have use the blockchain technology as backbone of the IV
data-sharing environment. The blockchain technology is provide the trust
environment between the vehicles with the based on proof of driving.
|
http://arxiv.org/pdf/1708.09721v1
|
Madhusudan Singh, Shiho Kim
|
cs.CR
|
cs.CR
|
Blockchain Based Intelligent Vehi cle Data sharing
Framework
Madhusudan Singh
Yonsei Institute of Convergence Technology
Yonsei University, Songdo, South Korea
msingh@yonsei.ac.kr
Shiho Kim
School of Integrated T echnology
Yonsei University, Seoul, South Korea
shiho@yonsei.ac.kr
ABSTRACT
The Intelligent vehicle (IV) is experiencing revolu
tionary growth in research and industry, but it still suf
fers from many security vulnerabilities. Traditional se
curity methods are incapable to provide secure IV dat
a sharing . The major issue s in IV data sharing are trus
t, data accuracy and reliability of data sharing data in
the communication channel. Blockchain technology
works for the crypto currency, Bit-coin, which is rece
ntly used to build trust and reliability in peer -to-peer
networks having similar topologies as IV Data sharin
g. In this paper, we have proposed Intelligent Vehicle
data sharing we are proposing a trust environment ba
sed Intelligent Vehicle framework . In proposed frame
work, we have use the blockchain technology as back
bone of the IV data sharing environment . The blockc
hain technology is provide the trust environment betw
een the vehicles with the based on proof of dr iving.
Keywords
Blockchain, intelligent vehicles, security, component;
vehicular cloud , ITS
1. INTRODUCTION
V ANET is the encapsulation of Vehicle -to-Vehicle
(V-to-V) and Veh icle-to-Infrastructure (V -to-I), for
providing notification of any safety critical incident
and hazard to the drivers [1]. This information is
gathered by the feedback of the nearby vehicles. This
system is prone to security attacks , by marking
incorrect feedback , which result s in higher congestion
and severe hazard s [2].
In IV data sharing network , security is a very
crucial issue during comm unication. These networks
require trust and privacy [3]. We have proposed a IV-
TP element, to build trust and transmit reliable data
among IV data sharing . IV-TP is a unique crypto
number, which is attach ed to the message format and
transmit ted during communication time . The cloud
storage based on Blockchain manages the IV-TP, and
is accessed ubiquitously . This IV-TP mechanism is also based on Blockchain technology, enabled to create the
crypto unique ID, self -executing digital contracts
and details of IV, controlled over the Blockchain Cloud
[4]. Fig. 1 shows the intelligent vehicle Information
sharing environment , showing V2V , V2I
communication .
Fig. 1. Intelligent Vehicles Information Environment
Previously , some researchers combined automotive
and blockchain technology but most of them
considered application s based on services and smart
contracts . However, our proposal concentrate s on
secure and fast communication between intelligent
vehicles (self-driving cars) [5]. We have proposed a
blockchain based trust environment for intelligent
vehicle information sharing based on blockchain
technology.
We organize our articles as follows; Section II
presents the motivation of using Blockchain based trust
environment for data sharing among Intelligent
Vehicles over traditional security methods. Section III
presents the introduction of blockchain technology and
existing work of blockchain technology for Intelligent
Vehicles data sharing . Section V, concludes our paper ,
and discuss our future work for our proposed
mechanism.
2. MOTIVATION
Current ITS system use s ad-hoc networks for Vehicle
communication such as DSRC, WA VE, Cellular
Network , which does not guarantee secure data
transmission. Currently, vehicle communication
application security protocols are based on cellular and
IT standard security mechanism which are not up -to-
date and suitable for ITS applications. Still many
researchers are working to provide standard securi ty
mechanism for ITS. Our proposed mechanism is
advantages as it is easy to implement, it’s a peer -to -
peer communication, it provides a secure and trust
environment for Vehicle communication with
immutable database and ubiquitous data access in a
secure way. Our proposal is based on a very simple
concept of using Blockchain based trust environment
for data sharing among Intelligent Vehicles using the
IV-TP (Intelligent Vehicle -Trust Point). We are
exploiting the features of Blockchain i.e. distributed
and open ledger which is encrypted with Merkel tree
and Hash function (SHA -256) and are based on
Consensus Mechanism (Proof of Work Algorithm). We
have not mentioned the details of the Blockchain
mechanism for our application Intelligent Vehicle data
sharing due to the limitation of space.
3. RELATED WORK
3.1.Blockchain Technology
Blockchain technology is distributed, open ledger ,
saved by each node in the network, which is self-
maintained by each node. It provides peer -to-peer
network without the interference of the third party. The
blockchain integrity is based on strong crypto graphy
that validates and chain blocks together on transactions,
making it nearly impossible to tamper with any
individual transaction without being detected [ 6].
Fig. 2. Blockchain technology
Fig.2 shows the Blockchain technology features such
as shared ledger, Cryptography, Sig ned blocks of
transactions, and digital sign atures [6].
3.2.Previous work: Blockchain technology
for Intelligent Transportation System .
Yong yuan, et.al [7] has proposed the blockchain
technology for ITS for establishment of secured,
trusted and decentralized autonomous ecosystem and
proposed a seven -layer conceptual model for the
blockchain.
Benjamin et.al [8], have also proposed the
blockchain technology for vehicular ad -hoc network
(V ANET). They have combine d Ethereum ’ blockchain
based smart contracts system with vehicle ad -hoc
network. They have proposed combination of two
application s, mandatory applications ( traffic regulation,
vehicle tax, vehicle insurance ) and optional
application s (applications which provides inf ormation
and updates on traffic jams and weather forecasts) of
vehicles. They have tried to connect the blockchain
with V ANET services. Blockchain can use multiple
other functionalities such as communication between
vehicles, provide security, provide peer -to-peer
communication with out disclosing personal
information etc. Ali dorri et.al. [9] have proposed the
blockchain technology mechanism without disclosing
any private information of vehicles user to provide and
update the wireless remote software and other
emerging vehicles ser vices. Sean Rowen et.al. [10]
have described the blockchain technolo gy for securing
intelligent vehicles communication through the visible
light and acoustic side channels. They have verified
their proposed mechanism through a new session
cryptographic key , leveraging both side -channels and
blockchain public key infrastruc ture.
We define our blockchain mechanism for the
intelligent vehicles communication environment. We
propose a framework for secure trust based
environment with peer-to-peer communication
between intelligent vehicles without
interfering/disturbing other intelligent vehicles.
4. BLOCKCHAIN BASED TRUST
ENVIORNMENT FOR INTELLIGENT
VEHICLES DATA SHARING
We propose a reward based intelligent vehicles
communication using blockchain technology . Our
proposed mechanism has three basics technologies
including communication network enabled connected
device , Vehicular Cloud Computing (VCC) and
blockchain technology (BT). Fig.3 has shown the
complete data-sharing environment for intelligent
vehicles .
Fig. 3. Proposed blockchain Intelligent Vehicle
Communication
4.1.Network enabled connected device
It is an internet -enabled device, which can organize ,
communicate in V ANET s uch as Smartphone, PDA,
Intelligent Vehicles, etc.
4.2.Vehicular Cloud Computing
VCC is a hybrid technology that has a remarkable
impact on traffic management and road safety by
instantly using vehicle resources, such as computing,
data storage, and internet decision -making.
4.3.Blockchain supported intelligent
vehicles
Blockchain consists of a technically unlimited
number of blocks which are chained together
crypt ographically in chronological order. In this, each
block consists of transactions, which are the actual data
to be stored in the chain.
In fig . 6 we present a s even-layer conceptual model
for standardizing blockchain architecture for the
intelligent commun ication network. W e briefly explain
the key features of our proposed network model. Due
to space limitation, we did not explain the technical
details of the proposed model. The implementation of
proposed model is beyond the scope of this paper and
thus is omitted.
Physical layer: This layer presents the
communication network enabled devices such as, IoT
devices, mobile, intelligent vehicles, camera, GPS, PDA, etc ., which can involve during communication
and easily adopt the blockchain mechanism.
Fig. 4. Proposed Blockchain based Intelligent Vehicle
Communication Network Framework
Data Layer: This layer process the data blocks with
cryptogra phy features such as hash algorithm, merkle
tree to make secure blocks.
The structure of block is shown in fig. 5, where
header pa rt specifies the previous hash and nonce with
current hash (root). Hash is made by double SHA 256
algorithm and is not easily hackable.
Fig. 5. The structure of blocks.
Network Layer: This layer represent s the data
forwarding peer-to-peer communication and
verification of the communication. This layer verifies
the legality of the broadcasted message and manage s
the peer connection between two IVs.
Handshake Layer: This feature is called consensus
layer in blockchain technology. It provides
decentralized commu nication with in network and
helps to develop trust between unknown users in the
communication environment . In intelligent vehicles
communication networks, more feasible consensus
algorithm is proof of driving (PoD), which verif y and
validate the vehicles involved in the communication
networks.
Reward layer: It provide s some crypto data, which
is called IV-TP in this paper. The IV-TP has a crypto
data which is assign ed to each vehicle and whenever
any vehicle wins the consensus competition, its get s
some IV-TP from t he benefiter IV . The vehicle having
the maximum IV-TP, lead s in the communication
network . IV-TP helps to make trust ed environment
between the vehicles communication .
Presentation Layer: The presentation layer
encapsulate s multiple scripts, contracts and algorithms,
which are provide d by the vehicles involve d in the
network.
Service Layer: This layer represents the scenario
and use cases of intelligent vehicles communication
system.
However, a lot of research organization and startup
companies are implementing blockchain in different
areas. One such area is , build ing ITS communication
trust enviro nment. Section VI, explains our proposed
mechanism through an intersection scenario based use
case example .
5. CONCLUSION
In this paper, we have presented a reward based
intelligent vehicle communication based on
blockcha in technology and not for specific services as
previously proposed by other researchers. We have
proposed crypto IV-TP that will help to improve the
privacy of IVs. IV-TP provide fast and secure
communication between IV s. It also helps to detect the
detailed history of IV s communication. IV
communication data will be stored on the VC, as long
as the user wants . During any accident, the IVs
communication history and their reputation s are
ubiquitously available to authorize d organization s
(hosp ital, insurance company, police etc.) and home
via VC.
In future , we will simulate our proposed framework
mechanism on real-time traffic data of vehicle
information sharing scenario s as well as analyze with
multiple use cases with a solution . 6. ACKNOWLEDGMENT
This work was supported by the MSIP (Ministry of
Science, ICT and Future), Korea, under the “ICT Con
silience Creative Program” (IITP -2017 - 2017 -0—1-01
015) supervised by the IITP (Institute of Information
& Communications Technology Promotion).
7. REFERENCES
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April,2017
|
{
"id": "1708.09721"
}
|
1810.00349
|
IDMoB: IoT Data Marketplace on Blockchain
|
Today, Internet of Things (IoT) devices are the powerhouse of data generation
with their ever-increasing numbers and widespread penetration. Similarly,
artificial intelligence (AI) and machine learning (ML) solutions are getting
integrated to all kinds of services, making products significantly more
"smarter". The centerpiece of these technologies is "data". IoT device vendors
should be able keep up with the increased throughput and come up with new
business models. On the other hand, AI/ML solutions will produce better results
if training data is diverse and plentiful.
In this paper, we propose a blockchain-based, decentralized and trustless
data marketplace where IoT device vendors and AI/ML solution providers may
interact and collaborate. By facilitating a transparent data exchange platform,
access to consented data will be democratized and the variety of services
targeting end-users will increase. Proposed data marketplace is implemented as
a smart contract on Ethereum blockchain and Swarm is used as the distributed
storage platform.
|
http://arxiv.org/pdf/1810.00349v1
|
Kazım Rıfat Özyılmaz, Mehmet Doğan, Arda Yurdakul
|
cs.CR, cs.NI
|
cs.CR
|
Presented at Crypto Valley Conference on Blockchain Technology (CVCBT 2018), 20-22 June 2018
Published version may differ
IDMoB: IoT Data Marketplace on Blockchain
Kazım Rıfat Özyılmaz
Computer Engineering Department
Bogazici University
Istanbul, Turkey
kazim@monolytic.comMehmet Do ˘gan
Computer Engineering Department
Bogazici University
Istanbul, Turkey
mehmet.dogan1@boun.edu.trArda Yurdakul
Computer Engineering Department
Bogazici University
Istanbul, Turkey
yurdakul@boun.edu.tr
Abstract —Today, Internet of Things (IoT) devices are the pow-
erhouse of data generation with their ever-increasing numbers
and widespread penetration. Similarly, artificial intelligence (AI)
and machine learning (ML) solutions are getting integrated to all
kinds of services, making products significantly more "smarter".
The centerpiece of these technologies is "data". IoT device
vendors should be able keep up with the increased throughput
and come up with new business models. On the other hand,
AI/ML solutions will produce better results if training data is
diverse and plentiful.
In this paper, we propose a blockchain-based, decentralized
and trustless data marketplace where IoT device vendors and
AI/ML solution providers may interact and collaborate. By
facilitating a transparent data exchange platform, access to
consented data will be democratized and the variety of services
targeting end-users will increase. Proposed data marketplace is
implemented as a smart contract on Ethereum blockchain and
Swarm is used as the distributed storage platform.
I. I NTRODUCTION
A new age of always listening, monitoring and commu-
nicating IoT devices are at our doorstep. Quoting IBM: "90
percent of the data in the world today has been created in
the last two years alone – and with new devices, sensors
and technologies emerging, the data growth rate will likely
accelerate even more" [1]. Increased amount of data enforces
companies to create and maintain large scale infrastructure
projects in the cloud. Unfortunately, every company, com-
petent or not, tackles these problems in their own way. In
the meantime, almost every company is building Artificial
Intelligence (AI) and Machine Learning (ML) solutions. Using
both publicly available and privately collected data, companies
aim to provide customized user experiences targeting each
individual differently. Vast amount of consented data is still
not tapped and currently there is no platform to search for it.
Today, IoT manufacturers use cloud-based solutions to im-
plement their data storage and business intelligence/dashboard
services. However implementing predictive, prescriptive and
adaptive solutions necessary for future businesses requires an
additional data processing step that extracts actionable triggers
from all the collected data [2] [3]. These next generation of
services, which is pushed by recent technology trends like
Industry 4.0 and Smart Agriculture, force IoT manufacturers
to develop a new skill set that they currently do not possess. On
the other side, there are plenty of AI and ML startups trying
to create insight using only tiny scraps of data. The solution is
straightforward: having a trustable, neutral platform that data
Fig. 1. Multi-party, multi-layer IoT solution
producers (IoT manufacturers) and data consumers (AI/ML
providers) can seamlessly trade. We propose blockchain to
facilitate such a trustless and secure digital trading platform.
Once the platform is in place, a complete “business intel-
ligence solution” can be created just like designing a layered
software stack. However, it will differ from existing solutions
as follows: our solution will be consisting of multiple stake-
holders (data providers and processors) that are connected to
each other in a certain way using the blockchain infrastructure,
to create actionable insights, i.e. information that can be acted
upon, for consumers (Figure 1). One of the many benefits
of using a blockchain-based solution is that it almost always
comes with a cryptocurrency attached, therefore it is very easy
to make economic incentives work. Consequently, as observed
in current blockchain/cryptocurrency realm (SegWit, block
size debate etc.), governance is yet another big aspect that
needs to be addressed in order to establish a living and working
marketplace. Eliminating IoT manufacturers that provide bad
data or ranking good data sets higher should be built while
designing the infrastructure.
Proposed idea facilitates an open environment with a low
barrier, where businesses or regular consumers will be able
to get the services or information from multiple providers.
These services may be acquired in exchange for sharing
device data with consent, instead of explicitly paying for
the services. Proposed marketplace is not designed for time-
critical systems or services that need complete user privacy or
had to comply with law and data protection requirements such
as General Data Protection Regulation (GDPR) (Regulation
(EU) 2016/679) [4].arXiv:1810.00349v1 [cs.CR] 30 Sep 2018
Fig. 2. Example benefit network with multiple stakeholders
In short, by creating a common, decentralized and trustless
infrastructure it will be possible to provide a) an always-
on data store for IoT manufacturers b) a searchable data
marketplace for AI/ML companies. In this paper, we aim
to give insights of how such a solution can be built by
using blockchain technology and lay out the mechanics and
governance guidelines for such a system.
Organization of this paper is as follows: an overview of the
IoT data marketplace concept and its benefits are presented
in the next section (Section II). Then, the requirements and
limitations for such a system is described in Section III. In
"Candidate Platforms" (Section IV), prospective blockchain
platforms for implementing the data marketplace are evaluated.
"Implementation Concepts" (Section V) and "Smart Contract"
(Section VI) sections focus on the implementation while pro-
viding insights on key features of the contract and the skeleton
code, respectively. Implementation sections are followed by a
discussion (Section VII) on the pain points and improvement
opportunities regarding the proposed system.
II. I OT M ARKETPLACE ON BLOCKCHAIN
Having a decentralized IoT data platform has multi dimen-
sional benefits for all of the contributing parties as detailed in
this section. Such a system will not only provide economical
benefits, but also technical and user-facing benefits as well.
As seen in Figure 2, IoT data can be collected, processed and
finally consumed by different parties. In this figure, there are
two IoT devices: a smart watch and a holter monitor. The
device manufacturer must have already listed the data of these
devices by using the proposed marketplace. Three different
AI/ML providers buy the data of these devices and process
them with their algorithms to produce insights such as tracking
a team's or an athlete's performance, location of a certain or a
group of individuals or non-real time monitoring of patients.
As a result, these insights may be exploited by individuals,
companies or organizations like hospitals, sport clubs or asingle patient. In the end, this approach will democratize the
way data is managed and will accelerate IoT adoption, creating
a positive feedback loop.
As a side note, although the concept of the decentralized,
trustless data marketplace is fairly new, IOTA platform [5]
has also proposed a data marketplace. In the last part of this
section, previous experiments and proposals for building such
systems are reviewed.
A. Benefits
1) Technical Benefits: Having a common, blockchain-based
data backend has clear technical benefits for all the parties in
the system:
IoT manufacturers don’t have to create and maintain
cloud backends for sensor data, because our solution
will provide the necessary data analytics services over
blockchain.
IoT manufacturers will use well-tested, maintained and
optimized code for their devices to interact with Swarm
and Ethereum. Software development cycles will be re-
duced and time-to-market for new products will decrease
significantly.
AI/ML providers may be able to tap into a vast pool
of data that they are unable to reach before. Therefore
AI/ML solutions will improve due to increased amount
of training and test data.
Consumers of the actionable insights, e.g. businesses,
organizations and end users will be able to build new
kinds of products and services. They will be able to
browse through a vast library of behavioural patterns that
are created by AI/ML providers.
2) Economical Benefits: Obviously, the most direct benefit
of the system will be in economic terms. Specifically, IoT
manufacturers, AI/ML providers and end users will benefit
directly and indirectly as detailed below:
IoT manufacturers will be able to monetize consented
user data which may ignite a new wave of business
models where IoT device costs may reduce to zero due
to the subsidies coming from data monetization.
AI/ML providers will be able to sell actionable insights
to businesses and users.
Businesses may finally provide predictive, prescriptive
and adaptive solutions to their customers.
End users may be able to use IoT devices and services
freely in exchange for their consent on data usage. The
scope of data collection is determined by both user’s
consent and legal framework that allows or denies it.
3) User-facing Benefits: In general, there is a skeptical ap-
proach to systems where user data is collected and processed,
mostly due to privacy concerns. However this is not a zero-
sum game. A transparent and trustless data marketplace that
only contains consented data may result high quality products
and services for consumers.
In the proposed model, there are two factors that will
reduce IoT device prices: the first one is the lack of
cloud backend management burden and the second one
is the data monetization capability. Assuming IoT device
manufacturers reflect decreased costs to device prices,
there will be a net incentive for end users to use these
products and services. It is likely that free-to-use or free-
if-consented business models bloom in every industry.
Such a data marketplace will bring democratization by
means of data access. This is one of the problems that
Web technologies have created, considering the enormous
amount of personal data accumulated and controlled by
Google and Facebook. In our proposed system, data is
intertwined with protocol. Hence, there is no way to
monopolize the data in the system.
A rich ecosystem filled with IoT manufacturers and
AI/ML providers will create a universal library, consisting
raw and processed data. It will be open to anyone who
wants to search for correlations for any set of inputs and
outputs.
B. Experiments
At the end of 2017, IOTA announced that it is going to
support a decentralized marketplace, "to open up the data silos
that currently keep data limited to the control of a few entities.
Data is one of the most imperative ingredients in the machine
economy and the connected world" [6]. Although the exact
number of IoT vendors and devices that use this platform is
not exactly known, IOTA's marketplace approach shares a lot
of goals and ambitions with the proposed design. The main
difference in our proposed approach is that there is no entity
that oversees the marketplace in any form. Only a transparent,
independent and auditable smart contract is in place, which
takes care of connecting data providers and consumers. The
data marketplace application is decentralized and trustless in
itself.
It is also imperative to mention about a previous experiment,
named "Contract Market" [7] where users can subscribe to IoTdevices. Hence, IoT vendors are able to manage them via a
smart contract. However, the details regarding where and how
the data is stored and accessed, or how the system economics
would work is missing.
There are also non-blockchain based attempts to create a
common data platform. One of them is "Big IoT Market-
place" [8] by the European IoT Platforms Initiative. Big IoT
Marketplace is a platform where IoT data providers will be
able to sell their data. However, it is both centralized and does
not provide a generic method to store and access data.
III. S YSTEM REQUIREMENTS AND LIMITATIONS
Finding the right blockchain platform for implementing an
IoT data infrastructure requires consideration of multiple key
aspects: data storage mechanism (on-chain or off-chain), tools
and capabilities for creating an IoT data platform and financial
incentivization for sustainability.
A. Data Storage
“Data storage mechanism” is a general term to describe
how IoT sensor data is pushed and where it will be stored.
First generation public blockchains have a cap on number of
transactions, either in form of block size (Bitcoin) or gas limit
per block (Ethereum). Pushing IoT data directly into these
systems is not feasible for the majority of IoT applications,
due to the high amount of transactions and the high amount
of data. Bitcoin is able to process 4.5 transactions per second
(2704 transactions per block on 21th of December, 2017) [9]
and Ethereum is able to clear out 15.6 transactions per
second (1349890 transactions on 4th of January, 2018) [10]
at their peak. On the other side, there are private blockchain
platforms like Hyperledger that has low latency requirements
for consensus but do not fully satisfy decentralization goals.
Benchmarking of Hyperledger platform shows that it fails to
scale beyond sixteen nodes [11].
Quorum [12] and Corda [13], which are both permissioned
and blockchain-inspired platforms targeting financial institu-
tions, proposing a different model where data is not stored
publicly on blockchain. Instead, data is kept off-chain by the
participating peers (financial institutions) and the consensus
function is designed to ensure agreements among interacting
parties. Although this approach may be practical for financial
institutions in terms of creating "business flows", it eliminates
one of our design goals where IoT device manufacturers use
this system as an "always-on data store". In addition, there are
custom blockchain platforms targeting IoT and decentralized
application development, such as IOTA [14] and EOS [15],
which will be analyzed separately below.
Although pushing the complete IoT data into blockchain is
problematic, it should be possible to push a so-called “file
handle”, that is tied to a specific IoT data chunk. Hence,
our proposed data marketplace targets non-real time and non-
critical IoT systems that push monitoring data to the data
backend in large time intervals (>30 mins). However, this
approach will need a secondary decentralized file storage layer.
IPFS [16] and Swarm [17] are two prominent alternatives that
can be used for this purpose. Both technologies are peer-to-
peer (P2P) with decentralized file transfer systems in which
files are addressed by the hash of their content. Moreover,
they are compatible with the concept of edge computing if
IPFS or Swarm nodes are executed on IoT gateways. On top
of that, highly used data sources will be retrieved with low
latency as mentioned in Swarm guide: “Nodes cache content
that they pass on at retrieval, resulting in an auto scaling elastic
cloud: popular (oft-accessed) content is replicated throughout
the network decreasing its retrieval latency” [18].
B. Decentralized Application
Proposed IoT data platform should be completely decen-
tralized and always in working condition in all circumstances.
Therefore not only the financial part of the trade, i.e., the
transactions, but also the application logic of the platform
should be in the blockchain. As a result, this narrows down
the list of blockchain platforms to the ones that utilize smart
contracts to ensure an always-on decentralized platform.
C. Financial Incentives
There are already clear benefits for IoT device manufac-
turers and AI/ML providers to use the proposed system. IoT
device manufacturers will be able to break free of developing
and maintaining a cloud backend. Besides, they will be able to
sell collected data in an open marketplace. AI/ML providers,
on the other hand, will be able to access a vast data library
where they can browse and buy as much as they can afford.
In addition, nodes in the decentralized storage network should
also be incentivized in order to keep bulk IoT sensor data
available, at least based on their usage [17]. This mechanism
is similar to Amazon Web Services (AWS) Simple Storage
Service (S3) in terms of functionality. Yet, it consists of
multiple independent peers committing their resources instead
of a single entity, where they are rewarded based on their
contribution. Storage incentivization can be done only if
decentralized storage system is deeply integrated with the
blockchain client. Having a built-in currency is a vital tool for
embedding incentives at the transaction level. Unfortunately,
permissioned blockchains like Hyperledger and Corda lack
this mechanism.
IV. C ANDIDATE PLATFORMS
The requirements and the challenges for such a system is
detailed in the previous section. Now we will study two avail-
able candidate platforms: one is customized for addressing IoT
needs (IOTA) and the other one is proposed for decentralized
application development (EOS). Then, we will briefly describe
our proposal, Ethereum and Swarm platform.
A. IOTA
IOTA is a relatively new project which uses "Tangle", a
directed acyclic graph data structure to store transactions. It
aims to provide a decentralized infrastructure and a data mar-
ketplace for IoT devices [14]. However due to centralization
concerns and persistent storage needs (permanode), IOTA is
not picked as the implementation platform for the time being."Coo" (Coordinator), which is a full node controlled by
IOTA Foundation, is employed to clear out transactions. If
"Coo" is down by any reason, IOTA network stops working.
IOTA plans to shutdown "Coo" when the system is able to
resist to a %34 attack. However, at the time of this writing,
it is still on. In addition, IOTA uses a mechanism called
"snapshot" where they prune history of transactions and the
attached data in order to prevent bloat. As a result, IOTA
full nodes will not be storing any data by default (even a
pointer to an external file) except the account balance. In order
to access persistent data, IoT vendors should run so-called
“permanodes” that store all the data starting from the genesis
block. This will be a huge burden for IoT vendors in terms
of storage compared to just incentivized independent Swarm
nodes for storage in a Ethereum-Swarm setup. As a part of
the announced milestones, IOTA is planned to take automatic
snapshots.
B. EOS and EOS Storage
EOS is another blockchain project aiming to create scalable,
decentralized applications on top of an existing blockchain
architecture [15]. EOS project addresses important aspects like
creating peer-to-peer terms of service agreements, separating
authentication from application. These aspects are very im-
portant if the aim is to create a decentralized peer-to-peer
data marketplace. Similar to the authors’ line of thinking,
EOS Whitepaper [15] emphasizes that the piece of data to
be stored in blockchain should be relevant to the application.
In other words, instead of the content itself, i.e. bulk IoT
data, a pointer to it should be stored in blockchain. Just
like Swarm, EOS recently proposed a decentralized storage
layer built on top IPFS technology [19]. In order to have a
replicated file on EOS Storage, two transactions should be
processed, one for creating the file on the blockchain and the
other one for the confirmation of a successful upload [19]. In
general, EOS is a well-thought platform tailored for the needs
of the next generation of application developers, However, for
our specific use case EOS storage mechanics will double the
amount transactions needed to store the file handles.
C. Ethereum and Swarm
Ethereum, being the first decentralized application platform,
has already established itself a high ranking among cryptocur-
rencies and sparked developer interest with its decentralized
application platform. Ethereum currently offers a widely used
programming language, called “Solidity”, and a complete
web based development environment, called “Remix IDE”.
In addition, Ethereum is deeply integrated with Swarm, a
decentralized, torrent-like storage service. As the result of this
deep integration, Swarm nodes can be financially incentivized
directly from Ethereum. Lastly, Ethereum platform’s currency
“Ether” is widely used which makes instant trades to fiat
currency possible. Such a feature may accelerate adoption of
the proposed system exponentially.
Based on the aspects detailed above, Ethereum is selected
as the blockchain and Swarm as the decentralized storage
platform based on the maturity of the platforms and deep
integration with each other.
V. I MPLEMENTATION CONCEPTS
This section will first go over the concepts used in the
development of our smart contract. Designing a generic IoT
backend on the blockchain requires some challenges which
can be listed as follows:
a flexible querying mechanism for data consumers (filter
data by vendor, sensor type, geo-location, time)
a voting system to rank data sources
a token-based economy where marketplace payments are
not exposed to heavy market fluctuations
payment channels to execute instant transfers
A. Data-as-a-Contract
Implementing the IoT data marketplace as a smart con-
tract, i.e. a decentralized application deployed as a part of
blockchain, facilitates a transparent data collection and shar-
ing environment. In addition, by being trustless, blockchain
infrastructure inherently provides a global safe-trade environ-
ment. Quoting Nick Szabo, “trusted third parties are security
holes” [20].
The presence of such a system proactively eliminates op-
erational risks of IoT device manufacturers, as there will be
no need to develop and maintain an actual data backend. In
our previous research, we demonstrated how the blockchain
platform can be used for integrating IoT devices [21] and
creating a generic data backend [22]. IoT device manufacturers
may maintain a custom data backend for certain purposes
like privacy, but in any case, a blockchain-based system will
enforce transparency and data democratization.
Nowadays, it is very common for cloud provides to create
solutions labeled as Software-as-a-Service (SaaS) or Platform-
as-a-Service (PaaS) that offer their customers pay-per-use
access. Following that model, consumers of the IoT data such
as AI/ML providers, or consumers of the actionable insight
such as business, organizations, regular end users who give
consent to data sharing, will pay as much as they use the
provided services.
B. Geographical Data on Blockchain
One of the key features for Internet of Things is geolocation
where the interaction with environment takes place. There
are many researches for determining geolocation of IoT end
devices without trusting the location information from IoT
device or IP packets, which may be blocked or compromised
by an adversary [23]. Still, detecting the geolocation of an IoT
device is not ethical due to violation of privacy. Actually, it is
possible to verify the geolocation of an IoT end-device by the
consensus of nearby IoT end devices on a mature ecosystem
unless more than 33% of devices behave maliciously.
Hence, we propose to use GeoHex that divides whole world
map into hexagons and map these hexagons with strings [24].
It is very practical when it comes to searching for nearby
geolocations. Whereas most of geolocation systems requirefloating point arithmetics, GeoHex limits the geolocation to a
string of at most 17-bytes. Considering its importance for a
data scientist in picking-up data from the market, we prefer
using GeoHex due to its ease of use in querying geolocations.
C. Validation and Feedback
V oting is a straightforward feedback mechanism that is used
by the AI/ML providers to rank the quality of the IoT device
manufacturers. Consumers of the data marketplace will mark
bad providers, which will in turn increase the overall data
quality in the system. V oting is one of the early concepts
explored in blockchain systems and due to its immutable
and trustless nature, such applications proved to be working
successfully.
D. Data Tokens
Ethereum tokens are ERC20-compatible smart contracts
that can act like a currency on top of Ethereum [25]. By
creating Ethereum tokens, it is possible to define a custom
currency, which can be used to interact with the proposed,
underlying system. In short, smart contracts can be extended
to define their own economic model. A direct benefit of such
an abstraction is the isolation of the token value from the price
fluctuations of Ethereum.
The proposed data marketplace uses Ether (Ethereum's
currency) as the medium of exchange. However, by extend-
ing the smart contract to support ERC20 standard, proposed
marketplace will be able to offer a custom token to be used
as a currency, therefore providing a stable and deterministic
data pricing. Even though it is purely economic, these type
of changes are required to facilitate mass adoption of the
proposed system.
E. Payment Channels
Blockchain systems process transactions by packaging them
into blocks, which inherently adds latency expressed in block
creation time. Average block creation time is 14 seconds in
Ethereum. This latency, however, is not ideal for scenarios
where high amount of small payments are taking place be-
tween two parties. In order to address this issue, an off-chain
scaling solution called payment channels has emerged. Pay-
ment channels are near-instant and low-fee payment networks,
complementing the original blockchain platform. Currently,
Lightning Network [26] provides this service for Bitcoin and
Bitcoin-variant currencies, and Raiden Network [27] provides
it for Ethereum blockchain and works with any ERC20 com-
patible token.
The proposed data marketplace will benefit from payment
channels, because marketplace should offer instant exchange
of token and data with its dedicated ERC20 token. By us-
ing Raiden micropayment network and instant token trans-
fers, it will become possible to introduce pay-as-you-go or
subscription-based solutions for data consumers.
Ethereum Swarm
IoT Manufacturer AI/ML ProviderIoT Gateway
Device
vendor_register(prefix,sensors,costs)
return vendor_adress
add_valid_device(device_address)
return device_address
upload_data
return swarm_url
return vendor_adress
sensor_data_pull(vendor_address, type,index)
return (schema,timestamp,spatial,price)[]
request_for_data(vendor_address, type, index)return vendor_adress[]query_sensor(type,index)sensor_data_push(vendor_address,type,schema,timestamp,spatial,swarm,key_index, encryption_id)
emit payload_request(from,to,pub_key ,type,index)customer_register(pub_key)
return customer_adress
transfer_key_and_data(dec_key ,to,type,index)
emit payload_response(from,to,dec_key ,type,index,swarm)Fig. 3. Data Flow Sequence Diagram
1 function vendor_register ( string prefix, uint [] sensors, uint [] costs) public returns (address);
2 function customer_register ( string pub_key) public returns (address);
3 function add_valid_device (address device_address) public returns (address);
4 function vendor_length () public view returns ( uint length);
5 function get_vendor (address addr) public view returns ( string prefix);
6 function vote_for_vendor (address vendor_address, uint vote) public returns ( uint );
7 function query_sensor ( uint sensor_type, uint index) public view returns (address result);
8 function sensor_data_push (address vendor_address, uint sensor_type, string schema, uint timestamp,
9 string spatial, string swarm, uint key_index, uint enc_id ) public returns (address);
10 function sensor_data_pull (address vendor_address, uint sensor_type, uint index)
11 public view returns ( string schema, uint timestamp, string spatial, uint price);
12 function sensor_data_length (address vendor_address, uint sensor_type) public view returns ( uint len);
13 function get_sensor_price ( uint sensor_type_index) public view returns ( uint );
14 function update_sensor_price ( uint sensor_type, uint price) public returns ( uint );
15 function request_for_data (address vendor_address, uint sensor_type, uint index) public returns (address);
16 function transfer_key_and_data ( string dec_key,address _to, uint sensor_type, uint index) public returns ( string );
Listing 1: Data Marketplace Core Functions
VI. S MART CONTRACT
A. Overview of Development Environment
Ethereum blockchain with a built-in Turing-complete pro-
gramming language allows us to write smart contracts [28].
In this paper, the smart contract implementation is done in
Solidity which is designed to target Ethereum Virtual Machine
(EVM). During the smart contract development process, we
used web-based Remix IDE, which contains Solidity compiler
and debugger. Ethereum client version geth 1.7.3, Solidity
version 0.4.19 and Remix IDE Online version 0.1.3 are used
for development.
The contract written in Solidity generates two components:
the bytecode to run on EVM and the Application Binary
Interface (ABI). Bytecode runs whenever a function is called
from the application, and stored into Ethereum blockchain
under contract address. ABI defines the structures and func-
tions that can be invoked explicitly. In other words, ABI
grants access to call functions in smart contracts. To sumup, three requirements should be satisfied to interact with a
smart contract: 1) Bytecode must be deployed to blockchain 2)
Address of bytecode must be known 3) ABI of smart contract
must be known.
Our proposed system will be able to provide IoT device
data to many users when the system reaches its maturity.
The relation between the data and its users is similar to
one-to-many relationship in traditional databases. Based on
this relationship, our platform can be seen as a decentralized
exchange platform that requires more reading operation from
smart contract than writing to it.
B. Action Flow
The main stakeholders of the application are “vendors” and
“customers”, who correspond to an IoT Manufacturer and an
AI/ML provider, respectively, in Figure 3. When a vendor
wants to get economic benefits from devices, it creates a new
registry on the application by calling vendor_register (List-
ing 1, line 1). For blocking an unauthorized device pushing
1 /*payload from a specific sensor type */
2 struct payload {
3 address device_id;
4 uint timestamp;
5 string swarm;
6 string schema;
7 string spatial;
8 uint key_index;
9 EncryptionScheme encryption_scheme;
10 string encrypted_key;
11 }
12 /*everything about vendors */
13 struct vendor {
14 string prefix;
15 // vendor supported sensor types
16 mapping( uint =>bool ) types;
17 // unit prices for every sensor type
18 mapping( uint =>uint ) prices;
19 // payload from a specific sensor type
20 mapping( uint => payload[]) payloads;
21 // devices belong to specific vendor
22 mapping(address => bool ) devices;
23 // total count of votes
24 uint votes;
25 }
26 struct customer {
27 payload[] paid_arr;
28 mapping(address => bool ) vote_map_used;
29 string pub_key;
30 }
31 mapping(address => vendor) private vendor_map;
32 mapping(address => customer) private customer_map;
33 address[] private vendor_arr;
34 mapping(address => uint ) balances;
Listing 2: Data Marketplace Core Data Structure
data to market on behalf of the vendor, vendor must declare its
device addresses by using add_valid_device method (Listing 1,
line 3).Then, any valid (registered by vendor) device can push
data to the system by stating the vendor, pre-defined schema,
file handle, timestamp, and geolocation (Listing 1, line 8).
In this manner, devices can upload many datasets from
different sensor types into the system. Then any user, such
as an AI/ML provider, can query data sets of a sensor by
calling query_sensor (Listing 1, line 7). It returns list of
vendors who own the datasets of the queried sensor type. From
this point on, the user selects a vendor and the application
calls sensor_data_pull to have more descriptive details such as
timestamp, geolocation or schema of the sensor data (Listing 1,
line 10). After desired dataset is matched, payload data can be
claimed by calling request_for_data (Listing 1, line 15). When
the user retrieves the data, voting option for the user is enabled
to evaluate the vendor. Through vote_for_vendor (Listing 1,
line 6), the user is able to vote as up or down according to
his/her experience.
C. Data Structures and Optimization
IoT device data is a form of digital asset which is controlled
by the smart contract in data market application. It is con-
sidered as a digital asset because data collection is a costly
operation for IoT vendors and the collected data provides
value for businesses. IoT device data or namely the payload
(Listing 2, line 2-11) is the fundamental structure, around
which the whole ecosystem gets shaped. Storing this data
directly on blockchain creates lots of transactions and incurs
high financial costs. Instead, IoT device data is uploaded fromgateway to Swarm file system in an encrypted form. Swarm
client returns file handle, which is cryptographic hash of the
data. The file handle is unique identifier and address of data.
Data schema, which will be used for parsing the payload, is an
important concern for AI/ML providers. Therefore, it is also
included in the payload structure. In addition, the identifier of
the device that uploads the data, the name of the encryption
scheme and the index of the encrpytion key is also present
in the payload structure. Details on encryption and security
will be given in the next subsection "Encrpytion and Data
Security" VI-D.
Second structure in the contract is vendor (Listing 2, line
13-25). Vendor is located at the lowest level in the stack-
like approach which is shown in (Figure 1). To show human-
readable names instead of addresses, we store prefix for each
vendor. In our implementation, we used a unique number
instead of a string to represent each sensor type. For example,
“1” for smart watch data, “2” for holter monitoring data,
etc. By doing so, we decrease the cost of transactions [29].
Based on this representation, we used sensor type as key
fortypes ,prices , and payloads mappings. Types store for
which sensor types registered by the vendor. Prices store the
corresponding prices for each sensor type. A vendor has the
ability to push multiple payloads per sensor type, with each
payloads store array of payload structures (Listing 2, line 20).
Device addresses that are allowed to push data in the name
of the vendor are also stored for automatization. This allows
the IoT device to export its data to Swarm with a script and
add a new payload to the application with a file handle on
behalf of vendor. Reliability and convenience of datasets can
be provided with a voting mechanism, so votes is also defined
in the vendor structure as a field.
The last structure of the system is customer , which can
be an AI/ML provider or an individual user. It stores the
public key of the AI/ML provider to be used in later stages
during data decryption. For browsing and voting purposes,
paid_arr and vote_map_used mappings are defined in this
structure (Listing 2, line 26-30). Vote_map_used stores address
of vendor as the key and a boolean value that shows whether
the customer has the right to vote or not.
While implementing functions, our main concern was to
minimize gas cost which is spent on each execution of opcodes
in EVM. Considering this cost, we avoided loops and mapped
data structures accordingly. We used two mapping structures
as global variables (Listing 2, line 31-32) for getting or
setting any field within stakeholders (customers and vendors)
of the system. Instead of storing whole vendor structs in an
array, we stored addresses of them for querying sensors and
corresponding prices (Listing 2, line 33).
Any operation like registering as a vendor with a fake ID
or adding random devices to vendor's space is punished by
Ethereum network itself. Therefore, we did not implement an
additional blocking mechanism.
D. Encryption and Data Security
Swarm file handles are openly visible on the blockchain. In
order to prevent a non-paying user get all the Swarm handles
and fetch the corresponding files, IoT data uploaded to Swarm
should be encrypted with a symmetric key before the upload.
To ensure that, payload metadata should contain the name of
the encryption scheme (DES, AES) and a key index beside
the device identifier.
We propose that IoT device vendors store the master keys
of their gateway devices and configure the devices to create
new symmetric encrpytion keys for each upload using a
hierarchically deterministic method as it is done in BIP32
Wallets [30]. This way, every Swarm file will be encrypted
with a different symmetric key and IoT device vendor will
be able to calculate any given key by using the master key
and the key index provided inside the metadata. IoT device
manufacturers store and manage master keys per device even
today, especially for low-power, long range protocols like
LoRa where a master key is used to encrypt data messages
on the field [31].
If a data consumer wants to buy a certain chunk of IoT
data, the payment and acquisition of the data will happen
as described in previous sections. The decryption process of
the acquired data will happen as follows, where the whole
process can be automated by Javascript code interacting with
the Ethereum client:
1) AI/ML provider will request data and pay for it by using
a smart contract function
2) IoT device vendor will be notified by the event invoked
by that call, passing the address of the AI/ML provider
3) IoT device vendor will use the address to get the public
key of the AI/ML provider
4) IoT device vendor will calculate the symmetric key that
is used to encrypt that particular Swarm file by using
that device's master key and key index
5) IoT device vendor will encrypt symmetric key with
AI/ML providers public key and create a transaction
6) AI/ML provider will receive the encrypted symmetric
key, decrypt it using its private key and then decrpyt the
Swarm file using the symmetric key
VII. D ISCUSSION
Encryption: Ethereum blockchain does not store data in
encrypted form. Similarly, the proposed data marketplace does
not impose any restrictions on the IoT data uploaded to Swarm.
It only sets the mechanics between data vendors and data
consumers. In the current design, it is assumed that the IoT
data sent to the platform is anonymized due to the rules and
regulations, like GDPR, that IoT device vendors are facing.
Although IoT device manufacturers may decide to encrypt
data and share the keys with data consumers by using an off-
chain method in the current system, it is not very practical.
It is planned to extend the data marketplace to support some
form of encryption where encrypt/decrypt operations can be
conducted in a decentralized manner.Real-Time Systems: Public blockchain systems add blocks,
i.e. packaged transactions, to the blockchain at every block
creation interval on average. On top of this, there is a block
propagation delay which adds additional latencies if a data
consumer tries to follow a real-time data feed using the data
marketplace. Therefore, with the current consensus functions
on widely used public blockchain platforms, proposed solution
does not support real-time or safety-critical applications due
to high latencies.
Data Collection and Consent: European Union has data
protection requirements such as General Data Protection Reg-
ulation (GDPR) (Regulation (EU) 2016/679) [4] already in
place, so IoT device manufacturers should comply with cur-
rent rules and regulations as data providers. Although data
marketplace smart contract does not store any user data (just
Swarm handles), data replication on Swarm filesystem should
be managed by device vendors.
VIII. C ONCLUSION
In our previous research, we have already explored ways to
integrate low-power IoT devices to a blockchain-based infras-
tructure [21] and created a decentralized data backend [22]. In
this paper, we extend that goal to a broader data marketplace
involving multiple parties, targeting non-real time, non-critical
IoT applications. Creating a decentralized and trustless plat-
form for storing and accessing IoT data will positively impact
IoT device manufacturers, AI/ML providers, and, obviously,
the end-users. Such a marketplace will democratize access to
consented data and increase both service quality and variety
of offerings, which will turn out to be beneficial for the users
in the end.
A proof-of-concept data marketplace is implemented as a
smart contract on Ethereum platform and uses Swarm as its
storage system. It provides a flexible querying mechanism
for data consumers and contains a voting mechanism for
eliminating unreliable data providers. Smart contract code is
open sourced on GitHub as "IDMoB: IoT Data Marketplace
on Blockchain" [32].
ACKNOWLEDGMENT
This project has been partially supported by Scientific
Research Fund of Bogazici University under grant number:
13500
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mediawiki
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https://github.com/kozyilmaz/idmob
|
{
"id": "1810.00349"
}
|
2007.09098
|
Actor-based Risk Analysis for Blockchains in Smart Mobility
|
Blockchain technology is a crypto-based secure ledger for data storage and
transfer through decentralized, trustless peer-to-peer systems. Despite its
advantages, previous studies have shown that the technology is not completely
secure against cyber attacks. Thus, it is crucial to perform domain specific
risk analysis to measure how viable the attacks are on the system, their impact
and consequently the risk exposure. Specifically, in this paper, we carry out
an analysis in terms of quantifying the risk associated to an operational
multi-layered Blockchain framework for Smart Mobility Data-markets (BSMD). We
conduct an actor-based analysis to determine the impact of the attacks. The
analysis identified five attack goals and five types of attackers that violate
the security of the blockchain system. In the case study of the public
permissioned BSMD, we highlight the highest risk factors according to their
impact on the victims in terms of monetary, privacy, integrity and trust. Four
attack goals represent a risk in terms of economic losses and one attack goal
contains many threats that represent a risk that is either unacceptable or
undesirable.
|
http://arxiv.org/pdf/2007.09098v2
|
Ranwa Al Mallah, Bilal Farooq
|
cs.CR
|
cs.CR
|
arXiv:2007.09098v2 [cs.CR] 4 Dec 2020Actor-based RiskAnalysis forBlockchainsin Smart Mobilit y
Forthcomingin theproceedingsofCryBlock/MobiCom2020
Ranwa AlMallah
Ryerson University
Toronto,Ontario
ranwa.almallah@ryerson.caBilalFarooq
Ryerson University
Toronto,Ontario
bilal.farooq@ryerson.ca
ABSTRACT
Blockchain technology is a crypto-based secure ledger for d ata
storage and transfer through decentralized, trustless pee r-to-peer
systems.Despiteitsadvantages,previousstudieshavesho wnthat
thetechnologyisnotcompletelysecureagainstcyberattac ks.Thus,
it is crucial to perform domain specific risk analysis to meas ure
how viable the attacks are on the system, their impact and con -
sequently the risk exposure. Specifically, in this paper, we carry
out an analysis in terms of quantifying the risk associated t o an
operationalmulti-layered Blockchainframework forSmart Mobil-
ity Data-markets (BSMD). We conduct an actor-based analysi s to
determinetheimpactoftheattacks.Theanalysisidentified fiveat-
tackgoalsandfivetypesofattackersthatviolatethesecuri tyofthe
blockchain system. In the case study of the public-permissi oned
BSMD,wehighlight thehighest riskfactorsaccordingtothe irim-
pact on the victims in terms of monetary, privacy, integrity and
trust.Fourattackgoalsrepresentariskintermsofeconomi closses
andoneattackgoalcontainsmanythreatsthatrepresentari skthat
is either unacceptableorundesirable.
CCS CONCEPTS
•Securityandprivacy →Databaseand storagesecurity .
KEYWORDS
blockchain,mobility,security,risk analysis
ACMReference Format:
Ranwa Al Mallah and Bilal Farooq. 2020. Actor-based Risk Analysis f or
Blockchains in Smart Mobility: Forthcoming in the proceedings of Cry-
Block/MobiCom2020.In ProceedingsofACMConference(Conference’17). ACM,
NewYork, NY, USA,6 pages.https://doi.org/10.1145/nnnnnnn.nnnnnnn
1 INTRODUCTION
Nowadays, transportationdata are shared across multiplee ntities
and stored in central servers that are susceptible to cybera ttacks.
In the recent years, many cybersecurity breaches have occur red
in transportation systems. In 2015, a group of civic hackers deci-
phered and exposed the unstandardized bus system location d ata
of Baltimore [13]. The San Francisco transit was hacked to gi ve
Permission to make digital or hard copies of all or part of thi s work for personal or
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for profit or commercial advantage and that copies bear this n otice and the full cita-
tiononthefirstpage.Copyrightsforcomponents of thiswork owned byothersthan
ACMmustbehonored.Abstractingwithcreditispermitted.T ocopyotherwise,orre-
publish,topostonserversortoredistributetolists,requ irespriorspecificpermission
and/or afee. Request permissionsfrompermissions@acm.or g.
Conference’17,July 2017,Washington,DC,USA
© 2020 Associationfor Computing Machinery.
ACM ISBN978-x-xxxx-xxxx-x/YY/MM...$15.00
https://doi.org/10.1145/nnnnnnn.nnnnnnnfreeaccesstocommutersfortwodays[14].Itwasrecentlydi scov-
ered that Google keeps collecting user locationdata even if users
explicitlydeactivatethetrackingsystem intheir mobiles [12].
Blockchain technology creates a distributed consensus, pr ovid-
ing entities with a secure platform that maintains past reco rds of
digitalevents [4].Inthecontextoftransportation,amult i-layered
Blockchain framework for Smart Mobility Data-market (BSMD )
wasrecentlyproposedbyLópezandFarooq[8].BSMDisapermi s-
sionedblockchainandisdesignedtosolvetheprivacy,secu rityand
management issues related to the sharing of passively as wel l as
activelysolicitedlarge-scalemobilitydata.Inanothers tudy,López
andFarooq[9]proposedadistributedtoolformobilitychoi cemod-
elling over BSMD, where participants do not share personal r aw
data, while all computations are done locally. Eckert et al. devel-
oped a user-centric emission monitoring and trading system for
multi-modal mobility over the BSMD [2]. Another applicatio n in
transportationused BSMDfor modechoice inference using fe der-
atedlearning over blockchain[10].
However, security issues related to blockchain are critica l in
termsofcybersecurity.Inthissense,securityexpertsnee dtofully
understand the risk in terms of scope and impact of the securi ty
andprivacychallenges relatedtoblockchainbeforepredic tingthe
potentialdamage from an attack. Furthermore, verifying wh ether
thecurrenttechnologycanwithstandpersistenthackingat tempts
is alsoof utmostimportance.
In 2017,Li et al.conducted a comprehensive cybersecurityr isk
analysisofblockchain[7].Theysystematicallystudiedth esecurity
threatstoblockchainandsurveythecorrespondingrealatt acksby
examining popular blockchain systems. Although they ident ified
the threats and their nature, the real scope of the risk that t hose
threats entail was not described and evaluated.Unlike thei r work,
we aim at quantifying the risk. We present ordinal values for the
identifiedrisks.Anotherdifferenceisthatinourwork,wean alyze
a real blockchainsystem developed fortransportationappl ication
i.e. BlockchainforSmart MobilityData-markets.
Theaim ofourworkis moreextensive thanthetraditionalris k
assessment.Weestimatetheriskthroughanactor-basedris kanal-
ysis based on the impact that the exploitation of vulnerabil ities
will have on the transportationsystems using BSMD. Efficient de-
tection mechanisms are desired in this context to reduce the risk
thatvariousthreatsentailontheblockchainsystemsusedi ntrans-
portationdomain.Countermeasurestoavoidcyberattacksm ustbe
implemented as a security-by-design practice.
This paper is organized as follows.Related work is provided in
Section 2. In Section 3, we present the methodology followed by
Conference’17, July 2017,Washington, DC,USA Ranwa AlMallahandBilalFarooq
the actor-based risk analysis of a blockchain in transporta tiondo-
maininsection4.Finally,conclusionandfutureworkareou tlined
in Section5.
2 RELATED WORK
In the last decade, several studies have exposed vulnerabil ities in
thetechnologiesemployedinblockchain.Particularly,fo rresearch
in blockchain cybersecurity risk analysis, we summarize th e find-
ings in Table1.
Lietal.[7]performedasystematicexaminationoftherisks asso-
ciatedwiththepopularblockchainsystems(i.e.Ethereum, Bitcoin,
Monero, RSK, Counterpaty, Stellar, Monax, Lisk), the corre spond-
ing real attacks, and the security enhancements. They evalu ated
the real attacks on popularblockchain systems from 2009 to 2 017
and analyzed thevulnerabilities exploitedinthese cases.
Ratherthanpopularblockchainsystems,Atzeietal.[1]foc used
on Ethereum smart contracts. From a security programming pe r-
spective,theirworkanalyzedthesecurityvulnerabilitie sofEthereum
smart contracts, and provided a taxonomy of common program-
ming pitfalls that may lead to vulnerabilities. They show a s eries
of attacks on smart contracts that exploit these vulnerabil ities, al-
lowing anadversary tosteal money orcauseother damage.
In contrast to previous studies, Homoliak et al. [5] propose d a
security reference architecture based on models that demon strate
thestackedhierarchy ofvariousthreats(similartotheISO /OSI hi-
erarchy). In order to isolate the various nature of security threats,
thestackedhierarchyconsistedoffourlayers(Networklay er,con-
sensus layer,replicated statemachine layer and applicati onlayer).
Theyalsoproposedathreat-riskassessmentofthereferenc earchi-
tectureusingtheISO/IEC15408template.Withthestackedm odel,
differentthreat-agentsandthreatsappearateachlayer.Th eyiden-
tified four threat-agents: service providers, consensus no des, de-
velopers and users.Theyare maliciousentities whoseinten tion is
tosteal assets, break functionalities, ordisruptservice s.
Although the previous studies identified the threats and the ir
nature, the real scope of the risk that those threats entail i s not
described.Weconsiderthatalthoughtherearevulnerabili ties ina
system, it is the impact that certain exploitationhas on the nodes
oftheblockchainnetworkthatdetermineswhetherthevulne rabil-
ity represents a significant risk or not. In this line of thoug ht, we
havecarriedoutanactor-basedriskanalysisthatweapplie dtothe
multi-layered blockchainfor smartmobilitydata-market.
3 METHODOLOGY
Theaimofourworkistoperformanactor-basedriskanalysis .Itis
moreextensivethanthetraditionalriskassessment inthem anner
thatthemethodologyenables ustoquantifytherisk.Quanti fying
theriskrefinestheanalysisandgivesanorderofmagnitudeo fthe
riskexposure.Also,measuringtheriskenables tobettergr aspthe
impact of theattacks on large-scale systems or ecosystems w here
many technologies are tobeevaluated bytherisk assessment .
In the following, we define some cybersecurity and risk asses s-
ment termsin ordertofacilitatethereading of ourwork.
•Actor:Anentitythatviolatesintegrity,privacyorconfiden-
tialitytoobtaincertainbenefit.
•Victim:An entitythatis thesubjectof a cyberattack.•Attackgoal: Finaleffectdesiredbytheactoraimingonpro-
ducingan impactonthevictim.
•Scenario: Set ofactions carriedoutbytheactortoachieve
its attackgoal.
•Impact:Quantificationoftheattackgoal’seffectonthevic-
tim.
•Threat:Often called the actor scenario pair, is the combi-
nationoftheentitywhocommitstheact,theactor,andthe
wayitiscommitted,thescenario,inordertoproduceaneg-
ative impact.
•Vulnerability: A flaw that offers the opportunity to dam-
age asystem.
Thefirststepoftheactor-basedriskanalysisistoidentify poten-
tialattackers,i.e.actorswhowouldbeinterestedintheec osystem
under study. The next step is to determine the attack goals of the
actors.Finally,Table2isusedtoquantifytheimpactonthe victim
ofsuch attackgoals according toa four-level scale.
We follow this methodology to determine the impact that re-
sults from attacks on the BSMD ecosystem, a realistic blockc hain
system.Specifically,LopezandFarooq[8]proposedtheBloc kchain
forSmartMobilityData-markets, which is composedofnodes :In-
dividuals,Companies,UniversitiesandGovernment(trans port,cen-
sus, planing and development agencies). The nodes collect t heir
owndataandstoreitinidentifications.Eachnodeisthesole owner
of their data and can share their information by showing othe r
nodes their identifications or parts of it. In the blockchain there
are smart contracts available that the nodes need to sign bef ore
any transaction of information is conducted. An actor-base d risk
analysis is conductedonthis system.
4 ACTOR-BASED RISK ANALYSIS OF BSMD
The methodology in our work can be applied to any cybersecu-
rity analysis. Here, we describe its application on BSMD, a c yber
physical transportation system, but the risk evaluation me thodol-
ogy is not constrained to blockchain and can be applied to oth er
technologies orgeneric blockchainsystems.
Inthefirststep,weneedtoidentifycyberthreatsources(po ten-
tialattackers) andtheir attackgoalsinthecontextofsmar tmobil-
itydata-markets.
4.1 Cyber threat sources
Cyber attackers exploit the systems for financial gains, obt aining
information, conducting sabotage activities, creating di sinforma-
tion, and degrading confidence in the ecosystem. The Industr ial
ControlSystemsCyberEmergencyResponseTeam(ICS-CERT)h as
characterizedacyberthreatsourceas:“personswhoattemp tunau-
thorizedaccesstoacontrolsystem deviceand/ornetwork us inga
datacommunicationspathway[3].”Itfurtherclassifiesthe sethreat
sourcesintofourgroups(A1throughA4).Thetaxonomyofcyb er
threat sources was introduced for traditional threats to pu rely In-
formation Technology (IT) infrastructure. We propose to us e the
same taxonomy in the context of cyber threats against the BSM D
ecosystem—since it is composed of both IT elements and of cy-
berphysicalsystemsthathaveanITcomponent.Inthecontex tof
threatsagainsttheBSMDecosystem,weaddedanewthreatgro up
that we call “insider threat”. We suppose it is a determined n ode
Actor-based Risk Analysis forBlockchains in SmartMobilit y Conference’17, July 2017,Washington, DC, USA
Table 1: Previousstudiesin blockchain cybersecurityrisk analysis.
Studies Methodology Drawbacks
Li etal., [7]- Systematic examination on security risks for
popularblockchainsystems.- The real scope of the risk that those
threats entail is notdescribed.
-Survey therealattacksonblockchainsystems
(Ethereum,Bitcoin,Monero,RSK,Counterpaty,
Stellar,Monax,Lisk).- Do not consider therisk as being a func-
tionofprobabilityand impact.
- Analyze relatedvulnerabilities exploited.
Atzei etal., [1]- Analyzed the security vulnerabilities of
Ethereum smartcontracts.-Isolatedtheanalysis fromasecuritypro-
gramming perspectiveonly.
- Show a series of attacks which exploit these
vulnerabilities.- They do not account for the various na-
tureofsecuritythreats.
Homoliaket al.,[5]- Stacked hierarchy of fourlayers. -They don’t quantifytherisk.
- Identified fourthreat agents. - Generic architecture was not evaluated
ona realistic blockchainsystem.
- Reportvulnerabilities at each layer.
Table2: Impactlevels for mobility data-marketadaptedfro m ICS-CERT[3]
Type LevelDescription of theimpact
Monetary1Minor monetaryloss
2Significant monetaryloss
3Severe monetaryloss
4Catastrophic monetary loss
Privacy1Minor impactontheprivacyof any ofthenodes
in BSMD(Individuals, Companies, Universities
and Government)
2Significant ontheprivacy ofthenodes inBSMD
3Severe ontheprivacy ofthenodes inBSMD
4Catastrophic ontheprivacyof thenodes in BSMD
Integrity1Minor impactontheintegrity of themobilitydata,
transactions and integrity oftheusers
2Significant impactontheintegrity ofthemobility
data,transactions and integrityof theusers
3Severe impactontheintegrity ofthemobilitydata,
transactions and integrity oftheusers
4Catastrophic impact ontheintegrity of themobility
data,transactions and integrityof theusers
Trust1Minor impactonthetrustoftheBSMDnetwork
2Significant impactonthetrustof theBSMDnetwork
3Severe impactonthetrustof theBSMDnetwork
4Catastrophic impact onthetrustoftheBSMD
with elevated economic means and high motives. The details o f
various actorsaredescribed below:
A1.Cybercriminals: Thisgroupincludestraditionalcybercrim-
inals that use compromised computer systems to commit ident ity
theft and leverage the blockchain network for a variety of ma li-
cious activities, mostly for monetary gain. They may be pass ive
nodessuchassmallbusinesses,individuals,datacollecto rs,oroth-
ers.
A2.Industrialspies: Organizations thatcompromisethecom-
putersystemstoillegallyacquireintellectualproperty, know-how,
trade, and commercial secrets, or other kinds of corporate c onfi-
dential information. This kind of espionage may occur betwe en
competingcorporations,foreconomic reasons.
A3.ForeignIntelligenceAgencies: Foreignstate-basedorga-
nizations that use computer systems to acquire sensitive in forma-
tion on opposingstates, corporationsor individuals, or ot herwise
influence their actions.A4. Terrorist groups: Organizations seeking to create public
disorderorsow nationalterror,bycommittingdestructive violent
acts.
A5. Insider threat: The insider threat is a corrupted active
node of the blockchain network (Government, University, Co m-
panyorother),oraninfrastructurenodesuchasanetworkop era-
tor,anInternetServiceProvider(ISP)thatmaliciouslyex ploitsthe
blockchainsystem.
4.2 Attackgoals
We identify five attack goals and present them in Table 3. We de -
scribethem herein.
G1- Gainknowledgeaboutthe data-market
There is significant competition between companies that pro -
ducetransportationinformation.Forexample,telecommun ication
companies generate data thatcanbeusedfor transportation mod-
eling. Similarly, the logs of available mobile devices regi stered by
cellphonetowers canbeusedtomonitortraffic.Companies are in
controloftheirdata.Accordingly,theBSMDecosystemcoul dbea
Conference’17, July 2017,Washington, DC,USA Ranwa AlMallahandBilalFarooq
Table 3: Attackgoals againsttheblockchain framework for s martmobilitydata-markets
Goal Example
G1 - Gain knowledge about the data-
marketIn 2015, a group of civic hackers deciphered and exposed the u nstan-
dardizedbussystem locationdataof Baltimore[13].
G2-Accesssensitivedataonthenodesof
thenetworkIt was recently discovered that Googlekeeps collecting use r location
dataeveniftheyexplicitlydeactivatethetrackingsystem intheirmo-
biles [12]. In 2016,information of 57 million Uber customer s and dri-
vers wereleaked [15].
G3 - Manipulate and modify blockchain
informationIn 2016, criminals manipulated Smart contracts in the Ether eum
blockchain with a DAO hack (Decentralized Autonomous Organ iza-
tion),tosteal around 60milliondollars[11].
G4- Sabotageactivities In 2016, the San Francisco transit was hacked to give free acc ess to
commutersfortwodays [14].
G5-Induceparticipantsintheblockchain
network tomakeerrorsIn the Bitcoin network, with a double spending attack on fast pay-
ments,itwasshownin[6]thatnodesinthenetworksmaynotde tect
aninvalidtransactions andadditintothesecureledger.Th eattacker
thusenjoys a service withoutpaying.
target for industrial spies (A2) aiming to obtain knowledge about
the data-market. Subsequently, such information could be s old to
competing companies. Furthermore, this information is als o valu-
able for criminal groups (A1), intelligence services (A3) a nd ter-
rorist groups (A4) because it allows them to undertake attac ks by
maliciouslyexploitingtheknowledge aboutthedata-marke t.
G2- Accesssensitivedataon the nodesof the network
Mobility data is continuously generated by different nodes o f
the BSMD network. According to the identification layer of th e
multi-layered blockchain model for smart mobility data mar ket,
transportationdata is storedin files calledIdentification . Each en-
tity contains metadata, static data and dynamic data. Nodes need
consentfromtheownertoaccessthestaticanddynamicdataw hich
can beattractivetomany actors.
On the other hand, nodes communicate with each other using
DecentralizedIdentifiers(DID).DIDsarethegatesforshar ingdata
via peer-to-peerconnections wheretheinformationistran sferred
using an asymmetric encryption. DIDs represent sensitive i nfor-
mation because a given nodewill have one unique DID per trans -
action. Any leakage of this information enables to correlat e DIDs
intheledgertotracksinglenodesandobtaininformationon trans-
actions.Intelligence services (A3) andterroristgroups( A4) would
be interested in having this information because it would al low
them to attain their ultimate goal of surveillance, assassi nation,
etc.Cybercriminalgroups(A1)wouldbeinterestedinthisi nforma-
tion to obtain monetary gain since mobility data is highly va lued.
Through unsolicitedsharing of information,their clients couldbe
forexampleinsurancecompanies(medicalorautomotive)th atmay
useinformationonanindividual’sdailyactivitypatterns toassess
the cost of insurance premiums or simply refuse coverage. In dus-
trial spies (A2) may take advantage of the blockchain to find c us-
tomers and improve their business.
G3– Manipulateandmodifyblockchain information
Blockchainsaremadetobepracticallyimmutable,where,in the-
ory no one can modify the blockchain’s ’distributed ledger’ of all
committed blocks. The blockchain relies on the distributed con-
sensusmechanismtoachieveimmutabilityandtoestablishm utual
trust.Vulnerabilitiesintheconsensusmechanismcanbeex ploitedbyattackerstocontroltheblockchainbymanipulatingandm odify-
ingtheblockchaininformation.Criminalgroups(A1),inte lligence
services (A3) and terrorist groups (A4) may want to reverse, ex-
cludeormodifytheorderingoftransactions.Aninsiderthr eat(A5)
mayhampernormalminingoperationsofotherminersorimped e
theconfirmationoperationof normaltransactions.
G4– Sabotageactivities
Cybercriminals (A1)mayusetheblockchainecosystemforsa b-
otageactivities.Sincetheprocessisanonymous,itishard totrack
user behaviors, let alone subject to legal sanctions. For ex ample,
criminals can leverage smart contracts for a variety of mali cious
activities,whichmayposeathreattoourdailylife.Crimin alSmart
Contracts can facilitate the leakage of confidential inform ation,
theftof cryptographickeys, and various real-world crimes .
Sabotage activities may also be conducted by intelligence s er-
vices (A3) and terrorist groups (A4) to disrupt the blockcha in net-
workormakevictimsviewoftheblockchainfiltered.Asprogr ams
running intheblockchain,smartcontractsmayhavesecurit yvul-
nerabilities caused by program defects. Attackers may expl oit the
vulnerabilities to send malware and infect nodes or to initi ate De-
nial of Service (DoS) attacks on the nodes of the blockchain. The
attacks may cause a waste of hard disk resources and decrease d
node speed. An insider threat (A5) may be a corrupted node who
wants to intercept the network traffic of the blockchain to pot en-
tiallydelay network messages. An attacker may want tomonop o-
lizeall of the victim’s incoming and outgoingconnections, which
isolates thevictim from the other peersin the network. Then , the
attacker can filter the victim’s view of the blockchain, or le t the
victim cost unnecessary computing power on obsolete views o f
theblockchain.
G5 – Induce participants in the blockchain network to
makeerrors
Thereareseveralcompanies,municipalitiesandindividua lspro-
ducing transportation information which is valuable to gov ern-
ments,researchersandpeople.Forexample,oneoftherespo nsabil-
itiesofthegovernmentistocollectdatainordertomodel,m anage
andimprovetransportationnetworks.Somemaliciousactor smay
inject the ecosystem with falsified transportationinforma tion via
Actor-based Risk Analysis forBlockchains in SmartMobilit y Conference’17, July 2017,Washington, DC, USA
hackedtrafficdetectors,tolls,parkingandsmartcards.The partici-
pantsoftheblockchainnetworkwouldendupmakingsub-opti mal
decision. Theattackers maybeinterested indamaging there puta-
tionofsomecompany,ortheymaysowdistrustinthegovernme nt
oranyentityofthenetwork.Attackerscouldincludeactors A1,A3,
A4andA5identifiedintheprevioussection.Thus,inducingp artic-
ipantstomakeerrorsnotonlywouldtheybeachieving theirg oal,
buttheywouldalsobeevadingtheresponsibilitiesoftheir actions
bymaking theirinterference less detectable.
4.3 Impact ofattackgoals
Independently ofthevariousactorsgoals,attackswillhav eanim-
pact on the victim. The victim may be the individuals, the BSM D
network or any of the active or passive node in the BSMD (data
collectors, Companies, Universities and Government). In o rder to
account for the various types of consequences that these att acks
couldhaveonthem,wemeasuretheimpactaccordingtofourse p-
arateaspects:Monetary(M),Privacy(P),Integrity(I)and Trust(T).
The impact scale ranges from 1 to 4, with 4 being the highest im -
pactlevel(mostsevere). Theimpactlevelsaredescribedin Table2.
Theactor-basedriskanalysis ispresented inTable4.Theex plana-
tionof theimpact analysis byattack goalfollows.
G1(M) Mobility data is very profitable, and competition be-
tweencompanies thatseek totakeadvantageoftheblockchai nto
usethedatatoimprovetheirbusinessisfierce.(P)Whilecon fiden-
tial,theinformationdisclosedwouldnothavesevereconse quences
ontheindividuals.(T) Theattackwillhaveaminor impacton the
trustintheBSMDnetwork.
G2(P) Whileconfidential, theinformationdisclosedwouldnot
have severe consequences (except maybe in terms of insurabi lity)
andislikelytobeotherwiseavailabletoactorsthroughoth ermore
traditionalformsofcyberattacksnotrelatedtotheBSMD.( M)The
disclosureofthisinformationmaybegroundsforlegalacti onagainst
theagency,government bodiesandconcernedcompanies.(T) The
attack willhave a significant impact onthetrustand will deg rade
confidence intheBSMDnetwork.
G3(M) In the context of data markets, tampering of a service
canproducelossesofmillionsofdollarstocompanies.Fore xample
companiesthattakeadvantageoftheblockchaintofindcusto mers.
Also,governmentandtransportagencies activitiestoimpr ovemo-
bilitywillsuffersevere monetarylosses.(P)Modifyingblo ckchain
information will have a significant impact on the privacy of a ny
of the nodes in BSMD (Individuals, Companies, Universities and
Government). (I) Atamperednode’s blockchainaccountwill have
catastrophic impact on the integrity of the mobility data, t ransac-
tions and integrity of the user. (T) Similarly, the attack wi ll have
catastrophicimpactonthetrustoftheBSMDnetworkbecause the
nodes will have no belief in the reliability or truth of the tr ansac-
tions in theledger.
G4(M)Maliciousactivitieswillinduceseveremonetarylosso n
the victim of the attack. For example, network hijacking, cr imi-
nalsmartcontractsandransomwareswillexploitthevictim inex-
change of money. Also, sabotage activities will induce the v endor
tonotgetrewardsforitsservice.(I)Theattackswillhavea signifi-
cantimpactontheintegrityofthemobilitydata,transacti onsandintegrity of the users. (T) Finally, disruption of the netwo rk will
have severe impactonthetrustoftheBSMD.
G5(M)Injecting theecosystemwithfalsifiedtransportationi n-
formation will induce significant monetary loss on the victi m of
theattack.(I)Theattackwillhaveasevereimpactontheint egrity
of the mobility data, transactions and integrity of the user s. (T)
Also, if participants relying on the mobility data acquired from
theblockchainmakeerrorsinthemodeling,management and i m-
provement of transportationnetworks, it will degrade confi dence
and have a severe impactonthetrustof theBSMD.
4.4 Discussion
Thisanalysisrespondstotheneedsofseveralgroupssuchas com-
panies,regulators,manufacturers,transportationagenc iesandeven
individuals.Eachwillbeabletoidentifytheriskiestthre at,theone
totreatwithpriority.FromtheresultsinTable4,dependin gonthe
rankingobtained,aspecificgivenriskmanagementstrategy canbe
applied.There are many strategies for managing therisk, na mely:
refuse, accept,transfer orreducetherisk.
Themostdrasticoneis refusing therisk. Itisappliedwhent he
latter is unacceptable because of the catastrophic consequ ences it
mayhave onthevictims. Thevictims may betheindividuals, u ni-
versities, companies,transportagencies oranyofthegove rnment
nodes of the blockchain network. In this case, vulnerabilit ies in
theblockchainsystemthatareexploitedbyattackersshoul dbere-
moved from the system because of the security threat they pos e.
The strategy of accepting the risk is applied when the risk is ei-
thernegligible oracceptable.Inthiscase,thebenefitstha tthesys-
tembringsaregreaterthanthepotentialrisk.Transferrin gtherisk
strategy consists in giving the risk management responsibi lity to
a thirdparty,i.e. insurance companies. Finally, therisk m itigation
strategy consists on reducing the risk as much as possible. T his
can be done through different means, such as the deployment of
preventivemechanisms,securityupdatesofthesystemsors tricter
regulations.
Interms of monetary, privacy, integrity and trust,we notet hat
G1 does not represent a potential risk that needs to be manage d.
However, G3 contains many threats that represent a risk that is
either unacceptableorundesirable.
From another angle, in terms of monetary impact, attack goal s
G2, G3, G4 and G5 represent a risk in terms of economic losses.
Forexample,networkhijacking, criminalsmartcontractsa ndran-
somwares willexploitthevictim inexchange ofmoney. Also, sab-
otage activities will induce the vendor to not get rewards fo r its
service. This monetary impact may concern certain groups or or-
ganizationsmorethananother.Thus,theywouldwanttomana ge
the risk by implementing the appropriate security defense m ech-
anisms. For example, it is essential to ensure that cryptogr aphic
keys are stored or maintained properly so that the attacker a im-
ingataccessingsensitive dataonthenodesofthenetworkdo not
exploit improper key protection mechanisms to attain the at tack
goal.
Ontheotherhand,intermsofprivacyimpact,theresultsrev eal
that G2 is the riskiest attack goal. For example, attacks con ducted
by A1 may represent an undesirable risk that needs to be man-
aged, i.e. cybercriminals that use compromised computersy stems
Conference’17, July 2017,Washington, DC,USA Ranwa AlMallahandBilalFarooq
Table4: Impacton thevictimsbyattackgoal-Monetary(M),P rivacy(P),Integrity(I)andTrust(T).Impactscaleranges from
1to 4, with 4being themostsevere.
Attackgoals MPIT
G1-Gain knowledgeaboutthedata-market 12-1
G2-Access sensitive data onthenodes of thenetwork 23-2
G3-Manipulateand modifyblockchaininformation 3244
G4-Sabotageactivities 3-23
G5-Induce participants in the blockchain network to
makeerrors2-33
tocommitidentitytheft.Thecompromisecouldleadtofraud ulent
transactions. Vulnerabilities should be eliminated by imp lement-
ing efficient privacy preservation techniques and adopting m ore
robustVPN solutions.
In terms of integrity impact, particularly when it comes to t he
manipulation and modificationof blockchain information, w e no-
ticefromtheresultsthatthisattackgoalrepresentsamajo rriskin
termsofintegrity.Also,thissameattackgoalhasacatastr ophicim-
pact on the trust of the blockchain system because the nodes w ill
have no belief in the reliability or truth of the transaction s in the
ledger. Among other solutions, threats should bemanaged by set-
tingproperidentitymanagement (MembershipServiceProvi ders).
Finally,whenitcomestoinducingparticipantsintheblock chain
network to make errors, we notice from the results, that this at-
tack goal represents a major risk in terms of integrity and tr ust
intheblockchainecosystem.Tomanagetherisk,solutionss hould
beimplemented specificallyregarding unauthenticateddat afeeds
producingwrongdata.
5 CONCLUSION
We have proposed an actor-based risk assessment for the mult i-
layered Blockchain framework for Smart Mobility Data-mark ets
(BSMD), a public-permissioned blockchain. Traditionally , the aim
of the risk assessment is to evaluate the risk in order to iden tify
the riskiest threat. The aim of our work is more extensive in t hat
we propose a risk analysis enabling the quantification of the risk
associated not only to the blockchain technology, but also t o its
ecosystem.Moreover,weconductedtheriskassessmentmeth odol-
ogyonarealisticblockchainforsmartmobilitydata-marke tswith
analysis of the attacks with regards to their impact at four s cales
(economy, privacy, integrity and trust). We quantify the ri sk by
presenting ordinalvalues,whichgives thedecisionmakers aclear
ranking interms ofprioritization.
Thisworkisthefirststepofasystematicriskassessmentfra me-
work.We consider thatalthoughthereare vulnerabilities i na sys-
tem, it is their probabilityof exploitation and the impact t hat this
exploitation has that determines whether the vulnerabilit y repre-
sents a significant risk or not. In future work, we will extend the
analysis and propose a scenario-based risk assessment foll owed
by a combined risk assessment. The scenario-based analysis de-
termines the probability of occurrence of each threat. The c om-
bined analysis aims at determining which attack outcomes ha ve
thehighestriskaccordingtotheirimpactonthevictims.We hope
touncover specificblockchaintechnologysecurityvulnera bilities
in the transportation ecosystem by exposing new attack vect ors.The systematic risk analysis can then be used to develop poss ible
countermeasuresagainstcybersecurityvulnerabilitiesi nthesmart
mobilityimplementations oftheblockchaintechnology.
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on ethereum smart contracts (sok). In International conference on principles of
security and trust .Springer,164–186.
[2] Johannes Eckert, David López, Carlos Lima Azevedo, and B ilal Farooq. 2019.
A blockchain-based user-centric emission monitoring and t rading system for
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[3] GAO. 2005. Cyber Threat Source Descriptions.
https://www.us-cert.gov/ics/content/cyber-threat-so urce-descriptions[Online;
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[4] Huru Hasanova, Ui-jun Baek, Mu-gon Shin, Kyunghee Cho, a nd Myung-Sup
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[5] IvanHomoliak,SaradVenugopalan,QingzeHum,andPawel Szalachowski.2019.
Asecurityreferencearchitectureforblockchains.In 2019IEEEInternationalCon-
ference on Blockchain (Blockchain) . IEEE, 390–397.
[6] GhassanOKarame,ElliAndroulaki,andSrdjanCapkun.20 12. Double-spending
fastpaymentsinbitcoin.In Proceedingsofthe2012ACMconferenceonComputer
and communicationssecurity . 906–917.
[7] Xiaoqi Li, Peng Jiang, Ting Chen, Xiapu Luo, and Qiaoyan W en. 2017. A sur-
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(2017).
[8] DavidLopez andBilal Farooq. 2020. A multi-layeredbloc kchainframeworkfor
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[9] D.LópezandB.Farooq.2019. Privacy-AwareDistributed MobilityChoiceMod-
elling overBlockchain.In 2019IEEEInternationalSmartCitiesConference(ISC2) .
187–192.
[10] D. López, A. Yazdizadeh, B. Farooq, and Z. Patterson. 20 19. Mode Choice In-
ference using Federated Learning overBlockchain. In 2019International Choice
Modelling Symposium (ICMC) .1–13.
[11] Muhammad Izhar Mehar,Charles Louis Shier, Alana Giamb attista,Elgar Gong,
Gabrielle Fletcher, Ryan Sanayhie, Henry M Kim, and Marek La skowski. 2019.
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[12] Ryan Nakashima. 2018. AP Exclusive: Google tracks your move-
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com/828aefab64d4411bac257a07c1af0ecb (2018).
[13] KRector.2018. MTAreal-timebusdata’hacked,’offered onprivatemobileappli-
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[14] JackStewart.2016. SF’STRANSITHACKCOULD’VEBEENWAY WORSE-AND
CITIES MUST PREPARE. Wired, Nov (2016).
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usersand drivers. TheGuardian 22 (2017).
|
{
"id": "2007.09098"
}
|
2209.13431
|
MTTBA- A Key Contributor for Sustainable Energy Consumption Time and
Space Utility for Highly Secured Crypto Transactions in Blockchain Technology
|
A Merkle tree is an information construction that is used in Blockchain to
verify data or transactions in a large content pool in a safe manner. The role
of the Merkle tree is crucial in Bitcoin and other cryptocurrencies in a
Blockchain network. In this paper, we propose a bright and enhanced
verification method, Merkle Trim Tree-based Blockchain Authentication (MTTBA)
for the hash node traversal to reach the Merkle Root in a minimum time. MTTBA
is a unique mechanism for verifying the Merkle Tree's accumulated transactions
specifically for an odd number of transactions. The future impact of
cryptocurrency is going to be massive and MTTBA proves its efficacy in
transaction speed and eliminating node duplication. Our method enables any
block to validate transactions' full availability without duplicating hash
nodes. Performance has been evaluated in different parameters and the results
show marked improvement in throughput(1680ms), processing time(29700kbps),
memory usage(140MB), and security(99.30%). The energy consumption factor is
crucial in the scenario, and MTTBA has achieved the lowest of 240 joules.
|
http://arxiv.org/pdf/2209.13431v1
|
M. Gracy, B. Rebecca Jeyavadhanam
|
cs.CR
|
cs.CR
|
MTTBA - A Key Contributor for Sustainable Energy Consumption Time and Space Utility for
Highly Secured Crypto Transactions in Blockchain Technology
M. Gracy1, B. Rebecca Jeyavadhanam2 *
1 SRM Institute of Science and Technology , Kattankulathur , India
2 SRM Institute of Science and Technology , Kattankulathur , India
*corresponding author. Email address : rebec caram2022@gmail.com
ABSTRACT
A Merkle tree is an information construction that is used in Blockchain to verify data or transactions
in a large content pool in a safe manner. The role of the Merkle tree is crucial in Bitcoin and other
cryptocurrencies in a Blockchain network. In this p aper, we propose a bright and enhanced verification
method, Merkle Trim Tree -based Blockchain Authentication (MTTBA) for the hash node traversal to
reach the Merkle Root in a minimum time. MTTBA is a unique mechanism for verifying the Merkle
Tree's accumul ated transactions specifically for an odd number of transactions. The future impact of
cryptocurrency is going to be massive and MTTBA proves its efficacy in transaction speed and
eliminating node duplication. Our method enables any block to validate trans actions' full availability
without duplicating hash nodes. Performance has been evaluated in different parameters and the results
show marked improvement in throughput(1680ms), processing time(29700kbps), memory
usage(140MB), and security(99.30%). The ener gy consumption factor is crucial in the scenario, and
MTTBA has achieved the lowest of 240 joules.
Keywords : Blockchain, Bitcoin, Merkle Tree, MTTBA , and Hashing.
1 Introduction
The internet has become an integral aspect of data transmission in our daily lives as a result of the
improved de velopment of Information Technologies . Blockchain Technology was first presented as a
public, decentralized, and trust -less digital currency ledger [1] and it has gained prevalent acceptance
in many fields . Blockchain [2] (e.g., Bitcoin and Ethereum) chron ologically keep track of transactions ,
grouped into a chain of blocks. Network nodes add to the ledger by creating and adding new blocks,
starting with the genesis block. Full nodes that download the whole block tree validate the transactions
in the receiv ed blocks. However, a blockchain must include light nodes [3], which may just be
interested in confirming a few specific transactions, for better scalability. In the original Bitcoin
protocol [4], which is a public database of financial transactions , the b lockchain is used to keep track
of coins [5]. The ledger network is also known as a decentralized peer -to-peer network [2]. The
characteristics of bilinear mapping can be used to achieve data integrity in the form of blockchain
transactions [6].
In this paper, we proposed a new optimized Merkle tree structure called MTTBA , to do the
verification process faster and easier with no duplication of nodes. This idea makes transaction
verification easier especially when the block consists of an odd number of tra nsactions. To solve the
space occupied by the duplicate nodes in the traditional Merkle tree method, the proposed technique,
MTTBA handles the problem in a novel way and helps in finding the tampered node with a quick
traversal of nodes. The traditional Me rkle tree uses node duplication when the block is accumulated
with an odd number of transactions . It needs extra memory space to accommodate the duplicate nodes.
Usharani et al suggested an idea [7], the Modi fied Merkle Tree data structure, which is used t o design
a system that handles data authentication, consistency verific ation, and data synchroni zation.
According to our design, the number of hash nodes appending to reach the root is reduced and storage
space is considerably reduced. The proposed MTTBA h as high throughput with low energy
consumption and has no compromise in terms of energy consumption, processing time utilization , and
security. The authentication process is inevitably fast [8]. The results and analysis section will
deliberately prove that this MTTBA variant is superior in all parameters.
Further sections can be divided as follows: S ection 2 gives the background of the Blockchain, Merkle
tree and Hashing. Related work presented in Section 3 , while Section 4 explains about the proposed
method. Imple mentation of the proposed work is presented in S ection 5 . Finally, the Conclusion and
Future directions are in Section 6.
2 Background
2.1 Blockchain
Cryptography, Mathematics, Consensus Algorithms, and Economic Models are among the
technologies that make up Blockchain Technology [9]. It is a safe distributed ledger (dataset) that
records all transactions as blocks. The blockchain uses peer -to-peer networks and consensus
procedures to solve the problem of distributed data synchronisation, eliminating the need for a trusted
centralized authority. A SHA256 cryptographic hash algorithm on the block header can be used to
identify each block [10]. Bitcoin is one of the most well -known blockchain -based applications. Each
transaction includes information on the sender and receiver and the numb er of coins to be exchanged.
Once confirmed by peers, a group of such transactions forms a new block [11]. Fuad Shamieh et al.
[12] envisioned a heuristic for selecting transactions from miners' transaction pools to form blocks
with a desired operational value to achieve predefined production outputs over blockchain -based
networks. The primary data is a list of transactions [ 13], whereas the header is a hash of the previous
and current blocks, Me rkle root, timestamp, nonce, and other information. Blockchains are used in
many scenarios like healthcare, IoT [14], and many more. Figure 1 gives the structure of the Block .
The basic features of Blockchain [15] [16]are transparency , decentralization and immutability .
Figure 1. Blockchain S tructure
2.2 The Merkle Tree
The Merkle trees are information constructions that are used to verify data in a big content pool in a
secure manner. It is an awesome concept and acclaim for coming up with a nice idea goes to Ralph
Merkle [4]. Merkle roots [ 17] are vital in the computation required to keep cryptocurrencies such as
bitcoin and ether operational . In their paper, Haojun Liu et al. [ 18] explain systematical insights into
Merkle trees in terms of their concepts, attributes, bene fits, and applications. . Merkle trees also provide
a way to authenticate [19][20]. The traditional Merkle tree construction is shown in Figure 2.
Figure 2. Traditional Merkle tree structure
All blocks are hashed using cryptographic string hash methods, such as SHA256 [21]. The data is
stored at the leaf nodes of a Merkle Tree, which is a binary tree [22][23]. The Merkle hash tree
technique can be used in securing smart g rid communication with smart meters having
computation -constrained resources [24] and enha ncing security with timestamps [25]. Figure
3 shows the clear architecture of the Merkle root, which forms from the group of transactions
in Block 3.
Figure 3. Merkle root architecture
Figure 4 shows five transactions and the Me rkle root is formed from the five transactions. Green -
colored hash nodes represent the verification of the transaction T3, which is in orange color . The hash
nodes involved in verifying T3 = H4, H7, H12
Figure 4. Transaction verification in Merkl e tree
2.3 Hashing
The hashing process is critical to the data integrity of blockchain. Hashing is a one -way fixed -size
visualisation that transforms any size of the file into a distinc tive, collision -restricted code . Ashok
Kumar Yadav [26] uses random pr ivate keys to implement ECC and RSA public generat ion,
encryption, and decryption. Yoon -Su Jeong[ 27] demonstrated an optimised hash computation method
that enables for stratified multiple processors on an n -bit blockchain. Monika et al . [28] propose that
hash functions be studied and that different cryptography algorithms be compared utilizing blockchain
technology .
3 Related Work
In this section, we present a detailed survey on related papers with the Merkle Tree and transactions
in the blockchain. Dong e t al found a new way in the form of Merkle tree-based operational online
authentication [29]which tells a simple modification that involves the addition of random inputs with
two tangibly separate d entities, prover and approver . Merkle Quad -Tree technique [ 30] is a method
that displays steady advantages with various levels of modifications and consumes about 1% of the
time of the traditional Merkle Tree method. Similar to Lamports' authentication approach, Yuji et al
proposed a method [31] in which the authe ntication act is carried out by releasing the digest values
associated with nodes that are higher in the hash chain than the digests of the leaf node. Lum Ramabaja
et al. [32] proposed a standard sparse Merkle multi -proof that involves storing an index for every non -
leaf hash in the multi -proof. To replace the Merkle tree, which has network delay, Patgir i et al .
developed an alternative model called H ex-Bloom [33]. Jia Kan et al [34] present MTFS, a
blockchain -based solution for private file storage. Marku s et al [35] propose an efficient algorithm for
traversing Merkle trees, as well as a technique for producing a classification of leaves and their
associated authentication paths. Using Merkle tree authentication, author Dong et al . [36] presented a
novel technique to safeguard outsourcing data. Wang et al. [ 37] propose a revised Merkle tree structure
for efficacious payment transactions i n blockchain -based IIoT systems . Ceaser Castellon et al.[38]
proposed an idea that employs an energy -saving algorithmic engineering technique for Merkle Tree
root calculations, a key component of blockchain simulations, to maintain the predicted protection
while sacrificing less system availability. Rasmus et al. proposed a sparse Merkle tree definition that
allows efficien t space -time exchange for different caching strategies when using SHA -512/256. For
to reduce the bandwidth required for Merkle Trees, John Kuszmaul [39] proposed a new data structure
called Verkle Trees, but this comes at the cost of increasing the computa tional power. Bruschi et
al.[40] proposed a system based on a Merkle tree representation to yield compact justification of the
content of web documents. Bayardo et al. [ 41] proposed using Merkle trees to support 200 and 404
response authentication while re quiring only a single cryptographic hash of trusted data per storage
system. Table 1 details the investigated five existing methods , including their methodology, benefits,
and drawbacks. Section 5 contains the detailed experimental results of the same.
S.No Author Title Methodology Benefits Drawbacks
1. Dongyoung
Koo et al.
2018
MTDALR [29] Insert ing ancillary random
sources into the integrity
verification proof on the
prover side. Maintains steady reliability
without being harmed by
continual data leaka ge
caused by authentication
process repetitions. More energy consumption
and more memory space
are needed.
2. Pavol Zajac
2021 EKAMT [42] A server -authenticated key
that is appropriate for TLS -
like handshake protocols . Provides efficient protocols
suitabl e for Internet of
Things application Use in constrained devices
limited due to the
operating time and
memory requirements.
3. Yi-Cheng
Chen et al.
2019 IASMTM[ 43] Tampered region detection
on the tampered image using
peak signal -to-noise ratio
value Subst antiates the reliability
of the image and repairs the
damaged area in the image. Takes more time for
authentication, Less
Throughput time , and
more end -to-end delay.
4. Teasung
Kim et al.
2020 SELCOM [44] SELCOM is used to solve
the storage problem for
blockchain nodes with
limited resources. It allows each node to
choose to maintain blocks
through the proposed
checkpoint chain . Security is a concern and
processing time is not
high nor low.
5. Mingchao
Yu et al.
2019 CMT[ 45] Using a peeling -decoding
techni que, a succinct proof
for data availability attack
on any layer is possible. Any node can validate the
entire availability of every
data block generated by the
system. It has an average
throughput time and need s
more memory space
Table 1. Existing papers methodology and its advantages and limitations for Experimental Analysis
4 Proposed Method
4.1 Preliminaries
We denote t n as the total number of nodes for the scheme . Let n= {1,2,3,..} be the number of nodes.
For any n≥1ϵ N, [N] denotes a set of even nu mber of transactions. N= {21,22,23,24,..2n}. for any n≥1ϵ
N-1, [N -1] denotes a set of the odd number of transactions. N=2n and N -1transactions are said to be
the odd number of transactions .
4.2 Construction of the Proposed Scheme
Traditional Merkle trees are time -consuming data structures that waste a significant amount of
computational resources. Figure 5 depi cts the MTTBA construction, where node duplication is
commletely removed . After successfully downloading a data block using Merkle root, it permits
verification of the data block's validity and integrity and takes a few hash values and does not impose
the Merkle tree on its whole . A block can accommodate 1MB of transactions, if the number of
transactions is even in number, the construction of the Merkle tree is done traditionall y. If the block
holds an odd number of transactions, then our proposed scheme MTTBA will be a handy one. As the
Blockchain has a high level of data integrity, transactions accommodat ed in a block cannot be
manipulated by any th ird person. The construction steps of the MTTBA for the block are as follows:
1) Set index (1) for the first node n1 with hash 1 in the Merkle Trim Tree for a block.
2) Append left and right nodes consecutively leaving the first block , index (1) all alone for an y t n
transactions,
for example, if the block has 2810 transactions, the hash nodes will keep on appending in pairs until it
reaches 2810 leaving the first hash node aside.
3) From 1 to tn, apply the hash function to each of the divided nodes.
4) After that , build the top level of the Merkle Tree until the Merkle Root is reached.
Figure 5. Construction of Merkle Trim Tree -Based Blockchain
Authentication Verification Method
Furthermore, the traditional Merkle tree has significant time complexit y and the Merkle tree server
takes a large amount of memory to keep all of the hash values; nevertheless, a user does not need to
save the complete Merkle tree. In contrast to traditional Merkle Tree construction and various
proposed ideas for verificatio n and validation of transactions, we proposed a method called Merkle
Trim Tree -based blockchain authentication , which is constructed using a combination of the Merkle
Tree's fundamental principle and the Merkle Trim Tree partition methods.
Figur e 6a. The comparison of transactions in the traditional Merkle tree (TMT)
and Merkle Trim tree (MTT) to form the root for three transactions.
The comparison of transactions in the traditional Merkle tree (TMT) and Merkle Trim tree (MTT) to
form the roo t is shown in Figures 6a and 6 b for three and five transactions respectively. The difference
in the nodes has been shown clearly in ‘n’.
Figure 6 b. The comparison of transactions in the traditional Merkle tree (TMT)
and Merkle Trim tree (MTT) to form the root for five transactions.
Merkle Trim Tree (MTT) stands tall as it can eliminate duplication as well as the number of nodes it
creates for forming the tree transactions. When the Merkle Trim Tree is compared with Traditional
Merkle Tree, the duplication of nodes is eliminated and the number of nodes has been reduced.
The number of nodes reduced can be shown in an equation
Number of nodes tn=n+(n -1)
tn=2n-1
Our proposed method of MTTBA helps in transaction verification and its Authentication. The primary
contribution is in eliminating node duplication . The objective of the Merkle Trim Tree is to take out
the duplication nodes when a block accommodates an odd number of transactions. When the nodes
are fewer, traversal becomes fast. The node dup lication happens when the block has an odd number
of transactions. MTTBA has incredible security when nodes are verified. The Merkle Trim Tree is
structured as, leaving the first node and pairing starts from the second node onwards. If the final node
in th e particular block is left with none to pair, the first node will pair with the left -out node. This
scenario happens when the transactions accumulated in the block were odd in number. The Merkle
Trim Tree -based blockchain authentication is an efficient dat a structure for the verification of nodes
and checking the integrity of data when transactions are odd in number. Our implementation results
show security is high and the packet delivery rate is glaringly good with very less collisions . The
overview of the proposed system with the neat setup and the different cases we are going to consider
the deal with the methodology.
Figure 7. Overview of the proposed scheme
Setup : Blockchain consists of a series of blocks in chronological order. Every block will have
components like block header, nonce, timestamp, hash, previous hash , and transactions. Consensus
mechanism s are used to validate a blockchain and add new blocks. Transactions will be selected to
join the block from the pool of transactions. This has been shown in Figure 7 . A block can hold 1MB
of transactions and after that new block will be appended and transactions continue to fill. The
accommodated transactions will be accessed with a single hash called Merkle root. The transactions
can be odd or even i n number. Our proposed work gives the methodology for verification of the odd
number of transactions. The verification of even the number of the transaction remains the same as
the traditional one.
Case 1 : Even the number of transactions for N=2n transacti ons, will be an even number of transactions ,
and no duplication is needed till reaches the root. n=1, 2, 3… nodes N = 21, 22,23,…2n
. [ 2,4,8,16,32,64,128,…] these number of transactions no need duplication.
Case 2 : Odd number of transactions. Needs du plication in the first level for appending.
N=2n and N -1 transactions are an odd number of transactions. if n=4, then 24 = 16
transactions and in case 2, it is N -1 and here 15 transactions. [1,3,5,7,9,11,…] these number
of transactions need duplication in the first level itself to find the pair to concatenate.
4.4.1 Algorithm
Set up the scenario for transaction verification.
N = set of even numbers, N-1 = set of odd numbers
tn is the total number of transactions,
H is the hash of the node .
If t n =N, then the block contains an even number of transactions
For any n≥1 and 1 ϵ N, n++
Append t1(H1) and t 2(H2) to form H 5. H 5 = hash of n1 and hash of n 2 (H1+H 2)
Append t 3(H3) and t 4(H4) to form H 6. H 6 = hash of n3 and hash of n 4 (H3+H 4)
H7 = H 5+H 6, H 7 is the root node or H n is the Merkle root
The n umber of nodes for the set of even numbers is tn=2n-1
If t n =N-1, then the block contains an odd number of transactions
t1(H1) is left idle for initial appending
For any n≥1 and 1 ϵ N -1, n++
Append t 2(H2) and t 3(H3) to form H 6. H 6 = hash of n2 and hash of n 3 (H2+H 3)
Append t 4(H4) and t 5(H5) to form H 7. H 7 = hash of n4 and hash of n 5 (H4+H 5)
H8 = H 1+H 7, H 8 is the root node or H n is the Merkle root
The n umber of nodes for the set of odd numbers is tn=2n-1
5. Implementation of MTTBA
All experiments were run on a single machine running Windows 10 with a 2.6 GHz CPU (Intel i5 -
3320M) and 16.0GB RAM (2601MHz 2x8 GB). Using the Hyper Caliper , a crypt analytic tool, each
algorithm was built as a Visual Studio 16.0 program. In addition, when the Merkle Trim tree was bu ilt,
the data was separated into 256 -byte blocks to allow for consistent comparison. Each parameter was
repeated numerous times in the same environment to produce the optimi zed report comparing the
proposed approach with the existing method, and then the p arameters were calculated and reported.
The computation time for each experiment was measured based on CPU time. The performance of
each algorithm for varying data sizes is analy zed and compared with various parameters like
throughput, end-to-end delay, av erage processing time, average authentication time, average energy,
packet delivery ratio, security, and memory utilization.
5.2.1 Throughput
MTTBA's throughput time has proven to be consistent, thus it was included in the experiment as a
baseline metric of efficiency . Figure 8 shows the graph ical representations to prove the proposed
MTTBA outperforms in throughput when compar ed with other existing methods.
Figure 8 . Throughput time
5.2.2 Average Processing Time
Processing time for a job is very important and this will de fine a genuine work methodology. In 1750
milliseconds, MTTBA performs the given task. This can be verified in various timestamps. Figure 9
gives a detailed representation of the graph .
Figure 9 . Average Processin g Time
5.2.3 Average Energy
In Blockchain, energy consumption [46] [47] is an important factor to be considered and different
methods consume different joules of energy when the process runs in different timeframe s. Figure 10
shows clear evidence of low e nergy consumption by MTTBA with the lowest of 240 joules .
Figure 10. Average Energy C onsumption
5.2.4 Average Authentication Time
The average authentication time of the proposed MTTBA is less than 100 milliseconds when
compar ed with IASMTM hav ing maximum authentication time and MTDALR having medium
authentication time. The comparisons have been shown in Figure 11 .
Figure 11. Average Authentication T ime
5.2.5 Security
Security plays a pivotal role in any application or tool, specifica lly in the decentrali zed form of
systems. Blockchain is a decentrali zed application and the security of data plays a major role . Figure
12 shows the ability of MTTBA, which stands tall in security. MTTBA never compensates for security
with any metrics.
Figure 12 . Security
5.2.6 Memory
Memory is the talk of argument whenever a new application or tool or method is developed.
Blockchain technology never hesitates this parameter. Blockchain’s main issue is its scalability since
every data inside the block will have a hash value and this will be duplicated as a ledger. Chances are
there for more memory consumption and relevant experiment in Figure 13 , where our proposed
MTTBA consumes less memory when compar ed with our existing methods. This can be easily
achieved by MTTBA as it completely obliterates the duplication nodes in the traditional Merkle Tree.
Figure 13 . Memory Consumption
The convenient way of sending data in the network is via packets, but the primary problem that arises
during packet delivery is data collision. If 100 or more packets are sent at a time, then there is a chance
of collision. Our proposed MTTBA variant’s Packet delivery ratio is very high. If the packet delivery
ratio is high, then the latency is obliviou sly low. Because throughput in KBPS is directly proportional
to the packet delivery ratio.
6 Conclusion a nd Future Work
The results of the studies show that hash duplication has been taken out with the help of the Merkle
Trim Tree construction. The Merkl e Trim Tree structure, especially when an odd number of
transactions is present in a block, helps to reduce duplication by appending with the initial transaction.
Energy consumption is always in check and MTTBA with its unique style can able to achieve its goal
of transaction verification more competitivel y. The data appending method proposed in MTTBA
results in limited and constant size and appears to be the source of the significant improvement .
Memory usage and Security help MTTBA to dominate the entire experimentation process. The
Merkle Trim Tree aiding approach, on the other hand, is identical to the different degrees of alterations
and takes around 1% of the time of the old way. In future work, we will merge Merkle Trim Tree with
the Bloom -filter alg orithm to create an advanced Merkle Trim Tree that can arrive with a high level
of data integrity as its main focus.
Author Contributions : Project administration, R.J.; Supervision, M.G.; Writing -original draft, M.G.;
Writing -review and editing, M.G. and R.J. All authors have read and agreed to the published version
of the manuscript.
Funding : This research received no external funding.
Institutional Review Board Statement : Not applicable.
Informed Consent Statement : Not applicable.
Data Availability S tatement : Not applicable.
Conflicts of Interest : The authors declare no conflict of interest
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|
{
"id": "2209.13431"
}
|
1805.12097
|
Social Signals in the Ethereum Trading Network
|
Blockchain technology, which has been known by mostly small technological
circles up until recently, is bursting throughout the globe, with a potential
economic and social impact that could fundamentally alter traditional financial
and social structures. Issuing cryptocurrencies on top of the Blockchain system
by startups and private sector companies is becoming a ubiquitous phenomenon,
inducing the trading of these crypto-coins among their holders using dedicated
exchanges.
Apart from being a trading ledger for tokens, Blockchain can also be observed
as a social network. Analyzing and modeling the dynamics of the "social
signals" of this network can contribute to our understanding of this ecosystem
and the forces acting within in.
This work is the first analysis of the network properties of the ERC20
protocol compliant crypto-coins' trading data. Considering all trading wallets
as a network's nodes, and constructing its edges using buy--sell trades, we can
analyze the network properties of the ERC20 network. Examining several periods
of time, and several data aggregation variants, we demonstrate that the network
displays strong power-law properties. These results coincide with current
network theory expectations, however nonetheless, are the first scientific
validation of it, for the ERC20 trading data.
The data we examined is composed of over 30 million ERC20 tokens trades,
performed by over 6.8 million unique wallets, lapsing over a two years period
between February 2016 and February 2018.
|
http://arxiv.org/pdf/1805.12097v1
|
Shahar Somin, Goren Gordon, Yaniv Altshuler
|
cs.SI, cs.CR
|
cs.SI
|
Social Signals in the Ethereum Trading Network
Shahar Somin1,3, Goren Gordon2,3, and Yaniv Altshuler1,3
1MIT Media Lab, MA, USA
{shaharso ,yanival }@media.mit.edu
2Curiosity Lab, Industrial Engineering Department, Tel Aviv University, Israel
goren@gorengordon.com
3Endor Ltd.
Abstract. Blockchain technology, which has been known by mostly
small technological circles up until recently, is bursting throughout the
globe, with a potential economic and social impact that could fundamen-
tally alter traditional financial and social structures. Issuing cryptocur-
rencies on top of the Blockchain system by startups and private sector
companies is becoming a ubiquitous phenomenon, inducing the trading
of these crypto-coins among their holders using dedicated exchanges.
Apart from being a trading ledger for tokens, Blockchain can also be
observed as a social network. Analyzing and modeling the dynamics of
the ”social signals” of this network can contribute to our understanding
of this ecosystem and the forces acting within in.
This work is the first analysis of the network properties of the ERC20
protocol compliant crypto-coins’ trading data. Considering all trading
wallets as a network’s nodes, and constructing its edges using buy–sell
trades, we can analyze the network properties of the ERC20 network.
Examining several periods of time, and several data aggregation variants,
we demonstrate that the network displays strong power-law properties.
These results coincide with current network theory expectations, however
nonetheless, are the first scientific validation of it, for the ERC20 trading
data.
The data we examined is composed of over 30 million ERC20 tokens
trades, performed by over 6 .8 million unique wallets, lapsing over a two
years period between February 2016 and February 2018.
Keywords: Complex Systems, Social Physics, Network Analysis, Blockchain,
Ethereum, Smart contracts, ERC20 tokens, cryptocurrency
1 Introduction
The Ethereum Blockchain, launched in July 2015 [1], is a public ledger that
keeps records of all Ethereum related transactions. It is shared between all par-
ticipants and is based on a reward mechanism as an incentive for users to run the
transactions network. A key characteristic of the Blockchain network is its heavy
reliance on cryptography to secure the transactions, addressed as the consensus
mechanism. Each account consists of a public and private key duo, where the
private key is used to digitally sign each account’s transactions, and the public
arXiv:1805.12097v1 [cs.SI] 30 May 2018
2 S. Somin, G. Gordon and Y. Altshuler
key can be used by all Blockchain participants in order to verify the transaction’s
validity, in a rapid, decentralized and transparent way.
The ability of the Ethereum Blockchain to store not only ownership, simi-
larly to Bitcoin, but also execution code, in the form of ”Smart Contracts” , has
recently led to the creation of a large number of new types of ”tokens”, based on
the Ethereum ERC20 protocol. These tokens are ”minted” by a variety of play-
ers, for a variety of reasons, having all of their transactions carried out by their
corresponding Smart Contracts, publicly accessible on the Ethereum Blockchain.
In this regards, the Ethereum Blockchain’s transactions, and ERC20 trans-
actions in particular, constitute a decentralized record of interactions among
participants, with two interesting properties that distinguish it from most of the
traditional interaction collections (such as social network activities, phone-call
records, financial bank transactions):
– Unlimited number of wallets — The Ethereum private key mechanism
enables any participant to create an unlimited amount of unique “wallets”.
Whereas the participant can control all of these wallets easily, it is impossible
for an outside observer to explicitly associate the wallets to each other (with
the exception of an implicit association, through a careful data analysis work,
as can be seen in [2]). This can be compared to a mobile phone network, in
which every participant may hold an infinite amount of different identities,
addressed as phone numbers, all of which can be used at will. Had this
property existed in reality, it would likely render most of recent seminal
works in this field (such as [3–8] and many more) highly impractical, if not
entirely obsolete, as demonstrated in [9].
– Unlimited number of tokens — The ability of participants to create
not only new wallet addresses, but also an unlimited number of new tokens
turns the Ethereum network from a single faceted means of communication
of storage and execution related transactions, to a multi faceted (and in fact,
an infinitely faceted) one, comprised of many different types of interactions,
whose nature widely varies from payment, through decentralized trading in
GPU resources [10], and to consumption of behavioral predictions [12].
As a result, the ERC20 ecosystem and the multitude of transactions it con-
sists of, constitutes one of the most fascinating examples for decentralized net-
works. However, to this day there has not been any in-depth analysis of the
ERC20 tokens network properties .
This work is the first attempt to analyze the ERC20 tokens through a net-
work theory prism. We study two years of ERC20 transactions over the Ethereum
Blockchain, by forming a social network from the participants and their corre-
sponding monetary actions. We show that the ERC20 tokens data, despite being
infinitely faceted and potentially comprised of unlimited amount of single-serving
wallet addresses, still strongly displays several key properties known in network
theory research to characterize sets of human interactions. The direct potential
implication of our discovery is that the ERC20 tokens data is likely to there-
fore also comply with additional known network properties – leading the way
Social Signals in the Ethereum Trading Network 3
for the development of an abundance of predictive and descriptive techniques
for the ERC20 tokens transactions, based on known network theory oriented
approaches from other domains.
The rest of the paper is organized as follows: Section 2 contains background
on the topics of this work and review of previous literature related to it. In
Section 3 we thoroughly describe the methodology that was used in this work,
whereas the results are discussed in Section 4. Concluding remarks and discussion
regarding future work appear in Section 5.
2 Background and Related Work
Blockchain’s ability to process transactions in a trust-less environment, apart
from trading its official cryptocurrency, the Ether , presents the most promi-
nent framework for the execution of “ Smart Contracts ” [13]. Smart Contracts
are computer programs, formalizing digital agreements, automatically enforced
to execute any predefined conditions using the consensus mechanism of the
Blockchain, without relying on a trusted authority. They empower developers
to create diverse applications in a Turing Complete Programming Language, ex-
ecuted on the decentralized Blockchain platform, enabling the execution of any
contractual agreement and enforcing its performance.
Moreover, Smart Contracts allow companies or entrepreneurs to create their
own proprietary tokens on top of the Blockchain protocol [14]. These tokens are
often pre-mined and sold to the public through Initial Coin Offerings (ICO) in
exchange of Ether, other crypto-currencies, or Fiat Money . The issuance and
auctioning of dedicated tokens assist the venture to crowd-fund their project’s
development, and in return, the ICO tokens grant contributors with a redeemable
for products or services the issuer commits to supply thereafter, as well as the
opportunity to gain from their possible value increase due to the project’s suc-
cess.
The most widely used token standard is Ethereums ERC20 (representing
Ethereum Request for Comment), issued in 2015. The protocol defines techni-
cal specifications giving developers the ability to program how new tokens will
function within the Ethereum ecosystem. In order to comply with the ERC20
standard, the token must adhere to rules regarding the form it will be transferred
between addresses and the manner in which data within the token is accessed.
The Contract stores the addresses of its corresponding token owners, alongside
with the amount of owned tokens, and allows token transfers only if the sender
proves ownership of the private key associated to the Contract address.
This brand new market of ERC20 compliant tokens is fundamental to ana-
lyze, as it is becoming increasingly relevant to the financial world. Issuing tokens
on top of the Blockchain system by startups and other private sector companies
is becoming a ubiquitous phenomenon, inducing the trade of these crypto-coins
to an exponential degree. Since 2017, Blockchain startups have raised over 7
Billion dollars through ICOs. Among the largest offerings, Tezos raised $232M
for developing a smart contracts and decentralized governance platform; File-
4 S. Somin, G. Gordon and Y. Altshuler
coin raised $205M to deploy a decentralized file storage network; EOS raised
over $185M to fund scalable smart contracts platform, Bancor managed to raise
$153M for deploying a Blockchain-based prediction market, Kin$98M to build a
decentralized social network and communication platform and Blockstack raised
$52M towards a decentralized browser, identity and application ecosystem.
Apart from being formed by countless stake-holders and numerous tokens, the
ERC20 transactional data also presents full data of prices, volumes and holders
distribution. This, alongside with daily transactions of anonymised individuals
is otherwise scarce and hard to obtain due to confidentiality and privacy control,
hence providing a rare opportunity to analyze and model financial behavior in
an evolving market over a long period of time.
In the past two decades, network science has exceedingly contributed to mul-
tiple and diverse scientific disciplines. Applying network analysis and graph the-
ory have assisted in revealing the structure and dynamics of complex systems
by representing them as networks, including social networks [15–17], computer
communication networks [18], biological systems [19], transportation [20, 21],
IOT [22], emergency detection [23] and financial trading systems [24–26].
Most of the research conducted in the Blockchain world, was concentrated
in Bitcoin, spreading from theoretical foundations [27], security and fraud [28,
29] to some comprehensive research in network analysis [30–32]. The world of
Smart contracts has recently inspired research in aspects of design patterns,
applications and security [33–36], policy towards ICOs has also been studied
[14]. However, the comprehensive analysis of ERC20 tokens, with emphasis on
the investigation of the transaction graph built from their related activity on the
Blockchain, is still lacking.
In this paper we aim to examine how this prominent field can enhance the
understanding of the underlying structure of the ERC20 tokens trading data.
3 Methodology
3.1 Data
In order to preserve anonymity in the Ethereum Blockchain, personal informa-
tion is omitted from all transactions. A User, represented by their wallet, can
participate in the economy system through an address, which is attained by ap-
plying Keccak-256 hash function on his public key. The Ethereum Blockchain
enables users to send transactions in order to either send Ether to other wal-
lets, create new Smart Contracts or invoke any of their functions. Since Smart
Contracts are scripts residing on the Blockchain as well, they are also assigned
a unique address. A Smart Contract is called by sending a transaction to its ad-
dress, which triggers its independent and automatic execution, in a prescribed
manner on every node in the network, according to the data that was included
in the triggering transaction.
Smart Contracts representing ERC20 tokens comply with a protocol defining
the manner in which the token is transferred between wallets. Among these re-
quirements, is the demand to implement a transfer method, which will be used
Social Signals in the Ethereum Trading Network 5
for transferring the relevant token from one wallet to another. Therefore, each
transfer of an ERC20 token will be manifested by a wallet sending a transaction
to the relevant Smart Contract. The transaction will encompass a call to the
transfer method in its data section, containing the amount being transferred
and its recipient wallet. Each such token transfer results in altering the ’to-
ken’s balance’, which is kept and updated in its corresponding Smart Contract’s
storage.
We obtain the ERC20 transactions basing on the further requirement of
the ERC20 protocol, demanding that each call to the transfer method will be
followed by sending a Transfer event and updating the event’s logs with all
relevant information regarding the token transfer. We therefore call an Ethereum
full node’s JSON API and fetch all logs matching to the Transfer event structure
[37]. Parsing these logs result in the following fields per transaction: Contract
Address - standing for the address of the Smart Contract defining the transferred
token, Value - specifying the amount of the token being transferred, Sender and
Receiver addresses, being the wallet addresses of the token’s seller and buyer,
correspondingly.
We have retrieved all ERC20 tokens transactions spreading between February
2016 and February 2018, resulting in 30 ,347,248 transactions and 18 ,517 token
address. Due to the restriction on changing and tempering Smart Contracts, any
modification made to a token’s designated Smart Contract involves a definite
change in it’s associated Contract Address. As a result, a token can change
addresses throughout it’s lifespan, though for any point in time, it will only be
assigned to a single relevant Contract Address . Therefore, the above mentioned
amount of unique contract addresses serves merely as an upper bound to the
amount of unique tokens. Since we do not restrict ourselves to a specific type
of token, but observe the network as a whole trading system, this non-unique
identification of tokens doesn’t affect our analysis of the network.
The dataset of ERC20 tokens transactions is extremely diverse and wide-
ranging, where not only any ERC20 token might correspond to multiple con-
tract addresses, and therefore being considered as various different tokens by
our analysis, but also the characteristics of the different tokens are extremely
varied. For instance, the tokens differ in their age, their economic value, activ-
ity volume and number of token holders, some merely serve as test-runs, others
aren’t tradable in exchanges yet, and some, according to popular literature, are
frauds, all residing next to actual real-world valuable tokens.
3.2 Graph Analysis
In order to perceive the network’s structure and assess the connectivity of its
nodes, one should examine the network’s degree distribution, considering both
in-degree and out-degree, indicating the number of incoming and outgoing con-
nections, correspondingly. The degree distribution P(k) signifies the probability
that a randomly selected node has precisely the degree k.
In random networks of the type studied by Erd¨ os and R´ enyi [38], where
each edge is present or absent with equal probability, the nodes’ degrees follow
6 S. Somin, G. Gordon and Y. Altshuler
aPoisson distribution. The degree obtained by most nodes is approximately
the average degree ¯kof the network. These properties are also manifested in
dynamic networks [39]. In contrast to random networks, the nodes’ degrees of
social networks (such as the Internet or citation networks) often follow a power
lawdistribution [40]:
P(k) =k−α(1)
The power law degree distribution indicates that there is a non-negligible
number of extremely connected nodes even though the majority of nodes have
small number of connections. Therefore the degree distribution has a long right
tail of values that are far above the average degree. Power law distributions
can be found in many real networks, Newman [17] summarized several of them,
including word frequency, citations, telephone calls, web hits, or the wealth of
the richest people.
4 Results
As discussed, we study an extremely diverse and wide-ranging dataset. In order
to present a first glimpse on the diversity of ERC20 tokens transactional data,
we explore the distribution of token popularity, in terms of buyers and sellers
amount. As Figure 1 reflects, ERC20 tokens’ popularity follows a power-law
distribution, thereby expressing the diversity of token holders along a 2 years
period, between February 2016 and February 2018. Particularly, it can be seen
that most tokens are traded by an extremely small amount of users and on the
other hand, a few popular tokens exist, traded by a very large group of users
during the examined timespan.
100101102103104105106
Token popularity among buyers108
106
104
102
100Token count
=1.501, R^2=0.990
(a) Tokens popularity among buyers
100101102103104105
Token popularity among sellers108
106
104
102
100Token count
=1.543, R^2=0.982
(b) Tokens popularity among sellers
Fig. 1: Crypto-coins popularity over a two year period lapsing from February 2016 to
February 2018. Depicting the probability a coin would have certain amount of buyers
(left panel) and sellers (right panel), both demonstrating a power-law distribution.
We further aim to examine whether the ERC20 network satisfies the known
characteristics of other real-world networks, first and foremost examining its de-
gree distribution. We therefore construct the following directed graph, GFT(V,E),
standing for ERC20 Full Transactions Graph , including all transactions in the
Social Signals in the Ethereum Trading Network 7
timespan between February 2016 and February 2018. The resulting graph con-
sists of 6,890,237 vertices and 17 ,392,610 edges.
The set of vertices Vconsists of all ERC20 trading wallets in this period,
where any vertex urepresents a trading wallet wu. Out-going edges depict trans-
actions in which wallet wusold any type of ERC20 token to other wallets, and
in-coming edges to uare formed as result of transactions in which wubought
any ERC20 token from others. Formally, E⊆V×Vs.t.:
E:={(u,v)/bardblwalletwusold any ERC20 token to wv} (2)
Out-degree of vertex urepresents the number of unique wallets buying tokens
fromwuand its in-degree depicts the number of unique wallets selling tokens to
it.
Surprisingly, despite the great variance between the traded tokens in the
network, we discovered that the degree distribution depicts a strong power-law
pattern, as presented in Figure 2. Hence the ERC20 Full Transactions Graph ,
GFT, displays similar connectedness structure to other real-world networks, pre-
senting a non-negligible number of extremely connected nodes even though the
majority of nodes have small number of connections, both in buying and selling
transactions.
100101102103104105
In-degree1011
109
107
105
103
101
density
=2.281, R^2=0.988
(a) In Degree Distribution
100101102103104105106
Out-degree1011
109
107
105
103
101
density
=1.935, R^2=0.992
(b) Out Degree Distribution
Fig. 2: Analysis of Blockchain network dynamics for a 2 years period from February
2016 to February 2018. The networks nodes represent ERC20 wallets and edges are
formed by ERC20 buy-sell transactions. Outgoing degree of a node reflects the number
of unique wallets receiving funds from that node, and vice-versa for incoming degree.
Both outgoing and incoming degrees present a power-law distribution.
We have additionally analyzed ERC20 transaction graphs based on varying
length periods between 3 days to 3 months, and validated our findings across 20
different points in time. We have observed that in all cases the power-law degree
distribution is preserved and presents roughly similar γvalues. We omit these
results from the current version, due to space limitations, and they will appear
in a future, extended version.
8 S. Somin, G. Gordon and Y. Altshuler
5 Concluding Remarks and Future Work
In this paper, we have demonstrated for the first time that the ERC20 tokens
transactional data displays several properties known to be associated with net-
works that are comprised of human interactions, and social networks specifically.
This occurs despite the fact that the Blockchain protocol enables the creation
of an unlimited number of “tokens”, causing diverse sub-domains to reside to-
gether over the same protocol, and regardless of an unlimited amount of wallets,
resulting in different identities controlled by a single individual.
Specifically, we have modeled the transactions as a network that is comprised
of wallets, connected through transactions, and found that the degree distribu-
tion of nodes in the network presents a power-law pattern. In addition, we have
shown that tokens popularity among buyers and sellers also follows a power-law
model. These preliminary results indicate that (somewhat surprisingly) despite
its diversity, ERC20 data presents a social behavior. This leads us to explore
whether other aspects of network theory can emerge from this data. Such fields
include short path lengths and clustering coefficient analysis [41], centrality mea-
sures [42], connected components behavior [5] and community structure study
[43]. We have already been able to demonstrate some of these phenomena using
the ERC20 data as well, however they were not included in this work due to
space considerations.
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|
{
"id": "1805.12097"
}
|
2102.01249
|
Blockchain-based Transparency Framework for Privacy Preserving
Third-party Services
|
Increasingly, information systems rely on computational, storage, and network
resources deployed in third-party facilities such as cloud centers and edge
nodes. Such an approach further exacerbates cybersecurity concerns constantly
raised by numerous incidents of security and privacy attacks resulting in data
leakage and identity theft, among others. These have, in turn, forced the
creation of stricter security and privacy-related regulations and have eroded
the trust in cyberspace. In particular, security-related services and
infrastructures, such as Certificate Authorities (CAs) that provide digital
certificate services and Third-Party Authorities (TPAs) that provide
cryptographic key services, are critical components for establishing trust in
crypto-based privacy-preserving applications and services. To address such
trust issues, various transparency frameworks and approaches have been recently
proposed in the literature. This paper proposes TAB framework that provides
transparency and trustworthiness of third-party authority and third-party
facilities using blockchain techniques for emerging crypto-based
privacy-preserving applications. TAB employs the Ethereum blockchain as the
underlying public ledger and also includes a novel smart contract to automate
accountability with an incentive mechanism that motivates users to participate
in auditing, and punishes unintentional or malicious behaviors. We implement
TAB and show through experimental evaluation in the Ethereum official test
network, Rinkeby, that the framework is efficient. We also formally show the
security guarantee provided by TAB, and analyze the privacy guarantee and
trustworthiness it provides.
|
http://arxiv.org/pdf/2102.01249v3
|
Runhua Xu, Chao Li, James Joshi
|
cs.CR, cs.NI
|
cs.CR
|
1
Blockchain-based Transparency Framework for
Privacy Preserving Third-party Services
Runhua Xu, Member, IEEE, Chao Li, Member, IEEE, and James Joshi, Senior Member, IEEE
Abstract —Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities
such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous
incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the
creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related
services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities
(TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving
applications and services. To address such trust issues, various transparency frameworks and approaches have been recently
proposed in the literature. This paper proposes TABframework that provides transparency and trustworthiness of third-party authority
and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TABemploys the
Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an
incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement
TABand show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also
formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.
Index Terms —Transparency, Trustworthiness, Third-party Authority, Blockchain, Ethereum, Smart Contract, Functional Encryption
F
1 I NTRODUCTION
INCREASINGLY , information systems are being built on
the third-party facilities or use external services. This is
beneficial to many enterprises as it lowers costs and allows
them to keep their focus on business missions. On the other
hand, increasing cybersecurity incidents such as cyberse-
curity attacks including those leading to data leakage and
identity theft are amplifying users’ concerns with regards
to their sensitive personal data that is collected, stored, and
processed on the third-party facilities. Furthermore, regula-
tions such as General Data Protection Regulation (GDPR)
and California Consumer Privacy Act (CCPA) introduce
stricter compliance requirements for enterprise informa-
tion systems. Fig. 1 illustrates the architecture of a typical
privacy-preserving third-party service enabled system, as
illustrated in a variety of existing work [1], [2], [3], [4],
where the personal data is protected by a cryptosystem, and
the encrypted data is collected and processed by a third-
party IaaS, while the public key and private key services
are provided by the third-party authority (TPA). Usually,
the third-party entities are assumed to be honest-but-curious ,
and a TPA is typically fully trusted .
To address the trust and compliance issues on these
service providers, especially, the security-related service
providers such as certificate authorities (CAs), key directo-
ries (KDs) and TPAs that provide services of certification,
Runhua Xu is with the IBM Research, San Jose, CA, United States,
95120. Chao Li is with Beijing Key Laboratory of Security and Privacy
in Intelligent Transportation, Beijing Jiaotong University, Beijing, China,
100044. Runhua Xu and Chao Li are corresponding authors. James Joshi
is with School of Computing and Information, University of Pittsburgh,
Pittsburgh, P A, United States, 15260.
E-mail:runhua@ibm.com, li.chao@bjtu.edu.cn, jjoshi@pitt.edu
Fig. 1. Illustration of privacy-preserving third-party service enabled sys-
tem.
public key lookup and private key generation in exist-
ing cryptographic key infrastructures, various transparency
approaches that provision openness and accountability have
been recently proposed [5], [6], [7], [8], [9], [10] to increase
users’ trust or confidence in such cryptographic key infras-
tructures. For instance, the CAs, as the underlying public
key infrastructure for SSL/TLS protocol, are responsible for
issuing digital certificates that certify the ownership of a
public key by the named principal of the certificate and
allows others to rely upon signatures made by the private
key corresponding to the certified public key.
Recent research have demonstrated that a variety of
attacks [11], [12] and mis-issuance problems [13], [14] might
cause complications during certificate issuance procedures.
For instance, Kumar et al. [13] analyze CAs’ certificate mis-
issuance incidents using a certificate linter named ZLint
and Scheitle et al. [14] focus on issues with certification
authority authorization (CAA) DNS records. To further
mitigate the threat of attack and mis-issuance, notions of
certificate transparency [5], [6], CertChain [15], verifiable key
directory [7], [9], [10], transparency overlay [8] and authority
transparency [16] have been proposed. To be more precise,arXiv:2102.01249v3 [cs.CR] 4 Jun 2022
2
certificate transparency frameworks such as those presented
in [5], [6] are intended to increase the transparency of users’
certificates, whereas the CONIKS [7], [9] and SEEMless [10]
are intended to be used with general key directories in end-
to-end encryption systems. Unlike the above-mentioned
conventional transparency frameworks, which focus on the
static binding of a public key and an identity, emerging
authority transparency [16] focuses on the dynamic key
generation interactions in TPA that is requisite by modern
cryptosystems such as attribute-based encryption (ABE)
[17], [18], functional encryption (FE) [19], [20], and multi-
key homomorphic encryption (HE) [21], [22].
However, the initial and formal design of authority
transparency has considerable limitations that hinder its de-
ployment and application in several areas, such as emerging
third-party service enabled privacy-preserving applications
[1], [2], [3], [4], [23]. Specifically, these limitations include:
(i) the definitions and protocols designed in authority trans-
parency model only work on, and relies on, the ABE cryp-
tosystems; and (ii) the implementation of authority trans-
parency framework is based on a secure logging system.
In short, existing authority transparency proposal does not
directly support other emerging cryptosystems such as the
FE and multi-key HE families that have been used to build
secure computation protocols [2], [3], [4], [24].
Besides the identity-to-public-key-binding stealthy targeted
attack and the private-key-service censorship attack as illus-
trated in [16], FE or multi-key HE based applications have
additional privacy threats; for instance, there is a potential
inference attack by manipulating a malicious functionality-
related vector, as illustrated in [2], [3]. Furthermore, the
deployment of secure logging system based authority trans-
parency solution pose a challenge with regards to being
broadly accepted by the Internet community because: (i) it
requires several commercial companies or non-profit orga-
nizations that have the computation and storage capabilities
to deploy a publicly auditable secure logging system such
as that used in the certificate transparency community (e.g.,
secure logging systems deployed by Google and Mozilla);
(ii) there is also a lack of a concrete mechanism for the
entities to participate in a transparency framework and
monitor and audit unintentional ormalicious behaviors.
To address the aforementioned limitations, in this
paper, we propose an approach that provides trans-
parency and trustworthiness of third-party authority and
IaaS using blockchain techniques - in short, TAB - for
emerging third-party service-enabled crypto-based privacy-
preserving applications. For simplicity, we use FE-based
privacy-preserving systems proposed in [2], [3], [4] as the
underlying application to illustrate the TAB approach. In
particular, to achieve the transparency and trustworthiness
goal, TAB employs the Ethereum blockchain as the un-
derlying public ledger infrastructure, and also includes a
novel and well designed Ethereum smart contract to sup-
port automatic accountability with an additional incentive
mechanism to motivate participants to participate in the
auditing process and punish unintentional misbehaviors or
malicious behaviors. We summarize our key contributions
as follows:
We first revisit the notion of authority transparency model
and propose our formal TAB model with new definitionsand protocols to address the entity trust issues considering
the scenarios of generic crypto-based privacy-preserving
applications where the cryptographic infrastructure TPA or
centralized key server is commonly assumed to be fully
trusted and the application entities (e.g., third-party IaaS and
data sources as illustrated in Fig. 1) is usually assumed to
behonest-but-curious .
Next, we design a novel smart contract to achieve auto-
matic accountability based on our design of the TAB model
and employ the Ethereum blockchain as the underlying
public ledger infrastructure.
We also design an incentive mechanism in the smart
contract to (i) reward a TPA if it fulfills its obligation;
(ii) punish any entity that violates its responsibility, and
(iii) encourage other entities to help audit and inspect the
potential malicious behaviors caused by the assumed fully
trusted TPA and assumed honest participants.
We finally analyze the security guarantee of TAB and
present the experimental evaluation on the smart con-
tract implemented in the Ethereum official test network -
Rinkeby. The evaluation result shows that TAB is efficient
and provides security and privacy guarantees.
2 B ACKGROUND AND PRELIMINARIES
Here, we briefly present preliminaries and background
of related concepts such as functional encryption and
its related applications, authority transparency, blockchain,
Ethereum, and smart contract.
2.1 TPA-based Cryptosystems and Applications
Emerging modern cryptographic schemes, especially those
that rely on a third-party authority (TP A) to provide key ser-
vices, are being adopted in privacy-preserving applications,
where data is encrypted and the data management opera-
tions such as querying, accessing control, and computation
are over the encrypted data. A TPA is a critical component
in these cryptosystems, and it is generally assumed to be
fully trusted. Such an assumption is very common in cryp-
tography research community. However, deploying such a
trusted TPA component in a real scenario is still a challenge
because there is a lack of (i) incentive mechanisms to encour-
age a participant (i.e., a third-party entity) to play the role
of the authority and (ii) a transparent mechanism to ensure
that such a TPA works as expected when considering the
attacks such as identity-to-public-key-binding stealthy targeted
attack and private-key-service censorship attack as illustrated in
[16].
Beyond authority transparency that addresses the trust
issues in a TPA caused by the aforementioned attacks, we
address incentive issues related to participants’ engage-
ment in a transparency framework via Ethereum blockchain
techniques, and tackle additional privacy leakage issue
caused by assuming that participants are hones-but-curious
in crypto-based privacy-preserving applications [2], [3]. Our
proposed blockchain-based TAB framework can support
various TPA-based cryptosystems such as ABE-enabled ap-
plications as illustrated in [16] and the emerging FE-enabled
privacy-preserving applications. Here, we briefly introduce
the FE cryptosystem and FE-based applications; we also dif-
ferentiate between the ABE and FE structures/components.
3
2.1.1 Functional Encryption (FE)
FE is a generalization of public-key encryption in which
any party with an issued functional secret key allows us
to compute a function of what a ciphertext is encrypting.
A FE scheme for functionality Fis a tupleEFE=(Setup,
KeyDerive, Encrypt, Decrypt) of four algorithms [19], [20],
where the Setup and KeyDerive algorithms are run by a
TPA that is assumed to be fully trusted . A data owner can
adopt the Encrypt algorithm to protect its data, while a data
user with the functional decryption key issued by its TPA
can compute the function over the ciphertext to acquire the
function result without learning the original data via Decrypt
algorithm.
2.1.2 FE-based Application and Potential Privacy Leakage
The feature of computing over encrypted data makes func-
tional encryption a promising approach for employing se-
cure multi-party protocols for privacy-preserving machine
learning (PPML) [2], [3]. While employing FE, a PPML also
inherits the assumption of a trusted TPA. Besides, PPML
techniques typically assume that the aggregator or coordi-
nator (that is, the decryption party when PPML uses a FE
scheme) is honest-but-curious .
Security guarantee provided by a FE scheme can ensure
that the encrypted data cannot be compromised by an ad-
versary [20]. However, there is still potential privacy leakage
in PPML approaches that use FE schemes, as demonstrated
in [2], [3]; here, an authorized honest-but-curious decryption
party may exploit a manipulated vector to request a func-
tional decryption key to repeatedly execute the decryption
algorithm over the encrypted data and store the intermedi-
ate data to infer partial information in the encrypted data.
For the specific inference attack, we refer the readers to [2],
[3] for more details.
2.1.3 Comparing ABE and FE
ABE is also a type of public-key encryption in which cipher-
texts are dependent upon access policy over a set of attribute
credentials (e.g., age, affiliation, etc.) and any party with
proper attribute credentials can be issued a secret key to
access the encrypted data. A (ciphertext-policy) attribute-based
encryption (ABE) scheme for access policy Ais a tupleEABE
=(Setup, KeyGeneration, Encrypt, Decrypt) of four algorithms
[25], [26]. As in ABE, the Setup and KeyGeneration algorithms
are run by a TPA that is assumed to be fully trusted . A data
owner uses the Encrypt algorithm with a specified access
policy to protect her data, while the data user with proper
attribute credentials that satisfy the access policy can access
(Decrypt) the encrypted data.
The main difference between ABE and FE is the creden-
tials that are used to generate or derive the private key. In
ABE, the private key is generated based on a set of attributes
of a data user, while the functional decryption key is derived
from a function-related vector in FE for the functionality
of inner-product scheme. Besides, the adoption of FE may
introduce potential inference threats as illustrated in [3].
Remark . Unlike authority transparency [16] that builds on the
ABE scheme, for simplicity, in this paper, we use the recently
proposed FE-based applications [2], [3], [4] as underlying
examples to illustrate the key features of TAB. Specifically,TAB focuses on providing transparency in cases related to
above-discussed assumptions, namely, a trusted TPA and a
honest-but-curious participants, to increase users’ trust in a
system. In Section 3.4, we analyze the applicability of TAB
in other TPA-based cryptosystems.
2.2 Authority Transparency
Authority transparency is defined as a publicly auditable set
of a TPA’s activities. The goal is to ensure that a TPA fulfills
its auditing obligations ( O) related to public parameter
distribution (Opp) and trustworthy key service ( Oks), con-
tinuously and transparently. We formally define authority
transparency as below; here, we adopt the notation from
[27].
Definition 2.1 (Authority Transparency [16]) .LetT;Land
Cdenote a third-party authority, a log server, and a client,
respectively, that use a set of interactive protocols. Let
C:actor;C:auditor andC:monitor represent the roles of the
actor, auditor, and monitor that execute the application,
auditing, and monitoring modules, respectively. We define
authority transparency ,ATT;L;C
O, as a set of six interactive
protocols:
ATT;L;C
O = (GenO;LogOpp;LogOks;CheckO;Inspect;Gossip );
and each protocol is defined as follows:
(SOpp; SOks) Run (1;GenO;fT;C:actorg;("; ")) (1)
(bT; ") Run (1;LogOpp;fT;Lg;(SOpp; ")) (2)
(bT; bC; ") Run (1;LogOks;fT;C:actor;Lg;(";Oks:SC;Oks:ST))
(3)
("; bC:auditor ) Run (1;Check O;fL;C:auditorg;("; ")) (4)
(bL; ") Run (1;Inspect ;fL;C:monitorg;("; ")) (5)
(evidence ) Run (1;Gossip ;fC:auditor;C:monitorg;("; ")) (6)
The order of parameters in the input tuple and the
order of elements in the output are consistent with par-
ticipating entities. For instance, in protocol (bT;")
Run (1;LogOpp;fT;Lg;(SOpp;")), there exists two partici-
pants:Thas the inputOppwhileLhas no input, as denoted
by".
We briefly introduce each interactive protocol as follows:
(1) GenOis a protocol between TandC:actor that gen-
erates the audit obligations to be logged;
(2) LogOppis a protocol between TandLthat is used to
recordOppin the public log;
(3) LogOksis a protocol involving T,LandC:actor that
is used to recordOksin the public log;
(4) CheckOis a protocol involving L,C:actor and
C:auditor that is used to check whether or not an
audit obligationOpporOksis in the log;
(5) Inspect is a protocol between LandC:monitor that is
used to allow the monitor to inspect the contents of
the log and find suspicious audit obligations fOig;
(6) Gossip is a protocol between C.auditor andC.monitor
that is used to compare different versions of a log and
detect any inconsistencies caused by misbehavior of
a participant or on behalf of the log server.
Unlike the authority transparency approach proposed
in [16] that is built on the secure logging system, our
4
proposed TAB relies on the Ethereum blockchain. Thus, the
above-mentioned protocols are not directly applicable in
our blockchain-based TAB framework. We will present our
relevant definitions in Section 3.3.
2.3 Blockchain, Ethereum and Smart Contract
A blockchain is a growing list of records (a.k.a, blocks) that
are linked via cryptographic techniques, where each block
contains a cryptographic hash of the previous block, a times-
tamp, and the transaction data. In particular, the blockchain
is a public distributed database of records, transactions, or
digital events that have been executed and shared among
various participants. In our proposed work we, employ a
blockchain as the underlying public ledger infrastructure
instead of the secure logging system adopted in [16].
Ethereum is an open-source and public blockchain-based
distributed computing platform supporting smart contracts
[28]. Usually, there are two types of accounts in Ethereum,
namely External Owned Accounts (EOAs) controlled by
private keys associated with users and Contract Accounts
assigned to smart contracts. A smart contract in Ethereum
refers to a piece of code, for instance, a Solidity1program
code that usually consists of multiple functions, few pa-
rameters and perhaps some modifiers. To deploy a smart
contract, an ordinary user can compile the contract to gen-
erate the corresponding bytecodes and application binary
interface (ABI), and then send a contract creation transaction
to the Ethereum network with the bytecodes and ABI. Upon
receiving a transaction, the miners of the Ethereum network
will include the bytecodes into the newly coming block
being added. Each successfully deployed contract account
can be viewed as a small decentralized computation and
storage unit that can execute specific functions defined in
the contract and also store data allowed by the contract. As
a result, the transactions, messages, as well as the inputs of
the functions are all recorded by the Ethereum blockchain,
and, hence, the outputs of the functions are deterministic
because the distributed miners can ensure that. Note that
it is not free to either deploy a smart contract or to call
a function of existing smart contracts in Ethereum. A user
needs to pay Gas2that can be exchanged with Ether, the
cryptocurrency used in Ethereum.
3TAB FRAMEWORK
3.1 Overview of TAB
3.1.1 Entities in TAB
Fig. 2 illustrates the architecture of TAB framework. Note
that the dashed lines represent the procedures of crypto-
based privacy-preserving applications, while the solid lines
denote the procedures of the TAB framework. TAB consists
of the following entities:
TPA . The TPA is the same role as in the ordinary FE
cryptosystem, but in TAB it has additional responsibilities
to fulfill, including: (a) submitting the public parameters
obligations (in particular, identity-to-public-key bindings),
(b) reporting its fulfillment of obligations in the key service
1. https://github.com/ethereum/solidity
2. https://github.com/ethereum/wiki/wiki/Whisperprocess, and (c) verifying that the submitted/reported obli-
gations are permanently recorded in the blockchain.
Actors . Actors include all users of an crypto-based privacy-
preserving application, namely, the entities (e.g., data owner )
that employ the encryption algorithm and the entities (e.g.,
data user ) that perform secure computation or access control
via the decryption algorithm. Besides, the actors may also
need to fulfill the obligations of key service because they are
involved in interaction with other actors and/or the TPA.
Monitors . Monitors are responsible for inspecting the con-
tents of the recorded auditing obligations to find suspicious
obligations. In TAB, the encryption entities or the additional
independent entities play the role of the monitors.
Administrator . An administrator is responsible for the de-
ployment, maintenance, and administration of the smart
contract. The smart contract mainly includes three modules:
(a) the obligation record module that provides various inter-
action functions for the entities to carry out recording, audit-
ing and inspection requirements related to the obligations,
(b) the incentive mechanism that provides the payment and
reward functions to the participants, and (c) the inference
prevention module (IPM) , previously deployed in a TPA as
illustrated in [3]. Note that the Ethereum blockchain can
ensure the trustworthiness of smart contracts; it can also en-
sure that the recorded obligations are distributed, open, and
tamper-proof. Note that once the smart contract deployed it
does not need a centralized administration.
3.1.2 Notations and Use Scenarios
To elaborate our TAB, we first present the notations, entities,
and scenarios of applying our TAB framework in a crypto-
based privacy-preserving environment. Here we use FE as
the underlying crypto scheme to present TAB framework.
In Section 3.4, we analyze the applicability of TAB in other
TPA-based cryptosystems. Suppose that we have a group
of data ownersfCowner
igi2[n]that will share their private
dataxxx=fxigi2[n]encrypted by an FE scheme where for
simplicity we assume that Cowner
i owns dataxi, a group of
data usersfCuser
jgj2[m], where each data user has a vector yyyj
and needs to acquire the inner-product hxxx;yyyjiover the ci-
phertext ofxxx, and a TPAAthat provides public and private
key services for these data owners and users. Furthermore,
letfCowner
kgk2[l]be the monitors. We use Bto represent the
Ethereum blockchain, and let BTAB
SC denotes our proposed
smart contract deployed in the blockchain.
3.2 Threat Model
Threat Model of Privacy-Enhanced Applications : Existing
crypto-based privacy-preserving applications are usually
based on some common assumptions: (i) a centralized TPA
or key server is assumed to be fully trusted and (ii) both
decryption and encryption entities are assumed to be honest-
but-curious . Hence, the threat models in such cases typically
focus on an adversary who attempts to compromise the
encrypted data and a curious entity that launches potential
privacy attacks (e.g., infer the private information), while
honestly following the protocols/algorithms.
Threat Model of TAB: As illustrated by the identity-to-
public-key-binding stealthy targeted attack and the private-key-
service censorship attack in [16], a TPA or key server may not
5
Fig. 2. Overview of the TAB framework. Note that the dashed lines represent the procedures of the crypto-based privacy-preserving applications,
while the solid lines denote the procedures of TABframework.
be trusted because of its unintentional misbehaviors and/or
malicious behaviors . Similarly, the honest-but-curious entities
may also behave dishonestly. In addition to addressing
above threats, TAB focuses on increasing entities’ trust on
the TPAs and other honest-but-curious entities through trans-
parency approach. In particular, TAB mitigates the depen-
dence of crypto-based privacy-preserving applications on
the assumptions of a trusted TPA or key server and an honest
entities.
To be more precise, we assume that such a dishonest
adversary may pretend to behave honestly without being
detected by other entities. Adversaries may not follow the
specifications in the protocols, and/or attempt to conceal
their activities. In general, the dishonest adversary includes
the TPA and actors, where a dishonest TPA may attempt
to forge a key service proof-of-work without actually pro-
viding a valid key service; and, a dishonest actor may try
to incorrectly blame other entities for misbehavior. Note
that misbehavior may be related to non-malicious misuse
by normal actors or the behavior of compromised actors
controlled by an attacker.
We note that the case of potential collusion between a
dishonest TPA and honest-but-curious actors is not fully con-
sidered in this paper. Rather than forbidding or preventing
collusion through technical means, such collusion between
two stakeholders (i.e., TPA and actors) can be solved by
resorting to game theory and incentive mechanism designed
in the smart contracts [29]. TAB also involves the incentive
mechanism from the aspects of each entity, and hence it can
prevent such collusion in a game theory manner. We will
not discuss that in the reset of the paper, and readers can
refer to [29] for more details.
Furthermore, unlike the secure logging system based
authority transparency framework in [16], where the logger
is treated as a potential dishonest adversary, in TAB, the
Ethereum smart contract is adopted as the public ledger
infrastructure that has been proved to be a trusted compu-
tation platform.
3.3 Proposed TAB Framework
3.3.1 TAB Model
Unlike the authority transparency approach in [16] that
builds on the secure logging system for ABE cryptosystem,
TAB uses the Ethereum blockchain, and to keep consistency,we adopt the similar concepts/notions of the authority
transparency but it considers generic crypto-based privacy-
preserving scenarios including emerging FE-based applica-
tions and a blockchain-based public ledger infrastructure.
Suppose that each entity einTAB is issued or self-
generates an identity-based public and private key pair
hpke;skei. Note that the key service interaction occurs
between entityCactorand authorityA, where each entity
has already received its public and private key pair. For
instance, lethpkactor;skactoriandhpkTPA;skTPAirepresent the
public/private key pairs of the actor and the TPA, respec-
tively. Here, we first present the notion of public parameter
audit obligation and key service audit obligation , and then
present the formal definition of TAB.
Definition 3.1 (Public Parameter Audit Obligation (PPAO)) .
A PPAOOppofeis a map structure as follows:
Oe
pp:=H(eid) :heid;pke;Sigske(eid;pke)i;
whereeidrepresents the descriptive identifier of e, H()is a
hash function, pkedenotes the public key binding of entity
e, and Sigskeis the signature using sk e.
Definition 3.2 (Key Service Audit Obligation (KSAO)) .A
KSAOOCactor;A
ksis a map structure consisting of a pair of key
service snapshots
OCactor;A
ks :=H(Cactor
id;Aid;r) :hSreq;Srespi;
where each snapshot is a 4-tuple as follows:
Sreq:=H(Cactor
id;Aid;r) :hr;f;tCactor;Sigskactor(r;f;tCactor)i;
Sresp:=H(Cactor
id;Aid;r) :hr;;tA;SigskTPA(r;;tA)i;
such that
Sreq:H(Cactor
id;Aid;r) =Sresp:H(Cactor
id;Aid;r)
Sresp:tA S req:tCactor>0
Sreq:tA S resp:tCactor<t
whereris a nonce selected by the key service requester, t
is the timestamp of key service processed by each entity, f
denotes the request content such as function related vector,
represents the proof-of-work that TPA has issued the key,
tis the threshold of timestamp difference indicating the
expected time of processing of the key service request by
the TPA.
6
Remark . In particular,Oppis an identity-to-public-key binding
with the issuer’s signature, while OCactor;A
ksis the proof-of-key-
service . In theOCactor;A
ks, for simplicity, to provide the proof-
of-work of issuing the functional decryption key sk ffor the
function related materials f, let Sigmabe H (skf).
Based on the notion of public parameter audit obligation
and key service audit obligation , we present the formal TAB
model as follows:
Definition 3.3 (TAB Model) .LetA;BandCdenote a third-
party A uthority, a B lockchain, and an A ctor, respectively,
which are parties involved in the interactive protocols. Let
C:actor andC:monitor represent the roles of the actor
and monitor that execute the functional and monitoring
modules, respectively. We define TAB model,M, as a set
of five interactive protocols:
MA;B;C
O = (GenO;LogOpp;LogOks;Inspect );
and each protocol is defined as follows:
(SOpp; SOks) Run (1;GenO;fA;C:actorg)
(bA; ") Run (1;LogOpp;fA;Bg;(SOpp; "))
(bA; bC; ") Run (1;LogOks;fA;C:actor;Bg;(Oks:SA;Oks:SC; "))
(bB; ") Run (1;Inspect ;fB;C:monitorg;("; "))
Theorem 3.1 presents similar security guarantee as used
in [16]. We present the details in Section 4.1.
Theorem 3.1. If the hash function is collision-resistant and
the signature scheme is unforgeable, then TAB model com-
prises a secure transparency framework.
Remark . Note that the formal definition of our TAB model
is inherited from the authority transparency model [16]
with needed changes considering the underlying Ethereum
blockchain infrastructure. Specifically, in the authority trans-
parency model, the gossip protocol essentially ensures the
consistency of distributed logs without being tampered by
an adversary, while the check protocol guarantees that the
submitted obligations are recorded by the logging system.
AsTAB adopts the Ethereum blockchain as the underlying
public ledger infrastructure, there is no need to run the
gossip and check protocols because these logging-related
functions are the features provided by the Ethereum smart
contract.
3.3.2 Design ofBTAB
SC
The TAB smart contract is a critical component in our
framework. To support the goal of TAB framework,BTAB
SC
includes various types of modules: administrative module ,
access control module ,obligation module ,inspection module , and
incentive module . As illustrated in Fig. 3, the access control
module verifies a user’s role within an application and
determines whether or not to execute corresponding mod-
ules or functions. The administrative module keeps track of
theTAB smart contract’s status and registration procedures.
The obligation module is concerned with the collection of
various obligation records and the prevention of inference
in privacy-preserving applications. The inspection module
enables authorized users to examine misbehavior or mali-
cious activity. The incentive module coordinates the above
modules in order to reward or punish users using the
Fig. 3. Overview of the TABSmart Contract Interface.
Ethereum network’s payment functionalities. We discuss
each module in details next.
Administrative module . This module allows the adminis-
trator role to deploy the smart contract into the Ethereum
network. The module also includes functions such as open-
ing and locking the enrollment, and allowing the partici-
pants to drop out.
Access control module . This module supports a basic role
based access control (RBAC) mechanism that allows the
account (a.k.a, the participating entities) have role-related
permissions to call various functions. In BTAB
SC, we define
four types of roles: the TP A, the actors of data owner , the actors
of data user , the monitors and the administrator (i.e., the smart
contract owner). The administrative entity that deploys
the smart contract becomes the smart contract owner . The
ownership can be transferred to a new account if necessary.
Besides, it is also possible to relinquish this administrative
privilege, which is a common pattern after an initial stage
when there is a need for a decentralized administration.
After the deployment, each entity needs to register to its
role by calling the corresponding function before it can use
the ordinary features of the smart contract.
Obligation module . This module assists in recording the
audit obligation into the public ledger. It also publishes its
identity-to-public-key binding to the Ethereum blockchain,
as illustrated above. Note that the identity of the entity
is the unique public address (i.e., 42 hex string characters
without case-sensitivity) of the blockchain account, which is
derived from the entity’s private key. With regards to the key
service audit obligation procedure, the key service requestor
(i.e., data owner) can call the corresponding function (that
includes role verification) with a randomly generated re-
quest identifier, the key-related request parameters, and the
corresponding signature. The function then automatically
analyzes the request parameters via the inference prevention
module (IPM) . Note that the IPM, previously deployed inside
the centralized trusted TPA in vanilla FE-based applications,
is now deployed in the smart contracts in a decentralized
trust setting in the TAB. Upon receiving the key service
request with the request identifier, the TPA first checks
the verification result of IPM. If the request passes the
verification, the TPA will issue the functional decryption
key and then publish a response snapshot to fulfill the key
service obligation.
Inspection module . This module mainly inspects the com-
7
pleteness of a pair of the key service snapshots to check
whether the TPA has fulfilled its key service obligation
or not. Besides, it also allows checking for the published
identity-to-public-key binding. Besides the inspection module
that can prevent potential misbehaviors, we have intro-
duced the RBAC mechanism to prevent partial misbehav-
iors and malicious behaviors as each entity only will be
allowed to call corresponding functions with limited privi-
lege.
Incentive module . This module, as part of BTAB
SC, includes
several functions to enforce the incentive mechanism, as
depicted in Fig. 3. The incentive mechanism is based on
payment features of the Ethereum network, where the to-
ken can be exchanged for real currency. As illustrated in
Fig. 3, we design several functions as ”publicly payable”,
which indicates that the smart contract is able to receive
the transaction value (e.g., the Ether) when the function is
successfully called and executed.
In general,mdata users need to pay equally for the cost
of calling the registration function for themselves as well as
forndata owners and the TPA. Each data user also needs
to pay for the cost of calling the request obligation record
function and that of calling the response obligation record
function by the TPA. Additionally, there exists a mechanism
to punish the misbehaviors and malicious activities by a
dishonest TPA and data users. To achieve that, the data
owners and the TPA first need to register and pay the cost
by themselves. The data users make a deposit equally for
all the entities’ registration cost after the enrollment phase.
Then, the data owners and the TPA can call the disposable
reward function to withdraw the registration cost. Besides,
we make the TPA and data users make a guaranteed deposit
after the registration phase. The monitors can register and
pay the cost by themselves, and then calls the inspection
function to check the suspicious behaviors. If monitors
find the malicious behaviors, they will acquire the reward
from a fine to the corresponding entity (i.e., the guaranteed
deposit of the entity). Without the guaranteed deposit, the
corresponding entity is not allowed to operate in/join the
system. We discuss the quantitative analysis of the cost of
each entity inBTAB
SCin Section 4.2.
3.3.3 TAB Procedures
As depicted in Fig. 4, we illustrate the four phases of the
TAB framework with specific procedures in a typical FE-
based privacy-preserving application scenario. Note that
the dashed arrows represent the functional procedures of a
typical FE-based application, while the solid arrows denote
procedures specific to TAB. In our design, each entity in the
FE-based application can also play the role of the auditor
and monitor, and we also allow additional monitors to help
inspect the misbehaviors and malicious behaviors. Below,
we present the specific procedures of each phase in TAB.
Phase I: entity initialization : For each entity ewith roleerole
and identifier eidin the framework, it generates a public
and private key pair hpke;skei. Then, entity eregisters its
roleerole toBTAB
SC, and publishes its id-to-public-key binding
heid;pkeiwith its signature Sigske(eid;pke)toBTAB
SC.
Phase II: FE (crypto) initialization . The TPAAsets up the
FE cryptosystem with the master public key and master
private key pairhmpkFE;mskFEi. Using the master key, theTPA generates and sends the common public key pkFE
com
for all entities (i.e., data owners and data users) in the
FE-based application. Then, the TPA publishes the binding
hAid;pkFE
comiwith its signature SigskA(Aid;pkFE
com)toBTAB
SC.
Phase III: secure data publishing . For each data owner Cowner
i ,
it first selects a nonce ras the key service identifier. Then
Cowner
i requests the entity-specific public key pkFE
Cowner
ifrom
the TPA with r. Meanwhile,Cowner
i also sends a request key
service snapshotSCowner
ireq toBTAB
SCas follows:
SCowner
ireq =hr;0;tCowner
i;SigskCowner
i(r;0;tCowner
i)i:
Then, the TPA generates pkFE
Cowner
iforCowner
i using its master
keys, and also publishes a corresponding response key
service snapshotSA
resp toBTAB
SCto fulfill its key service audit
obligationOCowner
i;A
kswith mapping key H (Cowner
i;id;Aid;r)as
follows:
SA
resp=hr;H(pkFE
Cowner
i);tA;SigskA(r;H(pkFE
Cowner
i);tA)i:
Each data owner then uses pkFE
eowner
ito encrypt its data as
follows:fEncpkFE
eowner
i(xi)gi2[n]. Finally, the data owner pub-
lishes a receipt for the received pkFE
eowner
i.
Phase IV: secure data computation . Suppose that a data user
Cuser
j who has a vector yyyj= (y1;:::;yn)jwould ap-
ply inner-product functionality over the encrypted data
fEnc(x1);:::;Enc(xn)g.Cuser
jalso selects a key service iden-
tifierr0first, and then requests the functional decryption
key skFE
yyyjto the TPA with the vector yyyjandr0. At the same
time,Cuser
jalso sends the request key service snapshot SCuser
j
req
toBTAB
SCas follows:
SCuser
j
req =hr0;yyyj;tCuser
j;SigskCuser
j(r0;yyyj;tCuser
j)i:
Unlike the approaches proposed in [2], [3] that deploy the
inference prevention module (IPM) within a TPA, we propose
to deploy IPM in a smart contract as the TPA is not fully
trusted in TAB. Thus, the TPA needs to query BTAB
SCto check
the validity of yyyi. Ifyyyiis valid, the TPA generates skFE
yyyjfor
Cuser
jusing its master keys, and then publishes a correspond-
ing response key service snapshot SA
resptoBTAB
SC to fulfill
its key service audit obligation OCuser
j;A
kswith mapping key
H(Cuser
j;A;r0)as follows:
SA
resp=hr0;H(skFE
yyyj);tA;SigskA(r0;H(skFE
yyyj);tA)i:
Otherwise, the TPA refuses the key service and also pub-
lishes key service snapshot indicating that it has refused the
key service,SA;refuse
resp , with refusing symbol ?toBTAB
SC to
fulfill its key service audit obligation as follows:
SA;refuse
resp =hr0;H(?;yyyj);tA;SigskA(r0;H(skFE
yyyj);tA)i:
With the received skFE
yyy, a data user can compute the inner-
product ofhxxx;yyyiby decryting as follows:
hxxx;yyyji=DecskFE
yyyj(fEncpkFE
Cowner
i(xi)gi2[n]):
Finally, the data owner publishes a receipt for the received
skFE
yyy.
8
Fig. 4. Illustration of the four phases with specific procedures in TABin a FE-based privacy-preserving application scenario.
Remark . To avoid redundant description, we do not present
the roles of auditor and monitor in the above-mentioned pro-
cedures. In particular, as illustrated in Fig. 4, the data users ,
data owners and the TP A also play the role of auditor that
checks whether the audit obligations are recorded into the
blochchain permanently. In our design, the data owners also
play the role of a monitor to check the suspicious obligations
caused by misbehaviors and malicious behaviors from the
TPA and adversarial data users . For instance, as illustrated
in [2], [3], an adversarial data user may infer the private
vectorxxxby manipulating a vector to request the functional
decryption key. The monitor can inspect Oeuser;eTPA
ksto find the
adversary’s suspicious behaviors. Furthermore, our design
can also address the case of intentionally issuing an incorrect
functional decryption key. For example, suppose the key
request material (i.e., xxx) from the authorized user is correct
after IPM checking, while the key is incorrect. In TAB, the
data user is also the monitor/auditor and can file a claim
by manually calling the inspect function based on existing
logged key service materials, where the inspection is based
on the rule defined in the formal model presented above.
3.4 Applicability of TAB
TAB is applicable to other popular TPA-based cryptosystems
as well. Specifically, we analyze the applicability of TAB
in the attribute-based encryption (ABE) scheme that is the
focus of authority transparency proposed in [16].
Differences in key service audit obligations in FE and
ABE schemes are as shown in TABLE 1. The main differ-
ence in the key service is the credential type, namely, the
function-related vector and the attributes that are usually
represented in a character string. These credentials are used
to generate or derive the private key in the Setup and
KeyGeneration phases. As presented in Section 3.3, TAB is a
general framework and is not restricted to the type of audit
obligation that builds on different key service credentials.
Specifically, the request content fand response proof-of-TABLE 1
Different key service audit obligations in FE and ABE
Types obligations in FE obligations in ABE
Setup-Output private key public parameter
KeyGen-Input function-related vector attribute set
KeyGen-Output functional decryption key attribute private key
workin Definition 3.2 are not limited to a function-
related vector and corresponding hashed generated key as
illustrated in Section 3.3.3. TAB is applicable to ABE-based
applications by replacing the following audit obligations:
SCuser
j
req =hr0;yyyj;tCuser
j;SigskCuser
j(r0;yyyj;tCuser
j)i;
SA
resp=hr0;H(skFE
yyyj);tA;SigskA(r0;H(skFE
yyyj);tA)i;
by the corresponding audit obligations:
SCuser
j
req =hr0;SSSj;tCuser
j;SigskCuser
j(r0;SSSj;tCuser
j)i;
SA
resp=hr0;H(skABE
SSSj);tA;SigskA(r0;H(skFE
SSSj);tA)i;
whereSSSjis the attribute set and H (skABE
SSSj)is the correspond-
ing access control private key generated by the TPA using
the attribute set SSSjin the hash format.
4 E VALUATION
4.1 Security, Privacy and Trustworthiness
4.1.1 Security Guarantee
The security for the transparency framework is defined
in terms of three properties [8], [16]: (i) log-consistency - a
dishonest public ledger cannot remain undetected if it tries
to present inconsistent versions of the recorded obligations;
(ii)unforgeable-service - a dishonest TPA cannot forge a key
service by sending valid key service snapshots, but not
provide the key service to the actors; (iii) non-fabrication - a
dishonest TPA or actors cannot blame the public ledger for
9
misbehavior if it has behaved honestly, and dishonest actors
cannot prove the TPA for misbehavior if it has behaved
honestly.
We note that log-consistency relies on the security prop-
erties of the Ethereum blockchain. The unforgeable-service
and non-fabrication properties depend on the designed smart
contract functions and the adopted signature scheme. Here,
we use the game simulation-based reduction methodology
to prove Theorem 3.1.
Proof. TAB is built on three fundamental security compo-
nents: the Ethereum blockchain as the public ledger in-
frastructure, the Secure Hash Algorithm 3 (SHA3) as the
collision-resistance hash function, the Elliptic Curve Digital
Signature Algorithm (ECDSA) to sign and validate the
origin and integrity of messages. The security of three
components has been proved in corresponding related work
[28], [30], [31]. We only prove the above-mentioned three
security properties.
Log-consistency . Unlike the existing transparency frame-
work, [8], [16], that relies on the customized public ledger,
TAB uses the public blockchain that has already been proved
to provide secure consistency feature [28], and hence we do
not present it here to avoid redundancy.
Unforgeable-service . In TAB, there are two possible issues
related to forgeable-service:
a dishonest TPA may publish SA
respto the blockchain,
but does not send the key sk fto the actors;
the dishonest TPA may send an invalid key sk0
fto
the actors, but publishes correct SA
respgenerated from
the valid key sk f.
For the first issue, the confirmation phase of key service
audit obligation cannot be accomplished in our designed
smart contract, and then such adversarial behavior is easily
detected by the monitors. For the second issue, suppose that
the dishonest TPA has the non-negligible advantage to
break the unforgeable-service security guarantee, and hence
it can forge the hashed key component HSHA3(sk0
f)for skf
with advantage AdvA
HSHA3(sk0
f)!skf.
To achieve that, the dishonest TPA hence needs the
ability to find potential collision HSHA3(skf) = HSHA3(sk0
f).
According to the security promise of SHA3, it is impossible
to find that collision with non-negligible advantage [30].
Thus, dishonest TPA does not have a non-negligible advan-
tage to provide an unforgeable key service without being
detected.
Non-fabrication . InTAB, a possible fabrication case is that dis-
honest actors may attempt to blame the TPA by publishing
SCactor
req to the blockchain but does not actually send the key
request to the TPA. Suppose that a dishonest actor has the
non-negligible advantage to break the non-fabrication secu-
rity promise. To launch the fabrication case, the dishonest
actor needs to forge a fake SA
respso that it can accomplish the
confirmation phase. Thus, the dishonest actor is able to forge
a fake signature of the TPA with advantage AdvCactor
skA.
However, it is impossible to break the ECDSA [31], as has
been proved, namely, the unforgeability of the signature
scheme. Thus, the actors do not have a non-negligible ad-
vantage to frame up the TPA.4.1.2 Privacy Guarantee
Typically, authorized users request public and private keys
from a TPA using (attribute) identities in privacy-preserving
applications built on cryptosystems like as ABE, FE, and
multi-key HE. The major objective of authority transparency
[16] and TAB is to audit the interactive key service discussed
previously without invading the original key service. As a
result, privacy assurance of TAB focuses on preventing data
leaking from audit materials.
Unlike the initial authority transparency that focuses on
ABE-based applications where partial attribute identities are
privacy-sensitive, TAB also supports the privacy-enhanced
computing applications that built on FE or multi-key HE
schemes. There is no privacy concern regarding the identity
in an FE or multi-key HE because those identities could
be any unique characters without any privacy-sensitive
information. For example, the identity of each entity in TAB
could be the Ethereum network’s public account address,
which is a random 64-character hex string produced from
the entity’s private key. Additionally, the TAB framework
only receives the hash of attribute identifiers in ABE-based
applications, not potentially sensitive attribute information.
As a result, such account identifiers or hash of auditing
materials do not reveal any private identifiable information.
4.1.3 Trustworthiness Goal
The purpose of the TAB approach is to deal with the trust
issues raised by potential dishonest entities by providing
transparency. TAB is able to prevent the attacks such as
stealthy targeted attack and censorship attack as illustrated
in [16]. Specifically, each dishonest entity needs to publish
the key service snapshot to prove that it has fulfilled its
obligation of public parameter distribution and private key
service. The designed smart contract can ensure that each
entity’s submitted audit obligations can be automatically
cross-validated based on our designed protocols before be-
ing honestly and permanently recorded into the blockchain.
Our security analysis has shown that the misbehaviors or
malicious behaviors of a TPA and the actors are easily de-
tected. Furthermore, the IPM is a critical component in FE-
based applications [2], [3] that helps to mitigate the inference
threats. In TAB, the IPM, which was deployed in a TPA in the
scheme proposed in [3], is moved to the smart contract, is
automatically executed in a publicly auditable environment,
and hence increase the transparency and trustworthiness of
IPM.
4.2 Experimental Evaluation
4.2.1 Implementation and Setup
The TAB model does not rely on the specific privacy-
enhanced applications that are built on FE or multi-key HE
cryptosystems, and hence for generality, we only present the
evaluation on a pure TAB model with the simulated audit
obligations where the key-related components are generated
by the FE-based application in an off-line manner.
Implemented Smart Contract : We implement the smart
contracts in Solidity programming language using a Truf-
fledevelopment environment, testing framework and as-
set pipeline for blockchains using the Ethereum Virtual
Machine (EVM). Fig. 3 illustrates the core modules and
10
interfaces of TAB smart contract, while the whole implemen-
tation is publicly available on Github3. We direct the reader
to the Github repository for implementation specifics and
discuss the fundamental implementation considerations be-
low.TAB mainly includes four types of functions as follows:
Access Control Modifiers . The modifier can be used to change
the behavior of functions in a declarative way. In our imple-
mentation, we use the modifier to automatically check the
privilege of each account that is defined in RBAC module
prior to executing the function. We employ Ownable and
AccessControl smart contracts from OpenZeppelin4as the
basis for our access control mechanism. To be specific, we
define various access control modifiers in which the basic
RBAC functions are integrated to satisfy our access control
requirement. Except for the registration related functions,
other functions are restricted by these modifiers.
Administrative and Incentive Functions . We define several
administrative functions such as ‘enrollLock()’ ,‘enrollOpen()’ ,
‘dropout()’ that allow the administrator to control the enroll-
ment status. In TAB, each entity can register if and only if
the enrollment is set as open by the administrator. After the
enrollment is locked, the deposit operations are opened to
the related entities. Besides, TAB also inherits the adminis-
trative functions such as ‘transferOwnership(newOwner)’ ,‘re-
nounceOwnership()’ . These two functions allow transferring
the ownership of the contract and leave the contract without
owner, respectively. Furthermore, we also define several
withdraw and deposit functions that help to establish a basis
for the incentive and penalty mechanisms.
Registration Functions . The registration functions mainly fo-
cus on the initialization phases of TAB (i.e., Phases I and II,
as illustrated in Section 3.3.3), where each entity is allowed
to register a role, and publish its identity-to-public-key bind-
ing in the blockchain.
Obligation Functions . The obligation functions address the
core features of the TAB model. As illustrated in Sec-
tion 3.3.3 Phases III and IV , we use a three-phase com-
mitment approach to achieve the obligation features. To
be specific, ‘recordKSPKReq’, and ‘recordKSSKReq’ allow the
actors to publish the key service request snapshots, while
‘recordKSPKResp’, and ‘recordKSSKResp’ allow the TPA to
record corresponding key service response snapshots. Then,
‘recordKSPKResp’ function allows us to confirm the receipt
of the key service.
Inspection Functions . The inspection functions address the
monitoring task for the recorded audit obligation as dis-
cussed in Section 3.3.3. To be specific, ‘inspectObligationKS’
allows to automatically inspect the completeness of the key
service obligations, while ‘inspectObligationPP’ permits the
monitor to verify the published identity-to-public-key bind-
ing. Regarding the incentive design, if a dishonest behavior
is detected, the corresponding entity will be fined a fixed
number of ethers that will be provided as an incentive
reward to the monitor.
Experimental Setup : Our experiments have been performed
on a Macbook Pro platform with 2.3GHz 8-Core Intel Core
i9 processors and 32GB DDR4 memory. Besides, we use the
Ethereum official test network, Rinkeby, as the experimental
3. https://github.com/iRxyzzz/tab
4. https://openzeppelin.com/contracts/
2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
training round020406080100120140time cost (min)party=6, Without TAB
party=6, with TAB
party=8, Without TAB
party=8, with TAB
party=10, Without TAB
party=10, with TABFig. 5. The time cost of TAB-enhanced privacy-preserving FL
environment to deploy our smart contract. Furthermore,
we write several JavaScript test-cases using the automated
testing framework of Truffle that is built on Mocha5and
provides a cleanroom environment.
Specifically, for demonstration, we use five Ethereum
accounts to simulate various entities in TAB, namely, the
role of the administrator , the TP A, the data owner , the data
user and the monitor . With regards to various scenarios, we
write corresponding test-cases to evaluate the performance
(i.e., the gas cost and the time cost for scenarios such as
administrative, registration, obligation, etc).
4.2.2 Experimental Results
We report the performance of TAB for selected functions
for various test scenarios in TABLE 2. In particular, the
performance includes two aspects: the gas cost and the test
time. Gas is spent in Ethereum for deploying smart contracts
or calling functions. As reported in TABLE 2, most functions
cost very little. Specifically, except for the smart contract
deployment, the cost of each function is at the level of
105gas in general. Regarding the most called functions for
obligation and inspection, to record an audit obligation for
one key service, the functions related to three-phase commit-
ment (i.e., recordKSSKReq ,recordKSSKResp ,recordKSConfirm )
cost 3:7105gas, 8:4105gas, 4:3105gas, respectively.
Besides, the cost of inspection for key service and public
parameter audit obligations is 2:4105gas and 3:7105
gas, respectively.
Furthermore, we also measure the time it takes to test the
selected functions. Except for the administrative functions,
the calling time of rest of the functions is less than 100ms.
Note that the time to test each function is measured in
the Ethereum test network. The testing time is related to
execution time instead of time taken to confirm the trans-
action . Thus, the deployment time of the smart contract is
only 183msrather than the general time taken to confirm a
transaction, namely, about 6 minutes.
To further assess the TAB framework’s scalability, we
integrate it into a specific privacy-preserving federated
learning application built on the FE cryptosystem with a
TPA for key service, where the number of enrolled data
owners (i.e., called party in the FL system) increased from
6 to 10 and the task of FL is to train a CNN model over
MNIST dataset. Due to the fact that TAB focuses exclusively
on the key interactions between the user and the TPA, and
5. https://mochajs.org/
11
TABLE 2
The gas cost and test time of selected functions in various test case scenarios in the TAB.
Test Cases Functions Gas Cost Time Description
Administrativedeployment 4125603 183ms deploy the smart contract
enrollOpen 44126 42ms open the enrollment
enrollLock 14531 46ms lock the enrollment
dropout 28293 178ms allow to drop out and withdraw the balance
IncentivedepositGuarantee 28083 48ms deposit the guarantee
rewardRegisterCost 52949 43ms reward registration cost for non-payable entity
rewardDeploymentCost 51584 41ms reward deployment for the administrator
RegisterationregisterAuthority 38276 80ms register the role of third-party authority
registerActorDataOwner 38335 71ms register the role of data owner
registerActorDataUser 36555 70ms register the role of data user
registerMonitor 36521 72ms register the role of monitor
ObligationrecordKSSKReq 43173 96ms publish the key service request snapshot
recordKSSKResp 84211 55ms publish the key service response snapshot
recordKSConfirm 43402 49ms confirm receipt of the key service obligation
InspectioninspectObligationKS 24511 41ms inspect the key service audit obligation
inspectObligationPP 37482 46ms check the correct of the public parameter
the time cost is negligible in comparison to the time cost
of FL training, the introduction of the TAB framework has
slight effect on the time performance of the original privacy-
preserving FL training, as shown in Fig. 5. Additionally, an
increase in the number of participants has an effect on the
amount of time spent on the supported privacy-preserving
application (i.e., FL training), but not obvious on the amount
of time spent on TAB.
5 R ELATED WORK
Privacy Enhanced Applications . Emerging FE schemes
[19], [20] have been shown to be a promising candidate
for secure computation in privacy-preserving application
scenarios where data is encrypted and outsourced, and
the computation is carried out over the encrypted data.
Especially, recently proposed functional encryption for the
functionality of computing the inner-product such as in [20],
[32] raises the possibility of applying functional encryption
in complex applications such as the federated learning and
deep neural networks, as demonstrated in [2], [3], [23], [24],
[33], [34].
Both HE and FE schemes are required to allow privacy-
enhanced computing in crypto-based federated learning
applications. While HE makes use of a centralized crypto
dealer to synchronize key pairs, FE makes use of a third-
party authority (TPA) to assist in the generation of public
parameters and the provision of functional decryption keys
for each function. The crypto dealer or TPA is a vital
component of those applications and is typically regarded
to be fully trustworthy . Furthermore, the entities such as
a coordinator in federated learning are also assumed to
behonest-but-curious . However, the trust issues caused by
malicious insiders in the TPA infrastructure [8], [16] and the
privacy inference issues caused by curious entities [2], [3]
have not been investigated adequately.
Transparency and Blockchain . The concept of transparency
issues have received more and more attention due to ma-
licious activities or misbehavior in various secure com-
puting infrastructures and components. For instance, the
certificate transparency proposed in [5], [6] aims to mitigate
the certificate-based threats caused by fake or forged SSL
certificates that are mistakenly or maliciously issued byinsiders. Most recent and related work such as CONIKS [7]
and its following up work EthIKS [9],SEEMless [10], and
transparency overlay [8] target the key transparency in end-to-
end encrypted communications systems and provide a for-
mal transparency model. The work closest to this proposed
work is the authority transparency framework proposed in
[16] that addresses the issues related to potentially dishonest
TPAs in ABE-based applications using a secure logging
based approach. Further, blockchain based techniques have
also been introduced to help increase the transparency of
existing certificate transparency framework, such as in [15],
[35]. However, there is still a lack of a mechanism to ad-
dress the authority transparency issues for emerging crypto-
based privacy-preserving applications without relying on
the complex secure logging systems. Such a transparency
approach is important to ensure the trustworthy deploy-
ment of generic crypto-enabled systems.
6 C ONCLUSION
This paper proposed the TAB framework to address trans-
parency and trustworthiness of third-party authorities (TP As)
and honest-but-curious entities for generic modern crypto
enabled privacy-preserving applications, as well as other
schemes that have components similar TPAs and many
entities that interact with them. TAB employs the Ethereum
blockchain as the underlying public ledger infrastructure
and also incorporates a novel smart contract to support
accountability with an additional incentive mechanism that
motivates participants to engage in auditing and punish
misbehaviors or malicious behaviors in the environment.
Our evaluation shows that TAB is efficient in the simulated
Ethereum, and achieves the security, privacy and trustwor-
thiness goals.
ACKNOWLEDGMENT
This work was performed while James Joshi was serving
as a Program Director at NSF; and the work represents
the views of the authors and not that of NSF. Chao Li is
supported in part by Beijing Natural Science Foundation
(No. M22039) and National Key R&D Program of China
(No. 2020YFB2103802).
12
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based certificate transparency and revocation transparency,”
TDSC , 2020.
|
{
"id": "2102.01249"
}
|
2307.14812
|
Impact of Black Swan Events on Ethereum Blockchain ERC20 Token
Transaction Networks
|
The Ethereum blockchain and its ERC20 token standard have revolutionized the
landscape of digital assets and decentralized applications. ERC20 tokens
developed on the Ethereum blockchain have gained significant attention since
their introduction. They are programmable and interoperable tokens, enabling
various applications and token economies. Transaction graphs, representing the
flow of the value between wallets within the Ethereum network, have played a
crucial role in understanding the system's dynamics, such as token transfers
and the behavior of traders. Here, we explore the evolution of daily
transaction graphs of ERC20 token transactions, which sheds light on the
trader's behavior during the Black Swan Events -- 2018 crypto crash and the
COVID-19 pandemic. By using the tools from network science and differential
geometry, we analyze 0.98 billion of ERC20 token transaction data from November
2015 to January 2023. Our analysis reveals that ERC20 financial ecosystem has
evolved from a localized wealth formation period to a more mature financial
ecosystem where wealth has dispersed among the traders in the network after the
crypto crash and during the pandemic period. Before the crash, most sellers
only sell the tokens, and buyers only buy the tokens. However, after the crash
and during the pandemic period, sellers and buyers both performed buying and
selling activities. In addition, we observe no significant negative impact of
the COVID-19 pandemic on user behavior in the financial ecosystem.
|
http://arxiv.org/pdf/2307.14812v1
|
Moturi Pradeep, Uday Kumar Reddy Dyapa, Sarika Jalan, Priodyuti Pradhan
|
nlin.AO
|
nlin.AO
|
arXiv:2307.14812v1 [nlin.AO] 27 Jul 2023Impact of Black Swan Events on Ethereum Blockchain ERC20 Tok en
Transaction Networks
Moturi Pradeepa, Uday Kumar Reddy Dyapab, Sarika Jalanc, Priodyuti Pradhanb,
aDepartment of Physics & Astrophysics, University of Delhi, India.
bDepartment of Computer Science & Engineering, Indian Insti tute of Information Technology Raichur, Karnataka -
584135, India
cComplex Systems Lab, Department of Physics, Indian Institu te of Technology Indore, Khandwa Road, Simrol,
Indore-453552, India
Abstract
The Ethereum blockchain and its ERC20 token standard have revolu tionized the landscape of digital
assets and decentralized applications. ERC20 tokens developed on the Ethereum blockchain have gained
significant attention since their introduction. They are programma bleand interoperable tokens, enabling
variousapplications and token economies. Transactiongraphs, re presentingthe flow ofthe value between
wallets within the Ethereum network, have played a crucial role in und erstanding the system’s dynamics,
such as token transfers and the behavior of traders. Here, we e xplore the evolution of daily transaction
graphs of ERC20 token transactions, which sheds light on the trad er’s behavior during the Black Swan
Events – 2018 crypto crash and the COVID-19 pandemic. By using t he tools from network science and
differential geometry, we analyze 0 .98 billion of ERC20 token transaction data from November 2015 to
January 2023. Our analysis reveals that ERC20 financial ecosyste m has evolved from a localized wealth
formation period to a more mature financial ecosystem where wealt h has dispersed among the traders
in the network after the crypto crash and during the pandemic per iod. Before the crash, most sellers
only sell the tokens, and buyers only buy the tokens. However, af ter the crash and during the pandemic
period, sellers and buyers both performed buying and selling activitie s. In addition, we observe no
significant negative impact of the COVID-19 pandemic on user behav ior in the financial ecosystem.
Keywords: Ethereum Blockchain, Financial Networks, Transaction gra ph, Forman-Ricci
Curvature
1. Introduction
The growing enthusiasm worldwide to understand the financial ecos ystem is largely due to several
Black Swan events like the credit crisis of 1772, the great depressio n of 1929 −39, the OPEC oil price
shock of 1973, the Asian crisis of 1997, and 2007 −2008 financial crisis [1, 2]. Modeling a financial
system as a network has helped us to understand a wide range of ph enomena crucial for financial
professionals, economists, and researchers [3]. Analysis of a finan cial network sheds light on underlying
salient features which may not be evident without the holistic approa ch of network science [5], thereby
providing a better understanding of how the traders interact with each other and how their interactions
affect the whole system [6, 7]. However, it was not as much of a succe ss as thought it would be,
as there exist constraints in modeling the underlying networks of tr aditional financial systems arising
due to many reasons; for example, confidentiality issues where a fin ancial institution or bank may not
fully provide all the transaction details due to the intellectual prope rty restrictions, and privacy rules.
Consequently, various features of the traditional economy still n eed to be explored. We consider the
Ethereum Blockchain transaction data to analyze the trader’s beh avior during the 2018 crypto crash
and the COVID-19 pandemic [8, 9].
Ethereum blockchain may be modeled using networks as entities are c onnected through transactions
of many assets [10, 11, 12]. Initially, transaction graphs within the E RC20 token financial ecosystem
were relatively simple and characterizedby straightforwardtrans fers between token holders. However, as
Email addresses: sarika@iiti.ac.in (Sarika Jalan), priodyutipradhan@gmail.com (Priodyuti Pradhan )
Preprint submitted to Journal of L ATEX Templates July 28, 2023
the financial ecosystem evolved, transaction graphs became incr easingly complex, reflecting the growth
and diversification of token-related activities [13]. New patterns, in cluding token swaps [14], lending
protocols [15], and decentralized exchanges [16], led to intricate and intertwined transaction graphs.
Analyzing and understanding ERC20 transaction graphs has becom e crucial for researchers, developers,
and regulators seeking to comprehend token movements, identify patterns, and assess network health.
Tools and techniques, such as graph analysis algorithms and visualiza tion frameworks, have emerged
to extract meaningful insights from transaction graphs, aiding in r isk assessment, fraud detection, and
market analysis within the Ethereum Financial Ecosystem [17, 18, 19 , 20, 21]. A few existing works
analyze the ERC20 transaction data and crypto crash [22]. Howeve r, the impact of critical events and
the behavior of traders still needs to be discovered as the system is continuously evolving.
This article studies the impact of the crypto crash and the COVID-1 9 pandemic on the trader’s
behavior of the ERC20 token transactions. We use network metho ds to model and analyze the structural
and dynamic behavior of the blockchain’s transaction graphs. To ex amine the financial ecosystem, we
create daily transaction graphs from November 2015 to January 2 023. We investigate the evolution of
traders’ behavior in the Ethereum blockchain. Our analysis unveils t hat before the crash, most sellers
only sell the tokens, and buyers only buy the tokens. Few transac tions among the small traders lead
to the localization of wealth among the individual traders. However, after the crash and during the
pandemic, the seller sells the token, and buyers buy the token. But at the same time, the seller buys the
tokens, and the buyer sells the token leading to the dispersal of th e wealth among the traders and making
the ERC20 financial ecosystem more stable during the pandemic. In addition, we show no significant
negative effect of the COVID-19 pandemic on user behavior in the fin ancial system.
The article is organized as follows: Section 2 discusses preliminaries of the Ethereum Blockchain
and ERC20 tokens. Section 3 illustrates the details of the extractio n and preprocessing of the ERC20
transaction data and the modeling of the daily transaction network . It also contains the notations and
definitions used in the later discussions. Section 4 explains the result s and analysis. Finally, section 5
summarizes the current study and discusses the open problems fo r further investigation.
2. Preliminary
Blockchain is an underlying technology on which the famous cryptocu rrency, BitCoin, was built;
nowadays, blockchain applications are widespread, which cover sup ply chains, financial services, health-
care, and public registers [23, 4, 24, 25]. The core components of b lockchain are transparency and
trustlessness, through which transactions are validated and bro adcasted. In the blockchain financial
ecosystem, a block comprises several transactions and is linked to its previous block via a digital link,
thus forming a chain of blocks.
2.1. Ethereum Blockchain
In the year 2015, Ethereum came into existence [8]. Ethereum allow s for the creation and direct
peer-to-peer exchange of digital assets without intermediaries. Ethereum platform is a software built on
blockchain technology that enables the creation of cryptocurren cy (Ether), crypto-assets (e.g., ERC20
tokens, ERC721 tokens [26]), and Decentralized Applications (DAp ps) [27]. The Ethereum blockchain
is a digital ledger where Ether and crypto-assets can be securely s tored and exchanged. Ether is the
backbone of the platform, which facilitates transactions and pays for the deployment of smart contracts
on the Ethereum Blockchain.
The primary focus of the platform is to use decentralized blockchain technology for smart contracts
[25, 28]. The smart contract is a computer protocol used to creat e and develop DApps, and crypto assets.
Smart contracts are conditional codes on the blockchain execute d when smart contract conditions are
met. In other words, they are “ if ...then... ” statements written in the form of code and deployed on
the blockchain. For example, a certificate contract in which the sma rt contractwill providethe certificate
when the participants attend the required number of classes of a c ourse and score more than or equal to
60 marks in that course. The usage of smart contracts is very dive rse and includes digital identity, real
estate [29], insurance, flash loans [15], gaming [30], and decentralized finance [15].
In the Ethereum Financial ecosystem, users interact with the Eth ereum network through their
Ethereum account. With the help of accounts, users can transfe r assets, create or invoke smart con-
tracts, and interact with DApps [8]. A user account consists of a 4 0-byte public address (like a bank
account number) with the prefix “0 x” (e.g., 0 x52d3fbd8fc248c...25c37c5f5), which other users use to
transfer assets. A transaction in the Ethereum platform can exe cute various things, such as transferring
2
Figure 1: Illustrate the ERC20 token transaction data over t he Ethereum blockchain and the associated transaction grap h.
For simplicity, we assign a unique integer number correspon ding to the ‘from’, ‘to’, and ‘token Address’ columns. Here,
‘from’ is the seller’s, ‘to’ is the buyer’s wallet, and the ed ge label shows which tokens are traded. The edge thickness re pre-
sents multiple transactions of the same tokens between buye r-seller. For instance, between node 5 and 7, two transactio ns
of ‘token 4’.
assets (ERC20 tokens), deploying smart contracts, and trigger ing the smart contract [8]. To deploy a
smart contract, a person uses an Ethereum account and sends a transaction containing compiled code
of the smart contract without the recipient of the transaction [31 ]. This article limits our discussion to
the transactions related to the ERC20 tokens.
2.2. ERC20 Token
Ethereum Blockchain platform provides a more accessible opportun ity for companies and individuals
to develop blockchain products instead of building their own blockcha in platform [13]. The Ethereum
Request for Comment 20 (ERC20) standard allows developers to cr eate smart contract-enabled tokens
that can be used with other products and services, such as DApps on the Ethereum network, which
started on Nov 2015 [32]. Sometimes we refer to tokens and coins as the same, but they are different
in what they represent and their functions. In both cases, they a re digital assets, but the coin is a
native asset of the platform, which facilitates operations on the pla tform, whereas tokens are built on
the platform for the creation and flow of wealth. For instance, Eth er is the native coin of Ethereum, and
Polygon MATIC [33] and USDT [34] are tokens built on the Ethereum pla tform.
In the Ethereum Blockchain, the digitalization of the value of a partic ular asset into tradeable digital
units is known as tokenization, and the digital assets are represen ted as tokens. Tokens allow a seamless,
borderless, and almost free flow of value in the form of digital asset s across the globe. Once any product
is tokenized, these tokens can be managed, detected, accounte d for, and leveraged in the context of
incentives that may promote fair wealth. For example, XAUt (Tethe r Gold) is a token representing gold
as a digital token on the Ethereum platform. One XAUt token equals 31.1035 grams of gold. Hence,
XAUt tokens digitally represent the value associated with gold asset s so that they can be traded across
the globe using the Ethereum Platform. The above example of the XA Ut token is an asset-backed token;
there are various other types of tokens on the Ethereum platfor m with multiple functions and features
[32]. The ERC20 token can be created by any individual or organizatio n that defines the rules governing
them, such as monetary policy, token features, user incentive sy stems, etc. The current market cap of
3
Figure 2: Portray the evolution of Ethereum blockchain tran saction data as wallets (nodes), transactions (edges), and the
number of unique traded tokens. Tokens are the attributes on the edges. We examine the daily transaction graph from
November 2015 to January 2023. The shaded region reflects the testing period of ERC20 tokens. We observe a rapid
increase in all three variables between July 2016 and July 20 18. After that, the number of nodes reaches stability, and
edges gradually increase, showing the growing activity bet ween the nodes.
Ethereum is approximately $229 .56B, and ERC20 tokens are approximately $112 .7B, around 49% of the
total Ethereum blockchain [35]. A high market capitalization implies tha t the market highly values the
asset, and our interest lies in studying the trader’s behavior involve d in ERC20 token transaction.
3. ERC20 Token Transaction Data and Network Modeling
3.1. Transaction Data sets
To analyze the underlying network of ERC20 transactions in the Eth ereum Blockchain, we use the
past 8 years of ERC20 transaction data [36]. We analyzed 982 ,119,361 ERC20 token transaction data
from November 2015 to January 2023. The data set consists of 9 c olumns (Fig. 1); each column gives
us specific information regarding the ERC20 transaction data and c an be summarized as follows.
1.blockNumber: block number in which the transaction information has been stored.
2.timeStamp: time in which the block was minted, and every transaction in a block has the same
timestamp.
3.transactionHash: unique identifier that serves as proof of transaction validation.
4.tokenAddress: the hash value refers to the actual smart contract address of t he ERC20 token,
which also acts as an identifier for an ERC20 token.
5.from:address of the sender of ERC20 token
6.to:address of the receiver of ERC20 token
7.fromisContract: if this field value is 1, it signifies the ‘from’ column is a smart contract ad dress
otherwise an externally owned account address.
8.toisContract: if this field value is 1, it signifies the ‘to’ column is a smart contract addr ess
otherwise an externally owned account address.
9.value:tells about the number of tokens transferred
Each row provides information about an ERC20 token transaction in the data set. The ‘from’ and ‘to’
columns are the addresses between whom the transaction has tak en place (Fig. 1). For our analysis, we
use four columns ‘timeStamp’, ‘tokenAddress’, ‘from’, and ‘to’. The ‘tim estamp’ column is in seconds,
which we convert into ( YYYY−MM−DD) format. For instance, after transforming the timestamp
in Fig. 1, 1455451585 becomes 2016 −02−14 where base time (1970 −01−01) is considered standard
time 00 : 00 : 00 UTC [37]. The rest of the three columns’ data are in h ash value which is very difficult
to analyze. For better viewing and analyzing the data, we iterated o ver the ‘from’ and ‘to’ columns and
mapped every unique address with a unique integer number. The sam e iteration process is carried out
for the ‘tokenAddress’ column. Finally, we divide the whole data set in day-wise.
4
Figure 3: Dynamic behavior of average degree ( /angbracketleftk/angbracketright), in-degree ( /angbracketleftkin/angbracketright), and out-degree ( /angbracketleftkout/angbracketright) of daily transaction graphs.
The average degree of the transaction graph provides the ave rage number of transactions a wallet carries out in a day. The
average in-degree (out-degree) is around 3. The fluctuation s in the inception period arise due to a large number of parall el
edges (transactions) between a pair of nodes (wallets) duri ng the testing of the ERC20 token.
3.2. Transaction Network
To model the ERC20 transaction data, we use the graph model [38]. In the Ethereum Financial
Ecosystem, wallets are the nodes that buy or sell ERC20 tokens, a nd transactions between two wallets
are the edges (links). For instance, let wallets Amake a transaction in which Asends 1 token to B,
then the link will be directed from AtoB(A/squigglerightB). Further, if a wallet, Amakes 2 transactions with 2
other wallets ( BandC) in a day, then there will be two directed edges between the nodes a sA/squigglerightBand
A/squigglerightC. Here, the node Ahas out-degree 2 and B,Cboth having in-degree 1. A node can have 10 edges
with another node if it makes 10 transactions with the same node in a d ay with different tokens; then,
there will be 10 parallel edges between them. Therefore, ERC20 to ken transaction graph is a multi-edges
directed graph consisting of source and target nodes, where sou rce nodes are the wallets that sell the
ERC20 tokens, and target nodes are the wallets that buy the ERC2 0 tokens. We can think of tokens as
the attributes on the edges of the transaction graphs (Fig. 1). T he transaction graph for a given day t,
represented as Gt(Vt,Et) where set of vertices ( Vt) consists of all wallets trading during that day as [17]
Vt={v||walletsvbuy or sell any assets at day t} (1)
and the set of edges Et⊆Vt×Vtis defined as:
Et={(u,v)||walletusell to wallet vany asset at day t} (2)
We denote the adjacency matrices corresponding to multi-edge dir ected graph GtasAt∈Rnt×ntand
which can be defined as aij=lif there are ledges from itojand 0 otherwise. The out-degree of
a node, ion daytcan be represented as kout
i,t=/summationtextnt
j=1aijand in-degree as kin
i,t=/summationtextnt
j=1aji. The
average out-degree and in-degree of Gtcan be defined as /an}bracketle{tkout
t/an}bracketri}ht=1
nt/summationtextnt
i=1kout
i,tand/an}bracketle{tkin
t/an}bracketri}ht=1
nt/summationtextnt
i=1kin
i,t,
respectively. Here, we consider number of wallets participated on d aytas|Vt|=nt, and number of
transactions as |Et|=/summationtextnt
i=1kout
i,t=/summationtextnt
i=1kin
i,t=mt, thus/an}bracketle{tkout
t/an}bracketri}ht=/an}bracketle{tkin
t/an}bracketri}ht. Further, a node that sent
the maximum number of transactions in a day as a max-out-degree n ode and represented as kout
max,t=
maxi∈Vtkout
i,t. Similarly, a node that receives a maximum number of transactions in a day as max-
in-degree and defined as kin
max,t= max i∈Vtkin
i,t. We can define sets containing all the nodes having
out-degree equal to αasDout
α,t={i∈Vt||kout
i,t=α,α= 1,2,...,kout
max,t}and in-degree equal to βas
Din
β,t={i∈Vt||kin
i,t=β,β= 1,2,...,kin
max,t}, whereNout
α,t=|Dout
α,t|andNin
β,t=|Din
β,t|, are the number of
elements inside the sets [17]. Hence, sets containing all the nodes h aving out-degree and in-degree equal
to 1 asDout
1,t={i∈Vt||kout
i,t= 1}andDin
1,t={i∈Vt||kin
i,t= 1}, whereNin
1,t=|Din
1,t|, andNout
1,t=|Dout
1,t|.
From the economic perspective – kout
max,tis a wallet that is a maximum selling hub, kin
max,tis a wallet
that is a maximum buying hub, Nout
1,tis the number of wallets which sell once and Nin
1,tis the number
of wallets which buy once on a daily basis. Note that in the later discuss ion, we omit tfrom the above
notations for convenience.
5
Figure 4: Represents the dynamics of the maximum selling hub (kout
max), maximum buying hub ( kin
max), number of wallets
buying once ( Nin
1), and number of wallets selling once ( Nout
1). We observe that Nin
1has more steeper increase than Nout
1
until July 2018, after that Nin
1reaches its stability whereas there is gradual increase in Nout
1. After July 2020, we observe
strong co-movement between Nin
1andNout
1.
4. Results and Discussion
In January 2018, the Ether price reached its record high of $1431 , and by the middle of December
2018, the Ether price was down by 94% [39]. This period was marked as the 2018 Crypto Market
Crash, where various other cryptocurrencies also hit record lows [40]. On the other hand, in March 2020,
COVID-19 was declared a pandemic by the World Health Organization, resulting in severe societal and
economic ramifications worldwide [41]. During these events, significan t changes occurred in the trading
behavior of the Ethereum ERC20 Financial Ecosystem. To underst and, we analyze the behavior of the
daily transaction graphs.
4.1. Dynamics of the System
After the inception of Ethereum ERC20 tokens, the number of walle ts and transactions was lower;
however,afterJuly2016,wecanseeanotableincreaseinthedeve lopmentofnodes, edges,andthevolume
of tokens traded over time (Fig. 2). After July 2018, the everyda y number of wallets (nodes) involved
in trading is approximately constant. Still, the number of daily transa ctions (edges) increases gradually,
which infers the growing activity between the wallets of the Ethereu m ERC20 Financial Ecosystem.
From the daily transaction graph, we can also predict that on avera ge, 105wallets perform around 105
transactions, and on average, 103distinct types of tokens traded (Fig. 2).
Additionally, for the initial period, the average number of transact ions carried out by wallets per day
is around 4 and gradually grows to around 6 after July 2020 (Fig. 3). However, if we separately look into
the average out-degree and in-degree, it is close to 3. On the cont rary, Fig. 4 reveals that the max-out-
degree (kout
max) is very large as compared to the average out-degree ( /an}bracketle{tkout/an}bracketri}ht). Also, we can notice a large
number of nodes having one out-degree ( Nout
1). Similar, behavior for the max-in-degrees ( kin
max,/an}bracketle{tkin/an}bracketri}ht
andNin
1). It infers degree distribution might be heavy-tailed where N1andkmaxare the extreme points
of the degree distribution [17]. If we randomly pick a daily transaction graph, it shows a heavy-tailed
degree distribution for both the out-degrees and in-degrees. Th e out-degree distribution is of the seller’s
wallet, and the in-degree distribution is of the buyer’s wallet of the ER C20 token. The distribution
clearly shows that the Ethereum ERC20 Financial Ecosystem follows heavy-tailed distribution for daily
transaction graphs, which coincides with numerous previous works showing that the degree distribution
of blockchain transaction data is heavy-tailed [13, 17].
To get insights on the buyers’ and sellers’ behavior before and aft er the crypto crash, we examine the
dynamical behavior of the extreme points of degree distribution – m aximum selling hub ( kout
max), number
of wallets which sell once ( Nout
1), maximum buying hub ( kin
max), and the number of wallets which buy
once (Nin
1) daily (Fig. 4). We observe that until July 2018, all four variables gr ow substantially. But
after that, the number of wallets buying once daily reached stability . In contrast, the number of wallets
selling once is still gradually increasing but not substantially, and ther e is a decrease in the maximum
selling and buying hub until July 2020 (Fig. 4).
6
Figure 5: Evolution of Tokens. Presents the dynamics of the a ddition of new ERC20 tokens to the network. For each
day, we extract the count of new tokens added to the network. F or instance, on 7thJuly 2017, 25 new tokens are added,
8thJuly 2017, 40 new tokens are added to the network, and so on. We observe that until July 2018, there is an increase
in the addition of new tokens to the network, but after that, t he count remains approximately constant until July 2020.
There exists a volatile behavior of the token evolution duri ng the COVID-19 pandemic. The total number of unique tokens
traded over the whole period is 301428.
Furthermore, we calculate the ratios between the extreme points of the degree distribution for each
day. In that case, we can observe significant changes in the netwo rk’s global dynamics during the 2018
crypto crash and the COVID-19 pandemic. We can define the ratio a s follows [17]
Rin(Gt) =log(Nin
1,t)
log(kin
max,t),andRout(Gt) =log(Nout
1,t)
log(kout
max,t)(3)
The ratios show the interplay between the buyers’ and sellers’ beh avior of the Ethereum ERC20 token
transactions and provide insight into their evolution over time. We ca n observe high volatility in the
dynamics of the ratios (Fig. 6). However, close observation of RinandRoutreveals a change in the
dynamicalbehaviorofthe ratiosbeforeand afterthecryptocra sh, whichsuggestsachangein the trader’s
trading behavior. The moving average of the ratios denoted as /an}bracketle{tRin/an}bracketri}htand/an}bracketle{tRout/an}bracketri}htcan prominently show
the behavioralchangesofthe buyers and sellers. We remarkthat before July 2018, when buyers’activity
Figure 6: Dynamical behavior of buyers and sellers ratios is represented as in-degree ratio ( Rin) and out-degree ratio
(Rout), respectively. For each transaction graph, we calculate Rin,Routusing Eq. (3). To observe the evolution of the
Financial Ecosystem’s dynamics, we calculate the moving av erage of RinandRout(/angbracketleftRin/angbracketrightand/angbracketleftRout/angbracketright) for each day. From
July 2016 to July 2018, we observe an anti-phase oscillation between RinandRout. However, after July 2018, we see
a change in the dynamics of RinandRoutwith co-movement between the two, which grows stronger afte r July 2020
(COVID-19 period). For a given day t,/angbracketleftRin/angbracketrightis calculated by taking the mean of window length p+t+sthat includes the
Rinvalue of the day t,pis the number of Rinvalues preceding the day tandsis the number of Rinvalues succeeding
the day t. The window length is truncated at the initial and final days w hen there are insufficient Rinvalues to fill the
window. The mean value is taken over only the Rinthat fill the window. Here, we consider the window size to be 70 . For
the initial days, the size of pis dynamically growing, and sis kept constant until pequals 34. For final days, the size of sis
dynamically growing, and pis kept constant when the successive days are less than 35. Si milarly, we calculate the /angbracketleftRout/angbracketright
values over time.
7
Figure 7: Buyer’s and seller’s behavior. Illustrate the rel ation between (a) largest seller ( kout
max) vs. small buyers ( Nin
1) (b)
largest buyer ( kin
max) vs. small seller ( Nout
1), (c) small sellers vs. small buyers and (d) largest seller v s. largest buyer. The
color bar corresponds to the date. We calculate the slope bet ween the entities for two different periods. The red line refe rs
to the slope from July 2016 to July 2018, and the blue line refe rs to the slope from July 2018 to January 2023. We can
observe a large slope value in the initial period for panels ( a-c) (kout
maxvs.Nin
1,kin
maxvs.Nout
1andNout
1vsNin
1) and a
decrease in the later periods. However, from July 2018 to Jan uary 2023, we can observe a larger slope value between Nout
1
vsNin
1, andkout
maxvskin
maxas compared to other panels.
(/an}bracketle{tRin/an}bracketri}ht) increasing, sellers’ activity ( /an}bracketle{tRout/an}bracketri}ht) decreasing and vice-versa (Fig. 6). We characterize this day-
wise phenomenon in transaction graphs as anti-phased oscillations [1 7]. Notably, after July 2018, there
was a co-movement of the buyers’ and sellers’ activity (Fig. 6). Th e daily transaction graph size is very
largeand dynamic, so it is difficult to understand the internal behavio r. Therefore, we use the correlation
measureandregressionanalysisamongthevariablesinEq. (3). Ant i-phaseoscillationof /an}bracketle{tRin/an}bracketri}htand/an}bracketle{tRout/an}bracketri}ht
to each other in the initial period is resulted due to a strong correlat ion between entities in Eq. (3) –
maximum selling hub ( kout
max) vs. number of wallets buying once ( Nin
1), and maximum buying hub ( kin
max)
vs. the number of wallets selling once ( Nout
1) (Fig. 7(a-b)). Simultaneously, the correlation between the
number of wallets selling once vs. the number of wallets buying once, a nd a weak correlation between
the maximum buying hub and maximum selling hub (Fig. 7(c-d)). The slop es in the regression analysis
also show that after July 2018, the value of the slope decreases (F ig. 7)(a-b). From the correlation and
slope analysis, we might conclude that during the initial period, most o f the transactions of the small
traders are with big traders, fewer among small traders, and simila rly, fewer transactions between big
traders.
However, after July 2018, both /an}bracketle{tRin/an}bracketri}htand/an}bracketle{tRout/an}bracketri}htshow co-movement to each other, which grows
stronger over the period, especially after July 2020 (COVID-19 pe riod). We observe the co-movement
of the ratios lead to a decrease in the correlations between the max imum selling hub and the number of
wallets buying once (Fig.7(a)), as well as the maximum buying hub and t he number of wallets selling
once (Fig. 7(b)). Simultaneously, there is an increase in the correla tion between the number of wallets
selling and buying once and between maximum buying and selling hubs (Fig .7(c-d)). One can notice the
decrement of the slopes during the co-movement for 2 relations an d an increase in other 2 relations. In
other words, the increase in the trading activity among small trade rs and among the big traders, and at
the same time, a decreasein the tradingactivity between big trader sand small traders hasresulted in the
co-movement of the ratios. From the above analysis of trading act ivity before and after the crypto crash,
there is an evolution in the trading behavior of the traders. Before the crash, small traders perform most
8
Figure 8: From an economic perspective, we get the Forman-Ri cci curvature ( R(e)) of a transaction ( e) between two wallets
uandvto be (a) R(e) = 2 when the wallet ucan not buy any token and wallet vcan not sell any token; however, wallet
ucan sell and wallet vcan buy tokens. (b) Similarly, R(e) = 1, we observe the same economic scenarios that we observe
inR(e) = 2, with additionally, wallet ucan buy once or wallet vcan sell once. (c) In the case of edge with R(e) = 0,
the wallet ucan buy at most twice, and wallet vcan not sell, or wallet ucannot buy, and wallet vcan sell at most twice.
However, the wallet ucan sell, and the vcan buy tokens. (d) Edge will have R(e)<<0 when wallet ucan buy and wallet
vcan sell tokens. In other words, R(e)<<0 when in-degree of uand the out-degree of vis high (Eq. (5)). The edges in
pink color contribute to the Forman-Ricci curvature of the e dgee(red) under consideration.
of the transactions with the big traders, but after the crash, sm all traders make most of the transactions
among themselves. Also, there was an increase in trading activity am ong the big traders after the crash.
Further from the dynamics of ratios, we observe a stronger co-m ovement during the pandemic period,
which indicates the absence of a significant impact of COVID-19 on th e trading behavior of the traders
in the Ethereum platform. However, volatility exists in the evolution o f the new ERC20 token inclusion
to the platform during the COVID-19 period, whereas, after the c rypto crash, the dynamics remained
constant until July 2020 (Fig. 5). Note that the key difference bet ween correlation and regression is that
correlation measures the degree of a relationship between two inde pendent variables. In other words, the
correlation between two variables captures how both variables are related. In contrast, regression is how
one variable affects another. Both of the measures can not say wh ether variables are directly interacting
with each other or not.
4.2. Forman-Ricci curvature analysis
Now we use discrete Forman-Ricci curvature of networks introdu ced by R. Forman [42] to provide
better insight into the trading behavior of the system. Forman-Ric ci curvature is an edge-based concept
that measures how fast edges spread in different directions [42]. Im portantly, edges with negative curva-
ture are vital in spreading information in a network. Previously, it ha s been used to characterizecomplex
networks, which yield insights into their dynamical structure [43]. Sin ce our networks are directed, we
use the Forman-Ricci curvature of directed networks. The curv ature of a directed edge eof weight ωe,
u/squigglerightvis defined as follows:
R(e) =ωe/parenleftBigg
ωu
ωe−/summationdisplay
eu∼eωu√ωeωeu/parenrightBigg
+ωe/parenleftBigg
ωv
ωe−/summationdisplay
ev∼eωv√ωeωev/parenrightBigg
(4)
whereeu,evare the edges connected to node u,vandωeu,ωevare weights associated with the edges,
9
Figure 9: Forman-Ricci Curvature of buyers’ and sellers’ be havior. We calculate the Forman-Ricci curvature ( R(e)) of each
edge (e) in a day using Eq. (5). We consider two snapshots of the R(e) vs. frequency plot from July 2016 to January 2023.
(a) theR(e) vs. frequency plot for 12thJuly 2018 shows fewer spreads of the negative curvature valu es, where n= 336542,
andE= 818739. On the other hand, (b) 12thJune 2021 plots large spreads the negative Forman-Ricci cur vature, where
n= 254903, E= 943758. We represent the total number of edges mt=m+
t+m−
t, where m+
tandm−
tare the number
of edges with positive and negative Ricci curvature on day tand after normalizing m+
t+m−
t= 1. (c) the light blue line
represents the fraction of negative Forman-Ricci curvatur e (m−) contribution from daily transaction graphs. The dark
blue line represents the moving average value ( /angbracketleftm−/angbracketright). We observe that the fraction keeps increasing after July 2 018 and
becomes stable during the COVID-19 pandemic. The moving ave rage window size is 70 and calculated as in Fig. 6.
respectively. Here, we only consider those directed edges that te rminate at node uand originate at node
v. Since edges are unweighted, the above expression (Eq. (4)) red uces to
R(e) = 2−indeg(u)−outdeg(v) (5)
whereuis the seller wallet, vis the buyer wallet and eis the transaction from utov. Here,R(e)≤2
as indeg( u)≥0 and outdeg( u)≥0. The curvature infers the structural properties of a network . Fig. 8
shows some examples of the respective curvature of edges and th e structure around them.
The positive curvature of an edge einfers limited types of trading activity between seller and buyer
(Fig. 8). For instance, if a seller wants to buy more than 2 times or a b uyer wants to sell more than 2
times, it can not be captured by the positive curvature (Fig. 8(a-c )). In other words, positive curvature
refers to buyer-seller interaction with other traders in an isolated or restricted manner. There are few in-
degree of the seller and fewer out-degree of the buyer, so wealth flows across the wallets in the network
will be very slow and sometimes localized among peers (Fig. 8). On the o ther hand, the negative
curvature of an edge R(e)<<0 refers to various trading activities carried out by the seller and bu yer.
It infers that the seller and the buyer can buy and sell multiple times. Therefore, increasing negative
curvature infers dispersion of wealth across the network.
We calculate the fraction of edges (m−)contributing to the negative Ricci curvatures for the daily
transaction network (Fig. 9). We observe an increase in the fract ion over time; it signifies an increase
in the trading activity among the traders where simultaneously the s eller sells and buys the tokens (Fig.
9(c)). Similarly, buyers can also buy and sell the tokens. It shows t he evolution in the behavior of
the traders, where before the COVID-19 pandemic, most of the s ellers only sold the tokens and buyers
only bought the tokens, which resulted in a small percentage of edg e with negative Ricci curvature, thus
resulting in large positive Ricci curvature, and wealth localizes among the buyers (Fig. 9(c)). However,
10
after the crypto crash and during the COVID-19 pandemic, sellers and buyers both performed buying
and selling activity which led to an increase in the percentage of edges having negative Ricci curvature
value. Notably, onecan observethat during both events, the num ber ofdailytransactionsremainsstable;
only the trader’s behaviors change.
5. Conclusion
In conclusion, the evolution of transaction graphs within the Ether eum blockchain’s ERC20 token
financial ecosystem from simple token transfers to complex DeFi p rotocols [15] reflects on the growth,
complexity, and innovation occurring in the tokenized economy. Und erstanding and harnessing the
insights from transaction graphs will be pivotal in addressing scalab ility challenges, fostering regulatory
compliance, and unlocking opportunities for decentralized finance a nd digital asset utilization.
Using complex network analysis and differential geometry tools, we a nalyzed the dynamic evolution
of transaction graphs in the ERC20 token financial ecosystem. We observed the evolution in the trading
activity of the traders and the dynamics of ERC20 tokens in the fina ncial ecosystem. We focused here
on two big events - the 2018 crypto crash and the COVID-19 pande mic. We started the investigation by
analyzing the evolution of wallets, transactions, and tokens for th e period of November 2015 to January
2023. There existed a constant addition of new tokens to the finan cial ecosystem until the pandemic;
however, after that, there were fluctuations. Our analysis of th e daily transaction graphs unveiled that
before the crash, the trading activities of the traders led to the lo calization of wealth among individual
traders. However, after the crash and during the pandemic, the change in trading activity by most
traders led to the dispersal or continuous flow of wealth over the n etwork.
Here though, we used the extreme points of the degree distributio n, incorporating other variables
(Nout
α,tandNin
β,t) in the analysis can provide more insight into the system which require s further investi-
gation. Moreover, we use 4 fields from the extracted data, and inc luding other data, fields can provide
greater insights into the financial ecosystem’s underlying feature s. For instance, if we include the ‘value’
field, the transaction networks become weighted and can provide in sights into the flow of wealth in the
financial ecosystem during the black swan events. Here, we examin ed the pairwise interactions of the
transaction data, which cannot capture the higher-order intera ctions of the traders’ behavior [44]. We
intend to pursue higher-order interactions, which will provide insigh ts into the existence and role of si-
multaneous many-body interactions in the financial market. As fina ncial ecosystems continue to mature,
further research and innovation in transaction graph analysis will b e essential to unlock the full potential
of ERC20 tokens and drive the adoption of decentralized application s built on the Ethereum blockchain.
6. Acknowledgement
MP is thankful to the Complex Systems Lab (IIT Indore) members f or the useful discussion. SJ
acknowledges DST grant SPF/2021/000136. PP acknowledges SERB grant TAR/2022/000657.
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12
|
{
"id": "2307.14812"
}
|
1703.04057
|
BLOCKBENCH: A Framework for Analyzing Private Blockchains
|
Blockchain technologies are taking the world by storm. Public blockchains,
such as Bitcoin and Ethereum, enable secure peer-to-peer applications like
crypto-currency or smart contracts. Their security and performance are well
studied. This paper concerns recent private blockchain systems designed with
stronger security (trust) assumption and performance requirement. These systems
target and aim to disrupt applications which have so far been implemented on
top of database systems, for example banking, finance applications. Multiple
platforms for private blockchains are being actively developed and fine tuned.
However, there is a clear lack of a systematic framework with which different
systems can be analyzed and compared against each other. Such a framework can
be used to assess blockchains' viability as another distributed data processing
platform, while helping developers to identify bottlenecks and accordingly
improve their platforms.
In this paper, we first describe BlockBench, the first evaluation framework
for analyzing private blockchains. It serves as a fair means of comparison for
different platforms and enables deeper understanding of different system design
choices. Any private blockchain can be integrated to BlockBench via simple APIs
and benchmarked against workloads that are based on real and synthetic smart
contracts. BlockBench measures overall and component-wise performance in terms
of throughput, latency, scalability and fault-tolerance. Next, we use
BlockBench to conduct comprehensive evaluation of three major private
blockchains: Ethereum, Parity and Hyperledger Fabric. The results demonstrate
that these systems are still far from displacing current database systems in
traditional data processing workloads. Furthermore, there are gaps in
performance among the three systems which are attributed to the design choices
at different layers of the software stack.
|
http://arxiv.org/pdf/1703.04057v1
|
Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan
|
cs.DB, cs.CR, cs.DC
|
cs.DB
|
BLOCKBENCH: A Framework for Analyzing Private
Blockchains
Tien Tuan Anh DinhzJi WangzGang ChenxRui LiuzBeng Chin OoizKian-Lee Tanz
zNational University of SingaporexZhejiang University
z{dinhtta, wangji, liur, ooibc, tankl}@comp.nus.edu.sgxcg@zju.edu.cn
ABSTRACT
Blockchain technologies are taking the world by storm. Pub-
lic blockchains, such as Bitcoin and Ethereum, enable secure
peer-to-peer applications like crypto-currency or smart con-
tracts. Their security and performance are well studied.
This paper concerns recent private blockchain systems de-
signed with stronger security (trust) assumption and perfor-
mance requirement. These systems target and aim to dis-
rupt applications which have so far been implemented on top
of database systems, for example banking, nance and trad-
ing applications. Multiple platforms for private blockchains
are being actively developed and ne tuned. However, there
is a clear lack of a systematic framework with which di erent
systems can be analyzed and compared against each other.
Such a framework can be used to assess blockchains' viabil-
ity as another distributed data processing platform, while
helping developers to identify bottlenecks and accordingly
improve their platforms.
In this paper, we rst describe Blockbench , the rst
evaluation framework for analyzing private blockchains. It
serves as a fair means of comparison for di erent platforms
and enables deeper understanding of di erent system de-
sign choices. Any private blockchain can be integrated to
Blockbench via simple APIs and benchmarked against
workloads that are based on real and synthetic smart con-
tracts. Blockbench measures overall and component-wise
performance in terms of throughput, latency, scalability and
fault-tolerance. Next, we use Blockbench to conduct com-
prehensive evaluation of three major private blockchains:
Ethereum, Parity and Hyperledger Fabric. The results demon-
strate that these systems are still far from displacing current
database systems in traditional data processing workloads.
Furthermore, there are gaps in performance among the three
systems which are attributed to the design choices at di er-
ent layers of the blockchain's software stack.
1. INTRODUCTION
Blockchain technologies are gaining massive momentum
in the last few years, largely due to the success of Bitcoin
crypto-currency [41]. A blockchain, also called distributed
ledger, is essentially an append-only data structure main-
tained by a set of nodes which do not fully trust each other.
All nodes in a blockchain network agree on an ordered set
of blocks, each containing multiple transactions, thus the
blockchain can be viewed as a log of ordered transactions.
In a database context, blockchain can be viewed as a solu-
tion to the distributed transaction management problems:
nodes keep replicas of the data and agree on an executionorder of transactions. However, traditional database sys-
tems work in a trusted environment and employ well known
concurrency control techniques [36, 48, 8] to order transac-
tions. Blockchain's key advantage is that it does not assume
nodes trust each other and therefore is designed to achieve
Byzantine fault tolerance.
In the original design, Bitcoin's blockchain stores coins
as the system states shared by all participants. For this
simple application, Bitcoin nodes implement a simple repli-
cated state machine model which simply moves coins from
one address to another. Since then, blockchain has grown
rapidly to support user-de ned states and Turing complete
state machine models. Ethereum [2] is a well-known ex-
ample which enables any decentralized, replicated applica-
tions known as smart contracts . More importantly, interest
from the industry has started to drive development of new
blockchain platforms that are designed for private settings
in which participants are authenticated. Blockchain systems
in such environments are called private (or permissioned ), as
opposed to the early systems operating in public (or permis-
sionless ) environments where anyone can join and leave. Ap-
plications for security trading and settlement [44], asset and
nance management [39, 40], banking and insurance [29] are
being built and evaluated. These applications are currently
supported by enterprise-grade database systems like Oracle
and MySQL, but blockchain has the potential to disrupt
this status quo because it incurs lower infrastructure and
human costs [29]. In particular, blockchain's immutability
and transparency help reduce human errors and the need
for manual intervention due to con
icting data. Blockchain
can help streamline business processes by removing dupli-
cate e orts in data governance. Goldman Sachs estimated 6
billion saving in current capital market [29], and J.P. Mor-
gan forecast that blockchains will start to replace currently
redundant infrastructure by 2020 [40].
Given this trend in employing blockchain in settings where
database technologies have established dominance, one ques-
tion to ask is to what extent can blockchain handle data
processing workload. Another question is which platform
to choose from many that are available today, because even
though blockchain is an open protocol, di erent platforms
exist in silo. In this work, we develop a benchmarking
framework called Blockbench to address both questions.
Blockbench is the rst benchmark for studying and com-
paring performance of permissioned blockchains. Although
nodes in a permissioned blockchain still do not trust each
other, their identities are authenticated, which allows the
system to use more ecient protocols for tolerating Byzan-arXiv:1703.04057v1 [cs.DB] 12 Mar 2017
tine failure than in public settings. We do not focus on
public blockchains because their performance (and trade-
o s against security guarantee) is relatively well studied [27,
38, 15, 9]. Our framework is not only useful for application
developers to assess blockchain's potentials in meeting the
application need, but also o ers insights for platform devel-
opers: helping them to identify and improve on the perfor-
mance bottlenecks.
We face three challenges in developing Blockbench . First,
a blockchain system comprises many parts and we observe
that a wide variety of design choices are made among di er-
ent platforms at almost every single detail. In Blockbench ,
we divide the blockchain architecture into three modular lay-
ers and focus our study on them: the consensus layer, data
model and execution layer. Second, there are many di er-
ent choices of platforms, but not all of them have reached
a mature design, implementation and an established user
base. For this, we start by designing Blockbench based
on three most mature platforms within our consideration,
namely Ethereum [2], Parity [22] and Hyperledger [31], and
then generalize to support future platforms. All three plat-
forms support smart contracts and can be deployed in a
private environment. Third, there is lack of a database-
oriented workloads for blockchain. Although the real Eth-
ereum transactions and contracts can be found on the pub-
lic blockchain, it is unclear if such workload is suciently
representative to assess blockchain's general data processing
capabilities. To address this challenge, we treat blockchain
as a key-value storage coupled with an engine which can
realize both transactional and analytical functionality via
smart contracts. We then design and run both transaction
and analytics workloads based on real and synthetic data.
Blockbench is a
exible and extensible framework that
provides a number of workloads, and comes with Ethereum,
Parity and Hyperledger as backends. Workloads are transaction-
oriented currently and designed to macro-benchmark and
micro-benchmark blockchain for supporting database-like ap-
plications. Speci cally, the current macro-benchmark in-
cludes a key-value (YCSB), an OLTP (Smallbank) workload
and a number of real Ethereum smart contract workloads.
For each of the consensus, data and execution layer, there
is at least a micro-benchmark workload to measure its per-
formance in isolation. For example, for the execution layer,
Blockbench provides two workloads that stress test the
smart contract I/O and computation speed. New workloads
and blockchains can be easily integrated via a simple set of
APIs. Blockbench quanti es the performance of a back-
end system in several dimensions: throughput, latency, scal-
ability and fault tolerance. It supports security evaluation
by simulating network-level attacks. Using Blockbench ,
we conduct an in-depth comparison of the three blockchain
systems on two macro benchmark and four micro bench-
mark workloads. The results show that blockchain systems'
performance is limited, far below what is expected of a state-
of-the-art database system (such as H-Store). Hyperledger
consistently outperforms the other two systems across seven
benchmarks. But it fails to scale beyond 16 nodes. Our
evaluation shows that the consensus protocols account for
the performance gap at the application layer for Ethereum
and Hyperledger. We also identify a processing bottleneck
in Parity. Finally, our evaluation also reveals bottlenecks in
the execution and data layer of Ethereum and Parity.
In summary, our contributions are:We present the rst benchmark for understanding and
comparing the performance of permissioned blockchain
systems.
We conduct a comprehensive evaluation of Ethereum,
Parity and Hyperledger. Our empirical results present
concrete evidence of blockchain's limitations in han-
dling data processing workloads, and reveal bottle-
necks in the three systems. The results serve as a
baseline for further development of blockchain tech-
nologies.
In the next section, we discuss blockchain systems in more
detail. Section 3 describes Blockbench design and imple-
mentation. Section 4 presents our comparative performance
studies of three systems. We discuss lessons learned from
the results in Section 5 and related work in Section 6, and
we conclude in Section 7.
2. PRIVATE BLOCKCHAINS
A typical blockchain system consists of multiple nodes
which do not fully trust each other. Some nodes exhibit
Byzantine behavior, but the majority is honest. Together,
the nodes maintain a set of shared, global states and perform
transactions modifying the states. Blockchain is a special
data structure which maintains the states and the historical
transactions. All nodes in the system agree on the transac-
tions and their order as stored on the blockchain. Because of
this, blockchain is often referred to as a distributed ledger.
Blockchain transactions. A transaction in a blockchain
is the same as in traditional database: a sequence of opera-
tions applied on some states. As such, a blockchain transac-
tion requires the same ACID semantics. The key di erence
is the failure model under consideration. Current transac-
tional, distributed databases [46, 14] employ classic concur-
rency control techniques such as two-phase commit to ensure
ACID. They can achieve high performance, because of the
simple failure model, i.e. crash failure. In contrast, the
original blockchain design considers a much hostile environ-
ment in which nodes are Byzantine and they are free to join
and leave. Under this model, the overhead of concurrency
control is much higher [11].
Bitcoin. In Bitcoin [41], the states are digital coins (crypto-
currency) available in the network. A Bitcoin transaction
moves coins from one set of addresses to another set of ad-
dresses. Each node broadcasts a set of transactions it wants
to perform. Special nodes called miners collect transactions
into blocks, check for their validity, and start a consensus
protocol to append the blocks onto the blockchain. Figure 1
shows the blockchain data structure, in which each block is
linked to its predecessor via a cryptographic pointer, all the
way back to the rst (genesis) block. Bitcoin uses proof-of-
work (PoW) for consensus: only a miner which has success-
fully solved a computationally hard puzzle ( nding the right
nonce for the block header) can append to the blockchain.
PoW is tolerant of Byzantine failure, but it is probabilistic
in nature: it is possible that two blocks are appended at
the same time, creating a fork in the blockchain. Bitcoin
resolves this by only considering a block as con rmed after
it is followed by a number of blocks (typically six blocks).
This probabilistic guarantee causes both security and per-
formance issues: attacks have been demonstrated by an ad-
versary controlling only 25% of the nodes [26], and Bitcoin
Smart contractBlock header
Transaction
roothashContract
roothash
Code
State
storageinput, outputSmart contractBlock header
Transaction
roothashContract
roothash
Code
State
storageinput, outputblock t block t+1
StorageCPUNetworkHardwareBlockchain Application
......Crypto-currencyCrypto-currency Asset
managementAsset
managementSecurities
settlementSecurities
settlement...
Figure 1: Blockchain software stack on a fully validating
node. A non-validating node stores only the block head-
ers. Di erent blockchain platforms o er di erent interface
between the blockchain and application layer.
transaction throughput remains very low (7 transactions per
second [15]).
Ethereum. Due to simple transaction semantics, Bitcoin
nodes execute a very simple state machine pre-built into the
protocol. Ethereum [2] extends Bitcoin to support user-
de ned and Turing complete state machines. In particular,
Ethereum blockchain lets the user de ne any complex com-
putations in the form of smart contracts. Once deployed,
the smart contract is executed on all Ethereum nodes as a
replicated state machine. Beside the shared states of the
blockchains (crypto-currency, for example), each smart con-
tract has access to its own states. Figure 1 shows the soft-
ware stack in a typical Ethereum node: a fully validating
node contains the entire history of the blockchain, whereas
a non-validating node stores only the block headers. One
key di erence with Bitcoin is that smart contract states are
maintained as well as normal transactions. In fact, a smart
contract is identi ed by a unique address which has its own
money balance (in Ether), and upon retrieving a transaction
to its address, it executes the contract's logics. Ethereum
comes with an execution engine, called Ethereum Virtual
Machine (EVM), to execute smart contracts. Figure 2 shows
a snippet of popular contract running on Ethereum, which
implements a pyramid scheme: users send money to this
contract which is used to pay interests to early participants.
This contract has its own states, namely the list of partici-
pants, and exports a function called enter . A user invokes
this contract by sending his money through a transaction,
which is accessed by the smart contract as msg.sender and
msg.amount .
Private blockchain. Ethereum uses the same consensus
protocol as Bitcoin does, though with di erent parameters.
In fact, 90% of public blockchain systems employ variants of
the proof-of-work protocol. PoW is non-deterministic and
computationally expensive, both rendering it unsuitable for
applications such as banking and nance which must han-
dle a lot of transactions in a deterministic manner. Recent
blockchain systems, e.g., Hyperledger, consider restrictedcontract Doubler{
struct Partitipant {
address etherAddress;
uint amount;
}
Partitipant[] public participants;
unit public balance = 0;
...
function enter(){
...
balance+= msg.value;
...
if (balance > 2*participants[payoutIdx].amount){
transactionAmount = ...
participants[payoutIdx].
etherAddress.send(transactionAmount);
...
}
}
...
}
Figure 2: An example of smart contract, written in Solidity
language, for a pyramid scheme on Ethereum.
ConsensusData ModelExecution EngineApplication
PoW, PoS,
PBFT, etc.Blocks
Transactions,
Indexing, etc.Compilers, VM,
Dockers, etc.ContractsYCSB,
Smallbank,
etc.
CPU-Heavy
Analytics,
IO-Heavy
CommitsBLOCKBENCH
workloadsBlockchain
layers
Figure 3: Abstraction layers in blockchain, and the corre-
sponding workloads in Blockbench .
settings wherein nodes are authenticated. Although PoW
is still useful in such permissioned environments, as in the
case of Ethereum, there are more ecient and determinis-
tic solutions where node identities are known. Distributed
fault-tolerant consensus in such a closed settings is a well
studied topic in distributed systems. Zab [33], Raft [42],
Paxos [35], PBFT [11] are popular protocols that are in ac-
tive use today. Recent permissioned blockchains either use
existing PBFT, as in Hyperledger [31], or develop their own
variants, as in Parity [22], Ripple [44] and ErisDB [5]. Most
of these systems support smart contracts, though in di erent
languages, with di erent APIs and execution engines (see a
more comprehensive comparison in the Appendix). As a
result, permissioned blockchains can execute complex appli-
cation more eciently than PoW-based blockchains, while
being Byzantine fault tolerant. These properties and the
commercial interests from major banking and nancial insti-
tutions have bestowed on private blockchains the potentials
to disrupt the current practice in data management.
3.Blockbench DESIGN
This section discusses blockchain's common layers of ab-
stractions and the benchmarking workloads.
3.1 Blockchain Layers
There are many choices of blockchains: over 200 Bitcoin
variants, Ethereum and other permissioned blockchains. To
meaningfully compare them, we identify four abstraction
layers found in all of these systems (Figure 3) and design
our workloads to target these layers. The consensus layer
contains protocols via which a block is considered appended
to the blockchain. The data layer contains the structure,
content and operations on the blockchain data. The execu-
tion layer includes details of the runtime environment sup-
port blockchain operations. Finally, the application layer
includes classes of blockchain applications. In a related
work, Croman et. al. [15] proposed to divide blockchain into
several planes: network, consensus, storage, view and side
plane. While similar to our four layers, the plane abstrac-
tions were geared towards crypto-currency applications and
did not take into account the execution of smart contracts.
Our layers model more accurately the real implementations
of private blockchains. We now discuss these layers in turn.
3.1.1 Consensus
The role of the consensus layer is to get all nodes in the
system to agree on the blockchain content. That is, if a node
appends (or commits) a block, the other nodes also append
the same block to their copy of the blockchain. Protocols for
reaching consensus in the crash-failure model play a key role
in distributed databases, wherein nodes agree on a global
transaction order. Blockchain systems, on the other hand,
employ a spectrum of Byzantine fault-tolerant protocols [49].
At one extreme, Ethereum, like Bitcoin, uses proof-of-
work whose diculty is agreed upon and adjusted gradually
to achieve a rate of (currently) one block per 14 s(Bitcoin's
diculty achieves a rate of one block per 10 m). In essence,
proof-of-work selects at each round a random node which
can append a block, where the probability of being selected
is determined by the node's total computing power. This
simple scheme works against Sybil attack [20] - a common
attack in open, decentralized environments in which the ad-
versary can acquire multiple identities. However, it con-
sumes a lot of energy and computing power, as nodes spend
their CPU cycles solving puzzles instead of doing otherwise
useful works. Worse still, it does not guarantee safety: two
nodes may both be selected to append to the blockchain, and
both blocks can be accepted. This causes fork in the block-
chain, and most PoW-based systems add additional rules,
for example, only blocks on the longest chain are considered
accepted. Ethereum, in particular, adopts a PoW variant
called GHOST [45] which accepts blocks in heavy branches.
In any case, a block can be con rmed as part of the block-
chain only with some high probability.
At the other extreme, Hyperledger uses the classic PBFT
protocol, which is communication bound: O(N2) where N
is the number of nodes. PBFT can tolerate fewer thanN
3
failures, and works in three phases in which nodes broadcast
messages to each other. First, the pre-prepare phase selects
a leader which chooses a value to commit. Next, the prepare
phase broadcasts the value to be validated. Finally, the com-
mitphase waits for more than two third of the nodes to con-
rm before announcing that the value is committed. PBFT
has been shown to achieve liveness and safety properties in
a partially asynchronous model [11], thus, unlike PoW, once
the block is appended it is con rmed immediately. It can
tolerate more failures than PoW (which is shown to be vul-nerable to 25% attacks [26]). However, PBFT assumes that
node identities are known, therefore it can only work in the
permissioned settings. Additionally, the protocol is unlikely
to be able to scale to the network size of Ethereum, because
of its communication overhead.
In between, there are various hybrid designs that combine
both scalability of PoW and safety property of PBFT [43].
For example, Bitcoin-NG [25] decouples consensus from trans-
action validation by using PoW for leader election who can
then append more than one block at a time. Similarly, Byz-
coin [34] and Elastico [37] leverage PoW to determine ran-
dom, smaller consensus groups which run PBFT. Another
example is the Tendermint protocol, adopted by ErisDB [5],
which combines proof-of-stake (PoS) and PBFT. Unlike PoW,
PoS selects a node which can append a block by its invest-
ment (or stake) in the system, therefore avoid expending
CPU resources. Parity [22] implements a simpli ed version
of PoS called Proof of Authority (or PoA). In this protocol,
a set of authorities are pre-determined and each authority
is assigned a xed time slot within which it can generate
blocks. PoA makes a strong assumption that the author-
ities are trusted, and therefore is only suitable for private
deployment.
3.1.2 Data model
In Bitcoin, transactions are rst class citizens: they are
system states representing digital coins in the network. Pri-
vate blockchains depart from this model, by focusing on
accounts . One immediate bene t is simplicity, especially
for applications involving crypto-currencies. For instance,
transferring money from one user to another in Bitcoin in-
volves searching for transactions belonging to the sender,
then marking some of them as spent, whereas it is easily
done in Ethereum by updating two accounts in one trans-
action. An account in Ethereum has a balance as its state,
and is updated upon receiving a transaction. A special type
of account, called smart contract , contains executable code
and private states (Figure 1). When receiving a transaction,
in addition to updating its balance, the contract's code is in-
voked with arguments speci ed in the transaction. The code
can read the states of other non-contract accounts, and it
can send new transactions during execution. Parity adopts
the same data model as in Ethereum. In Hyperledger, there
is only one type of account called chaincode which is the
same as Ethereum's contract. Chaincode can only access its
private storage and they are isolated from each other.
A block contains a list of transactions, and a list of smart
contracts executed as well as their latest states. Each block
is identi ed by the cryptographic hash of its content, and
linked to the previous block's identity. In Parity, the entire
block content is kept in memory. In Ethereum and Hy-
perledger, the content is organized in a two layered data
structure. The states are stored in a disk-based key-value
storage (LevelDB[4] in Ethereum and RocksDB[6] in Hyper-
ledger), and organized in a hash tree whose root is included
in the block header. Ethereum caches the states in memory,
while Hyperledger outsources its data management entirely
to the storage engine. Only states a ected by the block's
transactions are recorded in the root hash. The hash tree
for transaction list is a classic Merkle tree, as the list is
not large. On the other hand, di erent Merkle tree vari-
ants are used for the state tree. Ethereum and Parity em-
ploy Patricia-Merkle tree that supports ecient update and
search operations. Hyperledger implements Bucket-Merkle
tree which uses a hash function to group states into a list of
buckets from which a Merkle tree is built.
Block headers and the key-value storage together maintain
all the historical transactions and states of the blockchain.
For validating and executing transactions, a blockchain node
needs only a few recent blocks (or just the latest block for
PBFT-based systems). However, the node also interacts via
some RPC-like mechanisms with light-weight clients who do
not have the entire blockchain. Such external interfaces en-
able building of third-party applications on top of block-
chain. Current systems support a minimum set of queries
including getting blocks and transactions based on their IDs.
Ethereum and Parity expose a more comprehensive set of
APIs via JSON-RPC, supporting queries of account states
at speci c blocks and of other block statistics.
3.1.3 Execution layer
A contract (or chaincode) is executed in a runtime envi-
ronment. One requirement is that the execution must be
fast, because there are multiple contracts and transactions
in one block and they must all be veri ed by the node. An-
other is that the execution must be deterministic, ideally
the same at all nodes. Deterministic execution avoid unnec-
essary inconsistency in transaction input and output which
leads to blocks being aborted. In both PoW and PBFT,
aborting transactions wastes computing resources.
Ethereum develops its own machine language (bytecode)
and a virtual machine (called EVM) for executing the code,
which is also adopted by Parity. EVM is optimized for
Ethereum-speci c operations. For example, every code in-
struction executed in Ethereum costs a certain amount of
gas, and the total cost must be properly tracked and charged
to the transaction's sender. Furthermore, the code must
keep track of intermediate states and reverse them if the
execution runs out of gas. Hyperledger, in contrast, does
not consider these semantics in its design, so it simply sup-
ports running of compiled machine codes inside Docker im-
ages. Speci cally, chaincodes are deployed as Docker im-
ages interacting with Hyperledger's backend via pre-de ned
interfaces. One advantage of Hyperledger's environment
is that it supports multiple high-level programming lan-
guages such as Go and Java, as opposed to Ethereum's
own language. In terms of development environment, Hy-
perledger exposes only simple key-value operations, namely
putState and getState . This is restricted, because any
contract states must be mapped into key-value tuples. In
contrast, Ethereum and Parity support a richer set of data
types such as map, array and composite structures. These
high-level data types in Ethereum and Parity make it easier
and faster to develop new contracts.
3.1.4 Application layer
Many applications are being proposed for blockchain, lever-
aging the latter's two key properties. First, data in the
blockchain is immutable and transparent to the participants,
meaning that once a record is appended, it can never be
changed. Second, it is resilient to dishonest and malicious
participants. Even in permissioned settings, participants
can be mutually distrustful. The most popular application,
however, is still crypto-currency. Ethereum has its own cur-
rency (Ether) and a majority of smart contracts running on
it are currency related. Decentralized Autonomous Organi-
Ethereum ParityHyperledgerIBlockchainConnectorDriver StatsCollectorWorkloadClient
ConfigurationWorkloadClient
...
...ErisDB
Figure 4: Blockbench software stack. New workloads
are added by implementing IWorkloadConnector inter-
face. New blockchain backends are added by implement-
ingIBlockchainConnector . Current backends include Eth-
ereum, Parity and Hyperledger.
zation (DAO) is the most active application in Ethereum,
creating communities for crowd funding, exchange, invest-
ment, or any other decentralized activities. A DAO manages
funds contributed by participants and gives its users voting
power proportional to their contributions. Parity's main ap-
plication is the wallet application that manages Ether. As
major banks are now considering adopting crypto-currency,
some ntech companies are building applications that take
crypto-currency to mediate nancial transactions, for ex-
ample, in currency exchange market [44]. Other examples
include applying the currency and smart contracts for more
transparent and cost-e ective asset management [39, 40].
Some applications propose to build on blockchain's im-
mutability and transparency for better application work-
ows in which humans are the bottlenecks. For example,
security settlements and insurance processes can be sped up
by storing data on the blockchain [29]. Another example is
sharing economy applications, such as AirBnB, which can
use blockchain to evaluate reputation and trust in a decen-
tralized settings, because historical activities of any users
are available and immutable. This also extends to Inter-
net of Things settings, where devices need to establish trust
among each other [3].
3.2 Blockbench Implementation
Figure 4 illustrates the current Blockbench 's implemen-
tation. To evaluate a blockchain system, the rst step is
to integrate the blockchain into the framework's backend by
implementing IBlockchainConnector interface. The inter-
face contains operations for deploying application, invoking
it by sending a transaction, and for querying the blockchain's
states. Ethereum, Parity and Hyperledger are current back-
ends supported by Blockbench , while ErisDB integration
is under development. A user can use one of the existing
workloads (discussed next) to evaluate the blockchain, or
implement a new workload using the IWorkloadConnector
interface (we assume that the smart contract handling the
workload's logic is already implemented and deployed on the
blockchain). This interface essentially wraps the workload's
operations into transactions to be sent to the blockchain.
Speci cally, it has a getNextTransaction method which re-
turns a new blockchain transaction. Blockbench 's core
component is the Driver which takes as input a workload,
user-de ned con guration (number of operations, number
of clients, threads, etc.), executes it on the blockchain and
outputs running statistics.
Asynchronous Driver. One challenge in implement-
ing the Driver is that current blockchain systems are asyn-
chronous services , meaning that transactions submitted to
the systems are processed at a later time. This is in con-
trast to databases, especially transactional databases, in
which operations are synchronous, i.e. they block until the
systems nish processing. When a transaction is submit-
ted, Ethereum, Parity and Hyperledger return a transac-
tion ID which can be used for checking the transaction sta-
tus at a later time. Such asynchronous semantics could
result in better performance, but it forces the Driver to
periodically poll for status of the submitted requests. In
particular, Driver maintains a queue of outstanding trans-
actions that have not been con rmed. New transaction
IDs are added to the queue by worker threads. A polling
thread periodically invokes getLatestBlock(h) method in
theIBlockchainConnector interface, which returns a list of
new con rmed blocks on the blockchain from a given height
h. Ethereum and Parity consider a block as con rmed if it is
at least confirmationLength blocks from the current block-
chain's tip, whereas Hyperledger con rms a block as soon
as it appears on the blockchain. The Driver then extracts
transaction lists from the con rmed blocks' content and re-
moves matching ones in the local queue. getLatestBlock(h)
can be implemented in all three systems by rst requesting
for the blockchain's current tip t, then requesting the con-
tent of all blocks in the range ( h; t]. ErisDB provides a
publish/subscribe interface that could simplify the imple-
mentation of this function.
3.3 Evaluation Metrics
The output statistics of running a workload with di erent
con gurations can be used to evaluate the blockchain against
three performance metrics.
Throughput: measured as the number of successful
transactions per second. A workload can be con gured
with multiple clients and threads per clients to saturate
the blockchain throughput.
Latency: measured as the response time per transac-
tion. Driver implements blocking transaction, i.e. it
waits for one transaction to nish before starting an-
other.
Scalability: measured as the changes in throughput
and latency when increasing number of nodes and num-
ber of concurrent workloads.
Fault tolerance: measured as how the throughput and
latency change during node failure. Although block-
chain systems are tolerant against Byzantine failure, it
is not possible to simulate all Byzantine behaviors. In
Blockbench we simulate three failure modes: crash
failure in which a node simply stops, network delay
in which we inject arbitrary delays into messages, and
random response in which we corrupt the messages ex-
changed among the nodes.
Security metrics. A special case of Byzantine failures
that is important to blockchain systems is malicious behav-Smart contracts Description
YCSB Key-value store
Smallbank OLTP workload
EtherId Name registrar contract
Doubler Ponzi scheme
WavesPresale Crowd sale
VersionKVStore Keep state's versions (Hyperledger only)
IOHeavy Read and write a lot of data
CPUHeavy Sort a large array
DoNothing Simple contract, do nothing
Table 1: Summary of smart contracts implemented in
Blockbench . Each contract has one Solidity version for
Parity and Ethereum, and one Golang version for Hyper-
ledger.
ior caused by an attacker. The attacker can be a compro-
mised node or rouge participant within the system. Under
this threat model, security of a blockchain is de ned as the
safety property of the underlying consensus protocol. In par-
ticular, security means that the non-Byzantine nodes have
the same blockchain data. Violation of the safety property
leads to forks in the blockchain. Classic Byzantine tolerant
protocols such as PBFT are proven to ensure safety for a
certain number of failures, thus security is guaranteed. On
the other hand, in PoW systems like Bitcoin or Ethereum,
forks can occur due to network delays causing two nodes
to mine the same blocks. While such accidental forks can
be quickly resolved, forks engineered by the attackers can
be used for double spending and sel sh mining . In the for-
mer, the attacker sends a transaction to a block in the fork,
waits for it to be accepted by the users, then sends a con-
icting transaction to another block in the main branch. In
the latter, by withholding blocks and maintaining a private,
long fork, the attacker disrupts the incentives for mining and
forces other participants to join the attacker's coalition. By
compromising 25% of the nodes, the attacker can control the
entire network's block generation [26].
In this work we quantify security as the number of blocks
in the forks. Such blocks, called orphan or stale blocks, rep-
resent the window of vulnerability in which the attacker can
perform double spending or sel sh mining. To manipulate
forks, the key strategy is to isolate a group of nodes, i.e.
to partition the network. For example, eclipse attack [30]
exploits the application-level protocol to surround the tar-
geted nodes with ones under the attacker's control. At the
network level, BGP hijacking [7] requires controlling as few
as 900 pre xes to isolate 50% of the Bitcoin's total min-
ing power. Blockbench implements a simulation of these
attacks by partitioning the network for a given duration.
In particular, during partition Blockbench runtime drops
network trac between any two nodes in the two partitions.
Security is then measured by the ratio between the total
number of blocks included in the main branch and the total
number of blocks con rmed by the users. The lower the ra-
tio, the less vulnerable the system is from double spending
for sel sh mining.
3.4 Workloads
We divide the workloads into two major categories: macro
benchmark for evaluating performance of the application
layer, and micro benchmark for testing the lower layers. We
have implemented the smart contracts for all workloads for
Ethereum, Parity and Hyperledger, whose details are sum-
marized in Table 1. Ethereum and Parity use the same exe-
cution model, therefore they share the same smart contract
implementations.
3.4.1 Macro benchmark workloads
We port two popular database benchmark workloads into
Blockbench , and three other real workloads found in the
Ethereum blockchain.
Key-value storage. We implement a simple smart con-
tract which functions as a key-value storage. The WorkloadClient
is based on the YCSB driver [13]. It preloads each store with
a number of records, and supports requests with di erent ra-
tios of read and write operations. YCSB is widely used for
evaluating NoSQL databases.
OLTP (Smallbank). Unlike YCSB which does not con-
sider transactions, Smallbank [10] is a popular benchmark
for OLTP workload. It consists of three tables and four ba-
sic procedures simulating basic operations on bank accounts.
We implement it as a smart contract which simply transfers
money from one account to another.
EtherId. This is a popular contract that implements a
domain name registrar. It supports creation, modi cation
and ownership transfer of domain names. A user can re-
quest an existing domain by paying a certain amount to the
current domain's owner. This contract has been written for
Ethereum blockchain, and can be ported to Parity without
change. In Hyperledger, we create two di erent key-value
namespaces in the contract: one for storing the domain name
data structures, and another for users' account balances. In
domain creation, the contract simply inserts domain value
into the rst name space, using the domain name as the
key. For ownership transfer, it checks the second namespace
if the requester has sucient fund before updating the rst
namespace. To simulate real workloads, the contract con-
tains a function to pre-allocate user accounts with certain
balances.
Doubler. This is a contract that implements a pyramid
scheme. As shown in Figure 2, participants send money
to this contract, and get rewards as more people join the
scheme. In addition to the list of participants and their con-
tributions, the contract needs to keep the index of the next
payout and updates the balance accordingly after paying
early participants. Similar to EthereId, this contract has
already been written for Ethereum, and can be ported to
Parity directly. To implement it in Hyperledger, we need to
translate the list operations into key-value semantics, mak-
ing the chaincode more bulky than the Ethereum counter-
part.
WavesPresale. This contract supports digital token sales.
It maintains two states: the total number of tokens sold so
far, and the list of previous sale transactions. It supports
operations to add a new sale, to transfer ownership of a pre-
vious sale, and to query a speci c sale records. Ethereum
and Parity support composite structure data types, making
it straightforward to implement the application logic. In
contrast, in Hyperledger, we have to translate this structure
into key-value semantics by using separate key-value names-
paces.
3.4.2 Micro benchmark workloads
The previous workloads test the performance of block-chain as a whole. As discussed early in this section, a block-
chain system comprises multiple layers, and each layer may
have di erent impact on the overall performance. We design
several workloads to stress the layers in order to understand
their individual performance.
DoNothing. This contract accepts transaction as input
and simply returns. In other words, it involves minimal
number of operations at the execution layer and data model
layer, thus the overall performance will be mainly deter-
mined by the consensus layer. Previous works on perfor-
mance of blockchain consensus protocol [34, 43] use time
to consensus to measure its performance. In Blockbench ,
this metric is directly re
ected in the transaction latency.
Analytics. This workload considers the performance of
blockchain system in answering analytical queries about the
historical data. Similar to an OLAP benchmark, this work-
load evaluates how the system implements scan-like and ag-
gregate queries, which are determined by its data model.
Speci cally, we implement two queries for extracting statis-
tics from the blockchain data:
Q1: Compute the total transaction values committed be-
tween block i and block j .
Q2: Compute the largest transaction value involving a given
state (account) between block i and block j .
InClientWorkload , we pre-load the blockhain with trans-
actions carrying integer values (representing money trans-
ferring) and the states with integer values. For Ethereum,
both queries can be implemented via JSON-RPC APIs that
return transaction details and account balances at a speci c
block. For Hyperledger, however, the second query must
be implemented via a chaincode (VersionKVStore), because
the system does not have APIs to query historical states.
IOHeavy. Current blockchain systems rely on key-value
storage to persist blockchain transactions and states. Each
storage system may perform di erently under di erent work-
loads [50]. This workload is designed to evaluate the IO per-
formance by invoking a contract that performs a large num-
ber of random writes and random reads to the contract's
states. The I/O bandwidth can be estimated via the ob-
served transaction latency.
CPUHeavy. This workload measures the eciency of
the execution layer for computationally heavy tasks. EVM
may be fast at executing Ethereum speci c operations, but
it is unclear how it performs on general tasks for which ma-
chine native codes may be more ecient. We deploy a smart
contract which initializes a large array, and runs the quick
sort algorithm over it. The execution layer performance can
then be measured by the observed transaction latency.
4. PERFORMANCE BENCHMARK
We selected Ethereum, Parity and Hyperledger for our
study, as they occupy di erent positions in the blockchain
design space, and also for their codebase maturity. We eval-
uate the three systems using both macro and micro bench-
mark workloads described in the previous section1. Our
main ndings are:
Hyperledger performs consistently better than Eth-
ereum and Parity across the benchmarks. But it fails
to scale up to more than 16 nodes.
1We have released Blockbench for public use [1].
Ethereum and Parity are more resilient to node fail-
ures, but they are vulnerable to security attacks that
forks the blockchain.
The main bottlenecks in Hyperledger and Ethereum
are the consensus protocols, but for Parity the bottle-
neck is caused by transaction signing.
Ethereum and Parity incur large overhead in terms of
memory and disk usage. Their execution engine is also
less ecient than that of Hyperledger.
Hyperledger's data model is low level, but its
exibility
enables customized optimization for analytical queries
of the blockchain data.
We used the popular Go implementation of Ethereum,
geth v1.4.18 , the Parity release v1.6.0 and the Hyperledger
Fabric release v0.6.0-preview . We set up a private testnet
for Ethereum and Parity by de ning a genesis block and di-
rectly adding peers to the miner network. For Ethereum, we
manually tuned the difficulty variable in the genesis block
to ensure that miners do not diverge in large networks. For
Parity, we set the stepDuration variable to 1. In both Eth-
ereum and Parity, confirmationLength is set to 5 seconds.
The default batch size in Hyperledger is 500.
The experiments were run on a 48-node commodity clus-
ter. Each node has an E5-1650 3.5GHz CPU, 32GB RAM,
2TB hard drive, running Ubuntu 14.04 Trusty, and con-
nected to the other nodes via 1GB switch. The results below
are averaged over 5 independent runs. For Ethereum, we re-
served 8 cores out of the available 12 cores per machine, so
that the periodical polls from the client's driver process do
not interfere with the mining process (which is CPU inten-
sive).
4.1 Macro benchmarks
This section discusses the performance of the blockchain
systems at the application layer, by running them with the
YCSB and Smallbank benchmarks over multiple nodes.
4.1.1 Throughput and latency
We measured peak performance of the three systems with
8 servers and 8 concurrent clients over the period of 5 min-
utes. Each client sends transactions to a server with a re-
quest rate varying from 8 tx/s to 1024 tx/s. Figure 5 shows
the throughput and latency at peak, and how these metrics
change with varying transaction rates.
We observe that in terms of throughput, Hyperledger out-
performs other systems in both benchmarks. Speci cally, it
has up to 5 :5x and 28x higher throughput than Ethereum
and Parity respectively. Parity has the lowest latency and
Ethereum has the highest. The gap between Hyperledger
and Ethereum is because of the di erence in consensus pro-
tocol: one is based on PBFT while the other is based on
PoW. We measured CPU and network utilization during the
experiments, and observe that Hyperledger is communica-
tion bound whereas Ethereum is CPU bound (see Appendix
B). At 8 servers, communication cost in broadcasting mes-
sages is much cheaper than block mining whose diculty is
set at roughly 2 :5sper block.
The performance gap between Parity and Hyperledger is
not because of the consensus protocol, as we expect Parity's
PoA protocol to be simpler and more ecient than bothPoW and PBFT (indeed, we observe that Parity has the
same CPU utilization and lower network utilization than
Hyperledger). Figure 5[b,c] shows that Parity's throughput
and latency remains constant with increasing transaction
rates (beyond 40 tx/s). To understand its performance fur-
ther, we measure the queue of pending transactions at the
client. Figure 6 compares the queue sizes before and af-
ter the systems reach their peak throughput. With only 8
tx/s, the queues for Ethereum and Hyperledger remain at
roughly constant sizes, but Parity's queue size increases as
time passes. More interestingly, under high loads (512 tx/s
per client), Parity's queue is always smaller than Ethereum's
and Hyperledger's. This behavior indicates that Parity pro-
cesses transactions at a constant rate, and that it enforces a
maximum client request rate at around 80 tx/s. As a conse-
quence, Parity achieves both lower throughput and latency
than other systems.
Another observation is that there are di erences between
YCSB and Smallbank workloads in Hyperledger and Eth-
ereum. There is a drop of 10% in throughput and 20%
increase in latency. Since executing a Smallbank smart con-
tract is more expensive than executing a YCSB contract
(there are more reading and writing to the blockchain's states),
the results suggest that there are non-negligible costs in the
execution layer of blockchains.
At its peak throughput, Hyperledger generates 3 :1 blocks
per second and achieves the overall throughput of 1273 tx/s.
We remark that this throughput is far lower than what an in-
memory database system can deliver (see Appendix B). As
the throughput is a function of the block sizes and block gen-
eration rate, we measured the e ect of increasing the block
sizes in the three systems. The results (see Appendix B)
demonstrate that with bigger block sizes, the block genera-
tion rate decreases proportionally, thus the overall through-
put does not improve.
4.1.2 Scalability
We xed the client request rate and increased both the
number of clients and the number of servers. Figure 7 il-
lustrates how well the three systems scale to handle larger
YCSB workloads (the results for Smallbank are similar and
included in Appendix B). Parity's performance remains con-
stant as the network size and o ered load increase, due to
the constant transaction processing rate at the servers. In-
terestingly, while Ethereum's throughput and latency de-
grade almost linearly beyond 8 servers, Hyperledger stops
working beyond 16 servers.
To understand why Hyperledger failed to scale beyond
16 servers and 16 clients, we examined the system's logs
and found that the nodes were repeatedly trying and failing
to reach consensus on new views which contain batches of
transactions. In fact, the servers were in di erent views and
consequently were receiving con
icting view change mes-
sages from the rest of the network. Further investigation
reveals that con
icting views occurred because the consen-
sus messages are rejected by other peers on account of the
message channel being full. As messages are dropped, the
views start to diverge and lead to unreachable consensus.
In fact, we also observe that as time passes, client requests
took longer to return (see Appendix B), suggesting that the
servers were over saturated in processing network messages.
We note, however, that the original PBFT protocol guaran-
tees both liveness and safety, thus Hyperledger's failure to
YCSB Smallbank101102103104#tx/s 284 255
45 461273 1122Throughput
Ethereum
Parity
Hyperledger
YCSB Smallbank10-1100101102103second92114
3 43851Latency
Ethereum
Parity
Hyperledger(a) Peak performance
02004006008001000
#request/s101102103#tx/sThroughput
Ethereum
ParityHyperledger
02004006008001000
#request/s100101102secondLatency
Ethereum
ParityHyperledger (b) Performance with varying request rates
Figure 5: Blockchain performance with 8 clients and 8 servers.
0 50 100 150 200 250 300
time (second)02004006008001000#requestsQueue length, 8 tx/s
Ethereum
Parity
Hyperledger
0 50 100 150 200 250 300
time (second)0100002000030000400005000060000#requestsQueue length, 512 tx/s
Ethereum
Parity
Hyperledger
Figure 6: Client's request queue, for request rates of 8 tx/s
and 512 tx/s.
1248121620242832
#nodes101102103104#tx/sThroughput
Ethereum
Parity
Hyperledger
1248121620242832
#nodes10-1100101102103secondLatency
Ethereum
Parity
Hyperledger
Figure 7: Performance scalability (with the same number of
clients and servers).
8 12 16 20 24 28 32
#nodes0200400600800100012001400#tx/sThroughput
Ethereum
Parity
Hyperledger
812 16 20 24 28 32
#nodes020406080100120140160180secondLatency
Ethereum
Parity
HyperledgerFigure 8: Performance scalability (with 8 clients).
scale beyond 16 servers is due to the implementation of the
protocol. In fact, in the latest codebase (which was updated
after we have nished our benchmark), the PBFT compo-
nent was replaced by another implementation. We plan to
evaluate this new version in the future work.
The results so far indicate that scaling both the number of
clients and number of servers degrades the performance and
even causes Hyperledger to fail. We next examined the costs
of increasing the number of servers alone while xing the
number of clients. Figure 8 shows that the performance be-
comes worse as there are more servers, meaning that the sys-
tems incur some network overheads. Because Hyperledger
is communication bound, having more servers means more
messages being exchanged and higher overheads. For Eth-
ereum, even though it is computation bound, it still con-
sumes a modest amount of network resources for propagat-
ing transactions and blocks to other nodes. Furthermore,
with larger network, the diculty is increased to account for
the longer propagation delays. We observe that to prevent
the network from diverging, the diculty level increases at
higher rate than the number of nodes. Thus, one reason
for Ethereum's throughput degradation is due to network
sizes. Another reason is that in our settings, 8 clients send
requests to only 8 servers, but these servers do not always
broadcast transactions to each other (they keep mining on
0 50 100 150 200 250 300 350 400
time (second)020040060080010001200140016001800#txs#Transactions commited
Ethereum-12
Ethereum-16Parity-12
Parity-16Hyperledger-12
Hyperledger-16Figure 9: Failing 4 nodes at 250thsecond ( xed 8 clients)
for 12 and 16 servers. X-12 and X-16 mean running 12 and
16 servers using blockchain Xrespectively.
0 50 100 150 200 250 300 350 400
time (second)0100200300400500#blocks#Blocks generated
Ethereum-bc
Ethereum-totalParity-bc
Parity-totalHyperledger-bc
Hyperledger-total
Figure 10: Blockchain forks caused by attacks that parti-
tions the network in half at 100thsecond and lasts for 150
seconds. X-total means the total number of blocks generated
in blockchain X,X-bc means the total number of blocks that
reach consensus in blockchain X.
their own transaction pool). As a result, the network mining
capability is not fully utilized.
4.1.3 Fault tolerance and security
To evaluate how resilient the systems are to failures by
crashing, we ran the systems with 8 clients for over 5 min-
utes, during which we killed o 4 servers at 250thsecond.
Figure 9 shows that Ethereum is nearly una ected by the
change, suggesting that the failed servers do not contributing
signi cantly to the mining process. In Parity, each node gen-
erates blocks at a constant rate, thus failing 4 nodes means
the remaining nodes are given more time to generate more
blocks, therefore the overall throughput is una ected. In
contrast, the throughput drops considerably in Hyperledger.
For 12 servers, Hyperledger stops generating blocks after the
failure, which is as expected because the PBFT can only tol-
erate fewer than 4 failures in a 12-server network. With 16
servers, the system still generated blocks but at a lower rate,
which were caused by the remaining servers having to sta-
bilize the network after the failures by synchronizing their
views.
We next simulated the attack that renders the blockchain
vulnerable to double spending. The attack, described in
Section 3.3, partitioned the network at 100thsecond and
lasted for 150 seconds. We set the partition size to be half
1M 10M 100M
input size10-210-1100101102103104second10.5279.61
x3.0124.04232.78
0.190.331.94Execution time
Ethereum
Parity
Hyperledger
1M 10M 100M
input size105106107108MB
4,15022,819
x7182,07813,090
3764731,353Peak memory usage
Ethereum
Parity
HyperledgerFigure 11: CPUHeavy workload, `X' indicates Out-of-
Memory error.
of the original2. Figure 10 compares the vulnerability of
the three systems running with 8 clients and 8 servers. Re-
call that vulnerability is measured as the di erences in the
number of total blocks and the number of blocks on the
main branch (Section 3.3), we refer to this as . Both Eth-
ereum and Parity blockchains fork at 100thseconds, and
increases as time passes. For the attack duration, upto 30%
of the blocks are generated in the forked branch, meaning
that the systems are highly exposed to double spending or
sel sh mining attacks. When the partition heals, the nodes
come to consensus on the main branch and discard the forked
blocks. As a consequence, stops increasing shortly after
250thsecond. Hyperledger, in stark contrast, has no fork
which is as expected because its consensus protocol is proven
to guaranteed safety. We note, however, that Hyperledger
takes longer than the other two systems to recover from the
attacks (about 50 seconds more). This is because of the syn-
chronization protocol executed after the partitioned nodes
reconnect.
4.2 Micro benchmarks
This section discusses the performance of the blockchain
system at execution, data and consensus layers by evaluat-
ing them with micro benchmark workloads. For the rst
two layers, the workloads were run using one client and one
server. For the consensus layer, we used 8 clients and 8
servers.
4.2.1 Execution layer
We deployed the CPUHeavy smart contract that is ini-
tialized with an integer array of a given size. The array is
initialized in descending order. We invoked the contract to
sort the array using quicksort algorithm, and measured the
execution time and server's peak memory usage. The results
for varying input sizes are shown in Figure 11. Although
Ethereum and Parity use the same execution engine, i.e.
EVM, Parity's implementation is more optimized, therefore
it is more computation and memory ecient. An interest-
ing nding is that Ethereum incurs large memory overhead.
In sorting 10 Melements, it uses 22GB of memory, as com-
pared to 473MB used by Hyperledger. Ethereum runs out of
2We note that partitioning a N-node network in half does
not mean there are N=2 Byzantine nodes. In fact, Byzan-
tine tolerance protocols do not count network adversary as
Byzantine failure
memory when sorting more than 10M elements. In Hyper-
ledger, the smart contract is compiled and runs directly on
the native machine within Docker environment, thus it does
not have the overheads associated with executing high-level
EVM byte code. As the result, Hyperledger is much more
ecient in term of speed and memory usage. Finally, we
note that all three systems fail to make use of the multi-core
architecture, i.e. they execute the contracts using only one
core.
4.2.2 Data model
IO Heavy. We deployed the IOHeavy smart contract
that performs a number of read and write operations of
key-value tuples. We used 20-byte keys and 100-byte val-
ues. Figure 12 reports the throughput and disk usage for
these operations. Ethereum and Parity use the same data
model and internal index structure, therefore they incur sim-
ilar space overheads. Both use an order of magnitude more
storage space than Hyperledger which employs a simple key-
value data model. Parity holds all the state information in
memory, so it has better I/O performance but fails to han-
dle large data (capped by over 3M states under our hard-
ware settings). On the contrary, Ethereum only caches only
parts of the state in memory (using LRU for eviction policy),
therefore it can handle more data than Parity at the cost of
throughput. Hyperledger leverages RocksDB to manage its
states, which makes it more ecient at scale.
Analytic Queries. We implemented the analytics work-
load by initializing the three systems with over 120 ;000 ac-
counts with a xed balance. We then pre-loaded them with
100;000 blocks, each contains 3 transactions on average.
The transaction transfers a value from one random account
to another random account. Due to Parity's overheads in
signing transactions when there are many accounts, we con-
sidered transactions using only 1024 accounts. We then exe-
cuted the two queries described in Section 3.4 and measured
their latencies. Figure 13 shows that the performance for Q1
is similar, whereas Q2 sees a signi cant gap between Hyper-
ledger and the rest. We note that the main bottleneck for
both Q1 and Q2 is the number of network (RPC) requests
sent by the client. For Q1, the client sends the same num-
ber of requests to all systems, therefore their performance
are similar. On the other hand, for Q2 the client sends one
RPC per block to Ethereum and Parity, but only one RPC
to Hyperledger because of our customized smart contract
implementation (see Appendix C). This saving in network
roundtrip time translates to over 10x improvement in Q2
latency.
4.2.3 Consensus
We deployed the DoNothing smart contract that accepts
a transaction and returns immediately. We measured the
throughput of this workload and compare against that of
YCSB and Smallbank. The di erences compared to other
workloads, shown in Figure 13[c] is indicative of the cost
of consensus protocol versus the rest of the software stack.
In particular, for Ethereum we observe 10% increases in
throughput as compared to YCSB, which means that execu-
tion of the YCSB transaction accounts for the 10% overhead.
We observe no di erences among these workloads in Parity,
because the bottleneck in Parity is due to transaction sign-
ing (even empty transactions still need to be signed), not
due to consensus or transaction execution.5. DISCUSSION
Understanding blockchain systems. Our framework is
designed to provide better understanding of the performance
and design of di erent private blockchain systems. As more
and more blockchain systems are being proposed, each of-
fering di erent sets of feature, Blockbench 's main value
is that it narrows down the design space into four distinct
abstraction layers. Our survey of current blockchain sys-
tems (see Appendix A) show that the four layers are su-
cient to capture the key characteristics of these systems. By
benchmarking these layers, one can gain insights into the de-
sign trade-o s and performance bottlenecks. In this paper,
for example, by running the IOHeavy workload we identify
that Parity trades performance for scalability by keeping
states in memory. Another example is the trade-o in data
model made by Hyperledger. On the one hand, the sim-
ple key-value model means some analytical queries cannot
be directly supported. On the other hand, it enables opti-
mization that helps answering the queries more eciently.
Finally, we identify that the bottleneck in Parity is not due
to the consensus protocol, but due to the server's transac-
tion signing. We argue that such insights are not easy to
extract without a systematic analysis framework.
Usability of blockchain. Our experience in working with
the three blockchain systems con rms the belief that in its
current state blockchain are not yet ready for mass usage.
Both their designs and codebases are still being re ned con-
stantly, and there are no other established applications be-
side crypto-currency. Of the three systems, Ethereum is
more mature both in terms of its codebase, user base and de-
veloper community. Another usability issue we encountered
is in porting smart contracts from one system to another, be-
cause of their distinct programming models (see Section 3).
This is likely to be exacerbated as more blockchain platforms
are being proposed [44, 16].
Bringing database designs into blockchain. The chal-
lenge in scaling blockchain by improving its consensus proto-
cols is being addressed in many recent works [34, 37]. How-
ever, as we demonstrated in the previous section, there are
other performance bottlenecks. We propose four approaches
in applying design principles from database systems to im-
prove blockchain.
Decouple storage, execution engine and consensus layer
from each other, then optimize and scale them independently.
For instance, current systems employ generic key-value stor-
age, which may not be best suited to the unique data struc-
ture and operations in blockchain. UStore [19] demonstrates
that a storage designed around the blockchain data struc-
ture is able to achieve better performance than existing im-
plementations.
Embrace new hardware primitives. Many data processing
systems are taking advantage of new hardware to boost their
performance [47, 50, 21]. For blockchain, using trusted hard-
ware, the underlying Byzantine fault tolerance protocols can
be modi ed to incur fewer network messages [12]. Systems
like Parity and Ethereum can take advantage of multi-core
CPUs and large memory to improve contract execution and
I/O performance.
Sharding. Blockchain is essentially a replicated state ma-
chine system, in which each node maintains the same data.
As such, blockchains are fundamentally di erent to database
systems such as H-Store in which the data is partitioned (or
sharded) across the nodes. Sharding helps reduce the com-
0.8M 1.6M 3.2M 6.4M 12.8M
# tuples102103104second377 359 337
6913291181 1087
x x5618 5411 5123 4852 4527Average throughput (write)
Ethereum Parity Hyperledger(a) Write
0.8M 1.6M 3.2M 6.4M 12.8M
# tuples103104second 2019
1631
1282
5129329 9234 8974
x x8785 8758 8700 8670 8605Average throughput (read)
Ethereum Parity Hyperledger (b) Read
0.8M 1.6M 3.2M 6.4M 12.8M
# tuples103104105MB
2,3375,45912,80430,221
2,0865,04512,104
x x3606751,2832,4774,865Disk usage
Ethereum Parity Hyperledger (c) Disk usage
Figure 12: IOHeavy workload, `X'indicates Out-of-Memory error.
1 10 100 1,000 10,000
# blocks scanned10-210-1100101second
0.0200.0320.1681.37413.314
0.0200.0340.1290.9158.901
0.0190.0380.1350.9848.465Latency
Ethreum
Parity
Hyperledger
(a) Analytics workload (Q1)
1 10 100 1,000 10,000
# blocks scanned10-210-1100101second
0.0250.0330.1070.5954.907
0.0240.0310.0910.4273.472
0.019 0.0200.0230.0760.533Latency
Ethereum
Parity
Hyperledger (b) Analytics workload (Q2)
Ethereum Parity Hyperledger101102103104#tx/s256
451122
284
451273
328
461285Transaction througput
SmallBank
YCSB
DoNothing (c) DoNothing workload
Figure 13: Analytics and DoNothing workloads.
putation cost and can make transaction processing faster.
The main challenge with sharding is to ensure consistency
among multiple shards. However, existing consistency pro-
tocols used in database systems do not work under Byzan-
tine failure. Nevertheless, their designs can o er insights
into realizing a more scalable sharding protocol for block-
chain. Recent work [37] has demonstrated the feasibility
of sharding the consensus protocol, making important steps
towards partitioning the entire blockchain.
Support declarative language. Having a set of high-level
operations that can be composed in a declarative manner
makes it easy to de ne complex smart contracts. It also
opens up opportunities for low-level optimizations that speed
up contract execution.
6. RELATED WORK
Performance studies of blockchain systems have so far
been restricted to public blockchains. For example, [17, 15]
analyze the e ect of block sizes and network propagation
time on the overall throughputs. Recent proposals for im-
proving Bitcoin performance [27, 34, 37, 25, 43] have mainly
focused on the consensus layer, in which analytical models
or network simulations are used to validate the new designs.
Various aspects of Ethereum, such as their block process-
ing time (for syncing with other nodes) and transactions
processing time, have also been benchmarked [24, 23]. Our
analysis using Blockbench di ers from these works in that
it is the rst to evaluate private blockchains systems at scale
against database workloads. Furthermore, it compares two
di erent systems and analyzes how their designs a ect the
overall performances. Future extensions of Blockbench
would enable more comparative evaluations of the key com-
ponents in blockchain.There are many standard frameworks for benchmarking
database systems. OLTP-Bench [18] contains standard work-
loads such as TPC-C for transactional systems. YCSB [13]
contains key-value workloads. HiBench [32] and BigBench [28]
feature big-data analytics workloads for MapReduce-like sys-
tems. Blockbench shares the same high-level design as
these frameworks, but its workloads and main driver are
designed speci cally for blockchain systems.
7. CONCLUSION
In this paper we proposed the rst benchmarking frame-
work, called Blockbench , for evaluating private blockchain
systems. Blockbench contains workloads for measuring
the data processing performance, and workloads for under-
standing the performance at di erent layers of the block-
chain. Using Blockbench , we conducted comprehensive
analysis of three major blockchain systems, namely Eth-
ereum, Parity and Hyperledger with two macro benchmarks
and four micro benchmarks. The results showed that current
blockchains are not well suited for large scale data process-
ing workloads. We demonstrated several bottlenecks and
design trade-o s at di erent layers of the software stack.
Acknowledgment
We would like to thank the anonymous reviewers for their
comments and suggestions that help us improve the paper.
Special thanks to Hao Zhang, Loi Luu, the developers from
Ethereum, Parity and Hyperledger projects for helping us
with the experiment setup. This work is funded by the Na-
tional Research Foundation, Prime Minister's Oce, Sin-
gapore, under its Competitive Research Programme (CRP
Award No. NRF-CRP8-2011-08).
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APPENDIX
A. SURVEY OF BLOCKCHAIN PLATFORMS
We compare eleven promising blockchain platforms in Ta-
ble 2. We can see that all but Ripple support smart con-
tracts. Ethereum, Eris-DB, D nity and Parity execute the
contracts using Ehtereum Virtual Machine (EVM), whereas
Corda runs them in Java Virtual Machine (JVM). Hyper-
ledger, Stellar and Tezos employ Docker images, ScalableBFT
takes Haskell execution environment, and Sawtooth Lake
launches contracts on top of Trusted Execution Environment
(TEE) such as Intel Software Guard Extensions (SGX). These
platforms also support di erent languages to develop smart
contracts. For example, Solidity, Serpent and LLL are mainlyused in Ethereum, D nity and Parity, while Eris-DB only
supports Solidity. Hyperledger, Stellar, Corda and Sawtooth
Lake exploit various mature programming languages, such
as Python, Java, Golang, etc. ScalableBFT and Tezos even
develop their own smart contract languages. Most block-
chain platforms' data models are account-based. Two ex-
ceptions in the table are Ripple and Corda. Their data
models are similar to Bitcoin's unspent transaction outputs
(UTXO) which represents the coins in the network.
Each platform o ers di erent consensus protocols. Hy-
perledger implements PBFT in the version we evaluated,
while Ethereum implements a variation of PoW (Proof-of-
Work). Eris-DB builds on top of Tendermint protocol but
only works in the latest version (v 0.12). Ripple and Tezos
deploy Proof-of-Stake (PoS) schemes (the one in Ripple is
referred to Ripple Consensus Ledger) where the next block
is created based on accounts' wealth, i.e., the stake. Parity
takes another consensus protocol, Proof-of-Authority (PoA),
which holds a prede ned set of "authorities" to create new
blocks in a xed time slot and secure the blockchain network.
Sawtooth Lake uses Proof-of-Elapsed-Time (PoET) as its
consensus protocol, which in nature is a lottery algorithm
and decides the creator of block arbitrarily. Stellar develops
its own mechanism, Stellar Consensus Protocol, which is a
construction for decentralized Byzantine agreement. There
is no source code that helps determine which consensus pro-
tocol D nity uses, but its documents suggest that a Block-
chain Nervous System will govern the whole platform via a
voting mechanism based on neurons that interact with each
other and are controlled by users.
B. MACRO BENCHMARKS
We compared the performance of the three blockchain sys-
tems against a popular in-memory database system, namely
H-Store, using the YCSB and Smallbank workload. We ran
H-Store's own benchmark driver and set the transaction rate
at 100,000 tx/s. Figure 14 shows at least an order of mag-
nitude gap in throughput and two order of magnitude in la-
tency. Speci cally, H-Store achieves over 140K tx/s through-
put while maintaining sub-millisecond latency. The gap in
performance is due to the cost of consensus protocols. For
YCSB, for example, H-Store requires almost no coordination
among peers, whereas Ethereum and Hyperledger su er the
overhead of PoW and PBFT.
An interesting observation is the overhead of Smallbank.
Recall that Smallbank is a more complex transactional work-
load than YCSB, in which multiple keys are updated in a
single transaction. Smallbank is simple but is representative
of the large class of transactional workloads such as TPC-C.
We observe that in H-Store, Smallbank achieves 6 :6x lower
throughput and 4x higher latency than YCSB, which indi-
cates the cost of distributed transaction management proto-
col, because H-Store is a sharded database. In contrast, the
blockchain su ers modest degradation in performance: 10%
in throughput and 20% in latency. This is because each node
in blockchain maintains the entire state (replicated state ma-
chine), thus there is no overhead in coordinating distributed
transactions as the data is not partitioned.
The results demonstrate that blockchain performs poorly
at data processing tasks currently handled by database sys-
tems. However, we stress that blockchains and databases
Table 2: Comparison of blockchain platforms
ApplicationSmart
contract
executionSmart contract
languageData model Consensus
Hyperledger Smart contract Dockers Golang, Java Account-based PBFT
EthereumSmart contract,
Crypto-
currencyEVM Solidity, Serpent, LLL Account-based Ethash (PoW)
Eris-DB Smart contract EVM Solidity Account-based Tendermint (BFT)
RippleCrypto-
currency- - UTXO-basedRipple Consensus
Ledger (PoS)
ScalableBFT Smart contractHaskell
ExecutionPact Account-based ScalableBFT
Stellar Smart contract DockersJavaScript, Golang,
Java, Ruby, Python, C#Account-basedStellar Consensus
Protocol
D nity Smart contract EVM Solidity, Serpent, LLL Account-basedBlockchain
Nervous System
Parity Smart contract EVM Solidity, Serpent, LLL Account-based Proof of Authority
TezosSmart contract,
Crypto-
currencyDockersTezos Contract Script
LanguageAccount-based Proof of Stake
Corda Smart contract JVM Kotlin, Java UTXO-based Raft
Sawtooth
LakeSmart contract TEE Python Account-basedProof of Elapsed
Time
0 20 40 60 80 100
time (second)0102030405060708090%CPU utilization
Ethereum
Parity
Hyperledger
0 20 40 60 80 100
time (second)020406080100MbpsNetwork utilization
Ethereum
Parity
Hyperledger
Figure 16: Resource utilization.
Ethereum Parity Hyperledger10-1100101#block/s
0.341.005.20
0.220.563.10
0.120.281.75Block generation rate
Small
Medium
Large
Figure 15: Block generation rate.
are designed with di erent goals and assumptions. Specif-
ically, the protocols for Byzantine failure tolerance are an
overkill for traditional database settings where there areonly crash failures. Other features which are optional in
most database systems are cryptographic signatures on ev-
ery single transaction, and wide-area fully replicated state
machines. Although databases are designed without security
features and tolerance to Byzantine failures, we remark that
the gap remains too high for blockchains to be disruptive
to incumbent database systems. Nevertheless, the popular-
ity of blockchain is a clear indication that there is a need
for a Byzantine tolerant data processing systems which can
accommodate a large number of users.
Figure 15 shows the e ect of varying block sizes in the
overall throughput. While it is straightforward to set the
block size in Hyperledger by con guring the batchSize vari-
able, there is no direct way to specify the same in Ethereum.
An Ethereum miner uses gasLimit value to restrict the over-
all cost in constructing a block, thus we tuned this value to
simulate di erent sizes. In Parity, gasLimit is not applica-
ble to local transaction and it has no e ect on the block size.
Instead, we observe that the block size can be controlled by
tuning stepDuration value, which essentially decides how
much time a validator can use to build a block. In the exper-
iments, medium size refers to the default settings, whereas
large and small refer to 2x and 0 :5x of the default size. The
results show that increases in block sizes lead to proportional
decreases in block generation rate, meaning that the overall
throughput does not improve.
Figure 16 compares CPU and network utilization of the
three systems over the period of 100 seconds. It is easy to
see that Ethereum is CPU bound, as it fully utilizes 8 CPU
cores. Hyperledger, on the other hand, uses CPU sparingly
and spends the rest of the time on network communication.
Parity, in contrast, has lower resource footprints than other
two systems. For Ethereum and Hyperledger, the pattern
is the direct consequence of the consensus protocol: PoW is
CPU bound whereas PBFT is communication bound.
1248121620242832
#nodes101102103104#tx/sThroughput
Ethereum
Parity
Hyperledger
1248121620242832
#nodes10-1100101102103secondLatency
Ethereum
Parity
HyperledgerFigure 19: Scalability with Smallbank benchmark.
type account_t struct {
Balance int
CommitBlock int
}
type transaction_t {
From string
To string
Val int
}
func Invoke_SendValue(from_account string,
to_account string, value int) {
var pending_list []transaction_t
pending_list = decode(GetState("pending_list"))
var new_txn transaction_t
new_txn = transaction_t {
from_account, to_account, value
}
pending_list = append(pending_list, new_txn)
PutState('pending_list', encode(pending_list))
}
func Query_BlockTransactionList(block_number int)
[]transaction_t {
return decode(GetState("block:"+block_number))
}
func Query_AccountBlockRange(account string,
start_block int, end_block int)
[]account_t {
version := decode(GetState(account+":latest"))
var ret []account_t
while true {
var acc account_t
acc = decode(GetState(account+":"+version))
if acc.CommitBlock >= start_block &&
acc.CommitBlock < end_block {
ret = append(ret, acc)
} else if acc.CommitBlock < start_block {
break;
}
version -= 1
}
return ret
}
Figure 20: Code snippet from the VersionKVStore smart
contract for analytics workload (Q1 and Q2).
100
101
102
103
time (second)0.00.20.40.60.81.0cdfLatency distribution
Ethereum-YCSB
Ethereum-SB
Parity-YCSB
Parity-SB
Hyperledger-YCSB
Hyperledger-SB
Figure 17: Latency distribution.
0 50 100 150 200 250 300 350
time (second)01000020000300004000050000600007000080000#requestsQueue length
Ethereum
Parity
HyperledgerFigure 18: Queue length at the client.
Figure 17 shows the latency distribution. Ethereum has
both higher latency and higher variance, because PoW is
a randomized process which means the duration between
blocks are unpredictable. Parity has the lowest variance
because the server restricts the client request rate at 80 tx/s.
Figure 18 illustrates the request queue at the client for
the settings of 20 servers and 20 clients. The queue behav-
ior of Ethereum re
ects the normal case, i.e. the queue grew
and shrank depending on how fast the transactions are com-
mitted. Hyperledger failed to generate blocks in this case,
therefore the queue never shrank. However, there are du-
rations in which the queue size remains constant. Further-
more, at the beginning, the queue in Hyperledger is smaller
than that in Ethereum, even though the clients are send-
ing at the same rate. This suggests there is a bottleneck in
processing network requests at the Hyperledger servers.
Figure 19 illustrates the scalability of the three systems
using the Smallbank benchmark. We observe similar pat-
terns to the YCSB benchmark (Figure 7), except that Hy-
perledger failed to scale beyond 8 nodes instead of 16.
C. ANLAYTICS SMART CONTRACT
Figure 20 shows the implementation of the smart con-
tract method that answer Q2 of the analytics workload. To
support historical data lookup, we append a counter to the
key of each account. To fetch a speci c version of an ac-
count, we use key account:version . We store the latest
version of the account using key account:latest , and keep
aCommitBlock in the data eld for every version which in-
dicates in which block the balance of this version is com-
mitted. To answer query that fetches a list of balance of a
given account within a given block range, the method scans
all versions of this account and returns the balance values
that are committed within the given block range. Ethereum
and Parity provide JSON-PRC APIs getBalance(account,
block) to query information of an account at a given block
number. This API fetches only one version of the account
per HTTP roundtrip, so it is less ecient than pushing the
query logic to server side.
|
{
"id": "1703.04057"
}
|
1911.10187
|
Linear Consistency for Proof-of-Stake Blockchains
|
The blockchain data structure maintained via the longest-chain
rule---popularized by Bitcoin---is a powerful algorithmic tool for consensus
algorithms. Such algorithms achieve consistency for blocks in the chain as a
function of their depth from the end of the chain. While the analysis of
Bitcoin guarantees consistency with error $2^{-k}$ for blocks of depth $O(k)$,
the state-of-the-art of proof-of-stake (PoS) blockchains suffers from a
quadratic dependence on $k$: these protocols, exemplified by Ouroboros (Crypto
2017), Ouroboros Praos (Eurocrypt 2018) and Sleepy Consensus (Asiacrypt 2017),
can only establish that depth $\Theta(k^2)$ is sufficient. Whether this
quadratic gap is an intrinsic limitation of PoS---due to issues such as the
nothing-at-stake problem---has been an urgent open question, as deployed PoS
blockchains further rely on consistency for protocol correctness.
We give an axiomatic theory of blockchain dynamics that permits rigorous
reasoning about the longest-chain rule and achieve, in broad generality,
$\Theta(k)$ dependence on depth in order to achieve consistency error $2^{-k}$.
In particular, for the first time, we show that PoS protocols can match
proof-of-work protocols for linear consistency. We analyze the associated
stochastic process, give a recursive relation for the critical functionals of
this process, and derive tail bounds in both i.i.d. and martingale settings via
associated generating functions.
|
http://arxiv.org/pdf/1911.10187v1
|
Erica Blum, Aggelos Kiayias, Cristopher Moore, Saad Quader, Alexander Russell
|
cs.CR, cs.DM
|
cs.CR
|
arXiv:1911.10187v1 [cs.CR] 22 Nov 2019/T_he combinatorics ofthe longest-chain rule:
Linearconsistency for proof-of-stake blockchains∗
EricaBlum1, AggelosKiayias2,5, Cristopher Moore3,Saad/Q_uader4, andAlexander Russell4,5
1Universityof Maryland, College Park
2Universityof Edinburgh
3Santa Fe Institute
4Universityof Connecticut
5IOHK
November25, 2019
Abstract
Blockchain data structures maintained via the longest-cha in rulehave emerged as a powerful algorithmic tool
forconsensusalgorithms. /T_hetechnique—popularizedbythe Bitcoinprotocol—hasproventoberemarkablyflexible
andnowsupportsconsensusalgorithmsinawidevarietyofse /t_tings. Despitesuchbroadapplicabilityandadoption,
current analytic understanding of the technique is highly d ependent on details of the protocol’s leader election
scheme. Aparticularchallengeappearsintheproof-of-sta kese/t_ting, whereexistinganalyses suffer fromquadratic
dependence onsuffixlength.
We describe an axiomatic theory of blockchain dynamics that permits rigorous reasoning about the longest-
chainruleinquitegeneral circumstancesandestablishbou nds—optimaltowithinaconstant—ontheprobabilityof
a consistency violation. /T_his se/t_tles a critical open questio n in the proof-of-stake se/t_ting where we achieve linear
consistency forthefirsttime.
Operationally,blockchainconsensusprotocolsachieveco nsistencybyinstructingpartiestoremoveasuffixofa
certainlength from their localblockchain. While theanaly sis of Bitcoinguarantees consistency with error 2−kby
removing O(k)blocks, recent work on proof-of-stake (PoS) blockchains ha s suffered from quadratic dependence:
(PoS) blockchainprotocols,exemplifiedbyOuroboros(Cryp to2017),OuroborosPraos(Eurocrypt2018)andSleepy
Consensus (Asiacrypt 2017), can only establish that the len gth of this suffix should be Θ(k2). /T_his consistency
guaranteeis a fundamental design parameter for thesesyste ms, as thelength of thesuffixis a lower boundforthe
time required to wait for transactions to se/t_tle. Whether thi s gap is an intrinsic limitation of PoS—due to issues
such as the “nothing-at-stake” problem—has been an urgent o pen question, as deployed PoS blockchains further
rely on consistency for protocol correctness: in particula r, security of the protocol itself relies on this parameter.
Our general theory directly improves the required suffix leng th fromΘ(k2)toΘ(k). /T_hus we show, for the first
time, how PoS protocols can match proof-of-work blockchain protocols for exponentially decreasing consistency
error.
Ouranalysisfocusesonthearticulationofatwo-dimension alstochasticprocessthatcapturesthefeaturesofin-
terest,anexactrecursiveclosedformforthecriticalfunc tionaloftheprocess,andtailboundsestablishedforassoc i-
atedgeneratingfunctionsthatdominatethefailureevents . Finally,theanalysisprovidesanexplicitpolynomial-ti me
algorithmforexactlycomputingtheexponentially-decayi ng error functionwhich candirectlyinform practice.
∗EricaBlum’sworkwaspartlysupportedbyfinancialassistanceawar d70NANB19H126fromU.S.DepartmentofCommerce,NationalInstit ute
ofStandardsandTechnology. AggelosKiayias’researchwaspartl ysupportedbyH2020Grant#780477,PRIViLEDGE.CristopherMoo re’sresearch
waspartly supported by NSF grant BIGDATA-1838251. Alexander Russell’swork waspartly supported by NSF Grant #1717432.
1
1 Introduction
Ablockchainisadatastructureconsistingofacollectionofd atablocksplacedinlinearorder. Itfurtherrequiresthat
eachblockcontainsa collision-free hash ofthe previous block : thusblocksimplicitly committo the entire prefix of
theblockchainprecedingthem. /T_hiselementarydatastructure hasremarkableapplicationsindistributedcomputing,
and now appears as an essential component of consensus protocols in a wide variety of models and se/t_tings; this
notablyincludes boththe “permissionless” se/t_ting popularized by Bitcoin andthe classic “permissioned” model.
Suchconsensusprotocolscallforplayerstocollaboratively assembleablockchainbyrepeatedlyselectingplayers
to add blocks. Specifically,the protocoldetermines a stocha stic process resembling a lo/t_tery: each “leader” selected
by the lo/t_teryis then responsible for broadcasting a new block. W hile the algorithmicdetails of thislo/t_tery depend
heavily on the protocol, the outcome can be privately determ ined and provides the winning player a proof of lead-
ership that can be publicly demonstrated. Assuming that the ex pectedwait time for some player to win the lo/t_tery
is constant,the blockchainexperiences steady growthwhen pla yersfollow theprotocol.
Networkinfelicities, adversarial behavior,orthepossibi lity that two playerssimultaneously win thelo/t_tery can
leadtodisagreementsamongtheplayersaboutthecurrentblock chain. /T_husprotocolsadopta“chainselectionrule”
that determines how players should break ties among the variou s chains they observe on the network; ideally, the
combinationofthechainselectionruleandthelo/t_teryshouldg uaranteethattheplayers’blockchainsagree,perhaps
withtheexceptionofashortsuffix. /T_heemblematicchainselec tionstrategyamongsuchsystemsisthe longest-chain
rule,whichcallsforplayersto adopt the longestchain amongvario uscontenders.
/T_hefirstblockchainprotocolwasthecoreofthesensationalBit coinsystem[18];itadoptedalo/t_terymechanism
based on a cryptographic puzzle [7, 1]—also known as proof-of-work or PoW, for short —and a chain selection rule
favoring chains that represent more work. /T_he system is particul arly notable for its ability to survive in a permis-
sionlessse/t_ting—whereplayersmayfreelyjoinanddepart—even whenaportionoftheplayersareactivelya/t_tacking
the protocol. Unfortunately, the proof-of-work mechanism mak es quite striking energy demands: the system cur-
rently consumes as much electricity as a small country.1/T_his motivated the blockchain community to exploring
alternative lo/t_tery mechanisms, e.g., proof-of-stake (PoS) [ 3, 21, 13], proof of space [8, 20] and others [16]. /T_he
proof-of-stakemechanismis particularly a/t_tractive from th e perspective of efficiency,asit makesno assumption of
external computational resources.
/T_he fundamental consistency property—critical in all these bloc kchain systems—is common-prefix (cf. [9]). It
precisely captures the intuition described above: by trimmi ng ak-block suffix from the chain held by any honest
playertheresultingblockchainisaprefixoftheblockchainp ossessedbyanyhonestpartyatanyfuturepointofthe
execution. Aprincipalgoalintheanalysisofthesesystemsisa toguaranteecommonprefix,foranappropriatevalue
ofk,evenifsomeoftheplayerscolludetodisrupttheprotocol. Commonprefixistypicallyonlyshowntoholdwith
high probability 1−ε, whereεis an error term that is a function of k. /T_he exact dependency of εonkis critically
important: it determines the length of the suffix that is to be remo ved from a blockchain in order to ensure that
the remaining prefix will be retained at any future point of the exec ution. /T_his directly imposes a lower bound on
howlongonehastowait forinformationintheblockchain(sucha sapaymenttransaction)to“se/t_tle.” Additionally,
manyblockchainprotocolsinternallyrelyoncommonprefix forc orrectness;thustherelationship between εandk
is criticalto establishing the regime ofcorrectnessofthe ent ire protocol.
Arelativelystraightforwardlowerboundfor εisε≥exp(−αk)forsomeα >0. /T_hislowerboundapplieswhen
thereisacoalitionofadversarialplayersofconstantfracti on,thecaseofprimaryinterestinpractice. /T_heresultiseasy
toinferfromtheanalysisof[18],whereastrategyisdemonstra tedthatviolatescommonprefixwithsuchprobability
(this is referred to as a “double-spending” a/t_tack in that paper ). /T_he tightness of this bound is an important open
problem. For the special case of proof-of-work an upper bound ofexp(−Ω(k))was shown first in [9] and further
verified in extended security models by [11, 24]. In the proof- of-stake se/t_ting, on the other hand, the tightness of
the bound remains open. While recent proof-of-stake algorithms have been presented with rigorous analyses that
rival proof-of-work in many regards, they suffer from a quadra tic relationship between kandlog(ε). For example,
the Ouroboros protocols [13, 6, 2], as well as Snow White [4], p rovide an upper bound on εofexp(−Ω(√
k)); this
should be compared with ε= exp(−Θ(k))for proof-of-work. /T_he significant gap from the known lower bound
1See e.g.,https://digiconomist.net/bitcoin-energy-consumption where it is reported that Bitcoin annual energy
consumption is on theorderof at least50Twhrat thetime ofwriting.
2
wasa/t_tributedtoanotable,generala/t_tackthatdistinguishedPo SfromPoW:Knownasthe nothing-at-stake problem,
this refers to the ability of an adversarial coalition of pla yers to strategically reuse a winning PoS lo/t_tery to extend
multiple blockchains.
Our results. Our objective is to control the common-prefix error εas tightly as possible while making minimal
assumptions on the underlying blockchainprotocol. We work in a general model formulated by a simple family of
blockchainaxioms . /T_heaxiomsthemselvesareeasytointerpretandfewinnumber. /T_hi spermitsustoabstractmany
features of the underlying blockchain protocol (e.g., the det ails of the leader-election process, the cryptographic
security ofthe relevant signature schemesandhash functions,and randomnessgeneration),while still establishing
results that are strong enoughto directlyincorporateinto exis ting specificanalyses.
Ourmostinteresting findingisaquitetighttheoryofcommonprefi xthatdepends onlyonthescheduleofpartic-
ipantscertified to addablock . Undercommonassumptions about thisschedule,we achieve the optimal relationship
ε= exp(−Θ(k)). /T_his directly improves the common prefix guarantees (and se/t_tle ment times) of existing proof-
of-stake blockchains such as Snow White [4], Ouroboros [13], O uroboros Praos [6], and Ouroboros Genesis [2].
Specifically, this improves the scaling in the exponent from√
ktokand establishes a tight characterization for
ε= exp(−Θ(k)). (In fact, we even obtain reasonable control of the constants.) W e remark that our assumptions
about the schedule distribution can be weakened—without any e ffect on the final bounds—to apply to martingale-
style distributions such asthose that arise in theanalysis of adaptive adversaries [6,2].
Ournewanalysisoffersanadditional,butlowerorder,improvem entforseveraloftheseblockchains./T_heexisting
analysis of, e.g., Ouroboros Praos[6], required a union bound t o be taken over the entire lifetime of the protocol in
order to rule out a common prefix violation at a particular poi nt of time; thus such events were actually bounded
above by a function of the form Texp(−Ω(√
k)), whereTis the lifetime of the protocol. While this event does
dependontheentiredynamicsoftheprotocol,weshowhowtoavoi dthispessimistictailboundtoachievea“single
shot” commonprefix violation—at a particular time of interest —of form exp(−Θ(k)); this removes the dependence
onT.
Froma technicalperspective,we contrastthe structure ofour proofswith existing techniquesfor thePoWcase.
/T_he PoW results find a direct connection between common-prefix and th e behavior of a biased, one-dimensional
random walk. Interestingly, our results give a tight relationsh ip between the general (e.g., PoS) case and a pair of
coupledbiased random walks. A major challenge in the analysis is to bound the behavior of this richer stochastic
process. Ourtoolsyieldprecise,explicitupperboundsonth eprobabilityofpersistenceviolationsthatcanbedirectly
appliedto tunetheparametersofdeployed PoSsystems. SeeAp pendixAwherewe recordsomeconcreteresultsof
the general theory. /T_he importance of these results in the pract ice of PoSblockchainsystems cannot be overstated:
theyprovide,forthefirsttime,concreteerrorboundsforse/t_tl ementtimesforPoSblockchainsthatfollowthelongest
chainrule.
Furtheranalyticdetails. Ourapproachbeginswiththegraph-theoreticframeworkof forksandmargindeveloped
fortheanalysisoftheOuroboros[13]protocol.(Aforkisana bstractionoftheprotocolexecutiongiventheoutcomes
oftheleader-electionprocess.) Webeginbygeneralizingthe notionofmargintoaccountforlocal,ratherthanglobal,
features ofa leader schedule,and provide an exact,recursiv e closedformforthis new quantity (see Section 5). /T_his
proof identifies an optimal online adversary (i.e., a fork-buil ding strategy whose current decisions do not depend
on the future) for PoS blockchain algorithms with the remark able property that the sequence of forks produced
by this adversary simultaneously achieve the worst-case (slot) common-prefix violations assoc iated with all slots
(see Section 8). We then study the stochastic process generat ed when the characteristic string —a Boolean string
representing theoutcomeoftheleader electionscheme—isgiv en by afamilyofi.i.d. Bernoullirandomvariables. In
thiscase,weidentifyageneratingfunctionthatboundsthetaile ventsoffinterest, andanalyticallyupperboundthe
growth of the function. We then show how to extend the analysis to t he se/t_ting where the characteristic string is
drawn from a martingale sequence. Asit happens, this more genera ldistribution arises naturally in the analysesof
PoS protocols that survive adaptive adversaries; e.g., Our oboros Genesis [2]. We obtain the pleasing result that the
commonprefix error probability in the martingalecase is nomor e thanthat in the i.i.d. Bernoullicase.
3
Directconsequences. Ourresultsestablishconsistencyboundsinaquitegeneralse/t_ti ng—seebelow: Inparticular,
theydirectlyimply exp(−Θ(k))consistencyfortheSleepyconsensus(SnowWhite)[21],Ouroboro s[13],Ouroboros
Praos [6],and Ouroboros Genesis [2] blockchainprotocols. (/T_h e Ouroboros Praosand Ouroboros Genesis analyses
in factdirectly relied on an earlier e-print version of thepr esent article fortheir se/t_tlement estimates.)
Related work. Blockchain protocol analysis in the PoW-se/t_ting was initiated i n [9] and further improved in [24,
11]. /T_he established security bounds for consistency are linear i n the security parameter. Sleepy consensus [21,
/T_heorem 13] provides a consistency bound of the form exp(−Ω(√
k)). Note that [21] is not a PoS protocol per se,
but it is possible to turn it into one (as was demonstrated in [4]) . /T_he analysis of the Ouroboros blockchain [13]
achievesexp(−Ω(√
k)). We remarkthat theanalysesofOuroborosPraos[6]andOurobor osGenesis [2]developed
significant new machineryforhandling otherchallenges(e.g.,adap tive adversaries, partial synchrony),but directly
referred to apreliminary version ofthis article toconcludet heir guaranteesof exp(−Ω(k)).
Our results complement the recent results of [5], which also co nsiders longest-chain PoS protocols. [5] focuses
on identifying dynamics unique to longest-chain PoS protocols. I n particular, they show that longest-chain PoS
protocols that are predictable (i.e., for which some portion of the schedule of slot leaders is known ahead of time)
are necessarily vulnerable to “predictable double-spends.” /T_h e conventional defense against such a/t_tacks is to wait
for the specified se/t_tlement time to elapse before accepting a tr ansaction, which (until now) has resulted in slow
confirmation times. As such, [5] raised the question of whethe r long confirmation times are a necessary evil in
longest-chainPoSblockchains. Asdouble-spendinga/t_tacksimpl yaconsistencyviolation,ourresultsshowthatPoS
protocols can safely decrease se/t_tlement times to asymptotic ally match PoW protocols without sacrificing security
against double-spends.
Becausewefocusonthelongest-chainrule,ouranalysisisnota pplicabletoprotocolslikeAlgorand[15]which,
in fact, offer se/t_tlement in expected constant time without invoki ng blockchain reorganisation or forks; however,
Algorand lacksthe ability to operate in the “sleepy” [21] or “ dynamic availability” [2] se/t_ting. In our combinatorial
analysis, synchronous operation is assumed against a rushing adv ersary; this is without loss of generality vis-a-vis
the result of [6] where it was shown how to reduce the combinato rial analysis in the partially synchronous se/t_ting
tothesynchronousone. Wenotethatanumberofworkshaveshownhow touseablockchainprotocoltobootstrap
acryptographicprotocolthatcanofferfasterse/t_tlementtime understrongerassumptionsthanhonestmajority,e.g.,
Hybrid Consensus [22] or /T_hunderella [23]; our results are ortho gonal and synergistic to those since they can be
used to improve the se/t_tlement time boundsofthe blockchainpro tocolthatoperatesasa fallbackmechanism.
Outline. We begin in Section 2 by describing a simple general model for bl ockchain dynamics. Section 3 builds
on this model to set down a number of basic definitions required fo r the proofs. /T_he first part of the main proof is
described in Section 5, which develops a “relative” version of the theory of margin from [13]; most details are then
relegatedtoSection7inordertomovequicklytotheconsiste ncyestimatesinSection6. InSection8,wepresentan
optimalonlineadversarywhocansimultaneouslymaximizether elativemarginsforallprefixesofthecharacteristic
string. Finally, in Appendix A, we compute exact upper bounds on k-se/t_tlement error probabilities for various
valuesof kand describe a simple O(k3)-time algorithmto compute these probabilities in general.
2 /T_heblockchainaxioms and thesettlement security model
Typical blockchain consensus protocols call for each partici pant to maintain a blockchain ; this is a data structure
thatorganizestransactionsandotherprotocolmetadataintoano rderedhistoricalrecordof“blocks.” Abasicdesign
goal of these systems is to guarantee that participants’ block chainsalwaysagree on a commonprefix; the differing
suffixesofthechainsheldbyvariousparticipantsroughlycorr espondtothepossiblefuturestatesofthesystem. /T_hus
the majoranalytic challengeis to ensure that—despite evolving adversarial control ofsome of the participants—the
portion of honest participants’ blockchainsthat might pairwi se disagree is confined to a short suffix. /T_his analysis
in turn supports the fundamental guarantee of consistency for these algorithms, which asserts that data appearing
deep enoughin the chaincanbe considered to bestable, or “se/t_tl ed.”
Weadoptadiscretenotionoftimeorganizedintoasequenceof slots{sl0,sl1,...}andassumeallprotocolpartic-
ipantshavetheluxuryofsynchronizedclocksthatreportthecu rrentslotnumber. Asdiscussedabove,theprotocols
4
we consider rely on two algorithmicdevices:
• Aleader election mechanism , which randomly assigns to each time slot a set of “leaders” per mi/t_ted to post a
new blockin that slot.
• /T_helongest-chain rule , which calls for the leader(s) of each slot to add a block to th e end of the longest
blockchainshe hasyet observed, and broadcastthis new chain t oother participants.
/T_he Bitcoin protocol uses a proof-of-work mechanism to carry o ut leader election, which can be modeled using a
random oracle [9, 24, 11]. Proof-of-stake systems typically require more intricate leader election mechanisms; for
example, the Ouroboros protocol [13] uses a full multi-part y private computation to distribute clean randomness,
whileSnowWhite[4],Algorand[15],andOuroborosPraos[6]use hashingandafamilyofvaluesdeterminedon-the-
fly. Despite these differences, all existing analyses show that t he leader election mechanism suitably approximates
an ideal distribution, whichis also theapproachwe will adop t forouranalysis.
2.1 /T_he blockchain axioms and forks
Tosimplifyouranalysis,we assumeasynchronouscommunication networkinthepresence ofa rushingadversary:
in particular,anymessagebroadcastbyanhonest participant a t thebeginningofaparticular slotisreceived bythe
adversary first, who may decide strategically and individuall y for each recipient in the network whether to inject
additional messages and in what order all messages are to be del ivered prior to the conclusion of the slot. (See §2.5
below forcommentson thisnetwork assumption.)
Given this, the behavior of the protocol when carried out by a group of honest participants (who follow the
protocol in the presence of an adversary who may only reorganize messages) is clear. Assuming that the system is
initialized withacommon“genesisblock”correspondingto sl0andtheleaderelectionprocessinfactelectsasingle
leader perslot, theplayersobserve a common,linearly growing blockchain:
012...
Herenode irepresentstheblockbroadcastbytheleaderofslot iandthearrowsrepresentthedirectionofincreasing
time. (Note that the requirement of a single leader per slot is i mportant in this simple picture; it is possible for a
network adversary to induce divergent views between the player s by taking advantage of slots where more than a
single honest participant iselecteda leader.)
/T_he blockchain axioms: Informal discussion. /T_he introduction of adversarial participants or multiple slot
leaders complicates the family of possible blockchains that could emerge from this process. To explore this in the
context of our protocols, we work with an abstract notion of a bl ockchain which ignores all internal structure. We
consider a fixed assignment of leaders to time slots, and assume th at the blockchain uses a proof mechanism to
ensure that anyblocklabeled with slot sltwasindeed producedbya leader ofslot slt; thisis guaranteedin practice
byappropriate use of asecure digital signature scheme.
Specifically,we treat a blockchain asa sequenceofabstract blocks,eachlabeledwith a slot numbe r,so that:
A1. /T_heblockchainbeginswith a fixed “genesis” block,assigned to s lotsl0.
A2. /T_he(slot) labelsofthe blocksare in strictly increasing orde r.
Itisfurtherconvenienttointroducethestructureofadirected graphonourpresentation,whereeachblockistreated
as a vertex; in light of the first two axioms above, a blockchai n is a path beginning with a special “genesis” vertex,
labeled0,followedby vertices with strictly increasing labelsthat ind icate whichslot is associatedwith theblock.
024579
/T_he protocolsofinterest callforhonest playersto add a singleblockduringany slot. Inparticular:
5
A3. If a slot sltwas assigned to a single honest player, then a single blockis crea ted—during the entire protocol—
with thelabel slt.
Recallthatblockchainsare immutable inthesensethatanyblockinthechaincommitstotheentireprev ioushistory
of the chain; this is achieved in practice by including with each block a collision-free hash of the previous block.
/T_hesepropertiesimplythatifaspecificslot sltwasassignedtoauniquehonestplayer,thenanychainthatinclude s
the unique blockfrom sltmust also includethat block’sassociated prefix in its entiret y.
Asweanalyzethedynamicsofblockchainalgorithms,itisconve nienttomaintainanentirefamilyofblockchains
at once. As a ma/t_ter of bookkeeping, when two blockchains agree on a common prefix, we can glue together the
associated pathsto reflectthis, asindicated below.
024579
89
Whenwegluetogethermanychainstoformsuchadiagram,wecall ita“fork”—theprecisedefinitionappearsbelow.
Observe thatwhilethesetwoblockchainsagreethroughtheve rtex (block)labeled5,theycontain(distinct)vertices
labeled 9; this reflectstwo distinct blocksassociated with s lot 9 which,in light of the axiom above, must have been
producedbyan adversarial participant.
Finally, as we assume that messages from honest players are del ivered without delay, we note a direct conse-
quenceof thelongest chain rule:
A4. If two honestly generated blocks B1andB2are labeled with slots sl1andsl2for which sl1<sl2, then the
length of the unique blockchain terminating at B1is strictly less than the length of the unique blockchain
terminating at B2.
Recall that the honest participant assigned to slot sl2will be aware of the blockchain terminating at B1that was
broadcast by the honest player in slot sl1as a result of synchronicity; according to the longest-chain rul e, it must
have placed B2on a chain that was at least this long. In contrast, not all partic ipants are necessarily aware of all
blocks generated by dishonest players, and indeed dishonest play ers may o/f_ten want to delay the delivery of an
adversarial block to a participant or show one block to some par ticipants and show a completely different block to
others.
Characteristic strings, forks, and the formal axioms. Note that with the axioms we have discussed above,
whetherornotaparticularforkdiagram(suchastheonejustab ove)correspondstoavalidexecutionoftheprotocol
depends on how the slots have been awarded to the parties by the leader election mechanism. We introduce the
notion of a “characteristic” string as a convenient means of repres enting information about slot leaders in a given
execution.
Definition1 (Characteristic string) .Let sl1,...,slnbeasequence of slots. A characteristic string wis an element of
{0,1}ndefinedfora particular execution of ablockchain protocol s o that
wt=/braceleftBigg
0if sltwas assigned to asinglehonest participant ,
1otherwise.
Fortwo Booleanstrings xandw,we write x≺wiffxisastrict prefixof w. Similarly,we write x/√recedesequalwiffeither
x=worx≺w. /T_heemptystring εisaprefixtoanystring. Withthisdiscussionbehindus,wesetdo wntheformal
object we use to reflect the various blockchains adopted by hone st players during the execution of a blockchain
protocol. /T_hisdefinition formalizestheblockchainsaxiomsdi scussed above.
Definition 2 (Fork; [13]) .Letw∈{0,1}nand letH={i|wi= 0}. Aforkfor the string wconsists of a directed
androotedtree F= (V,E)withalabeling ℓ:V→{0,1,...,n}. Weinsistthat each edgeof Fisdirectedaway from
the rootvertex and further requirethat
6
(F1.) therootvertex rhaslabel ℓ(r) = 0;
(F2.) thelabels ofvertices along any directedpath are stri ctly increasing;
(F3.) each index i∈Histhe label for exactly one vertex of F;
(F4.) forany vertices i,j∈H, ifi < j, then thedepth of vertex iinFisstrictly less than thedepth of vertex jinF.
IfFis a fork for the characteristic string w, we write F⊢w. Note that the conditions (F1.)–(F4.) are direct
analogues of the axioms A1–A4 above. See Fig. 1 for an example f ork. A final notational convention: If F⊢xand
ˆF⊢w,wesaythat FisaprefixofˆF,wri/t_tenF⊑ˆF,ifx/√recedesequalwandFappearsasaconsistently-labeledsubgraphof
ˆF. (Specifically,eachpath of Fappears,with identical labels,in ˆF.)
w=01
122
03
14
44
05
06
17
18
09 0
Figure1: Afork Fforthecharacteristicstring w= 010100110 ;verticesappearwiththeirlabelsandhonestvertices
arehighlightedwithdoubleborders. Notethatthedepthsof the(honest)verticesassociatedwiththehonestindices
ofware strictly increasing. Note, also,that thisforkhastwo dis joint pathsofmaximumdepth.
Letwbe a characteristic string. /T_he directed paths in the fork F⊢woriginating from the root are called tines;
these are abstract representations of blockchains. (Note that a tine might not terminate at a leaf of the fork.) We
naturallyextend thelabelfunction ℓfortines: i.e., ℓ(t)/definesℓ(v)wherethetine tterminates atvertex v. /T_helengthof
a tinetis denotedby length(t).
Viable tines. /T_he longest-chain rule dictates that honest players build on ch ains that are at least as long as all
previously broadcast honest chains. It is convenient to distingui sh such tines in theanalysis: specifically,a tine tof
Fis calledviableif its length is at least the depth of any honest vertex vfor which ℓ(v)≤ℓ(t). A tinetisviable at
slotsiftheportionof tappearingover slots 0,...,shaslengthatleast thatofanyhonestverticeslabeledfromthis
set. (Asnoted,the properties(F3.) and(F4.) togetherimplyt hat anhonestobserver at slot swillonlyadoptaviable
tine.) /T_he honest depth functiond:H→[n]gives the depth of the (unique) vertex associated with an hones t slot;
by(F4.),d(·)isstrictly increasing.
2.2 Settlement and thecommon prefixproperty
We are now ready to explore the power of an adversary in this se/t_t ing who has corrupted a (perhaps evolving)
coalition of the players. We focus on the possibility that su ch an adversary can blatantly confound consistency of
thehonestplayer’sblockchains.Inparticular,weconsiderth epossibilitythat,atsometime t,theadversaryconspires
toproducetwoblockchainsofmaximumlengththatdivergeprio rtoapreviousslot s≤t;inthiscasehonestplayers
adopting the longest-chainrule mayclearlydisagree about th e history of theblockchaina/f_ter slot s. We call sucha
circumstancea se/t_tlement violation .
To reflect this in our abstract language, let F⊢wbe a fork corresponding to an execution with characteristic
stringw. Such a se/t_tlement violation induces two viable tines t1,t2with the same length that diverge prior to a
particular slot ofinterest. We recordthisbelow.
Definition3 (Se/t_tlementwithparameters s,k∈N).Letw∈{0,1}nbeacharacteristic string. Let F⊢w1...wtbe
aforkforaprefixof wwiths+k≤t≤n. Wesaythataslot sisnotk-se/t_tledinFiftheforkcontainstwotines t1,t2
7
of maximum length that “diverge prior to s,” i.e., they either contain different vertices labeled with s, or one contains a
vertexlabeledwith swhiletheotherdoesnot. Notethatsuchtinesareviablebyde finition. Otherwise, slotsisk-se/t_tled
inF. We say that a slot sisk-se/t_tled(for the characteristic string w) if it isk-se/t_tled in every fork F⊢w1,...wt, for
eacht≥s+k.
Common prefix. Se/t_tlement violations are a convenient and intuitive proxy for the not ion of common prefix
discussed in the introduction. Indeed, as we show in Section 4, t he two notions are equivalent, so we have the
luxuryofdiscussing se/t_tlementviolationswhichhavetheadvant ageofamorereadyinterpretation. Concretely,we
will simultaneously upper bound—using the same analytic techniq ues—the probability of se/t_tlement violations and
commonprefix violations.
Recallthatthecommonprefixpropertywithparameter kassertsthat,foranyslotindex s,ifanhonestobserver
atslots+kadoptsablockchain C,theprefixC[0 :s]willbepresentineveryhonestly-heldblockchainatora/f_tersl ot
s+k. (Here,C[0 :s]denotestheprefix oftheblockchain Ccontainingonlytheblocksissued fromslots 0,1,...,s.)
Wetranslatethispropertyintotheframeworkofforks. Conside ratinetofaforkF⊢w. /T_hetrimmedtinet⌈kis
definedastheportionof tlabeledwithslots{0,...,ℓ(t)−k}. Fortwotines,weusethenotation t1/√recedesequalt2toindicate
that the tine t1is a prefix oftine t2.
Definition 4 (Common Prefix Property with parameter k∈N).Letwbe a characteristic string. A fork F⊢w
satisfiesk-CPslotif, forall pairs (t1,t2)ofviabletines Fforwhich ℓ(t1)≤ℓ(t2), wehave t⌈k
1/√recedesequalt2. Otherwise, wesay
that the tine-pair (t1,t2)isa witness to a k-CPslotviolation. Finally, wsatisfiesk-CPslotif every fork F⊢wsatisfies
k-CPslot.
If a string wdoes not possess the k-CPslotproperty, we say that wviolatesk-CPslot. Observe that we defined
the common prefix property in terms of deleting any blocks assoc iated with the lastktrailing slots from a local
blockchainC. Traditionally (cf. [10]), this property has been defined in te rms of deleting a suffix of (block-)length
kfromC. We denote the block-deletion-based version of the common pre fix property as the k-CPproperty. Note,
however, that a k-CPviolation immediately implies a k-CPslotviolation, so bounding the probability of a k-CPslot
violation issufficient to ruleout both events.
2.3 Adversarialattacks onsettlementtime;thesettlement game
To clarify the relationship between forks and the chains at play in a canonical blockchain protocol, we define a
game-based model below that explicitly describes the relat ionship between forks and executions. By design, the
probabilitythattheadversary winsthisgameisatmostthepr obabilitythataslot sisnotk-se/t_tled. Weremarkthat
whilewe focusonse/t_tlement violations forclarity,onecouldeq uallywell have designedthe gamearound common
prefix violations.
Considerthe (D,T;s,k)-se/t_tlementgame ,playedbetweenanadversary AandachallengerCwithaleaderelec-
tion mechanismmodeledbyan ideal distribution D. Intuitively, thegameshould reflecttheability oftheadver sary
to achieve a se/t_tlement violation; that is, to present two maxima lly-long viable blockchains to a future honest ob-
server, thus forcing them to choose between two alternate hist ories which disagree on slot s. /T_he challenger plays
the role(s)of the honest playersduringthe protocol.
Notethat in typicalPoSse/t_tingsthe distribution Disdetermined bythecombinedstake heldbythe adversarial
players, the leader election mechanism,and the dynamicsof the protocol. /T_he most commoncase (asseen in Snow
White [21] and Ouroboros [13]) guarantees that the characteri stic string w=w1...wTis drawn from an i.i.d.
distribution for which Pr[wi= 1]≤(1−ǫ)/2; here the constant (1−ǫ)/2is directly related to the stake held by
the adversary. Se/t_tings involving adaptive adversaries (e.g., Ouroboros Praos [6] and Ouroboros Genesis [2]) yield
the weakermartingale-type guaranteethat Pr[wi= 1|w1,...,w i−1]≤(1−ǫ)/2.
8
/T_he(D,T;s,k)-settlementgame
1. A characteristic string w∈{0,1}Tis drawn fromDand provided toA. (/T_his reflects the results
ofthe leaderelection mechanism.)
2. LetA0⊢εdenotetheinitial forkfortheemptystring εconsisting ofasinglenodecorresponding
tothe genesis block.
3. Foreachslot t= 1,...,Tin increasing order:
(a) Ifwt= 0, this is an honest slot. In this case, the challenger is given th e forkAt−1⊢
w1...wt−1and must determine a new fork Ft⊢w1...wtby adding a single vertex (la-
beledwith t)totheendofalongestpathin At−1. (Ifthereareties,Amaychoosewhichpath
the challengeradopts.)
(b) Ifwt= 1, this is an adversarial slot. Amay setFt⊢w1...wtto be an arbitrary fork for
whichAt−1⊑Ft.
(c) (Adversarial augmentation.) Adeterminesan arbitrary fork At⊢w1...,wtfor which Ft⊑
At.
Recallthat F⊑F′indicates that F′contains, asaconsistently-labeled subgraph,the fork F.
Awinsthe se/t_tlement game if slotsis notk-se/t_tled in some fork At(witht≥s+k).
Definition5. LetDbea distribution on {0,1}T. /T_hen definethe (s,k)-se/t_tlement insecurity ofDto be
Ss,k[D]/definesmax
APr[Awinsthe(D,T;s,k)-se/t_tlementgame ],
this maximum taken over all adversaries A.
Remarks. Observe that the adversarial augmentation step permits the a dversary to “suddenly” inject new paths
in the fork between two honest players at adjacent slots; this c orresponds to circumstances when the adversary
chooses to deliver a new blockchain to an honest participant whi ch may consist of an earlier honest chain with
someadversarial blocksappendedtotheend. Observe, additio nally,thatthebehaviorofthechallengerin thegame
is entirely deterministic, as it simply plays according to the l ongest-chain rule (even permi/t_ting the adversary to
break ties). /T_hus the result of the game is entirely determined b y the characteristic string wdrawn fromDand the
choicesof theadversary A. We recordthe followingimmediate conclusion:
Lemma 1. Lets,k,T∈N. LetDbeadistribution on {0,1}T. /T_hen
Ss,k[D]≤Pr
w∼D[slotsisnotk-se/t_tled for w].
Inthesubsequentsections,wewilldevelopsomefurthernotati onandtoolstoanalyzethisevent. Wewillinvesti-
gatetwodifferentfamiliesofdistributions,thosewithi.i.d .coordinatesandthosewithmartingale-typeconditioning
guarantees. For T∈Nandǫ∈(0,1), letBǫ= (B1,...,B n)denote the random variable taking values in {0,1}n
sothat the Biareindependent and Pr[Bi= 1] = (1−ǫ)/2;we letBǫdenotethedistribution on {0,1}nassociated
withBǫ. Whenǫcan beinferred fromcontext,we simplywrite BandB.
We also study amore generalfamilyof distributions, defined next .
Definition 6 (ǫ-martingale condition) .LetW= (W1,...,W n)be a random variable taking values in {0,1}n. We
say thatWsatisfies the ǫ-martingalecondition iffor each t∈{1,...,n},
E[Wt|W1,···,Wt−1]≤(1−ǫ)/2.
Equivalently, Pr[Wt= 1|W1,...,W t−1]≤(1−ǫ)/2. /T_he conditioning on the variables W1,···,Wt−1isarbitrary
in both cases; as a consequence, Pr[Wt= 1]≤(1−ǫ)/2. As a ma/t_ter of notation, we let Wdenote the distribution
9
associated with the random variable W. We use the term “ ǫ-martingale condition” to qualify both a random variable
and itsdistribution.
/T_here are se/t_tings, such as Genesis [2], where this martingale-ty pe conditioning is important. Note that Bǫ
satisfies the ǫ-martingalecondition. Now we are ready to state ourmain theore m.
/T_heorem 1 (Main theorem) .Letǫ∈(0,1),s,k,T∈N. LetWandBǫbe two distributions on {0,1}TwhereBǫis
definedabove andWsatisfies the ǫ-martingalecondition. /T_hen
Ss,k[W]≤Ss,k[Bǫ]≤exp/parenleftbig
−Ω(ǫ3(1−O(ǫ))k)/parenrightbig
.
(Here, the asymptotic notation hidesconstants that donot d ependon ǫork.)
Bytechniques similar to the ones used to prove this result, we o btain the following theorem pertaining directly
tok-CPslot(andk-CP).
/T_heorem2 (Maintheorem; k-CPversion).Letǫ∈(0,1)andT∈N. Letw∈{0,1}Tbearandomvariablesatisfying
theǫ-martingalecondition. /T_hen
Pr[wviolatesk-CP]≤Pr[wviolatesk-CPslot]≤T·exp/parenleftbig
−Ω(ǫ3(1−O(ǫ))k)/parenrightbig
.
/T_he proofs of these theoremsare presented in Section 6.5. Addi tionally, we provide a O(k3)-time algorithm for
computingan explicit upperbound onthese probabilities; cf. AppendixA.
2.4 Survey oftheproofsof themain theorems
A central object in our combinatorial analysis is an “ x-balanced fork” for a characteristic string w=xy. Such a
fork contains two distinct, maximum-lengthtines that are disjoi nt overy; see Definition 9 for details. A se/t_tlement
violation for the slot |x|+1implies an x-balanced fork for the string xy; see Observation 1. In particular, for any
distribution oncharacteristic strings in {0,1}nands+k≤n,
Pr
w[slotsis notk-se/t_tled]≤Pr
w
there isa decomposition w=xyzand
a forkF⊢xy, where|x|=s−1and
|y|≥k+1,so thatFisx-balanced
.
(/T_hisis avariant ofLemma5fromSection 6.5.)
Aspromisedabove,commonprefixviolationscanbehandledthes ameway: welikewiseestablish(seeSection4;
/T_heorem3)thatacommonprefixviolationimpliesthatthereex istsabalancedforkforsomeprefixof w. Specifically,
forany distribution ofcharacteristicstrings,
Pr
w[wviolatesk-CPslot]≤Pr
w/bracketleftBiggthereisa decomposition w=xyzand
a forkF⊢xy, where|y|≥k+ 1, so
thatFisx-balanced/bracketrightBigg
. (1)
Next,inSection5,wegivearecursiveexpressionforthecom binatorialquantity“relative margin,”wri/t_ten µx(y)
(seeDefinition13inSection3). Weestablishthat,foranarbi trarydecompositionofthecharacteristicstring w=xy,
theevent “thereisan x-balancedforkfor xy”isequivalenttotheevent “therelative margin µx(y)isnon-negative;”
thisisFact1. InLemma3,wedevelopanexactrecursiveprese ntationfor µx(y);hencewecanboundtheprobability
ofacommonprefixviolation(orase/t_tlementviolation)byreaso ningaboutthenon-negativityoftherelativemargin
and,in particular,without reasoning directly aboutforks.
InSection 6,we prove two boundsforthe probability
Prw=xy
|x|=s[µx(y)≥0],
fora fixed length s. /T_he first boundpertains to the se/t_ting where w=xyis drawn fromBǫ. /T_he secondpertains to
anydistributionWsatisfyingthe ǫ-martingalecondition. Forcharacteristicstringswithdistri butionBǫ,weidentify
10
arandomvariablewhichstochasticallydominates µx(y)andisamenabletoexactanalysisviageneratingfunctions;
this yieldsthe bound
Pr
w=xy[µx(y)≥0]≤exp(−Ω(|y|)).
Noticethatthisbounddoesnotdependon s,thelengthof x. /T_heresult fordistributions satisfying the ǫ-martingale
condition then followsfromstochastic dominance(Lemma4). Se e Section 6fordetails.
Itimmediatelyfollowsthatan (s,k)-se/t_tlementviolation(ora k-CPslotviolation)isarareeventfordistributions
ofinterest. /T_hemultiplicative factor Tin /T_heorem2 comesfroma union boundtaken over all prefixes of w.
2.5 Comments onthemodel
Analysisinthe ∆-synchronoussetting. /T_hesecuritygameabovemostnaturallymodelsablockchainpro tocol
over a synchronous network with immediate delivery (because ea ch “honest” play of the challenger always builds
on a fork that contains the fork generated by previous honest play s). However, the model can be easily adapted to
protocolsinthe ∆-synchronousmodeladoptedbytheSnowWhiteandOuroborosPraos protocolsandanalyses. In
particular, David et al. [6] developed a “ ∆-reduction” mapping on the space of characteristic strings tha t permits
analysesofforks(and therelatedstatistics ofinterest, cf. § 3)in the∆-synchronousse/t_ting byadirectappeal tothe
synchronousse/t_ting.
Publicleaderschedules. Onea/t_tractivefeatureofthismodelisthatitgivestheadvers aryfullinformationabout
the future schedule of leaders. /T_he analysis of some protocols indeed demand this (e.g., Ouroboros, Snow White).
Other protocols—especially those designed to offer security against adaptive adversaries (Praos, Genesis)—in fact
contrive to keep the leader schedule private. Of course, as ou r analysis is in the more difficult “full information”
model,it appliesto all ofthese systems.
Bootstrappingmulti-phasealgorithms;stakeshi/f_t. Weremarkthatseveralexistingproof-of-stakeblockchain
protocols proceed in phases, each of which is obligated to ge nerate the randomness (for leader election, say) for
the next phase based on the current stake distribution. /T_he block chain security properties of each phase are then
individually analyzed—assumingcleanrandomness—whichyields arecursivesecurityargument;inthiscontextthe
gameoutlined above precisely reflectsthe single phase analysi s.
3 Definitions
WerelyontheelementaryframeworkofforksandmarginfromKia yiasetal.[13]. Werestateandbrieflydiscussthe
pertinent definitions below. With these basic notions behind us, we t hen define a new “relative” notion of margin,
whichwill allow usto significantly improve theefficacyofthese toolsforreasoning aboutse/t_tlement times.
Recallthatfora given execution oftheprotocol,we recordt he result ofthe leaderelectionprocessvia a charac-
teristicstring w∈{0,1}T,definedsuchthat wi= 0whenauniqueandhonestpartyisassignedtoslot iandwi= 1
otherwise. Avertex ofa forkissaid to be honestif it islabeledwith an index isuchthat wi= 0.
Definition7 (Tines,length,andheight) .LetF⊢wbeaforkforacharacteristicstring. A tineofFisadirectedpath
starting from the root. For any tine twe define its lengthto be the number of edges in the path, and for any vertex v
we define its depthto be the length of the unique tine that ends at v. If a tine t1is a strict prefix of another tine t2, we
writet1≺t2. Similarly, if t1isa non-strictprefixof t2, we write t1/√recedesequalt2. /T_helongest common prefixof two tines t1,t2
is denoted by t1∩t2. /T_hat is,ℓ(t1∩t2) = max{ℓ(u) :u/√recedesequalt1andu/√recedesequalt2}. /T_heheightof a fork (as usual for a tree)
isthe length of thelongest tine, denoted height(F).
Definition 8 (/T_he∼xrelations) .For two tines t1andt2of a fork F, we write t1∼t2whent1andt2share an
edge; otherwise we write t1≁t2. We generalize this equivalence relation to reflect whether tines share an edge over a
particular suffix of w: forw=xywe define t1∼xt2ift1andt2share an edge that terminates at some node labeled
with an index in y; otherwise, we write t1≁xt2(observe that in this case the paths share no vertex labeled b y a slot
11
associated with y). We sometimes call such pairs of tines disjoint(or, ift1≁xt2for a string w=xy,disjoint over y).
Note that∼and∼εarethe same relation.
/T_hebasic structure we use touse to reason aboutse/t_tlement time sis that ofa “balancedfork.”
Definition 9 (Balanced fork; cf. “flat” in [13]) .A forkFisbalanced if it contains a pair of tines t1andt2for which
t1≁t2andlength(t1) = length( t2) = height( F). We definea relative notion ofbalance as follows: afork F⊢xyis
x-balanced if itcontains apair of tines t1andt2for which t1/ne}ationslash∼xt2andlength(t1) = length( t2) = height( F).
/T_hus, balanced forks contain two completely disjoint, maximum- length tines, while x-balanced forks contain
two maximum-lengthtines that may share edgesin xbut must be disjoint over the rest of the string. See Figures 2
and 3forexamplesof balancedforks.
w=01
12
03
14
05
16
0
Figure 2: Abalancedfork
w=01
02
03
14
05
16
0
Figure 3: An x-balancedfork,where x= 00
Balanced forks and settlement time. A fundamental question arising in typical blockchain se/t_tings is how to
determine se/t_tlementtime ,thedelaya/f_terwhichthecontentsofaparticularblockofablo ckchaincanbeconsidered
stable. /T_he existence of a balanced fork is a precise indicator fo r “se/t_tlement violations” in this sense. Specifically,
consider a characteristic string xyand a transaction appearing in a block associated with the first s lot ofy(that is,
slot|x|+1). One clear violation of se/t_tlement at this point of the executio n is the existence of two chains—eachof
maximumlength—whichdiverge priortoy;inparticular,thisindicatesthatthereisan x-balancedfork Fforxy. Let
usrecordthis observation below.
Observation 1. Lets,k∈Nbe given and let wbe a characteristic string. Slot sis notk-se/t_tled for the characteristic
stringwif there exist adecomposition w=xyz,where|x|=s−1and|y|≥k+1,and anx-balanced forkfor xy.
In fact, every k-CPslotviolation produces a balanced fork as well; see /T_heorem 3 in Sec tion 4. In particular, to
provide a rigorous k-slot se/t_tlement guarantee—which is to say that the transaction can be considered se/t_tled once
kslots have gone by—it suffices to show that with overwhelming pro bability in choice of the characteristic string
determinedbytheleaderelectionprocess(ofafullexecutio noftheprotocol),nosuchforksarepossible. Specifically,
if the protocol runs for a total of Ttime steps yielding the characteristics string w=xy(wherew∈{0,1}Tand
thetransactionofinterest appearsinslot |x|+1asabove)thenit sufficestoensurethatthere isno x-balancedfork
12
forxˆy,whereˆyisanarbitraryprefixof yoflengthatleast k+1;seeCorollary1in Section6. Notethatforsystems
adoptingthelongestchainrule,thisconditionmustnecessaril yinvolve the entirefuturedynamics oftheblockchain.
We remarkthat our analysisbelow will in fact let ustake T=∞.
Definition10 (Closedfork) .AforkFisclosedifeveryleafishonest. Forconvenience, wesaythetrivialf orkisclosed.
Closed forks have two nice properties that make them especial ly useful in reasoning about the view of honest
parties. First, a closed fork must have a unique longest tine (si nce honest parties are aware of all previous honest
blocks,andhonest parties observe the longestchain rule). Sec ond,recallingour description of these/t_tlement game,
closedforksintuitively capturedecisionpointsfortheadve rsary. /T_headversarycanpotentiallyshowmanytinesto
manyhonestparties,butonceanhonestnodehasbeenplacedontopo fatine,anyadversarialblocksbeneathitare
partofthepublicrecordandare visible toallhonestparties. Forthesereasons, wewill o/f_ten findit easier to reason
about closedforksthanarbitrary forks.
/T_he next few definitions are the start of a general toolkit for reaso ning about an adversary’s capacity to build
highlydiverging pathsin forks,based onthe underlyingcharac teristic string.
Definition 11 (Gap, reserve, and reach) .For a closed fork F⊢wand its unique longest tine ˆt, we define the gapof
a tinetto begap(t) = length( ˆt)−length(t). Furthermore, we define the reserveoft, denoted reserve(t), to be the
numberof adversarial indicesin wthat appeara/f_ter theterminating vertex of t. Moreprecisely, if visthelast vertex of
t, then
reserve(t) =|{i|wi= 1andi > ℓ (v)}|.
/T_hesequantities together definethe reachof atine: reach(t) = reserve( t)−gap(t).
/T_he notion of reach can be intuitively understood asa measurement of the resources available to our adversary
inthese/t_tlementgame. Reserve tracksthenumberofslotsinwhi chtheadversaryhastherighttoissue newblocks.
When reserve exceeds gap (or equivalently, when reach is nonnega tive), such a tine could be extended—using a
sequenceof dishonest blocks—untilit is aslongasthelongest tine . Sucha tine couldbe offered to an honest player
who would prefer it over, e.g., the current longest tine in the fo rk. In contrast, a tine with negative reach is too far
behind to bedirectly useful to the adversary at that time.
Definition12 (Maximumreach) .Foraclosedfork F⊢w, wedefine ρ(F)tobethelargestreacha/t_tainedbyanytine
ofF, i.e.,
ρ(F) = max
treach(t).
Note that ρ(F)is never negative (as the longest tine of any forkalways has r each at least 0). We overload this notation
to denote the maximum reach over all forksfor agiven charact eristic string:
ρ(w) = max
F⊢w
Fclosed/bracketleftbig
max
treach(t)/bracketrightbig
.
Definition13 (Margin)./T_hemarginofa forkF⊢w, denoted µ(F), isdefinedas
µ(F) = max
t1≁t2/parenleftbig
min{reach(t1),reach(t2)}/parenrightbig
, (2)
wherethismaximumisextendedoverallpairsofdisjointtin esofF;thusmarginreflectsthe“secondbest”reachobtained
overalldisjointtines. Inordertostudysplitsinthechain overparticularportionsofastring,wegeneralizethistod efine
a“relative” notion of margin: If w=xyfortwo strings xandyand, as above, F⊢w, we define
µx(F) = max
t1≁xt2/parenleftbig
min{reach(t1),reach(t2)}/parenrightbig
.
Note that µε(F) =µ(F).
Forconvenience, weonceagainoverload thisnotation toden otethemarginofastring. µ(w)referstothemaximum
value ofµ(F)over all possibleclosed forks Fforacharacteristic string w:
µ(w) = max
F⊢w,
Fclosedµ(F).
13
Likewise, if w=xyfortwo strings xandywedefine
µx(y) = max
F⊢w,
Fclosedµx(F).
Notethat,atleastinformally,“second-best”tinesareofnatur alinteresttoanadversaryintentontheconstruction
ofanx-balancedfork,whichinvolves two (partially disjoint) long ti nes.
Balanced forks and relative margin. Kiayias et al. [13] showed that a balanced fork can be construct ed for a
given characteristicstring wif andonlyif thereexists someclosed F⊢wsuchthat µ(F)≥0. We recordarelative
version ofthistheorembelow,whichwill ultimatelyallow u stoextend the analysisof[13]to moregeneralclassof
disagreement and se/t_tlement failures.
Fact 1.Letxy∈{0,1}nbeacharacteristic string. /T_hen there isan x-balanced fork F⊢xyif andonly if µx(y)≥0.
Proof./T_he proofis immediatefrom thedefinitions. We sketchthe detail s forcompleteness.
SupposeFis anx-balanced fork for xy. /T_henFmust contain a pair of tines t1andt2for which t1/ne}ationslash∼xt2and
length(t1) = length( t2) = height( F). We observe that (1) gap(ti) = 0forbotht1andt2,and (2)reserve isalways
a nonnegative quantity. Together with the definition of reach, these two facts immediately imply reach(ti)≥0.
Becauset1andt2are edge-disjoint over yandmin{reach(t1),reach(t2)}≥0,we conclude that µx(y)≥0, as
desired.
Supposeµx(y)≥0. /T_hen there is some closed fork Fforxysuch that µx(F)≥0. By the definition of relative
margin, we know that Fhas two tines t1,t2such that t1≁xt2andreach(ti)≥0. Recall that we define reach by
reach(t) = reserve( t)−gap(t), and so in this case it follows that reserve(ti)−gap(ti)≥0. /T_hus, an x-balanced
forkF′⊢xycanbe constructedfrom Fbyappending apath of gap(ti)adversarial vertices to each ti.
Asindicated above,we candefine the “forkability” ofa charact eristic string in termsofits margin.
Definition 14 (Forkable strings) .A charactersitic string wisforkableif its margin is non-negative, i.e., µ(w)≥0.
Equivalently, wisforkable ifthere isabalanced forkfor w.
Althoughthisdefinition is notnecessary forour presentation, it reflectstheterminologyof existing literature.
4 Common prefix violation and balanced forks
In this section, we show that a common prefix violation implies the existence of a balanced fork. /T_his allows us to
bound consistency errors by reasoning about balanced forks. In par ticular, inequality (1) is a direct consequenceof
the theorembelow.
/T_heorem 3. Letk,T∈N. Letw∈{0,1}Tbe a characteristic string which violates k-CPslot. /T_hen there exist a
decomposition w=xyzand afork ˆF⊢xy, where|y|≥k+1, so thatˆFisx-balanced.
Proof.Recall that ℓ(t)is the slot index of the last vertex of tine t. DefineA/defines/uniontext
F⊢wAFwhere, for a given fork
F⊢w,define
AF/defines/braceleftBigg
(τ1,τ2) :τ1,τ2are two viable tines in the fork F,
ℓ(τ1)≤ℓ(τ2), and the pair (τ1,τ2)is a
witness to a k-CPslotviolation/bracerightBigg
.
Define the slot divergence of two tines as divslot(τ1,τ2)/definesℓ(τ1)−ℓ(τ1∩τ2)whereτ1∩τ2denotes the common
prefix of thetines τ1andτ2. Recallingthe definition ofa k-CPslotviolation, it isclearthat
divslot(τ1,τ2)≥k+1forall(τ1,τ2)∈A. (3)
Notice that there must be atine-pair (t1,t2)∈Awhichsatisfies the followingtwo conditions:
divslot(t1,t2)ismaximalover A,and (4)
|ℓ(t2)−ℓ(t1)|isminimal amongalltine-pairs in Aforwhich(4)holds. (5)
/T_he tines t1,t2will play aspecial rolein ourproof;let Fbea forkcontainingthese tines.
14
/T_he prefix x, forkFx, and vertex u.Letudenote the last vertex on the tine t1∩t2, as shown in the diagram
below,andlet α/definesℓ(u) =ℓ(t1∩t2). Letx/definesw1,...,w αandletFxbethefork-prefixof Fsupportedon x. Wewill
arguethat umustbehonestand,inaddition,that Fxmustcontainauniquelongesttine tuterminatingatthevertex
u. We will also identify a substring y,|y|≥k+1such that wcanbe wri/t_ten as w=xyz. /T_hen we will construct a
balanced fork ˜Fy⊢yby modifying the subgraph of Fsupported on y. We will finish the proof by constructing an
x-balancedforkbysuitably appending ˜FytoFx.
ut1
t2
umustbeanhonestvertex. We observe,first ofall,thatthe vertex ucannotbeadversarial: otherwise it iseasy
to construct an alternative fork F′⊢wand a pair of tines in F′that violate (4). Specifically, construct F′fromF
byaddinganew(adversarial)vertex u′toFforwhich ℓ(u′) =ℓ(u),addinganedgeto u′fromthevertexpreceding
u, and replacing the edge of t1following uwith one from u′; then the other relevant properties of the fork are
maintained,but the slot divergence of the resulting tines hasincr eased by atleast one. (See the diagrambelow.)
uu′
t1
t2
Fxhasaunique,longest(andhonest)tine tu.Asimilarargumentimpliesthatthefork Fxhasauniquevertex
of depthdepth(u): namely, uitself. In the presence of another vertex u′(ofFx) with depth depth(u), “redirecting”
t1throughu′(as in the argument above) would likewise result in a fork with a larger slot divergence. To see this,
noticethat ℓ(u′)mustbestrictlylessthan ℓ(u)sinceℓ(u)isanhonestslot(whichmeans uistheonlyvertexatthat
slot). /T_hus ℓ(·)wouldindeed beincreasing alongthisnew tine (resulting fromredir ectingt1). Asαisthelast index
of the string x, this additionally implies that Fxhas no vertices of depth exceeding depth(u). Lettu∈Fxbe the
tine with ℓ(tu) =α.
/T_hehonest tine tuis the unique longest tine in Fx. (6)
Identifying y.Letβdenotethesmallesthonestindex of wforwhich β≥ℓ(t2),with theconvention thatifthere
is no such index we define β=T+ 1. Observe that β−1≥ℓ(t1). (Ifℓ(t2)is an honest slot then β=ℓ(t2)but
ℓ(t1)< ℓ(t2). /T_he case ℓ(t1) =ℓ(t2)is possible if ℓ(t2)is an adversarial slot; but then β > ℓ(t2).) /T_hese indices, α
andβ,distinguish thesubstrings y=wα+1...wβ−1andz=wβ...wT;wewillfocuson yintheremainderofthe
proof. Since the function ℓ(·)is strictly increasing alonganytine, observe that
|y|=β−α−1≥ℓ(t1)−ℓ(u)≥k+1.
Henceyhasthe desired length and it sufficesto establish that it is fork able. We canextract from Fa balancedfork
(fory)intwosteps: (i.) wesubjectthefork Ftosomeminorrestructuringtoensurethatall“long”tinespassth rough
u; (ii.) we construct a flat fork by treating the vertex uas the root of a portion of the subtree of Flabeled with the
indices of y. At the conclusion of the construction, the segments of the two ti nest1andt2will yield the required
“long,disjoint, equal-length”tines satisfying thedefinition of a balancedfork.
Honestindicesin xyhavelowdepths. /T_heminimalityassumption(5)impliesthatanyhonestindex hforwhich
h < βhasdepth no morethan min(length( t1),length(t2)): specifically,
h < β =⇒d(h)≤min(length( t1),length(t2)). (7)
15
Toseethis,consideranhonestindex h,h < β andatine thforwhich ℓ(th) =h. Recallthat t1andt2areviableand
thath < ℓ(t2). (Ifℓ(t2)is honest, it is obvious. Otherwise, h < ℓ(t2)< βsinceℓ(t2)is adversarial.) As t2is viable,
it follows immediately that d(h) = length( th)≤length(t2). Similarly, if h≤ℓ(t1)thend(h)≤length(t1)since
t1is viable aswell. /T_he remaining case,i.e., when ℓ(t1)< h < ℓ(t2),canbe ruledout bythe argumentbelow.
/T_hereisno honest indexbetween ℓ(t1)andℓ(t2).We claim that
/T_here is nohonest index hsatisfying ℓ(t1)< h < ℓ(t2). (8)
/T_he claim above is trivially true if ℓ(t1) =ℓ(t2). Otherwise, suppose (toward a contradiction) that his an honest
index satisfying ℓ(t1)< h < ℓ(t2). Letthbe the (honest) tine at slot h. /T_he tine-pair (t1,th)may or may not be in
A. We will show that bothcaseslead to contradictions.
• If(t1,th)isinAandℓ(t1∩th)≤ℓ(u),divslot(t1,th)isatleast divslot(t1,t2). Infact,dueto(4),thisinequality
must bean equality. However, the assumption ℓ(t1)< h < ℓ(t2)contradicts(5).
• If(t1,th)isinAandℓ(t1∩th)> ℓ(u),it followsthat divslot(th,t2)>divslot(t1,t2). Asthe la/t_terquantity is
atleastk+1,(th,t2)must be in A. /T_he precedinginequality,however, contradicts(4).
• If(t1,th)/ne}ationslash∈A,divslot(t1,th)is at most k. Asdivslot(t1,t2)is at least k+ 1,thandt1must share a vertex
a/f_terslot ℓ(u). Sinceℓ(t1)< h < ℓ(t2)byassumption, divslot(th,t2)>divslot(t1,t2)≥k+1and,asaresult,
(th,t2)∈A. However, the precedingstrict inequality violates condition (4).
A forkF⊲u⊳where all long tines go through u.In light of the remarks above, we observe that the fork F
may be “pinched” at uto yield an essentially identical fork F⊲u⊳⊢wwith the exception that all tines of length
exceeding depth(u)pass throughthe vertex u. Specifically,the fork F⊲u⊳⊢wis defined to be the graphobtained
fromFby changing every edge of Fdirected towards a vertex of depth depth(u) +1so that it originates from u.
To see that the resulting tree is a well-defined fork, it suffices t o check that ℓ(·)is still increasing along all tines of
F⊲u⊳. Forthispurpose,considertheeffectofthispinchingonanindiv idualtine tterminatingataparticularvertex
v—it is replacedwith a tine t⊲u⊳defined so that:
• Iflength(t)≤depth(u),thetine tisunchanged: t⊲u⊳=t.
• Otherwise, length(t)>depth(u)andthas a vertex vof depthdepth(u)+1; note that ℓ(v)> ℓ(u)because
Fxcontains no vertices of depth exceeding depth(u). /T_hent⊲u⊳is defined to be the path given by the tine
terminating at u, a (new) edge from utov, and the suffix of tbeginning at z. (Asℓ(v)> ℓ(u)this has the
increasing labelproperty.)
/T_hus the tree F⊲u⊳is a legal fork on the same vertex set; note that the depths of ve rtices inFandF⊲u⊳are
identical.
Constructing a shallow fork Fy⊢y.By excising the tree rooted at ufrom this pinched fork F⊲u⊳, we may
extract a fork for the string wα+1...wT. Specifically, consider the induced subgraph Fu⊳ofF⊲u⊳given by the
vertices{u}∪{v|depth(v)>depth(u)}. By treating uas a root vertex and suitably defining the labels ℓu⊳of
Fu⊳sothatℓu⊳(v) =ℓ(v)−ℓ(u),thissubgraphhasthedefiningpropertiesofaforkfor wα+1...wT. Inparticular,
consideringthat αishonestitfollowsthateachhonestindex h > αhasdepth d(h)>length(u)andhence hlabels
avertex in Fu⊳. Foratine tofF⊲u⊳,welettu⊳denotethesuffixofthistine beginningat u,whichformsatine in
Fu⊳. (Iflength(t)≤depth(u), we define tu⊳to consist solely of the vertex u.) Note that t1u⊳andt2u⊳share no
edgesin thefork Fu⊳.
Finally,let Fydenotethesubtreeobtainedfrom Fu⊳astheunionofalltines tu⊳ofFu⊳sothatalllabelsof tu⊳
are drawn from y(asit appearsasa prefix of wα+1...wT),and
length(tu⊳)≤max
h≤|y|
hhonestd(h). (9)
It isimmediate that Fy⊢y.
16
Two longest viabletines in Fy.Consider the tines t1u⊳andt2u⊳. As mentioned above, they share no edges in
Fu⊳and hence the prefixes ˇt1andˇt2(oft1u⊳andt2u⊳) appearing in Fyshare no edges. We wish to show that
these prefixes have the maximal length in Fy, making Fybalanced, as desired. Let hbe the largest honest index in
y. Sincethelengthsofthetinesin Fyareatmost d(h),itsufficestoshowthatthelengthsof ˇti,i∈{1,2}isatleast
d(h).
/T_hisisimmediateforthetine ˇt1sincealllabelsof t1u⊳aredrawnfrom yand,considering(7),itsdepthisatleast
that of all relevant honest vertices. As for ˇt2, observe that if ℓ(t2)is not honest then β > ℓ(t2)so that, as with ˇt1,
thetineˇt2islabeledby yso thatthesameargument,relying on(7),ensures thatthe length(ˇt2)isat least thedepth
of all relevant honest vertices. If ℓ(t2)is honest, β=ℓ(t2), and the terminal vertex of t2u⊳does not appear in Fy
(asℓ(t2u⊳)falls outside y). In this case, however, length(t2u⊳)>d(h)for any honest index hofy. It follows that
length(ˇt2), which equals length(t2u⊳)−1, is at least the depth of any honest index of y, as desired. /T_hus we have
proved
ˇt1andˇt2are two maximallylongviable tines in Fy⊢y. (10)
Constructingaflatfork ˜Fy⊢y.Letusidentifytheforkprefix ˜Fy⊑Fywhichiseitheridenticalto Fyordiffers
fromFyin only one of the tines ˇt1,ˇt2. In particular, if length(ˇt1) = length( ˇt2), we set˜Fy=Fy. Otherwise, let ˇta
be the longer of the two tines ˇt1,ˇt2; letˇtbbe the shorter one. We modify Fyby deleting some trailing adversarial
nodes from ˇtauntil it has the same length as ˇtb; we set˜Fyas the resulting fork and, in addition, set ˜tb=ˇtband˜ta
asthe tine a/f_ter trimming ˇta.
We claim that ˜Fyis balanced. /T_he claim is obvious if length(ˇt1) = length( ˇt2). Otherwise, thanks to (10), it
remains to show that the longer tine, ˇta, has sufficiently many trailing adversarial nodes which, if delet ed, yields
length(˜t1) = length( ˜t2). Tothat end,let hibe theindex ofthe last honest vertex on ˇti∈Fy,i∈{1,2}.
Supposelength(ˇt2)>length(ˇt1). By (8), we also have length(ˇt1)≥d(h2)and hence we can trim some of
the trailing adversarial nodes from ˇt2to get the tine ˜t2whose length is the same as that of ˇt1. Otherwise, suppose
length(ˇt1)>length(ˇt2). Sincet2is a viable tine in F, we also have length(ˇt2)≥d(h1). /T_hus we can trim some
of the trailing adversarial nodes from ˇt1to have a tine ˜t1whose length is the same as that of ˇt2. In any case, the
quantitymin(length( ˜t1),length(˜t2))remainsthesameas min(length( ˇt1),length(ˇt2)). /T_husthefork ˜Fyhasatleast
two tines, ˜t1and˜t2,that achieve the maximumlength ofalltines in ˜Fy;hence˜Fyis balanced.
Anx-balanced fork ˆF⊑F.Let us identify the root of the fork ˜Fywith the vertex uofFxand letˆFbe the
resulting graph(a/f_ter“gluing”therootof ˜Fytou). By(6),itiseasytoseethatthefork ˆF⊑Fisindeedavalidfork
onthestring xy. Moreover, ˆFisx-balancedsince ˜Fyisbalanced. /T_heclaimin/T_heorem3followsimmediatelysince
|y|≥k+1.
5 Asimple recursive formulation of relative margin
A significant finding of Kiayias et al. [13] is that the margin of a ch aracteristic string µ(w)—the maximumvalue of
a quantity taken over a (typically) exponentially-large famil y of forks—can be given a simple, mutually recursive
formulationwith the associated quantity of reach ρ(w). Specifically,they prove the followinglemma.
Lemma 2 ([13,Lemma4.19]) .ρ(ε) = 0whereεistheempty string, and, forall nonempty strings w∈{0,1}∗,
ρ(w1) =ρ(w)+1,andρ(w0) =/braceleftBigg
0 ifρ(w) = 0,
ρ(w)−1otherwise.(11)
Furthermore, marginsatisfies themutually recursive relat ionshipµ(ε) = 0and for all w∈{0,1}∗,
µ(w1) =µ(w)+1,andµ(w0) =/braceleftBigg
0 ifρ(w)> µ(w) = 0,
µ(w)−1otherwise.(12)
Additionally, there exists aclosed fork F⊢wsuch that ρ(F) =ρ(w)andµ(F) =µ(w).
17
We prove ananalogousrecursive statement forrelative margin, recordedbelow.
Lemma 3 (Relative margin) .Given a fixed string x∈{0,1}*,µx(ε) =ρ(x)whereεis the empty string, and, for all
nonempty strings w=xy∈{0,1}*,
µx(y1) =µx(y)+1,andµx(y0) =/braceleftBigg
0 ifρ(xy)> µx(y) = 0,
µx(y)−1otherwise.(13)
Additionally, there exists aclosed fork F⊢xysuch that ρ(F) =ρ(xy)andµx(F) =µx(y).
We delay the proof of Lemma 3 to Section 7, preferring to immedi ately focus on the application to se/t_tlement
times in Section 6.
Discussion. /T_heproofofLemma3sharesmanytechnicalsimilaritieswiththe proofofLemma2givenbyKiayias
etal.[13]. However,thereisanimportantrespectinwhichth eproofsdiffer. Eachoftheproofsrequiresthedefinition
of a particular adversary (which,in effect, constructs a fork achieving the worst case reach and margin guaranteed
by the lemma). /T_he adversary constructed by [13] can create a ba lanced fork for wwhenever µ(w)≥0(i.e.,wis
“forkable”). However, the adversary onlyfocuseson theprob lem ofproducingdisjoint tines over the entirestring w
(consistent with the definition of µ(·)). /T_he “optimal online adversary,” developed in Section 8, uses a more sophis-
ticated rule for extending chains (tines) of the fork. Notably, t his adversary can simultaneously maximize relative
marginover all prefixes of thestring .
6 General settlement guarantees and proof ofmain theorems
With the recursive formulation for relative margin in hand, w e study the stochastic process that arises when the
characteristic string wis chosen from a distribution satisfying the ǫ-martingale condition. Let us write w=xy
(where the decomposition is arbitrary) and let Ebe the event that the relative margin µx(y)is non-negative. As
Fact 1and Observation 1 point out,thisevent hasa direct bearing on the se/t_tlement violation on w.
In this section, we prove two bounds on the probability of the ev entE. /T_he first bound corresponds to the
distributionBǫwhereasthesecondboundappliestoanydistributionthatsatis fiestheǫ-martingalecondition. (Recall
thatthedistribution Bǫ,mentionedin/T_heorem1,satisfiesthe ǫ-martingaleconditionwithequality.) Ourexposition
in this section culminatesin the proofsof ourmain theorems.
We start with thefollowing theoremwhichisa directconsequenc esof these bounds;see Section 6.1foraproof.
/T_heorem 4. LetT,k∈N. Letw∈{0,1}Tbea random variable satisfying the ǫ-martingale condition. Consider the
decomposition w=xy,|y|=k. /T_hen
Pr
w=xy[there isan x-balanced forkfor xy] = Pr
w=xy[µx(y)≥0]≤exp(−Ω(k)).
(/T_heasymptotic notation hidesconstants that dependonly on ǫ.)
Notice how the final bound does not depend on |x|. Indeed, as we show in Lemma 4, the reach of a Boolean
stringxdrawn from the distribution Bǫconverges to a fixed exponential distribution as |x|→∞. /T_his limiting
distribution“stochasticallydominates”anydistributiont hatsatisfiesthe ǫ-martingalecondition;seeSection6.2. /T_he
followingcorollaryis immediate.
Corollary1. LetT,s,k∈N. Letw∈{0,1}Tbearandom variablesatisfying the ǫ-martingalecondition. /T_hen
Pr
w/bracketleftbigg
thereisadecomposition w=xyz,where|x|=
s−1and|y|≥k, so thatµx(y)≥0/bracketrightbigg
≤O(1)·exp(−Ω(k)). (14)
Proof.Notice that /T_heorem 4works for anyprefixxof the characteristic string w=xy. /T_huswe can fix the prefix
xwith length s−1and sum the bound in /T_heorem 4 over all suffixes ywith length at least k. /T_his would give an
upper boundto the le/f_t-hand side of ourclaim,the boundbeing/summationtext
t≥kexp(−Ω(t)) =O(1)·exp(−Ω(k)).
18
We obtain anotherimporant corollarybyse/t_ting |x|= 0and|y|=nin /T_heorem4.
Corollary2. Letw∈{0,1}nbea randomvariablesatisfying the ǫ-martingalecondition. /T_hen
Pr[wisforkable ] = Pr[µ(w)≥0]≤exp(−Ω(n)).
/T_husforkable strings are rare where “forkable” is defined in Definition 14. /T_his result significa ntly strengthens
theexp(−Ω(√n))bound obtained in /T_heorem 4.13 of [13]. /T_he improvement comes in tw o respects: first, Corol-
lary 1 improves the exponent from√nton, and second, the characteristic string is allowed to be drawn fr om any
distribution satisfying the ǫ-martingalecondition. For comparison,the characteristic str ing in /T_heorem 4.13of [13]
hasthe distribution Bǫ,i.e., the bits were i.i.d. Bernoullirandomvariableswith ex pectation (1−ǫ)/2.
6.1 Two boundsfornon-negative relative margin
/T_he main ingredients to proving /T_heorem 4 are two bounds on the event t hat for a characteristic string xy, the
relative margin µx(y)is non-negative.
Bound1. Letx∈{0,1}mandy∈{0,1}kbeindependentrandom variables, each chosen according to Bǫ. /T_hen
Pr[µx(y)≥0]≤exp(−ǫ3(1−O(ǫ))k/2).
Bound 2. Letx∈{0,1}mandy∈{0,1}kbe random variables (jointly) satisfying the ǫ-martingale condition with
respect to the ordering x1,...,x m,y1,...,y k. Letx′∈{0,1}mandy′∈{0,1}kbe independent random variables,
each chosen independentlyaccording to Bǫ. /T_hen
Pr[µx(y)≥0]≤Pr[µx′(y′)≥0]≤exp(−ǫ3(1−O(ǫ))k/2).
Proofof/T_heorem4. /T_he equalityis Fact 1and theinequality is Bound 2.
6.2 A stochastically dominant prefix distribution
Stochastic dominance plays an important role in the argumentsbe low. First of all, we observe that the distribution
Bǫstochastically dominatesany distribution satisfying the ǫ-martingalecondition; thisyields the first inequality in
/T_heorem1. Amoredelicate applicationofstochasticdominance isusedin ordertoachieving bounds,suchasthose
of Section 6.1, that are independent of the length of x. /T_his follows from the fact that reach(Bǫ)converges to a
particular,dominant distribution asits argumentincreases in length.
For notational convenience, we denote the probability distributi on associated with a random variable using up-
percase script le/t_ters; for example, the distribution of a ra ndom variable Ris denoted byR. /T_his usage should be
clearfromthe context.
Definition 15 (Monotonicity and stochastic dominance) .LetΩbe a set endowed with a partial order ≤. A subset
A⊂Ωis monotone if for all x≤y,x∈Aimpliesy∈A. LetXandYbe random variables taking values in Ω. We
say thatXstochastically dominates Y, wri/t_tenY/√recedesequalX, ifX(A)≥Y(A)for all monotone A⊆Ω. As a special case,
whenΩ =R,Y/√recedesequalXifPr[X≥Λ]≥Pr[Y≥Λ]for every Λ∈R. We extend this notion to probability distributions
in thenatural way.
Observe that for any non-decreasing function udefined on Ω,Y/√recedesequalXimpliesu(Y)≤u(X). Finally, we
note that for real-valued random variables X,Y, andZ, ifY/√recedesequalXandZis independent of both XandY, then
Z+Y/√recedesequalZ+X.
Lemma 4. SupposeW= (W1,...,W n)∈{0,1}nsatisfies the ǫ-martingale condition. Let ǫ∈(0,1)andB=
(B1,...,B n)∈{0,1}nwhere each Biis independent with expectation (1−ǫ)/2. LetR∞∈{0,1,...}be a random
variablewhose distribution R∞isdefinedas
R∞(k) = Pr[R∞=k]/defines/parenleftbigg2ǫ
1+ǫ/parenrightbigg
·/parenleftbigg1−ǫ
1+ǫ/parenrightbiggk
fork= 0,1,2,... . (15)
/T_henρ(W)/√recedesequalρ(B)/√recedesequalR∞.
19
Proof.We begin by observing that Bstochastically dominates W. As a ma/t_ter of notation, for any fixed values
w1,...,w k∈{0,1}k,let
θ[w1,...,w k] = Pr[Wk+1= 1|Wi=wi,fori≤k]≤(1−ǫ)/2
andθ[ε] = Pr[W1= 1]whereεis the empty string. /T_hen consider nuniform and independent real numbers
(A1,...,A n),eachtakingavalueintheunit interval [0,1];weusethese randomvariablestoconstructamonotone
coupling between WandB. Specifically, define β: [0,1]n→{0,1}nby the rule β(α1,...,α n) = (b1,...,b n)
where
bt=/braceleftBigg
1ifαt≤(1−ǫ)/2,
0ifαt>(1−ǫ)/2,
and define B= (B1,...,B n) =β(A1,...,A n); theseBis are independent zero-one Bernoulli random variables
withexpectation (1−ǫ)/2. Likewisedefinethefunction ω: [0,1]n→{0,1}nsothatω(α1,...,α n) = (w1,...,w n)
where each wtis assigned bythe iterative rule
wt+1=/braceleftBigg
1ifα≤θ[w1,...,w t],
0ifα > θ[w1,...,w t],
and observe that the probability law of ω(A1,...,A n)is precisely that of W= (W1,...,W n). For convenience,
we simply identify the random variable Wwithω(A1,...,A n). Note that for any α= (α1,...,α n)and for each
i, theith coordinates of β(α)andω(α)satisfyω(α)i≤β(α)i(which is to say that Wi≤Biwith probability 1).
But this is equivalent to saying W/√recedesequalB. (See [14, Lemma 22.5].) Now consider the following partial or der≤on
then-bit Boolean strings: for x,y∈{0,1}n, we write x≤yif and only if xi= 1impliesyi= 1,i∈[n]. Since
ρis non-decreasing with respect to this partial order, we have ρ(ω(α))≤ρ(β(α))with probability 1 and hence
ρ(W)/√recedesequalρ(B)aswell.
Tocompletetheproof,wenowestablishthat ρ(B)/√recedesequalR∞. Weremarkthattherandomvariables ρ(B)(andR∞)
haveanimmediateinterpretation intermsoftheMarkovchain correspondingtoabiasedrandomwalkon Zwitha
“reflectingboundary” at-1. Specifically,consider the Markovc hainon{0,1,...}given bythe transition diagram
012...
where edges pointing right have probability (1−ǫ)/2and edges pointing le/f_t—including the loop at 0—have prob-
ability(1 +ǫ)/2. Examining the recursive description of ρ(w), it is easy to confirm that the random variable
ρ(B1,...,B n)is precisely given by the result of evolving the Markov chain a bove for nsteps with all probabil-
ity initially placed at 0. It is further easy to confirm that the d istribution given by (15) above is stationary for this
chain.
Toestablishstochasticdominance,itisconvenienttoworkwitht heunderlyingdistributionsandconsiderwalks
ofvaryinglengths: let Rn:Z→Rdenotetheprobabilitydistributiongivenby ρ(B1,...,B n);likewisedefineR∞.
For a distributionRonZ, we define [R]0to denote the probability distribution obtained by shi/f_ting all probability
masson negative numbersto zero; thatis, for x∈Z,
[R]0(x) =
R(x) ifx >0,/summationtext
t≤0R(t)ifx= 0,
0 ifx <0.
We observe that if A/√recedesequalCthen[A]0/√recedesequal[C]0for any distributions AandConZ. It will also be convenient to
introduce the shi/f_t operators: for a distribution R:Z→Rand an integer k, we define SkRto be the distribution
given by the rule SkR(x) =R(x−k). With these operatorsin place,we maywrite
Rt=/parenleftbigg1−ǫ
2/parenrightbigg
S1Rt−1+/parenleftbigg1+ǫ
2/parenrightbigg/bracketleftbig
S−1Rt−1/bracketrightbig
0,
20
withtheunderstandingthat R0isthedistributionplacingunitprobabilityat 0. /T_heproofnowproceedsbyinduction.
It isclearthatR0/√recedesequalR∞. Assuming thatRn/√recedesequalR∞,we note thatfor any k
SkRn/√recedesequalSkR∞and,additionally,that [S−1Rn]0/√recedesequal[S−1R∞]0.
Finally,itisclearthatstochasticdominancerespectsconvexc ombinations,inthesensethatif A1/√recedesequalC1andA2/√recedesequalC2
thenλA1+(1−λ)A2/√recedesequalλC1+(1−λ)C2(for0≤λ≤1). We concludethat
Rt+1=/parenleftbigg1−ǫ
2/parenrightbigg
S1Rt+/parenleftbigg1+ǫ
2/parenrightbigg/bracketleftbig
S−1Rt/bracketrightbig
0/√recedesequal/parenleftbigg1−ǫ
2/parenrightbigg
S1R∞+/parenleftbigg1+ǫ
2/parenrightbigg/bracketleftbig
S−1R∞/bracketrightbig
0.
Byinspection, theright-hand side equals R∞,asdesired. Hence ρ(B)/√recedesequalR∞.
Remark. In fact, the random variable ρ(B)actually converges to R∞asn→∞. /T_his can be seen, for example,
bysolvingforthestationary distribution oftheMarkovchain intheproofabove. However,wewillonlyrequirethe
dominance for our exposition. Importantly, since µx(ε) =ρ(x), andPr[µx(y)≥0]increases monotonically with
an increase in Pr[µx(ε)≥r]for anyr≥0, it suffices to take |x|→∞when reasoning about an upper bound on
Pr[µx(y)≥0].
6.3 Proof ofBound 1
Anticipating the proof, we make a few remarks about generating fu nctions and stochastic dominance. We reserve
the term generating function to refer to an “ordinary” generating function which represents a s equencea0,a1,...
of non-negative real numbers by the formal power series A(Z) =/summationtext∞
t=0atZt. WhenA(1) =/summationtext
tat= 1we say
thatthegeneratingfunctionisa probabilitygeneratingfunction ;inthiscase,thegeneratingfunction Acannaturally
be associated with the integer-valued random variable Afor which Pr[A=k] =ak. If the probability generating
functions AandBareassociatedwith therandomvariables AandB,itiseasy tocheckthat A·Bisthegenerating
function associated with the convolution A+B(whereAandBare assumed to be independent). Translating
the notion of stochastic dominance to the se/t_ting with generating fu nctions, we say that the generating function A
stochastically dominates Bif/summationtext
t≤Tat≤/summationtext
t≤Tbtfor allT≥0; we write B/√recedesequalAto denote this state of affairs. If
B1/√recedesequalA1andB2/√recedesequalA2thenB1·B2/√recedesequalA1·A2andαB1+βB2/√recedesequalαA1+βA2(foranyα,β≥0). Moreover,if B/√recedesequalA
then it can be checked that B(C)/√recedesequalA(C)for any probability generating function C(Z), where we write A(C)to
denote the composition A(C(Z)).
Finally, we remark that if A(Z)is a generating function which converges as a function of a complex Zfor
|Z|< Rforsomenon-negative R,Riscalledthe radiusofconvergence ofA. Itfollowsfrom[26,/T_heorem2.19]that
limk→∞akRk= 0and|ak|=O(R−k). In addition, if Ais a probability generating function associated with the
randomvariable Athen it followsthat Pr[A≥T] =O(R−T).
We define p= (1−ǫ)/2andq= 1−pand asin the proofof Bound 2,consider the independent {0,1}-valued
random variables w1,w2,...wherePr[wt= 1] =p. We also define the associated {±1}-valued random variables
Wt= (−1)1+wt.
Although our actual interest is in the random variable µx(y)from (13) on a characteristic string w=xy, we
begin byanalyzing thecase when |x|= 0.
Case1:xistheemptystring. Inthiscase,therandomvariable µx(y)is identical to µ(w)from(12)with w=y.
Our strategy isto study the probability generatingfunction
L(Z) =∞/summationdisplay
t=0ℓtZt
whereℓt= Pr[tisthe last time µt= 0]. Controlling the decay of the coefficients ℓtsuffices to give a bound on the
probability that w1...wkisforkablebecause
Pr[w1...wkis forkable ]≤1−k−1/summationdisplay
t=0ℓt=∞/summationdisplay
t=kℓt.
21
It seems challenging to give a closed-form algebraic expressi on for the generating function L; our approach is to
developaclosed-formexpressionforaprobabilitygenerati ngfunction ˆL=/summationtext
tˆℓtZtwhichstochasticallydominates
Land apply the analytic properties of this closed form to bound th e partial sums/summationtext
t≥kˆℓk. Observe that if L/√recedesequalˆL
then the series ˆLgives rise to anupper boundon the probability that w1...wkis forkableas/summationtext∞
t=kℓt≤/summationtext∞
t=kˆℓt.
/T_he coupled random variables ρtandµtare Markovian in the sense that values (ρs,µs)fors≥tare entirely
determined by (ρt,µt)and the subsequent values Wt+1,...of the underlying variables Wi. We organize the se-
quence(ρ0,µ0),(ρ1,µ1),...into “epochs” punctuated by those times tfor which ρt=µt= 0. With this in mind,
we define M(Z) =/summationtextmtZtto be the generating function for the first completion of such an e poch,corresponding
to the least t >0for which ρt=µt= 0. As we discuss below, M(Z)is not a probability generating function, but
ratherM(1) = 1−ǫ. It followsthat
L(Z) =/parenleftBigg
1+(1−ǫ)·M(Z)
M(1)+/parenleftbigg
(1−ǫ)·M(Z)
M(1)/parenrightbigg2
+···/parenrightBigg
·ǫ
= (1+M(Z)+M(Z)2+···)·ǫ
=ǫ
1−M(Z). (16)
/T_heexpressionaboverepresentsthefollowinggeometricproce ss: beforethebeginningofanepoch,we“ask”whether
thewalkisevergoingtocomebacktozero. Withprobability ǫ,theansweris“no”andwestoptheprocess. Otherwise,
i.e., with probability 1−ǫ,we commencean epochwhichis guaranteedto finish; thenwe askag ain.
Below we develop an analytic expression for a generating functio nˆMfor which M/√recedesequalˆMand define ˆL=ǫ/(1−
ˆM(Z)). We then proceed as outlined above, noting that L/√recedesequalˆLand using the asymptotics of ˆLto upper bound the
probability thata string is forkable.
In preparation for defining ˆM, we set down two elementary generating functions for the “descent” and “ascent”
stoppingtimes. Treatingtherandomvariables W1,...asdefininga(negatively)biasedrandomwalk,define Dtobe
the generating function for the descent stopping time of the walk; this is the first time the random walk, starting at
0,visits−1. /T_henaturalrecursiveformulationofthedescenttimeyieldsa simplealgebraicequationforthedescent
generating function, D(Z) =qZ+pZD(Z)2,and fromthis we mayconclude
D(Z) =1−/radicalbig
1−4pqZ2
2pZ.
We likewise consider the generating function A(Z)for theascent stopping time , associated with the first time the
walk,starting at 0,visits 1: we have A(Z) =pZ+qZA(Z)2and
A(Z) =1−/radicalbig
1−4pqZ2
2qZ.
Note that while Dis a probability generating function, the generating function Ais not: according to the classical
“gambler’s ruin” analysis [12], the probability that a negativ ely-biased random walk starting at 0 ever rises to 1 is
exactlyp/q;thusA(1) =p/q.
Returning to the generating function Mabove, we note that an epochcan have one oftwo “shapes”: in the fi rst
case,theepochisgiven byawalkforwhich W1= 1followedbyadescent(so that ρreturnstozero); inthe second
case, the epoch is given by a walk for which W1=−1, followed by an ascent (so that µreturns to zero), followed
by the eventual return of ρto 0. Considering that when ρt>0it will return to zero in the future almost surely, it
follows that the probability that such a biased random walk wi ll complete an epoch is p+q(p/q) = 2p= 1−ǫ,
as mentioned in the discussion of (16) above. One technical difficu lty arising in a complete analysis of Mconcerns
the second case discussed above: while the distribution of th e smallest t >0for which µt= 0is proportional to A
above,thedistributionofthesmallestsubsequenttime t′forwhich ρt′= 0dependsonthevalue t. Morespecifically,
the distribution of the return time depends on the value of ρt. Considering that ρt≤t, however, this conditional
distribution (ofthereturn time of ρtozero conditionedon t)is stochasticallydominated by Dt,the timeto descend
tsteps. /T_his yieldsthe following generatingfunction ˆMwhich,asdescribed,stochasticallydominates M:
ˆM(Z) =pZ·D(Z)+qZ·D(Z)·A(Z·D(Z)).
22
It remains to establish a bound on the radius of convergence of ˆL. Recall that if the radius of convergence of
ˆLisexp(δ)it follows that Pr[w1...wkis forkable ] =O(exp(−δk)). A sufficient condition for convergence of
ˆL(z) =ǫ/(1−ˆM(z))atzis that that all generating functions appearing in the definition of ˆMconverge at zand
that the resulting value ˆM(z)<1.
/T_he generating function D(z)(andA(z)) converges when the discriminant 1−4pqz2is positive; equivalently
|z|<1/√
1−ǫ2or|z|<1 +ǫ2/2 +O(ǫ4). Considering ˆM, it remains to determine when the second term,
qzD(z)A(zD(z)),converges;this is likewise determined bypositivity ofthe d iscriminant, whichis to saythat
1−(1−ǫ2)/parenleftBigg
1−/radicalbig
1−(1−ǫ2)z2
1−ǫ/parenrightBigg2
>0.
Equivalently,
|z|</radicalBigg
1
1+ǫ/parenleftbigg2√
1−ǫ2−1
1+ǫ/parenrightbigg
= 1+ǫ3/2+O(ǫ4).
Note thatwhentheseries pz·D(z)converges,it convergesto avalue lessthan 1/2;thesameistrue of qz·A(z). It
followsthat for|z|= 1+ǫ3/2+O(ǫ4),|ˆM(z)|<1andˆL(z)converges,asdesired. We concludethat
Pr[w1...wkis forkable ] = exp(−ǫ3(1+O(ǫ))k/2). (17)
Case 2:xis non-empty. /T_he relative margin before ybegins is µx(ε). Recalling that µx(ε) =ρ(x)and condi-
tioning ontheevent that ρ(x) =r,let usdefine therandomvariables {˜µt}fort= 0,1,2,···asfollows: ˜µ0=ρ(x)
and
Pr[˜µt=s] = Pr[µx(y) =s|ρ(x) =rand|y|=t].
If the˜µrandom walk makes the rth descent at some time t < n, then˜µt= 0and the remainder of the walk is
identical to an (k−t)-stepµrandom walk which we have already analyzed. Hence we investigate the probability
generating function
Br(Z) =D(Z)rL(Z)with coefficients b(r)
t:= Pr[tis the last time ˜µt= 0|˜µ0=r]
wheret= 0,1,2,···. Our interest lies in the quantity
bt:= Pr[tisthe last time ˜µt= 0] =/summationdisplay
r≥0b(r)
tRm(r),
where the reach distributionRm:Z→[0,1]associated with the randomvariable ρ(x),|x|=mis defined as
Rm(r) = Pr
x:|x|=m[ρ(x) =r]. (18)
LetRm(Z)be the probability generating function for the distribution Rm. Using Lemma 4 and Definition 15, we
deducethat Rm/√recedesequalR∞forevery m≥0sinceRm/√recedesequalR∞. Inaddition,itiseasytocheckfrom(15)thattheprobabilit y
generatingfunctionfor R∞isinfactR∞(Z) = (1−β)/(1−βZ)whereβ:= (1−ǫ)/(1+ǫ). /T_husthegenerating
function correspondingto the probabilities {bt}∞
t=0is
B(Z) =∞/summationdisplay
t=0btZt=∞/summationdisplay
r=0Rm(r)∞/summationdisplay
t=0b(r)
tZt=∞/summationdisplay
r=0Rm(r)Br(Z)
=L(Z)∞/summationdisplay
r=0Rm(r)D(Z)r=L(Z)Rm(D(Z))/√recedesequalˆL(Z)R∞(D(Z))
=(1−β)ˆL(Z)
1−βD(Z). (19)
23
/T_he dominancenotation above followsbecause L/√recedesequalˆLandRm/√recedesequalR∞.
ForB(Z)toconverge,weneedtocheckthat D(Z)shouldnever convergeto 1/β. Onecaneasily checkthatthe
radius of convergenceof D(Z)—whichis 1//radicalbig
1−ǫ2—is strictly less than 1/βwhenǫ >0. We concludethat B(Z)
convergesif both D(Z)andL(Z)converge. /T_he radius ofconvergence of B(Z)would be the smallerof the radiiof
convergenceof D(Z)andL(Z). Wealreadyknowfromthepreviousanalysisthat ˆL(Z)hasthesmallerradiusofthe
two; therefore,the boundin (17)applies to the relative marg inµx(y)for|x|≥0.
6.4 Proof ofBound 2
Letǫ∈(0,1),W∈{0,1}m,W′∈{0,1}kwhere both (W1,...,W n)and(W′
1,...,W′
n)satisfy the ǫ-martingale
condition. Let B∈{0,1}m,B′∈{0,1}kwherethecomponentsof B,B′areindependentwithexpectation (1−ǫ)/2.
ByLemma4,
W/√recedesequalBandW′/√recedesequalB′. (∗)
Letusdefine the partial order ≤onBooleanstrings {0,1}k,k∈Nasfollows: a≤bif andonlyif forall i∈[k],
ai= 1impliesbi= 1. Letµ:{0,1}k→Zbe the marginfunctionfromLemma3. Observe that forBooleans trings
a,a′,b,b′with|a|=|a′|and|b|=|b′|, (i.)b≤b′impliesµa(b)≤µa(b′)and (ii.)a≤a′impliesµa(b)≤µa′(b).
/T_hat is,
µa(b)is non-decreasingin both aandb. (†)
Using (∗)and (†),it followsthat µW(W′)/√recedesequalµB(B′). Writing x=Wandy=W′,we have
Pr[µx(y)≥0] = Pr[µW(W′)≥0]≤Pr[µB(B′)≥0]
wheretheinequalitycomesfromthedefinition ofstochasticdo minance. Aboundontheright-handsideisobtained
in Bound 1.
InAppendixB,wepresentaweakerboundon Pr[µx(y)≥0]wherethesequence x1,...,x m,y1,...,y ksatisfies
ǫ-martingaleconditions. /T_heproofdirectlyusestheproperties ofthemartingaleandAzuma’sinequalitybutitdoes
not use a stochastic dominance argument. Although it gives a bound of3exp/parenleftbig
−ǫ4(1−O(ǫ))k/64/parenrightbig
, the reader
mightfind theproofof independent interest.
6.5 Proof ofmain theorems
Proof of /T_heorem 1. Let us start with the following observation. It allows us to for mulate the (s,k)-se/t_tlement
insecurity of adistribution Ddirectly in termsofthe relative margin.
Lemma 5. Lets,k,T∈N. LetDbeany distribution on {0,1}T. /T_hen
Ss,k[D]≤Pr
w∼D/bracketleftBiggthere is a decomposition w=xyz, where
|x|=s−1and|y|≥k+1,sothatµx(y)≥
0/bracketrightBigg
.
Proof.Lemma1impliesthat Ss,k[D]isnomorethantheprobabilitythatslot sisnotk-se/t_tledforthecharacteristic
stringw. By Observation 1, this probability, in turn, is no more than th e probability that there exists an x-balanced
forkF⊢xywhere we write w=xyz,|x|=s−1,|y|≥k+ 1,|z|≥0. Finally, Fact 1 states that for any
characteristic string xy, the two events “exists an x-balanced fork F⊢xy” and “µx(y)is non-negative” have the
same measure. Hencethe claimfollows.
IfthedistributionDin thelemmaabove satisfies the ǫ-martingalecondition,the probability in thislemmaisno
morethantheprobabilityinthele/f_t-handsideofCorollary1. Finally,byretracingtheproofofCorollary1usingthe
explicitprobabilityfromBound2,weseethattheboundinCoro llary1isO(1)·exp/parenleftbig
−Ω(ǫ3(1−O(ǫ))k)/parenrightbig
. SinceBǫ
satisfies the ǫ-martingalecondition, we concludethat Ss,k[Bǫ]isno morethan thisquantity aswell.
Foranyplayerplayingthese/t_tlementgame,thesetofstringsonwh ichtheplayerwinsismonotonewithrespect
to the partial order ≤defined in Section 6.4. To see why, note that if the adversary wins with a specific string
24
w, he can certainly win with any string w′wherew≤w′. AsBǫstochastically dominates W, it follows that
Ss,k[W]≤Ss,k[Bǫ].
Proof of /T_heorem 2 For the first inequality, observe that if wviolatesk-CP, it must violate k-CPslotas well. It
remainstoprove thesecondinequality. Let Dbeanydistribution on {0,1}T. WecanapplyFact1onthestatement
of/T_heorem 3to deducethat
Pr
w∼D[wviolatesk-CPslot]≤Pr
w∼D/bracketleftbiggthere is a decomposition w=xyz,
where|y|≥k,so thatµx(y)≥0/bracketrightbigg
.
Byusing aunion bound over |x|, the above probability is atmost
T−k+1/summationdisplay
s=1Pr
w/bracketleftbiggthere is a decomposition w=xyz,where
|x|=s−1and|y|≥k,sothatµx(y)≥0/bracketrightbigg
.
Sincewsatisfies the ǫ-martingale condition, we can upper bound the probability inside the sum using Corollary 1.
Aswe have seen in the proofof /T_heorem1,the bound in Corollary1 isO(1)·exp/parenleftbig
−Ω(ǫ3(1−O(ǫ))k)/parenrightbig
. It follows
that the sumabove isat most Texp/parenleftbig
−Ω(ǫ3(1−O(ǫ))k)/parenrightbig
.
It remains to prove the recursive formulation of the relative margin from Section 5; we tackle it in the next
section.
7 Proof oftherelative margin recurrence
We set thestage by formallydefining forkprefixes .
Definition16 (Forkprefixes) .Letw,x∈{0,1}∗so thatx/√recedesequalw. LetF,F′betwo forks for xandw, respectively. We
saythatFisaprefixofF′ifFisaconsistently labeledsubgraphof F′. /T_hatis,allvertices andedgesof Falso appear
inF′andthe label of any vertex appearingin both FandF′isidentical. Wedenote thisrelationship by F⊑F′.
Whenspeakingaboutatine thatappearsinboth FandF′,weplacetheforkinthesubscript ofrelevant properties,
e.g.,writing reachF,etc.
Observe that for any Boolean strings xandw,x/√recedesequalw, one can extend(i.e., augment) a fork prefix F⊢xinto
a larger fork F′⊢wso thatF⊑F′. Aconservative extension is a minimal extension in that it consumes the least
amountofreserve(cf.Definition11),leavingtheremainingreser vetobeusedinfuture. Extensionsand,inparticular,
conservative extensions playa critical role in the exposition thatfollows.
Definition17 (Conservative extension ofclosedforks) .LetwbeaBoolean string, Faclosedforkfor w, andletsbe
an honest tine in F. LetF′be a closed fork for w0so thatF⊑F′andF′contains an honest tine σ,ℓ(σ) =|w|+1.
Wesaythat F′isanextension of For,equivalently, that σisanextension of s,ifs≺σ. If, inaddition, length(σ) =
height(F)+1,we call this extension a conservative extension .
Clearly,σisthelongesttinein F′. Sinceσishonest,itfollowsthat length(σ)≥1+height( F) = 1+length( s)+
gap(s). /T_he root-to-leaf path in F′that ends at σcontains at least gap(s)adversarial vertices u∈F′so that
ℓ(u)∈[ℓ(s)+1,|w|]andu/ne}ationslash∈F. Ifσisaconservative extension, thenumberofsuchverticesisexact lygap(s)and,
in particular,the height of F′is exactlyone morethan theheight of F.
/T_he main ingredients to proving Lemma 3 are a fork-building strate gy for the string xyand Propositions 1
and 2. Specifically,recallequation (13). /T_he first propositio n shows that the fork F⊢xy0built by the said strategy
achieves µx(F)≥µx(y0)while the second proposition shows that this value, in fact, i s the largest possible, i.e.,
µx(y0)≤µx(y0). In addition, any fork-building strategy whose forks satisfy t he premise of Proposition 1 can be
used to prove Lemma3.
25
7.1 A fork-buildingstrategyto maximize x-relative margin
Any fork F⊢xycontains two tines tx,tρso thatreach(tρ) =ρ(F),reachF(tx) =µx(F), and the tines tx,tρare
disjoint over the suffix y. We say that the tine-pair (tρ,tx)is awitnesstoµx(F).
Letx,y∈{0,1}∗andwrite w=xy. Recursivelybuildclosedforks F0,F1,...,F |w|whereFi⊢w1...wi,i≥1
andF0⊢εisthetrivialforkconsistingofasinglevertexcorrespondingto thegenesisblock.For i= 0,1,...,|w|−1
inincreasingorder,doasfollows. If wi+1= 1,setFi+1←Fi. Ifwi+1= 0,setFi+1⊢w0asaconservativeextension
ofFi⊢wso thatσ∈Fi+1,ℓ(σ) =i+ 1is a conservative extension of a tine s∈Fiidentified as follows. If Fi
containsnozero-reachtine, sistheuniquelongesttinein Fi. Otherwise,firstidentifyamaximal-reachtine tρ∈Fi
asfollows: if i≥|x|+1,tρisamaximal-reachtinein Fiwhichbelongstoatine-pair witnessing µx(Fi);otherwise,
tρcan be an arbitrary maximal-reachtine in Fi. Finally, sis the zero-reach tine in Fithat diverges earliest from tρ.
Ifthere are multiple candidates for sortρ,breaktie arbitrarily.
Proposition1. Letx,ybearbitraryBooleanstrings, |y|≥1andw=xy. LetF⊢wandF′⊢w0betwoclosedforks
built by the strategy above so that F⊑F′and suppose, in addition, that ρ(F) =ρ(xy)andµx(F) =µx(y). /T_hen
ρ(F′) =ρ(xy0)andµx(F′)≥µx(y0).
7.2 Proof ofProposition1
Beforeweproceedfurther,letusrecordtwousefulresultsr elatedtoconservativeextensionsandclosedforkprefixes.
Claim 1 (A conservative extension has reach zero) .Consider closed forks F⊢w,F′⊢w0such that F⊑F′. If a
tinetofF′isaconservative extension then reachF′(t) = 0.
Proof.We have assumed that tis a conservative extension, so its terminal vertex must be the new honest node. By
definition, reachF′(t) =reserve F′(t)−gapF′(t). Honest players will only place nodes at a depth strictly greate r
than all other honest nodes, so we infer that tis the longest tine of F′, and so gapF′(t) = 0. Moreover, we observe
that there are no 1s occurring a/f_ter this point in the characteris tic string, and so reserve F′(t) = 0. Plugging these
valuesinto ourdefinition of reachwe see that reachF′(t) = 0−0 = 0.
Claim2(Reachofnon-extendedtines) .Consideraclosedfork F⊢wandsomeclosedfork F′⊢w0suchthat F⊑F′.
Ift∈FthenreachF′(t)≤reachF(t)−1. /T_he inequality becomes and equality if F′is obtained via a conservative
extension from F.
Proof.Definitionally, we know that reachF′(t) =reserve F′(t)−gapF′(t).FromFtoF′, the length of the longest
tine increases by at least one, and the length of tdoes not change, so we observe that gapF′(t)≥gapF(t) + 1
with equality only for conservative extensions. /T_he reserve of tdoes not change, because there are no new 1s
in the characteristic string. /T_herefore, reachF′(t) =reserve F′(t)−gapF′(t)≤reserve F(t)−gapF(t)−1 =
reachF(t)−1.
Assume the premise of Proposition 1. /T_hat is, Fis a forkfor xyso thatρ(F) =ρ(xy),µx(F) =µx(y), and the
tinetρidentified bythefork-buildingstrategyinSection7.1belongs toanF-tine-pair (tρ,tx)thatwitnesses µx(F).
To recap, this means reachF(tρ) =ρ(F) =ρ(x),reachF(tx) =µx(F) =µx(y), and the tines tρ,txare disjoint
overy(i.e.,ℓ(tρ∩tx)≤|x|). In addition, since σ∈F′is a conservative extension of s, we have reachF′(σ) = 0.
Finally,let Sbe theset ofall zero-reachtines in F.
We will breakthis part ofthe proofinto several casesbased on the relative reachand marginofthe fork.
Case 1:ρ(xy)>0andµx(y) = 0.We wish to show that ρ(F′) =ρ(xy0)andµx(F′)≥0. Sinceρ(F)>0,
s/ne}ationslash=tρand therefore, By (11) and Claim 2, /T_hus ρ(F′)≥reachF′(tρ) = reach F(tρ)−1 =ρ(xy)−1 =ρ(xy0).
/T_herefore, ρ(F′) =ρ(xy0).
Sinceµx(y) = 0,txisacandidateforbeingselectedas sandhence ℓ(s∩tρ)≤ℓ(tx∩tρ)≤|x|. /T_husσ,tρ∈F′
are disjoint over y0and,therefore, µx(F′)≥reachF′(σ) = 0.
26
Case 2:ρ(xy) = 0.We wish to show that ρ(F′) =ρ(xy0)andµx(F′)≥µx(y)−1. Since there is at least one
zero-reach tine, reachF(s) = 0and, in addition, tρ∈S,|S|≥1. SincereachF′(σ) = 0 = ρ(xy0)by (11),σhas
the maximal reach in F′and, in particular, ρ(F′) =ρ(xy0). Depending on Sands, there are three possibilities.
Ifs=tρ, this means S={tρ},tx’sF′-reach is one less than its F-reach, and σ,txare still disjoint over y0.
Henceµx(F′)≥reachF(tx)−1 =µx(y)−1. Ifs=tx, thentρ’sF′-reach is one less than its F-reach and
σ,tρare disjoint over y0. Henceµx(F′)≥reachF(tρ)−1 =ρ(xy)−1≥µx(y)−1. Finally, suppose s/ne}ationslash=tρ
ands/ne}ationslash=tx. /T_henµx(y) = reach F(tx)<0and, in addition, s(andσ) must share an edge with tρsomewhere
overysince otherwise, we would have achieved µx(y) = 0. As a result, txandσmust be disjoint over y0. Hence
µx(F′)≥reachF′(tx) = reach F(tx)−1 =µx(y)−1.
Case 3:ρ(xy)>0,µx(y)/ne}ationslash= 0.We wish to show that ρ(F′) =ρ(xy0)andµx(F′)≥µx(y)−1. In this case,
s/ne}ationslash=tρands/ne}ationslash=txand therefore, reachF′(ti) = reach F(ti)−1fori= 1,2. /T_he tines tρ,txare still disjoint over
y0. Inaddition, tρwillstill havethemaximalreachin F′sincereachF′(tρ) =ρ(xy)−1 =ρ(xy0)by11. /T_herefore,
ρ(F′) =ρ(xy0)and,in addition, µx(F′)≥reachF′(tx) = reach F(tx)−1 =µx(y)−1.
/T_hiscompletethe proofof Proposition 1.
7.3 Proof ofLemma 3
LetFbe a closed fork for the characteristic string xy. Lettρ,tx∈Fbe the two tines that witness µx(F), i.e.,
reach(tρ) =ρ(F),reachF(tx) =µx(F),andtρ,txare disjoint over y. Letˆtbe thelongest tine in F.
Inthebasecase,where y=ε,weobservethatanytwotinesof Faredisjointover y. Moreover,evenasingletine
tρisdisjointwithitselfover ε. /T_herefore,therelativemargin µx(ε)mustbegreaterthanorequaltothereachofthe
tinetthat achieves reach (t) =ρ(x). /T_herelative marginmust also be lessthan orequal to ρ(x), because that is,by
definition, the maximumreachover alltines in allforks F⊢w. Pu/t_ting these factstogether,we have µx(ε) =ρ(x).
Moving beyond the base case, we will consider a pair of closed for ksF⊢xyandF′⊢xybsuch that F⊑F′,
x,y∈{0,1}∗,|y|≥1,andb∈{0,1}.Ifb= 1,wehaveset F′=F. /T_hereachofeachtineincreasesby1from Fto
F′since the gaphasnot changedbut the reserve hasincreased byone. /T_h erefore,µx(y1) =µx(y)+1,asdesired.
Ifb= 0,however, thingsare more nuanced. Consider the following propos ition:
Proposition2. Letx,ybearbitrary Boolean strings, |y|≥1, andw=xy0. /T_henµx(y0)≤0ifρ(xy)> µx(y) = 0,
andµx(y0)≤µx(y)−1otherwise.
Recall that µx(F′)≥µx(y0)by Proposition 1. Combining this with Proposition 2 above, we c onclude that
µx(F′) =µx(y0)and,inaddition,thatthefork F′actuallyachievesthemaximumreachandthemaximumrelative
marginforthe characteristic string xy0. It remainsto prove Proposition 2.
Proof of Proposition 2. SupposeF′⊢xy0is a closed fork such that ρ(xy0) =ρ(F′)andµx(y0) =µx(F′). Let
tρ,tx∈F′to be a pair of tines disjoint over yinF′such that reachF′(tρ) =ρ(F′)andreachF′(tx) =µx(F′) =
µx(y0). LetF⊢xybe the unique closed fork such that F⊑F′. Note that while F′is an extension of F, it is not
necessarily a conservative extension.
Case 1:ρ(xy)>0andµx(y) = 0.We wish to show that µx(y0)≤0. Suppose (toward a contradiction) that
µx(y0)>0. /T_hen neither tρortxis a conservative extension because, as we proved in Claim 1, co nservative
extensionshavereachexactly0. /T_hismeansthat tρandtxexistedin F,andhadstrictlygreaterreachin Fthanthey
do presently in F′(byClaim 2). Because tρandtxare disjoint over y0,they must also be disjoint over y; therefore
theµx(F)must be at least min{reachF(tρ),reachF(tx)}. Following this line of reasoning, we have 0 =µx(y)≥
mini∈{1,2}{reachF(ti)}>mini∈{1,2}{reachF′(ti)}=µx(F′) =µx(y0)>0,a contradiction,asdesired.
Case 2:ρ(xy) = 0.We wish to show that µx(y0)≤µx(y)−1or, equivalently, that µx(y0)< µx(y). First, we
claimthat tρmustarise fromanextension. Suppose,toward acontradiction,t hattρisnot anextension, i.e., tρ∈F.
/T_he fact that tρachieves the maximum reach in F′implies that tρhas non-negative reach since the longest honest
tine alwaysachievesreach0. Furthermore,Claim 2states tha talltines otherthantheextended tine see their reach
27
decrease. /T_herefore, tρ∈Fmust have had a strictly positive reach. But this contradicts the central assumption of
the case, i.e., that ρ(xy) = 0. /T_herefore, we conclude that tρ∈F′,tρ/ne}ationslash∈F, and, since F′differs from Fby a single
extension, tx∈F.
Lets∈Fbe the tine-prefix of tρ∈F′so thattρis an extension of s. SincereachF′(tρ) =ρ(xy0) = 0by (11),
reachF(s)must be at least 0. Additionally, since ρ(xy) = 0,reachF(s)≤0. Together, these statements tell us
thatreachF(s) = 0. Restricting our view to F, we see that sandtxare disjoint over yand so it must be true that
min{reachF(s),reachF(tx)}≤µx(y). Because reachF(s) = 0andreachF(tx)≤ρ(xy) = 0,we can simplify that
statement to reachF(tx)≤µx(y). Finally, since tx∈F, Claim 2 tells us that reachF′(tx)<reachF(tx). Taken
together,these two inequalities show that µx(y0) = reach F′(tx)<reachF(tx)≤µx(y).
Case 3:ρ(xy)>0,µx(y)/ne}ationslash= 0.We wish to show that µx(y0)≤µx(y)−1or,equivalently, that µx(y0)< µx(y).
Note that by11, ρ(xy0) =ρ(xy)−1≥0. We will breakthis case into two sub-cases.
If bothtρ,tx∈F./T_hentρ,tx∈Fand,consequently, min{reachF(tρ),reachF(tx)}≤µx(y)sincetρandtxmust
be disjoint over y. Furthermore, by Claim 2, reachF′(ti)<reachF(ti)fori∈{1,2}. /T_herefore, µx(y0) =
reachF′(tx) = min{reachF′(tρ),reachF′(tx)}<min{reachF(tρ),reachF(tx)}≤µx(y),asdesired.
If eithertρ/ne}ationslash∈Fortx/ne}ationslash∈F.It must be true that reachF′(tx)≤0, because either txis the extension (and therefore
has reach exactly 0) or tρis the extension and we have reachF′(tx) =µx(y0)≤ρ(xy0) = reach F′(tρ) = 0.
Recall that we have assumed µx(y)/ne}ationslash= 0. Ifµx(y)>0,we are done: certainly µx(y0)≤0< µx(y). If,
however, µx(y)<0, there is more work to do. In this case, we claim that tx∈F, i.e.,txdid not arise
from an extension. To see why, consider the following: if txarose from extension, then there must be some
s∈Fso thats≺txandreachF(s)≥0. Additionally, by our claim about non-extended tines, we see
thatreachF(tρ)>reachF′(tρ) =ρ(xy0)≥0. /T_herefore, µx(y)≥min{reachF(tρ),reachF(s)} ≥0,
contradictingour assumption that µx(y)<0. /T_hustx∈F.
/T_he only remaining scenario is the one in which µx(y)<0andtρarises from an extension of some tine
s∈F,reachF(s)≥0. In this scenario, txcannot have been the extension (since there is only one). By
Claim 2,reachF(tx)>reachF′(tx). Using a now-familiar line of reasoning, note that the two tines txand
sare disjoint over yand, therefore, µx(y)≥min{reachF(s),reachF(tx)}. Since,µx(y)<0by assumption
andreachF(s)≥0,it followsthat µx(y)≥reachF(tx)>reachF′(tx) =µx(y0),asdesired.
/T_hiscompletestheproof ofLemma3.
8 Canonical forks and an optimal onlineadversary
Letwbe a characteristic string, wri/t_ten w=xy, and recall the online fork-building strategy from Section 7.1. In
Proposition 1, we showed that the fork produced by this strat egy (for the string w) always contains a tine-pair
(tρ,tx)thatwitnesses µx(y). Inthissection,wepresentanonlinefork-buildingstrategywhi chproducesaforkthat
simultaneously contains, for every prefix x/√recedesequalw, a tine-pair that witnesses µx(y). /T_hese forks are called canonical
forks,defined below.
Definition 18 (Canonical forks) .Letw1...wT∈ {0,1}T. Forn= 0,1,...,T, acanonical fork Fnforw=
w1...wnis inductively defined as follows. If n= 0thenF0is the trivial fork for the empty string; it consists of a
single (honest) vertex and no edge. If n≥1, the following holds: Fnis a closed fork so that Fn−1⊑Fn.Fncontains
an honest tine τρso thatreach(τρ) =ρ(Fn) =ρ(w). For every decomposition w=xy,x≺w,Fncontains two
honest tines τx,τρxso that the tine-pair (τρx,τx)witnesses µx(Fn) =µx(y). /T_he (possibly non-distinct) designated
tinesτρ,τρx,τx,x≺warecalled the witness tines .
Note that if one’s objective is to create a fork which contains ma ny early-diverging tine-pairs witnessing large
relative margins,a canonicalforkisthe best one canhopefor.
28
8.1 Anonline strategyfor building canonical forks
Letwbeacharacteristicstring,wri/t_tenas w=xy,andletFbeaforkfor w. Ifthetines t1,t2∈Faredisjoint over
y, we sayt1andt2arey-disjoint, or equivalently, t1isy-disjointwith t2. Note that this means ℓ(t1∩t2)≤|x|. Let
≤πbethelexicographicalorderingofthetineswhereeachtineisr epresentedasthelistofvertexlabelsappearingin
thetine’sroot-to-leafpath. Iftwotineshavethesamevertex labels,≤πmustbreaktieinanarbitrarybutconsistent
way.
For a fixed fork, let A,Bbe two sets of tines. We define the early-divergence witness for (A,B)as follows. Let
CABbe an ordered set of tine-pairs (t′
a,t′
b),a′∈A,b′∈Bthat minimize ℓ(ta∩tb),ta∈A,tb∈B. /T_he order of
the elements in CABis the following: (t1,t2)≤(t′
1,t′
2)if and only if t1≤πt′
1andt2≤πt′
2. /T_he first element of
CABis calledtheearly-divergence witness for (A,B).
/T_he fork-building strategy A∗presented in Figure 4 builds canonicalforksin an online fashion,i .e., it scans the
characteristicstring wonce,fromle/f_ttoright,maintainsa“currentfork,”andupdatesit a/f_terseeingeachnewsymbol
byonlyaddingnewvertices. Sincethefinalfork F⊢wiscanonical,itsatisfies µx(F) =µx(y)simultaeneouslyfor
all decompositions w=xy;hencewe callA∗theoptimal onlineadversary .
/T_he strategyA∗
Letw=w1...wn∈{0,1}nandwn+1∈{0,1}. Ifn= 0, setF0⊢εas the trivial fork comprising a
single vertex. Otherwise, for n≥0, letFnbe the closedfork built recursively by A∗forthe string w. If
wn+1= 1, setFn+1=Fn. Otherwise, the closed fork Fn+1⊢w0is the result of a single conservative
extension of a tine s∈Fninto a new honest tine σ∈Fn+1,ℓ(σ) =n+1; /T_he tine scan be identified
asfollows. If Fncontainsno tine with reach zero, sisthe unique longest tine in Fn. Otherwise, sis the
reach-zero tine that diverges earliest with respect to the se t of maximal-reach tines in Fn. If there are
multiple candidates for s,selectthe one with the smallest ≤π-rank.
Designatingthewitnesstines
Writingw′=wwn+1,F=Fn,andF′=Fn+1,identify thetines τρ,τw,τx,τρx∈F′,x≺wasfollows.
LetR(resp.R′) be the set of F-tines (resp. F′-tines) with the maximal F-reach (resp. F′-reach). Set τρ
as the element of R′with smallest≤π-rank. Set (τw,τρw)as the early-divergence witness for (R,R′).
For every decomposition w=xy,|y|≥1,|x|≥0, do as follows. Let Bxbe the set of F′-tines that are
ywn+1-disjoint with somemaximal-reach tine in R′. LetCx⊆Bxcontain the tines with the maximal
F′-reach,the maximumtaken over Bx. Set(τx,τρx)asthe early-divergence witness for (Cx,R′).
Figure 4: Optimal online adversary A∗
/T_heorem5 (A∗builds canonicalforks) .Letw∈{0,1}nandb∈{0,1}. LetF⊢wandF′⊢wbbetwo closed forks
builtbythe strategy A∗so thatF⊑F′and suppose, inaddition, that Fiscanonical. /T_hen F′iscanonical aswell.
We remark that the fork-building strategy A∗would certainly satisfy Proposition 1 and, therefore, satisf y the
recurrencerelation (13)aswell.
8.2 Winning the (D,T;s,k)-settlementgame, optimally
Consider the player in the (D,T;s,k)-se/t_tlement game who, at the first step, samples a characterist ic stringw∼
D,w=w1w2...wT. Since thechallengerisdeterministic, thegameiscompletely determinedbythecharacteristic
stringandthechoicesoftheplayer. Inparticular,foragiven prefixx≺w,|x|=s−1,considerthedecompositions
w=xyz. /T_heplayer’schanceofwinningthegamewillbemaximizedif,fore veryy,|y|≥k+1(sothatn=|xy|≥
s+k),thefork Fn⊢xycontainsatine-pair (τρx,τx)thatwitnesses µx(y). Infact,if µx(y)≥0forsome ythen,as
shown in Fact 1,theplayer wins the gamebyaugmenting Fntoanx-balancedfork An⊢xy.
29
Note,in addition, that if Fnis canonical,the player canoptimally play (D,T;s,k)-se/t_tlementgames simultane-
ouslyforevery s∈[n−k]. /T_hatis,givenadistribution D,acanonicalfork Fngivestheplayerthelargestprobability
ofcausing a se/t_tlement violation at asmanyslots s∈[n−k]aspossible, at once.
8.3 Proof of/T_heorem5
For convenience,let us recordthe followingfactwhichcompacts Claims1and 2.
Fact2.LetF⊢wandF′⊢w0beclosedforkssothat F⊑F′andF′differsfrom Fbyasingleconservativeextension
σ∈F′,ℓ(σ) =|w|+1. /T_henreachF′(t) = reach F(t)−1for everyt∈Fand,in addition, reachF′(σ) = 0.
In the rest of the proof, we will frequently use the above fact a long with Lemma 2 and Lemma 3, o/f_ten without
an explicit reference.
Byassumption, Fisacanonicalfork. /T_hus reachF(tρ) =ρ(w)andforeveryprefix x≺w,reachF(tx) =µx(y).
Letw′=wband letτρ,τw,τρw,τx,τρx∈F′,x≺wbe the purported witness tines in F′. Note that τxmust beyb-
disjointwith τρxbyconstruction. Similarly, τwmustbewn+1-disjointwith τρwsincebothcannotcontaintheunique
vertexfromslot n+1. Itisevidentfromtheconstructionthat ρ(F′) = reach F′(τρ) = reach F′(τρw) = reach F′(τρx)
forx≺w. /T_herefore,wewishtoshowthat reachF′(τρ) =ρ(wb),reachF′(τw) =µw(b)andreachF′(τx) =µx(yb)
forx≺w.
Ifb= 1.In this case, F′=Fandw′=w1. Examining the rule for assigning τρ,τx,τρx, andτw, we see that
τρ=tρ,τw=tρ,τx=tx, andτρx=tρxfor allx≺w. SinceF′=Fandb= 1, theF′-reach of every
F-tine is one plus its F-reach. /T_hus for any x,x≺w, writing w′=xy1, we have µx(y1) = 1 + µx(y) =
1 +reach F(tx) = reach F′(tx) = reach F′(τx). Similarly, ρ(w1) = 1 + ρ(w) = reach F′(tρ) = reach F′(τρ).
By construction, τwhasthe largest reach in F; but this means reachF′(τw) = reach F′(tρ) =ρ(F′) =ρ(w1)
but,onthe other hand, µw(1) = 1+ µw(ε) = 1+ρ(w) =ρ(w1); hencereachF′(τw) =µw(1).
Ifb= 0./T_he contingenciesof thiscase are covered byPropositions 3,4,and 5 below.
Proposition3. Assumethepremiseof/T_heorem5with b= 0. /T_henF′containsawitnesstine τρsothatreachF′(τρ) =
ρ(w0).
Proof.Recall that the tine σ∈F′,ℓ(σ) =|w|+ 1is a conservative extension to a tine s∈F,reachF(s) = 0so
thatreachF′(σ) = 0. Also recall that µz(ε) =ρ(z)for any characteristic string z. Finally, note that it suffices to
show that reachF′(τρ)≥ρ(w0).
Supposeρ(w)>0. Using Fact 2, Lemma 3, and examining the rule for assigning τρ, we see that reachF′(τρ)≥
reachF′(tρ) = reach F(tρ)−1 =ρ(w)−1 =ρ(w0). On the other hand, if ρ(w) = 0thenρ(w0)is zero as well. It
followsthat reachF′(τρ)≥reachF′(σ) = 0 =ρ(w0).
Proposition 4. Assume the premise of /T_heorem 5 with b= 0. /T_henF′contains a tine-pair (τρw,τw)that witnesses
µw(0).
Proof.Recallthatthetine σ∈F′,ℓ(σ) =|w|+1isaconservative extension toatine s∈F,reachF(s) = 0sothat
reachF′(σ) = 0. Inaddition,since F′containsa singlevertex atslot |w|+1,τwandτρware disjoint over thesuffix
wn+1and, moreover, reachF′(τρw) =ρ(F′) =ρ(w0)by Proposition 3. Now consider the following contingencies
based on ρ(w).
Ifρ(w)>0./T_husµw(0) =µw(ε)−1 =ρ(w)−1 =ρ(w0). /T_here are two mutuallyexclusive scenariosbased on
τρwandσ. Ifτρw=σthen,byconstruction, τw/ne}ationslash=σ(sinceℓ(τρw,τw)≤|w|) and,in addition, reachF(τw) =
ρ(w). /T_his implies reachF′(τw) = reach F(τw)−1 =ρ(w)−1 =µw(0). On theother hand,if τρw/ne}ationslash=σthen
τρw∈F. Sinceτwis theF-tine with the largest F′-reach, it follows that reachF′(τw) = reach F′(τρw) =
ρ(w0) =µw(0).
30
Ifρ(w) = 0.Sinceρ(F) =ρ(w) = 0,Fact2tellsusthatevery F-tinemusthaveanegativereachin F′. Sinceρ(F′)
is non-negative, it must be the case that τρw=σ. We can reuse the argumentfrom the subcase “ τρw=σ” of
theprecedingcase and concludethat reachF′(τw) =µw(0).
Proposition 5. Assume the premise of /T_heorem 5 with b= 0. Letx≺wand write w=xy. /T_henF′contains a
tine-pair(τρx,τx)that witnesses µx(y0).
Proof.Byconstruction, reachF′(τx) =µx(F′)and,bythedefinition ofrelative margin, µx(F′)≤µx(y0). Inlight
of (13),it sufficestoshow that reachF′(τx)≥0ifρ(xy)> µx(y) = 0,andreachF′(τx)≥µx(y)−1otherwise.
LetRbethesetof F-tineswiththemaximal F-reachandlet R′bethesetof F′-tineswiththemaximal F′-reach;
thusτρx∈R′. We knowthat txisy-disjoint with tρinF. Consider the followingmutually exclusive cases.
Ifρ(w)>0andµx(y) = 0.Inthiscase, µx(y0) = 0using Lemma3. Since reachF(s) = 0<reachF(tρx) =ρ(w),
itfollowsthat s/ne}ationslash=tρx. Inaddition,observethat tρxmustbein R′. Byourchoiceof s,ℓ(s∩tρx)≤ℓ(tx∩tρx)
sincereachF(tx) =µx(y) = 0 = reach F(s). Sincetxisy-disjoint with tρx,soiss. Recallthat reachF′(τx)is
thelargest amongalltines that are y0-disjoint with τρx.
Ifτρx=tρx./T_hustxisy0-disjoint with τρx. Sinceℓ(σ) =|w|+1,σmust bey0-disjoint with tρx=τρx, it
followsthat reachF′(τx)≥reachF′(σ) = 0 =µx(y0).
Ifτρx/ne}ationslash=tρx./T_his happens when ρ(w) = 1,ρ(w0) = 0, andtρx,σ∈R′. Note that|R′|≥2since both
σ,tρx∈R′butσ/ne}ationslash=tρx. If there are two y0-disjoint tines r′
1,r′
2∈R′thenreachF′(τx)≥0 =µx(y0).
Otherwise, all tines r′∈R′share a vertex indexed by y. Sincetxisy-disjoint with tρx,txmust be
y-disjoint (and thus y0-disjoint) with every r′∈R′as well. Examining the rule for assigning τx, we
concludethat τx=txand,therefore, reachF′(τx) = reach F′(tx) =µx(y) = 0 =µx(y0).
Ifρ(w) = 0.Letx≺wand note that µx(y0) =µx(y)−1. Sinceρ(w) = 0,reachF(s) = 0allF-tines will have
a negative reach in F′; by Fact 2, σis the only tine in F′with the maximal reach ρ(F′) =ρ(w0) = 0, i.e.,
τρx=τρ=σ. In addition, we must also have reachF(s) = 0,i.e.,s∈R; we conclude that shasthe smallest
≤πrank among all members of Rand, therefore, s=tρ. It follows that τxisy0-disjoint with s=tρand,
in particular, τx∈F. Considering tx, if it isy-disjoint with tρthen we must have τx=tx; in this case,
reachF′(τx) = reach F′(tx) = reach F(tx)−1 =µx(y)−1 =µx(y0). Otherwise, ℓ(tx∩tρ)≥|x|+ 1
and there must be a tine tρx∈Fthat isy-disjoint with tx(and hence, with τρx). /T_herefore, reachF′(τx)≥
reachF′(tρx)≥reachF′(tx) = reach F(tx)−1 =µx(y)−1. Here, the first inequality follows from the
constructionof τxandthe secondone followssince tρx)hasthe maximalreachin F.
Ifρ(w)>0andµx(y)/ne}ationslash= 0./T_here canbe two casesdepending onwhether shaszero reachin F.
IfreachF(s) = 0./T_hens/ne}ationslash∈{tρx,tx}. Observe that reachF′(tρx) = reach F(tρx)−1 =ρ(w)−1 =ρ(w0).
It follwos that tρx∈R′. Sincetxisy0-disjoint with tρx∈R′and, in addition, that τxhas the largest
reach among all tines that are y0-disjoint with some member of R′, we conclude that reachF′(τx)≥
reachF′(tx) = reach F(tx)−1 =µx(y)−1 =µx(y0).
IfreachF(s)≥1.In this case, sis the longest tine in F. Considering fork F′, if some tine r′∈R′isy0-
disjoint with txthenreachF′(τx)≥reachF′(tx) = reach F(tx)−1 =µx(y)−1 =µx(y0). Otherwise,
ℓ(r′∩tx)>|x|for every tine r′∈R′, i.e., no maximal-reach F′-tine isy0-disjoint with tx. Since
ℓ(tx,tρx)≤|x|by assumption and τρx∈R′, it follows that ℓ(τρx∩tρx)≤|x|, i.e.,tρxisy0-disjoint
withτρx. /T_herefore, reachF′(τx)≥reachF′(tρx) = reach F(tρx)−1 =ρ(w)−1≥µx(y)−1 =µx(y0).
Here,the secondinequality istrue since µx(y)≤ρ(xy) =ρ(w).
31
/T_hiscompletestheproof of/T_heorem5.
In regards to the canonical fork F⊢wproduced by the strategy A∗(see Figure 4), it is possible to maintain
witness tines τρ,τ′
m∈F, for integers m=−|w|,...,|w|, so that for every prefix x≺w, the tine-pair (τρ,τ′
µx(y))
witnesses µx(y). In particular, a single maxmimal-reach tine τρappears in every witness tine-pair. We omit futher
details.
Acknowledgments
WearegratefultoShreyasGandlurandBruceHajek(UIUC)forth eirsuggestionaboutusingthedominanceargument
in the proofofBound 2.
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[19] Jesper Buus Nielsen and Vincent Rijmen, editors. Advances in Cryptology - EUROCRYPT 2018 - 37th Annual
International Conference on the /T_heory and Applications of C ryptographic Techniques, Tel Aviv, Israel, April 29 -
May3, 2018Proceedings, Part II ,volume 10821of Lecture Notes in Computer Science , 2018.Springer.
[20] Sunoo Park, Krzysztof Pietrzak, Albert Kwon, Jo ¨el Alwen, Georg Fuchsbauer, and Peter Gazi. Spacemint: A
cryptocurrencybased onproofsofspace. IACR CryptologyePrint Archive , 2015:528,2015.
[21] Rafael Pass and Elaine Shi. /T_he sleepy model of consensus. In T suyoshi Takagi and /T_homas Peyrin, editors,
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Cryptology and Information Security, Hong Kong, China, Dec ember 3-7, 2017, Proceedings, Part II , volume 10625
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[22] Rafael Pass and Elaine Shi. Hybrid consensus: Efficient consensu s in the permissionless model. In Andr ´ea W.
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[23] Rafael Pass and Elaine Shi. /T_hunderella: Blockchains with op timistic instant confirmation. In Nielsen and
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33
A Exact settlement probabilities
Letm,k∈Nandǫ∈(0,1]. Letwbe a characteristic string of length T=m+ksuch that the bits of ware i.i.d.
Bernoulli with expectation α= (1−ǫ)/2. Writewasw=xywhere|x|=m,|y|=k. /T_he recursive definition of
relative margin(cf. Lemma3)impliesanalgorithmforcompu tingtheprobability Pr[µx(y)≥0]intimepoly(m,k).
Intypicalcircumstances,however,itismoreinterestingtoes tablishanexplicitupperboundon Pr[µx(y)≥0]where
|x|→∞; this corresponds to the case where the distribution of the ini tial reach ρ(x)is the dominant distribution
R∞in Lemma 4. Due to dominance, R∞(m)serves as an upper bound on ρ(x)for any finite m=|x|. For this
purpose,onecanimplicitlymaintainasequenceofmatrices (Mt)fort= 0,1,2,···,ksuchthat M0(r,r) =R∞(r)
forall0≤r≤2kandthe invariant
Mt(r,s) = Pr
y∼B(t,α)[ρ(xy) =randµx(y) =s]
issatisfied forevery integer t∈[1,k],r∈[0,2k],ands∈[−2k,2k]. Here,M(i,j)denotestheentry atthe ithrow
andjthcolumnofthematrix M. Observe that Mt(r,s)canbecomputedsolelyfromtheneighboringcellsof Mt−1,
that is, fromthe values Mt−1(r±1,s±1). Of course,only the transitions approved by the recursions in Le mma2
and Lemma3should beconsidered.
Finally, one can compute Pr[µx(y)≥0]by summing Mk(r,s)forr,s≥0. Table 1 contains these probabilities
whereαrangesfrom 0.05to0.40andkrangesfrom 50to1000. Inaddition,Figure5showsthebase- 10logarithmof
these probabilities. /T_he pointscorresponding to a fixed αappear to forma straight line. /T_his meansthe probability
decaysexponentially in k,or equivalently,that theexponent dependslinearly on k,as stipulated by Bound1.
AC++implementationoftheabovealgorithmispubliclyavailable ath/t_tps://github.com/saad0105050/forkable-strings-cod e[25].
Table 1: Exact probabilities Pr[µx(y)≥0]where the bits of the characteristic string xyare i.i.d. Bernoulli with
expectation α. Eachrow ofthe table correspondstoa different k=|y|.
kα
0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
505.37E-15 1.16E-09 1.02E-06 8.68E-05 1.96E-03 1.86E-02 9.36E-02 2.92E-01
1001.23E-28 5.10E-18 3.52E-12 2.28E-08 1.03E-05 8.00E-04 1.72E-02 1.37E-01
1502.83E-42 2.24E-26 1.22E-17 6.05E-12 5.54E-08 3.57E-05 3.30E-03 6.74E-02
2006.49E-56 9.82E-35 4.21E-23 1.61E-15 2.98E-10 1.60E-06 6.40E-04 3.36E-02
2501.49E-69 4.31E-43 1.46E-28 4.27E-19 1.61E-12 7.21E-08 1.25E-04 1.69E-02
3003.42E-83 1.89E-51 5.05E-34 1.14E-22 8.67E-15 3.25E-09 2.44E-05 8.52E-03
3507.84E-97 8.29E-60 1.75E-39 3.02E-26 4.67E-17 1.46E-10 4.78E-06 4.31E-03
4001.80E-110 3.64E-68 6.06E-45 8.02E-30 2.52E-19 6.59E-12 9.37E-07 2.18E-03
4504.13E-124 1.60E-76 2.10E-50 2.13E-33 1.36E-21 2.97E-13 1.84E-07 1.11E-03
5009.47E-138 7.00E-85 7.26E-56 5.67E-37 7.32E-24 1.34E-14 3.60E-08 5.62E-04
5502.17E-151 3.07E-93 2.51E-61 1.51E-40 3.95E-26 6.02E-16 7.05E-09 2.86E-04
6004.98E-165 1.35E-101 8.70E-67 4.00E-44 2.13E-28 2.71E-17 1.38E-09 1.45E-04
6501.14E-178 5.91E-110 3.01E-72 1.06E-47 1.15E-30 1.22E-18 2.71E-10 7.37E-05
7002.62E-192 2.59E-118 1.04E-77 2.83E-51 6.19E-33 5.51E-20 5.31E-11 3.75E-05
7506.02E-206 1.14E-126 3.61E-83 7.52E-55 3.33E-35 2.48E-21 1.04E-11 1.91E-05
8001.38E-219 4.99E-135 1.25E-88 2.00E-58 1.80E-37 1.12E-22 2.04E-12 9.69E-06
8503.17E-233 2.19E-143 4.33E-94 5.31E-62 9.69E-40 5.04E-24 4.00E-13 4.93E-06
9007.27E-247 9.61E-152 1.50E-99 1.41E-65 5.23E-42 2.27E-25 7.84E-14 2.50E-06
9501.67E-260 4.22E-160 5.19E-105 3.75E-69 2.82E-44 1.02E-26 1.54E-14 1.27E-06
10003.83E-274 1.85E-168 1.80E-110 9.98E-73 1.52E-46 4.61E-28 3.01E-15 6.48E-07
34
0 200 400 600 800 1 ,000−300−200−1000
Lengthof ylog10Pr[µx(y)≥0]α= 0.40
α= 0.35
α= 0.30
α= 0.25
α= 0.20
α= 0.15
α= 0.10
α= 0.05
Figure 5: /T_he probabilities from Table1drawn in the base- 10logarithmicscale.
B Aforkability boundforstrings satisfying the ǫ-martingale condition
Below we present a bound (Bound 3) on the probability that a chara cteristic string satisfying the ǫ-martingale con-
dition has a non-negative relative margin. We remark that the bou nd below is weaker than Bound 2. Before we
proceed,recallthefollowing standard largedeviation bound f orsupermartingales.
/T_heorem6 (Azuma’sinequality (Azuma;Hoeffding). See[17,4.16]fora di scussion).LetX0,...,X nbeasequence
of real-valued randomvariables sothat, forall t,E[Xt+1|X0,...,X t]≤Xtand|Xt+1−Xt|≤cfor someconstant
c. /T_henPr[Xn−X0≥Λ]≤exp/parenleftbig
−Λ2/2nc2/parenrightbig
foreveryΛ≥0.
Bound 3. Letx∈{0,1}mandy∈{0,1}kbe random variables, satisfying the ǫ-martingale condition (with respect
to the ordering x1,...,x m,y1,...,y k). /T_hen
Pr[µx(y)≥0]≤3exp/parenleftbig
−ǫ4(1−O(ǫ))k/64/parenrightbig
.
Proof.Letw1,w2,...be random variables obeying the ǫ-martingale condition. Specifically, Pr[wt= 1|E]≤
(1−ǫ)/2conditionedonanyevent Eexpressedinthevariables w1,...,w t−1. Forconvenience,definetheassociated
{±1}-valuedrandomvariables Wt= (−1)1+wtand observe that E[Wt]≤−ǫ.
Ifxis empty. Observe that in this case, the relative margin µx(y)reduces to the non-relative margin µ(y)from
Lemma2. Sincethesequence y1,y2,...inthestatementoftheclaimisidenticaltothesequence w1,w2,...defined
above, we focus on the reach and margin of the la/t_ter sequence. Sp ecifically, define ρt=ρ(w1...wt)andµt=
µ(w1...wt)to be the two random variables from Lemma 2 acting on the string w=w1...wt. /T_he analysis will
relyontheancillaryrandomvariables µt= min(0,µt). Observethat Pr[wforkable] = Pr[µ(w)≥0] = Pr[µk= 0],
sowemayfocusontheeventthat µk= 0. Asanadditionalpreparatorystep,definetheconstant α= (1+ǫ)/(2ǫ)≥1
and define the randomvariables Φt∈Rbythe inner product
Φt= (ρt,µt)·/parenleftbigg1
α/parenrightbigg
=ρt+αµt.
/T_heΦtwill act as a “potential function” in the analysis: we will establ ish thatΦk<0with high probability and,
considering that αµk≤ρk+αµk= Φk,thisimplies µk<0,asdesired.
Let∆t= Φt−Φt−1; we claim that—conditioned on any fixed value (ρ,µ)for(ρt,µt)—the random variable
∆t+1∈[−(1+α),1+α]has expectation no more than −ǫ. /T_he analysis hasfour cases, depending on the various
35
regimes of ρandµfrom Lemma 2. When ρ >0andµ <0,ρt+1=ρ+Wt+1andµt+1=µ+Wt+1, whereµ=
max(0,µ);then∆t+1= (1+α)Wt+1andE[∆t+1]≤−(1+α)ǫ≤−ǫ. Whenρ >0andµ≥0,ρt+1=ρ+Wt+1
butµt+1=µso that∆t+1=Wt+1andE[∆t+1]≤−ǫ. Similarly,when ρ= 0andµ <0,µt+1=µ+Wt+1while
ρt+1=ρ+max(0 ,Wt+1); we maycompute
E[∆t+1]≤1−ǫ
2(1+α)−1+ǫ
2α=1−ǫ
2−ǫα=1−ǫ
2−ǫ/parenleftbigg1
ǫ·1+ǫ
2/parenrightbigg
=−ǫ.
Finally,when ρ=µ= 0exactlyoneofthetworandomvariables ρt+1andµt+1differsfromzero: if Wt+1= 1then
(ρt+1,µt+1) = (1,0);likewise, if Wt+1=−1then(ρt+1,µt+1) = (0,−1). Itfollowsthat
E[∆t+1]≤1−ǫ
2−1+ǫ
2α≤−ǫ.
/T_husE[Φk] =E/summationtextk
t=1∆t≤−ǫk. We wish to apply Azuma’s inequality to conclude that Pr[Φk≥0]is exponen-
tially small. For this purpose, we transform the random variab lesΦtto a related supermartingale by shi/f_ting them:
specifically,define ˜Φt= Φt+ǫtand˜∆t= ∆t+ǫso that˜Φt=/summationtextt
i˜∆t. /T_hen
E[˜Φt+1|˜Φ1,...,˜Φt] =E[˜Φt+1|W1,...,W t]≤˜Φt,˜∆t∈[−(1+α)+ǫ,1+α+ǫ],
and˜Φk= Φk+ǫk. ItfollowsfromAzuma’sinequality that
Pr[wforkable] = Pr[µk= 0]≤Pr[Φk≥0] = Pr[˜Φk≥ǫk]
≤exp/parenleftbigg
−ǫ2k2
2k(1+α+ǫ)2/parenrightbigg
= exp/parenleftBigg
−/parenleftbigg2ǫ2
1+3ǫ+2ǫ2/parenrightbigg2
·k
2/parenrightBigg
≤exp/parenleftbigg
−2ǫ4
1+35ǫ·k/parenrightbigg
. (20)
Ifxis not empty. In this case, we go back to study the sequences xandyas in the statement of the claim.
Recall the reach distribution (i.e., the distribution of th e random variable ρ(x))Rm:Z→[0,1]from (18). Since
x= (x1,...,x m)satisfies the ǫ-martingalecondition,Lemma4states that Rm/√recedesequalR∞. Wereserve thesymbol µ(r)
x
for the relative margin random walk µxwhich starts at a non-negative initial position r. /T_husρ(x) =µx(ǫ) =r,
and
Pr[µx(y)≥0] =/summationdisplay
r≥0Rm(r)Pr[µ(r)
x(y)≥0]≤/summationdisplay
r≥0R∞(r)Pr[µ(r)
x(y)≥0] (21)
since the sequence (Pr[µ(r)
x(y)≥0])∞
r=0is non-decreasing and Rm/√recedesequalR∞. Fix a “large enough” positive integer
r∗whosevalue will beassigned later in theanalysis. Let usdefine t he followingevents:
•EventBr: it occurswhen r∈[0,r∗]and theµ(r)
xwalk is strictly positive on every prefix of ywith length at
mostk/2;and
•EventCr,s:itoccurswhen r∈[0,r∗]andˆyisthesmallestprefixof yoflength s∈[r,k/2]suchthat µ(r)
x(ˆy) =
0. We say that ˆyisa witnesses to the event Cr,s.
/T_he right-handside of (21) canbe wri/t_ten as
/summationdisplay
r>r∗R∞(r)Pr[µ(r)
x(y)≥0]+/summationdisplay
r≤r∗R∞(r)Pr[Br]·Pr/bracketleftBig
µ(r)
x(y)≥0|Br/bracketrightBig
+/summationdisplay
r≤r∗R∞(r)k/2/summationdisplay
s=rPr[Cr,s]·Pr[µ(r)
x(y)≥0|Cr,s].
36
We observe that the probabilities Pr[µ(r)
x(y)≥0]andPr[µ(r)
x(y)≥0|Br]are at most one. In addition, recall that
for two non-negative sequences (ai),(bi)of equal lengths,we have/summationtextaibi≤maxbiif/summationtextai≤1. /T_hus (21) canbe
simplified as
Pr[µx(y)≥0]≤/summationdisplay
r>r∗R∞(r)+/summationdisplay
r≤r∗R∞(r)Pr[Br]
+/summationdisplay
r≤r∗R∞(r) max
r≤s≤k/2Pr[µ(r)
x(y)≥0|Cr,s]
≤/summationdisplay
r>r∗R∞(r)+max
r≤r∗Pr[Br]+ max
r≤r∗
r≤s≤k/2Pr[µ(r)
x(y)≥0|Cr,s]. (22)
/T_hefirstterm in (22) istheright-tail ofthe distribution R∞. Using Lemma4,thisquantity is atmost βr∗where
β:= (1−ǫ)/(1+ǫ). Furthermore,it canbe easily checkedthat theabove quantit y is at most exp(−5ǫ/3).
/T_hesecond term in (22) concernstheevent Brand callsformorecare. Define
S(r)
k:=k/summationdisplay
t=0Wt
whereW0=rand the random variables Wtare defined at the outset of this proof for t≥1. We know that the
µ(r)
xwalk starts with ρ(x) =µ(x) =r≥0. SinceBrholds, both the margin µx(ˆy)and the reach ρ(xˆy)remain
non-negative for all prefixes ˆyof length t= 1,2,···,k/2. /T_hese two facts imply that the random variable µ(r)
x(ˆy)
is identical to the sum S(r)
tforallprefixes ˆyoflength t= 1,2,···,k/2.
Tobe precise,
Pr[Br] = Pr[S(r)
t≥0forallt≤k/2].
/T_he la/t_terprobability isat most Pr[S(r)
k/2≥0]becausetheevent S(r)
k/2≥0doesnotconstrain theintermediate sums
S(r)
tfort < k/2. SincePr[S(r)
k/2≥0]increases monotonically in r, we conclude that the second term in (22) is at
mostPr[S(r∗)
k/2≥0]. Now we are free to shi/f_t our focus from the relative margin wal k to the sum of a martingale
sequence.
For notational clarity, let us write S:=S(r∗)
k/2. Since the sequence (wt)obeys the ǫ-martingale condition, ESis
at mostM:=r∗−kǫ/2. Letus set r∗=W0=kǫ/4. /T_henESisat most−kǫ/4and Azuma’sinequality gives us
Pr[S≥0] = Pr[( S−ES)≥kǫ/4]≤exp/parenleftbigg
−(kǫ/4)2
2(k/2)·22/parenrightbigg
= exp/parenleftbigg
−kǫ2
64/parenrightbigg
.
/T_his isan upper boundon thesecond termin (22).
/T_hethirdtermin (22)concernstheevent Cr,sanditcanbeboundedusingourexistinganalysisofthe |x|= 0case.
Specifically,suppose y= ˆyzwhereˆyisawitnesstotheevent Cr,s. Sincethe µ(r)
xwalkremainsnon-negativeoverthe
entirestring ˆy,itfollowsthat ρ(xˆy) =µ(xˆy) = 0andasaconsequence,the µxˆywalkonzisidenticaltothe µwalkon
z. Ouranalysisinthe |x|= 0casesuggeststhat Pr[µ(z)≥0]isatmost A(k−s,ǫ)where|z|=k−sandA(k,ǫ)isthe
boundin(20). Since A(·,ǫ)decreasesmonotonicallyinthefirstargument, A(k−s,ǫ)isatmost A(k/2,ǫ). However,
since the last quantity isindependent of r, the third term in (22)is at most A(k/2,ǫ) = exp/parenleftbig
−kǫ4/(1+35ǫ)/parenrightbig
.
Returningto (22) andusing r∗=kǫ/4,we get
Pr[µx(y)≥0]≤exp/parenleftbigg
−5ǫ
3·kǫ
4/parenrightbigg
+exp/parenleftbigg
−2ǫ4
1+35ǫ·n
2/parenrightbigg
+exp/parenleftbigg
−kǫ2
64/parenrightbigg
.
It iseasy to checkthat theabove quantity is at most 3exp/parenleftbig
−kǫ4/(64+35ǫ)/parenrightbig
= 3exp/parenleftbig
−ǫ4(1−O(ǫ))k/64/parenrightbig
.
37
|
{
"id": "1911.10187"
}
|
2107.08970
|
Indexing structures for the PLS blockchain
|
This paper studies known indexing structures from a new point of view:
minimisation of data exchange between an IoT device acting as a blockchain
client and the blockchain server running a protocol suite that includes two Guy
Fawkes protocols, PLS and SLVP. The PLS blockchain is not a cryptocurrency
instrument; it is an immutable ledger offering guaranteed non-repudiation to
low-power clients without use of public key crypto. The novelty of the
situation is in the fact that every PLS client has to obtain a proof of absence
in all blocks of the chain to which its counterparty does not contribute, and
we show that it is possible without traversing the block's Merkle tree.
We obtain weight statistics of a leaf path on a sparse Merkle tree
theoretically, as our ground case. Using the theory we quantify the
communication cost of a client interacting with the blockchain. We show that
large savings can be achieved by providing a bitmap index of the tree
compressed using Tunstall's method. We further show that even in the case of
correlated access, as in two IoT devices posting messages for each other in
consecutive blocks, it is possible to prevent compression degradation by
re-randomising the IDs using a pseudorandom bijective function. We propose a
low-cost function of this kind and evaluate its quality by simulation, using
the avalanche criterion.
|
http://arxiv.org/pdf/2107.08970v2
|
Alex Shafarenko
|
cs.CR, C.4; H.3.1
|
cs.CR
|
Indexing structures for the PLS blockchain
Alex Shafarenko∗
University of Hertfordshire, AL10 9AB, UK; Ada Finex Ltd
∗Correspondence address: University of Hertfordshire, U.K.; email: a.shafarenko@herts.ac.uk
1arXiv:2107.08970v2 [cs.CR] 3 Aug 2021
Abstract
This paper studies known indexing structures from a new point of view: minimisation of
data exchange between an IoT device acting as a blockchain client and the blockchain server
running a protocol suite that includes two Guy Fawkes protocols, PLS and SLVP. The PLS
blockchain is not a cryptocurrency instrument; it is an immutable ledger o ering guaranteed
non-repudiation to low-power clients without use of public key crypto. The novelty of the
situation is in the fact that every PLS client has to obtain a proof of absence in all blocks of
the chain to which its counterparty does not contribute, and we show that it is possible without
traversing the block's Merkle tree.
We obtain weight statistics of a leaf path on a sparse Merkle tree theoretically, as our
ground case. Using the theory we quantify the communication cost of a client interacting with
the blockchain. We show that large savings can be achieved by providing a bitmap index of the
tree compressed using Tunstall's method. We further show that even in the case of correlated
access, as in two IoT devices posting messages for each other in consecutive blocks, it is possible
to prevent compression degradation by re-randomising the IDs using a pseudorandom bijective
function. We propose a low-cost function of this kind and evaluate its quality by simulation,
using the avalanche criterion.
keywords: PLS blockchain, Guy Fawkes protocol, content-addressable storage, data{structure
statistics, Tunstall coding, pseudorandom bijections
1 Introduction
This paper gives statistical analysis of some known data structures required for the implementation
of the PLS (permissioned) blockhain [12] or PLSB for short, whose purpose is to support a swarm
of IoT devices, or things operating on the premises of a single administrative authority, for example
a smart hospital. The use of a blockchain is for the purposes of audit trail, authentication and non-
repudiation of allactors, both human and unmanned, including small, bare-metal microcontrollers
that supply critical sensor data and those which drive actuators.
The utility of permissioned distributed ledger systems (permissioned blockchains, or PBCs for short)
is based on two fundamentals: (i) distributed validity check of messages and (ii) an immutable,
linearly-ordered ledger. In IoT applications, especially in sensor-networks, (ii) tends to be more
important than (i). Indeed, typically messages are not transactions in the nancial sense, so checks
such as double spending are not relevant; value checks are domain-speci c and are best performed
by smart contracts, which leaves the authenticity and provenance of each message posted on the
ledger as the only general validity concerns. The PLS blockchain [12] assures (ii) by employing
Guy-Fawkes Protocols (GFPs) [2].
A GFP is a post-quantum signature protocol based on an unlimited series of interlocking crypto-
graphic hashes. GFP computations are fast, messages short and secrets neither moved nor kept for
a long time; the GFPs are resistant to quantum computing as they do not use operations such as
prime-number factorisation or discrete logarithm. Finally, by their recursive nature, GFPs de ne
a single sequence of signatures that is very hard to split; this makes them quite suitable as a basis
of a blockchain.
In the next section we will brie
y outline the architecture and protocols of the PLS blockchain
published in an earlier paper[12]. Operational di erences between the PLS and other blockchains,
such as Etherium, call for re-evaluation of major data structures required for its implementation.
2
In Section 4 we argue that the limited number of users (IoT devices on the premises and human
actors1) and the limit on their communication duty cycle and disposable energy need ecient secure
data structures to avoid communicating irrelevant data. We propose a Merkle-Tunstall Tree for
that purpose (Section 5) and provide a statistical evaluation of its eciency. The eciency of the
Tunstall compressor depends on the lack of correlations between di erent user's contributing to
the same block. To decorrelate block access we propose to use a random permutation function to
map users' true IDs onto local IDs for a given block, see Section 6. To illustrate how proposed
technologies work together we give one illustrated example in section 7. Finally, there is a section
on related work and some conclusions.
The main contributions of the paper are as follows:
1.Statistical analysis of a sparse Merkle tree with uniform, uncorrelated probabilistic leaf occu-
pancy. We have obtained the path-weight probability distribution function (as a recurrence
relation in the tree height) analytically, without Monte-Carlo simulation. It is easy to quantify
the function numerically for any given height.
2.The proposal and evaluation of a compressed bitmap and local enumeration of block users.
This makes it possible for a user to obtain the proof of absence in the block directly from
the broadcast root of trust without accessing the Merkle tree. We have also shown that the
local enumeration results in a path weight similar to that on the original sparse Merkle Tree
on average, but the variation is tightly bounded from above, which makes it possible to limit
the packet length when communicating a secure leaf path using our structure. By contrast,
the path across the original tree varies more widely depending on the leaf statistics and may
result in paths exceeding the maximum packet size.
3.The proposal and evaluation of shift-shue as a low-cost pseudorandom permutation tech-
nique sucient to break a possible correlation between occurrences of di erent users' records
in block contents. We quanti ed the number of rounds in the permutation algorithm to be
used taking the avalanche criterion as a basis.
2 PLS blockchain: architecture and protocols.
The details of the protocols and their security analysis are available from [12]. We present them
here for completeness. However, for the contributions of the present paper we only need to discuss
the logistics of the PLSB, whose architecture is shown in gure 1.
Blocks are formed from transactions communicated by things viaproxies that make it possible for
allthings to rely on low-power radio communication. To authorise a transaction, things run another
GFP protocol, called SLVP. That protocol's messages are forwarded by one or more proxies to the
Fog Server (FS) to be included in the next block. The FS forms blocks regularly, on a xed wall-
clock schedule, by validating incoming SLVP messages from things , and adding them to the current
block.
1In the sequel, when it is not important what kind of actor is meant we will call all actors users for short and
apply the pronoun 'it'.
3
Sequencer
Fog Server
Proxy …
CAS
Local TCP/IP Subnet
Proxy
Thing
Thing
Thing
hard shell …
Figure 1: Architecture of a PLS-blockchain system
Chain. By regular deadlines the current blocks are stored in CAS and their hash is signed by
producing messages of the main protocol, PLS. All PLS messages are generated and transmitted by
radio via a sealed unit, Sequencer, which receives the current block's hash from the FS on a private
radio channel. The Sequencer does not contain a changeable program and is not connected to the
Internet, so it is not hackable. The PLS sequence, i.e. the sequence of PLS messages, requires a
short-term secret, which is produced inside the Sequencer using a physical source of randomness in
one time interval and is revealed in the next interval at the same time as selecting a new random
secret. All things must receive each PLS message, validate it, and unlock the corresponding block's
hash, which is a le name of the block in CAS, see g 2. P- and L- messages cross-validate as shown
in the gure, and S-messages contain some redundancy, which, after deciphering, indicates whether
the message is valid or not. For example, Jcan include a run of zeros at the end; this would be
sucient to thwart a \random message" attack, which is a possible DoS action of the attacker
jamming the radio channel2. Also notice that blocks of the blockchain are, as usual, key-value
collections, where the key is the originator's ID.
Any invalid messages, possibly sent by an attacker will fail the validity check with a very high
probability. Progress is assured by limiting the number of invalid messages using various tech-
niques discussed in [12], but those are exclusively DoS countermeasures which do not in
uence the
semantics of the blockchain. The initial message P0is authenticated by all blockchain users via
external credentials. Users joining the system later would require external authentication of the
latest P-message instead of P0. The unlocked hashes of all the subsequent blocks are as secure as
the weaker of the credential and the computational hardness of the full hash preimage problem
(i.e. nding allbit-strings of a given length whose hash is a given value). The latter is not feasible
for a SHA-2 hash even Post Quantum. Also notice that the veri cation and unlocking computa-
2Since the attacker does not know the preimage of Pat the time when an attack is possible, it can only send
an arbitrary message; after unlocking, it would produce a near-random bitstring as a would-be block hash. The
requirement for it to have rtrailing zeros will only be satis ed with the probability 2 r
4
tions are fast (single microseconds) even for a small bare-metal microcontroller equipped with a
crypto-accelerator, e.g. ESP32[7].
Transactions. As mentioned earlier, a thing publishes a transaction on the blockchain by running
the SLVP protocol with the FS. A transaction requires one round of the SLVP protocol, which takes
three blockchain blocks. For the security of the protocol it is required that the originating thing
check that the latest sent SLVP message has appeared on a block. As soon as it has, the next
protocol message can be sent. The rst message to send is an S-message, which contains the data
object to be signed. Then an LV-message is posted on the blockchain, which provides interlocking
hashes and veri cation data (the latter is needed to thwart jam-spoof attacks, see [12]). Finally,
thething posts a (proof) P-message. The FS validates the P-message using the data contained in
the previous round's P-message and the content of the LV-message sent in between. The FS will
only include a P-message in a block if the P-message is valid, while LV- and S- are posted right
away, the reason being that invalid LV- or S-messages will be recognised as such by the protocol
itself only after the next P-message is posted. In practice The FS and a user may share a secret to
help the FS to authenticate incoming messages early to make it dicult for an attacker to post a
large number of invalid S- and LV-messages. However, this does not help a counterparty that must
be mistrustful of the FS. So additional authentication, if present, is purely a DoS countermeasure;
we needn't focus on it as we concern ourselves only with the machinery of the blockchain.
The protocol is summarised in gure 3. The diagram shows two users, blue and brown, posting
their SLVP messages on the blockchain using the transmissions shown in the table below, which
is presented on behalf of a single blockchain user. Users are independent in transmitting protocol
messages for themselves and in verifying messages sent by others. The veri cation formulae in
the penultimate column enable users (as well as the FS that does it rst) to prove to themselves
that the other party has genuinely signed its data object M. If the next P-message, Pk+1checks
out, they use its value to unlock the data object Mkas de ned in the bottom of the gure3. Just
like blockchain blocks, the data objects have some redundancy. For example, a certain number of
leading zeroes, not necessarily very few as this does not require computation, will adequately defend
against random S messages, sent by an attacker. Notice that the SLVP protocol de nes variable
length encryption for S-messages using a block cipher in PCBC mode. Encryption is bijective, i.e.
information-preserving, and the redundancy required for validation in the presence of a random-
message attack is just a few bytes (e.g. 4 bytes gives the attacker 1 chance in a few billion to post
valid random data, but even then it only subverts a single S-message).
Also note that the FS has authority to introduce a new user by posting their very rst P-message.
The rst P-message is always marked as such on the blockchain for other users exchanging trans-
actions to recognise it as the originator's identity.
Operations. Transactions can be posted by both things and human users. Each thing has one or
more masters , which are typically users (but could be other things ). Not only does each thing check
the posting of each of its SLVP messages on the blockchain, it also monitors the postings of all its
masters and any relevant counterparties, and validates their data objects by applying the SLVP
protocol. Alternatively, the thing can participate in a smart contract which would only require it
to follow and validate messages from the contract engine. In this paper we limit ourselves to the
3The original paper [12] has a slightly di erent arrangement for S-messages since in the original design CAS was
trusted for progress, but in the present paper we eliminate this requirement.
5
Interval Transmit/Receive Verify Unlock
[T0;T1]L0=H(N1)N0
S0=EN0(J0H(N1))P0out of band
P0=H(N0)
[T1;T2]L1=H(N2)N1
S1=EN1(J1H(N2))H(L0P1) =P0H(B0) =P1DL0P1(S0)
P1=H(N1)
... ... ... ...
[Tk;Tk+1]Lk=H(Nk+1)Nk
Sk=ENk(JkH(Nk+1))H(Lk 1Pk) =Pk 1H(Bk 1) =PkDLk 1Pk(Sk 1)
Pk=H(Nk)
Notation:
H(): a cryptographic hash function
Ji: a digest of blockchain block Bi, e.g.H(Bi)
E : encipherment under symmetric key ; use PCBC mode if Jexceeds cipher block size
D : decipherment under symmetric key , matching E
Figure 2: Structure of the PLS protocol
6
id1id2id3… Block …
id1
S S
id1
LV CAS
t
id1id2id3… Block … … LV
id1id2id3… Block
id1id2id3… Block P
id3
S
id3
LV S LV
id1 P
id3
P Block Transmit Verify BC Action
b0P1=H(N1) Out of Band (Enrolment) Post
b1S1=E
N1(H(N2);M1) | Post
b2LV1=H(N2)N1jjH(H(N2)jjN1) | Post
... ... ... ...
bnPk=H(Nk) as forbn+3, assume success Post
bn+1Sk=E
Nk(H(Nk+1);Mk) | Post
bn+2LVk=H(Nk+1)NkjjH(H(Nk+1)jjNk)| Post
bn+3Pk+1=H(Nk+1) forbn<b<bn+3:
nd rst LV such that
H(Pk+1jjLPk+1) =V
if found in block ^b:
unless9b2(bn;^b);L02Bb:
H(L0Pk+1) =Pk Post
else Reject
To unlock the data object MkcomputeM=D
LPk+1(Pk+1;S) for every S-message in the interval
[bn;^b) and accept the rst valid M. HereE
k(a;b) is the encryption of bunder keykin PCBC mode
withaas IV;D
k(a;b) is the matching decryption.
Figure 3: Structure of the SLVP protocol. Table on behalf of a single user
7
mechanics of transaction processing, while leaving higher-level protocol to further work. We will
assume in the sequel that each thing is interacting with a very small number of other actors and
needs to follow a few SLVP threads (perhaps 2 or 3). Our focus will be on how to make these
interactions as computationally and communicationally ecient as possible.
Addresses. Each PLS user has an address, which is a small number. Since we concern ourselves
with a localised enterprise solution (e.g. a smart hospital) covered by a direct link radio network
(e.g. smart sensors equipped with a LoRa[1] transceiver), we do not expect the number of things
greater than circa 1000. The total number of actors should be a small factor of that to account for
human users and smart contracts, so 2{4K addresses is our target. Transactions have an originating
address and a destination.
Frequency. In IoT networks of interest, communication is limited by the duty cycle to save the
limited bandwidth that all things have to share. This is in addition to the constraints imposed
by the energy budget of an individual IoT device. Consequently a small fraction (typically a few
percent) of the registered users will be posting a transaction in any given block.
3 Block structure and optimisation challenge
Immutable dictionary. In the previous section blockchain blocks were shown in the diagrams
that consisted of records attributed to various users as key-value pairs, where the key is the user
ID. In a given block only some of the registered users would be represented by records. Since the
frequency of posting on the blockchain in our case is severely limited by the things ' communication
duty cycle (if using LoRa) or energy budget (if using public networks or LoRa), the proportion of
users posting to any given block is expected to be very small. Still, the user can only authenticate
the block by the S-message of the PLS protocol, which, when unlocked, contains the block hash.
On the other hand, as we mentioned in the previous section the user is typically interested in two
or three other users' contributions, which, given a typical enterprise IoT network of a thousand
things , is still much less than the expected volume of activity in a block. Indeed, given a block
production rate of 4 blocks per hour and a thing data production rate of 2{4 samples per day, and
bearing in mind that each data post requires 3 blocks according to the SLVP protocol, we arrive
at 6 to 12 blocks per device per day and circa 100 blocks per day in total. This means that in
the absence of correlated activities we should expect about 5 to 10 percent of the swarm to post
in every given block. For a 1000-strong swarm, the block may contain an estimated 50 to 100 user
records authenticated by a single hash. If a thing wishes to access just a few of these, it would have
to rst read the whole block and check the hash to validate it, and then dispose of most of these
records as they would be irrelevant.
Merkle tree. The standard solution to the above problem is called the Merkle Tree (MT)[11], see
gure 4. It is a labelled binary4tree each node of which has two children, with some labels L
andR, and its own label is =H(LjjR). A child can either be a leaf or a full node in its own
right; in both cases it has a label but in the latter case it also has two children of its own. It is
quite clear that a change in any leaf will change the root label (also known as the root hash ), so the
4The tree does not have to be binary, but higher-based trees, and higher-based MPTs, discussed later, are
inecient for a small number of leaves.
8
r
!00
!000
!001
!01
!010
!011
!0
!1
!10
!100
!101
!11
!110
!111
Figure 4: Merkle Tree.
authenticated root makes the whole tree authentic. For each node except the root there exists one
other node with the same parent, which we call adjunct . What makes the MT useful is that it can
also authenticate a single leaf by providing a root-path list of adjunct nodes' labels, or root-adjunct
path for short. For example, to authenticate the leaf 101shown in red in the gure, given the root
labelr, we only need to know the labels of the blue (adjunct) nodes: 100,11and0, since
r=H(0jjH(H(100jj101)jj11)):
Generally speaking, for a tree with Kleaves one needs to communicate h=dlog2Kehashes, which
is much less than Kfor the number of leaves in the hundreds that we are considering. The tree
thus represents an array of leaves indexed by the path: a left edge represents 0 and a right edge
1; the edges traversed en route to the leaf form a bit-string that represents the key. The leaf itself
represents the value of the key-value pair.
Blocks represented as Merkle trees. Common practice in Blockchain construction is to rep-
resent a block as an MT, each leaf of which carries the hash of a user's record included in the block,
with the user ID being the key gleaned from the leaf's root path. A user requesting another user's
record (or the one of its own) from an intelligent CAS could just receive the root-adjunct path
corresponding to the requested ID and hash it through to match with the root hash value. If the
PBC signs the root hash of every block it creates, no further security is required to authenticate
any user records. Our investigation is of a special case when the maximum number of users is small
and is known in advance, and where good communication eciency is important. We could use an
MT with the tree height hclose to 10 (to accommodate our expected 2101000 users). Since, as
we have mentioned earlier, we expect only around 50 (maybe up to 100) users to contribute to any
given block, a great majority of the leaves will not be used.
Mask-controllable sparse MT. The number of leaves in an MT does not have to be a power
of 2. Also leaves can have no value associated the root-path key. We can think of such leaves as
unoccupied. An MT with no-value leaves is called a sparse MT, or SMT. There are several ways of
9
organising an SMT, but proposals usually focus on mutable trees that are used for secure updatable
key-value storage. Our interest is in immutable SMTs, where eciency is understood in narrow
terms as eciency of retrieval only. Below we de ne our own version of the SMT, geared towards
our objectives.
We can assume that a leaf without value has a special label NULL and the parent of two NULL
nodes has the label NULL as well. This assumption does not diminish security due to the fact that
a NULL child of any node is implicitly associated with the node height. Consequently, the shape
of the NULL subtree associated with the child is completely de ned by its root position. All such
NULL trees are identical anyway, so a single label value fully represents them.
For the veri er to be able to verify a path with NULL nodes, it requires a bit mask of length h,
where bit-value 1 indicates that the corresponding adjunct node is non-NULL; and the bit-value
0, that it is NULL. The NULL labels can then be omitted from the path. Finally we extend the
domain ofH(x) to include NULL-concatenated strings by de ning that for any bit-string x
H(xkNULL) =H(xkx0); (1)
H(NULLkx) =H(x0kx);
wherex0is the bit string obtained form xby
ipping all bits. Interestingly, a simpler exten-
sion
H(xkNULL) =H(NULLkx) =H(x)
would not be secure, as it allows one to construct a second preimage by rotating the subtree or
swapping nodes along a NULL path. It is easy to see that the hashing process introduced by Eq
1 is not invariant to any such transformation. It is impossible to create a new valid SMT with the
same root hash and a di erent leaf sequence without solving the second preimage problem.
If the path is mask-controlled, CAS only needs to communicate up tohhashes in addition to the
bit mask for the veri er to successfully compute the root hash. Extending the above example, if
11were unoccupied, CAS could supply the bit mask 101 (the second adjunct is missing in path
order), and the values of 0and100. Notice that the bit mask does not need to be secured: if it is
incorrect, the veri er will compute an incorrect path expression and the result will not match the
root hash. Also notice that the mask is very small compared to the hash length: for a tree of 1024
leaves (counting both NULL and non-NULL) the root-adjunct path contains from 1 to 10 hashes
256 bits each, i.e. 256 to 2560 bis, but the mask length is only 10 bits regardless.
Merkle-Patricia Trie. The idea of mask-control path is similar to that of the so called Merkle-
Patricia Trie (MPT)[15] where not only the nodes but also the edges can be labelled. If a node has
a single active edge (i.e the other edge leads to a NULL subtree), the node is eliminated and its
parent uses the pre x of the other edge as its label, see gure 5. The example in the gure is of
a block where, out of the maximum 8, only users 2, 4, 5, and 7 (010, 100, 101, 111 in binary) are
present. Notice that we still have a binary tree, but the root-adjunct path augmented with edge
labels requires from one (for 010) to three (for 100 and 101) adjunct hashes for validation, depending
on the leaf. The edge labels are typically much shorter bit-strings than a single cryptographic hash
(log2K 256) and so can be neglected in determining the communication eciency of the access
scheme. The same is true of masks with our version of the SMT.
10
r
!00
!000
!001
!01
!010
!011
!0
!1
!10
!100
!101
!11
!110
!111
010
11
Figure 5: Merkle-Patricia Trie. All unlabelled edges are assumed to have the label `0' if they lean
to the left and `1' if they lean to the right.
How is the edge label secured? It is simply hashed together with its child content in determining
the node label:
=H(0jj1jj0jj1);
where0;1are the edge labels of the left and right child, respectively.
It is easy to see that there is a direct correspondence between the MPT and the SMT with mask-
controlled paths. Our construction requires more work when validating a path: each node, irre-
spective of its path quality involves hhash calculations for veri cation, where his the height of
the tree, but in the MPT case the number of times a hash is calculated is the same as the number
of adjunct hashes supplied with the MPT path, although each hash calculation also involves edge
labels, which may increase the cost. The total length of edge labels along the root path in the MPT
case is equal to the length of the mask in the SMT case. However, an MPT path requires markers
to partition the path string into individual edge labels. Our construction is slightly more frugal in
this respect, and it is simpler, which is why we prefer it.
4 Motivation and optimisation idea
It is obvious from the SLVP protocol that an actor engaging in transactions with another on
the PLSB must check each block to determine the presence of a transaction message from the
counterparty. Due to the low duty-cycle of thing -to-FS communication, the counterparty will not
be present in a great majority of blocks. However, to securely establish the absence the actor must
traverse the block and verify that the counterparty's record is not there. In the MT case we can
use the mask-controlled path to the unoccupied leaf which can contain up to hadjunct hashes. In
the MPT case CAS will supply the longest path in the direction of , rather than to, the unoccupied
leaf. By examining the last node on that path the user will be able to verify that the necessary
edge is missing. For example, looking at the MPT in gure 5, if an actor requested the unoccupied
11
leaf 110, the one-step path r!1with adjunct material will be sent back for validation:
010;1;010
0;11;10;111
The number of hashes to be communicated is the same as that for the mask-controlled MT: in the
current example the latter would require hashes 111,10, and0. An advantage of the MPT is
that it saves the veri er extra hash computations by providing segments of the path as (hashed-
controlled) edge labels. While saving some compute time, the e ect of it is negligible, since the
maximum root-adjunct path length is logarithmic in the number of leaves ( .12), and a modern
microcontroller can compute tens of thousands of hashes per second. A disadvantage of the MPT
is that it requires communication of edge labels in addition to the hash for each node on the path,
but again, compared to the hash length this is negligible, too.
What is considerably more important here is that neither mask-controlled MT, nor MPT reduce
the maximum root-adjunct path length. In our example the number of leaves present is 4, but the
hight of the MPT as a binary tree is 3, not log24 = 2; it is as if all leaves were present. As a
result, the system would require to accommodate longer communication packets, which may a ect
the guaranteed duty cycle limit of an IoT device.
This brings us to the central idea of the paper: to index a block, one might prefer to locally
renumber the users to achieve a contiguous range of IDs rather than a scattering over a regular
structure with subsequent remedies such as the MPT.
However, before proceeding to our solution, we would like to evaluate the base case, the mask-
controlled MT. We would like to establish some quantitative characteristics of MT paths under a
random distribution of leaf occupancy.
4.1 Sparse MT Statistics
Let us number the levels of the MT from the leaves up, starting with 0. We will call the number
of adjunct hashes associated with a path its weight . Let function PDF k(i) of integer ibe the
probability for a path from a given leaf to a hight- knode on the tree to be of weight i. Recall that
we only count nodes on the path with both children being non-NULL as those require an adjunct
hash for a Merkle proof. Let us take a closer look at an example path, see gure 6. From level
0 we move to level 1 as a left child, then to level 2 as the left child, and nally to the level 3 as
the right child. Clearly di erent leaves' paths di er by the choice of left- and right- ascension at
each level, but the signi cance of the node does not depend on it: the node is only counted when
bothits children are non-NULL. The shaded triangles signify the subtrees that represent the other,
non-path child of the corresponding path node, which, if non{NULL, produces what we termed
above the adjunct hash. If the non-path node is NULL, this fact is noted in the path bit mask, but
no adjunct hash is produced.
At level 0, the subtree is of height 0 (it is a leaf), at level 1 it is of height 1 (connects two leaves),
etc.
Statistical model. We adopt a model relevant to the IoT case that the PLS blockchain was
developed for. A thing submits a message for inclusion in a block very infrequently. It does so at
12
3
2
1
0
Figure 6: A path across a sparse MT
random with some small probability p, whose value depends on the duty cycle restriction, urgency
of the sensor data and the available energy budget. Without loss of generality let us assume that
p0:1 in our examples, which will give us some intuition of what kind of gures may arise in
practical work. This level of activity means that a thing participates in roughly one block out of
10 or that about 1/10 of all blockchain users are active in any given PLS round. We also assume
that the activities of di erent things are uncorrelated, so any given leaf is either present or absent
(NULL) irrespective of the presence/absence of other leaves.
Path weight. The subtrees in gure 6 will consequently be NULL-valued with the probabil-
ity
k= (1 p)2k; (2)
wherekis the level at which the subtree is rooted. Let us introduce the Probability Distribution
Function PDF k(L) as the probability for the weight of a path from level kto a leaf to be equal to
L. Clearly PDF k(L>k ) = 0, and we also assume for convenience that for all k, PDFk(L<0) = 0.
It is easy to calculate PDF 1directly:
PDF 1(0) = 1 p;PDF 1(1) =p: (3)
Indeed, the other child of a given leaf of a height-1 tree is NULL with the probability 1 p, producing
no adjunct hash, so L= 0 with that probability; otherwise (with the probability p) the other leaf
is non-NULL, supplying a single adjunct hash.
For a hight- kpath we have a combinatorial problem of calculating the probabilities of 2kcombina-
tions of absence/presence of each adjunct hash (remember that these probabilities are completely
independent as per our chosen statistical model). Instead of doing this, we observe the following
recurrence relation between the paths to neighbouring levels:
PDFk+1(i) = kPDFk(i) + (1 k)PDFk(i 1) (4)
Indeed, if the non-path child of the height-( k+ 1) path node is NULL (this happens with the
probability k) the number of adjunct hashes that the path to height k+ 1 produces is the same as
that to height k. Alternatively, if the non-path child is non-NULL, it produces one adjunct hash,
and so the probability to produce L hashes for the whole path is the same as the probability to
13
k mean 0 1 2 3 4 5 6 7 8 9 10
==============================================================================
2 0.200 81.0 18.0 1.0
3 0.390 65.6 30.0 4.2 0.2
4 0.734 43.0 42.2 13.1 1.6 0.1
5 1.303 18.5 42.7 29.7 8.1 0.9 0.0
6 2.118 3.4 23.0 40.3 25.7 6.8 0.8 0.0
7 3.084 0.1 4.1 23.6 39.8 25.0 6.6 0.7 0.0
8 4.083 0.0 0.1 4.1 23.6 39.8 25.0 6.6 0.7 0.0
9 5.083 0.0 0.0 0.1 4.1 23.6 39.8 25.0 6.6 0.7 0.0
10 6.083 0.0 0.0 0.0 0.1 4.1 23.6 39.8 25.0 6.6 0.7 0.0
Table 1: Numerical evaluation of Eq 2{4. Probability Distribution Function (%) of path weight vs
height in a sparse MT ( p= 0:1)
produceL 1 for the path to height k. The above equation is the weighted sum of those two
outcomes, a mixture distribution.
The signi cance of Eqs. 2{4 is in the fact that they permit direct calculation of the PDF at any
level above 1 very cheaply given the value of p. The PDF obtained can deliver various practical
parameters: the average path weight:
Lk=X
iiPDFk(i);
the standard deviation, the probability that a certain limit Lmaxis exceeded, etc., which are useful
in designing bandwidth-limited communication protocols.
Table 1 presents the outcome of a direct calculation of Eqs. 2{4 for p= 0:1 and also includes the
value of Lkin the second column (heading \mean"). The table shows the value (%) of PDF k(i),
whereiruns horizontally. For obvious reasons nontrivial evolution only happens until kdrops to
small values, whereupon Eq. 4 degenerates to
PDFk+1(i)PDFk(i 1)
making the PDF( i) shift to the right by 1 without change of shape as kincreases. For p= 0:1
sparsity is present in the rst 7 levels of the tree; from level 8 up the tree becomes dense.
Another noteworthy feature of the distribution is its breadth: 95% of the paths require from 4 to
8 hashes, with the mean being around 6, which would necessitate variable length communication,
since a factor of 2 di erence cannot be ignored. This variability comes despite the compression we
have already applied by introducing the bitmap-controlled MT.
The sparse MT is indexed by the user ID, and a set of active users for an individual block is random
as de ned by our statistical model. To get a feel of how ecient the sparse MT is in terms of path
weights, we compare its mean path weight with that of a truncated dense tree carrying the same
number of non-NULL leaves. We use the least sucient hight of the dense tree to accommodate
all non-NULL leaves, and place all NULLs on the right hand side of level 0, so that the non-NULL
leaves may be contiguous, and use Eqs: 1 to deal with none values (that is what we mean by
truncation). Figure 7 compares the path weights of the two trees. The dependence of the path-
weight averaged across the truncated tree on the number of non-NULL leaves is not smooth, as the
14
0 1 2 3 4 5 6 7 8
0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 W
pFigure 7: Mean path weights. The curve: sparse MT, n= 1024 leaves each occupied with probability
p. Scattered dots: the average path weight of a truncated dense MT with 2dlog2(np)eleaves
tree hight leaps up when the number of non-NULLs crosses power-of-two boundaries. Nevertheless,
one can clearly see that the dense tree has a lighter path weight at the majority of probability
values, becoming slightly worse just before the probability reaches a value that the equivalent dense
tree must grow at.
Challenge. Now we are prepared to argue our case. A user engaging in the SLVP protocol as
veri er must examine every consecutive block to see if the prover has placed a message in it (i.e. an
S-, LV- or P-message). According to our statistical model, in a great majority of the blocs, in fact,
in a factor of 1 pof them, the prover's message is likely to be absent. Nevertheless, the veri er
needs to satisfy itself that indeed, no message from the prover is present. With the classic MT as
well as MPT and our own version of sparse MT, the absence of a leaf is almost as expensive to
prove as its presence with a particular value. The di erence is that for an absent leaf the label is
NULL and it is not communicated, but that is a di erence of 1 against, as our calculation shows,
circa 6 adjunct hashes to be communicated when 1024 users participate at probability p= 0:1.
This means that on average 60 (!) hashes would be required to certify the start of an SLVP round.
Worse still, each active user, even when all it does is wait for a possible signed message from a
counterparty, will be actively requesting the counterparty's root-adjunct path from CAS every time
a block is released, which pretty much destroys the advantages of a low-bandwidth Guy Fawkes
protocol.
However, a simple remedy exists, which we consider next.
15
5 Tunstall-Merkel Tree
Basic idea. We kill two birds with one stone by providing a one-time renumbering of users in
each block while broadcasting the renumbering information together with the root of the tree. The
purpose of the renumbering is to achieve a contiguous range of ID numbers. This way absent users
will be recognised as such immediately by any counterparty involved. As a result the cost of absence
proof will be zero (plus the cost of the one-for-all broadcast message, which need not be requested).
The indexing structure in terms of new IDs will be the kind of tree we have already studied and
shown the superior access cost of: a dense, truncated one.
How do we enumerate users that are present? Imagine a bitmap sized 2h, wherehis the height
of the original (sparse) MT. In the bitmap 1s mark the presence of the corresponding user/leaf
and 0s its absence. Under our statistical model (see page 12) on average 2kpbits (as per binomial
distribution) of the bitmap will be 1s. Users are renumbered according to the bitmap: the user's
new ID is the number of 1s in the bitmap preceding the bit that corresponds to the user's actual
ID.
Our statistical model assumes that all users are engaged the whole time. A user that decides not
to use the blockchain for a while will not be able to maintain a factor of pmessages per block on
average until the user becomes active again; the user's bit position in the bitmap will be 0 during
that period. If there are many such users, the bitmap may have signi cantly fewer 1s than the
aforementioned expectation 2kp. In this sense the expectation is pessimistic.
The maximum number of users is within a near-unity factor from the number of things in the
swarm, since non-IoT users have typically a one-to-many relation with things : a human or a server
would be in control of several IoT devices. The majority of the users tend to be always-on, active
things , which work according to a near-periodic schedule. Another useful circumstance here is that
the bitmap can be e ectively and eciently compressed to a fraction of its length, provided that
the distribution of 1s is close to random and that the number of 1s is known to both the sender
and the recipient. The former can be made true by pseudorandomisation, and the later is easy to
achieve by including a small integer (typically 10-12 bits in length) in the message that broadcasts
the bitmap. In this section we describe the compression technique, and in the next one we will
propose a simple and ecient pseudorandomisation.
Tunstall code. Given a bit string of length nwhich is expected to contain m=pn;p< 1 ones in
random positions (which makes pa true probability), or alternatively a bit string which is known
to containmones,m=pnin random positions (which makes panempirical probability), with the
positions of ones pairwise uncorrelated (this is called a zero-order environment), we set ourselves
the task of nding a bijective function C:Bn!Brw,rw<n that maps the string to a sequence of
rcodewords of length w. We wish to minimise rw, or, for a given w, to minimise r. The theoretical
limit of compression is well known from information theory: rwH0n, where the zero-order per-bit
entropyH0is de ned thus:
H0= plog2p (1 p) log2(1 p):
The mapping Cis realised by partitioning the source bit string into (generally unequal length)
chunks and assigning a codeword to each. A chunk b0;b1;:::;bkis found at any given position in
16
1−p
0
1
p
1−p
0
1
p0.4 0.6
(1−p)2
0
1
p(1−p)0.24 0.36 0.4
1−p
0
1
p
(1−p)2
0
1
p(1−p)0.24 0.36
p(1−p)
0
1
p20.16 0.24
startstep 1step 2Figure 8: Tunstall Tree for p= 0:4. First two steps of the algorithm.
our random string with the likelihood
pc=kY
i=0pbi(1 p)1 bi: (5)
The best code with the word length wshould assign its 2wcodewords to the 2wchunks with the
highest likelihood. It must also make sure that the code is complete, i.e. any bit sequence can be
represented as a sequence of codewords. It is intuitive that such a code would be optimal, and it
can be proven that it is also asymptotically e ective, i.e. that its compression ratio tends to the
entropy limit as wtends to in nity.
It is not easy, however, to turn Eq. 5 into a practical encoder/decoder. The main reason for it is
that the value of kis not bounded, and neither is the search for suitable chunks to nd the top 2w
ones in terms of their likelihood. The problem is not so much the amount of work required for the
search, since we could take the logarithm of Eq 5 and maximise the linear form
logpc=llogp+ (k l) log(1 p); (6)
where
l=kX
i=0bi;
in the (0lk;k > 0) area of the ( l;k)-plane starting at the maximum (0 ;1). The problem is
that each (l;k) point corresponds to k
l
chunks of length kwithl1s in each. Their enumeration
and mapping at di erent kwould be rather awkward.
Tunstall in his PhD thesis[13] proposed a greedy search which at the same time builds a compact
dictionary structure (the Tunstall tree) that can be used for encoding/decoding eciently, without
sharing the dictionary (as long as pis known to both the encoder and the decoder). The greedy
search turns out to be of excellent quality, too, delivering the entropy limit asymptotically[13], and,
17
as a recent study shows [9], with a rapidly decreasing redundancy as wincreases. The redundancy
formula from [9] being useful for our analysis, we present it below (without derivation and rewritten
in our notation):
rwnH0+O(nH0log(1=p)
w): (7)
We will return to Eq 7 later and present our own measurements for the relevant range of parameters,
but let us rst introduce the dictionary idea, see gure 8. The dictionary is a (generally imbalanced)
labelled binary tree. Both the nodes and the edges are labelled. The edge labels are 0 and 1 as usual,
and a node's label is the likelihood value of the chunk composed by reading the edge labels along
the path from the root to the node. The algorithm builds the tree node-by-node, as follows:
1. Create a root node with two edges labelled 1 and 0 to two child nodes labelled with the value
ofpand 1 p, respectively.
2. Find and mark the maximum likelihood leaf node. Denote its label as R.
3. Create two leaf children of the marked node and connect them with edges labelled 1 and 0.
Make the node labels the value of RpandR(1 p), respectively.
4. Repeat steps 2 and 3 until the tree has 2wnodes besides the root.
5. Now relabel each leaf by its consecutive leaf number while visiting the leaves in some order
agreed between the encoder and decoder5.
Tunstall encoding is achieved by running the source bit-string down the Tunstall tree bit by bit until
a leaf is reached, at which point the codeword is read o from the leaf label and the process returns
to the root. Tunstall decoding requires a 2w-entry table where variable-length path sequences are
set against codewords, with the former read o from the path to the leaf labelled by the latter. We
notice with satisfaction that Tunstall decoding has the cost O(1).
Implementation. Tunstall encoding (and especially decoding) is very undemanding, well within
reach of a small, system-on-chip smart sensor. To avoid accuracy/under
ow problems with repeated
multiplication in generating the dictionary at the receiver (Step 3 of the algorithm above), one could
use log-likelihoods as node labels. Then instead of multiplication, log pand log(1 p) are added to
the parent label to produce labels for the 1- and 0-child, respectively6. This way for any reasonable
table size, computational accuracy will not be a problem. We implemented the algorithm to see
what kind of residual redundancy we could be getting from a speci c Tunstall code. The results
of our running a Tunstall compressor through 1 million random bits are presented in table 2.
Comparing this with Eq 7, we conclude that at p= 0:05 the compressor already reaches asymptotic
mode when doubling the codeword length roughly halves the redundancy . At the same time,
the dependency (p) in the interesting range of p, i.e. in the area around p= 0:1 is not quite
asymptotic: the contrast in redundancy between p= 0:05 andp= 0:15 is nowhere near a factor of
2 that the formula suggests.
From the practical point of view, if we target a blockchain with 1Kusers, with 10% of them
posting messages in any given block, p= 0:1 suggests a compressed bitmap of at least 1024 0:47 =
5For example, left to right, or in pre x order. The properties of the code remain the same under any permutation
of the codeword assignments but the practicalities of encoding/decoding require a shared order
6We use the fact that the greatest number has the greatest logarithm
18
w p H 0(%)
4 0.05 0.37 0.29 30.4
4 0.10 0.50 0.47 7.5
4 0.15 0.65 0.61 6.9
8 0.05 0.32 0.29 13.4
8 0.10 0.49 0.47 4.6
8 0.15 0.63 0.61 3.8
Table 2: Observed redundancy of Tunstall code. Sample length before compression: 106. Column
headers:w, codeword length; p, probability of 1; , compression ratio; H0, per-bit entropy; =
( H)=H, residual redundancy(%)
codeword log2pcchunk
0000 3.06 0000000000000
0001 5.55 0000000000001
0010 5.32 000000000001
0011 5.08 00000000001
0100 4.85 0000000001
0101 4.61 000000001
0110 4.38 00000001
0111 4.14 0000001
1000 3.91 000001
1001 3.67 00001
1010 3.44 0001
1011 3.20 001
1100 3.20 010
1101 5.70 011
1110 2.97 10
1111 5.47 11
Table 3: 4-bit Tunstall code for p= 0:15
482 bits or 61 bytes. An 8% residual redundancy would increase this by only 5 bytes. However,
the bitmap is broadcast together with the root hash, 32 bytes long, and a few extra bytes of forced
redundancy for the purposes of S-message veri cation (as per PLS protocol). This increases the
length of the S-message up to nearly 100 bytes, and at this level a redundancy of the compressor
to the tune of 5 to 10 bytes makes little di erence.
If the number of users drops to 0.05, even the poor compression quality for w= 4 results in only 379
bits (though 82 bits, or 11 bytes, more than the entropy limit), which is still less than the already
acceptable 482 bits we observed for p= 0:1. An alternative is to use a list of raw ID numbers, circa
52 in total, each requiring 10 bits. This is 520 bits, far worse than the compressor's output, but
not signi cantly worse than 482, and the list length would decrease in proportion to p. Ifpwere
to drop further below 0.05, and if the Tunstall compressor further deteriorated, the uncompressed
`list' option could at some point be preferred, with the switch controlled by a single additional bit
in the message.
19
o set eld size description
(bits) (bits)
0 Ti 256 root hash of the Merkle Tree for the new block Bi
built using new user-IDs
256 n,m 24n: total number of users, m: how many present
280
ags 8 bits 0,1: bitmap type (plain, compressed, list, empty)
bit 2: (0:w= 4, 1:w= 8)
bits 3{7: pre-randomisation parameter (see next section)
288 bitmap M L 1024 processed bitmap content
L+ 288 redundancy 32 all zeros, for PLS validation
Table 4: Structure of the proposed block root-of-trust Ji
We conclude that a four-bit Tunstall code is all that is required to implement the PLS S-message
within half of the maximum LoRa message length (250 bytes). To aid the reader's intuition,
we present an example of a 4-bit Tunstall code for p= 0:15 in table 3. For each codeword we
additionally show its log-likelihood. Notice that unless the log-likelihoods are exactly identical, as
is the case for codewords 1010 and 1100 which correspond to chunks with the same number of 1s
and 0s, the di erences between log-likelihoods manifest themselves in the rst (decimal) fractional
digit already, so computational accuracy should not be a concern7.
New structure of the root of trust. In the original PLS protocol [12] the S-record was a
message that contains the block's root of trust Ji, which was the root hash of the Merkle tree
representing the new block Bi. In the light of our analysis of indexing costs presented in Section
4.1 and the properties of Tunstall encoding described in the current section, we propose to modify
the root of trust Jias shown in Table 4.
The total message length is L+320 bits or L+40 bytes. We expect Lto be close to 60 bytes in most
cases (which is the entropy limit for 1024 users at 10% occupancy per block on average), which makes
the S-message circa 100 bytes long, but if necessary Lcan be increased to 128 bytes resulting in the
packet length 168 bytes, still well within the length limit (255 bytes) for LoRa communications. A
128-byte bitmap would support the number of users up to 1024 without Tunstall compression, or
about twice as many if Tunstall compression is used at 10% occupancy.
The hashTrequires the server to renumber the users, building a new Merkle tree and computing
its root hash. The client will recompute Tfrom any leaf hash, adjunct hashes and the path mask
{ all sent to it by CAS (unauthenticated, unsigned) at request, and then check it against the Tin
the S-record.
Notice that the redundancy eld is only 32 bits, since it is impossible to crack the S-message
directly: both the plaintext and the key are unknown, the former due to the XORing of the next
P-message, yet to be received, to the plain text, see Figure 2. As mentioned earlier, the purpose of
the redundancy eld is to thwart a random message attack for the DoS purposes, and so a 32-bit
redundancy translates into a less than 1-in-a-billion chance to cause the recipient to accept a false
message, which is more than sucient in the IoT world.
7the main danger would be that the sender and the receiver use di erent
oating-point arithmetic, incur di erent
rounding errors and end up using di erent dictionaries; this example shows that for a small dictionary there is no
such danger
20
Finally, let us dwell a little on the block's MT whose root Tiis included in the block's root-of-trust
Ji. The leaves of that tree are hashes of the user records with the user ID corresponding to the
path label sequence as usual, except the IDs are now new IDs calculated from the block bitmap
and occupying a range from 0 to m 1 without gaps. Since mis not necessarily a power of 2,
the MT generally consists of a complete half with leaf labels in the interval [0 ;2blog2mc) without
gaps and a truncated half with labels in the interval [2blog2mc;m), also without gaps, with the
rest of the leaves labelled with NULL. The shape of the MT depends solely on one parameter, m,
which is part of the root-of-trust. Consequently, no further information, such as path masks, etc,
is required for access and validation of the root hash, Ti, except, of course, we must use Eqs. 1 to
calculate the root-adjunct paths in the truncated half. For our running example of 10% occupancy
and the total number of users 1024, the value of mwill have an expectancy of around 102, which
means this path will be between 1 and 7, and never longer. Table 1 indicates that the standard
(MPT or mask-controlled MP) would require from 3 to 9 adjunct hashes. The di erence between
7 and 9 is not big, but notice that 8 hashes would already require more than one LoRa packet to
transmit.
We would like to emphasise here that the main e ect of using the Tunstall-Merkle Tree (TMT,
which is how we wish to call our construction) rather than, say, MPT is notthat fewer hashes have
to be communicated with the former than the latter, but the fact that the latter requires a full path
irrespective of the presence or absence of the leaf for secure retrieval. By contrast, a TMT provides
an absence proof directly from the root-of-trust bypassing the Merkle Tree entirely. Since the SLVP
protocol requires every thing to check the presence of its own S- and LV- messages before advancing
the protocol, and since a thing would typically monitor another user's infrequent activity, the cost
of absence proof dominates over the cost of secure retrieval. Nevertheless, it is reassuring to see
that the latter is also improved, in terms of limits if not necessarily average, by our approach. The
price we are paying is some additional calculations well within the capabilities of resource-limited
systems such as most things tend to be.
Returning to the compression issue, there is one factor yet to be accounted for. We remarked
earlier that our statistical calculations are based on the zero-order assumption, i.e. that di erent
users' behaviours are uncorrelated. Obviously it is not the case when users engage in a higher-level
protocol with one another, e.g. producer/consumer. This may skew the chunk statistics resulting
in a longer codeword sequence for the block bitmap. In the next section we will propose a simple
remedy.
6 Pre-randomisation
The idea is to apply a bijective function to the source user ID which depends on an extra parameter,
block number i. A di erent block number should result in a very di erent permutation. This way a
position in the block bitmap will have the value 1 in a proportion of bitmaps that does not depend
on the value of other positions. A user ID will be associated with a pseudorandom sequence of
positions as new blocks are produced. Invertibility (bijection) is very important, as it prevents
di erent users from being mapped on the same bit-position in the bitmap, thus ensuring that the
mapping a pseudorandom permutation.
A simple and e ective pseudorandom permutation based solely on the block number ican be
achieved by analogy with randomising the order in a deck of playing cards. One player performs
21
the deck shue: for the card in position iin the deck i2[0;n= 2d) represented in binary as
i=idid 1:::i 0, the new position of the card i0, represented in binary as i0
di0
d 1:::i0
0is obtained
from the current position by applying the following operator
i0
di0
d 1:::i0
0=id 1id 2:::i 0id=(i);
which is a cyclic shift left. This corresponds to dividing the deck into two halves and interleaving
them, exactly as an experienced dealer would.
When the shue is nished, the other player shifts the deck, i.e. divides it into two unequal parts
and transposes them. In terms of card numbers, this corresponds to adding a pseudorandom value
vmodulo 2d:
i0=i+vmodn= v(i)
Applying the shue-shift operator Qv= vto a range= [0;n)ttimes with a pseudorandom
choice ofv:
Qvt:::Qv1Qv0
has the same e ect as repeatedly shuing/shifting a deck of cards, which, the intuition suggests,
delivers a rather arbitrary permutation. We call tthe number of rounds.
Note that the operators and vhave a negligible cost even when executed by the least powerful
platform, as they take literally a few machine instructions. The cost of pseudorandom generator that
produces a series of v-values is similarly small if we use a standard Linear Congruential Generator
(LCG):
vk+1=Fvk+ 1 modn;0k<t;
wherenis a power of 2, v0is set to the block number, and the factor Fis any positive integer that
satis es the well-known Hull-Dobell constraint: F= 5 mod 8. We chose for Fthe hex value 5EED
which satis es the constraint and which has more than enough signi cant digits for any reasonable
n.
Our solution appears quite attractive from the point of view of its cost; however, while bijectiveness
is guaranteed by construction, we need to be reassured that the solution can deliver sucient
randomness of mapping at a reasonably small number of rounds t.
Avalanche test. How do we judge the quality of a pseudorandom mapping? A common test is
based on the so-called avalanche criterion[14], used in evaluation of symmetric ciphers and hash
functions. We consider it next in relation to our mapping Q.
Select the block-number v0for the test. Next, select a number xfrom the range and some integer
0k<d and prepare two numbers x1=xandx2same asx1, except bit kof it is
ipped. Apply
the mapping Qto both and take the bitwise XOR of the results: Ai(x;k) =Q(x1)Q(x1). Let
Ai(x;k;l ) be thelth bit ofAi(x;k). De ne the correlation matrix Kklthus:
Kkl=hAi(x;k;l )ix;i; (8)
Here the averaging is done over values of x2and block numbers v0. Good randomness of the
mapping manifests itself in the closeness of all matrix elements of Kklto 1/2:
max
kl(Kkl 1=2) 1=2: (9)
22
d t d t d t
10 100 0.061 11 100 0.113 12 100 0.164
10 150 0.014 11 150 0.026 12 150 0.042
10 200 0.008 11 200 0.009 12 200 0.013
Table 5: Avalanche test of the pre-randomiser. d: input length (bits), t: number of rounds, :
mapping quality, = maxkl(Kkl 1=2), see Eq 9
This means that if we
ip a random bit in a random value xthe probability that any bit in the
image ofxunderQ
ips in response is close to 1/2. In other words, if we
ip one bit in x, on
average close to one half of the bits in the result will
ip. The name \avalanche e ect" is to do
with the fact that small changes cascade through the rounds of the computation causing further
changes until all bits of the result are a ected in a complex and unpredictable, though of course
deterministic, way.
We have applied the avalanche criterion to our proposed randomiser to estimate the acceptable
minimum value of t, the number of rounds. As it is impossible to average over all potential values
of the block number v0, we limited ourselves to 50 random samples taken from the interval [0 ;10000]
The results have proven quite insensitive to the averaging over v0, which in not surprising given
that we established that the required t-numbers are in the hundreds. The averaging over xwas
done by sweeping the whole range .
The results of the avalanche test are presented in Table 5. For practical purposes we limited ourselves
ton= 1024, 2048 and 4096, since more users are unlikely to be supported by the communication
infrastructure of a single site. The results show that a surprisingly large number, around 200, of
rounds is required to achieve good randomisation. It is large compared to the number of rounds
one expects to be necessary to randomise a deck of cards (of the order of 10), but it is not large
technically: a microprocessor would have to execute only a few thousand instructions to compute
the imagei0giveni. This has to be done as many times per block as the number of counterparties
that the user has to monitor on the blockchain. For a thing this would be a number of the order
unity, hence the cost would be negligible even on a tight energy budget. On the other hand, setting
tto 200 would ensure that possible correlations between bits in the image do not exceed 3% (0.013
normalised by 1/2), which should be good enough for practical purposes.
Finally, let us recall that the IoT platform is low-power, but the server running the PLS protocol
via the Sequencer is not. It has ample capacity to analyse the quality of the permutation in terms
of its in
uence on Tunstall compression. We have reserved 5 bits in to pass to the shue-shifter
an integer value in the interval [0 ;32). It is convenient to use the value 0 to indicate that a random
permutation is not required8, whereas a nonzero value is added to the round counter t. The server
can try up to 31 additional rounds and choose the one that gives the best compression. The users
receiving the root of trust will be aware of how many additional rounds should be performed and
will maintain consistency.
23
T
block i = 26920
user 45
shuffle-shifter
45 0000 0100 0010 0101 0000 1000 0010 0 1 2 3 4 5 0 1 2 3 4 5 CAS
!
Tunstall decoder
!
n,! n,m
PLS protocol S-message for block 269 n,m,! block bitmap
0 4 8 12 16 20 24 Figure 9: Retrieving a user record from block 269 for ID= 45; for this block m= 5.
T
block i = 30523
user 17
shuffle-shifter
17 0000 0011 0000 1000 0001 0000 0100 01 2 3 4 !
Tunstall decoder
!
n,! n,m
PLS protocol S-message for block 305 n,m,! block bitmap
0 4 8 12 16 20 24
Figure 10: Obtaining proof of absence.
24
7 Putting it all together
Next we consider a complete example of a user attempting to retrieve a contribution to a block
that has been made either by itself or a counterparty.
Figure 9 presents the
ow of data when a block-269 S-message is received and successfully unlocked
by a user. The user is about to request the contribution to block 269 from user ID 45. To accomplish
this, it needs to decode the received block bitmap by feeding it to the Tunstall decoder together
with the parameters nandm(total number of users and the number of users contributing to block
269, respectively). The decoder produces the uncompressed bitmap. At the same time the user ID
(45) and the block number (269) along with the total number of users and con guration parameters
are fed to the shue-shifter, which will extract the number of additional rounds from and produce
its output value, 20. The bit in position 20 of the uncompressed bitmap happens to be 1, which
means that the contribution from ID 45 is present in block 269. The number of 1s in the bitmap
to the left of position 20 is 4, so the index in the truncated Merkle tree for the contribution in
question will be 4. The path to leaf 4 is highlighted in red in the gure.
The user's CAS request will include the block number, 269, and the ID index, 4. CAS will respond
with the leaf hash h4and the adjunct sequence
V0=h5; V2=H(H(h0kh1)kH(h2kh3));
which consists of the labels of the two nodes of the tree marked in blue. Because the user acquired
mfrom the unlocked S-message, i.e. the root of trust, it knows the shape of the tree. Consequently,
no mask is communicated, but the user is able to reconstruct the mask anyway.
To validate the requested h4, the user checks that the following equation holds:
H(V2kH(h4kV0)kH(h4kV0)0)) =T:
whereTis the root hash received with the unlocked S-message.
An alternative scenario is shown in gure 10. When attempting to retrieve the contribution of
user ID=17 to block 305, it turns out that the output of the shue-shifter points to a 0 in the
uncompressed block bitmap. Since the unlocked S-message is the root of trust, this constitutes a
proof that block 305 has no contribution from user 17. Notice that CAS is not involved in the
process at all.
8 Related work
The PLS blockchain and the protocols in basic form were proposed in [12]. The idea of sparse Merkle
Tree has an unclear origin. To the best of our knowledge it was rst put forward by Bauer[5] and
was recently improved on in [6]. Both studies are concerned with mutable trees, with objectives
very di erent from ours, although, like ourselves, the authors remark on the importance of proofs of
absence (non-membership). Tree statistics is tackled theoretically in [4] in the context of optimising
mutable MTs for the Bitcoin blockchain in the context of Bitcoin transactions. The objectives of
this study are similar to ours as the authors attempt to group the leaves together to minimise the
8This could be advantageous when, for example, no compression is used.
25
proof length, but they do it using tree transformations (taking the data structure red-black tree as
a starting point), while we achieve a similar objective by renumbering the keys (user IDs in our
case).
The compression technique we use is due to Tunstall [13] and this seems to be uniquely suitable
for our case since it is based on empyrical probability of leaf occupancy, which is available to the
Fog Server running PLS and which takes next to no resources to communicate to the client. The
eciency of our technique depends on this method.
We used our own pseudorandom permutation as a combination of a perfect shue and a random
shift, using a classical LCG source [10]. There exist various methods of pseudorandom permutation,
an oft-cited one being Fisher-Yates shue[8], rst published in the 1930s (citation unavailable). The
idea there is to choose a (pseudo)random element of a sequence of source items and exchange it
with the rst element on the sequence. Clearly, if this is repeated enough times then any possible
permutation could be achieved9. A recent paper [3] presents a fast, parallel algorithm that mimics
the technique of merge-sort except the merge makes a pseudorandom choice when ordering two
elements for the output.
However, our situation is quite di erent. Not because we are dealing with a contiguous range of
numbers rather than an abstract sequence of objects: one could enumerate the objects and the
problem would boil down to the one we are faced with. Our situation is di erent because the
sender and the recipient must choose the same permutation. To encode an arbitrary permutation
ofnnumbers would take close to ( m 1) log2mbits, which is the same order of magnitude as the
block bitmap we are trying to make more compact. Of course the ability to perform an arbitrary
permutation is not required: all we want is break correlations between user IDs in a series of block
bitmaps, and for this any suciently rich subgroup would do.
Conclusions
Statistical analysis of a sparse Merkel Tree under the assumption of uniform, uncorrelated leaf
occupancy has been presented. The model obtained allows direct computation of the Probability
Distribution Function for path lengths given the leaf-value probabilities. The path weight was
quanti ed in terms of the number of adjunct hashes required for its leaf proof. We determined that
the mean path weight of a sparse MT tree is close to that of a dense, truncated MT tree, with the
latter being slightly better at most leaf-probability values pin the practically interesting interval.
We proposed an alternative structure, a Tunstall-Merkle tree, which combines a dense, truncated
MT and a Tunstall-compressed bitmap indicating leaf ocupancy. We tested the compressor at
several practical values of code size and quanti ed its residual redundancy. We found that a very
small code table (16 or 256 codewords) proves sucient for achieving near-limit compression, which
means that Tunstall decoding presents no storage problem whatsoever to an IoT platform. To
improve the e ect of compression we further proposed a decorellation facility in the form of a shue-
shifting algorithm and tested its properties using the standard avalanche criterion to determine the
number of rounds. Both the Tunstall decoder and the shue-shifter with the codes size and the
number of rounds, respectively, sucient for our purposes are quite processor-ecient as well, since
they involve inexpensive operations (table indexing, cyclic shift and binary addition) and short
instruction sequences in implementation.
9unless the pseudorandom generator producing the selections is caught in a cycle rst
26
The main e ect of the proposed technology is a drastic improvement in the cost of the SLVP proto-
col. Indeed an SLVP veri er has to check every block for the presence of counterparty (prover) con-
tributions, and no such contribution would be present in a great majority of blocks. Our proposed
Tunstall-Merkle tree has zero proof-of-absence cost, and when a leaf is present the communication
cost of retrieval is in most cases better than that for the standard MT and MPT. Obviously our
technique o ers no advantage to a system with an unlimited and dynamic number of users, but it is
bene cial for at least the PLS blockchain situation. The statistical analysis of a sparse MT/MPT
has signi cance beyond the area of our study; it could be useful for planning and designing any
secure storage structure that involves Merkle trees.
Future work will concentrate on higher-level protocols which control the interaction of things with
a smart contract within the same limited-resource set of assumptions.
References
[1] LoRa and LoRaWAN: A Technical Overview. Technical report, Sentech Corporation, December
2019.
[2] Ross Anderson, Francesco Bergadano, Bruno Crispo, Jong-Hyeon Lee, Charalampos Mani-
favas, and Roger Needham. A new family of authentication protocols. SIGOPS Oper. Syst.
Rev., 32(4):9{20, October 1998.
[3] Axel Bacher, Olivier Bodini, Alexandros Hollender, and J er emie Lumbroso. Mergeshue: A
very fast, parallel random permutation algorithm. CEUR Workshop Proceedings , 2113:43{52,
2018.
[4] Bolton Bailey and Suryanarayana Sankagiri. Merkle trees optimized for stateless clients in
bitcoin. IACR Cryptol. ePrint Arch. , 2021:340, 2021.
[5] Matthias Bauer. Proofs of zero knowledge. CoRR , cs.CR/0406058, 2004.
[6] Rasmus Dahlberg, Tobias Pulls, and Roel Peeters. Ecient sparse merkle trees - caching
strategies and secure (non-)membership proofs. In Billy Bob Brumley and Juha R oning,
editors, Secure IT Systems - 21st Nordic Conference, NordSec 2016, Oulu, Finland, November
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2016.
[7] Espressif Systems. ESP32 Technical Reference Manual. Available as https://www.espressif.
com/sites/default/files/documentation/esp32_technical_reference_manual_en.pdf .
[8] R. A. Fisher and F Yates. Statistical Tables for Biological, Agricultural and Medical Research .
Oliver & Boyd, Edinburgh and London, 6th edition, 1963.
[9] Seungbum Jo, Stelios Joannou, Daisuke Okanohara, Rajeev Raman, and Srinivasa Rao Satti.
Compressed Bit vectors Based on Variable-to-Fixed Encodings. The Computer Journal ,
60(5):761{775, 12 2017.
[10] Derrick H. Lehmer. Mathematical methods in large-scale computing units. In Proceedings of the
Second Symposium on Large Scale Digital Computing Machinery , pages 141{146, Cambridge,
United Kingdom, 1951. Harvard University Press.
27
[11] Ralph C. Merkle. A digital signature based on a conventional encryption function. In Carl
Pomerance, editor, Advances in Cryptology | CRYPTO '87 , pages 369{378, Berlin, Heidel-
berg, 1988. Springer Berlin Heidelberg.
[12] Alex Shafarenko. A PLS blockchain for IoT applications: protocols and architecture. Cyber-
security , 4(1):4, 2021.
[13] B.P. Tunstall. Synthesis of noiseless compression codes . PhD thesis, Georgia Tech, 1967.
[14] A F Webster and S E Tavares. On the design of s-boxes. In Lecture Notes in Computer
Sciences; 218 on Advances in Cryptology|CRYPTO 85 , pages 523{534, Berlin, Heidelberg,
1986. Springer-Verlag.
[15] Cong Yue, Zhongle Xie, Meihui Zhang, Gang Chen, Beng Chin Ooi, Sheng Wang, and Xiaokui
Xiao. Analysis of indexing structures for immutable data. In Proceedings of the 2020 ACM
SIGMOD International Conference on Management of Data , SIGMOD '20, page 925{935, New
York, NY, USA, 2020. Association for Computing Machinery.
28
|
{
"id": "2107.08970"
}
|
2101.00378
|
Speeding up Block Propagation in Blockchain Network: Uncoded and Coded
Designs
|
We design and validate new block propagation protocols for the peer-to-peer
(P2P) network of the Bitcoin blockchain. Despite its strong protection for
security and privacy, the current Bitcoin blockchain can only support a low
number of transactions per second (TPS). In this work, we redesign the current
Bitcoin's networking protocol to increase TPS without changing vital components
in its consensus-building protocol. In particular, we improve the compact-block
relaying protocol to enable the propagation of blocks containing a massive
number of transactions without inducing extra propagation latencies. Our
improvements consist of (i) replacing the existing store-and-forward
compact-block relaying scheme with a cut-through compact-block relaying scheme;
(ii) exploiting rateless erasure codes for P2P networks to increase
block-propagation efficiency. Since our protocols only need to rework the
current Bitcoin's networking protocol and does not modify the data structures
and crypto-functional components, they can be seamlessly incorporated into the
existing Bitcoin blockchain. To validate our designs, we perform analysis on
our protocols and implement a Bitcoin network simulator on NS3 to run different
block propagation protocols. The analysis and experimental results confirm that
our new block propagation protocols could increase the TPS of the Bitcoin
blockchain by 100x without compromising security and consensus-building.
|
http://arxiv.org/pdf/2101.00378v1
|
Lihao Zhang, Taotao Wang, Soung Chang Liew
|
cs.NI, cs.CR
|
cs.NI
|
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 1
Speeding up Block Propagation in Blockchain
Network: Uncoded and Coded Designs
Lihao Zhang, Taotao Wang, Member, IEEE, and Soung Chang Liew, Fellow, IEEE
Abstract —We design and validate new block propagation protocols for the peer-to-peer (P2P) network of the Bitcoin blockchain.
Despite its strong protection for security and privacy, the current Bitcoin blockchain can only support a low number of transactions per
second (TPS). In this work, we redesign the current Bitcoin’s networking protocol to increase TPS without changing vital components in
its consensus-building protocol. In particular, we improve the compact-block relaying protocol to enable the propagation of blocks
containing a massive number of transactions without inducing extra propagation latencies. Our improvements consist of (i) replacing
the existing store-and-forward compact-block relaying scheme with a cut-through compact-block relaying scheme; (ii) exploiting
rateless erasure codes for P2P networks to increase block-propagation efficiency. Since our protocols only need to rework the current
Bitcoin’s networking protocol and does not modify the data structures and crypto-functional components, they can be seamlessly
incorporated into the existing Bitcoin blockchain. To validate our designs, we perform analysis on our protocols and implement a Bitcoin
network simulator on NS3 to run different block propagation protocols. The analysis and experimental results confirm that our new
block propagation protocols could increase the TPS of the Bitcoin blockchain by 100x without compromising security and
consensus-building.
Index Terms —Blockchain, Networking Protocol, Cut-through Forwarding, Rateless Coding.
F
1 I NTRODUCTION
BLOCKCHAIN was proposed as a supporting technol-
ogy for Bitcoin [1], the first decentralized cryptocur-
rency. After Bitcoin, other decentralized cryptocurrencies
(e.g., Litecoin [2], Ethereum [3]) quickly emerged. The
blockchains of these cryptocurrencies use the Nakamoto’s
proof-of-work (PoW) protocol to build consensus among
distributed nodes. Blockchain has, by now, become a
cutting-edge technology in the fields of FinTech, Internet
of Things (IoT), and supply chains [4], [5], [6], thanks to its
ability to enable Byzantine agreement over a permission-less
decentralized network [7].
A weakness of the current blockchains is the low on-
chain transaction throughput. For example, the throughput
of Bitcoin is around 5 7 transactions per second (TPS),
and that of Ethereum is around 40 TPS [8]. Both are ex-
tremely low compared to around 110 TPS of PayPal and
1700 TPS of Visa. Its low transaction throughput hampers
the widespread adoption of today’s blockchain technology.
A straightforward method to increase TPS is to enlarge
the block size so that a block can carry more transactions.
However, the propagation of large blocks in the network
may incur huge delays that compromise the blockchains’
security and integrity [9], and thus it is not a good idea
to increase the TPS by merely increasing the block size.
Consequently, new consensus-building protocols and new
specially deployed networking infrastructures have been
Lihao Zhang and Soung Chang Liew are with the Department of Informa-
tion Engineering, ,The Chinese University of Hong Kong, Hong Kong.
E-mail: zl018@ie.cuhk.edu.hk, soung@ie.cuhk.edu.hk
Taotao Wang is with the College of Electronics and Information
Engineering, Shenzhen University, Shenzhen 518060, China. E-mail:
ttwang@szu.edu.cn
Manuscript received xxx, xxx; revised xxx, xxx.proposed as solutions to increase the TPS of blockchains
(see discussions of these related works in Section 2).
In this work, we put forth a new block propagation pro-
tocol to propagate large blocks containing a large number of
transactions without increasing the block relay delay. Unlike
the existing solution that requires changing the consensus-
building protocol or deploying new network infrastructures,
we build our block propagation protocol upon the compact-
block relaying protocol that is already adopted by the cur-
rent Bitcoin network [10]. Compact-block relaying reduces
the block relay delay by compressing blocks that contain
transactions (around 250 Bytes each in Bitcoin) into compact
blocks that contain transaction hashes (6 Bytes each). There-
fore, compact-block relaying can include more transactions
into each compact block while maintaining the same relay
delay, thus increasing the TPS without compromising the
blockchain’s security.
In this paper, we further boost TPS by further increas-
ing compact-block size including even more transaction
hashes into each compact block. However, simply increasing
compact-block size induces extra propagation delays that
may compromise blockchain’s security. We devise methods
to keep the propagation delays at bay while increasing the
compact-block size. We adopt a two-pronged approach: 1)
we replace the store-and-forward compact-block relaying
scheme with a cut-through forwarding scheme; 2) we apply
rateless erasure codes to increase the efficiency of block
propagation. The contributions of this work are listed as
follows.
1) We put forth a new block propagation protocol
that replaces the store-and-forward compact-block
relaying scheme with a cut-through forwarding
scheme. Our new cut-through compact-block re-arXiv:2101.00378v1 [cs.NI] 2 Jan 2021
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 2
laying scheme can propagate large compact blocks
without inducing extra relay. The original compact-
block relaying [10], [11] is a store-and-forward
scheme in which a whole compact block must be re-
ceived before it is forwarded. With cut-through for-
warding, a node receives small chunks of a compact
block while forwarding earlier-received chunks, al-
lowing reception and forwarding of a compact block
to progress in parallel.
2) We apply rateless erasure codes to compact blocks.
Rateless erasure codes allow the recovery of the
source symbols using a subset of the encoded sym-
bols. Instead of distributing the original compact
block’s source symbols, the compact block’s source
distributes the compact block’s encoded symbols.
Importantly, rateless erasure codes allow a peer to
construct source symbols by retrieving the encoded
symbols from multiple peers. Our coded design
benefits from the peer-to-peer (P2P) network topol-
ogy of the Bitcoin blockchain by efficiently utilizing
the upload bandwidths of all peers [12].
3) We perform a theoretical analysis of our protocols
assuming a simple linear network. The analysis
results confirm that our protocols can obtain signif-
icant TPS gain. Moreover, to evaluate our protocols
in a practical network, we implemented a Bitcoin
network simulator. We simulated propagating huge
compact blocks containing a large number of trans-
action hashes to improve TPS. Our results indicate
that our block propagation protocols can increase
TPS by 100x while maintaining the same propaga-
tion delay as the conventional block propagation
protocols.
Since our design is built upon compact-block relaying that
has been implemented into the Bitcoin network, we be-
lieve our design can be readily deployed in the existing
blockchain networks.
The rest of this paper is organized as follows. Section 2
discusses related works. Section 3 presents the background
of blockchain. Section 4 and Section 5 introduce our un-
coded and coded designs. Section 6 analyzes the perfor-
mance of our two-pronged approach. Section 7 discusses
our experimental results, and Section 8 concludes this work.
2 R ELATED WORK
To improve the TPS of the Bitcoin blockchain, [13], [14]
changed the Nakamoto’s PoW consensus protocol . These
clean-slate designs modified data structures and many
crypto functional components in the Bitcoin blockchain’s
consensus protocol. Consequently, these new consensus pro-
tocols are incompatible with the today’s Bitcoin blockchain.
By contrast, our work aims to increase TPS by redesigning
the networking protocol without changing other key func-
tional components. Furthermore, our work does not require
the building of new network infrastructures. There has been
little prior work along this line.
Ref. [15] discussed the shortcoming of the networking
aspects of the Bitcoin blockchain. To reduce block propa-
gation delay, [10], [11] devised compact-block relaying that
transmits a compressed block consisting of the hashes oftransactions in the block; transactions are transmitted (upon
feedback from the receiver) only if they are missing at
the receiver. Investigation in [16] preemptively announce
a block’s availability before the complete reception of the
whole block. Exploiting the multicasting capability of net-
work nodes in blockchain, [17] employs fountain codes [18]
to enable a full node to obtain a block from multiple peers.
However, [17] still relies on store-and-forwarding rather
than cut-through forwarding.
Several works advocated building new network infras-
tructures for speeding up the block propagation of the
Bitcoin blockchain. For example, [19] used a blockchain
distribution network (BDN) with high throughput servers
to speed up the propagation of large blocks. In particular,
[19] adopted cut-through forwarding within the BDN, but
not at distributed blockchain nodes. The BDN approach
requires building a new infrastructure controlled by a cen-
tral authority, partially offsetting decentralized blockchain
systems’ many advantages. FIBRE (Fast Internet Bitcoin
Relay Engine) in [20] is a transport-layer protocol that uses
UDP with forward error correction to decrease the delays
caused by packet loss. It also introduces data compression
to reduce the amount of network traffic. Our work, focusing
on the blockchain networking protocol1, is fully compatible
with FIBRE and can be merged with it.
The idea of using cut-through forwarding to improve
block propagation is also employed by Falcon [21]. The dif-
ferences between Falcon and our work here are as follows:
i) Falcon implements cut-through forwarding on specially
deployed relay nodes rather than on the existing blockchain
nodes; ii) Falcon is a commercial project and there lacks
performance analysis on its block propagation; iii) Falcon
does not employ rateless erasure codes to increase the
efficiency of block propagation.
3 B ITCOIN BLOCKCHAIN BACKGROUND
This section reviews the background for the Bitcoin
blockchain, including its data structure, PoW consensus pro-
tocol, network topology, and block propagation protocols.
After that, we present our compact block relaying scheme.
3.1 Data Structure of Blockchain
Blockchain is a decentralized append-only ledger for digital
assets. A blockchain is replicated and shared among par-
ticipants. The transactions in the system are contained in
concatenated blocks in the blockchain. A block contains a
header and multiple transactions. The state of a blockchain
maintained by a participant changes according to the blocks
in the blockchain.
We can write a block as B= [H; T 1; T2;; TK]where
Hdenotes the header, fT1; T2;; TKgdenotes the transac-
tions in the block, and Kdenotes the number of transactions
in a block. The header Hcontains the hash computed from
the content of the preceding block, the Merkle root of the
transactions in this block, a nonce generated by the PoW
consensus protocol and a number indicating the mining
1. In the TCP/IP and OSI nomenclature, the blockchain networking
protocol resides in the application layer. In this paper, we do not change
IP or TCP protocol at all.
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 3
target. Each block must refer to its preceding block by
placing the hash of its preceding block in its header, and
the blocks form a chain of blocks arranged in chronological
order.
3.2 PoW Consensus Protocol
A consensus protocol coordinates the blockchain’s updates
to ensure chronological ordering of the blocks and to ensure
the blockchain’s integrity and consistency across geograph-
ically distributed nodes. Bitcoin [1], as the first implemen-
tation of blockchain, introduces the Proof-of-Work (PoW)
consensus protocol. Tens of thousands of distributed nodes
adopt the PoW consensus protocol to achieve data consis-
tency (i.e., ensuring the blockchains maintained by them are
the same). Before adding blocks to the blockchain, a Bitcoin
node has to prove that it has performed some work known
as PoW. In essence, a node must find a nonce input to a hash
function so that the hash value is less than a target number,
as expressed by
h(n; p; m )< D (1)
where nis the nonce, pis the hash of the preceding block,
mis the Merkle root of the transactions in the block, h()
is a hash function, and Dis the mining target that is small
with respect to the whole range of possible hash function
outputs. The target Dis determined by a difficulty level set
by the Bitcoin network. The header Hof a block contains
n,p,mandD. The difficulty level is dynamically tuned
by the Bitcoin protocol, which ensures that the participating
nodes, as a whole, produce an average of one block every
ten minutes. The process of solving the PoW puzzle is called
mining, and the nodes that perform the mining function are
known as miners.
When other nodes receive a new block broadcasted from
the miner, they verify the block locally and independently.
The verification of a block can be divided into two parts: 1)
the verification of the PoW solution, i.e., verifying whether
the nonce ncontained in the block header fulfills (1) given
the other block header’s contents; 2) the verification of
the transactions contained in the block body, i.e., verifying
the validity of each transaction and verifying whether the
Merkle root of all the transactions included in the block
body is consistent with the Merkle root contained in the
block header. If block passes verification, this block is ap-
pended to the local blockchain of the node.
3.3 Blockchain Network Topology
The network of the Bitcoin blockchain is based on an
unstructured P2P network. The Bitcoin Core project [22]
implements the Bitcoin networking protocol. When a node
initializes, it attempts to discover a set of peers to establish
outgoing or incoming2TCP connections. These connections
are used for transaction and block propagation. Each node
2. Outgoing connections are initiated by the node itself, and incoming
connections are initiated by other nodes. When a Bitcoin node boots
up, it asks the DNS seed nodes for a list of Bitcoin nodes’ IP addresses.
Then, it selects a subset of these addresses and initiates up to 8 outgoing
connections. Meanwhile, this node’s IP address will be logged and,
going forward, sent to other nodes by the DNS seed nodes. This node
will accept up to 117 incoming connections. Super nodes may establish
more than 8 outgoing connections.maintains a list of peers’ IP addresses. According to the
default protocol in the Bitcoin Core client, a node in the
Bitcoin network initiates up to 8 outgoing connections and
accepts up to 117 incoming connections. While the Bitcoin
infrastructure does not support the discovery of the overall
network topology — this is to secure the network from
potential network attacks such as Eclipse [23], Sybil [24] —
there have been works that acquire the network topology
using the information in the Bitcoin blockchain protocol
messages. A Bitcoin P2P Network Sniffer [25] can connect
to a Bitcoin node and listen to network events such as block
broadcast or transaction broadcast. The collected data can
be used to infer the size of the Bitcoin P2P network and
the geographic distributions of nodes. We use this type
of network topology information to model our blockchain
network simulator, as discussed in Section 7.
3.4 Transaction and Block Dissemination
The network that supports the dissemination of transactions
and blocks in the blockchain is a P2P network overlaid
over the Internet. Each node keeps a replica of the com-
plete blockchain. There is no central coordinator to ensure
the consistency of the replicas across the different nodes.
Rather, the nodes use a simple gossip protocol to propagate
messages containing transactions and blocks to update and
synchronize their ledger replicas.
Let us first describe the block propagation protocol of
standard relaying (SR) as illustrated in Fig. 1(a). To avoid
unnecessary block forwarding, a peer does not forward
blocks to its peer immediately. Instead, an inv [16] contain-
ing a summary of the blocks available—specifically, hashes
of the blocks—is sent to a neighbor (node B in Fig. 1(a)).
When a node receives an inv message, it issues a getdata
message to the sender, only requesting for blocks that it does
not have. The SR protocol, although simple, is inefficient
and incurs large propagation delay and has low transaction
throughput.
Compact-block relaying (CBR) [10], as illustrated in Fig.
1(b), is a solution to decrease propagation delay. CBR also
announces blocks available at the sender through an inv.
However, upon receiving a getdata message, the sender
only returns a compact block containing a summary of the
transactions included in the entire block (i.e., the hashes of
the transactions). The receiver then only requests for the
transactions in the block that it does not have (blocktxn in
Fig. 1(b)) rather than the whole block (block in Fig. 1(a)).
Since blocktxn contains only a fraction of all transactions
in a block, the bandwidth needed for data propagation is
reduced. Although CBR decreases the propagation delay,
the sender still needs to have the whole compact block in
hand before relaying it. This relaying is called ”stored-and-
forward”. While store-and-forward relaying works well for
small compact blocks, if we intend to increase the block size
to boost TPS, the compact blocks become large themselves.
When relaying compact blocks of large size, the store-and-
forward CBR can incur large latency.
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 4
Node P
Node Q
block
inv
getdata
Node R
inv
(a) Original block propagation
protocolgetdata
Node P
Node Q
block
getblocktxn
Node R
blocktxn
(b) Compact block relaying
protocol compact
blockinv
getdata
inv
getdata
Compact
block
(c) Compact block relaying
protocol with Cut-through
forwarding
Node P
Node Q
block
getblocktxn
Node R
blocktxnblock
header
Query
blockblock
header
Query
getblocktxn
blocktxn
Fig. 1: The forwarding of a block over two hops by three different block propagation protocols: (a) the original block
propagation protocol; (b) the compact block relaying protocol; (c) the cut-through compact block relaying.
4 U NCODED DESIGN : COMPACT -BLOCK RELAY -
ING WITH CUT-THROUGH FORWARDING
This section presents the design of a new CBR protocol. Al-
though the original store-and-forward CBR can increase the
TPS to some extent, according to our experiment result (see
Section 7.2), using the store-and-forward CBR to propagate
compact blocks of large size can lead to large propagation
delays.
As illustrated in Fig. 1(c), the main essence of our new
CBR protocol is to allow parallel relaying of large compact
blocks. A node P forwards a compact block to another node
Q in small chunks. Node Q then forwards the earlier chunks
to node R as later chunks are received. In particular, node
Q does not wait for all the chunks of the compact block to
be received before forwarding the earlier received chunks.
We refer to our block propagation protocol as cut-through
compact-block relaying (Cut-through CBR). We elaborate on
the details of Cut-through CBR in the following.
For a block B= [H; T 1; T2;; TK], the original CBR
constructs the compact block as Bc= [H; I 1; I2;; IK],
where His the header of the block and Ikis a the 6-
byte short hash of transaction Tk. When relaying a compact
block, instead of sending out the inv message first as in
the original CBR, the sender node in our Cut-through CBR
immediately broadcasts the block header Hto its adjacent
peers. In particular, our Cut-through CBR requires the nodes
to check whether the block header is valid. Specifically, after
receiving the block header, the nodes need to perform the
header validation by checking whether the nonce contained
inHfulfills (1) to validate the PoW solution. Compared
with the full validation3of a compact block in the original
CBR, this header validation is provisional. As soon as the
3. In the original CBR for Bitcoin, the full validation of a compact
block consists of the header validation and the transaction validation.
The header validation checks whether the PoW is valid, while the
transaction validation check whether the transactions conveyed by the
compact block are valid.header His received, the provisional header validation can
begin before the whole compact block is received, consum-
ing much less computation time. This provisional header
verification can prevent malicious nodes from generating
fake blocks at low cost. Furthermore, to maintain security, if
a node in our Cut-through CBR finds an invalid transaction
later during the compact block relaying, this node will
suspend the compact-block relaying and alert its peers by
sending an abort message, as explained in the following.
To further elaborate our protocol, as illustrated in Fig.
1(c), we consider a scenario in which a peer P sends out H,
and peer Q is one of P’s neighbors. After peer Q receives H
from peer P , peers Q and P execute the following operational
steps in order:
1) Peer Q first checks whether the received His in its
local pool. If it is, peer Q does nothing and discards
H. If it is not, peer Q sets peer P as this block’s seed
peer and goes to the next operation.
2) Peer Q performs the provisional header validation
ofHto verify the PoW solution. If the provisional
header validation passes, peer Q puts Hinto its
local pool and broadcasts it to all adjacent peers
(except the peer P). Furthermore, peer Q sends a
query message to peer P to ask for the transaction
hashes in the compact block and then goes to step 4).
If the provisional header validation is not fulfilled,
peer Q suspends all the operations related to this
block.
3) After receiving the query message from peer Q, peer
P sends peer Q the transaction hashes in chunks.
Specifically, every consecutive Mtransaction hashes
are grouped into a hash chunk, and the hash chunks
are sent one after another. Peer P keeps sending the
hash chunks to peer Q as long as it has any of the
hash chunks (either generated from peer P itself if
peer P is the miner of this block or received from
peer P’s seed peers).
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 5
4) Peer Q receives the hash chunks sent from peer P
and performs the chunk validation by validating
the transactions indexed by the transaction hashes.
In case peer Q lacks some of the transactions on
its local storage, it requests the missing transactions
from other nodes by sending the getblocktxn mes-
sage, as in the original CBR [10]. Meanwhile, peer
Q also listens for query messages from other peers
who treat peer Q as their seed peer of the compact
block. As illustrated in Fig. 1(c), peer Q receives a
peer R’ query message, peer Q then answers peer R
with its validated hash chunks.
5) If peer Q finds an invalid transaction during the
chunk validation, it will suspend the propagation
of this compact block. Furthermore, it will clear all
the compact-block related messages related to the
compact block from its storage and send an abort
message to alert its peers that query for the same
compact block. If all transactions pass validation,
this block is valid, and peer Q reconstructs the block
and appends it to the blockchain.
Our Cut-through CBR protocol processes the reception and
the forwarding of a compact block’s small chunks in paral-
lel, in a pipeline manner. It thus speeds up the propagation
of a huge compact block.
5 C ODED DESIGN : LEVERAGING RATELESS COD-
ING
In Cut-through CBR, a peer can only retrieve the compact
block’s chunks from one peer. This section leverages rateless
erasure codes to allow a peer to retrieve the chunks from
multiple peers. This coded design incorporates fountain
codes [18] (a kind of rateless erasure codes) into Cut-through
CBR to efficiently utilize the upload bandwidths of multiple
peers [12].
In the coded design, instead of sending Mtransac-
tion hash chunks fp1; p2;; pMg, the block miner per-
forms fountain coding over the hash chunks to generate
the equal-size coded symbols fx1; x2;xi;g accord-
ing to the coding scheme proposed in [26]. Specifically,
the coding scheme chooses a set of Mcoding cofficients
Ci=fc1; c2; c3: : : ; c Mgin a finite field. Then we have
xi=MX
j=1cjpj (2)
We refer tofx1; x2: : : ; x i; :::gas the coded hash chunks.
Then the miner sends these coded hash chunks ( Ciis also
sent out together with xi) to the peers querying this compact
block. After receiving a coded hash chunk, a peer relays it
to some of its peers querying for the same compact block.
In this way, each node can be the seed peer of a number
of its peers during the compact-block relaying process.
Furthermore, fountain code guarantees that the likelihood
of decoding a set of coded chunks into the compact-block
approaches 1when n=M(1 +")coded chunks have been
received, where "is typically less than 2%[18].
We name the rateless-erasure-codes enhanced protocol
Coded Cut-through CBR. We describe the operational steps
of Coded Cut-thought CBR using an example networktopology illustrated in Fig. 2, where peer P is a miner with
two neighbor peers: peer Q and peer S. In particular, peer
Q and peer S are also the neighbors of each other. After
peer P mines a block, it sends the block header Hto peer
Q. After peer Q receives H, peers Q and P execute the
following operational steps in order (these operational steps
are illustrated in Fig. 3):
1) Peer Q first checks whether it has the block using in
H. If it has the block, peer Q does nothing. If it does
not have the block, peer Q sets peer P as this block’s
seed peer and goes to step 2.
2) Peer Q performs the provisional header validation
over Hto validate the PoW. If the PoW is valid,
peer Q puts Hinto its local pool and relays Hto its
neighbor peer (in this case, its neighbor peer is peer
S). After that, peer Q sends a query message to peer
P querying the transaction hashes. If the PoW is not
valid, peer Q aborts and does not relay this compact
block.
3) After receiving the query message from peer Q,
peer P sends peer Q the coded hash chunks gener-
ated by fountain coding. Specifically, peer P keeps
generating and sending the coded hash chunks
xi; i= 1;2;to peer Q until peer Q sends back an
ACK message announcing that it receives sufficient
coded hash chunks to decode the fountain code. In
Fig. 3, peer P sends a total of ncoded hash chunks
to peer Q.
4) When receiving coded hash chunks xi;1in
from peer P , peer Q also relays its received coded
hash chunks to peer S that queries the chunks after
receiving Hfrom peer Q.
5) After receiving the ACK, peer P stops sending its
coded hash chunks to peer Q. Peer P then sends H
to peer S seeking to be a potential seed peer of peer
S. Once receiving the query message sent from peer
S, peer P starts generating and sending coded hash
chunk xi; in+ 1to peer S.
6) At this time, peer S has two seed peers of this
compact block, namely, peer P and peer Q. Peer S
keeps receiving the coded hash chunks sent from
either peer P or peer Q. Once it gets sufficient coded
hash chunks for decoding the compact block, peer
S sends the ACK to peer P and peer Q one after
another. In Fig. 3, peer S receives j; jncoded
hash chunks from peer Q and k; kn0coded
hash chunks from peer P . Here, for peer S, we have
j+k=n0,n0is the number of coded hash chunks
required to decode the compact block.
After receiving sufficient coded chunks, peer Q and peer
S decode the fountain-code and validate all transactions. If
all transactions pass validation, each peer reconstructs this
block and appends it to the blockchain.
In Fig. 3, if the peer Q’s upload bandwidth is much lower
than that of other peers, then we have j n. In this case,
if using standard relaying (SR) (see Section 3.4) and Cut-
through CBR, peer S can do nothing except receiving the
information sent from peer Q. In Coded Cut-through CBR,
however, the available upload bandwidth of other nodes
(in this case, the available upload bandwidth of peer P)
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 6
Q
P
Coded Hash Chunk n+k
S
Block
Header
Coded Hash Chunk 2
Coded Hash Chunk n
Coded Hash Chunk 1
Block
Header
Coded Hash Chunk 2
Coded Hash Chunk 1
Block
Header
Coded Hash Chunk j
Coded Hash Chunk 1
Coded Hash Chunk n+1
Coded Hash Chunk n+k
Coded Hash Chunk 3
Fig. 2: The network topology of three nodes (peer P , peer Q,
and peer S) relaying one compact block using Coded Cut-
through CBR. Node P is the miner of the compact block in
this example.
P Q P
Coded Hash Chunk 1
Coded Hash Chunk 2
Coded Hash Chunk nS
Peer P starts to
communicate with
peer S
Peer P stops the
communication
with peer Q here
Peer S receives
sufficient coded hash
chunks from peer Q and
peer P
Fig. 3: The illustration for Coded Cut-through CBR.
can be further used to relay the compact block to peer S.
Hence, Coded Cut-through CBR leverages the P2P network
topology to use the upload bandwidths of all peers effi-
ciently. Thus, it can speed up the information propagation
in the Bitcoin blockchain. We theoretically analyze our Cut-
through CBR and Coded Cut-through CBR protocols to
validate their potential gains in the next section.
6 P ERFORMANCE ANALYSIS
This section presents the effect of the block propagation
delay on the Bitcoin blockchain’s TPS and the analysis of
the block propagation latencies of the four propagation pro-
tocols (SR, CBR, Cut-through CBR and Coded Cut-through
CBR).
6.1 The effect of the block propagation delay on TPS
We first calculate the TPS of the Bitcoin blockchain. We
denote the block size by Sblock , the average transaction sizebyStransaction , and the inter-block generation time by TB.
The TPS of the blockchain is calculated as
TPS =Sblock
Stransaction TB(3)
In Bitcoin, we have Sblock = 1 MB,Stransaction = 380 :04B
andTB= 600 s, and thus the TPS of Bitcoin is TPS Bitcoin =
1048576
380:046004:6.
According to (3), a simple way to increase the TPS
is to increase Sblock or to decrease TB. However, merely
increasing Sblock or decreasing TBcannot increase the TPS,
as explained below. It takes a certain time to propagate a
block over the p2p network of the blockchain. Let Ldenote
the time latency of propagating a block from the miner to
almost all nodes in the whole network (e.g., 90% of the
nodes in the network). First, the increase of Sblock leads to an
increase of L, which, in turn, may compromise the security
of the blockchain [9]. In particular, Lshould be sufficiently
smaller than TB. The closer Lgets to TB, the more forks,
more orphan blocks, and more chain re-organizations there
will be. According to [19], the probability for a fork to occur
at another miner is approximately
P(forkjTB= 600) = 1 e L
TB (4)
In the extreme case, the system will be exposed to security
vulnerabilities such as double-spend attacks [9]. Motivated
by (4), this paper uses a performance metric, propagation
divergence factor, to quantify the effect of block propagation
latency.
Propagation Divergence Factor : The propagation diver-
gence factor is defined as
=L
TB(5)
where 0since L0. The larger , the larger the
divergence (e.g., asynchronies and discrepancies) is among
the different local replicas of the blockchain maintained by
different nodes. In the extreme case where L= 0, we have
= 0 meaning that the blockchains of all the nodes in the
network become instantaneously synchronized.
The main purpose of our protocols is to suppress the
increase of Lwhen we increase Sblock . In this way, we can
boost the TPS of the Bitcoin blockchain while maintaining
the security of the blockchain. According to (3), increas-
ingSblock by 100x can increase the TPS by 100x, yielding
TPS Bitcoin460. In the next subsection, we analyze
the block propagation latency Lcaused by different block
propagation protocols.
6.2 The comparison of the block propagation latency
using different protocols
We next analyze the block propagation latencies of the
four propagation protocols (SR, CBR, Cut-through CBR
and Coded Cut-through CBR). For simple illustration, we
consider a network with a linear topology consisting of n+1
nodes as shown in Fig. 4. We assume the communication
bandwidth of each node is B. We calculate the block prop-
agation latency of propagating a block from node 0to node
nin this linear network.
LetSipbe the IP packet’s payload size and Shbe the
IP packet header size. When using SR to propagate blocks,
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 7
ch cb chSVOCut-through CBR and Coded Cut -through CBR
cb cbSOCBR
bbSOSR
bV
bV
cb CBRSO
Node 0 Node 1 Node 2Node n Node 0 Node 1 Node 2Node n Node 0 Node 1 Node 2Node n
Fig. 4: A simple linear network with n+ 1nodes.
with IP packet fragmentation, a block needs to be cut into
Sblock
SipIP packets. The block propagation latency from node
0to node ncaused under the SR protocol is given by
LSR=n(Sblock+Ob)
B+nVb (6)
where Ob=Sblock
SipShis the overhead caused by the IP
packet fragmentation when relaying a block over one hop
between two neighbor nodes, and Vbis the time incurred by
a full-block validation process.
LetNtbe the number of transactions in a block. The size
of a compact block is given by
Scb=NtSI+Shcb (7)
where SIis the size of a short transaction hash and Shcb
is the size of the compact-block header. When using CBR, a
compact block needs to be cut intoScb
SipIP packets. Recall that
in Bitcoin, nodes have 10 min ( TB= 10 min) to propagate
their collected transactions to all other nodes before the
transactions are announced in the latest block. For the sake
of simplicity, we assume that all transactions within the
block are already available at the nodes given the long
duration of 10 min, and there is no need to go and fetch the
transactions. In fact, [10] found that in the Bitcoin network,
before a new block is found, transactions obtained by all
nodes are almost synchronized. Then, the block propagation
latency from node 0to node nunder CBR is given by
LCBR =n(Scb+Ocb)
B+nVb (8)
where Ocb=Scb
SipShis the overhead caused by the IP packet
fragment when relaying a compact block over one hop.
From (6) and (8), we can see that CBR speeds up the
block propagation significantly, since Scb Sb. In particu-
lar, if Vbis negligible, CBR can reduce the block propagation
latency bySblock
Scbtimes compared with SR. However, LCBR
is still proportional to nScb. Consequently, the increase of
Scbcan lead a significant increase of LCBR , making it
impossible to increase TPS by inserting more transaction
hashes into each compact block.
We next analyze the propagation latency of the cut-
through forwarding scheme used in both Cut-through CBR
and Coded Cut-through CBR. Let k1be the number of
chunks per compact block and Schbe the chunk size. We
thus have Scb=kSch. LetVch=Vb
kbe the time needed tovalidate the transactions in a chunk and Ochbe the overhead
of IP packet fragmentation when relaying a chunk over one
hop. The block propagation latency from node 0to node n
under Cut-through CBR is given by
LCT CBR =k(Sch+Och
B+Vch)+(n 1)(Sch+Och
B+Vch)
(9)
The first term of (9) is the block propagation latency in the
first hop (from node 0to node n) and the second term of
(9) is the block propagation latency over the rest of the hops
(from node 0to node n). We further investigate the effect
ofkwhen using the cut-through forwarding scheme in the
following two cases:
If1kScb
Sip, then we haveScb
k=SchSip, which
means that we need to perform IP packet fragmentation
over each chunk. Specifically, a chunk needs to be cut into
Sch
SipIP packets and Och=Sch
SipSh=Ocb
k. From (9), we then
have
LCT CBR = (Scb+Ocb
B+Vb) +(n 1)
k(Scb+Ocb
B+Vb)
(10)
Compared with (8), the increase of kin (10) will significantly
reduce the block propagation latency. We denote the gain
obtained from the cut-through forwarding scheme over the
original CBR by gCT CBR =LCBR LCT CBR . The gain
gCT CBR can be computed as
gCT CBR =n(Scb+Ocb)
B+nVb (k+n 1)(Sch+Och
B+Vch)
=n(Scb+Ocb) (k+n 1)(Scb
k+Ocb
k)
B+nVb (k+n 1)Vb
k
= (1 1
k)(n 1)((Scb+Ocb)
B+Vb)
(11)
Note that since k1and1 1
k>0, we have gCT CBR>0.
That is, the cut-through forwarding scheme outperforms the
traditional CBR when 1kScb
Sip. Eqn. (8) and Eqn. (11)
lead to several conclusions:
1) When k= 1 orn= 1, both Cut-through CBR and
Coded Cut-through CB downgrade to CBR.
2) When kis fixed, the gain obtained from the cut-
through forwarding scheme becomes larger with the
increase of Scb. The cut-through forwarding scheme
hence is expected to perform well even if Scbis very
large.
3) When Scbis fixed, LCT CBR is a decreasing func-
tion of k. The increase of kachieves lower propaga-
tion latency.
Ifk >Scb
Sip, then we haveScb
k=Sch< Sip. In this case,
we use one IP packet to transmit one chunk with the IP
packet size smaller than the maximum IP packet size. Hence
Och=Shand the gain gCT CBR is given by
gCT CBR =n(Scb+Ocb)
B+nVb (k+n 1)(Sch+Och
B+Vch)
=n(Scb+Ocb) (k+n 1)(Scb
k+Sh)
B+nVb (k+n 1)Vb
k(12)
From (12), we can see that LCT CBR is no more a decreas-
ing function of k. To find the optimal k that maximizes
the gain expressed in (12), we can set the derivative of
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 8
TABLE 1: PARAMETERS OF THE BITCOIN SIMULATOR
Parameter Bitcoin Simulator
# of the nodes 6000
Block interval 10 min
# of the connectionDistribution according to
Miller et al. [30]
Geographical distributionDistribution according to
actual blockchains
Bandwidth propagation delay6 regional bandwidth and
propagation delay
LCT CBR with respect to kto zero:dgCT CBR
dk= 0, and
solve the equation. The optimal k is given by
k=s
B(n 1)(Vb+Scb)
Sh(13)
where the gain LCT CBR achieves its maximum.
The above analysis for a linear network shows the poten-
tial gain brought by our Cut-through CBR protocol. In the
next section, we will investigate the performances of our
Cut-through CBR and Coded Cut-through CBR protocols
under a practical network setup.
7 S IMULATION RESULTS
This section presents simulation results comparing the per-
formance of the four block-propagation protocols: SR, CBR,
Cut-through CBR, and Coded Cut-through CBR. Specifi-
cally, using the network simulator 3 (NS3) [27], we inves-
tigate the block-propagation latencies of these four propa-
gation protocols in a simulated Bitcoin network.
7.1 Simulation Setup
Our investigation is performed on a simulator extended
from the Bitcoin simulator implemented by [9]. The Bitcoin
simulator, written in NS3, is designed to simulate Bitcoin
nodes’ behavior in the Bitcoin network. The Bitcoin simula-
tor can simulate a Bitcoin network with thousands of nodes.
The Bitcoin simulator retrieved the current geographical
node distribution from bitnodes.21.co and adopted the dis-
tribution to define its simulated nodes’ locations. In the
simulated Bitcoin network, the connection between two
nodes is established as a point-to-point (P2P) link. Each
P2P link has a random bandwidth and transmission delay
(according to the geographical location) following a statis-
tical distribution from Verizon [28] and testmy.net [29]. The
parameters in Table I presents the parameters used in the
Bitcoin simulator [9].
The original Bitcoin simulator already included the SR
protocol. We implemented CBR, Cut-through CBR and
Coded Cut-through CBR in the simulator. The extended
simulator allows us to set parameters such as the block size
and the chunks per block for Cut-through CBR and Coded
Cut-through CBR. We performed simulations to investigate
the effect of block size on the network block propagation
latency and the propagation divergence factor defined in
(5) under different block propagation protocols. Our overall
simulation experiment consists of several simulation runs.
We fixed a block size for each run and then ran separate
simulations to evaluate SR, CBR, Cut-through CBR, and
Fig. 5: Median block propagation latency versus block size
for different block propagation protocols.
Coded Cut-through CBR. Each simulation run, for a specific
relay protocol, monitored the statistics of up to 10 000 blocks
created during the simulation to smooth the experimental
results.
7.2 Experimental Results
Fig. 5 and Fig. 6 show the median and the CDF of the block
propagation latency, respectively. We have the following
observations:
1) When relaying a large block, cut-through forward-
ing can significantly speed up block propagation
with respect to SR and CBR. For example, Fig. 5 and
Fig. 6 show that when relaying a block larger than
25MB, Cut-through CBR and Coded Cut-through
CBR reduce both the median and the tail of block
propagation latency by up to three times compared
with CBR and by up to more than one hundred
times compared with SR.
2) Coded Cut-thought CBR can further speed up the
block propagation compared to Cut-through CBR.
For example, Fig. 5 and Fig. 6 show that the median
and the tail of block propagation latency of Coded
Cut-through CBR are both up to two times smaller
than that of cut-through CBR for different k.
Fig. 7 shows the stale-block rate versus the propagation
divergence factor . We can see that large increases
stale-block rate significantly. The reason is that when the
block propagation divergence factor is large, a miner cannot
receive the latest announced block (mined by others) in
time and may still announce its mined block with the same
block height, leading to a large stale-block rate. Fig. 8 shows
the propagation divergence factor of different protocols
when different block sizes are used. From Fig. 8, we can see
that the propagation divergence factors of SR and CBR
increase significantly when the block size is large, leading
to a large stale-block rate. For example, increasing block
size from 1 MB to 100 MB (100x) increases the propagation
divergence factor by 30x and increases the stale-block
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 9
(a) BLOCK SIZE: 1 MB
(b) BLOCK SIZE: 25 MB
(c) BLOCK SIZE: 50 MB
(d) BLOCK SIZE: 100 MB
Fig. 6: CDF of block propagation latency for different block
propagation protocols.
Fig. 7: Stale-block size versus propagation divergence factor
derived using (4).
Fig. 8: Propagation divergence factor versus block size for
different block propagation protocols.
rate by around 30x for CBR. However, Cut-through CBR
and Coded Cut-through CBR can suppress the increase of
with the increase of block size. In particular, the of
Coded Cut-through CBR with the optimal k(the optimal k
is obtained through experiments), when the block size is 100
MB, is roughly equal to the of SR when the block size is
1MB.
In summary, Cut-through CBR and Coded Cut-through
CBR can be used for propagating 100 MB blocks while
maintaining the propagation latency to the level of the
traditional block propagation protocol that propagates 1 MB
blocks, hence increasing the TPS capacity by 100x.
8 C ONCLUSION
We proposed a new blockchain networking protocol to
increase the TPS of the Bitcoin blockchain. When a large
block size is used in the Bitcoin blockchain, original block
propagation protocols, such as SR and CBR, suffer from
large block propagation delays to large stale-block rate,
JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, DECEMBER 2020 10
compromising the blockchain’s security. In this work, we
put forth a two-pronged approach to increase the block
size without inducing extra propagation latency. First, we
design a Cut-through CBR that enables parallel reception
and forwarding of compact blocks at relay nodes. Second,
we design a Coded Cut-through CBR that incorporates
rateless erasure codes into Cut-through CBR to further
increase efficiency. Our simulation results demonstrate that
our protocols can significantly reduce the block propagation
latency and suppress the stale-block rate. Specifically, our
protocols can increase the TPS of the Bitcoin blockchain
by 100x without compromising the blockchain’s security.
More importantly, our approach only needs to rework the
communication and networking architecture of the current
Bitcoin blockchain without changing the data structures
and crypto-functional components in them. Therefore, our
protocols can be seamlessly incorporated into the existing
Bitcoin blockchain. The implementation of our protocols in
Bitcoin-like blockchains may allow the blockchains to be
used in many use cases not possible currently.
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|
{
"id": "2101.00378"
}
|
2206.08401
|
Is decentralized finance actually decentralized? A social network
analysis of the Aave protocol on the Ethereum blockchain
|
Decentralized finance (DeFi) has the potential to disrupt centralized finance
by validating peer-to-peer transactions through tamper-proof smart contracts,
thus significantly lowering the transaction cost charged by financial
intermediaries. However, the actual realization of peer-to-peer transactions
and the levels and effects of decentralization are largely unknown. Our
research pioneers a blockchain network study that applies social network
analysis to measure the level, dynamics, and impacts of decentralization in
DeFi token transactions on the Ethereum blockchain. First, we find a
significant core-periphery structure in the AAVE token transaction network
where the cores include the two largest centralized crypto exchanges. Second,
we provide evidence that multiple network features consistently characterize
decentralization dynamics. Finally, we document that a more decentralized
network significantly predicts a higher return and lower volatility of the
decentralized market of AAVE tokens on the Ethereum blockchain. We point out
that our approach is seminal for inspiring future extensions related to the
facets of application scenarios, research questions, and methodologies on the
mechanics of blockchain decentralization.
|
http://arxiv.org/pdf/2206.08401v4
|
Ziqiao Ao, Lin William Cong, Gergely Horvath, Luyao Zhang
|
econ.GN, cs.CR, q-fin.EC, q-fin.ST, stat.CO, E.0; G.1; G.3; I.6; J.4; J.6
|
econ.GN
|
Is decentralized finance actually decentralized? A
social network analysis of the Aave protocol on the
Ethereum blockchain
ZIQIAO AO∗†, LIN WILLIAM CONG‡∗, GERGELY HORVATH∗†§, and LUYAO
ZHANG∗†§
Decentralized finance (DeFi) has the potential to disrupt centralized finance by validating
peer-to-peer transactions through tamper-proof smart contracts, thus significantly lowering
the transaction cost charged by financial intermediaries. However, the actual realization of
peer-to-peer transactions and the levels and effects of decentralization are largely unknown. Our
research pioneers a blockchain network study that applies social network analysis to measure
the level, dynamics, and impacts of decentralization in DeFi token transactions on the Ethereum
blockchain. First, we find a significant core-periphery structure in the AAVE token transaction
network where the cores include the two largest centralized crypto exchanges. Second, we
provide evidence that multiple network features consistently characterize decentralization
dynamics. Finally, we document that a more decentralized network significantly predicts a
higher return and lower volatility of the decentralized market of AAVE tokens on the Ethereum
blockchain. We point out that our approach is seminal for inspiring future extensions related to
the facets of application scenarios, research questions, and methodologies on the mechanics of
blockchain decentralization.
Keywords : blockchain, decentralized finance, social network analysis, AAVE, Ethereum,
core-periphery, number of components, giant component ratio, modularity, degree centrality,
number of cores, market return, market volatility, blockchain operation management.
ACM CCS : E.0, G.1, G.3, I.6, J.4, J.6
JEL Code : C15, C22, C23, C58, C63, C81, G17
Acknowledgments : We have benefited from the intellectual conversations at the 29th Annual Global
Finance Conference featuring the Keynote speaker Nobel Prize Laureate, Prof. Robert Engle in Braga,
Portugal, June 20-22, 2022, the Crypto Economics Security Conference (CESC) hosted by Berkeley Center
for Responsible Decentralized Intelligence (DCI) at University of California, Berkeley, C.A., United States,
Oct. 31-Nov.1, 2022, and the 12th Portuguese Financial Network Conference in Madeira, Portuguese, July
2023. We Thank Prof. Claudio J. Tessone, Prof. William Goetzman, and Prof. Subrahmanyam Avanidhar
for their insightful comments. Ziqiao Ao contributed to the research as an undergraduate advisee and
signature work mentee of Prof. Luyao Zhang and the Summer Research Scholar (SRS) mentee of Prof.
Gergely Horvath in the Class of 2022, at Duke Kunshan University, supported by the undergraduate program.
Ziqiao Ao is now at McCormick School of Engineering, Northwestern University. Luyao Zhang is supported
by National Science Foundation China on the project entitled “Trust Mechanism Design on Blockchain: An
Interdisciplinary Approach of Game Theory, Reinforcement Learning, and Human-AI Interactions (Grant
No. 12201266). The authors declare no conflict of interest.
∗Names by the alphabetical order of the last names.
†Duke Kunshan University, No. 8 Duke Ave, Kunshan, Suzhou, Jiangsu, China, 215316.
‡Cornell SC Johnson College of Business, IC3, & NBER, Ithaca, NY, 14853, USA.
§Contact Luyao Zhang: (email: lz183@duke.edu, institutions: Data Science Research Center and Social
Science Division) and Gergely Horvath (email:gergely.horvath@dukekunshan.edu.cn, institutions: Social
Science Division) at Duke Kunshan University.
Authors’ address: Ziqiao Ao; Lin William Cong; Gergely Horvath; Luyao Zhang.arXiv:2206.08401v4 [econ.GN] 1 Dec 2023
2 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
1 INTRODUCTION
DeFi, short for decentralized finance, is a blockchain-powered peer-to-peer finan-
cial system [Werner et al .2021]. Harvey et al .[2021] predict that DeFi could disrupt
centralized finance by validating peer-to-peer transactions by tamper-proof smart
contracts and thus significantly lower the transaction cost charged by financial in-
termediaries. However, the actual realization of peer-to-peer transactions and the
levels of decentralization are largely unknown [Cong et al .2022; Zhang et al .2022b;
Zhang 2023]. Moreover, how the levels of decentralization would affect the economic
performance of the blockchain platform is broadly unexplored. How can decentraliza-
tion be measured? The comparative study of decentralized and centralized financial
markets is not new [Canales and Nanda 2012; Miao 2006]. However, before blockchain
technology existed, decentralized markets tended to have worse performance. For
example, decentralized over-the-counter markets and the lack of a central market-
maker induce high trading costs and give rise to intermediations between trading
partners [Battiston et al .2012; Bosma et al .2017; Yun et al .2019]. Moreover, Bovet
et al.[2019]; Motamed and Bahrak [2019]; Vallarano et al .[2020] find that network
features, a proxy for the market structure in decentralized markets affect important
market outcomes (e.g., liquidity and volatility) that individual traders, at the hub of
the network, make more profit, in general, [Di Maggio et al .2017; Hollifield et al .2017;
Li et al .2019]. Does blockchain live up to its promise of empowering peer-to-peer
transactions in decentralized financial markets? Our research applies social network
analysis [Jackson 2008; Otte and Rousseau 2002; Scott 1988] to blockchain transaction
data and aims to answer the following research questions:
•Realization of decentralization: Are the transactions in decentralized banks
on blockchain indeed decentralized?
•Blockchain network dynamics: How do different network features of
blockchain transactions correlate and change over time?
•Network features and market activities: How do network features predict
and interact with the economic performance of decentralized markets on
blockchains?
We pioneer a blockchain network study that applies social network analysis to measure
the level, dynamics, and impacts of decentralization in DeFi token transactions on the
Ethereum blockchain. We analyze our research questions with an application to the
transaction network of AAVE, the native utility token of a top-ranked decentralization
finance application on Ethereum. We have three main findings.
(1)There exists a significant core-periphery structure in the AAVE token transac-
tion network where the cores include the two largest centralized exchanges
and central smart contracts with specific functions.
(2)Multiple network features including the number of components, the relative
size of giant components, modularity, and standard deviation of degree cen-
trality consistently characterize decentralization dynamics.
Is decentralized finance actually decentralized? 3
(3)A more decentralized network as represented by the network measures sig-
nificantly predicts a higher return and lower volatilities of the AAVE token
transaction network.
The structure of the remainder of this paper is meticulously outlined as follows:
Section 2 embarks on a comprehensive review of the pertinent literature. Section 3
delineates the foundational aspects of network features and elucidates the methodol-
ogy for measuring decentralization utilizing these network characteristics. Section 4
details the data source, data generating mechanics, and data processing workflow. Sec-
tion 5 delineates our approach of empirical analysis and expounds upon the findings
pertaining to the three posed research inquiries. Finally, Section 6 synthesizes the
key takeaways, reflecting on the implications of our findings and proposing avenues
for future scholarly inquiry in this domain. Additionally, to ensure transparency and
replicability, the data and code supporting our analyses are made available on GitHub
at [removed for anynomous review].
2 LITERATURE REVIEW
Our research contributes to the literature on the interplay of the financial market,
social network studies, and crypto-economics.
Fig. 1. Contribution Map of this Study. Note: This figure displays the contribution map of our study
on the existing literature.
2.0.1 Social network analysis in the financial market and core-periphery structures.
The application of social network analysis (SNA) to financial markets has gained
momentum after the 2008-2009 financial crisis. Lending relationships among banks and
other financial institutions proved to be conduits of contagion of liquidity shortages
and financial distress [Anand et al .2013; Blasques et al .2018; Gai et al .2011; Langfield
4 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
et al.2014]. Regulators came to realize that studying the structure of financial networks
is key to identifying sources of systemic risk, for example, in the form of financial
institutions that are ‘too central to fail’, meaning that their failure may generate
a cascade that ruins the whole financial system [Bardoscia et al .2017; Battiston
et al.2012; Bosma et al .2017; Yun et al .2019]. The decentralized financial market on
blockchain in our research thus serves as a potential solution to the “too central to fail”
problem. However, one of the most important conclusions of this literature was that in
many financial markets, transactions are not carried out in an anonymous market but
through stable trading relationships that reduce transaction costs [Babus and Kondor
2018; Duffie et al .2005]. The peer-to-peer transactions on the blockchain are instead
anonymous by default and might not benefit from stable exchange relationships like
those observed in the traditional financial market.
A commonly adopted notion in social network analysis is the core-periphery struc-
ture, which presents two qualitatively distinct components: “core” nodes that are
densely connected, and “periphery” nodes that are loosely connected to the core
members but not necessarily to each other [Gallagher et al .2021]. The core-periphery
structure enables us to compare and analyze the properties of the two types of nodes
more accurately and efficiently under different contexts structurally and functionally
[Csermely et al .2013]. SNA has already been widely applied to study financial net-
works. For example, Sui et al .[2019] compares the resilience between core-peripheral
networks and complete networks by analyzing the financial contagion of interbank
networks. Barucca and Lillo [2016] proposes methods to identify different network
architectures including bipartite and the core-periphery structure in the case of the
interbank network. In the cryptocurrency market, the core-periphery structure of
stable assets based on liquidity and capital inferred by a network feature has been
examined to study the market impact and evolution [Polovnikov et al. 2020].
A variety of algorithms have been developed by scholars for extracting the core-
periphery structure [Malliaros et al .2019], which differ typologically based on their
definitions of the nature of the network and the way in which core and peripheral
nodes are connected. The most popular is the Borgatti-Everett (BE) algorithm, which
partitions the network into a central hub with interlacing nodes and a periphery
radiating outward from the hub [Borgatti and Everett 2000]. Cucuringu et al .[2016]
detected the core-periphery structure through spectral methods and geodesic paths
based on the transportation networks. In addition to the classical two-block methods,
there exist algorithms that build a continuous spectrum between a core and a periphery
which have been applied in examples related to collaboration, voting, transportation,
etc. [Boyd et al .2010; Rombach et al .2017; Rossa et al .2013]. The continuous structure
enables the exploration of network components and features that are not apparently
categorized as core or periphery [Rombach et al .2017]. Considering that there may be
more than one core-periphery pair in the network, Kojaku and Masuda [2017, 2018a,b]
propose scalable algorithms to detect multiple core-periphery groups in a network
and demonstrate their application in networks of political blogs and airports.
Given the diverse core-periphery structures defined by algorithms, we apply the
Borgatti-Everett (BE) algorithm [Borgatti and Everett 2000] to our AAVE transfer
Is decentralized finance actually decentralized? 5
network while examining algorithms of multiple pairs [Kojaku and Masuda 2018a]
along with SNA to test the level of decentralization comprehensively. Details of
algorithms and packages utilized will be given in the data section to provide reliable
network inference and methodology for further studies.
2.0.2 Crypto tokens and decentralized banking. Crypto tokens are digital assets that
utilize blockchain and cryptography technology to ensure security [Halaburda et al .
2022]. As of Jan. 20, 2022, the market value of crypto tokens was beyond 1.9 trillion U.S.
dollars. Cong and Xiao [2021] categorize cryptocurrencies into general security, utility
(general payment and platform), and product tokens based on their functions. Bitcoin,
the first cryptocurrency, was designed as a transaction mechanism and classified as
utility (general payments) tokens. Although Bitcoin dominated the market between
2009 and 2016 [Liu and Zhang 2022; Liu et al .2022b], other alternatives emerged
later on [Härdle et al .2020]. Ethereum blockchain proved revolutionary in its support
for smart contracts that allow automatic transactions and the issuance of Ethereum
Request for Comments (ERC) tokens [Lehar and Parlour 2021; Liang et al .2018; Liu
et al.2022a]. Our research studies the transaction network of AAVE, an ERC-20 token
that is the native utility (platform) token of Aave, a top-ranked decentralized finance
application on Ethereum. The market value of AAVE was beyond 2.9 billion U.S.
dollars as of Jan. 20, 2022 [coinmarketcap 2022]. Aave is a decentralized bank that
allows users to lend and borrow crypto assets and earn interest on assets supplied
to the protocol [Whitepaper.io 2020]. In general, decentralized banks differ from
centralized banks in two aspects: 1) they replace centralized credit assessments with
coded collateral evaluation [Gudgeon et al .2020], and 2) they employ smart contracts
to execute asset management automatically [Bartoletti 2020]. The open-source codes
of the decentralized bank, Aave, and the transparent trading data of the AAVE token
enable us to reproduce the historical network dynamics.
2.0.3 Network studies in cryptoeconomics. An extensive body of literature explores
the key features of trading networks and the way in which they relate to the price
dynamics of cryptocurrencies. Liang et al .[2018] show that both Bitcoin and Ethereum
trading networks display fluctuations in growth rates. For example, the clustering
coefficient1of Bitcoin was initially 0.15, qualifying it as a small-world network, and
decreased to approximately 0.05 later [Baumann et al .2014; Kondor et al .2014]; in
contrast, the clustering coefficient of Ethereum has fluctuated between 0.15 and 0.2
over time and has never been identified as a small word2[Ferretti and D’Angelo
2019]. Motamed and Bahrak [2019] built both monthly and accumulative networks
of Ethereum. They found that the number of components3is approximately ten and
increases over time; however, similar to that of the Bitcoin trading network, the
1The clustering coefficient describes the extent to which a network is aggregated [Baumann et al. 2014].
2A small-world network refers to a network in which most nodes are not neighbors of each other, but most
nodes can be reached from other nodes by a small number of steps [Baumann et al. 2014].
3Components are parts of the network that are disconnected from each other [Vallarano et al. 2020].
6 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
network density4of Ethereum decreases over time [Vallarano et al .2020]. Liang et al .
[2018] also found that the largest components of Bitcoin and Ethereum have large
sizes in terms of both their diameters (approximately 100 for Bitcoin and gradually
increasing for Ethereum) and percentages (40-60%). The network structures differ
significantly across blockchains. For instance, Chang and Svetinovic [2016] found
that the Bitcoin network has grown denser over time, with more nodes tending to be
connected with each other, leading to a strong community while Namecoin has shown
a decrease in density, resulting in an unclear community structure. [De Collibus et al .
2021] hints that the growth and concentration indexes can be measured by network
calculations via analyzing the aggregated transaction networks of Ethereum-based
crypto assets, and conclude that wealth is much more concentrated than in-degree and
out-degree. Polovnikov et al .[2020] demonstrate a core-periphery structure [Gallagher
et al. 2021] in cryptocurrency exchange networks.
The literature has also found an effect of network features on economic variables
such as price and volatility. Motamed and Bahrak [2019] found that the price of
Bitcoin, Ethereum and Litecoin is positively correlated with the size of the graph and
the number of nodes and edges. Vallarano et al .[2020] showed that the price of Bitcoin
is negatively correlated with the average outdegree. Cong et al .[2022] discovered
that the current Ethereum is more and more centralized in block rewards, ownership,
and transactions. Bovet et al .[2019], using a Granger causality test, found that the
past degree distributions, especially the outdegree5of the Bitcoin trading network
can predict future price increases [Bovet et al .2019]. Several studies have also used
network features. Li et al .[2019] built an ARIMA time-series model to forecast price
anomalies using network features. [Zhang et al .2022a] is a follow-up study of ours
that extends the analysis to compare several decentralized banks.
Our research extends network studies on Bitcoin and Ethereum to DeFi tokens.
Moreover, we aim to conduct a comprehensive analysis of the blockchain network and
the core-periphery structure by comparing a variety of network features including
the numbers of nodes and edges, the mean and standard deviation of degree., top
10 degrees mean ratio, relative degree, modularity, the count of components, the
count of core, and giant component ratio, etc. Furthermore, we identify the effects of
decentralization measured by network features on economic performance at different
time horizons.
3 CONCEPTUAL FRAMEWORK
The computer science literature has defined three types of communication networks
since [Baran 1964], these are depicted in Figure 2, borrowed from [Barabási 2016].
In a centralized network, one central node connects all other nodes, and the degree
distribution is unequal since one node has N-1 links while all other nodes have only
1 link. In a decentralized network, there are several hubs that connect to peripheral
4Network density describes the portion of the potential connections in the network that are actual connec-
tions [Vallarano et al. 2020].
5Outdegree is the number of edges that are directed out of a node in the directed network graph [Vallarano
et al. 2020].
Is decentralized finance actually decentralized? 7
nodes and to each other. In this network, the degree distribution is equal to that
in the centralized network but there are hubs that have considerably more links
than the peripheral nodes. The third type of network is the distributed network
in which there are no hubs and all nodes have approximately the same number of
neighbors. We regard the network on the left as the most centralized and the network
on the right as the most decentralized network structure. Ideally, DeFi aims to be
completely decentralized, facilitating peer-to-peer transactions, which corresponds to
the distributed network in the computer science literature.
Fig. 2. Different types of network structures. Note: This figure illustrates three types of communication
networks, borrowed from [Barabási 2016].
[Campajola et al .2022] in their paper presents a wide array of indexes to display
the different levels of decentralization of Bitcoin, including clustering index, degree
distribution, core-periphery structure, etc. In our study, we introduce various network
measures to capture the differences between these network features in the token
networks of Aave. The first thing to note is that in Figure 2, the network consists of
only one component, that is, all nodes are connected by direct or indirect paths. In our
transaction data, however, the network consists of many disconnected components.
In an ideally centralized market, all nodes should be connected to a single hub; thus
the number of components should be one. In a very fragmented market, in contrast,
we may observe many components. This argument leads to our first measure of
centralization: the number of components (disconnected parts in the network)
shows how centralized or fragmented the network is. We compute this measure for
every day observed in the data.
The second related measure is the relative size of the largest (giant) component .
If the network is more centralized, we expect the largest component to cover a high
fraction of nodes, in a fragmented network we observe many small components. We
calculate the size of the giant component divided by the total number of nodes in the
daily transaction network; the larger this value is, the more centralized the network.
8 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
A related measure is the modularity score (formally defined in Table 1) which
measures the strength of division of a network into small groups (Newman 2006). A
market structure with lower modularity is more centralized, which means that there
are no separate communities in the transaction network. This measure can be applied
to both connected and disconnected graphs.
Fourth, we capture the characteristics of the structures in Figure 2 by computing
thestandard deviation of the number of neighbors (degree) in the network. In a
centralized network, we have the largest disparity in degree, while in the distributed
case, the degree distribution is equal.
Last but not least, we use the concept of core-periphery networks to measure
the degree of centralization. In a core-periphery network, a limited number of core
members constitute a densely connected hub that are connected to each other and
to the peripheral nodes. The peripheral nodes are not connected to each other, only
the core members. Note that the centralized structure in the left panel of Figure 2
corresponds to a core-periphery structure with one core member, and the decentralized
structure to a core-periphery network with multiple core members. The distributed
network in the right panel is not a core-periphery network.
Based on these arguments, we run statistical tests to detect the core-periphery struc-
ture in the data, comparing it to a random network with the same degree distribution
[KOJAKU 2022]. Our first measure of decentralization is the significance level of
this test, which we convert to a binary measure. This measure is equal to 1 when the
p-value of the test is less than 0.05, which indicates the presence of a core-periphery
structure. In the other case, when the p-value is larger than 0.05, the measure takes
the value 0, which indicates that the presence of a core-periphery structure can be
rejected.
In addition, for the days when a core-periphery structure describes the data well, we
measure the number and degree of core members in the network. In a more centralized
network, the number of core members is lower and each core member has a larger
degree.
Table 1 summarizes the network measures that we use to capture market central-
ization in the data.
Turning to the economic variables of the market, we focus on two common variables
of interest: price and 30-day volatility. We expect market network centralization to
affect these outcome variables. Table 2 summarizes the economic variables.
4 DATA QUERYING, MECHANICS, AND PROCESSING
4.1 Data Source
Our data are from three open sources: general economic variables of the AAVE token
from Coinmetrics [Coinmetrics 2022], TVL in Aave from DeFi Pulse [Pulse 2022], and
blockchain transaction records of AAVE token from Bigquery public datasets on the
Ethereum blockchain [Bigquery 2022], ranging from Oct. 10, 2020, to Jul. 30, 2023.
Our processed datasets for analysis are at a daily level. We include a detailed dataset
overview and dictionary for each dataset in Section A in the Appendix.
Is decentralized finance actually decentralized? 9
Table 1. Major Network Features. Note: This table gives the general definitions of the network features
included in our study with an explanation and equation.
Name Definition
num_nodes Number of unique addresses in the daily transaction net-
work.
num_edges Number of transactions in the daily transaction network.
Components_cnt The various disconnected parts of the network, where there
is no path that can connect from a node in one component
to a node in another component. Components_cnt here
refers to the number of components in the daily transaction
network
giant_com_ratio Size of the giant component divided by the total number of
nodes in the daily transaction network.
DCstd Standard deviation of degree centrality. Degree centrality
measures the number of neighbors one node has: the higher
the number, the more central the node is.
Modularity Measure of the strength of a network divided into modules.
A network with a high degree of modularity has dense
connections between nodes within a module but sparse
connections between nodes in different modules.
cp_test_pvalue P value of the significance test of the core-periphery struc-
ture.
cp_significance 1 if cp_test_pvalue is less than 0.05 and, else 0 otherwise.
core_cnt Number of nodes in the core based on the BE core-periphery
structure algorithm in the daily transaction network.
avg_core_neighbor the Average number of neighbors (degree) of the core nodes
detected by the core-periphery structure algorithm in the
daily transaction network.
Table 2. Major Economic Variables. Note: This table gives the general definitions of the economic
variables used as dependent variables in regressions.
Name Definition
PriceUSD Fixed closing price of the asset in USD.
VtyDayRet30d Volatility over 30 days, measured as the standard deviation of
the natural log of daily returns over the past 30 days.
4.2 Data Generating Mechanics Explained in AAVE Token Functions
In our network analysis of the AAVE token transfers on the Ethereum blockchain, we
define the nodes and edges as follows:
10 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
•Nodes : In our network, nodes represent the addresses involved in AAVE token
transactions. These addresses can be either the sender or receiver in a transfer
ofAAVE tokens.
•Edges : The edges in our network are the token transfers themselves. Each
edge denotes a transfer of AAVE tokens from one address (node) to another.
This framework allows us to analyze the transaction network of AAVE , providing
insights into the flow and distribution of tokens between various participants in the
ecosystem.
There are two main types of AAVE token transfers on the Ethereum blockchain:
(1)Internal Transfers : These transfers occur between addresses within the Aave
protocol. Examples include a user transferring AAVE tokens from their wallet
to the Aave protocol as collateral or the Aave protocol transferring AAVE
tokens to a user as a reward. Internal transfers are typically faster and cheaper
as they do not require gas fees to be paid to the Ethereum network.
(2)External Transfers : These transfers occur between addresses outside of the
Aave protocol, such as transferring AAVE tokens between wallets or from
an exchange to a user’s account. External transfers can be slower and more
expensive due to the required gas fees.
Specific types of AAVE token transfers include:
•Lending :
–Deposit : Users deposit AAVE tokens into the protocol as collateral to
borrow other assets.
–Withdrawal : Users withdraw assets from the lending pool by burning
their AAVE tokens.
–Borrow : Users borrow assets using AAVE tokens as collateral.
–Repay : Borrowers repay loans, returning assets to the lending pool.
–Liquidation : If a loan is not repaid, the collateral can be liquidated.
•Staking :
–Stake : Users stake AAVE tokens to earn rewards.
–Unstake : Users remove their tokens from the staking pool.
•Governance :
–Proposing : Suggesting changes to the AAVE protocol.
–Voting : Casting votes on proposals related to the AAVE protocol.
•Exchange :
–Transfer : Transferring AAVE tokens between addresses.
–Swap : Swapping AAVE tokens for other assets.
These are the main examples of the many types of AAVE token transfers that can
occur on the Ethereum blockchain, depending on the features of the Aave protocol
and the Ethereum network.6
6Readers can check the details of each AAVE token tranfer at https://etherscan.io/token/
0x7fc66500c84a76ad7e9c93437bfc5ac33e2ddae9
Is decentralized finance actually decentralized? 11
4.3 Data Processing Workflow
4.3.1 Calculate network features. Using the Python NetworkX package [Hagberg et al .
2008], we build daily transaction network graphs using from_address, to_address as
nodes and the values as weights. We consider an undirected network between the
addresses with weights equal to the total value of transactions between the accounts.
This means that we add the transaction values between two duplicate accounts without
considering the direction before network building. Based on the daily network, we
calculate 24 network features using the NetworkX algorithms [Hagberg et al .2008],
given in Appendix A. We keep only the network features listed in Table 1 to answer
the research questions of this paper but open source the rest for future research.
4.3.2 Extract the core-periphery structure. To extract the core-periphery structure,
we utilize the Python cpnet package [KOJAKU 2022], which contains algorithms
implemented in Python for detecting core-periphery structures in networks. cpnet.BE
[KOJAKU 2022] is the algorithm used for the Borgatti-Everett (BE) algorithm, which,
identifies nodes either as either core or periphery in a single group [Borgatti and
Everett 2000]. cpnet.KM_config [KOJAKU 2022] is used for examining multiple pairs
of core-periphery nodes, which can return the coreness and pair of each node. This
package also allows us to conduct significance testing via the q-s test on the core-
periphery structure of the daily network to assess the fitness of this algorithm for our
data, where we use 0.05 as the significance level. The core-periphery structure detected
for the input network is considered significant if it is stronger than those detected
in randomized networks [Kojaku and Masuda 2018b]. We calculate the number of
cores and the average number of neighbors of the core nodes to further investigate the
levels of decentralization given the core-periphery structure. Additionally, based on
the daily networks constructed using the core-periphery algorithm, we record all core
addresses that appear during the period and the number of days that they become
core. The type (contract or address) and information links of those cores are extracted
from Etherscan.io [etherscan.io 2019] and recorded for further comparison.
4.3.3 Analyze interactions with economic variables. Among the economic metrics
queried as shown in Appendix B, the price in USD PriceUSD , 30-day volatility Vty-
DayRet30d and total value locked (TVL) in USD tvlUSD are chosen as the dependent
variables in our regression models since they are significant and commonly used
economic metrics in market valuation. Specifically, the price intuitively reflects the
market value of the AAVE token, which is perfectly correlated with the market capital-
ization for the Aave protocol7during the time range of our data. The 30-day volatility
can reflect the degree of volatility in the token market over the past month and the
potential existence of risks or tendencies [Coinmetrics 2022]. The total value locked,
which is the overall value of crypto assets deposited in the Aave protocol in USD
[George 2022], is a unique economic metric in the context of the cryptocurrency
7Rather than matching a lender to a borrower, lenders deposit funds into the Aave liquidity pools, en-
suring a continuous supply of funds, which leads to market capitalization perfectly correlated with price
[Whitepaper.io 2020].
12 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
market. Furthermore, before regression, we need to ensure stationarity of time-series
data-that is, that the mean, variance, and autocorrelation structure remain constant
over time [Brownlee 2017], and the values give an approximately normal distribution.
We transform the variables using methods including differencing the data and taking
the logarithm difference, or percentage change based on the Dickey-Fuller test results
to ensure that they become stationary. After that, we scale all the stationary variables
between 0 and 1 for scale comparison and test the correlation for all the independent
variables (after transformation) to avoid high correlation in the regressions.
5 EMPIRICAL ANALYSIS METHODS AND RESULTS
5.1 The realization of decentralization (the core-periphery structure)
5.1.1 Construct core-periphery structures. As introduced and discussed in the previous
sections, we apply the Borgatti-Everett (BE) algorithm [Borgatti and Everett 2000] and
the multiple-pairs core-periphery structure algorithm [Kojaku and Masuda 2018b]
to the AAVE transaction network to investigate whether the daily graphs can be
separated into dense transactions between some active addresses (defined as the core)
and some other loose transactions between some small addresses (defined as the
periphery) in either a single pair or multiple pairs. In this section, we connect the
properties of the core-periphery structure in the AAVE transaction network with the
real functions and types of the specific addresses (some typical addresses) to further
explore the question of centralization vs. decentralization.
We test the significance of all 365 observations. The results indicate that the AAVE
daily transaction network is insignificant in the multiple-pair core-periphery structure,
but partially significant in the one-pair structure with 232 significant (64%) and 133
insignificant (36%) days. Comparing the distribution (displayed in Figure 3) of the
number of nodes in the core and the average number of degrees of the core nodes
(described in Table 1) in the significant and nonsignificant daily graphs that we tested,
we find that the number of nodes in the core in the significant graph is much smaller.
The average number of neighbors of the core nodes is more prominent in those
significant graphs and vice versa. By interpretation, when the transaction network
significantly fits the core-periphery structure, it can be divided into small groups of
denser and looser connections. The transaction difference between nodes is larger;
thus, the degree of centralization is greater. In this case, a few addresses are likely to
dominate most transactions.
To depict the structure and comparison in a more intuitive and explainable way, we
pick two representative days of the significant and nonsignificant graph to visualize
the network, as displayed in Figure 4. It shows the core-periphery network graphs
of transactions among the identified core accounts based on the cpnet.BE algorithms
in a spiral layout on 2020-10-12 (left panel) and 2021-02-22 (right panel), where the
dark dots represent the core nodes and the light dots are the periphery nodes. The
two panels clearly illustrate that on a significant core-periphery graph (left panel), all
core nodes are closely linked, and each core node forms an aggregation group with
periphery nodes (each with a high degree) so that any two nodes can connect in a
Is decentralized finance actually decentralized? 13
Fig. 3. Core-periphery structure features distribution box plots. Note: This figure plots the distribu-
tion of the number of core nodes (left panel) and the average number of neighbors of core nodes (right
panel) on days with significant (p-value smaller than 0.05) and insignificant core-periphery test results.
few steps. The overall network structure of the transactions among the identified
core accounts is very compact and cohesive, resulting in a significant core-periphery
structure, which is also more centralized in the transaction since the core nodes
are dominant. In contrast, the right panel shows a looser overall connection. Each
identified core node has a smaller degree, with many scattered periphery nodes;
some nodes require a long step size to connect to other nodes, which prevents the
structure from being significantly certified as core-periphery and adds to the level of
decentralization in the transaction network.
5.1.2 Compare the core-periphery structure for externally owned and contract account.
Furthermore, we investigate addresses/accounts that were once identified as core
members in the graphs. There are two types of accounts defined on Ethereum: exter-
nally owned accounts (EOAs) and contract accounts (CAs, also called smart contracts)
[Hu et al .2021]. EOAs are created and owned by users with a private key set and can
be utilized to deposit and transfer assets and call smart contracts [Hu et al .2021]. The
CAs are execution programs composed of smart contract code, which also possesses
asset balance and will be automatically executed if the trigger condition in met [Szabo
1997]. We record the accounts that appear to be identified as core nodes during the
period and the number of days that they become core, based on the daily networks
constructed by the Borgatti-Everett (BE) algorithm [Borgatti and Everett 2000]. We
extract the type of account (EOA or CA) according to Etherscan.io [etherscan.io 2019].
14 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
Fig. 4. Network graphs on 2020-10-12 (left panel) and 2021-02-22 (right panel). Note: This figure
shows the core-periphery network graphs of transactions among the identified core accounts based on the
cpnet.BE algorithms in a spiral layout on 2020-10-12 (left panel) and 2021-02-22 (right panel), where the
dark dots represent the core nodes and the light dots are the periphery nodes. The left panel returns a.
significant p-value in the significance test, while the right panel is nonsignificant.
Figure 5 plots the distribution of the number of core days for EOAs and CAs. From
the graph, the range of day counts is generally larger for the contract accounts, where
the extreme value is much larger than its counterpart for externally owned accounts.
The four outliers identified (two for CA and two for EOA) appear to be in the core for
many of the days, resulting in a centralized transaction that could significantly affect
the level of decentralization. We investigate the detailed account information of these
four accounts by Etherscan.io [etherscan.io 2019] for further explanation.
The two outliers among EOAs are Binance andCoinbase , which are the two top
centralized exchanges in the cryptocurrency market. A centralized exchange is a
significant online platform for users to buy and sell cryptocurrencies, offering secu-
rity and monitoring for the individual to complete the transaction in a trustworthy
environment [Reiff 2019]. Due to the popularity of these two centralized exchanges,
a tremendously large number of transfers occur through these two accounts daily
between the same and different types of cryptocurrencies by many EOAs. Given
the results of the core-periphery structure, the centralized exchange exerts a signif-
icant influence on the AAVE daily transaction network and brings a high level of
“centralization” to it.
The two outliers among CAs are decentralized exchanges, we find that one of them
is an automated market maker on Uniswap, a decentralized exchange for transactions
between AAVE and Ether. Automated market makers (AMMs), first introduced by
Hanson’s logarithmic market scoring rule (LMSR) [Hanson 2003], are contracts that
allow liquidity to be automatically provided to the crypto market automatically [Fritsch
and Zürich 2021]. Based on the AMM, this contract is built on Uniswap, which allows
agents to trade between AAVE and Ether at the price and rates specified by the
pricing function, and the price is kept enormous centered transaction network around
this account, which greatly influences the core-periphery structure and increases
the centralization of the AAVE transaction graph. Another is the smart contract of
Is decentralized finance actually decentralized? 15
Fig. 5. Core day count distribution box plots. Note: This figure plots the distribution of the number
of core days for EOAs and CAs, where the y-axis is the number of days in which the node is in the core.
Stated AAVE, which performs the functions in the Aave decentralized bank of staking
(moving assets to long-term saving account), redeeming (getting collateral back),
getting rewards (claiming interest rates), etc. [Whitepaper.io 2020].
On the one hand, the two outliers of centralized exchanges put the promise of
blockchain decentralization in doubt; on the other hand, the two outliers of decen-
tralized exchanges evidence that blockchain can mitigate the dependence on trusted
centralized entities.
5.2 Blockchain network dynamics and correlations
In addition to the core-periphery structure, we use other network properties to capture
market centralization. We find that all the intertemporal network features indicate
consistent dynamics whereby the AAVE token transaction network first becomes
more decentralized and then reverts to being more centralized.
5.2.1 Numbers of components. When the market is more centralized, we expect to
have fewer components in the network since most transactions go through a central
node that connects most nodes indirectly, forming a network component. The left
upper panel of Figure 6 plots the number of components over time. The graph suggests
that the AAVE market first became increasingly decentralized, as indicated by the
increase in the number of components up to February 2021, and then showed a
tendency to centralize as the number of components increased. The network structure
of the market converged to approximately 100 components after July 2021.
16 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
Fig. 6. Time-series plots of network features. Note: This figure gives time-series plots of network
features included in our study, with the feature name in the title of each panel.
5.2.2 Giant component size ratio. A related network property is the relative size of the
giant component. In a centralized market, the giant component covers a large fraction
of the nodes, while in a decentralized market, the giant component is relatively small.
The lower right panel of Figure 6 shows that the relative size of the giant component
first decreased and then increased. This again suggests that the market was initially
decentralized and then became more centralized.
5.2.3 Modularity score. The modularity score is small when the market is centralized,
meaning that there are no separate communities in the network. In contrast, in a
decentralized market, many communities are not or are only weakly connected to each
other, implying a high modularity score. The right upper panel of Figure 6 shows the
evolution of modularity. The modularity score increased first, indicating a tendency
to decentralize, and then it started to decrease, suggesting centralization.
5.2.4 The standard deviation of degree centrality. The standard deviation of degree
centrality is large (small) when the market is centralized (decentralized) since, in a
centralized market, a few hubs have a high degree while the other nodes have only a few
connections. In the right middle panel of Figure 6, we see that the standard deviation
Is decentralized finance actually decentralized? 17
of degree centrality first decreased and then increased, suggesting that the market
first showed a tendency toward decentralization and then toward centralization.
Fig. 7. Correlation heatmap of network features. Note: This figure plots the correlation between
network features, red represents a positive correlation while blue represents a negative correlation. The
depth of the color is proportional to the correlation coefficient value.
5.3 The impact of network features on market return and volatility
5.3.1 Background and Methods. Liu and Tsyvinski [2018]’s study demonstrated that
there is strong evidence of time-series momentum at various time horizons of the
cryptocurrency network features; this evidence could potentially indicate a momentum
effect of the network on DeFi economic metrics. We utilize the Python package smf.ols
for the OLS regression to test the following two hypotheses:
•Return of Investment (ROI) : The decentralization level measured by network
features predicts higher future ROI.
•Market Volatility Growth Rate (MVGR) : The decentralization level mea-
sured by network features predicts a lower increase in volatility.
We utilize Python smf.ols for the OLS regressions with return (ROI) to test the two
hypotheses. We generate the results for ROI and MVGR at different windows from
one-day, one-week, to nighty-day horizons. Each dependent variable is regressed on
each of the network features and control variables. To avoid the issues of heterogeneity
and autocorrelation, we generalize the regressions with Newey-West estimators [Liu
and Tsyvinski 2018].
5.3.2 Results on market returns. Figure 8 displays the outcomes of our analysis on
token market returns (in USD), utilizing a 7-day moving average of network variables.
Detailed regression results are provided in Table 4 and Table 5 in the Appendix.
Upon examining token market returns, we observe a notable correlation: a higher
degree of market decentralization appears to be a predictive factor for long-term
token returns. Specifically, in markets characterized by greater decentralization, token
returns tend to be more substantial. This finding aligns with the prevalent expectation
among stakeholders that a blockchain’s value increases with its decentralization.
For instance, a more decentralized transaction network in DeFi tokens correlates
18 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
Fig. 8. Results of the token market returns (USD). Note: This figure reports the results of predicting
the token market returns (USD) using the 7-day moving average of network variables.
with optimistic stakeholder projections about future returns, culminating in a self-
reinforcing equilibrium of enhanced returns.
Our regression analyses, centered on network attributes, corroborate this trend. We
note a significant and positive correlation between the number of network components—
a marker of decentralization—and token returns after a 14-day period. Similarly, an
elevated modularity value, indicative of market decentralization, demonstrates a sig-
nificant and positive association with market returns over a 21-day span.
In contrast, lower values in the standard deviation of degree centrality and the rela-
tive size of the largest network component, both indicative of decentralization, exhibit
a significant and negative correlation with long-term market returns. Additionally,
the core-periphery structure’s significance, representative of a centralized network,
bears a negative correlation with market returns for most periods exceeding 7 days.
It is crucial to note the high volatility of short-term market returns, which renders
our network measures less predictive in this timeframe. This observation is mirrored
in the R-squared values of our regressions: they approach zero for short-term fore-
casts but increase with longer time horizons. The premise that short-term returns of
cryptocurrency tokens are less predictable by network features is in agreement with
Is decentralized finance actually decentralized? 19
the findings of Liu et al. (2022) [Liu and Zhang 2022].
PC1=−0.26×Components_cnt+0.87×giant_com_ratio
−0.32×modularity+0.26×DCstd , (1)
PC2=0.78×Components_cnt+0.41×giant_com_ratio
+0.08×modularity−0.47×DCstd , (2)
PC3=0.56×Components_cnt−0.19×giant_com_ratio
−0.43×modularity+0.68×DCstd . (3)
To further substantiate our results, we employed principal component analysis
(PCA) to distill the essence of the five network features representing centralization
measures. The PCA formulas are presented in the collection of Equations 1, 2, 3. The
optimal number of factors was determined by maximizing the variance explained
by these features. In our regression outputs, we found Factor 3, which quantifies
decentralization, to be significantly and positively correlated with market returns. This
reaffirms our earlier observations: a heightened level of decentralization is consistently
associated with increased market returns.
Fig. 9. Interpretation of PCA. Note: This figure depicts the correlation coefficient between the original
variables and the components. Positive and negative values in the graph reflect the positive and negative
correlation of the variables with the PCs. Red represents a positive correlation, blue represents a negative
correlation, and the depth of the color is proportional to the correlation coefficient value.
5.3.3 Results on market volatility. In our examination of market volatility, as depicted
in Figure 10, we conducted a parallel analysis, the results of which are detailed in
Table 6 and Table 7 in the Appendix. Our findings indicate a discernible positive
correlation between the degree of decentralization and the growth rate of market
volatility. Specifically, an increase in the number of network components, a proxy
for market decentralization, is significantly and positively correlated with future
20 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
market volatility, particularly for mid-range horizons of 42, 49, and 56 days. This
observation suggests that markets with higher levels of decentralization are prone to
increased volatility. Interestingly, this result aligns with the theoretical expectation
that decentralized markets should exhibit lower volatility, as the impact of market
shocks tends to be more dispersed in such environments.
Fig. 10. Results of the 30-day volatility growth rate. Note: This figure reports the results of predicting
the 30-day volatility growth rate using the 7-day moving average of network variables.
6 CONCLUSIONS AND DISCUSSION
6.1 Extensions in three facets
Our study shows that social network analysis is instrumental in characterizing the
level, dynamics, and impacts of decentralization in DeFi token transactions. Our
research is also seminal in terms of inspiring future research on the three facets of
application scenarios, research questions, and methodology.
(1)Application Scenarios . Our methods can be generally applied to trans-
action tokens issued by other DeFi protocols, such as decentralized pay-
ment, exchange, assets, derivatives and even non-financial applications on
blockchains.8
(2)Research Questions . We can extend our analysis to study the interplay of
other network features and economic variables. For example, one straightfor-
ward follow-up research is to extend the analysis to include other network
features for which we have provided open-source data as defined in Appendix
A.
8Refer to [Zhang et al. 2022b] and the references therein.
Is decentralized finance actually decentralized? 21
(3)Methodology . We can further explore the interplay of network dynamics
and token economics by causal inference through advanced econometrics and
prediction algorithms in machine learning [Athey 2015].
6.2 On the mechanics of blockchain decentralization
Is decentralized finance actually decentralized? The answer from our pioneering
blockchain network study is intriguing. We found that the current research on de-
centralization tends to neglect two important aspects of the mechanics of blockchain
decentralization:
(1)How do incentives affect agents’ behavior in transaction network formations?
(2) How do incentives affect the final realizations of network decentralization?
These two gaps neglections in the literature leave a door for future research to im-
prove the mechanism to support a truly decentralized economy. Why? The blockchain
infrastructure only provides the possibility for peer-to-peer transactions. However,
the actually realized decentralization of blockchain transaction networks depends
on the behavior of stakeholders, who are affected by incentives. If we can better
understand the incentives that govern the stakeholders’ behavior and the formation
of transaction networks, we can design incentives schemes to support desired levels
of decentralization.9Future research can experiment with other scientific methods
of theoretical modeling and simulations. For example, we can further apply network
game theory [Azouvi and Hicks 2020] to distributed systems to study how incen-
tives affect agents’ strategic behaviors and the transaction network formations on
the blockchain. We can also apply agent-based modeling [Iori and Porter 2012] to
simulate the transaction networks on blockchain to systematically evaluate the effect
of incentive design changes.
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A NETWORK FEATURES
Table 3 contains 24 daily network features calculated from the AAVE transaction data
we calculated using the NetworkX package, which can be used as a reference for
further network studies.
B SUPPLEMENTARY REGRESSION TABLE
Table 4 gives the regression result table of the token market returns (USD). Table 5
gives the regression result table of the token market returns (USD) with the control
variable. Table 6 gives the regression result table of the 30-day volatility growth rate.
Table 7 gives the regression result table of the 30-day volatility growth rate with the
control variable.
26 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
Table 3. Network Features. Note: This table gives the general definitions of all network features
calculated in our study with the explanation.
Name Definition
num_nodes Number of unique addresses in daily transaction network.
num_edges Number of transactions in daily transaction network.
Degree mean The number of edges a node has, an average of nodes.
Degree std The number of edges a node has the standard deviation of nodes.
Top10Degree mean Average degree of the addresses with top 10 highest degree values during
the whole period.
Top10Degree std Standard deviation of the degree of the addresses with the top 10 highest
degree values during the whole period.
Top10 Degree mean ratio Top 10 addresses’ degree mean divided by the general degree mean.
Relative degree Network density. The portion of the potential connections in a network
is actual connections.
DCmean The average value of degree centrality.
DCstd The standard deviation of degree centrality.
Cluster_mean Mean of clustering coefficient. The degree to which nodes in a graph
tend to cluster together.
Cluster_std Standard deviation of clustering coefficient. The degree to which nodes
in a graph tend to cluster together.
Modularity Modularity is a way to measure the strength of a network divided into
modules. A network with a high degree of modularity has dense connec-
tions between nodes within a module, but sparse connections between
nodes in different modules.
Transitivity Transitivity is the overall probability for the network to have adjacent
nodes interconnected, thus revealing the existence of tightly connected
communities.
eig_mean Mean of eigenvector centrality. Measures the degree to which the division
of a network into communities.
eig_std Standard deviation of eigenvector centrality. Measures the degree to
which the division of a network into communities.
closeness_mean Mean of closeness centrality. The reciprocal of the farness.
closeness_std Standard deviation of closeness centrality. The reciprocal of the farness.
giant_com_ratio Size of the giant component divided by the total number of nodes in the
daily transaction network.
Components_cnt The components of the network are the various disconnected parts, where
there is no path that can connect from a node in one component to a
node in another component. Components_cnt here refers to the number
of components in the daily transaction network
cp_test_pvalue P-value of the significant test of the core-periphery structure.
cp_significance 1 if cp_test_pvalue is less than 0.05, else 0.
core_cnt Number of nodes in the core based on the BE core-periphery structure
algorithm in the daily transaction network.
avg_core_neighbor the Average number of neighbors (degree) of the core nodes detected by
the core-periphery structure algorithm in daily transaction network.
Is decentralized finance actually decentralized? 27
Table 4. Results of the token market returns (USD). Note: This table reports the results of predicting
the future market return (USD) using the 7-day moving average of network variables. Columns (1)-(10)
represent one day, one week to eight weeks, and 90 days respectively. *, **, and *** denote significance
at the 10%, 5%, and 1% levels. The data frequency is daily. The residual standard errors are reported in
parentheses.
Time horizon t, t+1 t, t+7 t, t+14 t, t+21 t, t+28 t, t+35 t, t+42 t, t+49 t, t+56 t, t+90
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
△component cnt -0.034 0.125 0.247** 0.384*** 0.323*** 0.301*** 0.234** 0.261** 0.289** 0.233***
𝑅20 0.006 0.018 0.032 0.028 0.027 0.016 0.018 0.018 0.013
Residual Std. Error (0.105) (0.100) (0.120) (0.138) (0.123) (0.119) (0.122) (0.125) (0.138) (0.134)
△giant com ratio 0.005 -0.039 -0.060* -0.065* -0.062* -0.040 -0.044 -0.099* -0.129** -0.083***
𝑅20 0.002 0.003 0.003 0.003 0.001 0.002 0.008 0.011 0.005
Residual Std. Error (0.105) (0.101) (0.120) (0.140) (0.125) (0.120) (0.123) (0.126) (0.139) (0.135)
△log(modularity) 0.008 0.057 0.127 0.191 0.201 0.142 0.158** 0.203** 0.195** 0.279***
𝑅20 0 0.001 0.001 0.002 0.002 0.009 0.015 0.016 0.035
Residual Std. Error (0.105) (0.101) (0.121) (0.140) (0.125) (0.120) (0.122) (0.125) (0.139) (0.133)
△log(DCstd) 0.012 -0.041 -0.097** -0.178*** -0.207*** -0.161*** -0.153*** -0.186** -0.189** -0.241***
𝑅20 0 0.004 0.016 0.028 0.018 0.016 0.022 0.018 0.032
Residual Std. Error (0.105) (0.101) (0.120) (0.139) (0.123) (0.119) (0.122) (0.125) (0.138) (0.133)
cp significance -0.014 -0.090** -0.163*** -0.278*** -0.322*** -0.324** -0.314* -0.188 0.056 1.834***
R2 0.007 0.031 0.039 0.061 0.046 0.028 0.018 0.005 0 0.124
Residual Std. Error (0.080) (0.242) (0.391) (0.528) (0.718) (0.931) (1.138) (1.269) (1.351) (2.432)
PCA component1 -0.015 -0.027 -0.018 0.007 0.002 0.006 0.012 0.018 0.062 0.327***
PCA component2 0.046 0.091* 0.128* 0.144* 0.115* 0.097 0.076 0.062 0.057 -0.067
PCA component3 0.046 0.159*** 0.275*** 0.378*** 0.305*** 0.277*** 0.262*** 0.277*** 0.322*** 0.558***
𝑅20.005 0.045 0.083 0.105 0.087 0.076 0.060 0.060 0.064 0.252
Residual Std. Error (0.105) (0.098) (0.116) (0.133) (0.120) (0.116) (0.119) (0.122) (0.135) (0.117)
Table 5. Results of the token market returns (USD) with control variable. Note: This table reports
the results of predicting the future market return (USD) using the 7-day moving average of network
variables with Eth price as a control variable. Columns (1)-(10) represent one day, one week to eight weeks,
and 90 days respectively. *, **, and *** denote significance at the 10%, 5%, and 1% levels. The data frequency
is daily. The residual standard errors are reported.
Time horizon t, t+1 t, t+7 t, t+14 t, t+21 t, t+28 t, t+35 t, t+42 t, t+49 t, t+56 t, t+90
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
△component cnt -0.046 0.095 0.197** 0.316*** 0.258*** 0.236*** 0.164** 0.183* 0.199* 0.146***
ETHPriceUSD -0.046*** -0.110*** -0.182*** -0.249*** -0.234*** -0.235*** -0.252*** -0.274*** -0.318*** -0.308***
Adjusted R2 0.008 0.064 0.13 0.189 0.205 0.22 0.231 0.261 0.287 0.29
Residual Std. Error (0.105) (0.097) (0.112) (0.126) (0.111) (0.106) (0.108) (0.108) (0.118) (0.114)
△giant com ratio 0.007 -0.033 -0.05 -0.052 -0.05 -0.027 -0.028 -0.081* -0.104** -0.058**
ETHPriceUSD -0.045*** -0.112*** -0.186*** -0.255*** -0.239*** -0.240*** -0.255*** -0.277*** -0.320*** -0.310***
Adjusted R2 0.007 0.002 0.003 0.003 0.003 0.001 0.002 0.008 0.011 0.005
Residual Std. Error (0.105) (0.097) (0.113) (0.128) (0.113) (0.107) (0.108) (0.109) (0.118) (0.114)
△log(DCstd) 0.024 -0.011 -0.047 -0.109** -0.142*** -0.095** -0.082* -0.109* -0.096 -0.151***
ETHPriceUSD -0.046*** -0.112*** -0.184*** -0.250*** -0.232*** -0.235*** -0.251*** -0.272*** -0.317*** -0.302***
Adjusted R2 0.008 0.061 0.121 0.174 0.199 0.21 0.228 0.26 0.283 0.297
Residual Std. Error (0.105) (0.097) (0.113) (0.127) (0.112) (0.107) (0.108) (0.108) (0.118) (0.113)
△log(modularity) -0.011 0.011 0.052 0.088 0.105 0.057 0.076 0.117* 0.105 0.192***
ETHPriceUSD -0.045*** -0.112*** -0.186*** -0.256*** -0.239*** -0.240*** -0.253*** -0.274*** -0.318*** -0.302***
Adjusted R2 0.007 0.061 0.119 0.168 0.187 0.204 0.226 0.257 0.283 0.301
Residual Std. Error (0.105) (0.097) (0.113) (0.128) (0.113) (0.107) (0.108) (0.109) (0.118) (0.113)
PCA component1 -0.011 -0.019 -0.004 0.026 0.02 0.025 0.027 0.035 0.082** 0.339***
PCA component2 0.046 0.092* 0.129** 0.146* 0.117* 0.098* 0.077 0.063 0.058 -0.071*
PCA component3 0.031 0.124** 0.217*** 0.297*** 0.227*** 0.197*** 0.173*** 0.180*** 0.208*** 0.450***
ETHPriceUSD -0.042*** -0.100*** -0.167*** -0.231*** -0.220*** -0.224*** -0.242*** -0.264*** -0.308*** -0.286***
Adjusted R2 0.009 0.09 0.173 0.235 0.237 0.244 0.251 0.278 0.308 0.484
Residual Std. Error (0.105) (0.096) (0.110) (0.123) (0.109) (0.105) (0.106) (0.107) (0.116) (0.097)
28 Ziqiao Ao, Lin William Cong, Gergely Horvath, and Luyao Zhang
Table 6. Results of the 30-day volatility growth rate. Note: This table reports the results of predicting
the 30-day volatility growth rate using the 7-days moving average of network variables. Columns (1)-(10)
represent one day, one week to eight weeks, and 90 days respectively. *, **, and *** denote significance
at the 10%, 5%, and 1% levels. The data frequency is daily. The residual standard errors are reported in
parentheses.
Time horizon t, t+1 t, t+7 t, t+14 t, t+21 t, t+28 t, t+35 t, t+42 t, t+49 t, t+56 t, t+90
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
△component cnt -0.051 -0.059 -0.026 -0.069 -0.113 -0.132 -0.218*** -0.393*** -0.423*** -0.186
𝑅20.002 0.002 0 0.001 0.002 0.002 0.006 0.014 0.016 0.004
Residual Std. Error (0.078) (0.082) (0.121) (0.134) (0.161) (0.178) (0.184) (0.220) (0.221) (0.194)
△giant com ratio -0.014 -0.008 -0.028 -0.05 -0.042 -0.085 -0.085 -0.078 -0.069 0.044
𝑅20 0 0.001 0.002 0.001 0.003 0.003 0.002 0.001 0.001
Residual Std. Error (0.078) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.221) (0.222) (0.194)
△log(DCstd) 0.058** 0.043 0.017 0.005 0.058 0.108 0.126 0.198** 0.148 -0.042
𝑅20.006 0.003 0 0 0.001 0.004 0.005 0.008 0.005 0
Residual Std. Error (0.077) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.220) (0.222) (0.194)
△log(modularity) 0 0.027 0.006 -0.001 0.001 0 0.037 0.042 0.026 0.055
𝑅20 0.001 0 0 0 0 0 0 0 0.001
Residual Std. Error (0.078) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.221) (0.223) (0.194)
PCA component1 -0.037 -0.076*** -0.139*** -0.172*** -0.212*** -0.255*** -0.262*** -0.283*** -0.245*** -0.286***
PCA component2 -0.039 -0.062* -0.099** -0.123** -0.140** -0.156** -0.113 -0.055 0.078 0.310***
PCA component3 -0.028 -0.091*** -0.167*** -0.215*** -0.268*** -0.300*** -0.356*** -0.442*** -0.413*** 0.150**
𝑅20.008 0.036 0.052 0.067 0.071 0.075 0.073 0.066 0.052 0.103
Residual Std. Error (0.077) (0.081) (0.118) (0.130) (0.155) (0.172) (0.178) (0.214) (0.217) (0.184)
Table 7. Results of the 30-day volatility growth rate with control variable. Note: This table reports
the results of predicting the 30-day volatility growth rate using the 7-days moving average of network
variables with Eth price as a control variable. Columns(1)-(10) represent one day, one week to eight weeks,
and 90 days respectively. *, **, and *** denote significance at the 10%, 5%, and 1% levels. The data frequency
is daily. The residual standard errors are reported in parentheses.
Time horizon t, t+1 t, t+7 t, t+14 t, t+21 t, t+28 t, t+35 t, t+42 t, t+49 t, t+56 t, t+90
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
△component cnt -0.048 -0.055 -0.021 -0.065 -0.109 -0.127 -0.212** -0.384*** -0.408*** -0.144
ETHPriceUSD 0.012 0.012 0.017 0.015 0.016 0.016 0.019 0.033 0.052 0.148***
Adjusted R2 0.001 0.001 -0.001 0 0.001 0.001 0.005 0.013 0.017 0.034
Residual Std. Error (0.078) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.220) (0.221) (0.191)
△giant com ratio -0.015 -0.009 -0.029 -0.051 -0.043 -0.087 -0.087 -0.081 -0.074 0.033
ETHPriceUSD 0.013 0.013 0.018 0.017 0.019 0.02 0.026 0.044 0.063 0.151***
Adjusted R2 0 -0.001 0 0.001 0 0.002 0.002 0.002 0.003 0.032
Residual Std. Error (0.078) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.221) (0.222) (0.191)
△log(DCstd) 0.055** 0.04 0.012 0 0.053 0.104 0.121 0.188* 0.132 -0.089
ETHPriceUSD 0.01 0.011 0.017 0.016 0.015 0.013 0.018 0.032 0.054 0.157***
Adjusted R2 0.004 0.002 -0.001 -0.001 0 0.002 0.003 0.007 0.006 0.034
Residual Std. Error (0.077) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.220) (0.222) (0.191)
△log(modularity) 0.004 0.032 0.012 0.003 0.006 0.006 0.045 0.054 0.044 0.1
ETHPriceUSD 0.013 0.014 0.018 0.016 0.018 0.019 0.026 0.045 0.064 0.156***
Adjusted R2 -0.001 0 -0.001 -0.001 -0.001 -0.002 -0.001 0 0.002 0.034
Residual Std. Error (0.078) (0.082) (0.121) (0.134) (0.161) (0.178) (0.185) (0.221) (0.222) (0.191)
PCA component1 -0.038 -0.076*** -0.139*** -0.172*** -0.212*** -0.255*** -0.262*** -0.284*** -0.247*** -0.291***
PCA component2 -0.039 -0.062* -0.099** -0.123** -0.140** -0.156** -0.113 -0.055 0.079 0.315***
PCA component3 -0.024 -0.087*** -0.163*** -0.213*** -0.266*** -0.299*** -0.355*** -0.435*** -0.398*** 0.221***
ETHPriceUSD 0.012 0.009 0.01 0.005 0.004 0.003 0.004 0.016 0.039 0.185***
Adjusted R2 0.005 0.032 0.049 0.063 0.067 0.071 0.069 0.062 0.05 0.149
Residual Std. Error (0.077) (0.081) (0.118) (0.130) (0.155) (0.172) (0.178) (0.214) (0.217) (0.179)
|
{
"id": "2206.08401"
}
|
1812.10345
|
Bitcoin Payment-channels for Resource Limited IoT Devices
|
Resource-constrained devices are unable to maintain a full copy of the
Bitcoin Blockchain in memory. This paper proposes a bidirectional payment
channel framework for IoT devices. This framework utilizes Bitcoin
Lightning-Network-like payment channels with low processing and storage
requirements. This protocol enables IoT devices to open and maintain payment
channels with traditional Bitcoin nodes without a view of the blockchain.
Unlike existing solutions, it does not require a trusted third party to
interact with the blockchain nor does it burden the peer-to-peer network in the
way SPV clients do. The contribution of this paper includes a secure and
crypto-economically fair protocol for bidirectional Bitcoin payment channels.
In addition, we demonstrate the security and fairness of the protocol by
formulating it as a game in which the equilibrium is reached when all players
follow the protocol.
|
http://arxiv.org/pdf/1812.10345v1
|
Christopher Hannon, Dong Jin
|
cs.CR
|
cs.CR
|
1
Bitcoin Payment-Channels for Resource Limited
IoT Devices
Christopher Hannon, Student Member, IEEE, and Dong Jin, Member, IEEE,
Abstract —Resource-constrained devices are unable to maintain
a full copy of the Bitcoin Blockchain in memory. This paper
proposes a bidirectional payment channel framework for IoT
devices. This framework utilizes Bitcoin Lightning-Network-like
payment channels with low processing and storage requirements.
This protocol enables IoT devices to open and maintain payment
channels with traditional Bitcoin nodes without a view of the
blockchain. Unlike existing solutions, it does not require a trusted
third party to interact with the blockchain nor does it burden the
peer-to-peer network in the way SPV clients do. The contribution
of this paper includes a secure and crypto-economically fair
protocol for bidirectional Bitcoin payment channels. In addition,
we demonstrate the security and fairness of the protocol by
formulating it as a game in which the equilibrium is reached
when all players follow the protocol.
I. I NTRODUCTION
INTERNET of Things (IoT) services and devices are ex-
panding at an exponential pace due to the rapid expan-
sion of networking technologies. Today many companies are
jumping into an IoT arms race across various application
domains including smart home, connected health, wearables,
connected car, smart retail, supply chain, and many more.
One key observation is that smart IoT devices are increasingly
replacing our physical credit cards, enabling a faster and easier
way for us to order products and pay services on demand.
However, many problems exist concerning payment services
through IoT devices, such as identity verification, security and
privacy (e.g., financial information protection), scalability and
flexibility (e.g., accidental ordering, refunds).
Blockchain technology has been proposed to play a pow-
erful role to address those challenges in IoT payment ser-
vices. Blockchains are based on cryptographically secured,
immutable distributed ledger technology which operate in a
distributed fashion, and thus have the potential to enhance
IoT solutions with better automated resource optimization,
data security and reliability. For example, [1] describes how
blockchain can facilitate sharing of services and the automa-
tion of work flows; [2] overviews the blockchain integration
and projects within the energy sector including markets, op-
erations and stability, and security of the grid.
The integration of IoT and blockchain has huge potential
in revolutionizing IoT. IoT devices that interact with the
physical world can transfer value in exchange for services and
blockchains can provide a value transfer protocol. Applications
C. Hannon and D. Jin are with the Department of Computer Science, Illinois
Institute of Technology, Chicago, IL, 60616 USA e-mail: channon@iit.edu,
dong.jin@iit.edu.
Manuscript received December 18, 2018.in Industrial IoT such as metering infrastructure in utilities
including gas, electricity and water as well as electric vehicle
charging and supply chain management can benefit from
value transfer using blockchain technology. Transactions on
blockchains can build trust between devices without relying on
a trusted third party intermediary. Decentralized trustless value
ledgers in the form of blockchains have gained increasing
traction as trust-less value transfer protocols. Another novelty
of blockchain technology is the design of a crypto-economic
consensus algorithm which relaxes the assumption that some
number of agents are honest to economically rational. This
creates a state that as long as participants value money (or
digital cash), they will behave in a way that results in their
own best interest, i.e., highest profits. By design, blockchain
consensus ensures correct operations of a decentralized public
database that records users’ account balances. Blockchain
technology allows for users to transfer value to other users
without the help of trusted third parties such as PayPal or
Visa.
The main advantage of blockchain is its trust-less value
transfer protocol, which is securely maintained through de-
centralized participants. However, the blockchain technology
does not solve all problems, specifically, blockchains suffer
from limited scalability due to their decentralized nature.
Additionally, the limited scalability can drive up the cost of
using the blockchain network through high fees. In this work,
we focus on using blockchain payment channels which enables
scalability. We design a payment channel protocol based on
the Lightning Network, which enables IoT devices with few
computational and storage resources to transfer value. In
particular, our protocol enables a party to transact with another
using the Bitcoin blockchain without storing the complete
blockchain using untrusted third parties. Figure 1 shows the
architecture of our blockchain protocol in reference to the rest
of the blockchain.
The remaining paper is organized as follows: Section II
provides background on blockchain and its payment channels.
Section III presents a protocol design that enables real-time
payment channels for IoT devices to gateway services. We
analyze the security of the protocol by formulating it as a
game in Section IV. Finally, we describe the related work
in Section V and conclude in Section VI with future work
directions.
II. B ACKGROUND
Blockchain technology at its core is an immutable pub-
lic digital ledger containing transactions. The novelty ofarXiv:1812.10345v1 [cs.CR] 26 Dec 2018
2
IoT Gateway IoT Device
Blockchain Bitcoin Node IoT Field Device
IoT Gateway P2P Link TCP/IP Payment Channel
Fig. 1. IoT devices in the Bitcoin Blockchain context. The IoT device has a
payment channel open to a gateway service and does not have a local copy
of the blockchain. Instead, the IoT device relies on untrusted third parties to
connect on its behalf through economic incentives.
blockchain is its ability to unequivocally agree on the state
of the ledger in a decentralized setting. Through the process
ofmining , the global state of the blockchain advances.
The Bitcoin blockchain [3] provides the ability to send
transactions which consist of inputs, outputs, and rules gov-
erning the redemption of the outputs. Inputs to transactions
map to the source of the funds (a previous transaction) while
the outputs represent the destination of the transaction value.
The governing rules included in the transaction dictate how the
recipient of the transaction is able to spend the received value
in the future. A common rule is for the recipient to prove
ownership of a private key associated with the destination
address in the transaction. However, more detailed rules can
be expressed to provide more complicated value redemption
logic.
In general, Bitcoin follows the UTXO model which says
that an input to a new Bitcoin transaction is the output
of an unspent previous transaction along with a script that
redeems the previous transactions output. The output of the
new transaction provides the recipient and a script telling the
recipient how to redeem the values. Since the blockchain is
an immutable public database, transactions cannot be revoked
and the total of all unspent transaction outputs represent the
current state of the Blockchain. Thus, there is no concept of
users or accounts included in the Bitcoin Blockchain.
Transactions are organized into blocks which remain pend-
ing until a partial pre-image is found for the sha-256 hash
algorithm which meets a specific criteria quantified as the
blockchain’s difficulty. This difficulty is a dynamic variablethat corresponds to the processing power of participants work-
ing to add new blocks and transactions into the blockchain.
The result of this process maintains that on average new blocks
are added to the blockchain every 10 minutes. In alternative
blockchain implementations, the target block interval varies,
e.g., 15 seconds in Ethereum [4]. This interval is important to
note because until a transaction is included in a block it is not
considered verified by the blockchain network. Furthermore,
due to the consensus algorithm that governs the blockchain,
there may be a temporary fork where multiple valid blocks
are at the same height. A block is only valid if it is part
of the longest blockchain. Confidence of immutability grows
exponentially in relation to the depth of the block, i.e., number
of subsequent blocks. The original Bitcoin white paper [3]
provides a more detailed analysis. However, one heuristic used
in practice is 6 blocks (about 60 minutes)[5].
Figure 2 shows the cost of sending a transaction converted
to USD over four months in 2018. The cost of a transaction
on the blockchain as well as the time required to publish
the transaction makes frequent real-time transactions impos-
sible. Another method to reduce fees is to use an alternative
blockchain that has larger block sizes or more frequent blocks.
Although alternative blockchains can have weaker security,
and greater price volatility that does not satisfy our goals in
this work. In this paper, we propose a protocol using off-chain
payment channels that can provide real-time payment but do
not incur large fees with frequent posting to the blockchain.
A. Off-Chain Bitcoin Transactions
Bitcoin’s Forth-like scripting language enables more com-
plex functionality by placing conditions on the redeeming of
transaction outputs. For example, time locks can be used on
transactions and transaction outputs to place temporal restric-
tions on the ability to spend or create transactions. Time locks
can be both relative and absolute and can prevent a transaction
output from being spent until after a certain time. Time locks
are one of the most important building blocks in off-chain
Bitcoin Transactions, such as in the Bitcoin Lightning Network
[6]. Multisignatures can require multiple keys to spend a
transaction output. One use case for multisignatures is joint
savings accounts where both parties need to agree to make a
transaction. Time lock contracts include primitives [5] such as
nLockTime specifies the minimum height of the
blockchain that a transaction can be included in.
CheckLockTimeVerify requires that the blockchain
be at a certain height for the output of an already included
transaction to be spent.
Relative LockTime places restrictions on the inclu-
sion of an input to a new transaction based on the time
that the input was included in the previous transaction.
CheckSequenceVerify provides a relative time that
the output of a transaction becomes valid after inclusion
in a block.
Multisig can be used to require m of nsignatures
to become valid.
nLockTime andRelative Locktime are corresponding
counterparts for absolute and relative time respectively for in-
clusion into the blockchain while CheckLockTimeVerify
3
andCheckSequenceVerify are counterparts for absolute
and relative time respectively for making outputs of a valid
transaction spendable.
Hashlocks on the other-hand provide encumbrance on the
outputs of transaction that requires a specified secret value
being publicly revealed. Upon unlocking the hashlock, all
other hashlocks with the same secret value are also unlocked
due to the secret value being recorded on the blockchain.
The combination of hashlocks and timelocks can create
timed hashlock contracts (HTLCs) which can be used to put
Bitcoin transactions ’off-chain’ through what is called L2
or layer 2 scaling solutions, such as the Lightning Network
[6], Duplex micropayment channels[7], and Raiden [8]. Our
protocol uses CheckSequenceVerify andMultisig for
payment channels similar to Lightning Network Transactions.
Off-chain transactions are properly formatted Bitcoin transac-
tions that are deferred from being published immediately on
the blockchain. The benefit is that off-chain transactions can be
updated many times before published to the blockchain result-
ing in reduced on-chain transactions and ultimately fewer fees.
In [7], channels can be created unidirectional or in duplex, and
transactions can be updated so that the final channel balances
are guaranteed to be included. This is accomplished by newer
transactions having smaller timelocks than earlier transactions,
and thus being able to be published sooner than old invalidated
transactions. A limitation of this approach is that channels will
have finite lives. References [6] and [8] allow for channels to
remain open indefinitely or until the owners decide to close
the channel.
In those solutions, transactions are properly formatted so
that either party involved can post the transaction at any time
to the blockchain in case of dispute. This property allows
for the protocol to remain trustless. To avoid the problem of
excessive fees, transactions are updated off-chain to reflect
a new balance and only publish them upon closure to the
blockchain. Therefore, fees only need to be paid when opening
and closing a channel and updating the balance within the
channel is fee-less.
Because IoT devices are resource limited, it is infeasible
to assume that they can remain connected to the blockchain.
Therefore, we need to adopt the state channel model to
allow for one (or both) parties to be offline from the Bitcoin
blockchain. In this work, we assume that the IoT devices have
networking capability to untrusted third parties. By providing
financial incentives to the third parties, the IoT devices do not
need to have direct access to the blockchain and can instead
rely on the untrusted third parties to act as a bridge to ensure
correct operation of the protocol.
III. P ROTOCOL DESIGN
The intuition of our protocol is to use a multisig transaction
to fund the channel. Intermediate states are made revocable as
developed in [6]. Our contribution is to ensure correctness
and crypto-economical fairness when one party does not have
access to the blockchain. To do this, we use a third party to
post transactions to the blockchain by creating an additional
output that is spendable by the third party. By creating an
Feb
2018Mar Apr May Jun0246810121416USDAverage Transaction fee in USDFig. 2. Average transaction fee from Feb. to June 2018 in USD. Calculated
using the blocksci library [9].
economic incentive, a third party is willing to participate in
the protocol. Furthermore, to ensure that the second party does
not violate the protocol by publishing a revoked state, a third
party is used as a watchdog , which informs the first party when
the funding transaction’s output is used as an input to a new
transaction. This watchdog will report when an intermediate
state is posted to the blockchain, which prevents publishing
an expired state. Additionally, to prevent collusion between
the second party and the third parties, we use a pool of third
parties for each service. Since any member in the third party
is able to take the role, the incentive needs to be higher than
any incentive from colluding. In Section IV, we formulate the
problem as a game and show that the equilibrium is reached
by following the protocol.
The details of the protocol are as follows. For each
IoT device A, the IoT payment gateway Bcreates
a payment channel. Both AandBgenerate 3 sets
ofjkeypairs (pkfBjAgjfajbjcg;skfBjAgjfajbjcg)such
that each intermediate transaction uses a different key-
pair making jthe number of intermediate states. Ad-
ditionally, we generate a keypair for opening and clos-
ing the channel, (pkfBjAgfFTjcloseg;skfBjAgfFTjcloseg),
and another pair for Atransacting with the third parties,
(pkA3rdfajrcg;skA3rdfajrcg). To minimizes the IoT de-
vices’ memory requirements, we use BIP32 that provides
a deterministic hierarchical key generation algorithm with a
highly compacted data structure [10]. All keys can then be
generated with a given master key and an index in the data
structure. Effectively this enables the storing of an index in
place of a keypair, as the total requirements for storing a state
is the key index and the balances.
The two parties agree to place a Funding Transaction TFT
on the blockchain that sends
Aand
Bas input funds from A
andBrespectively. The output of the channel is a 2-of-2 multi-
signature requiring both AandB’sskfBjAgFTto spend.
Additionally, the two parties agree on an initial commitment
transactionTC1that is a valid spending of the funds from TFT
4
as the input and returning
Aand
Bas the outputs. Note
that the transaction is not published to the blockchain, and
its purpose is to denote the starting balance in the payment
channel. Reference [6] shows how two transactions, TC1B
andTC1Acan be constructed so that Bis the only party that
can publish TC1AandAis the only party that can publish
TC1B. This mechanism is accomplished by supplying one of
the 2-of-2 input signatures required to spend TFT’s output,
i.e., partially signing the transaction.
Furthermore, the transactions TC1BandTC1Aare made
revocable by encumbering the outputs of the corresponding
party’s ability to send the output. For example, if Apublishes
TC1B, the output of
Bcan be redeemed immediately by
B. However,
Afunds are locked. There are two ways to
redeem the locked funds. The first is to use AandB’s secret
keyssk1s for a 2-of-2 multi-signature. The second is to use a
timelock to redeem the fund to AafterWblocks, where W
is the number of blocks specified in the timelock. Bmay use
the first mechanism to steal all ofAs funds after both parties
update the state of the channel to the new transactions TC2B
andTC2A. Upon updating, Asendssk1toB(andBsends
sk1toA). The stealing of funds relies on the mechanism to
prevent oldtransactions from being published, which can be
achieved by Bchecking the blockchain before some Wblocks
afterTC1Bis published. This mechanism is a way to ensure
that both parties follow the protocol even if the two parties do
not trust each other.
However, since the IoT devices are assumed not to have a di-
rect access to the blockchain, two disjoint groups of untrusted
third parties are used to interface between the IoT device and
the blockchain. Each group has multiple members, K1and
K2respectfully, to prevent collusion with B. The first group
is used to publish a transaction to the blockchain incentivized
through a small fee as an output to the transaction. The second
group ensures that if Bpublishes an old transaction that the
IoT device is notified of the transaction and is able to spend
the transaction output before the timelock Wexpires for B
to redeem their funds. This second group is also incentivized
through small fees in Bitcoin smart contracts. Transactions 3-5
show the method to do this. In order to prevent third parties
colluding with each other or B, the number of members in
each third party has to be chosen with respect to the quantity
of fees as well as to the channel balances.
When both parties agree on the closing of a channel,
they can create a new transaction TFin that uses
Afin
and
Bfin as the final output balances, and post it to
the blockchain. If both parties follow the protocol properly,
only two transactions, TFTandTFin, are published to the
blockchain. If one party tries to publish an old state of the
channelTCi(B=A ), the other party can detect this and take all
the funds in the channel.
Transaction 1, the funding transaction, contains two or more
inputs, and one or more outputs. The channel funding output is
a multisignature output requiring both parties to sign in order
to use as an input into a new transaction.
Transaction 2, is used upon mutual channel closing, it uses
the multisignature output from the funding transaction and
uses multiple outputs to both AandB. If the IoT devicewishes to post the transaction, a fee can be placed in an
input for a third party to be incentivized to publish.
Transaction 3 takes the funding transactions output as input
and creates 3 outputs. The first output is local to A, the party
with the ability to publish it. This output is encumbered by
a timelock of Wblocks toA’s address to ensure that if the
transaction is old. In other words, A has given a key pair
(pkfAgjfcg;skfAgjfcg)to B, and B can redeem this input.
The second output is the remote output to B. Finally, there is
a third output, which is the incentive for a third party to send
the transaction to the blockchain and make sure the transaction
gets included in a block.
Transaction 4 takes the funding transactions output as input
and creates 2 outputs. Because Apartially signs the input to
this transaction, only Bis able to publish it. The first output
is timelocked by Wblocks with an output to B.Ais also
able to redeem this input given (pkfBgjfcg;skfBgjfcg).
There is a third party watching the blockchain, whose key is
required for Ato redeem this input. By doing so, the third
party also gets a fee in return.
Transaction 5 takes this as input and can be presigned. This
recovery transaction is used as a smart contract to incentivize
a third party to watch the blockchain by providing a fee
determined by the members of the third party. The second
output to Transaction 4 is used for a remote output to A.
The Bitcoin Scripts used for all the aforementioned transac-
tions are shown in Appendix A to provide a real-time payment
channel for IoT devices with an IoT gateway.
IV. S ECURITY ANALYSIS
To prove that our protocol design is crypto-economically
fair, we model the protocol as a game and demonstrate that
the equilibrium can always be reached as long as the players
follow the protocol and fees are appropriately set.
The payment channel in the Bitcoin Lightning Network
[6] can be modeled as a game between two actors. After
the channel is funded, Player I may post any previous states
and then Player II may choose to follow the protocol or
deviate. Following the protocol means to take the maximum
amount of funds, i.e., the remote transaction as well as the
local transaction, if the transaction is rescinded. Deviating
means to do nothing or to take just the remote funds. Let
us consider 3 transactions with Players I and II balances ,
respectively. We define TX 1= ( 1; 1),TX 2= ( 2; 2), and
TX 3= ( 3; 3), such that 2> 1> 3and 3> 1> 2,
whereTX 1is the current state of the channel, and TX 2and
TX 3are previous states where and are the values each
party has respectively in the channel at a state. Additionally,
1+ 1= 2+ 2= 3+ 3since the total amount of funds
in the channel is fixed. Player I’s strategies are which TX to
publish to the blockchain. Following the protocol, the strategy
is publishing TX 1, whileTX 2andTX 3is deviating from
the protocol. Player II’s strategies are Follow, Deviate 1, and
Deviate 2 as described above. The payoff matrix for this game
is shown in Table I. Player I experiences a maximum payout
under strategy D 1 if Player II chooses a deviating strategy.
However, since Player II has a pure strategy always to follow
5
I/II F D 1 D 2
F 1/ 1 2+ 2/0 3+ 3/0
D1 1/ 1 2/ 2 3/ 3
D2 0/ 1 0 / 2 0 / 3
TABLE I
PAYOFF MATRIX OF PAYMENT CHANNEL GAME BETWEEN 2PARTIES
the protocol, the equilibrium is reached when Player I also
follows the protocol. In this game, we show that in the life of
the channel, no player will be able to increase their profit if
the other player follows the protocol.
In our approach, we assume that one party does not have
access to the blockchain, and also with the addition of more
players, the game gets further complicated. The interesting
cases to evaluate are when one of the parties posts an inter-
mediate state to the blockchain. Let us consider four players in
the game, the IoT gateway, an IoT device, and two groups of
untrusted third parties. Player 1 is the IoT gateway with three
strategies, using the same set of transactions as the previous
game. Each strategy refers to posting a transaction to the
blockchain where strategy 1 is following the protocol. In the
first game where player 1 goes first (see Figure III), Player
4, the 3rd party that watches the blockchain for an output
of the funding transaction in a new transaction plays next.
Player 4 can either tell player 2 about the transaction (Strategy
F) or deviate from the protocol. Since Player 4 represents a
group of players, K2, any one of them can follow the protocol.
Therefore, in order to deviate, all Players in the group must
collude. If the players collude to perform a denial-of-service
attack, then no profit is gained. Therefore, it is not rational. On
the other hand, if the Player 4 members collude with Player 1
(Strategy D), then they can receive some payoff
2
K2, whereK2
is the number of members in the group and
2is the amount
that Player 1 offers which is bounded by 1.
If we show that
2
K2is less than
1, then Player 4 will not
be incentivized to deviate from the protocol. Player 2 will not
deviate from the protocol because they have a pure strategy to
follow the protocol. Finally, similarly to Player 4, Player 3 can
follow the protocol where any one member of K1will earn
1. After many games, the average payout is1
K1.1must be
large enough to cover the fee of spending the transaction as
well as for the bandwidth requirements in order to maintain a
connection to the IoT device. Additionally, in order to prevent
collusion between all the members in Player 3 and Player
1,2
K1is less than 1. This forced inequality is the reason
for using multiple members in each group. If2
K1< 1and
2
K2<
1are true, then the equilibrium is reached when all
players follow the protocol correctly. In order to ensure these
inequalities hold, a minimum/maximum channel balance must
be enforced. Recall that 2> 1> 3, to evaluate the fees
ofand
, we can set TX 2to the intermediate state where
2=8i MAX ( i)and similarly TX 3, 3=8i MIN ( i)
Player 1 has the potential to maximize their potential
earnings when they deviate with strategy 2, by posting TX 2
to the blockchain with earnings of 2 2and 2
2andby colluding with Players 3 and 4 respectively. By enforcing
that
1>2
K11> 2 3
K1
and similarly for Player 4,
1>
2
K2
1> 2 3
K2
we can show that the equilibrium is met when all players
follow the protocol because Player 3 and Player 4 will not
collude with Player 1.
If Player 2 makes the first move by posting a transaction
to the blockchain, the game is similar to the original payment
channel game shown in Table I. Player 1 will have a pure
strategy to follow the protocol. However, Player 3 can deviate
from the protocol by performing a denial-of-service attack
against Player 2. If Player 3 does this through collusion, the
game actually restarts. By incentivizing Player 3 and because
there is a pool of members in Player 3, it is not economically
rational to take that strategy. Therefore, using the fee structure
foras in the game when Player 1 goes first, equilibrium is
reached when all players follow the protocol. Although in both
equilibrium cases Player 4 does not get an incentive, they do
not know which strategy Player 1 has taken, thus their profit
is still maximized when following the protocol.
V. R ELATED WORK
There are many alternative blockchains with various prop-
erties including privacy, support for more complex smart con-
tracts, and client software for interfacing with the blockchain,
as well as with various uses of blockchain technology in IoT.
A. Simplified Payment Verification
Simplified Payment Verification (SPV) clients are
lightweight Bitcoin clients [3], that do not need to store the
full state of the blockchain. Instead, they store the 80-byte
block headers. The block header contains a lot of information
as they are chained together and contain the Merkle root of
the transactions in each block. By providing an SPV client
with a Merkle proof, any node can convince an SPV client
that a transaction is included in the blockchain with high
security as it is not efficient to create fake block headers.
However, SPV clients rely on blockchain nodes to watch
for payments. With many IoT devices making payments,
there is no incentive for regular Bitcoin nodes to watch the
blockchain for specific transactions. Therefore, we argue that
SPV clients are a burden to the Bitcoin network and we
design our protocol to avoid these scalability limitations. In
practice, if the IoT device has storage for approximately 4
MB per year for storing block headers, then it is reasonable
to include the SPV client in addition to our protocol for even
greater security. However, our solution requires significantly
less data storage for IoT devices.
6
P1
P4
P2
P3
( 1; 1 1;1
K1;0)F
( 1;0;0;0)DF
( 1;0;0;0)DF
( 1;0;0;0)DS1
P4
P2
P3
(0; 2+ 2 1
1;1
K1;
1
K2)F
( 2 2; 2;2
K1;0)DF
( 2+ 2;0;0;0)DF
( 2+ 2
2;0;0;
2
K2)DS2
P4
P2
P3
(0; 3+ 3 1
1;1
K1;
1
K2)F
( 3 2; 3;2
K1;0)DF
( 3+ 3;0;0;0)DF
( 3+ 3
2;0;0;
2
K2)DS3
Fig. 3. Extensive form when Player 1 goes first. The optimal solution is reached when all players follow the protocol.
P2
P3
P1
( 1; 1 1;1
K1;0)F
(0; 1 2;2
K1;0)DF
(0;0;0;0)DS1
P3
P1
( 2+ 2 1;0;1
K1;0)F
( 2; 2 2;2
K1;0)DF
(0;0;0;0)DS2
P3
P1
( 3+ 3 1;0;1
K1;0)F
( 2; 2 2;2
K1;0)DF
(0;0;0;0)DS3
Fig. 4. Extensive form when Player 2 goes first. The optimal solution is reached when all players follow the protocol.
B. Payment Channels
This work expands the Lightning Network [6] payment
channel system for IoT devices. The full Lightning Network
enables payments to be routed through third parties too while
we leave routing through third parties as future work in order
to fully integrate with the Lightning Network.
Bolt [11], is a protocol that enables private off-chain pay-
ment channel transactions. Because of the privacy-preserving
nature, it is a challenge to interface with such a system on
low resource devices commonly seen in IoT. However, privacy
preservation in state channels can be a desired property in IoT
payment systems, such as smart meters in the power grid.
Raiden [8], is an Ethereum based payment channel similar
to the Lighting Network. Our protocol can also be adapted to
this style network. Plasma [12], is a scaling solution designed
on Ethereum that enables payment channels as well as more
complex smart contracts to be deployed.
IOTA (MIOTA) [13], is a blockchain designed with low
computation for IoT and web 3.0 protocols. In our case, how-
ever, the security model of this blockchain is quite different ofthat from Bitcoin and Ethereum. Additionally, we choose to
use Bitcoin because it is the most widely used and thus easier
to adopt in practice.
C. IoT Integrated with Blockchain
Christidis and Devetsikiotis explore the challenges and
opportunities of blockchain and smart contracts for IoT in [1].
Their work focuses on discovering use cases that distributed
ledger technology can solve and challenges found with inte-
gration of IoT. One challenge that they do not discuss is the
resource limitations of IoT, which is the problem we propose a
solution for. Our solution only covers the value transfer portion
of blockchain technology.
In [14], Aitzhan and Svetinovic propose a token-based
system similar to Bitcoin and coupled with an anonymous
messaging system to provide security and privacy for peer-
to-peer energy trading. They also include the ability to open
unidirectional payment channels for partial payment. Their ap-
proach designs an anonymous market revolving around energy
7
trading. In our work, we focus solely on bidirectional payment
channels for the Bitcoin blockchain. While application-specific
blockchains and token systems including [15] may provide a
solution in specific domains, we would like to explore general
purpose solutions within IoT payment systems.
In [16], the authors focus on creating a local energy market
for matching energy orders in a decentralized manner. Their
proposals call for a private or permissible blockchain, which
operates as a decentralized trusted application. In this work,
we focus on integrating the existing end-user devices to IoT
gateways on public trustless blockchains.
In [17], Blockchain is evaluated for use in smart homes for
IoT, but the blockchain proposed does not use a trustless con-
sensus algorithm, which makes it a decentralized database for
recording internet-of-things devices activity. Reference [18]
attempts a similar objective for smart grid sensors and actua-
tors. In our work, we design a protocol that can be generally
applied to public trustless blockchains. Additionally, we aim
to solve the problem of value transfer rather than information
assurance.
Our approach uses the Bitcoin blockchain for our protocol
design. There are many other blockchains that have useful
properties, such as Ethereum [4], Litecoin, various Bitcoin
forks, and many others which have a different block time
and can implement Turing-complete scripting languages or
provide differing features, such as privacy and anonymity.
However, payment channels are still in development and on-
chain transactions will still suffer from the high fee problem
that Bitcoin on-chain transactions do. In our future work, we
will analyze trade-offs between blockchain ecosystems for IoT
and cyber-physical system payments.
VI. C ONCLUSION
We design a real-time blockchain-based payment channel
for IoT devices to gateway services, which is less resource
intensive than existing solutions, and show that off-chain pay-
ment channels are feasible for applications where IoT devices
transfer value. We also demonstrate that the protocol is crypto-
economically fair by modeling the protocol as a game, in
which the equilibrium is reached as long as the players follow
the protocol and set the fees appropriately. In the future, we
would like to expand our protocol for IoT devices to interact
with other IoT devices as well as generalize the protocol to
work with payment channels including interoperability with
the existing Lightning Network [6]. We would also like to
explore the ability of privacy-preserving blockchain payment
channels, such as Bolt [11], in order to protect the rights of
end-users in IoT and cyber-physical systems.
APPENDIX A
BITCOIN TX S CRIPTS
REFERENCES
[1] K. Christidis and M. Devetsikiotis, “Blockchains and smart contracts for
the internet of things,” IEEE Access , vol. 4, pp. 2292–2303, 2016.
[2] J. Basden and M. Cottrell, “How utilities are using blockchain to
modernize the grid,” Harvard Business Review , 2017.
[3] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system.” http:
//bitcoin.org/bitcoin.pdf, 2008.Transaction 1 Funding Transaction Script OUT1
1:Redeem Script (rS)
2: 2<pkAFT> <pkBFT>2 CHECKMULTISIG
3:Locking Script (lS)
4: HASH160<rSHash>EQUAL
5:Unlocking Script (uS)
6: 0<sigAFT> <sigBFT> < rS> < lS>
7:where
8: rSHash = RIPEMD160 (SHA256 (r S))
Transaction 2 Mutual Close Transaction
1:In1 .FT
2: Funding Transaction (FT)
3:Out1 .A
4: Locking Script (lS)
5: DUP HASH160 <H(pkAclose)>
EQUALVERIFY CHECKSIG
6: Unlocking Script (uS)
7:<sigAclose> <pkAclose>
8:Out2 .B
9: Locking Script (lS)
10: DUP HASH160 <H(pkBclose)>
EQUALVERIFY CHECKSIG
11: Unlocking Script (uS)
12:<sigBclose> <pkBclose>
13:where
14: H(pk) = RIPEMD160 (SHA256 ( pk))
[4] V . Buterin, “Ethereum white paper: A next gerneration smart contract
and decentralized application platform,” tech. rep., 2014.
[5] “Bitcoin wiki.” https://en.bitcoin.it/wiki. Accessed: 2018-05-28.
[6] J. Poon and T. Dryja, “The bitcoin lightning network,: Scalable off-chain
instant payments,” 2016.
[7] C. Decker and R. Wattenhofer, “A fast and scalable payment network
with bitcoin duplex micropayment channels,” in Proceedings of the
17th International Symposium on Stabilization, Safety, and Security
of Distributed Systems - Volume 9212 , (Berlin, Heidelberg), pp. 3–18,
Springer-Verlag, 2015.
[8] brainbot, “The raiden network.” https://raiden.network, 2018. Accessed:
2018-05-28.
[9] H. A. Kalodner, S. Goldfeder, A. Chator, M. M ¨oser, and A. Narayanan,
“Blocksci: Design and applications of a blockchain analysis platform,”
CoRR , vol. abs/1709.02489, 2017.
[10] P. Wuille, “Bip32: Hierarchical deterministic wallets.” https://github.
com/bitcoin/bips/blob/master/bip-0032.mediawiki. Accessed: 2018-05-
28.
[11] M. Green and I. Miers, “Bolt: Anonymous payment channels for
decentralized currencies.” Cryptology ePrint Archive, Report 2016/701,
2016. https://eprint.iacr.org/2016/701.
[12] J. Poon and V . Buterin, “Plasma: Scalable autonomous smart contracts.”
https://plasma.io/plasma.pdf, 2017. Accessed: 2018-10-28.
[13] S. Popov, “The tangle.” https://www.iota.org/research/academic-papers,
2018. Accessed: 2018-10-28.
[14] N. Z. Aitzhan and D. Svetinovic, “Security and privacy in decentralized
energy trading through multi-signatures, blockchain and anonymous
messaging streams,” IEEE Transactions on Dependable and Secure
Computing , pp. 1–1, 2016.
[15] M. Mihaylov, S. Jurado, N. Avellana, K. Van Moffaert, I. M. de Abril,
and A. Now ´e, “Nrgcoin: Virtual currency for trading of renewable
energy in smart grids,” in European Energy Market (EEM), 2014 11th
International Conference on the , pp. 1–6, IEEE, 2014.
[16] E. Mengelkamp, B. Notheisen, C. Beer, D. Dauer, and C. Weinhardt, “A
blockchain-based smart grid: towards sustainable local energy markets,”
8
Transaction 3 Commitment ia(Publishable by A)
1:In1 .FT
2: Funding Transaction (FT)
3:Out1 .A
4: Locking Script (lS)
5: IF
6: W CHECKSEQUENCEVERIFY DROP DUP HASH160 <H(pkAia)>EQUALVERIFY CHECKSIG
7: ELSE
8: DUP HASH160 <H(pkBib)>EQUALVERIFY CHECKSIG DROP
DUP HASH160 <H(pkAic)>EQUALVERIFY CHECKSIG
9: ENDIF
10:Out2 .B
11: Locking Script (lS)
12: DUP HASH160 <H(pkBib)>EQUALVERIFY CHECKSIG
13:Out3 .3rd Party A (Economically Incentivized to publish to the blockchain)
14: 1<pk 3rda0> <pk 3rdaK> K 1CHECKMULTISIG
Transaction 4 Commitment ib(Publishable by B)
1:In1 .FT
2: Funding Transaction (FT)
3:Out1 .B
4: Locking Script (lS)
5: IF
6: W CHECKSEQUENCEVERIFY DROP DUP HASH160 <H(pkBia)>EQUALVERIFY CHECKSIG
7: ELSE .3rd Party B (Economically Incentivized to watch the blockchain)
8: 1<pk 3rdb0> <pk 3rdbK> K 2CHECKMULTISIG DROP
DUP HASH160 <H(pkBic)>EQUALVERIFY CHECKSIG DROP
DUP HASH160 <H(pkAib)>EQUALVERIFY CHECKSIG
9: ENDIF
10:Out2 .A
11: Locking Script (lS)
12: DUP HASH160 <H(pkAib)>EQUALVERIFY CHECKSIG
Transaction 5 Recovery Transaction
1:In1 .Commitment TX b
2: (Transaction 4, Output 1)
3: Unlocking Script (uS)
4:<sigAib> <pkAib> <sigBic> <pkBic> <pk 3rdb >
5:Out1 .A
6: Locking Script (lS)
7: DUP HASH160 <H(pkAirc)>EQUALVERIFY CHECKSIG
8: Unlocking Script (uS)
9:<sigAirc> <pkAirc>
10:Out2 .3rd Party b
11: Locking Script (lS)
12: DUP HASH160 <H(pk 3rdb!)>EQUALVERIFY CHECKSIG
13:Out3 .3rd Party a
14: Locking Script (lS)
15: 1<pk 3rda0> <pk 3rdaK> K 1CHECKMULTISIG DROP
9
Computer Science-Research and Development , vol. 33, no. 1-2, pp. 207–
214, 2018.
[17] A. Dorri, S. S. Kanhere, R. Jurdak, and P. Gauravaram, “Blockchain for
iot security and privacy: The case study of a smart home,” in 2017 IEEE
International Conference on Pervasive Computing and Communications
Workshops (PerCom Workshops) , pp. 618–623, March 2017.
[18] G. Liang, S. R. Weller, F. Luo, J. Zhao, and Z. Y . Dong, “Distributed
blockchain-based data protection framework for modern power systems
against cyber attacks,” in IEEE Transactions on Smart Grid (Early
Access) , 2018.
|
{
"id": "1812.10345"
}
|
1803.09028
|
(Short Paper) Towards More Reliable Bitcoin Timestamps
|
Bitcoin provides freshness properties by forming a blockchain where each
block is associated with its timestamp and the previous block. Due to these
properties, the Bitcoin protocol is being used as a decentralized, trusted, and
secure timestamping service. Although Bitcoin participants which create new
blocks cannot modify their order, they can manipulate timestamps almost
undetected. This undermines the Bitcoin protocol as a reliable timestamping
service. In particular, a newcomer that synchronizes the entire blockchain has
a little guarantee about timestamps of all blocks.
In this paper, we present a simple yet powerful mechanism that increases the
reliability of Bitcoin timestamps. Our protocol can provide evidence that a
block was created within a certain time range. The protocol is efficient,
backward compatible, and surprisingly, currently deployed SSL/TLS servers can
act as reference time sources. The protocol has many applications and can be
used for detecting various attacks against the Bitcoin protocol.
|
http://arxiv.org/pdf/1803.09028v2
|
Pawel Szalachowski
|
cs.CR
|
cs.CR
|
(Short Paper) Towards More Reliable
Bitcoin Timestamps
Pawel Szalachowski
SUTD
pawel@sutd.edu.sg
Abstract —Bitcoin provides freshness properties by forming a
blockchain where each block is associated with its timestamp and
the previous block. Due to these properties, the Bitcoin protocol is
being used as a decentralized, trusted, and secure timestamping
service. Although Bitcoin participants which create new blocks
cannot modify their order, they can manipulate timestamps
almost undetected. This undermines the Bitcoin protocol as a
reliable timestamping service. In particular, a newcomer that
synchronizes the entire blockchain has a little guarantee about
timestamps of all blocks.
In this paper, we present a simple yet powerful mechanism
that increases the reliability of Bitcoin timestamps. Our protocol
can provide evidence that a block was created within a certain
time range. The protocol is efficient, backward compatible, and
surprisingly, currently deployed SSL/TLS servers can act as
reference time sources. The protocol has many applications and
can be used for detecting various attacks against the Bitcoin
protocol.
I. I NTRODUCTION
Bitcoin [18] is a cryptocurrency successful beyond all
expectations. As a consequence of this success and properties
of Bitcoin, developers and researchers try to reuse the Bit-
coin infrastructure to build new or enhance existing systems.
One class of such systems is a decentralized timestamping
service. For instance, the OpenTimestamps project [1] aims
to standardize blockchain timestamping, where a timestamp
authority, known from the previous proposals [2], is replaced
by a blockchain. Other, more focused applications that rely
on the blockchain timestamps include trusted record-keeping
service [10], [15], decentralized audit systems [16], [20],
document signing infrastructures [14], timestamped commit-
ments [5], or secure off-line payment systems [7]. Reli-
able timestamps are also vital for preventing various attacks
against the Bitcoin protocol. For instance, Heilman proposed
a scheme [13] which with unforgeable timestamps can protect
from the selfish mining strategy [9].
By design, the Bitcoin protocol preserves the order of
events (i.e., weak freshness ), however, accurate time of events
(i.e., strong freshness ) is questionable, despite the fact that
each block has a timestamp associated. In practice, Bitcoin
timestamps can differ in hours from the time maintained by
Bitcoin participants ( nodes ), and in theory can differ radically
from the actual time (i.e., time outside the Bitcoin network).
Effectively, the accurate time cannot be determined from the
protocol, which limits capabilities of the Bitcoin protocol as
This work was supported in part by the National Research Foundation
(NRF), Prime Minister’s Office, Singapore, under its National Cybersecurity
R&D Programme (Award No. NRF2016NCR-NCR002-028) and administered
by the National Cybersecurity R&D Directorate.a timestamping service, and which impacts the security of the
protocol [11].
In this work, we propose a new mechanism for improving
the security of Bitcoin timestamps. In our protocol, external
timestamp authorities can be used to assert a block creation
time, instead of solely trusting timestamps put by block
creators. Our scheme is efficient, simple, practical, does not
require any additional infrastructure nor any changes to the
Bitcoin protocol, thus can be deployed today. Interestingly,
currently existing SSL/TLS servers can act as time authorities.
II. B ACKGROUND AND PRELIMINARIES
A. Freshness in the Bitcoin Blockchain
Bitcoin is an open cryptocurrency and transaction system.
Each transaction is announced to the Bitcoin network, where
nodes called miners collect and validate all non-included
transactions and try to create ( mine ) ablock of transactions by
solving a cryptographic proof-of-work puzzle, whose difficulty
is set such that a new block is mined about every 10 minutes.
Each block has a header that contains the block’s metadata.
Transactions are represented as leaves of a Merkle tree [17]
whose root is included in the header ; hence, with the header, it
is possible to prove that a transaction is part of the given block.
Every block header contains also a field with a hash of the
previous header to link the blocks together. Due to this link, the
blocks create an append-only blockchain . Additionally, headers
include Unix timestamps that describe when the corresponding
block was mined. These timestamps are used as an input
for proof-of-work puzzles and are designed to impede an
adversary from manipulating the blockchain.
Freshness properties offered by the Bitcoin protocol are
unclear. Since the blockchain is append-only, weak freshness
is provided by design (i.e., blocks are ordered in the chronolog-
ical order). Timestamps associated with blocks are validated
in a special way. Namely, a node considers a new block’s
timestamp Tas valid if:
1)T > the median timestamp of previous eleven blocks, and
2)T 2h < network time (defined as the median of the
timestamps returned by all nodes connected to the node).
Each node maintains its local Bitcoin timer, which is
defined as the node’s local system time plus the difference
between this time and the network time. However, the timer
cannot be adjusted more than 70 minutes from the local system
time.
As it is not required that all nodes have accurate time,
timestamps encoded in headers may not be even in order,arXiv:1803.09028v2 [cs.CR] 18 May 2018
and their accuracy is estimated to hours. Manipulation of
the Bitcoin network time is possible and can result in severe
attacks [4]. Furthermore, as Bitcoin timestamps depend only
on time of nodes, timestamps can differ radically from the
actual time, outside the network. Another issue is that nodes
synchronizing the entire blockchain have hardly any guarantees
about the previous blocks’ creation times. Given that, it is clear
that the Bitcoin protocol does not provide strong freshness,
what limits the Bitcoin blockchain applicability for time-
sensitive applications (like accurate timestamping).
B. Timestamping Service
The time-stamp protocol (TSP) [2] is a standard timestamp-
ing protocol built on top of the X.509 public key infrastructure
(PKI). In the protocol, a client that wishes to timestamp data
contacts a timestamp authority (TSA) with the data’s hash.
The TSA signs the hash along with the current timestamp and
returns the signed message to the client. The message, with
the TSA’s certificate and the data, allows everyone to verify
that the data was timestamped at the given time.
For simple description, we present our protocol as com-
pliant with TSP. However, with minor or no changes, our
scheme can be combined with other services, like currently
existing PKIs or secure time synchronization services (see
subsection V-B).
C. System Model
Our protocol introduces the two following parties:
Timestamping authority (TSA) runs a service that
timestamps documents according to the TSP protocol
presented in subsection II-B.
Verifier is an entity that wants to verify when a new
(upcoming) blockchain’s block was mined. A verifier can
interact with the Bitcoin network by reading blocks and
sending transactions and can interact with a (chosen)
trusted TSA.
We assume that the used cryptographic primitives are secure.
We assume an adversary able to mine Bitcoin blocks, and her
goal is to introduce a new block with an incorrect timestamp
(i.e., deviating from the TSA’s time) undetected.
D. Notation
Throughout the paper we use the following notation:
fmsggAdenotes the message msg signed by A,
h(:) is a cryptographic hash function,
k is the string concatenation,
rR S denotes that ris an element randomly
selected from the set S,
f0;1gnis a set of all n-bit long strings,
Bi denotes the ith blockchain’s block,
Hi denotes the ith block header,
Tx is a Unix timestamp expressed in seconds.
III. D ESCRIPTION OF THE PROTOCOL
A. High-Level Overview
The main idea behind our scheme is to combine an external
TSA with the blockchain, such that a verifier can create acryptographically-provable series of events that asserts when a
given block was mined (i.e., when all transactions associated
with the block were published). A simplified description of
our protocol is presented in Figure 1.
VerifierTSAprev: h(Hi-1)
time: 1508135779
...
trans: h( )prev: h(Hi-2)
time: 1508104751
...
trans: ...prev: h(Hi)
time: 1508139750
...
trans: ...BlockchainBi-1BiBi+1h( )h( )
(1a) timestamp Hi-1(1b) timestamped Hi-1(2) publish the
timestamped Hi-1(3a) timestamp Hi(3b) timestamped Hi
Fig. 1. A high-level overview of the protocol.
The protocol starts, when a verifier sees a new block Bi 1.
The verifier extracts the block header Hi 1and contacts a
TSA to timestamp Hi 1. Then, the TSA returns a timestamped
and signed Hi 1to the verifier. This message states that
the block Bi 1is older than the message itself (i.e., than
its timestamp). Next, the verifier publishes the timestamped
and signed message in the blockchain. The corresponding
transaction is published in the subsequent block Bi. As the
transaction is included in the block, it implies that the block
is newer than the transaction (i.e., the block is newer than the
timestamp associated with the transaction). Finally, the verifier
extracts the header Hiof this block and timestamps it with
the TSA. Now, the verifier has evidence that the block Biwas
created between the timestamped messages (i.e., between their
timestamps).
B. Details
As presented above the verifier interacts with the TSA and
the Bitcoin network. Everyone can act as a verifier, and TSAs
can be chosen arbitrarily by verifiers. The protocol is initiated
independently by a verifier by executing the following:
1) On receiving the (i 1)th block Bi 1with the block
header Hi 1, the verifier:
a) selects a random value R0R f0;1gn,
b) prepares data D0=h(R0kHi 1)to be timestamped.
2) The verifier contacts a TSA to timestamp D0.
3) The TSA returns a timestamped and signed message
fD0; T0gTSA.
4) On receiving this message the verifier:
a) computes C=h(fD0; T0gTSA)as a commitment,
b) encodes Cwithin a Bitcoin transaction, and
c) propagates the transaction across the network, such
that it is included in the subsequent block Bi.
5) On receiving the ith block Bi, with the block header Hi,
the verifier:
a) selects a random value R1R f0;1gn,
b) prepares data D1=h(R1kHi)to be timestamped,
c) creates PCas a Merkle tree inclusion proof of the
transaction containing C.
6) The verifier contacts the TSA to timestamp D1.
7) The TSA returns a signed message fD1; T1gTSA.
8) Now, the verifier has the following information
R0; Hi 1;fD0; T0gTSA; R1; Hi;fD1; T1gTSA; PC;(1)
which constitutes a proof that the block Biwas mined
between T0andT1.
9) To verify whether the block has a correct timestamp, the
verifier checks if the following is satisfied:
T0< H i’s timestamp < T 1:
The verifier can terminate the protocol at the step 9.
However, to verify the creation time of the subsequent block,
he can compute a new commitment C=h(fD1; T1gTSA)and
conduct the protocol from the step 4b onwards.
For the sake of a simple presentation, we include D0and
D1in the proof (see Equation 1), but they are redundant as
can be computed from R0; Hi 1andR1; Hi, correspondingly.
We also describe the protocol with a single TSA. However,
it is easy to extend the scheme to multiple TSAs. In such a
case, the verifier timestamps the D0andD1messages with
multiple TSAs, and computes the commitment Cas a hash
over the TSAs messages corresponding to D0.
IV. A NALYSIS
First, we claim that the verifier executing the protocol
obtains a provable series of events that given block was
mined in a given time range. Hence, an adversary cannot
introduce a block with an invalid timestamp undetected. (Al-
though we present our protocol in the adversarial setting,
invalid timestamps can be introduced by benign miners with
desynchronized clocks.)
The timeline of the protocol events is presented in Figure 2.
When the verifier notices the block Bi 1he creates D0by
hashing a random value R0and the block’s header Hi 1.D0is
timestamped by the TSA, and the commitment Cis computed
as a hash of this timestamped message. With Cthe verifier can
check that it was indeed created after the Bi 1as the header
ofBi 1(i.e.,Hi 1) was used to create it. Therefore, the block
Bi 1is older than the timestamp T0. Then, the commitment
is propagated among the network and finally included in the
newly created block Bi. The verifier, with the header Hiof
the new block can prove that Cis part of this block (using
the Merkle inclusion proof PC), thus it has to be older than
the block. Next, the verifier from a random value R1and the
timeblock Bi 1
is createdmining periodblock Bi
is created
timestamp D0
and publish Ctimestamp D1
Fig. 2. Timeline of the events in the protocol.block’s header Hicreates D1, which is timestamped by the
TSA. The message from the TSA ( fD1; T1gTSA) states that
D1was created before T1, and because D1is derived from Hi,
it implies that the block Biwas created before T1. Finally,
the verifier equipped with this information (see Equation 1)
can check whether the block’s timestamp is correct (i.e., 2
[T0; T1]).
Our protocol provides much better freshness properties
than the Bitcoin protocol alone. As depicted in Figure 2, if the
verifier creates and publishes Cimmediately after the block
Bi 1is observed and timestamps D1after the block Biis
observed, then the accuracy of timestamping is approximately
equal the block creation time (currently, estimated as 10
minutes). The verifier can increase the accuracy by creating
and publishing multiple commitments in a sequence, such that
the difference between timestamped D0andD1decreases.
The protocol is described in the scenario where the com-
mitment Cappears in transactions corresponding to the block
Bi. Although the propagation in the Bitcoin network is fast
when compared to the average block creation time [6], it may
happen that Cis included in a later block. In such a case, our
protocol still provides guarantees about the blocks in between.
For example, if the commitment Cappears not in Bibut in
the block Bi+1, then the proof states that blocks BiandBi+1
were mined between T0andT1.
The verifier generates a random value R0that together with
Hi 1is timestamped by the TSA as fD0; T0gTSA, which
in turn is hashed into the commitment C. The commitment
is published in the blockchain, however, R0is not revealed.
This construction protects the protocol from censorship by an
adversary that wishes to manipulate the timestamp. Without
this random value, the adversary could just keep timestamping
hash of Hi 1every second, learn all possible commitments for
the block header, and censor the verifier’s transaction. With a
large random value (e.g., chosen from f0;1g128), generating all
possible commitments is infeasible, hence the adversary cannot
distinguish between a regular transaction and the verifier’s
transaction.
Although we do not consider malicious TSAs, the protocol
provides means to keep them accountable. If the TSA returns
the signed messages such that T0> T 1, then the verifier has
an evidence that the TSA misbehaved. More specifically, the
verifier can show that D0is older than D1(by showing that
D1was created using Hi, which contains Ccreated from D0
which was timestamped at T0), which proves that the TSA
contradicted itself. Moreover, the TSA does not know secret
random values R0,R1, hence cannot learn what is being
timestamped. (However, colluding TSA and adversary could
censor commitments.)
V. P RACTICAL CONSIDERATIONS
A. Commitments Encoding
In our protocol, a verifier publishes commitments in the
blockchain (see the step 4c of the protocol). This message
is computed as a hash thus is short and can be encoded
on the blockchain in many ways. One way is to publish a
transaction with the commitment encoded within the 20-byte
long receiver of transaction (pay-to-pubkey-hash ) field.
An alternative could be to encode messages into other fields
or to use the OP_RETURN instruction [19].
Storing non-transaction data in the Bitcoin blockchain is
regarded by many members of the Bitcoin community as a
spam or even a vandalism. We agree that using the Bitcoin
blockchain as a highly distributed database negatively influ-
ences its performance. However, we believe that our protocol
will be seen as a positive contribution to the ecosystem,
as firstly, it aims to improve the security of the protocol,
and secondly, the overhead introduced is marginal. Moreover,
this overhead can be minimized by publishing commitments
through a system like OpenTimestamp, which aggregates and
publishes data in the blockchain efficiently.
B. Timestamping Service
We describe our protocol to be compliant with the times-
tamping service as defined in the RFC 3161 [2] (see sub-
section II-B). There are many providers of this service, both
commercial and free. However, our protocol, with minimal or
no changes, can be combined with other currently existing
infrastructures.
Surprisingly, today’s SSL/TLS servers can act in our proto-
col as TSAs. The SSL/TLS protocol supports Diffie-Hellman
(DH) as a key-exchange algorithm. In such a case, a server
sends to a client the ServerKeyExchange message, that
among other parameters, signs the DH parameters, and client’s
and server’s random values. These random values start with a
timestamp field, hence it is possible to timestamp a document
by the server’s key by setting the client’s random value to a
document’s hash [8]. As, SSL/TLS is becoming ubiquitous and
the DH exchange is widely supported [3], [21], web servers of
reputable organizations (e.g., mozilla.org ) or high-profile
websites (like google.com orlive.com ) can be used as
TSAs.
Another infrastructure that with minimal changes can im-
plement the TSA functionality is secure time synchronization
infrastructure. For instance, Roughtime [12], a recent proposal
by Google, provides signed timestamps. To prevent replay at-
tacks, a client inputs its nonce which together with a timestamp
is signed by the server. To implement the TSA functionality,
a client just inputs Di 1as a nonce, like in the protocol. One
small change is caused by the design of Roughtime where, for
efficiency reasons, servers sign responses in batches. Hence,
values returned by servers are encoded differently, however
still are verifiable and can be used analogically as the TSA’s
output from the protocol (see the steps 3 and 7).
VI. C ONCLUSIONS
In this paper, we presented a method of strengthening the
reliability of Bitcoin timestamps. Our protocol is efficient,
backward compatible, and can provide much stronger freshness
guarantees than the Bitcoin protocol alone. Our method can
be combined with currently existing and widespread security
infrastructures like the SSL/TLS PKI. Although we presented
our scheme in the Bitcoin context, it is also applicable to other
blockchain-based platforms.
The protocol can be deployed in many applications. Ver-
ifiers can run the protocol to detect misbehaving nodes. Theprotocol can be part of a detection system against time-related
attacks or can be combined with a system like OpenTimes-
tamps to enhance it. Proofs can be also publicly published,
so nodes that in the future download and validate the entire
blockchain will have much better assurance about the event
timeline.
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|
{
"id": "1803.09028"
}
|
1801.03528
|
Applications of Blockchain Technology beyond Cryptocurrency
|
Blockchain (BC), the technology behind the Bitcoin crypto-currency system, is
considered to be both alluring and critical for ensuring enhanced security and
(in some implementations, non-traceable) privacy for diverse applications in
many other domains including in the Internet of Things (IoT) eco-system.
Intensive research is currently being conducted in both academia and industry
applying the Blockchain technology in multifarious applications. Proof-of-Work
(PoW), a cryptographic puzzle, plays a vital role in ensuring BC security by
maintaining a digital ledger of transactions, which is considered to be
incorruptible. Furthermore, BC uses a changeable Public Key (PK) to record the
users' identity, which provides an extra layer of privacy. Not only in
cryptocurrency has the successful adoption of BC been implemented but also in
multifaceted non-monetary systems such as in: distributed storage systems,
proof-of-location, healthcare, decentralized voting and so forth. Recent
research articles and projects/applications were surveyed to assess the
implementation of BC for enhanced security, to identify associated challenges
and to propose solutions for BC enabled enhanced security systems.
|
http://arxiv.org/pdf/1801.03528v1
|
Mahdi H. Miraz, Maaruf Ali
|
cs.CR
|
cs.CR
|
Annals of Emerging Technologies in Computing (AETiC)
Vol. 2, No. 1, 2018
Article
Applications of Blockchain Technology
beyond Cryptocurrency
Mahdi H. Miraz1, *, Maaruf Ali2
1School of Computer Studies, AMA International University BAHRAIN (AMAIUB), Bahrain
m.miraz@amaiu .edu.bh
2Department of Science and Technology, University of Suffolk, Ipswich, Suffolk, UK
m.ali2@uos.ac.uk
*Correspondence: m.miraz@amaiu.edu.bh
Received: 25th November 2017 ; Accepted: 15th December 2017; Published: 1st January 2018
Abstract: Blockchain (BC), the technology behind the Bitcoin crypto -currency system, is considered to be both
alluring and critical for ensuring enhanced security and (in some implementations, non -traceable ) privacy for
diverse applications in many other domains - including in the Internet of Things (IoT) ec o-system. Intensive
research is currently being conducted in both academia and industry applying the Blockchain technology in
multifarious applications . Proof -of-Work (PoW), a cryptographic puzzle, plays a vital r ôle in ensuring BC security
by maintaining a digital ledger of transactions , which is considered to be incorruptible. Furthermore, BC uses a
changeable Public Key (PK) to record the users’ identity , which provides an extra layer of privacy. Not only in
cryptocurrency has the successful adoption of BC been implemented but also in multifaceted non -monetary systems
such as in: distributed storage systems, proof -of-location, healthcare , decentralized voting and so forth. Recent
research articles and projects/applications were surveyed to assess the implementation of BC for enhanced s ecurity,
to identify associated challenges and to propose solutions for BC enabled enhanced security systems .
Keywords: Blockchain (BC); Bitcoin ; Crypto -currency; IoT; Proof of Work (PoW); Distributed Digital Ledger
1. Introduction
The goal of this research paper is to summarise the literature on implementation of the
Blockchain and similar digital ledger techniques in various other domains beyond its application to
crypto -currency and to draw appropriate conclusions. Bl ockchain being relatively a new technology,
a representative sample of research is presented, spanning over the last ten years, starting from the
early work in this field. Different types of usage of Blockchain and other digital ledger techniques,
their ch allenges, applications, security and privacy issues were investigated. Identifying the most
propitious direction for future use of Blockchain beyond crypto -currency is the main focus of the
review study.
Blockchain (BC), the technology behind Bitcoin cryp to-currency system, is considered to be
essential for forming the backbone for ensuring enhanced security and privacy for various
applications in many other domains including the Internet of Things (IoT) eco -system. International
research is currently bein g conducted in both academia and industry applying Blockchain in varied
domains. The Proof -of-Work (PoW) mathematical challenge ensur es BC security by maintaining a
digital ledger of transactions that is considered to be unalterable . Furthermore, BC uses a changeable
Mahdi H. Miraz and Maaruf Ali , "Applications of Blockchain Tech nology beyond Cryptocurrency ”, Annals of Emerging Technologies in
Computing (AETiC), Print ISSN: 2516 -0281, Online ISSN: 2516 -029X, pp. 1 -6, Vol. 2, No. 1, 1st January 2018, Published by
International Association of Educators and Researchers (IAER) , Available: http://aetic.theiaer.org/archive/v2n1/p1.pdf .
AETiC 2018 , Vol. 2, No. 1 2
Public Key (PK) to record the users’ identity that provides an extra layer of privacy. The successful
adoption of BC has been implemented in diverse non- monetary systems such as in online voting,
decentralized messaging, distributed cloud stora ge systems, proof -of-location, healthcare and so
forth. Recent research articles and projects/applications were surveyed to as certain the
implementation of BC for enhanced s ecurity and to identify its associated challenges and thence to
propose solutions f or BC enabled enhanced security systems.
The knowledge domain of the research is in the realm of the digital ledger, specifically, in
Blockchain and crypto -currency.
2. Technology Fundamentals of Blockchain
This section briefly describes the fundamentals of the technology behind the Blockchain. A
Blockchain comprises of two different components, as follows:
1. Transaction: A transaction, in a Blockchain, represent s the action triggered by the
participant.
2. Block: A block, in a Blockchain, is a collection of da ta recording the transaction and
other associated details such as the correct sequence, timestamp of creation, etc.
The Blockchain can either be public or private , depending on the scope of its use. A public
Blockchain enables all the users with read and w rite permissions such as in Bitcoin , access to it .
However, there are some public Blockchain s that limit the access to only either to read or to write.
On the contrary, a private Blockchain limit s the access to selected trusted participants only, with the
aim to keep the users’ detail s concealed. This is particularly pertinent among st government al
institutions and allied sister concerns or their subsidies thereof .
One of the major benefits of the Blockchain is that it and its implementation technology is
public. Each participating entities possesses an updated complete record of the transactions and the
associated blocks. Thus the data remains unaltered, as any changes will be publicly verifiable.
However, the data in the blocks are encrypted by a private ke y and hence cannot be interpreted by
everyone.
Another major advantage of the Blockchain technology is that it is decentralized. It is
decentralized in the sense that:
• There is no single device that stores the data (transactions and associated blocks), ra ther
they are distributed among the participants throughout the network supporting the
Blockchain.
• The transactions are not subject to approval of any single authority or have to abide by
a set of specific rules, thus involving substantial trust as to reac h a consensus.
• The overall security of a Blockchain eco -system is another advantage. The system only
allows new blocks to be appended. Since the previous blocks are public and
distributed, they cannot be altered or revised.
For a new transaction to be added to the existing chain, it has to be validated by all the
participants of the relevant Blockchain eco -system. For such a validation and verification process,
the participants must apply a specific algorithm. The relevant Blockchain eco -system defines what is
perceived as “valid”, which may vary from one eco -system to another. A number of transactions,
thus approved by the validation and verification process, are bundled together in a block. The newly
prepared block is then c ommunicated to all other participating nodes to be appended to the existing
chain of blocks. Each succeeding block comprises a hash, a unique digital fingerprint, of the
preceding one.
Figure 1 demonstrates how Blockchain transactions takes place, using a step -by-step example.
Bob is going to transfer some money to Alice. Once the monetary transaction is initiated and hence
triggered by Bob, it is represented as a “transaction” and broadcast to all the involved parties in the
networks. The transaction now has to get “approv al” as being indeed “valid” by the Blockchain
eco-system. Transaction(s) once approved as valid along with the hash of the succeeding block are
then fed into a new “block” and communicated to all the participating nodes to be subsequently
appended to the existing chain of blocks in the B lockchain digital ledger .
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AETiC 2018 , Vol. 2, No. 1 3
Figure 1 . Operation of the Blockchain.
3. Use of Blockchain beyond Cryptocurrency
Although the Internet is a great tool to aid every sphere of the modern digital life, it is still
highly flawed in terms of the lack of security and privacy, especially when it comes to FinTech and
E-commerce. Blockchain, the technology behind cryto -currency, brought forth a new revolution by
providing a mechanism for Peer -to-Peer (P2P) transacti ons without the need for any intermediary
body such as the existing commercial banks [1]. BC validates all the transactions and preserves a
permanent record of them while making sure that any identif ication related information of the users
are kept incognito. Thus all the personal information of the users are sequestered while
substantiating all the transactions. This is achieved by reconciling mass collaboration by cumulating all the transactions in a computer code based digital ledger. Thus, by applying Blockchain or similar
crypto -currency techniques, the users neither need to trust each other nor do they need an
intermediator; rather the trust is manifested within the decentralized network system itse lf.
Blockchain thus appears to be the ideal “Trust Machine” [2] paradigm .
In fact, Bitcoin is just an exemplary use of the Blockchain. Blockchain is considered to be a novel
revolution in the domain of computing enabling limitless applications such as storing and verifying
legal documents including deeds and various certificates, healthcare data, IoT, Cloud and so forth.
Tapscott [3] rightly indica ted Blockchain to be the “World Wide Ledger” , enabling many new
applications beyond verifying transactions such as in: smart deeds, decentralized and/or
autonomous organizations/ government services etc.
In the cloud [4,5] environment, the history of creation of any cloud data object and its
subsequent operations performed thereupon are recorded by the data structure mechanism of ‘Data
Provenance ’, which is a type of cloud metadata. Thus this is very important to provide the utmost
security to the data provenance for ensuring its data privacy, forensics and accountability. Liang et
al. [6] puts forward a Blockchain based trusted cloud data provenance architecture, ‘ ProvChain ’,
which is fully decentralized. Such adoption of the Blockchain in a cloud environment can provide
strong protection against records being altered thus enabling an enha nced transparency as well as Triggering
Transaction•Bob is going to transfer some money to Alice;
•Once the monetary transaction is initiated and hence triggered by Bob, it is represented as a
“transaction” and broadcast to all the involved parties in the network.
Validation &
Verification•The transaction now has to get “approval” as being indeed “valid” by the Blockchain eco -system.
•For such a validation and verification process, the participants must apply a specific algorithm;
•The relevant Blockchain eco -system defines what is perceived as “valid”, which may vary from one
eco-system to another.
Creating a
New Block•Transaction(s) once approved as valid along with the hash of the succeeding block are then fed into a new “block”.
Adding Block
to the Chain•The new block is then communicated to all the participating nodes to be subsequently appended to the existing chain of blocks in the blockchain digital ledger.
www. aetic.theiaer .org
AETiC 2018 , Vol. 2, No. 1 4
additional data accountability. This also increases the availability , trustworthiness, privacy and
ultimately the value of the provenance data itself .
In an IoT ecosystem [7,8], most of the communication is in the form of Machine -to-Machine
(M2M) interactions . Thus establishing trust among the participating machines is a big challenge that
IoT technology still has not been met extensively . However, Blockchain may act as a catalyst in this
regard by enabling enhanced scalability, security, reliability and privacy [9]. This can be achieved by
deploying Blockchain technology to track billion s of devices connected to the IoT eco -systems and
used to enable and/or coordinate transaction processing. Applying Blockchain in the IoT ecosystem
will also increase reliability by axing the Single Point of Failure (SPF). The cryptographic algorithms
used for encryption of the block data as well as the hashing techniques may provide better security.
However, this shall demand more processing power which IoT devices currently suffer from. Thus
further research is required to overcome this current limitation .
Underwood [10] considers the application of Blockchain technology to completely overhaul the
digital economy. Ensuring and maintaining trust is both the primary and initial concern of the application of the B lockchain. BC can also be used to gather chronological and sequence information
of transactions, as it may be seen as an enormous networked time- stamp ing system. For example,
NASDAQ is using its ‘Linq B lockchain’ to record its private securities tr ansactions. Meanwhile the
Depository Trust & Clearing Corporation (DTCC, USA) is working with Axoni in implementing
financial settlement services such as post -trade matters and swaps. Regulators are also interested for
BC’s ability to offer secure, private, traceable real -time monitoring of transactions.
4. The Future of Blockchain
According to the Gartner Hype Cycle for Emerging Technologies 2017, shown in Figure 2, below,
Blockchain still remains in the region of “Peak of Inflated Expectation” with forecast to reach plateau in “five to ten years” . However, this technology is shown going downhill into the region of the
“Trough of Disillusionment” . Because of the wide adoption of the Blockchain in a wide range of
applications beyond cryptocurrency, the authors of this paper are forecasting a shift in classification
from “five to ten years” to “two to five years” to reach maturation . Blockchain possesses a great
potential in empower ing the citizens of the developing countries if widely adopted by e -gover nance
applications for identity management, asset ownership transfer of precious commodities such as
gold, silver and diamond , healthcare and other commercial uses as well as in financial inclusion.
However, this will strongly depend on national political decisions.
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AETiC 2018 , Vol. 2, No. 1 5
Figure 2. Gartner Hype Cycle, 2017 [11]
6. Concluding Discussions
The application of the Blockchain concept and technology has grown beyond its use for Bitcoin
generation and transactions. The properties of its security, privacy, traceability, inherent data
provenance and time- stamping has seen its adoption beyond its initial application areas. The
Blockchain itself and its variants are now used to secure any type of transactions, whether it be
human- to-human communications or machine -to-machine. Its adoption appears to be secure
especially with the global emergence of the Internet -of-Things. Its decentralized application across
the already established global Internet is also very appealing in terms of ensuring data redundancy
and hence survivability.
The Blockchain has been especially identified to be suitable in developing nations where ensuring
trust is of a major concern. Thus the invention of the Blockchain can be seen to be a vital and much
needed additional component of the Internet that was lacking in security and trust before. BC
technology still has not reached its maturity with a prediction of five years as novel applications
continue to be implemented glo bally.
www. aetic.theiaer .org
AETiC 2018 , Vol. 2, No. 1 6
References
[1] Nir Kshetri, "Can Blockchain Strengthen the Internet of Things?," IT Professional, vol. 19, no. 4, pp. 68 - 72, May
2017, Available: http://ieeexplore.ieee.org/document/801230 2/
[2] Mahdi H. Miraz, "Blockchain: Technology Fundamentals of the Trust Machine," Ma chine Lawyering, Chinese
University of Hong Kong, 23rd December 2017, Available: http://dx.doi.org//10.13140/R G.2.2.22541.64480/2
[3] Don Tapscott and Alex Tapscott, Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money,
Business, and the World, 1st ed. New York, USA: Penguin Publishing Group, 2016.
[4] Maaruf Ali and Mahdi H Miraz, "Cloud Computing Applications," in Proceedings of the International Conference on Cloud Computing and eGovernance - ICCCEG 2013, Internet City, Dubai, Unite d Arab Emirates, 2013, pp. 1-8,
Available: http://www.edlib.asdf.res.in/2013/iccceg/paper001.pdf
[5] Maaruf Ali and Mahdi H. Miraz, "Recent Advances in Cloud Computing Applications and Services," International Journal on Cloud Computing (IJCC), vol. 1, no. 1, pp. 1 -12, February 2014, Available:
http://asdfjournals.com/ijcc/ijcc -issues/ijcc -v1i1y2014/ijcc -001html -v1i1y2014/
[6] Xueping Liang et al., "ProvChain: A Blockchain-based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability," in Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid '17), Madrid, Spain, May 14 - 17, 2017, pp. 468 -477, Available:
https://dl.acm.org/citation.cfm?id=3101176&CFID=994896989&CFTOKEN=44228545
[7] Mahdi H. Miraz, Maaruf Ali, Peter Excell, and Picking Rich, "A Review on Internet of Things (IoT), Internet of Everything (I oE) and Internet of Nano Things (IoNT)," in the Proceedings of the Fifth International IEEE Conference
on Internet Technologies and Applications (ITA 15), W rexham, UK, 2015, pp. 219 – 224, Available:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7317398
[8] Mahdi H. Miraz, Maaruf Ali, Peter S. Excell, and Richard Picking, "Internet of Nano -things, Things and Everything:
Future Growt h Trends," (to be published) Future Internet, 2018 .
[9] Mahdi H. Miraz and Maaruf Ali, "Blockchain Enabled Enhanced IoT Ecosystem Security," (accepted) in proceedings
of the First International Conference on Emerging Technologies in Computing 2018 (iCETiC '18), London, UK, 23 August 2018.
[10] Sarah Underwood, "Blockchain Beyond Bitcoin," Communications of the ACM, vol. 59, no. 11, pp. 15-17, November
2016, Available: https://doi.org/10.1145/2994581
[11] Gartner, "Top Trends in the Gartner Hype Cycle for Emerging Technologies, 2017," Gartner, Inc ., Gartner Hype Cycle
2017, August 2017, Available:
http://www.gartner.com/smarterwithgar tner/top -trends -in-the-gartner -hype -cycle -for-emerging -technologies -2017/
© 2018 by the author (s). Published by Annals of Emerging Technologies in Computing
(AETiC), under the terms and conditions of the Creative Commons Attribution ( CC BY )
license which can be accessed at http://creativecommons.org/licenses/by/4.0/ .
www. aetic.theiaer .org
|
{
"id": "1801.03528"
}
|
1802.01315
|
Gosig: Scalable Byzantine Consensus on Adversarial Wide Area Network for
Blockchains
|
Existing Byzantine fault tolerance (BFT) protocols face significant
challenges in the consortium blockchain scenario. On the one hand, we can make
little assumptions about the reliability and security of the underlying
Internet. On the other hand, the applications on consortium blockchains demand
a system as scalable as the Bit-coin but providing much higher performance, as
well as provable safety. We present a new BFT protocol, Gosig, that combines
crypto-based secret leader selection and multi-round voting in the protocol
layer with implementation layer optimizations such as gossip-based message
propagation. In particular, Gosig guarantees safety even in a network fully
controlled by adversaries, while providing provable liveness with
easy-to-achieve network connectivity assumption. On a wide area testbed
consisting of 140 Amazon EC2 servers spanning 14 cities on five continents, we
show that Gosig can achieve over 4,000 transactions per second with less than 1
minute transaction confirmation time.
|
http://arxiv.org/pdf/1802.01315v1
|
Peilun Li, Guosai Wang, Xiaoqi Chen, Wei Xu
|
cs.DC
|
cs.DC
|
Gosig: Scalable Byzantine Consensus on Adversarial Wide Area Network
for Blockchains
Peilun Li
Tsinghua UniversityGuosai Wang
Tsinghua UniversityXiaoqi Chen
Princeton University
Wei Xu
Tsinghua University
Abstract
Existing Byzantine fault tolerance (BFT) protocols
face significant challenges in the consortium blockchain
scenario. On the one hand, we can make little assump-
tions about the reliability and security of the underlying
Internet. On the other hand, the applications on consor-
tium blockchains demand a system as scalable as the Bit-
coin but providing much higher performance, as well as
provable safety. We present a new BFT protocol, Gosig,
that combines crypto-based secret leader selection and
multi-round voting in the protocol layer with implemen-
tation layer optimizations such as gossip-based message
propagation. In particular, Gosig guarantees safety even
in a network fully controlled by adversaries, while pro-
viding provable liveness with easy-to-achieve network
connectivity assumption. On a wide area testbed con-
sisting of 140 Amazon EC2 servers spanning 14 cities
on five continents, we show that Gosig can achieve over
4,000 transactions per second with less than 1 minute
transaction confirmation time.
1 Introduction
The rise of cryptocurrencies, such as Bitcoin [43], in-
creases the awareness and adoption of the underly-
ingblockchain technology. Blockchain offers a dis-
tributed ledger that serializes and records the transac-
tions. Blockchain provides attractive properties such as
full decentralization, offline-verifiability, and most im-
portantly, scalable Byzantine fault tolerance on the In-
ternet. Thus, Blockchain has become popular beyond
cryptocurrencies, expanding into different areas such as
payment services, logistics, healthcare, and Internet-of-
Things (IoT) [8, 54, 48].
While Bitcoin provides a permissionless protocol,
where everyone can join, we focus on consortium
blockchains (aka permissioned blockchains), where a
participant needs offline authentication to join. It is use-
ful in many commercial applications [25]. While we nolonger have to worry about Sybil attacks [21], there are
still some other significant challenges.
The blockchain is replicated to each participant, and
the key problem is how to reach consensus on these repli-
cas. Comparing to traditional distributed transaction sys-
tems, the three biggest challenges of a blockchain are:
1) Players are from different organizations without
mutual trust. Failures, even Byzantine failures, are com-
mon. Thus, we cannot rely on a small quorum (e.g.,
Chubby [9] or ZooKeeper [27]) for consensus. Instead,
we need Byzantine consensus and allow all players to
participate, i.e., supporting a consensus group with thou-
sands of servers.
2) The system runs on an open Internet. The network
can drop/delay communication arbitrarily. Even worse,
as the addresses of the participants are known to all, an
adversary can easily launch attacks targeting any chosen
participant at any time, using techniques like distributed
denial-of-service (DDoS) [26, 49]. The adversary can
strategically choose which players to attack, and victims
will remain unavailable until the adversary adaptively
choose to attack others. We call these attacks adaptive
attacks , which strongly threatens the special nodes in a
protocol, such as the leaders.
3) Different from Bitcoin that allows temporary incon-
sistency (i.e., a fork), people usually expect a permis-
sioned chain to provide more traditional transaction se-
mantics, i.e., a committed transaction is durable. Also,
applications on a permissioned chain [1] require much
higher throughput and lower latency than Bitcoin.
While there are many Byzantine fault-tolerant (BFT)
protocols, most of them do not sufficiently address these
three challenges. For example, PBFT [13] and its suc-
cessors [33, 38] and even some Bitcoin variants [23] all
depend on a leader or a small quorum to participate mul-
tiple rounds of communications, and thus they are vul-
nerable to adaptive attacks on the leader. Other protocols
that try to avoid faulty leaders by changing leaders in a
fixed order [35, 42, 51, 3] cannot avoid adaptive leader
1arXiv:1802.01315v1 [cs.DC] 5 Feb 2018
attacks, because once the adversaries know who is the
next leader, they can change their target accordingly.
There is a new generation of BFT protocols designed
to run over the Internet. To avoid adaptive attacks, Al-
gorand [24] hides the leader identity. To improve scal-
ability, ByzCoin[32] combines Proof of Work (PoW)
with multi-signature-based PBFT. To tolerate arbitrary
network failures, HoneyBadgerBFT[41] adopts asyn-
chronous atomic broadcast [11] and asynchronous com-
mon subset (ACS) [4].
Unfortunately, as we will detail in Section 3.3, none of
these BFT protocols offer the following properties at the
same time: 1) liveness under adaptive attack , 2) scalabil-
ity to 10,000s of nodes with low latency (15 seconds in
our simulation) for commitment, and 3) provable safety
(i.e., no fork at any time) with arbitrary network fail-
ure. Also, there is no straightforward way to combine
the techniques used in these protocols.
We present Gosig1, a new BFT protocol for permis-
sioned blockchains. Gosig can achieve all three proper-
ties above, and also provide provable liveness with par-
tially synchronous network (details in Section 3.2).
Gosig elects different leaders secretly for every block,
and it eliminates the leader’s involvement after it pro-
poses a block to defend against adaptive attacks on lead-
ers. At the implementation level, we use gossip-based
communications to fully exploit the link redundancy on
the Internet while keeping the source safe. Since we
need to gather signatures during gossip, we adopt asyn-
chronous multi-signature [6, 46] to largely reduce the
network overhead of consensus messages.
We evaluate Gosig on an Amazon EC2-based 140-
server testbed that spans 14 cities on five continents. We
can achieve a throughput of 4,000 tps (transactions per
second) with an average transaction confirmation latency
of 1 minute. Even when 1/4 of the nodes fail, we can
still maintain over 3,500 tps with the same latency. With
over 100 participants, it effectively doubles the through-
put and reduces the latency by 80% comparing to Honey-
BadgerBFT [41], the state-of-the-art protocol that offers
the same level of safety guarantee. Also, using simula-
tions, we show that Gosig is able to scale to 10K nodes.
In summary, our major contributions are:
1) We propose a new BFT protocol that achieves scal-
ability, provable safety and resilient to adaptive attack.
2) We propose a novel method of combining secret
leader selection, random gossip, multi-round voting , and
multi-signature into a single BFT protocol in a compati-
ble way.
3) We provide a real Gosig implementation, evaluate
it on a real-world geographically distributed testbed with
1The name is a combination of Gossip and Signature aggregation,
two techniques we use.140 nodes, and achieve promising performance results.2
2 Related Work
Bitcoin and its variants. Permissionless public
blockchains like Bitcoin [43], Ethereum [53], PP-
Coin [31] need proof of work (PoW) or proof of stake
(PoS) to prevent Sybil attacks. They also need incen-
tive mechanisms to encourage people to join the public
network to keep the system safe. Other designs [17, 20]
try to avoid chain forking but retain the design of PoW
or PoS. We assume consortium blockchains [10, 45, 52],
and mainly focus on the performance and safety of the
system, instead of the other economic aspects.
Byzantine fault tolerance. The most important fea-
ture of a BFT protocol is safety. Unfortunately, many
open source BFT protocols are not safe [12]. There are
two major approaches to design provable BFT agree-
ment protocols. 1) Using multi-round voting: exam-
ple systems include PBFT [13] and its successors [33,
38, 14]; 2) Using leader-less atomic broadcast: Hon-
eyBadgerBFT [41] and [16, 34]. To prevent malicious
leaders from affecting the system, Aardvark [15] use
performance metrics to trigger view changes and Spin-
ning [51], Tendermint [35] or others [3, 42] rotates leader
roles in a round robin manner. However, there methods
can not avoid adaptive attacks because the leader role is
known to all in advance, and thus can be muted by at-
tacks like DDoS right before it becomes a leader. Gosig
adopts similar voting mechanism like PBFT to get good
performance without failure, and keeps safety and live-
ness under attacks.
In order to scale the system, many systems adopt
the “hybrid consensus” design [30, 32, 44] that uses a
Bitcoin-like protocol to select a small quorum, and use
another (hopefully faster) BFT protocol to commit trans-
actions. If adversaries can instantly launch adaptive at-
tacks on leaders, it is hard for these protocols to main-
tain liveness. Algorand [24] leverages secret leader elec-
tion and quorum member replacement methods to keep
liveness. Gosig lets every player participate in the con-
sensus, but combines similar secret leader selection with
signature-based voting to prevent such attacks.
We use similar methods and adversary models pro-
posed in Algorand [24]. We adopt the idea of multi-
round voting from PBFT and HoneyBadgerBFT [41],
and the idea of multi-signature from ByzCoin [32].
Gosig combines these incompatible methods in a coher-
ent protocol and achieves good performance. We com-
pare the key differences of these protocols in Section 3.3.
Overlay network and gossip. Most BFT protocols and
blockchains use broadcast as a communication primitive.
2We will opensouce Gosig when the paper is published.
2
To improve broadcast reliability on the Internet, people
often use application-layer overlay networks. We adopt
techniques like gossip from reliable multicast [5], prob-
abilistic broadcast [22, 29] and other peer-to-peer (P2P)
networks [28, 50]. Existing P2P networks may tolerate
some Byzantine failures, but do not provide convergence
guarantee [37]. By combining network optimizations
like gossip with a robust protocol layer design, we can
greatly improve both system resilience and availability.
3 Problem Definition and Assumptions
The goal of Gosig is to maintain a blockchain. In Gosig,
clients submit transactions toplayers (or servers), who
pack these transactions into blocks in a specific order.
All committed blocks are then serialized as a blockchain ,
which is is replicated to all players. On the blockchain,
one block extends another by including a hash of the pre-
vious block. In a blockchain, a transaction is confirmed
only when the consensus group commits the block con-
taining the transaction. Gosig, as a consensus protocol,
ensures that all blockchain replicas are the same. In par-
ticular, we want to prevent forks on the blockchain, i.e.
two different blocks extending the same block.
3.1 Problem Definition
We consider a system with Nplayers, p1;p2;;pn. We
can tolerate bstatic Byzantine failures and c adaptive
attacks where b+c=f=b(N 1)=3c. The Byzan-
tine faulty nodes can do anything including colluding to
break the protocol. The honest players under adaptive
attacks act like crash failure, but they come to life again
when the attacks are over. All other (at least 2 f+1) play-
ers are honest and follow the protocol.
All players form a consensus group G. Each player pi
outputs (commits) a series of ordered blocks Bi[1];Bi[2];
;Bi[ni], where ni, the length of the blockchain after
attaching a new block, is the height of this block.
A transaction is an operation on a state machine, and
we say it is valid when it is a legal operation on the cur-
rent state. A block Bi[h]is valid if: 1) all transaction
included are valid if executed sequentially, and 2) the
block header contains the correct reference to the previ-
ous block Bi[h 1], like the Nakamoto blockchain [43].
The goal of Gosig is to let Greach a consensus on the
blockchain , i.e. the following two conditions hold.
1. Safety : (1) Any block committed by any honest
player is valid; (2) at any time, for any two honest players
piandpj,Bi[k] =Bj[k]for any kmin(ni;nj).
2. Liveness : For any finite time tand any honest
player pi, there exists a time t0>twhen picommits a
block packed by an honest player.Here we define safety and liveness of blocks instead
of transactions for simplicity. We rely on gossip mecha-
nisms to ensure that a transaction will reach most play-
ers, and an honest player will pack the transaction when
it becomes the leader. Intuitively, the safety condi-
tion requires a total order of committed blocks on the
blockchains at all honest players, meaning there is no
fork at any time. The liveness condition says that all
honest players will always make progress, i.e., if a trans-
action can reach all honest players, it will eventually be
confirmed. Both conditions are based on certain assump-
tions about the system, and we detail them next.
3.2 System Model and Assumptions
We summarize our key assumptions that we use to prove
the safety and liveness of Gosig.
Strong cryptography and PKI. We only consider per-
missioned chain, and there is a trusted public key infras-
tructure (PKI) to authenticate each player - a common
assumption in today’s Internet. We also assume the cor-
rectness of the cryptographic primitives. These assump-
tions are the foundation of the safety in Gosig.
Asynchronous network for safety. Our protocol can
keep safety under asynchronous network [41] condition,
which means messages can be arbitrarily dropped, re-
ordered, or duplicated.
Liveness under partial synchrony. We also guarantee
liveness if the network has partial synchrony , a common
assumption [14, 33, 42, 51]. We say a network has partial
synchrony if there exists a time t0such that for any time
t>t0, all messages sent between any two honest players
during the interval can be delivered within a limited time
bound Dt. Specially, we assume adaptive attacks on any
player only take effect after a delay of Dtat any time.
Partially synchronized clock. Similar to [24], we as-
sume a partially synchronized clock for getting liveness.
That is, at any wall clock time t, for any two players pi
andpj, their local time readings Cpi(t)andCpj(t)satisfy
thatjCpi(t) Cpj(t)j<D. Practically, it is easy to ensure
aDof several seconds using standard Network Time Pro-
tocol (NTP) on the Internet.
3.3 Key Features of Gosig
Comparing to existing blockchains and other Byzantine-
fault-tolerant atomic ordered broadcast protocols, Gosig
has achieved scalability ,liveness under adaptive attacks
and safety under asynchronous networks at the same
time. While existing protocols provide one or more of
these features, to our knowledge, Gosig is the first proto-
col that offers all three together.
3
ByzCoin [32] offers excellent scalability by com-
bining PBFT, signature collection and proof-of-work
(PoW). However, like PBFT, it loses liveness under adap-
tive attacks given that it is still PBFT-based. Even with-
out PBFT, its two-phase multi-signature and Bitcoin-
NG [23]-like mechanism that allows elected leaders to
keep serving are also vulnerable to adaptive attacks.
Algorand [24] has excellent scalability, and tolerate
adaptive attack using secret consortium election. How-
ever, the safety of its Byzantine Agreement is based on
aweak synchrony assumption about the network. This
additional requirement comes from the idea of randomly
selecting a small quorum, which is the key to Algorand’s
scalability. We only adopt the secret leader election from
Algorand to avoid adaptive attacks, but completely re-
design the BFT protocol using multi-round signature col-
lections to achieve provable safety in asynchronous net-
works, like PBFT. We solve the scalability problem by
combing protocol design with implementation optimiza-
tions like multi-signatures.
HoneyBadgerBFT [41] achieves provable optimal
liveness and provable safety in any situation. However,
each node needs to send O(N2)messages per round.
Batching up O(N2logN)transactions per round helps
amortize the cost, but the large batch results in a latency
as high as O(N2logN), limiting the scalability. In com-
parison, the network overhead for each Gosig player is
O(NlogN)per round. Therefore, experimentally, we can
double the HoneyBadgerBFT throughput with only 1/5
of the latency on a similar testbed with more participates.
In summary, we insist that Gosig has provable safety
under a strong adversary model, but we choose to relax
the liveness goal a little, in exchange for better scalabil-
ity. We achieve this goal by adopting some originally
incompatible ideas and provide alternative implementa-
tions, so they can be combined seamlessly with our ac-
cordingly designed protocol.
4 Gosig Protocol Overview
We provide an intuitive overview of Gosig and leave for-
mal descriptions and analysis to Section 5.
Players. Every players participates in the protocol, and
knows all other players’ public key. It receives transac-
tions submitted by clients and gossip transactions among
all players. An honest player is responsible for verifying
transaction validity and block validity. A player drops in-
valid transactions and blocks, and blacklist the senders.
Rounds and stages. Gosig is a partially-synchronous
protocol. We divide the execution of Gosig into rounds
with a fixed time length (30 seconds by default). Each
round consists of a leader selection step (no communica-
tions) and two subsequent stages with fixed length. Thus,
Bp1p2p3p4PR(B)P(B)TC(B)BBBBlock commitmentBlock proposalStage IStage IIRound rRound r+1Round r-1Leader SelectionLeader SelectionFigure 1: Overview of a Gosig round (happy path only).
all players know the current round number and stage by
referring to the local clock.
Figure 1 provides an overview of a typical round. At
the start of each round, some players secretly realize they
arepotential leaders of this round with the cryptographic
sortition algorithm (Section 5.2). Thus, the adversary can
not target the leaders except randomly guessing.
At Stage I, a selected potential leader packs non-
conflict uncommitted transactions into a block proposal,
disseminates it with gossip, and acts just like a normal
node afterwards. Note that a potential leader’s identity
can only be discovered by others (including the adver-
sary) after she has full-filled her leader duties.
The goal of Stage II is to reach an agreement on some
block proposal of this round by vote exchange. A player
“votes” for a block by adding her digital signature of
the block to a prepare message (“ Pmessage”) and send-
ing it. An honest player only votes for a single pro-
posal per round. Upon receiving at least 2 f+1 signa-
tures from Pmessages for a block, the player starts send-
ingtentatively-commit messages (“ TCmessage”) for it.
She finally commits the block to her local blockchain
replica once she receives 2 f+1TCmessages.
The above process only covers the “happy path” of the
protocol. We provide the details how Gosig handles fail-
ures in the next section.
5 Gosig Protocol Details
We formally describe the Gosig protocol details.
Throughout the paper, we adopt the following notation:
subscripts denote who have signed the message3and su-
perscripts denote the round in which the message is sent.
For example, Pr
Xis aPmessage collectively signed by
the set Xof players in round r4. We also use a shorthand
to describe the action that pisends a Pmessage about a
3A message may be collectively signed by multiple players.
4For brevity, we denote Mr
figbyMr
i.
4
block Bas “piPsB”, and the action that pisends a TC
message about a block Bas “piTCsB”.
5.1 Player’s Local State
We describe each player as a finite state machine in
Gosig. Each player maintains a local state and decides
her next actions based on the state and external events
(receiving a certain message or selected as a leader).
The local state on player piis a 4-tuple si=hBroot;
hroot;Btc;Fi. The tuple contains two types of informa-
tion. The first two values are about the last committed
block in her local blockchain copy. Brootis the block it-
self, and hrootis the height ofBroot.
The rest values in sidescribe the pending block that the
player plans to add to her chain at height hroot+1.Btcis
the pending block itself. Btcis non-null if pihasTCed
Btc. Otherwise, Btcis a null value e. The last variable F
characterizes the timeliness of the block of Btc.
Besides the elements appearing in the tuple, pialso
implicitly maintains two variables: crootandctc.crootis
acommitment certificate that proves the validity of Broot,
andctcis aproposal certificate ofBtc. We define both
later in this section. For brevity, we do not explicitly
include crootandctcin the situple.
When Gosig starts running, a player’s local state is ini-
tialized as si=hBe;0;e;0i.
5.2 Leader Selection: The First Step
Leader selection is the first step of each round. The
objective of this step is to secretly and randomly select
thepotential leaders who are entitled to propose a next
block. The greatest challenge is to keep the election re-
sult unpredictable until the potential leaders have sent out
thepropose messages ( PRmessage). Otherwise, the ad-
versary can attack the leaders beforehand, and thus break
the liveness of the system.
The cryptographic sortition algorithm. We use a sim-
plified version of the cryptographic sortition mechanism
from Algorand [24] to select a set of potential leaders .
Similarly, we use a number Qhto implement crypto-
graphic sortition. In Gosig, Qhis recursively defined as
Qh=H(SIGlh(Qh 1)) ( h>0) (1)
where his the height of a committed block B5,His a
secure hash function shared by all players, lhis defined
as the leader who has proposed the block B(the signer of
the proposal certificate of B), and SIG i(M)is the digital
signature of message Msigned with pi’s private key.
Based on Qh, we define a player pi’sleader score
Lr(i)at round rasLr(i) =H(SIG i(r;Qh)), where his
the height of the latest committed block at round r.
5Q0is a random number shared among all players.At the beginning of each round r, each player picom-
putes her Lr(i), and if the score is less than a leader prob-
ability parameter q , she knows that she is a potential
leader of the round. A potential leader can prove to other
players about her leader status with the value SIG i(r;Qh).
We define the value as the leader proof for round r,lcr
i.
The process requires no communication among players.
The cryptographic sortition algorithm has two good
properties: 1) the signature SIG iuses pi’s private key,
and thus cannot be forged by others; 2) If the hash func-
tionH()is perfectly random, the potential leader selec-
tion is uniformly random. Thus, there is no way for the
adversary to know who is selected, nor can it change any
node’s chance of becoming a leader.
Choosing a right qis important. If qis too small, some
rounds may not have a leader and thus fail to proceed. If
qis too large, there may be many competing potential
leaders, wasting resources to resolve the conflict. Sim-
ilar to Algorand, we set q=7=Nwhere Nis the total
number of players. This qis sufficiently large to reduce
the probability of no-leader rounds to less than 0.1%.
Of course, a faulty node may still become a potential
leader. In this case, the worst damage it can cause is to
stall the round without affecting the correctness.
5.3 Potential Leaders Propose Blocks
When a player pirecognizes that she is a potential leader,
she needs to decide which block to propose and then gen-
erate a proposal message.
Given pi’s current local state si=hBroot;hroot;Btc;Fi
(Btcmay be e), she first gathers a list of candidate blocks
to propose. If pifinds she has a non-empty Btc, the block
Btcis a good candidate to propose again, and the height
stays the same as Btc. Alternatively, she can also con-
struct a new block extending her own chain, at height
(hroot+1). The following procedure defines which block
she will propose.
To make a proposal valid, a potential leader needs to
provide a valid certificate c for the proposed block Bat
height h. In addition to serving as a proof of the block
validity, calso determines the proposal round r p(B)6of
the block proposal. The leader decides which candidate
block to propose based on rp(B).
We can understand the proposal round as the round
number when the block is first generated. Intuitively,
a block with a larger proposal round is more likely to
get accepted by peers. Therefore, in Gosig, we stipulate
that a potential leader should propose the block with the
largest proposal round among all candidate blocks.
6More precisely, the proposal round is an attribute of a proposal,
which is defined in the next paragraph, rather than of a block. We use
rp(B)notation for brevity.
5
A proposal certificate cisvalid if and only if it
matches one of the following two cases. We also specify
how we compute the proposal round in each case.
Case 1 :c=TCr0
i(Br0;h) (r0<r), where Bandhare ex-
actly the proposed block and its height, respectively. In
this case, rp(B) =r0;
Case 2 :c=TCr0
X(B0r0;h 1) (r0<r), where Xcontains
at least 2 f+1 different players. In this case, rp(B) =
r0+1.
Finally, the potential leader piassembles a proposal
message ( PRmessage) in the form of PRr
i(B;h;c;lc)
containing: 1) the proposed block B, 2)B’s height h, 3)
the proposal certificate c, and 4) the leader proof lc(de-
fined in Section 5.2). Then pisigns the message with her
private key. Everyone can easily verify the validity of the
PRmessage by checking the included block, signatures
and certificates.
5.4 Stage I: Block Proposal Broadcast
After the leader selection step, Gosig enters Stage I:
block proposal dissemination. The objective of this stage
is to propagate blocks proposed by all potential leaders
to as many honest players as possible.
We use the well-known gossip protocol [47] to dissem-
inate messages on the application layer overlay network
formed by the same set of players. A player sends/for-
wards a message to mrandomly selected players in each
hop. Parameter mis called the fanout and determines
how fast the message propagates.
Potential leaders initiate the gossip of their PRmes-
sages. Each honest player, upon receiving a PRmes-
sage, will first check its validity, and then forward all
valid PRmessages. Note that there can be more than one
valid block proposed in the same round, either because
there are multiple potential leaders, or because a mali-
cious leader can propose multiple valid blocks. Players
forward all valid blocks in this stage and leave the con-
flict resolution to Stage II.
At the end of Stage I (after a fixed time interval T1), we
expect that most players have seen all block proposals in
the round, assuming everything goes well. Nevertheless,
Stage II is able to handle all complicated situations.
5.5 Stage II: Signature Collection
The objective of Stage II is to disseminate signed mes-
sages of players’ votes for the block proposals, in the
hope that honest players can commit a single valid block.
Same as Stage I, we use gossip to propagate all messages.
Message types. There are two message types involved
in this stage. Players can collectively sign a message by
appending their signatures. We say a message Mr
Xisk-signed ifXcontains at least k distinct players’ signa-
tures.
Pmessage. Aprepare message ( Pmessage) digitally
signs a block. Specifically, Pr
i(Br;h)means player pi
signs her vote for the block Brat height hproposed in
round r. In short, we say piprepares or “ P”sBr.
TCmessage. Atentatively-commit message ( TCmes-
sage) signs a proof of a (2f+1)-signed Pmessage.
Specifically, TCr
i(Br;h)proves that at least 2 f+1 play-
ers (including pi) have Ped the block Brat height h
in round r. In short, we say pitentatively-commits or
“TC”sBr.
Stage II protocol. Algorithm 1 outlines the expected
behavior of an honest player piin Stage II. We model
each pias a finite state machine with local states listed at
the beginning of Algorithm 1. It performs actions based
on current local state and the incoming messages.
Lines 1 to 7 describe the initialization procedure, in
which pichecks all block proposals she receives in Stage
I (by calling the function DecideMsg in Algorithm 2).
Ifpireceived valid proposals in Stage I, she needs to
decide which block to prepare (function DecidePMsg in
Algorithm 2). In general, piprefers a block proposal
with larger proposal round, as it indicates a more recent
block (lines 2.14 to 2.20). Finally, pichooses exactly
one block ¯Bfor height h, and Ps it (line 1.4 and line 1.7,
respectively).
After initialization, the state machine of pistarts to
handle incoming messages. Lines 8 to 29 in Algorithm 1
outlines handler routines for these three different mes-
sage types. pionly signs messages about the same block
that she has Ped (line 1.9 and line 1.21), she can only
TCa block Bafter she collects at least 2 f+1 signatures
from the Pmessage about B, and she can only commit a
block Bafter she collects at least 2 f+1 signatures from
theTCmessages about B(line 1.13 and 1.25). These
rules ensure the safety of Gosig.
5.6 Reducing Signature Sizes
Gosig protocol requires signatures from over 2/3 of
the players. To reduce the storage and communica-
tion overhead of signatures, we adopt the techniques
in [7, 6] to aggregate these signatures into a compact
multi-signature form.
The cryptographic signature of a player piinvolves a
hash function H, a generator G, a private key xi, and a
public key Vi=Gxi. A player holding the private key xi
can sign a message Mby computing Si=H(M)xi, and
7Other players’ signatures are in the received messages.
8Among all blocks with the largest proposal round in S,Bis the
block whose proposer has the smallest leader score.
9Note that rp(B0)is actually the proposal round of the proposal mes-
sage about B0, which is not necessarily equal to rp(Btc).
6
Algorithm 1 Stage II workflow for each player pi.
Constants:
–G: the consensus group of N players
–r: the current round number
State Variables:
–si:pi’s local state, i.e.,hBroot;hroot;Btc;Fi
–S: the set of all valid proposals received in Stage I
1:phase Init
2:msg DECIDE MSG .See Algorithm 2
3:ifmsg6=null then
4: P(B;h) msg .Bis the block pivotes for
5: phase Ped
6: XP fig .The players that have PedB
7: Prepare Bby gossiping msg
8:Onreceiving a valid Pr
X0(B;h)message Do
9: ifphase =PedandB=Bthen
10: XP XP[X0
11: sigP signatures of players in XP7
12: Sign Pr
XP(B;h)with sigP
13: ifPr
XPis (2f+1)-signed then
14: phase TCed
15: XTC fig .The players that have TCedB
16: si hBroot;hroot;B;ri
17: Tentatively commit Bby gossiping TCr
i(B;h)
18: else
19: Forward the signed message Pr
XPwith gossip
20:Onreceiving a valid TCr
X0(B;h)message Do
21: ifphase6=InitandB=Bthen
22: XTC XTC[X0
23: sigTC signatures of players in XTC
24: Sign TCr
XTC(B;h)with sigTC
25: ifphase6=Ced andTCr
XTCis (2f+1)-signed then
26: si hB;h;e;0i
27: Commit Bon the local blockchain
28: phase Ced
29: Forward the signed message TCr
XTCwith gossip
others can verify it by checking whether e(G;Si)is equal
toe(Vi;H(M))with a given bilinear map e. To track
which signatures we have received, we append an inte-
ger array nof size Nto the signature, and by signing a
message M, a player computes Si=H(M)xi, and incre-
ments the i-th element of nSi. The combination is the
signature for aggregation, and we denote this process by
sign i(M) = ( Si;nSi).
An important property of the aggregated signature is
that we can put in new signatures in an arbitrary or-
der, avoiding the risk of adaptive chosen-player attack
that Byzcoin[32] faces. Aggregating signatures is sim-
ply multiplying the BLS signature and adding up the
array n. Thus, the aggregated signature (aka multi-
signature ) is S=H(M)åixinS[i]. We denote the pro-
cess by aggregate (S1;S2;:::) = ( S;nS). Let (S1;nS1)and
(S2;nS2)be two multi-signatures, we can combine themAlgorithm 2 Deciding which block to prepare in Stage
II.
1:function DECIDE MSG
2: ifS=fthen .Received no valid proposals
3: return null
4: else .Received one or more valid proposals
5: return DECIDE PM SG
6:function DECIDE PM SG
7: rp max B2Srp(B)
8: B argminBj2S;rp(Bj)=rpLr(j)8
9: Denote by PR(B;h;c)the proposal message about B
10: ifh=hroot+1then
11: ifBtc=ethen
12: si hBroot;hroot;e;0i
13: return Pr
i(B;h)
14: else
15: ifrp(B)>Fthen
16: si hBroot;hroot;e;0i
17: return Pr
i(B;h)
18: else if9B02Ss.t.B0=Btcand rp(B0)F9
then
19: si hBroot;hroot;B0;rp(B0)i
20: return Pr
i(B0;h)
return null
by computing aggregate (S1;S2) = ( S1S2;nS1+nS2).
The array ntracks who have signed the message. Every-
one can verify the multi-signature by checking whether
e(G;S) =e(ÕiVnS[i]
i;H(M)).
[40, 6] points out that aggregating signatures of the
same message can be vulnerable to chosen-public-key
attack. This attack can be avoided if the participates
can prove they have the private key to their announced
public key, either forced by a trusted third party or by
a zero-knowledge-proof bootstrap process proposed by
[40]. We choose this method because it’s acceptable with
the help of PKI.
Another method proposed in [6] computes H(M+VI)
instead of H(M)so each player signs different messages.
Since everyone knows each others’ public keys, the result
is still verifiable without increasing the data size. This
method does not involved a trusted third party or online
bootstrap process, but it forces the algorithm to compute
the bilinear map Ntimes, instead of one time when the
messages are the same. It can cause the verification time
100 times slower, and thus can only be adopted when the
number of players in a system is small (less than 200).
The signature aggregation process significantly re-
duces memory utilization. Although the multi-signature
still has size O(N)asymptotically, a 4-byte integer is
enough for each element in nSin most cases. With 1,000
players using a 2048-bit signature, naively it takes 256
KB to store these signatures, but with aggregation, it re-
quires only 4256 bytes, or 1/60 of the original size. The
7
optimization is more efficient as the system scales larger.
For the case when the number of signers is small, the
array is sparse and thus easily compressible.
5.7 Player Recovery from Temporary Fail-
ures
In normal cases, blocks and signatures are broadcast to
all players. If a player misses a block in round rdue to
temporary failures, it can catch up in subsequent rounds
using the following (offline) recovery procedures:
If player pireceives a valid signature of enough sign-
ers in round rbut fails to receive the block itself, piwill
check the signature and try to contact someone who has
signed the block to retrieve it.
If player pirecovers from an extended crash period
and/or data loss, it should try to retrieve all lost blocks
and proofs. She can only continue participating in the
protocol after she recovers the entire history. As the com-
mitted blocks are offline-verifiable, blocks with valid sig-
natures from any player are sufficient for recovery.
5.8 Security Analysis
We can prove that Gosig provides safety (as defined in
Section 3) in fully asynchronous networks. Adding par-
tial synchrony assumption, it also achieves liveness. We
only list some key lemmas here and leave the complete
proofs in Appendix A and B.
Lemma 1. If an honest player p icommits a block B at
height h in round r, no player will ever TC any other
block B0at any height h0h in any later rounds.
Proof sketch. At least f+1 honest players will not P
any blocks whose proposal rounds are no larger than r
(line 14 to 20 in Alg. 2). Therefore, at least f+1 hon-
est players will not Pany other block at height h0h
after round r, proving Lemma 1. And Lemma 1 leads to
safety, because no block can be committed without hon-
est players signing TCmessages.
The following two lemmas prove the liveness under
the partial synchrony assumption.
Lemma 2. If in round r, for any honest player p iwe have
si=hBroot;hroot;e;0i, then there exists a round r0>r and
an honest player p jsuch that p jPs some block at height
h=hroot+1in round r0.
Lemma 3. If in round r, there exists some honest player
piwith state s i=hBroot;hroot;Btc;Fi(Btc6=e), then there
exists a round r0>r and an honest player p jsuch that
pjcommits a block at height h =hroot+1in round r0.
Attacks beyond the protocol layer. In addition to the
adaptive chosen-player attacks and other attacks causingcommunication problems, an adversary can design at-
tacks on the system implementations, including: 1) com-
putation resource saturation attack, where the adversary
may disseminate a large number of invalid messages to
the honest players, consuming their CPU cycles for use-
less signature verification, and 2) signature counter over-
flow attack, where the adversary may craft valid multi-
signatures where some counters are close to the maxi-
mum integer, and thus careless signature aggregating of
honest players may cause an integer overflow resulting
in incorrect signatures.
Note that both attacks can only cause liveness prob-
lems, rather than correctness problem. We will describe
our countermeasures to both attacks in Section 6.
6 Implementation-level Optimization
As we mentioned, Gosig combines protocol level design
and implementation level optimizations to achieve high
performance. In this section, we introduce the important
optimizations.
Asynchronous transaction dissemination and hash-
only blocks. In our protocol, messages only con-
tainhashes of the transactions to reduce message size.
Raw transactions are gossiped among all players asyn-
chronously, independent of protocol stages. In the case
that a player does not have the raw transaction data when
she receives the blocks, she retrieves the transactions
from others before she can process the block. In practice,
the gossip protocol often does a good job replicating the
transactions, and thus this retrieval finishes fast.
Continuous gossiping in Stage II voting. As we need
all honest players to receive proposed blocks and oth-
ers’ votes to achieve liveness, we do not limit the fanout.
Instead, each player continuously sends block or P/TC
messages to random neighbors until the end of the stage.
However, we do put on a limit of concurrent connections
to avoid overloading any player, which we set to 5 by
default in our implementation. Section 7.3 provides a
detailed analysis of the fanout limit.
Blacklisting obvious problematic players. While
there is no way to ensure message delivery, if a player
detects an obvious communication problem (connection
failure, timeout etc.) with a peer, she will “blacklist” the
peer (i.e. stop sending to it) for a short time period To
(typically half a round time). On subsequent failure with
the same peer, she will additively increase To, until she
receives a message from that peer, or successfully retries.
This backoff mechanism effectively limits the wasted at-
tempts to connect to failed nodes.
LIFO processing stack. In Stage II, each node can con-
currently receive multiple messages with signatures for
processing (verification + aggregation). Sometimes the
8
messages arrive faster than the server can process them.
We put them in a last-in-first-out (LIFO) stack , instead
of a queue. This is because it is likely that later arriving
messages contain more signatures, or sometimes even a
super-set of signatures in earlier messages.
Preventing signature overflow. In the aggregated sig-
nature described in Section 5.6, we have the array nwith
N B-bit integers (we have B=32 as default). An adver-
sary can craft a valid signature so that the element corre-
sponding to her signature is 2B 1. This attack prevents
honest players who have this signature from further ag-
gregating the signature array because otherwise, the ele-
ment will overflow.
We prevent such attack by restricting the growth of
the maximum element in n,max, based on the number of
signers s. On receiving a new message, the player tries
to aggregate it into her local signature array, and check if
the result satisfies maxsor log2(max)<Bs=N. If so,
the player updates her local array; otherwise, she drops
the incoming message.
7 Evaluation
We evaluate the performance of Gosig using both simu-
lations and real testbed experiments.
7.1 Evaluation Setup
Gosig prototype implementation. We implement the
Gosig prototype in Java. We use pbc [39] library (with
JPBC [19] wrapper) for cryptographic computation and
usegrpc-java [2] for network communication. As
for signature parameters, we choose the default a-type
parameter provided by JPBC [18], and use it to gener-
ate 1024-bit BLS signature. The entire system contains
about 5,000 lines of Java code excluding comments.
Testbed. We build a testbed with up to 140 t2.medium
instances evenly distributed on Amazon EC2’s all 14 re-
gions on 5 continents. We experimentally measure the
network condition between the instances. Within a re-
gion, we have less than 1ms latency and about 100 MBps
bandwidth, and latencies across regions are hundreds of
milliseconds with the bandwidth varying from 2 MBps
to 30 MBps. We believe the testbed is a good emulation
of a typical multi-datacenter WAN.
Each instance acts both as client and server in the sys-
tem. As the client, it generates transactions with an expo-
nentially distributed inter-arrival time. Each transaction
is 250 bytes, a typical Bitcoin transaction size (and used
in evaluations of [41] too). These transactions are sub-
mitted to the servers on the same instance.
Simulation for larger scales. Limited by the testbed
scale, we depend on simulations to analyze larger scalebehavior of our signature collection process. We set
the network latency to an exponential distribution with a
mean of 300 ms, a typical value for today’s Internet [36],
and set the bandwidth to 500 KBps, a generous estima-
tion after subtracting the bandwidth consumed by con-
stantly gossiped transactions. We set the packet loss rate
to 1% across all links (higher than many Internet links).
In the simulator, we do not actually verify signatures, but
set the signature verification time to be consistent with
the performance we get on AWS t2.medium instances.10
All faulty players in testbed experiments and simulation
simply fail by crashing.
Key Configuration parameters. There are several
configuration parameters to tune. The most important
ones include the round time Tand maximum block size
maxblock size . Both are correlated and affect sys-
tem scalability and performance significantly. We dis-
cuss their impacts in Section 7.4.
We set T=30 sec (25 seconds for Stage I and 5 sec-
onds for Stage II), and maxblock size =8MB. Re-
call that in Gosig we only propagate transaction hashes
(and send actual transactions asynchronously). Given
that each transaction hash is 256 bit, a block can contain
at most 250K transactions. With an average transaction
size of 250 bytes, the corresponding raw transaction data
for a block is about 62.5MB.
7.2 Real Testbed Performance
Here we present the performance metrics on the EC2-
based testbed. We test two configuration settings, one
optimized for throughput (the default setting), and the
other optimized for transaction commit latency.
7.2.1 Throughput-optimized configuration
Using default parameter settings (Section 7.1), we run
experiments on 35, 70, 105, 140 instances for 1200 sec-
onds each using different workload from 1,000 tps to
7,000 tps. Figure 2(b) and 2(a) plot the throughput and
average commit latency, respectively. We have the fol-
lowing observations:
1) Without overloading the system, the average com-
mit latency is a little over 40 seconds. This is consistent
with our theoretically expected latency of 1.5 rounds.
2) With 35 players, we can sustain a 6,000 tps work-
load. With 140 players, we can still support 4,000
tps. Comparing to the reported numbers in HoneyBad-
gerBFT [41] (using similar EC2 testbeds, but fewer geo-
locations), we double the throughput and reduce the la-
tency by 80% with even more players and more regions.
10The verification time consists of an 11 ms constant overhead for
computing bilinear map functions and another 0 :11kms for ksigners.
That means 12.1ms for 10 signers and 1111ms for 10000 signers.
9
0 1000 2000 3000 4000 5000 6000 7000
Workload (tx / s)406080100120140Latency (s)
N=140, max_block_size=4MB
N=140, f=35
N=140
N=105
N=70
N=35(a) Latency with varying workload.
0 1000 2000 3000 4000 5000 6000 7000
Workload (tx / s)100020003000400050006000Throughput (tx / s)
N=140, max_block_size=4MB
N=140, f=35
N=140
N=105
N=70
N=35 (b) Throughput with varying workload
0 500 1000 1500 2000 2500
Throughput (tx / s)0102030405060Latency (s)
N=140
N=105
N=70
N=35 (c) Latency vs. throughput
Figure 2: Performance under different configurations. (a) and (b) are throughput-optimized. (c) is latency-optimized.
3) When the system gets overloaded, the throughput
actually drops . This is because, with a 30-second round
time, there is not enough time to propagate all blocks to
everyone, causing incomplete rounds and thus reducing
the effective throughput (aka. goodput). To prevent such
situation, we limit the maxblock size as an admission
control mechanism, just like most blockchains do. In
fact, the dashed line in Figure 2(b) shows that when lim-
iting maxblock size to 4MB (i.e. 125K transaction
per block, or 4167 tps), we can sustain the maximum
throughput even on overloading. Of course, overloading
still causes the latency to go up, but there is no difference
from any queuing system.
4) Gosig tolerates failures quite well with small over-
head. As Figure 2(a) and 2(b) shows, 35 faulty ones
among 140 total nodes show little influence on the sys-
tem’s throughput or latency without overloading. The
only impact of these failures is decreasing the maximal
throughput by about 10%, from 4,000 tps to 3,600 tps.
7.2.2 Latency-optimized configuration
The default setup uses large block sizes and long round
time (30 seconds) to improve overall throughput. For ap-
plications that are more latency-sensitive, we provide an
alternative configuration. We reduce the round time Tto
10 seconds with 5 seconds for each stage. We also dis-
able block-existence probing (see Section 6) to further
reduce latency. Then we repeat the same set of experi-
ments, and Figure 2(c) shows the latency we can achieve
under different workloads.
Like the previous case, we can get less-than-17-second
latency and stable throughput until overloading. We can
sustain over 200 tps with 140 nodes, 600+ tps with 70
nodes or 2,400+ tps with 35 nodes. However, the 10-
second round offers very tight time budget for blocks
to propagate. Larger blocks have little chance to com-
plete propagation, causing the latency to go up quickly
on overloading. Thus, a carefully controlled block size
is even more essential.
In comparison, HoneyBadgerBFT cannot offer a low
latency configuration in a relatively large group because
of its O(N2)-message-per-node complexity. Evaluation
0 1 2 3 4 5
Time in a stage (s)0.00.20.40.60.81.0CDF
Stage I
Stage IIFigure 3: CDF for each player’s stage completion times,
using latency-optimized configuration. The error bars are
95% confidence intervals.
101
102
103
104
Total number of players (log scale)05101520253035Time for Stage II (s)
Default (5) fanout w/o failures
3 fanout w/o failures
20 fanout w/o failures
Default fanout w/ 3/N failures
Default fanout on AWS testbed
Figure 4: Simulation results for Stage II completion time.
in [41] shows that the latency with only 64 players cannot
even go below 200 seconds.
7.2.3 Latency breakdown
While the transaction commit latency is largely deter-
mined by the round time, we want to take a closer look
at how fast a player can commit a block within a round.
We plot the cumulative distribution (CDF) of the time
taken for players to commit a block, using the same low-
latency configuration with 140 nodes and 200 tps work-
load. Figure 3 shows the CDF of completion time for
both stages on each node.
We can see that Stage II is only slightly slower than
Stage I, especially at the slowest player (both at about 4
seconds). It is a little counter-intuitive as Stage II con-
sists of 2 rounds of messaging (P and TC messages) vs.
Stage I has only one. The reason is that to complete Stage
I, every player has to receive the block from all leaders,
in order to determine the least leader score . In compari-
son, Stage II only need votes from any 2 f+1 players.
10
7.3 Scalability
Limited by the resources on the testbed, we evaluate the
scalability on systems larger than 140 nodes using sim-
ulation. In the simulation, we focus on the completion
time of Stage II, because it is the core and most compli-
cated part of Gosig, while the performance of Stage I is
no different from other gossip-based systems.
Using the default settings, we show the time required
to complete stage II with 10 to 10,000 players, i.e., all
honest players receives a TCmulti-signature signed by
more than 2/3 players. Figure 4 shows the results using
different combinations of failure modes and optimiza-
tions. The key observations are:
1) To calibrate the simulator, we also reproduce the
testbed results (for up to 140 nodes) in Figure 4. We can
see that it fits our simulation results quite well.
2) Gosig scales well. Even with 10,000 players, we
can still finish Stage II within 15 seconds only. This is
a direct benefit of using gossip-based application overlay
network and asynchronous multi-signature, which fully
exploits the redundant paths while keeping the band-
width consumption small. The time grows faster when
the number of players Nis large, because the over-
head for signature verification increases linearly with N.
But this overhead only becomes significant after the to-
tal number goes beyond 3000, and can be reduces by
stronger hardwares.
3) With 10000 players and 1/3 of them being faulty
by crashing, Stage II completion time slowdowns from
14.97 seconds to 19.53 seconds. The robustness of
the protocol comes from the gossip mechanism and the
order-independent signature aggregation algorithm.
4) A small gossip fanout, i.e., the number of outbound
connections, can fit most environments. A large fanout
(like 20 in Figure 4) will saturate the network and cause
higher latency due to queueing effects. Although a small
fanout may not fully utilize the network when the system
has fewer players, the cost is not significant since most
time of a round is allocated to stage I.
7.4 Configuration Parameters
As we have seen in Section 7.2.1 and 7.2.2,
maxblock size and round time Taffect system per-
formance significantly.
With Nplayers, the maxblock size is proportional
to three parameters [17, 47]: 1)1
logN, 2) round time T,
and 3) the network bandwidth. That means, in order to
increase the number of nodes from 100 to 10,000, we
need to either decrease the maxblock size by half or
double the round time Tgiven a fixed bandwidth.
In the remaining of the section, we experimentally
evaluate their impacts using all 140 nodes in the testbed.
2MB 4MB 6MB 8MB 10MB
max block size0100020003000400050006000Throughput (tx / s)Throughput
020406080100120140
Latency (s)
LatencyFigure 5: Influence of maxblock size on the perfor-
mance.
0 10 20 30 40 50 60 70
Round time (s)0100020003000400050006000Throughput (tx / s)Throughput
020406080100120140
Latency (s)
Latency
Figure 6: Influence of round time on the performance.
Max block size. As we have discussed, the parameter
maxblock size serves as an admission control mecha-
nism to avoid overloading the system.
Keeping N=140 and T=30s, we vary
maxblock size from 2MB to 10MB, correspond-
ing to 60K to 300K transactions per block, or 2K to 10K
tps. In each round, we generate workload that equals the
maxblock size . Figure 5 plots the results. The dashed
line in Figure 5 shows the ideal case where the system
has infinite capacity.
The actual throughput of the system is around 4,000
tps, as we presented in Section 7.2.1. We can see
that for a maxblock size smaller than 4MB, the ac-
tual throughput increases with the maxblock size and
roughly follows the ideal line. At around 4,000 tps, the
system saturates. If the maxblock size is significantly
larger than what the system can handle, the throughput
decreases because some rounds will end before the play-
ers can fully propagate the blocks.
Round time Tvs. throughput. Given a round time T,
it is easy to calculate the expected transaction commit
latency when the system is under a normal workload,
which is 1 :5T. A latency significantly larger than this
value indicates system overloading.
Here we experimentally find the maximum throughput
we can obtain under different Ts, without overloading
the system. Of course, because of the global clock syn-
chronization error Dand message propagation latency,
we require Tbe at least 10 seconds.
Figure 6 plots both the throughput and latency under
different Tsettings. We verify that we are able to keep
the latency very close to the expected latency of 1 :5T.
We observe that choosing a small Tsignificantly reduces
the maximum throughput. The throughput even drops
11
super-linearly as Tgets smaller than 30 seconds. This
is because when Tandmaxblock size are both small,
the network setup overhead becomes non-negligible, fur-
ther reducing the number of block transfers we can com-
plete during the round. Fortunately, a Tof 40 seconds
already supports the max throughput in a 140-node sys-
tem, still much faster than existing solutions.
8 Conclusion and Future Work
There are two types of approaches to build scalable
blockchain systems: Some focus on theoretically prov-
able protocols (e.g. HoneyBadgerBFT) even with
high performance overhead, and others adopt best-effort
hacks and hope it works most of the time (e.g. Bitcoin).
We believe there should be a middle ground: we insist
on a system with provable safety under a very strong
adversary assumption, while adopting the best-effort ap-
proaches to increase the probability of staying alive. We
use Gosig to demonstrate such a system. At the proto-
col level, using cryto-based secrete leader election and
multi-round signature-based voting, we can guarantee
safety, and by adding the partial synchrony assumption,
we can also prove liveness. At the same time, we adopt
implementation-level techniques such as gossiping, fail-
ure discovery and asynchronous signature verification to
increase the probability of liveness. With evaluations on
a real 140-server wide-area network, we show that Gosig
both scales well and provides high performance.
As the next steps, we want to extend the protocol to
allow players to join/exit. We also want to explore the
approaches of integrating Gosig with other data-center-
focused protocols to create a hybrid protocol, further im-
proving its performance.
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A Safety
Lemma A.1. If an honest player p icommits a block B at height h in
round r, no player will ever TC any other block B0at any height h0h
in any later rounds.
Proof. An honest player icommits a block Bwith a height of h
means that at least 2f+1 players have TCedBin round r, so at least
f+1 honest players have TCedBin round r. Those who commit the
block Bsuccessfully will reject all messages for blocks of a height no
higher than h, so we only consider the case where they fail to commit
the block B.
There is no other block proposed before round rwhose proposal
round is larger than r. These f+1 honest players should have set their
local F=raccording to line 16 in Algorithm 1, so they will not Pany
blocks proposed before round r.
Also, these f+1 honest player will not send TCmessage for any
block of height less than hafter round r, because they have committed
13
the same block of height h 1. Thus, no block of height less than hcan
be committed after round r, meaning no one is able to propose a new
valid block with a height of h0hand with a proposal round larger
than rafter round r.
The only case left is that there can be more than one valid block
proposed in round r, whose proposal rounds are the same with B’s. In
this case, by Algorithm 1, the f+1 honest players who have PedBin
round rwill not Pany other round- rblock any more, so no other blocks
proposed in round- rcan be TCed, which requires Pmessages from at
least f+1 honest players.
To sum up, at least f+1 honest players will not Pany other block
with a height of h0hafter round r, and thus no one can TCany other
block with a height of h0hafter round r, which requires at least 2f+1
Pmessages.
Lemma A.2. If an honest player p icommits a block B at height h in
round r, no player will ever commit any other block B0at any height
h0h in any later rounds.
Proof. By Lemma A.1, we get that no one can TCany new block with
a height of h0hafter round r, so no honest player can commit any
block with a height of h0hafter round r, as he cannot collect enough
TCmessages.
Theorem 1. Gosig protocol achieves safety.
Proof. Any committed block will be prepared by at least f+1 honest
players. Since honest players will only Pvalid blocks, condition (1) of
safety is true.
Condition (2) of safety can be directly proved by Lemma A.2.
B Liveness
Since our potential leaders are elected secretly, the adversaries can not
prevent honest players from becoming potential leaders of the least
leader score, and can not prevent the block proposed by such an honestplayer from propagation since gossip peers are also secretly randomly
chosen.
We base our proof on a partially synchronous network assumption.
And when the network is synchronous, a transaction will fail to reach
all honest players in a finite time with negligible probability. If there
are always blocks packed by honest players committed, all transactions
will be confirmed eventually.
Lemma B.1. For any round r, there exists a round r0>r and an honest
player p jsuch that p jcommits some block at height h =hroot+1in
round r0.
Proof. Without losing generality, we assume that at round r, the last
committed Brootis the same for all honest players.
(Case 1) No player has TCed. Because the network will become
synchronous infinitely, there exists a round where an honest player be-
comes the potential leader with the least leader score and the network
is synchronous. In this round, the proposal round of the block proposed
by this honest leader is equal to the local freshness Fof all honest play-
ers, so it will be prepared and committed by all honest players.
(Case 2) Some player has TCed. Because at most one block can
beTCed in a round, if two players TCed different blocks, these two
players will have different local F. Thus, all players with the largest
Fwill always have the same Bp, meaning if any of them becomes the
leader in a synchronous round, this Bpwill be prepared by all honest
players and committed. Similar to Case 1, because the network will
become synchronous infinitely, there exists a round where an honest
player with the largest Fbecomes the potential leader with the least
leader score and the network is synchronous, so its Bpcan be proposed,
prepared and committed.
To sum up, the lemma is proved.
Theorem 2. Gosig protocol achieves liveness.
Proof. By Lemma B.1, the theorem is easily proved.
14
|
{
"id": "1802.01315"
}
|
2009.00319
|
Transaction Pricing for Maximizing Throughput in a Sharded Blockchain
Ledger
|
In this paper, we present a pricing mechanism that aligns incentives of
agents who exchange resources on a decentralized ledger with the goal of
maximizing transaction throughput. Subdividing a blockchain ledger into shards
promises to greatly increase transaction throughput with minimal loss of
security. However, the organization and type of the transactions also affects
the ledger's efficiency, which is increased by wallet agents transacting in a
single shard whenever possible while collectively distributing their
transactions uniformly across the available shards. Since there is no central
authority to enforce these properties, the only means of achieving them is to
design the system such that it is in agents' interest to act in a way that
benefits overall throughput. We show that our proposed pricing policy does
exactly this by inducing a potential game for the agents, where the potential
function relates directly to ledger throughput. Simulations demonstrate that
this policy leads to near-optimal throughput under a variety of conditions.
|
http://arxiv.org/pdf/2009.00319v1
|
James R. Riehl, Jonathan Ward
|
eess.SY, cs.DC, cs.SY
|
eess.SY
|
Transaction Pricing for Maximizing
Throughput in a Sharded Blockchain
Ledger
James R. RiehlJonathan Ward
Fetch.ai, St. John's Innovation Centre, Cowley Road, Cambridge,
CB4 0WS, UK (e-mail: fjames.riehl, jonathan.ward
@fetch.ai).g
Abstract: In this paper, we present a pricing mechanism that aligns incentives of agents
who exchange resources on a decentralized ledger with the goal of maximizing transaction
throughput. Subdividing a blockchain ledger into shards promises to greatly increase transaction
throughput with minimal loss of security. However, the organization and type of the transactions
also a ects the ledger's eciency, which is increased by wallet agents transacting in a single
shard whenever possible while collectively distributing their transactions uniformly across the
available shards. Since there is no central authority to enforce these properties, the only means
of achieving them is to design the system such that it is in agents' interest to act in a way
that bene ts overall throughput. We show that our proposed pricing policy does exactly this by
inducing a potential game for the agents, where the potential function relates directly to ledger
throughput. Simulations demonstrate that this policy leads to near-optimal throughput under
a variety of conditions.
Keywords: blockchain, decentralized ledger, network throughput, potential game
1. INTRODUCTION
Decentralized ledgers, commonly implemented as en-
crypted linked lists of transaction records, or blockchains ,
allow individuals to trade resources and maintain a com-
mon state machine securely and without a central author-
ity (Narayanan et al., 2016; Crosby et al., 2016). As the
demand for such systems grows, the slow throughput of
established systems, e.g. 7-15 transactions per second on
Bitcoin and Ethereum (Croman et al., 2016), is becoming
a major obstacle to more widespread adoption and suc-
cess, especially in applications requiring high frequency or
time-critical transactions. One innovation that promises
to signi cantly increase transaction throughput is subdi-
viding each the transaction records into distinct shards ,
allowing parallel communication, execution and storage of
transactions that use di erent shards (Saraph and Herlihy,
2019). These subdivision methods have proven e ective in
conventional databases (Corbett et al., 2013). By evenly
distributing transactions across multiple shards, the sys-
tem can process transactions much faster than a serial
blockchain ledger. However, in a decentralized system,
di erent external users submit transactions, and there
is no guarantee that they will choose to distribute their
transactions in a way that enables the system to operate
eciently. In this paper, we investigate the use of small
transaction surcharges as incentives to align the goals of
the individual users with the system-wide goal of maxi-
mizing throughput.
Decentralized ledgers pose additional challenges due to
various stakeholders having di erent and often compet-ing objectives. For example, it is in the agents' inter-
est that transactions are fast and cheap, which is more
likely to occur when congestion is low, but miners or
validators (agents responsible for reaching a consensus on
which transactions are valid) bene t when transactions
are expensive, which may be the case when congestion is
high. These con
icting incentives must be accounted for in
the design of an e ective sharding system for blockchain
ledgers.
Several proposals for sharding block chains have previously
been put forth (Kokoris-Kogias et al., 2018; Luu et al.,
2016; Zamani et al., 2018; Buterin, 2016), each of which
aims to make the ledgers more scalable while maintaining
appropriate levels of security. However, these approaches
all rely on randomization for distributing transactions
among shards and do little to explicitly mitigate the prob-
lems of load imbalance and frequent cross-shard transac-
tions, which consume resources in communication between
shards and potentially, depending on the sharding imple-
mentation, force the pausing of execution threads on one
or both shards.
We address these problems here with a transaction pricing
policy that incentivizes agents to choose shards in a
way that maximizes ledger throughput. In particular, the
proposed pricing function is based on a novel transaction
eciency measure that induces a potential game for the
agents, where the potential function relates directly to
overall transaction throughput.
The problem considered here resembles that of congestion
games, a classic example of potential games, in which thearXiv:2009.00319v1 [eess.SY] 1 Sep 2020
goal is to minimize congestion in transportation or commu-
nication networks, for example, by setting prices to align
agent incentives with this goal (Monderer and Shapley,
1996). Indeed if the only goal were to minimize congestion
on the transaction network, this would be a straight-
forward application of congestion game theory. However,
the reduced eciency caused by cross-shard transactions
introduces additional complexity that must be accounted
for in the pricing mechanism. By combining transaction
size and the distribution of transactions across shards into
a single quantity called transaction eciency , we are able
to express the throughput objective as a function of agents'
shard choices and previous transactions, which we then use
to set the price. Decentralized ledgers sometimes use trans-
action fees as incentives for other agents to maintain the
ledger by validating transactions, so our proposal would
simply weight such fees to promote overall eciency.
2. LEDGER AND TRANSACTION MODEL
The model consists of a set of agents who transact with
their neighbors in the network via a blockchain ledger.
2.1 Agents and network
The network consists of a set Aofnagents who are
interconnected by the edges E A2, where an edge
(i;j)2E means that agent ican request a transaction
from agent j. We denote the set of neighboring agents
from which agent irequests transactions by Ni:=fj2
A: (i;j)2 Eg . Each agent imaintains a balance of
resources bi:= [b1
i;:::;bm
i]>, wherebs
idenotes the amount
of resources agent iowns in shard s2S:=f1;:::;mg, and
its objective is to choose shards and execute transactions
in a way that minimizes transaction fees.
2.2 Transactions
Assume that transactions are fully asynchronous and ar-
rive in a sequence where one agent ( i, the receiver ) requests
a transaction of an amount ( a) from another agent ( j, the
sender ), such that the sender is in the neighbor set of the
receiver (j2Ni). LetT:= (i;j; ) denote a transaction re-
quested by agent ifrom agent j, where := [ 1;:::; m]>
lists the amounts to be transferred in each shard, which we
assume are non-negative (each s>= 0), and sum to the
total amount (Pm
s=1 s=a). The cardinalityjSTjis the
number of shards used in the transaction. Let Pdenote the
set (or pool) of transactions that are waiting to be added to
a block. We de ne the price of a transaction as a function
of the transaction itself and the current transaction pool:
:=f(T;P), to be given in precise terms in section 4. The
transaction process proceeds as follows:
(1)Request: Agentirequests a transaction from agent
j2Niand speci es an amount aand a shard si2S
in which to receive the transaction, with the goal of
minimizing current and expected future transaction
fees. Only the sender pays the transaction fees, but
the receiver has an incentive to minimize these in
order to maximize the probability that the sender
accepts and ful lls the transactions. We can express
the receiver's shard choice as
si2arg min
s2S
rf(T;P) + (1
r)E[ ij;si]
;(1)whereE[ ij;si] denotes the expected price of future
transactions from the sender to the receiver if the
receiver requests the current transaction in shard si,
and
r2[0;1] denotes the priority receiving agents
place on the current transaction fee relative to future
transactions. Estimating E[ ij;si] is a key element
in the pricing mechanism and is discussed further in
section 3.1.
(2)Ful llment: Agentjaccepts and ful lls the trans-
action if there are sucient funds (including fees)
and chooses a set of shards SjS from which to
send resources, with the goal of minimizing current
and expected future transaction fees. Since the nal
transaction always includes the receiver's requested
shardsi, the transaction set is given by ST:=Sj[
fsig. The set of all feasible transaction shard sets is
then
Sj(a) :=n
STS:X
s2Sjbs
ja+f(T;P)o
:
For a requested transaction, we assume that the
sender chooses a shard set that minimizes the fol-
lowing expression:
Sj2arg min
ST2Sj(a)
sf(ST;P)+(1
s)E[ ij;s]
;(2)
where
s2[0;1] denotes the priority sending agents
place on the current transaction fee relative to future
transactions. The sender withdraws the resources to
be transferred plus transaction fees from the shards
inS
T, and the receiver adds the transferred balance
to shardsi. The result is a transaction Tthat goes
into the transaction pool P P[fTg.
(3)Block assembly:
We assume that the blockchain is divided into m
shards, each of which contains slots, and that the
blocks in each shard are produced synchronously.
LetPsdenote the set of transactions in pool Pthat
use shards:
Ps:=f(i;j; )2P: s>0g:
In this paper, we assume that when any shard be-
comes full (i.e., there exists a shard ssuch thatjPsj>
) the block is assembled from all transactions in the
pool andPis reset to empty. The maximum theoreti-
cal capacity of the blockchain ( m) is reached when
the cardinality of all transactions is one (no cross-
shard transactions) and each shard contains exactly
the maximum number of transactions. Note that it
may not be possible to execute both sides of multi-
shard transactions leading to failure of transactions of
this type. Therefore, assuming that all transactions in
the pool are executed, as we do in this study, leads
to optimistic estimates of the throughput in the case
of frequent cross-shard transactions. This leads to
conservative estimates of the performance gains that
would arise from implementing our proposed pricing
policy. Although this serves as a reasonable approxi-
mation for our purposes, coordinating states between
shards is a nontrivial problem and provides further
motivation to incentivize single-shard transactions.
We note that this analysis generalizes to blockchains
with complex state execution rules such as smart
contracts where cross-shard transactions require at
least twice the computation of an otherwise identical
single-sharded transaction.
3. TRANSACTION THROUGHPUT
Transaction throughput measures the number of transac-
tions processed in a given unit of time, but since we do
not explicitly include time in our model, we seek a time-
independent alternative quantity. Speci cally, we de ne
the transaction eciency of a poolPas the fraction
of the theoretical maximum number of transactions that
could be included in a block in which all transactions have
cardinality one and are evenly distributed across shards.
There are two primary factors that determine transaction
eciency: cardinality and shard balance . We say that
shard balance is high when the transaction pool uses the
shards in roughly equal proportions and low when the
transaction pool uses some shards much more than others.
Figure 1 illustrates three partially completed blocks from
transaction pools exhibiting varying degrees of cardinality
and shard balance.
We measure shard balance in terms of the deviation from
uniform shard usage in the transaction pool. The usage
of each shard relative to the pool is given by u(P) :=
[u1(P);:::;um(P)]>, where
us(P) :=jPsjP
T2PjSTj:
Since we consider only positive transaction fees, we focus
on those shards with greater usage than average and de ne
the loading of shard s2Sby
s(P) := max
0;us(P) 1
m
:
These values are collected in the loading vector (P) :=
[1(P);:::;m(P)]>, and we can now quantify the shard
balance in the transaction pool as
BP(S) := 1 X
s2Ss(P): (3)
Note that by de nition, BP(S)2[0;1). We can now ex-
press the total transaction eciency as the shard balance
divided by the mean cardinality of all transactions in the
pool:
FP:=BP(S)
1
jPjX
T2PjSTj: (4)
As an example, for the middle block in Figure 1, the
shard usage is u(P) = (0
8;4
8;4
8;0
8)>, resulting in the
loading vector (P) = (0;1
4;1
4;0)>and total shard balance
BP(S) =1
2. Since the mean cardinality is one, the
transaction eciency is1
2. For the left block in Figure 1,
since the shard balance is one and the mean cardinality is
two, the transaction eciency is also1
2. The block on the
right achieves the maximum transaction eciency of 1.
3.1 Expected transaction eciency
We introduce for each edge ( i;j)2 E along which a
transaction can take place, a shard request distributionwij:= [wij1;:::;wijm]>, wherewijsdenotes the proba-
bility that agent iwill choose shard swhen requesting
a transaction from agent j. Similarly, we de ne a shard
sending distribution vij:= [vij1;:::;vijm]>, wherevijs
denotes the probability that agent jwill choose shard s
when sending a transaction to agent i. These randomized
distributions, which lie on a probability simplex (each
wijs0 andPm
s=1wijs= 1, and similarly for vij), model
the initial uncertainty about the shards used by neigh-
boring agents and how such uncertainty evolves toward
deterministic choices.
Expected cardinality: We can now write the expected
cardinality of transactions between agents iandjas:
E[jSTj] =w>
ijPcardvij
= [wij1wij2wijm]"1 22
2 12
............
2 21#2
4vij1vij2
...
vijm3
5;(5)
wherePcardis a matrix that encodes the expected car-
dinality when the agents request transactions from each
other in the shards corresponding to the row and column
of each entry.
Expected shard balance: Similarly, we can express the
expected shard balance as follows:
E"
1 X
s2STs(P)#
=w>
ijPbalvij=
[wij1wijm]2
41 11 1 21 1 m
1 1 21 21 2 m
............
1 1 m1 2 m 1 m3
5"vij1
...
vijm#
;
(6)
wherePbalencodes the shard balance values correspond-
ing to the shards used in the transaction (we omit the
argumentPfor a more compact expression).
Expected transaction eciency: Based on the de -
nition of transaction eciency for the entire transaction
pool (4), we de ne the eciency of a single transaction in
a given pool as the shard balance of the transaction shards
divided by the cardinality:
FP(T) :=BP(ST)
jSTj: (7)
Using (7), we can write the expected eciency of transac-
tions from agent jtoi:
E[FP(T)] =w>
ijPe vij=
[wij1wij2wijm]2
6641 11 1 2
21 1 m
2
1 1 2
21 21 2 m
2
............
1 1 m
21 2 m
2 1 m3
7752
4vij1vij2
...
vijm3
5:
(8)
4. TRANSACTION PRICING
We propose the use of transaction fees to align the individ-
ual goals of minimizing fees with the system-wide goal of
maximizing throughput. A natural choice is to make the
fee proportional to the desired objective, which we have
quanti ed as the transaction eciency. Hence, we propose
the following pricing function:
Transaction T
Lane
High cardinality
High shard balanceLow cardinality
Low shard balanceLow cardinality
High shard balanceFig. 1. Cardinality and shard balance in three di erent transaction pools assembled into blocks. Rows corresponds to
shards and columns represent block slices. The gray boxes symbolize transactions on the underlying shards.
f(T;P) :=p0(T) +
1 BP(ST)
jSTj
max; (9)
wherep0(T) is the nominal transaction price, which can
vary with computational requirements and market de-
mand, maxis the maximum transaction fee, and is a
free parameter that can be used to calibrate the cardinality
estimate or to further discourage multi-shard transactions.
Note that the price includes one minus the transaction
eciency since the price should be low when the eciency
is high. To simplify the remaining analysis, we assume that
p0(T) = 0 and max= 1 unless otherwise stated. However,
it is straightforward to extend the analysis to include these
parameters.
While (9) de nes the price for a particular transaction, the
expected price of a future transaction requested by agent
ifrom agent jis given by:
E[ ij] = 1 w>
ijPvij= 1
[wij1wij2wijm]2
6641 11 1 2
2 1 1 m
2
1 1 2
2 1 21 2 m
2
............
1 1 m
2 1 2 m
2 1 m3
7752
4vij1vij2
...
vijm3
5:
(10)
The expression (10) provides a direct link from the trans-
action price (9) to the agents' optimal choice of shards
in which to request transactions for the case of agents
that wish to minimize long-term expected transaction fees
(
r=
s= 0), allowing us to rewrite (1) as follows:
w
ij:= arg max
wij2mw>
ijPvij; (11)
where w
ijdenotes an update to the shard request distri-
bution wijand mdenotes the m-dimensional probability
simplex. The update (11) is indeed a best response of agent
i(in mixed-strategy space) to the mixed strategy of agent
j. Similarly, the sender's optimal shard choice distribution
is given by
v
ij:= arg max
vij2mw>
ijPvij; (12)
Since the sender also seeks to minimize long-term expected
transaction fees, this distribution update is indeed inde-
pendent from the choice of the receiver.
There is an important class of multi-player games called
potential games , in which players choose actions to max-imize their individual utility functions, which in turn in-
creases some global utility function (Monderer and Shap-
ley, 1996). A key property of potential games is that when
agents act to improve their utility functions, the system
is guaranteed to converge to a Nash equilibrium, which
is a state in which no action by any single agent will
increase their utility. In potential games, Nash equilibria
also correspond to maxima of the global utility function.
We de ne potential games in precise terms below.
LetSidenote the space of actions for a single agent and let
S:=Sn
idenote the set of all actions in the system. Denote
byui: the utility function of agent i. Together these de ne
a game for the nagents, and such a game is said to be a
potential game if there exists a function H:S!Rsuch
that for any agent i2Aand any pair of actions a;a02Si
(wherea idenotes the actions of all agents except i):
H(a0
i;a i) H(ai;a i) =ui(a0
i;a i) ui(ai;a i):
Indeed we can show that the proposed pricing mechanism
induces a potential game over the transaction edges with
the following potential function:
H:=X
(k;l)2Ew>
klPvkl: (13)
Theorem 1. The game where nagents connected by the
edgesEupdate their shard request and sending distri-
butions according to the edge-utility functions uij:=
w>
ijPvijis a potential game with the potential function
(13).
Proof. Given an edge ( i;j)2E, suppose agent iupdates
its shard request distribution for this edge from wijtow0
ij.
Then, the change in the edge utility function is equal to
u0
ij uij= (w0
ij wij)>Pvij. The resulting change in the
potential function is
H0 H:= (w0
ij wij)>Pvij;
since the only change was to agent i's shard request
distribution from agent j, which is exactly equal to the
change in the edge utility function uij. Similarly, if agent
jupdates its shard sending distribution for this edge from
vijtov0
ij, then the change in the edge utility function is
equal toH0
ij Hij=w>(v0
ij vij), which is also equal
to the change in the potential function, completing the
proof.
Although the case we have analyzed is somewhat sim-
pli ed, the fact that it constitutes a potential game is
important because it ensures not only that the agents'
incentives are aligned with the global objective, but that
rational choices by the agents will result in convergence of
the system to a maximum of the global potential function,
which in our case corresponds to transaction throughput.
And their choices need not be optimal { the only require-
ment is that agents take actions that increase their local
utility function (lower their expected future transaction
price). This means that even deterministic shard choices
(pure strategy best or better responses) will lead to conver-
gence. For example, a pure strategy best response update
is given by replacing the probability simplex min (11)
with the set of all pure strategies m:=fw2m:ws2
f0;1gfor eachs2Sg .
A potential problem with the approach described so far
is that the update (11) assumes that agents know the
shard request or sending distribution of their transacting
neighbors along each edge, which would require additional
communication between agents. To resolve this, let's as-
sume that agents keeps estimates ( ^vijfor the receiver,
^wijfor the sender) of the shard request and sending
distributions of each neighbor, constructed simply from
the normalized histogram of past transaction requests. The
new update rules that rely only on information available
to the respective agents are then:
w+
ij:= arg max
wij2mw>
ijP^vij; (14)
v+
ij:= arg max
vij2m^w>
ijPvij: (15)
This turns out to be an example of ctitious play in game
theory, where agents estimate strategies of other players
based on empirical distributions. Indeed, multiplayer po-
tential games in which players act to improve their utility
using ctitious play are known to converge to a Nash
equilibrium (Marden et al., 2009).
The optimizations (14)-(15) are readily solved via linear
programming, and the deterministic (pure-strategy) case
is an integer program that reduces to nding the maximum
entry in am-dimensional vector:
w+
ij:= arg max
wij2mw>
ijP^wji
=es;wheres= arg max
s2SPs^wji; (16)
where esrefers to column sof themmidentity
matrix, and Psdenotes row sof the matrix P. A similar
modi cation can be made for the sending shard update.
The computational complexity of the pure-strategy and
mixed-strategy optimizations are linear and polynomial
(due to the complexity of linear programming, e.g. (Cohen
et al., 2019)), respectively.
5. SIMULATIONS
In this section, we investigate the performance of a simu-
lated blockchain ledger with transaction price (9) in which
agents update their shard request distributions with pure
strategy best response updates (16).5.1 Ideal case
We begin with a simple scenario to test the pricing mech-
anism under ideal conditions. Suppose that 20 agents
transact with two neighbors each via a ring network on
a ledger with 4 shards. Each block contains 2500 slices
meaning that the maximum capacity is 10000 transac-
tions per block. In this scenario, agents start with an
arbitrarily large initial balance (1 e6) in one shard (such
that the resources in these shards will not be depleted),
staggered among the agents, and transactions of a small
xed amount (10) are generated randomly by rounds.
That is, each of the 38 edges in the network executes a
transaction in random order, and then the process repeats
in a new random sequence until 5 blocks are eventually
completed.
In the gures that follow, the x-axis corresponds to the
transaction index, where anytime one shard reaches maxi-
mum capacity, a block is assembled (indicated by the verti-
cal grid lines) and the transaction pool resets to zero. The
top panel shows the proportion of transactions contained
in each shard, resulting in the balance value shown on the
second panel. The third panel shows the mean transaction
cardinality and the fourth shows the transaction eciency
calculated for each block. As a baseline, Figure 2 shows
the result of assigning transactions to random shards,
modeling a standard hash-based sharding protocol.
We observe that the randomized policy achieves a reason-
ably even distribution among the shards, but since the
mean cardinality is quite high, the eciency is only about
50%, yielding 25899 transactions in 5 blocks.
0.250.50trans. pool
0.81.0shard bal.
012cardinality
0 5216 10416 15603 20756 25899
trans.0.00.51.0efficiency
Fig. 2. Fixed price and random shard requests on 20-agent
ring network with 4 shards.
Next, we apply load-minimizing pricing by setting = 0 in
(9) and allowing the agents to update their shard requests
with best-responses. Figure 3 shows that the loading stays
very low, but the transaction eciency remains below 60%,
because the agents still have no incentive to transact in a
single shard and therefore engage in frequent multi-shard
transactions.
Finally, we see in Figure 4 that combining the proposed
pricing mechanism ( = 0:001) with best-response updates
results in high shard balance and low cardinality and
therefore almost perfect transaction eciency.
0.250.50trans. pool
0.81.0shard bal.
01cardinality
0 6358 12837 19247 25626 31982
trans.0.00.51.0efficiencyFig. 3. Congestion pricing with best-response update on
20-agent ring network with 4 shards.
0.20.4trans. pool
0.51.0shard bal.request
actual
01cardinality
0 9323 19231 29207 39197 49187
trans.0.00.51.0efficiency
Fig. 4. Eciency pricing with best-response update on 20-
agent ring network with 4 shards ( = 0:001).
5.2 Larger random networks
In the next simulation study, we construct a directed
random network with a long-tailed degree distribution
using a preferential attachment model. Also, instead of
generating transactions on repeated random sequences of
edges, we generate them uniformly at random on the edges.
Blocks in this scenario contain 12500 slices and thus a
maximum capacity of 100000 transactions. In this case,
we initialized the agents with arbitrarily large balances in
each shard. Figure 6 shows that network still converges
to a high transaction eciency compared to the baseline
shown in Figure 5. The gray data in the loading plot shows
how evenly distributed the shard requests are throughout
the network. As the agents acquire more information about
the shard preferences of their neighbors, the shard requests
converge to align with the corresponding send requests
while maintaining a balance across shards. The values of
in both simulations were chosen by manual tuning, and
the results are somewhat sensitive to small changes in this
parameter. An automated tuning method for would be
a useful direction for future research.
0.10.2trans. pool
0.91.0shard bal.
012cardinality
0 50340 100653 150926 201270 251549
trans.0.00.51.0efficiencyFig. 5. Fixed pricing with random shard requests on 100-
agent scale-free network with 8 shards.
0.10.2trans. pool
0.81.0shard bal.request
actual
01cardinality
0 98702 198385 297663 397049 495244
trans.0.00.51.0efficiency
Fig. 6. Eciency pricing with best-response update on
100-agent directed random network with 8 shards
( = 0:00015).
6. CONCLUSIONS AND FUTURE WORK
The pricing function (9) induces a potential game for the
users of a sharded blockchain ledger, such that is in their
interests to choose shards in a way that promotes ideal
throughput conditions on the ledger. Both the estima-
tion of shard request distributions and the deterministic
best-response policy are implementable with simple and
fast computations, and guarantee convergence to a Nash
equilibrium under some practical simplifying assumptions.
Simulations demonstrate the e ectiveness of the policy in
various conditions.
We plan to extend the approach in several ways. For
example, there is currently no mechanism that would
encourage agents to request from di erent neighbors in
the same lane, although this would seem reasonable in
practice, especially if resources are scarce. This could be
achieved by adding a term that encourages agents to align
shard requests with their resource balance. We also intend
to more explicitly account for varying costs of transaction
and smart contract execution and storage.
ACKNOWLEDGEMENTS
We are grateful to Marcin Abram and Jin-Mann Wong for
insightful technical discussions and for help in editing the
paper.
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Saraph, V. and Herlihy, M. (2019). An empirical study of
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|
{
"id": "2009.00319"
}
|
2204.05884
|
A Decentralized Resource Management System Proposal For Disasters:
NGO-RMSD (STK-AKYS)
|
Disaster and emergency management are under the responsibility of many
organizations and there are serious coordination problems in post-disaster
crisis management. This paper proposes a decentralized non-governmental
organization resource management system for disasters (NGO-RMSD / STK-AKYS).
This system is based on blockchain technology and it will enable the
non-governmental organizations (NGO) and public institutions to manage and
coordinate the resources in a trusted environment in the case of disasters. A
proof of concept implementation is developed by using the Quorum blockchain
framework which is more energy-efficient than crypto currency-based blockchain
solutions. Smart contracts are developed for the autonomous working of the
system. These smart contacts are used for the verification of the needs of the
one who is in need, delivering resources to the right people, and identifying
the urgent needs. The system aims to reach more disaster victims in a more
timely manner. NGO-RMSD is designed according to the needs of the NGOs in the
field. The application is shared with the free software license and further
development with the community is aimed.
|
http://arxiv.org/pdf/2204.05884v1
|
Arzu Özkan, Umutcan Korkmaz, Cemal Dak, Enis Karaarslan
|
cs.CY
|
cs.CY
|
A Decentralized Resource Management System
Proposal For Disasters: NGO-RMSD (STK-AKYS)
1stArzu ¨Ozkan
Department of English Language & Literature
Mu˘gla Sıtkı Koc ¸man University
Mu˘gla, Turkey
arzuozknn8@gmail.com2ndUmutcan Korkmaz
Department of Computer Engineering
Mu˘gla Sıtkı Koc ¸man University
Mu˘gla, Turkey
kmmzurtkcn@gmail.com3rdCemal Dak
Department of Computer Engineering
Mu˘gla Sıtkı Koc ¸man University
Mu˘gla, Turkey
cemal.dak@gmail.com
4thEnis Karaarslan
Department of Computer Engineering
Mu˘gla Sıtkı Koc ¸man University
Mu˘gla, Turkey
enis.karaarslan@mu.edu.tr
Abstract —Disaster and emergency management are under
the responsibility of many organizations and there are serious
coordination problems in post-disaster crisis management. This
paper proposes a decentralized non-governmental organization
resource management system for disasters (NGO-RMSD). This
system is based on blockchain technology and it will enable the
non-governmental organizations (NGO) and public institutions to
manage and coordinate the resources in a trusted environment
in the case of disasters. A proof of concept implementation is
developed by using the Quorum blockchain framework which
is more energy-efficient than crypto currency-based blockchain
solutions. Smart contracts are developed for the autonomous
working of the system. These smart contacts are used for the
verification of the needs of the one who is in need, delivering
resources to the right people, and identifying the urgent needs.
The system aims to reach more disaster victims in a more timely
manner. NGO-RMSD is designed according to the needs of the
NGOs in the field. The application is shared with the free software
license and further development with the community is aimed.
Index Terms —Disaster, Non-Governmental Organizations, Cri-
sis management, Organization, Source management, Coordina-
tion, Blockchain
I. I NTRODUCTION
The disaster management processes of the local govern-
ments are not sufficient even in developed countries such as
Japan and America [1]. Disaster and emergency management
are under the responsibility of many organizations, there are
critical coordination problems in post-disaster crisis manage-
ment.
Non-governmental organizations and volunteers have signif-
icant roles and contributions in the case of disasters. Today,
NGOs intensely work on the following about the disasters:
Support the preparation of communities for the disasters
Work on the reduction of pre-disaster risks and post-
disaster losses,
Work on short-term intervention, and long-term improve-
ment efforts.
Without the help of voluntary organizations, it does not
seem possible for local governments and the state to meetthe needs of disaster victims. There is a need for Joint
disaster management that is possible by assigning the roles
of voluntary organizations at all stages of the disasters and
establishing coordination between parties [2].
The duty of NGOs is to support local governments and
the public, as needed especially in times of crisis within the
framework of their chosen mission [3]. As an example; Ahbap
NGO took an active role in the post-disaster process after the
6.8 magnitude earthquake in Elazig Sivrice/TURKEY , which
happened in January 2020. One of our team members, who
was an active Ahbap volunteer in this aid team, observed the
following:
Non-governmental organizations working in the disaster
area act independently of each other
There is a deficiency and problem to reach the real needy
people
The resources can not be used efficiently.
When NGOs are analyzed, it is seen that there are a variety
of NGOs which have different missions and visions. Each
has various resources and strengths. The most significant defi-
ciency that we have observed in the post-disaster management
is; the coordination problem between NGOs and the govern-
ment. This results in the efficient usage of the capabilities.
This situation causes deficiencies in resource management and
slows down the post-disaster recovery efforts.
In the next section, the fundamentals will be given. Then
the related works will be given in Section 3. In Section 4,
details of the system proposal are given. The proof of concept
implementation of NGO-RMSD system is given in Section 5.
Conclusion and future works will be given in the last section.
II. F UNDAMENTALS
A. Blockchain
Blockchain is a distributed ledger technology that ensures
trust between the users without an intermediary. This is
accomplished by keeping track of records linked to eacharXiv:2204.05884v1 [cs.CY] 12 Apr 2022
other[4]. The records are called transactions and transactions
are bundled into blocks at the nodes (the computers that
are in charge of the blockchain system). These records are
stored in immutable record storage called the ledger. Since
the blockchain system is distributed, there is a need for a
way that the nodes can agree upon the transactions, blocks,
etc. Consensus protocols are used in blockchain networks
to provide that functionality to the distributed system. The
consensus protocol in the blockchain is chosen according to
the type of the blockchain.
Different types of blockchain networks exist. Their types
mainly depend on who can join as a node and participate in
the network and also the visibility of the blockchain records.
Public/private types are according to the visibility of the
records. In public blockchain networks; all the transactions
in the network are visible by anyone on the internet. Private
networks only allow authorized users to reach the records.
Permissioned/Permissionless type determines who can join
and participate in the network. Anyone can join permissionless
networks. In Permissioned networks, nodes need to be allowed
by an authority of the network to join in and participate.
Bitcoin [4] is an example of a Public/Permissionless network
while Quorum [5] and R3 Corda [6] are examples of Pri-
vate/Permissioned networks.
B. Resource Management Need
According to The Republic of Turkey Ministry of the
Interior Disaster and Emergency Management Presidency
Earthquake Office Management’s general earthquake statistics
report by years in Turkey; Earthquake rates in Turkey are
increasing every year. In the last century, 192 damaging
earthquakes occurred in Turkey, one hundred thousand citizens
died, and more than 650 thousand houses were demolished or
damaged heavily [7].
After our researches and interviews, ˙It’s concluded that
there is a lack of coordination and resources management in
post-crisis management in Turkey. These problems are in the
following areas:
Coordination between NGOs
Management of human resources
Resource management
–Logistics
–Storage
–Distribution
Post-disaster necessity tracking
–Verifying the necessity
–Support tracking and management
–Tracking the provided support
–Preventing abuse of resources and supports
Protection of trust in NGOs
C. The need for coordination Between NGO’s
NGOs, which have voluntary activities in disaster situations,
have access to different resources such as aid and human
resources. It is very important to provide coordination betweenNGOs to manage these resources correctly. The collaboration
of NGOs will enhance the post-disaster recovery.
Autonomous systems can be used to manage these resources
effectively and promptly. In this context, utilizing the tech-
nology’s facilities can have the potential to simplify resource
management and ensure coordination.
III. RELATED WORKS
There are few studies on disaster management. The use
of decision support systems for this purpose is discussed in
[8]. Usage of digital twins is given in a recent study [9].
Interestingly, there are comprehensive open-source solutions
such as SAHANA EDEN (https://sahanafoundation.org/) that
have several functions. Interestingly, Sahane Eden or similar
solutions are not that widely used. Our interviews with experts
in the field and NGOs revealed some possible reasons for that.
It is mainly because these solutions are not built with people
in the field. Also, there is always a need for transparency of
the transactions and a trust issue in the used systems.
Blockchain technology can be used to overcome these
needs. Blockchain technology can remove intermediaries and
ensure trust. Blockchain technology was mostly used for
cryptocurrency implementations and is famous for high energy
usage. However, with the advance in enterprise blockchain
frameworks, it is possible to obtain a trusted environment with-
out high energy usage. Different fields of using blockchain are
possible such as supply chain, sharing medical information, AI
marketplace, DAO machines economy, etc. Also, blockchain
can be used for social impact [10].
There are a few decentralized system proposals in the
literature regarding this topic. These are mostly for collecting
monetary aid [11] or disaster/refugee aid scenarios [12]. An
interesting study [13] is about integrating IoT and blockchain
in the supply chains of various units to increase the efficiency
and effectiveness of humanitarian aid. These papers mostly
do not contain technical details and also do not involve
NGOs. A very recent study [14] gives a theoretical model
which questions the applicability of a trusted supply chain for
humanitarian aid. We are not aware of a resource management
system for disasters that focuses on NGO collaboration. We
focused on generating a prototype to make a social impact.
IV. SYSTEM PROPOSAL
The proposed NGO-RMSD is shown in Figure 1, nodes
to be found in the blockchain network will be added by
non-governmental organizations within the system by using
blockchain technology. All transactions performed on the
system for each node will be saved encrypted in a tamper-proof
way. Actors within the distributed application to be created
with smart contracts to be deployed on the blockchain network
are as follows;
Non-Governmental Organizations: V oluntary platforms
that are approved by the ministry, supporting local gov-
ernments and ministries in case of disasters.
People In Need: Materially and morally damaged victims
due to disasters.
Fig. 1. Basic design of the NGO-RMSD System
Supporter: People, institutions, or organizations that are
wishing to provide in-kind and cash support to disaster
victims.
A. Architecture
The overall system consists of two parts that are given in
Figure 2. A Quorum network for the nodes of the NGO’s
and the web service. Those two parts will communicate
via Web3JS library from node to JS Back-End. Front-End
includes HTML and CSS code to present users from varying
platforms with a web page interface. Front-End and Back-
End components of the web service will communicate via the
ReactJS framework.
To initialize the system, our research group will install the
initial (startup) nodes of the network. There is no defined
minimum node number, the more the better. The first node
account (of the first node) added to the system will have the
role “admin”. After the initialization of the system, the account
with the admin role will distribute the admin roles to the
regarding accounts (pre-defined accounts on the pre-defined
nodes) according to the protocol of the community.
At least one node for each NGO is required in the
blockchain network to represent the institution. As the system
will run in a docker image, there is no need to reserve the
node machine only for this purpose. This machine may also
be used for general computing purposes by the NGO.
The volunteers or staff of the NGOs are going to create
their accounts via those nodes. These accounts are needed to
check the support offers or requests and take action.
RAFT is chosen as the consensus protocol of the proposed
system. This protocol uses the “leader-follower” role principle
where the leader node is selected internally in the network.
The leader and follower nodes can be thought of as minter
and verifier nodes in Ethereum. The leader node mines a
block to the network while follower nodes verify it. Every
node has the chance to be a leader, they have to mine a
block for this. Every node has an enode ID that has the
raftport parameter as a query string. This parameter will be
used to define the communication port that will be used for
communicating with the other nodes. These definitions are
grouped in the static-nodes.json file. Every node should have
the same static-nodes.json file in its filesystem.
The addition of another node or another NGO to the system
is as follows:
Fig. 2. NGO-RMSD System Architecture
1) The candidate NGO node installs the required Docker
image.
2) A node can not be initialized as the leader, there is an
election for it in RAFT protocol. So every node is added
as a new follower node.
3) The required files with the candidate node’s information
(static-nodes.json, node keys, etc. ) should be set. The
details will be given in the project Github page.
4) The static-nodes.json files of all nodes in the network
need to be updated.
5) One node in the network has to run eth.addPeer()
command from its geth RPC console with the enode
ID of the candidate node. Then, a new node is added
and running functionally in the network.
We are currently working on automation for this process
and the automatization process will be given on the project
Github page.
B. Smart Contracts
Although the blockchain concept is said to be decentralized,
not all solutions have to be like that. Private/Permissioned
networks like Quorum are not fully decentralized. The system
gives privileges to some authorities to add nodes or users.
The proposed system aims to form trust and transparency.
NGOs have the ADMIN role which has the privileges that can
set the user’s roles as CHECKER or CREATOR. Also, NGOs
with the ADMIN privileges add the new NGOs to the system.
Users are assigned roles in the smart contract structure. These
roles are as follows:
1) Admin: Admin is the system administrator.
2) Checker: The role of the checker belongs to NGOs and
is used to check whether the proposed support or request
is correct.
3) Creator: The role of the creator is open to everyone and
it is required to make a request or support offered.
Smart contracts are written for the following functions:
1) Role Assignment and Listing Functions: These are
called by the system administrator and they aim to
assign the role to the user with the setUser function
and to display the role of the user with the getUserAuth
function.
2) Authority-Role Control Modifier Functions: These func-
tions are called by the organizer to compare the hash of
the user’s role with the hash of the role assigned to the
user.
3) Requirement Creation Function: This function enables
users in the role of “Creator” to create a new need with
the data that they enter. Need; is created with features
such as type, and amount. The created need is put in
the need list and the label ”waiting for confirmation” is
added.
4) Support Creation Function: This function enables users
in the role of ”Creator” to create new support with the
data that they enter. Support; is created with features
such as type, amount, and shipping type. The created
support is added to the support list and the label ”waiting
for approval” is added.
5) Approve Functions: These functions enable users with
the role of ”Checker” to confirm the needs and supports
labeled ”waiting for approval”. While the approveNeed
function confirms the pending needs, the approveSupport
function confirms the pending supports. Once the need
is approved, the label changes to ”approved” and not
”waiting for approval”. Once the support has been
approved, it is added to the approved support list.
6) Request-Support Listing Functions: These functions are
used to display all needs and supports. While the
showSupport function listed the known support, the
showSupports function shows all the supports. The
showAllApprovedSupports function lists approved sup-
ports. The showNeedOffers function lists all the needs
records while the showNeed function lists the known
need records.
The last 2 functions (showNeedStatus and showSupportSta-
tus) could be run by users with the role of ”Checker”, and
these are in charge to list the known requests and supports
confirmation status.
C. System Application Flow
Application flow of the system as shown in Figure 3, the
user chooses their application type. That application is sent to
the Quorum node and stored as an unapproved application.
A transaction is sent to the network and the ledgers of
nodes get updated. If there are any unapproved applications
in the network, an NGO Staff checks the application by
communicating with the user that applied. If the application
is valid, in that case, the requirement is real or support is
accurate; the information of the applicant user is written into
the local database, and the application gets approved. After the
application validation, the application is stored as an approved
application. Then, another transaction is sent to the network,
and the ledgers of nodes get updated again. If there are
no unapproved applications, the application flow process is
finished.
Fig. 3. System Activity Diagram
V. IMPLEMENTATION
The default system works initially as three nodes with two
NGOs. To participate in the network and the system, each
NGO must add at least one node. The scenario with five
nodes was executed. The proof of concept implementation of
NGO-RMSD is implemented on the Quorum framework. The
Quorum framework, which provides corporate solutions, is
used as blockchain technology. It offers a private/permissioned
blockchain structure with low energy consumption [5]. Docker
container technology is used to integrate this system into every
platform. The system does not need special hardware and does
not need high power. Ordinary virtual machines (single CPU,
4 GB ram, 256 GB disk) are used for the nodes.
Web3JS is used for the communication between the Quorum
network and Web interface backend. Solidity language is
used to write smart contracts. RAFT protocol is used as the
consensus protocol as it can support more nodes than Istanbul
Byzantine Fault Tolerance (IBFT). NodeJS is used in the
backend because it provides the asynchronous functionality
that this application needs.
The privacy of personal data is prioritized in the design.
Turkish Law on the Protection of Personal Data (KVKK)
[15] and the EU General Data Protection Rules (GDPR) [16]
requirements are taken into consideration. Since it is not
possible to delete the data on the blockchain, the storage
of personal data in the blockchain constitutes a situation
that is not suitable for personal data protection laws. In our
design, personal data will never be stored on the blockchain.
Personal data will be stored in a private database instead,
which could be deleted at any time by NGOs. In a privacy
breach case, it will be the NGO’s responsibility if it didn’t take
the required actions to apply to the necessary legal processes.
However, Non-interactive zero-knowledge proof (NIZK) based
autonomous codes can be developed to ensure privacy [17],
which is out of the scope of this study.
In this project, open-source and free software licensed soft-
Fig. 4. Request and Support Forms
Fig. 5. Interface to track approved requests
ware is used, and dependency on any company or institution is
minimized. This project is also published on the Github page.
The prototype is created appropriately with the scenario
specified. Requirement Creation, Requirement Confirmation,
Support Creation, Support Confirmation, Support, and Re-
quirement Listing processes are directed to smart contracts
running on nodes in the Quorum network through the web
interface.
A. Smart Contracts
The smart contract is open to improvement and updating
and debugging process continues. The recent version of the
contract structure and functions are given on the public Github
page https://github.com/MSKU-BcRG/akys.
B. GUI Design
Prototype web interfaces of the system are designed. Disas-
ter victims can make requests or give support through forms
that are shown in Figure 4. All operations in the system are
stored on the blockchain as transactions. All the users can
track the status of the transactions through the web interface
that is given in Figure 5.VI. ACKNOWLEDGEMENT
This study won the second prize in the TUB ˙ITAK 2242
University Students Research Project Competition in the field
of “Information and Communication Technologies” in 2020.
VII. RESULTS AND CONCLUSION
Decentralized resource management and NGO coordination
system are proposed for disasters in this study. The proposed
NGO-RMSD system will enable NGOs and public institutions
to act in coordination in the case of disasters. A trusted
system is designed where all transactions are transparent. Proof
of concept implementation of the proposed system NGO-
RMSD had promising results. Smart contracts which allow
the autonomous working of the system is written and tested
on the live platform.
Coordination of NGOs will be provided with this system
which will lead to improvements in workforce and resource
management. The proposed system will enable NGOs to reach
more people in need. Also, it will ensure urgent needs could
be determined to resolve as soon as possible. Users’ personal
data’s unauthorized sharing will be prevented and made sure
that the data-sharing is legal.
The smart contracts and project details of the system are
shared on the project’s Github page with a free software
license. We are in contact with related NGOs and further
development with the community will continue. The live
tests of the proposed system is being implemented in the
DS4H blockchain research network. Dynamically establishing
new nodes and the automation for the authentication will be
given on the project’s Github page. Details of the project can
be reached at http://wiki.netseclab.mu.edu.tr/index.php?title=
STK-AKYS.
In future works, it is aimed to ensure that the system is
easily integrated into new laws and regulations. The personal
data will be protected autonomously with new smart contracts.
It is also aimed to create a more efficient system by integrating
the data collected in the system with the data collected from
the territory. The collected data will be processed with artificial
intelligence. The requirement and support requests will be
matched by using natural language processing and machine
learning. A dataset for machine learning processes will be
formed with the collected data on the live system. Thus,
the data to be entered into the system could be cleared by
using Natural Language Processing (NLP) and a classification
algorithm could be applied with keywords. It will be possible
to apply the need and support matching infrastructure through
this classification algorithm. In all these processes, preliminary
studies will be done to receive extra information from the
Twitter environment with sustained queries to be made with
these keywords.
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Refugee Relief Operations.” Prehospital and Disaster Medicine 34.s1
(2019): s106-s106.
[13] Aranda, Daniel Arias, Luis Miguel Molina Fern ´andez, and Vladimir
Stantchev. ”Integration of Internet of Things (IoT) and Blockchain to
increase humanitarian aid supply chains performance.” 2019 5th Inter-
national Conference on Transportation Information and Safety (ICTIS).
IEEE, 2019.
[14] Dubey, Rameshwar, et al. ”Blockchain technology for enhancing swift-
trust, collaboration and resilience within a humanitarian supply chain
setting.” International Journal of Production Research (2020): 1-18.
[15] [dated 24.3.2016 numbered 6698 Personal Data Protection Law] https:
//www.mevzuat.gov.tr/MevzuatMetin/1.5.6698.pdf, Last access date : 2
April 2020
[16] Regulation (EU) 2016/679 of the European Parliament and of the
Council of 27 April 2016 on the protection of natural persons with
regard to the processing of personal data and on the free
[17] Feng, Q., He, D., Zeadally, S., Khan, M. K., & Kumar, N., 2019. A
survey on privacy protection in blockchain system. Journal of Network
and Computer Applications, 126, 45-58.
|
{
"id": "2204.05884"
}
|
2211.01000
|
SoK: Play-to-Earn Projects
|
Play-to-earn is one of the prospective categories of decentralized
applications. The play-to-earn projects combine blockchain technology with
entertaining games and finance, attracting various participants. While huge
amounts of capital have been poured into these projects, the new crypto niche
is considered controversial, and the traditional gaming industry is hesitant to
embrace blockchain technology. In addition, there is little systematic research
on these projects. In this paper, we delineate play-to-earn projects in terms
of economic & governance models and implementation and analyze how blockchain
technology can benefit these projects by providing system robustness,
transparency, composability, and decentralized governance. We begin by
identifying the participants and characterizing the tokens, which are products
of composability. We then summarize the roadmap and governance model to exposit
there is a transition from centralized governance to decentralized governance.
We also classify the implementation of the play-to-earn projects with different
extents of robustness and transparency. Finally, we discuss the security &
societal challenges for future research in terms of possible attacks, the
economics of tokens, and governance.
|
http://arxiv.org/pdf/2211.01000v1
|
Jingfan Yu, Mengqian Zhang, Xi Chen, Zhixuan Fang
|
cs.CR
|
cs.CR
|
SoK: Play-to-Earn Projects
Jingfan Yu1y, Mengqian Zhang2z, Xi Chen3x, and Zhixuan Fang1;4
1Tsinghua University, Beijing, China
2Shanghai Jiao Tong University, Shanghai, China
3New York University, New York, USA
4Shanghai Qi Zhi Institute, Shanghai, China
Abstract. Play-to-earn is one of the prospective categories of decentral-
ized applications. The play-to-earn projects combine blockchain technol-
ogy with entertaining games and finance, attracting various participants.
While huge amounts of capital have been poured into these projects, the
new crypto niche is considered controversial, and the traditional gaming
industry is hesitant to embrace blockchain technology. In addition, there
is little systematic research on these projects. In this paper, we delineate
play-to-earn projects in terms of economic & governance models and im-
plementation and analyze how blockchain technology can benefit these
projects by providing system robustness, transparency, composability,
and decentralized governance. We begin by identifying the participants
and characterizing the tokens, which are products of composability. We
then summarize the roadmap and governance model to exposit there
is a transition from centralized governance to decentralized governance.
We also classify the implementation of the play-to-earn projects with
different extents of robustness and transparency. Finally, we discuss the
security & societal challenges for future research in terms of possible
attacks, the economics of tokens, and governance.
Keywords: Play-to-Earn ·Gamefi ·Tokenomics.
1 Introduction
The development of blockchain, especially the emergence of fungible tokens(FTs)
and non-fungible tokens (NFTs), is seeing big changes in the business model of
gaming industries. By giving value back to the players, the novel play-to-earn
(P2E) model has attracted huge attention from the market. In September 2022,
there are over 900 thousand active wallets interacting with blockchain-based
gaming projects and over 20 million transactions are sent [24].
The earliest blockchain-based gaming projects date back to 2015. These
projects store the ownership of players’ in-game assets in blockchains to pro-
vide crypto-assets to the players. Launched in 2015, Spells of Genesis [38] is the
yyujf20@mails.tsinghua.edu.cn
zmengqian.cs@gmail.com
xxchen3@stern.nyu.edu
Corresponding author: Zhixuan Fang ( zfang@mail.tsinghua.edu.cn )arXiv:2211.01000v1 [cs.CR] 2 Nov 2022
2 J. Yu et al.
1st blockchain-based mobile game and is partnered with Counterparty [13]. In
the game, players have to collect cards and fight their enemies. Players interact
with the game server governed by the creator EverdreamSoft, except that there
is a ‘blockchainization’ button that converts in-game cards to blockchain-based
digital assets. When a player with digital wallets of the corresponding blockchain
chooses a card and clicks the button, the server will send a transaction to the
blockchain to record the ownership of the card on it. Launched in 2017, Cryp-
toKitties is a successful game based on Ethereum smart contract [100], hitting
over 14,914 users a day at its peak [19]. In the game, players can trade and breed
their CryptoKitties through the smart contract by sending transactions to the
Ethereum network.
Thedevelopmentoftokenstandardsanddecentralizedfinance(Defi)[97]lead
to the emergence of various token designs attracting various investors, e.g.Mak-
erDAO [27] established its stablecoin [81] in 2017. Defi projects affect the designs
of blockchain-based games and also provide secondary markets for game assets
trading. The combination of Defi and game is also called Gamefi. Launched in
2018, Axie Infinity [6] is one of the most popular Gamefis, hitting a total NFT
transaction volume of over $2 billion by September 2021 [46]. It is a battling
game and allows players to collect, raise, breed, battle, and trade digital pets
known as Axies. The game attracted 2.8 million monthly active users at its
peak by rewarding players with tokens [5]. On the other hand, the high return
and the high entry cost incur criticism that there are economic bubbles in these
projects, and some may consider these projects as Crypto Ponzi schemes. There-
fore, many projects also offer a free-to-play mechanism to reduce the entry cost,
e.g., Illuvium and a new version of Axie called Axie Origin [6]. In addition, the
transparency of the blockchain may mitigate financial fraud.
A P2E project is a typical Gamefi project where players can earn crypto-
assets depending on the engagement level in the game as its rule suggests. The
rule is called game protocol in this paper and is created by the project founder,
e.g., in CryptoKitties, the entrepreneur creates the smart contract. The in-game
assets like virtual land, characters, as well as skins, are in the form of crypto
tokens, either FTs or NFTs. Also, there can be tokens for the monetary system
of the game, such as gems, points, or coins, with which players can purchase
the assets. The information of ownership, transaction activities of these tokens
are stored on blockchains, which could be public blockchains like Ethereum, or
program-specific chains with preferred features. Other than entertaining games
mentionedabove,someprojectscalledMetaverse[92],X-to-earn5orcrypto-game
ecosystems are also included in our definition of P2E projects, as long as there is
a rule or a protocol and players can earn crypto-assets by taking actions defined
in the protocol.
The P2E project is different from the traditional game in terms of the fol-
lowing properties:
5There are move-to-earn and read-to-earn projects, such as stepN [40] and
ReadON [34].
SoK: Play-to-Earn Projects 3
– Robustness against single-point failure: any single party can not affect
the state of the game. For example, malicious behaviors of the entrepreneur
orthefailureofasingleentity’sserversthatexecutethegamewillnotchange
the asset ownership of players.
– Transparency: the execution of the smart contract is tractable by anyone.
– Composability [97] to secondary markets: the function of the smart con-
tract of tokens is callable by other contracts so that any standard token can
be traded in secondary markets.
– Decentralized governance: the updates of the game are decided by the
players instead of the project founder.
Traditional games are played in a centralized ecosystem where developers
retain almost all power over the experience. Players devote their time and effort
totheirfavoritegamesandcollectvariouscharacters,weapons,extrapowers,and
more. These achievements, however, only value within games. When the game
ends, the precious collections will be worthless. People cannot transfer their
hard-earned items to another game, exchange them on third-party marketplaces
or simply cash them out. In a traditional game model, players do not own the
value they created. By contrast, in a P2E project, the four features mentioned
above make the tokens valuable outside of the game and realize true ownership of
tokens for the players. These features depend on the implementation and token
designs, which will be discussed in the remaining of the paper.
To the best of our knowledge, this paper is the first systematical paper to
review P2E projects. We identify the participants, characterize the economic &
governance model, and summarize various purposes of tokens. We discuss the
technical roadmap and implementation realization and propose a classification
of popular projects. We discuss the security & societal challenges of the projects.
Therestofthepaperisorganizedasfollows:InSection2,wediscusstheeconomic
& governance model. In Section 3, we discuss the implementation, especially how
the data are stored and processed, which reflects different design principles. In
Section 4, we discuss the challenges and solutions.
2 Play-to-Earn Games
In this section, we discuss the economic & governance model. In Section 2.1, we
identify the participants. In Section 2.2, we summarize the tokens in terms of
their purposes. Section 2.3 summarizes the flow of tokens in P2E projects and
Section 2.4 discusses their development roadmap. In Section 2.5, we discuss the
decentralized governance of P2E projects.
2.1 Participants
In this paper, we call the world in the game universe and focus on three mainly
involved parties of a P2E game: entrepreneur, investor, and player.
4 J. Yu et al.
–Theentrepreneur is an individual or a team who designs the game proto-
col and launches the game universe. By realizing a series of new ideas, the
entrepreneur aggregates capital and provides the game service for profit.
–Aninvestor is the person who provides the game project with capital in the
form of fiat money or cryptocurrency like Bitcoin. Meanwhile, the investor
receives the token issued by the game. This investment process is mainly
done through initial coin offerings (ICOs), which is a popular way for the
entrepreneur to raise funds in the blockchain. Usually, investors do so out
of the expectation that the received tokens will appreciate and bring high
returns for them in the future.
–Aplayeris the user of the game. He engages in activities allowed in the game
ecosystem for entertainment. In this process, the player can obtain various
tokens in the game.
The players and the investors are differentiated by whether they play the game,
or say, take actions that are defined by the game protocol. Investors will buy
tokens before the game protocol is created, while the players won’t.
2.2 Tokens
In this subsection, we discuss different token designs in P2E projects. For ex-
ample, some projects use the same fungible token for funding and incentivizing
players,whileotherslikeAxieInfinity[6]andstepN[40]usedualfungibletokens.
There are usually many kinds of tokens in a P2E game. The aforementioned
token involved in ICOs (also referred to as ICO token in this paper) is fungible
and its total circulation is limited or scheduled, e.g., AXS of Axie Infinity [6].
Besides, the game may issue other fungible tokens, e.g., SLP of Axie Infinity
(which is called utility token ), mainly for players’ use in the game. Its total
supply can be unlimited. In addition, some NFTs will be generated during the
play. All tokens in a game including the ICO token, utility token, and NFTs are
collectively referred to as project tokens . The possible design purposes of project
tokens are summarized as follows.
– Funding: to raise money for creating or updating the game universe. For
example, ICO tokens can work as funding tokens.
– Incentive for players: to reward players for participating in games ( e.g.,
winning a tournament, or creating content).
– Payment: for players to pay to the universe for assets or playing games.
– Voting: for voters to participate in the voting when governing the universe.
Tokens with the voting function are also called governance token .
– Incentive for investors: to reward investors for their staking of funding
tokens they bought.
– Tokenizinguser-generatedcontent(UGC): torepresentuser-generated
contentandmakethecontenttradableasassets.Itisusuallythenon-fungible
tokens that achieve this function.
SoK: Play-to-Earn Projects 5
– Incentive for implementation: to reward users for their implementation
of the project. For example, for the project using project-specific consensus,
miners receive the tokens with this purpose as the reward for mining.
– Stakeproofforconsensus: toelectleadersorvoterswhenadoptingProof-
of-Stake (PoS) [78,87] for the consensus.
Table 2 lists the design of token purposes in several popular P2E projects. As
can be seen, each project can be characterized by its token designs, e.g., whether
to use different tokens as the incentive for players and investors, whether the
governance token can be earned by playing, and whether there are tokens that
cannot be earned by playing. In addition, we can see that one token can be used
for multi-purpose [82], and there can be multiple tokens for the same purpose.
This allows game designers to combine tokens with various features to design
distinctive projects and deal with complex game ecology.
2.3 Tokenomics
In this subsection, we summarize three mechanisms that exist in most P2E
projects and introduce the overall flow of tokens.
Minting Mechanism. The minting mechanism is a set of rules allowing players
togeneratenewtokensinthegame.Thiscanrequireplayers’effortsandconsume
players’ tokens. For example, in Axie Infinity, a player consumes its AXS and
SLP to breed new Axies NFTs. After mining an NFT, players can do more other
than just own it in the universe. They can use the NFT to participate in various
events to earn more project tokens, rent them to other players for revenue, or
sell them in the market.
Staking Mechanism. Staking is a way for investors to earn in the game. Al-
though the players can earn this way, we mainly consider the earnings of the
investors because if players stake all their tokens, they cannot play the game. A
staking mechanism requires token holders to lock their project tokens for a while
and promises to pay them interest for this. The interests are also in the form of
project tokens and proportional to the number of locked tokens. This could serve
as a bright spot to attract investors who can gain wealth by staking to earn. In
addition, regarding staking as an approach to money-making money, it can also
prevent token holders from overselling their tokens by increasing interest rates.
Typically, the system reserves a certain amount of tokens in advance as staking
interest (stated in the whitepaper), as well as collecting assets from transaction
fees and token flows in the universe ( e.g., consumed tokens for minting NFTs).
Treasury Mechanism. To develop a sustainable economy, the treasury mech-
anism is designed to collect assets from the universe and then allocate them
to certain entities. The treasury funds are primarily in the form of fees from
game-related economic activities. Specifically, when consuming tokens to mint
NFTs in the game, part of them will be recycled into the treasury. Besides, when
a successful NFT sale occurs in the marketplace, some transaction fees will be
extracted from the trade. Also, in some activities where the entrepreneur makes
money ( e.g., advertisement and subscription service), part of the revenue will
6 J. Yu et al.
be put into the treasury. Later, treasury funds are allocated to the ecosystem
according to some rules. For example, they can serve as the staking interest or
monetary rewards to encourage active contributions to the game’s growth.
Fig. 1.The flow of tokens.
Flow of Tokens. In this paragraph, we describe the flow of tokens as well
as currency in a P2E game, which is illustrated in Figure 1. First, as Line 1
shows, players can gain project tokens or currency through participating in the
universe, i.e., taking actions specified by the universe. As a cost, this process also
consumes some tokens (see Line 2). Those project tokens paid to the universe
will either go to a treasury account for further use or be transferred to a burn
address where the token can never be retrieved. Moreover, it may be necessary to
hold an NFT when playing. For example, in Axie Infinity, players can earn AXS
and SLP by participating in tournaments and attaining top rankings. To join
the competition, players should have an Axie NFT in advance. For a new player
without such an NFT, he can choose to buy one from other players, using project
tokens, fiat money, or cryptocurrencies like ETH. Actually, all project tokens
can be traded by players, investors, and the entrepreneur in the marketplace
(see Lines 3, 4, and 5). As mentioned, part of the tokens or currency involved
in the trade may go to the treasury in the form of transaction fees. In Axie
Infinity, ETH can be used for trading an Axie NFT, and after a trade, 4.25%
ETH will be paid to the Axie Common Treasury. Those collected project tokens
and currencies in the treasury can be distributed to some token holders (see
Lines 6 and 7). The distributed method is originally specified in the whitepaper
or later decided by the token owners through weighted voting. For example,
when Axie Infinity evolves into the period of decentralized organization, AXS
holders will vote for the rules on how to distribute AXS and ETH in the Axie
Common Treasury to the community.
Lines 8 and 9 show the staking process that distributed tokens to particular
token holders. In this process, this particular token needs to be staked in a
dashboard for some specified period to get the reward, and can not be used for
SoK: Play-to-Earn Projects 7
Fig. 2.Roadmap of P2E projects.
trading during the period. The token portfolio earned can be different depending
on what token is staked and how long the token is staked.
2.4 Roadmap
In this subsection, we present the evolution of P2E games, which are built from
scratch and then change from centralization to decentralization with the flow of
tokens. Most P2E games plan to delegate the governing power to the community,
using the project token as a medium. Holders of the governance token will have
the right to participate in the decision-making of a certain event. Besides the
ICO token, NFTs or other project tokens may be also regarded as the governance
token, which varies among different P2E games.
At the very beginning, the entrepreneur is the only token holder. During the
ICO stage, some ICO tokens are sold to the investors for funds, as shown in
Figure 2(a). Along with the token itself, these investors also have the potential
rights to manage this game project in the future, if the ICO token is regarded
as the governance token. Then in Phase 2, tokens are further dispersed among
participants. Players also own the governance token. But in this phase, the game
universe is still mainly managed by the entrepreneur. Ideally, the system will
eventuallyevolvetoPhase3,namely,itiscompletelymanagedbythegovernance
tokenholdersincludingplayersandinvestors.Theycanvotefortheimprovement
of the game. For example, they may vote on whether to pass a proposal about
addinganewfeaturetothegame.Iftheproposalpassed,theyalsoneedtodecide
the amount of reward for attracting someone to implement this function. Thus,
in Phase 3, the players can also earn money by contributing to the universe.
8 J. Yu et al.
In addition, the original entrepreneur acts as a player or investor in this phase.
Likewise, it can obtain tokens by playing, contributing, or just staking, and it
can also participate in governance.
2.5 Decentralized Governance
Most P2E projects will gradually evolve into the phase that uses Decentralized
Autonomous Organization (DAO) to govern the universe so that players can
make decisions for the development of the universe. They can vote for specified
parameters or proposals proposed by the community. Their votes are usually
weighted by their governance tokens either linearly or quadratically. Various
rules, such as the votes threshold to pass a proposal, starting and ending time
of voting, and voting rewards are initially decided by the entrepreneur and then
updated by the DAO. If multiple tokens are allowed for voting, their relative
weightshouldbespecified.Inaddition,participants’behaviorsmayalsoinfluence
their voting weight. For example, it can be weighted by the staking period.
The governance tokens can be gained through various behaviors in the uni-
verse, reflecting different design principles of the project. If a project prefers the
longtime or skillful players to be governors, it can make the tokens mainly main-
tained by higher-level players have a larger weight when voting. For example,
GMT, which is the governance token in Stepn, can only be gained when reaching
level 50 and Axie Infinity’s governance token AXS is distributed to high-rank
players in tournaments.
3 Implementation
In this section, we discuss various implementations of the P2E projects. We focus
on how the state of the game universe is stored and updated upon receiving the
actions of the participants, where states are the view of the game universe shared
by all the participants. Different implementations reflect the design principles of
the project, e.g., decentralization, transparency, security, and performance. In
Tabel 1, we briefly list the implementations of the projects mentioned in this
section and summarize the implementations of several popular P2E projects in
Table 3 in more detail. We discuss the principles behind these designs in this
section.
The structure of this section is as follows. First, we discuss decentralized
data storage in Section 3.1. The data in P2E projects can be stored in either a
central server or decentralized nodes connected by P2P networks. Data stored
in central servers are vulnerable to single-point failure, i.e., the entity fails or
becomes malicious. In addition, data operations are not transparent. On the
other hand, data stored in decentralized nodes may suffer from inconsistency
andpoorperformance.Therefore,consensusprotocolsareusedtoresolveconflict
states, which is discussed in Section 3.2. In Section 3.3, we discuss bridges and
oracles, which are used for communication between separate components. In
Section 3.4, we classify these implementations into fully decentralized and hybrid
architectures, and discuss the design principles of different implementations.
SoK: Play-to-Earn Projects 9
Table 1. The implementations of P2E projects mentioned in Section 3.
projects architecture dataimplementationpublicpermission consensusbridgea
(Y/N)b(Y/N)cprotocol
My Neighbordecentralized alldChromia [12] Y Y BFT -Alice [30]
Gala Games [17] hybridAOEthereum [100] Y NNakamoto-otherseGala Node [18] N Nmonitoredf
The Sandbox [36] hybridAOEthereum [100] Y NNakamoto -
UGC IPFS [50] - - - -
GP AWS - - - -
Decentraland [15] hybridAOEthereum [100] Y NNakamoto -
UGCBitTorrent/IPFS - - - -
GPUser front-end - - - -
Axie Infinity [6] hybridAOEthereum [100] Y NNakamotoYAORonin chain [35] N Y BFT
Illuvium [22] hybridAOEthereum [100] Y NNakamoto -
GP AWS - - - -
Stepn [40] hybridAO Solana [37] Y N BFT
Y AO BSC YY(PoSA) -
AO Ethereum Y NNakamoto
abridge: Y means the project designs its own bridge.
bpublic: Y means public, N means program-specific.
cpermission: Y means permissioned, N means permissionless.
dincluding asset ownership (AO), UGC, game protocol & its execution (GP).
eother game content.
fmonitored means monitored by Gala Game.
3.1 Decentralized Data Storage
In this subsection, we discuss what to store and how to store it for improving
the performance of decentralized data storage.
Data in P2E Projects. Various types of data needed to be stored for P2E
projects, e.g., game protocols & designs of the universe, player actions, UGC,
asset ownership, and various proofs. To explain these data in detail, we consider
a Turing machine that stores these data and calculates the corresponding state
of the game. This Turing machine is called the systemof the project. The system
can contain centralized or decentralized subsystems storing different data. The
game protocol and other designs of the game universe are sent to the system by
the entrepreneur, to kick off the game universe. The game protocol defines how
theplayerscanplaythegameandwhatplayerscanearn, e.g.,howtobattle,gain
rewards, and breed Axie NFT in Axie Infinity [6]. It also defines how to update
the protocol itself. Other designs can be the appearance of the environment or
avatars in the game, e.g., the character design of Axie character. Actions defined
by the game protocol are sent by the players to play the game, e.g., registering
the identity, purchasing assets, and controlling avatars. Then, the system can
calculate asset ownership from the game protocol and the actions of players.
UGC is content generated by the players. The content can be tokenized as NFT
by the system, and become an asset that can be traded between players. For
example, in The Sandbox [36], players can create items with its custom Voxel
Engine on top of the Unity engine. In addition, the system can calculate succinct
10 J. Yu et al.
proofs for large data and store the proofs and the data differently to improve
performance.
Next, we discuss how the above-mentioned data are stored in decentralized
nodes. An intuitive way is to require each node to maintain a replica of all data.
However, there will be a huge cost to store the growing data this way. There-
fore, it is necessary to classify the data and store them differently. According
to whether it is required for nodes to reach a consensus, data in P2E projects
can be basically divided into two categories, necessary data, and unnecessary
data. For example, the ownership of assets is necessary because when trading
an asset, nodes should verify if the seller owns it. Instead, the raw data of UGC
is unnecessary because nodes can use its hash value to represent the data dur-
ing the consensus. This will consume less storage and network resources. Also,
some intermediate process data is unnecessary after they are aggregated. The
necessary data is stored by all nodes participating in the consensus, thus easy to
access during the consensus process. On the contrary, the data unnecessary for
the consensus can only be saved by part of the nodes. The data inaccessibility
issue caused by distributed storage will be discussed in Section 4.1.
In addition, we discuss some techniques to enable secure decentralized stor-
age in Appendix A, including data structure, upload and download process, and
incentives for nodes that provide storage. They are different from those of cen-
tralized storage because the decentralized nodes may be malicious, fail, or hard
to find, for not having a registered address.
3.2 Consensus
In order to have the same view of the game universe, nodes run consensus pro-
tocols to maintain consistency, during which they communicate with each other
to reach continuous unanimous agreements on the states. Consensus protocols
should satisfy safety and liveness [56] so that participants can hold consistent
states at any time and their inputs to the system will eventually act on the
state. The consensus of a P2E project can be categorized in terms of whether
the project relies on a public blockchain, how the nodes join the network ( i.e.,
permissioned or permissionless), and the protocol, i.e., how each node decides
on the state.
Public or Project-specific Chain. P2E projects may use a public blockchain
or a project-specific chain. Different ways can affect the stability of the system.
For projects that run on a project-specific blockchain, the entrepreneur or DAO
can control the parameters of the chain, such as transaction fees, the number of
nodes, and the type of consensus. For example, Gala Games [17] allows at most
50,000 consensus nodes and anyone who wants to join the consensus should buy
a Founder’s Node License, the price of which is set by the entrepreneur. How-
ever, using a project-specific blockchain means that the consensus rewards are
endogenous tokens of the chain. If this niche currency is not attractive to nodes,
the security and decentralization of the consensus can not be ensured, making
the system vulnerable. To bootstrap the project-specific chain, an unpredictable
genesis block [70] should also be prepared for the project. This block can either
SoK: Play-to-Earn Projects 11
be provided by a trusted centralized party or a decentralized party, such as an
existing blockchain. For example, the Ronin sidechain is boosted by a block of
Ethereum. There are also plenty of P2E projects using public chains. For ex-
ample, hundreds of P2E projects are based on Solana chain [42]. To maintain
low transaction fees, high throughput, and fast transaction finalization, various
methods are used, such as Layer-2 methods [73], sharding [95,101], and DAG-
based blockchains [96]. However, some of these methods may induce security
problems, which will be discussed in Section 4.1.
Permissioned or Permissionless. In terms of how the nodes join the network,
there can be permissionless and permissioned settings. For the latter, its identity
management makes the confirmation speed faster and the realization easier,
but meanwhile, the consensus nodes are more likely to collude with each other,
posing a threat to the consensus security. On the other hand, consensus with
permissionless nodes is considered to be more decentralized and transparent.
P2E projects with permissioned designs usually adopt proof of authority (PoA)
as the consensus algorithm while the consensus nodes are determined by the
entrepreneur. For example, in the Ronin chain of Axie, the entrepreneur selects
trusted nodes to build the consensus. Nodes can also be added upon the approval
of the existing consensus nodes. In the permissionless setting, nodes use proof
of work (PoW) [71], PoS, Delegated Proof-of-Stake (DPoS) [89] to elect voters
and leaders [72,85] to prevent Sybil attack and claim incentives. For example,
in Gala Games [17] players can purchase a Founder’s Node License, which is
similar to PoS, and download the Gala Node Software to run the node. Running
a Founder’s Node is motivated by project tokens. Permissioned consensus can
anchor blocks in permissionless consensus. For example, Chromia [12] plans to
record hashes of its blocks in Bitcoin and Ethereum.
ConsensusProtocol. TherearevariousprotocolsP2Eprojectscanusetoreach
a consensus, e.g., Nakamoto protocol, Byzantine Fault Tolerant (BFT) protocol,
or RAFT. Different protocols resist different proportions of malicious nodes to
ensure security. In Nakamoto protocol, a leader is elected at each round to in-
cludeinputs,voteforpreviousstateswiththemostvotes,andcalculatethelatest
state. The protocol can maintain safety and liveness with 50% malicious nodes
for a synchronous network. For a partially synchronous model, where the upper
bound in the delay of messages is unknown to the honest parties, the number of
malicious nodes it can resist will decrease according to the delay [69]. In Byzan-
tine protocol, leaders and a voting committee are elected at each round [72].
The leaders include inputs and calculate their states. Voters will initially vote
for some of their received states and amplify the votes that they receive more
than a threshold [56]. The protocol can resist 33% malicious nodes in the voting
committee. RAFT [83] can ensure safety in a fail-stop model and can not resist
any number of Byzantine malicious nodes.
3.3 Bridges and Oracles
The system of the project can be made up of several subsystems. Each subsys-
tem can be a P2P network of decentralized nodes or a centralized server. For
12 J. Yu et al.
example, the nodes that participate in the consensus protocol and the nodes
that respond to the data accessing request can be two groups in different net-
works. The system can also contain several groups of nodes that run consensus
severally. This subsection introduces bridges and oracles that connect different
decentralized subsystems.
Oracles. Oracles are used to link consensus to other information sources, e.g.,
discussion forums, random numbers, order of transactions, economic information
such as the price of the dollar, and any other external events. In P2E projects,
the update of the game protocol is often discussed in some online forums, and
players can vote for the proposals proposed in these forums. Random numbers
generated in the consensus may be predictable to some adversaries, therefore
more unpredictable random numbers should be generated by the oracle. Also,
someoftheconsensuseshavethetemporarilycentralizedproblemthattheleader
candeterminepartoftheorderoftransactions, i.e.,minerextractablevalues[63].
Therefore, oracles can be used to determine the order of transactions [76].
The data source of the oracle can be software source ( e.g., online databases,
websites,blockchainmempools)orhardwaresource( e.g.,sensors)[52].Anoracle
that uses a single source is considered to be centralized for it is vulnerable to
single-point failure, while decentralized oracles aggregate the information from
multiple sources [80]. There can be a hierarchical structure for a decentralized
oracle, namely, a project can aggregate the information from different oracles,
and an oracle can aggregate the information from several nodes that contract
with different data sources to prevent malicious behaviors at any step. Nodes in
the oracle can also form a consensus to validate the data [54].
Bridges. Bridges are used to exchange or migrate assets between different con-
sensuses [102]. There are many cross-chain P2E projects. For example, players
can play Stepn in Solana, BNBchain, and Ethereum, which are called different
realms in Stepn [41]. Bridges across these chains help the participants react to
the possible risks from the chains, such as transaction fee fluctuations, enabling
them to choose their preferred chain at any time. In addition, for those project-
specific chains, bridges are needed for users to exchange other commonly used
cryptocurrencies for the project tokens. For example, the Ronin bridge is used
to connect the Ronin chain and Ethereum so that players can buy Axie NFTs
and other Axie tokens by ETH.
Furthermore, Bridges enables the migration process, in which assets in one
subsystem can be locked in the bridge accounts and some ‘wrapped’ assets are
issued in another subsystem, and if the ‘wrapped’ assets are paid back, the
original assets will be unlocked. So through this, players can migrate their assets
from one chain to another. Projects can add rules on the usage of the ‘wrapped’
assets. For example, in Stepn, Energy assets can only be shared across realms
under specific conditions through Energy Bridge.
Bridges can be realized by a centralized entity. For example, Axie Ronin
bridge is controlled by the entrepreneur of Axie project. Bridges can also be
realized by a decentralized third party, such as chain relay or synchronization
methods like HTLC [102].
SoK: Play-to-Earn Projects 13
3.4 Hybrid Architecture
In the above sections, we mainly discussed the realization when all the data are
stored in decentralized nodes, while many existing P2E projects use a hybrid
architecture. In a hybrid architecture, there will be centralized servers storing
and processing part of the data, and communicating with the decentralized sub-
systems of the project. A centralized server contains data storage and processing
nodes owned by a single entity, e.g., entrepreneur, a third-party service provider
likeAmazonWebServices(AWS),andInterPlanetaryfilesystem(IPFS)pinning
service providers such as Pinata [31] and Infura [25]. There are several reasons
to adopt a hybrid architecture. One reason is that the realization and the mod-
ification of the game protocol are easier for the entrepreneur when starting the
project.Althoughtherighttomodifytheprotocolmayharmplayers’interests,it
is hard to design a perfect protocol that does not need subsequent modification,
for there can be many uncertainties in the start-up period. Another reason is
that the hybrid architecture can provide better performance because its require-
ments for the network throughput and data processing speed are much less. The
main concern of the hybrid architecture is the failure of the centralized server.
There can be inconsistency between the centralized server and the decentralized
nodes due to attacks from an attacker or misbehavior of the entity who owns or
rents the server, which will be discussed in Section 4.
Here, we give some examples of hybrid architecture that reflect different
design principles. Usually, the most critical data like asset ownership, UGC, and
the game protocol are stored in a decentralized way. In P2E projects, earning
assets from the game is one of the most important features. Usually, the asset
ownership will be stored by the decentralized nodes. For example, Illuvium [22]
is a collection game where players can control the avatars, explore the game
universe and capture the encountered Illuvials. The ownership of Illuvials is
written in an Ethereum smart contract. On the other hand, the project uses
centralized AWS as its backend to store other data, such as the movement of the
avatars. During the game, the action data of avatars are sent to the centralized
server, while the trading data related to asset ownership are widely sent to the
decentralized nodes. After that, the centralized server and decentralized nodes
synchronize the latest state of the system. The design principle of this hybrid
architecture is the transparency of the asset transfer, for the transfer process is
realized in decentralized nodes. Therefore, players can monitor the centralized
server’s behavior and partially transfer their rights. For example, players can
make necessary their authorization when the centralized server intends to move
assets out of their accounts.
The accessibility and censorship resistance of UGC can be important in some
projects.Therefore, the project can store UGCin decentralized nodes. For exam-
ple, in The Sandbox, after players send their created content to the centralized
server of the project, the server will calculate the hash of the content, tokenize
the content, and send the raw data to decentralized storage nodes. The cen-
tralized server in The Sandbox is AWS and the decentralized storage is IPFS.
Therefore, if the decentralized storage is properly designed, players can access
14 J. Yu et al.
the contents without the centralized server. The user front-end can also be used
to tokenize the content and send the content to the decentralized nodes to ensure
censorship resistance.
The immutability of the game protocol is essential to a P2E project. For
example, My Neighbor Alice [30] emphasizes that for a game fully controlled by
players, the logic of items should be stored in a decentralized way instead of only
storing the ownership of the items.
Some technologies can be used to improve the performance of the hybrid
architecture. Trusted Execution Environments, e.g., Intel SGX [62], can be used
to replace the centralized server. Then the system will be secure under the trust
in the hardware and the institution that authenticates attestation keys [58]. User
front-ends can be utilized to store and process data, as they are motivated to do
so without incentives. For example, in Decentraland, players can play games on
the servers hosted by landowners.
4 Challenges
In this section, we discuss security & societal challenges of the projects. In Sec-
tion 4.1, we discuss security challenges. In Section 4.2, we discuss economic
challenges. In Section 4.3, we discuss governance challenges.
4.1 Security Challenges
ICO Rug Pull. During the ICO phase, investors take the risk that the en-
trepreneur may abruptly shut down the project after raising money. Many rug
pulls have been observed in various blockchain projects [68]. In the field of P2E
games, there are also such scams. Along with the shutdown of the project, the
entrepreneur deletes their social accounts and transfers their raised cryptocur-
rency to money launderers. Towards this problem, one possible approach for the
investors is to judge the quality of a project by the sold fraction of the project
token [59]. If the proportion of tokens issued in the ICO stage is quite high, such
a project is likely to have the risk of rug pull.
Private Key Compromise. To obtain a large amount of money, the common
accounts in the game universe, bridges of multi-chain projects, and cryptocur-
rency wallets are mostly targeted, especially when they are not fully decentral-
ized. Attackers may try to get hold of the participants’ private keys to steal
tokens in their wallets. Actually, private keys can be compromised by bribing
the key owner, hacking or phishing, or just because of the reuse of private keys.
In addition, they may attempt to compromise the private keys of consensus
nodestocontroltheentirechaindirectly.Topursueefficiency,manyP2Eprojects
use private chains or layer-2 scaling schemes instead of the public chain for
recording most of the game operations. In a private chain, several specified nodes
take responsibility for the consensus. The private chain is tied to other chains
through two-way bridges for cross-chain coordination. If larger than 50% nodes
are corrupted in a private chain, the attacker can take control of the whole chain.
SoK: Play-to-Earn Projects 15
As a result, they can corrupt all the accounts, including the game treasury and
all bridges, and take the tokens therein. For example, Axie Infinity uses the
Ronin, which is a private sidechain linked to Ethereum. On March 29, 2022,
Axie Infinity reported a loss of over $625 million caused by an attack on the
Ronin sidechain. The attacker hacked to get the private keys of over half of
Ronin nodes and take the cryptocurrency to Ethereum through a bridge.
Likewise, Layer-2 scaling schemes also face this kind of problem. Layer-2
schemes can provide P2E games with high throughput by allowing offline pro-
cesses. Layer-2 protocols rely on a set of computational nodes. A classic solution
is that any computational node could provide periodic checkpoints to the main
public chain while others can challenge it by offering fraud proofs. For example,
the Polygon blockchain uses this kind of solution and several P2E projects ( e.g.,
Arc8 by GAMEE, Crazy Defense Heroes, and Pegaxy) are built on it. In such
schemes, if all computational nodes are corrupted, the attacker can also operate
the system arbitrarily [75,77].
Smart Contract Vulnerabilities. As a blockchain project, the P2E game de-
ployssmartcontractstodealwiththegamerulesliketokenflowsandtheminting
mechanism. Researchers have found that there are multiple security issues in the
publicly deployed smart contracts, which may be maliciously exploited by the
attackers [49,86]. Although extensive literature investigates vulnerability detec-
tion and security analysis [88], some blockchain projects still get attacked due
to the improper design or implementation of smart contracts, and P2E games
are no exception.
In P2E projects, contracts transferring NFTs are usually the target. For
example, in the initial CryptoPunks contract, the ETH paid to buy the punk
NFT was wrongly sent to the buyer instead of the NFT seller. As a result,
attackers take advantage of the loophole to earn NFTs for free. There are also
other designs of the smart contract that may cause an unexpected loss of other
honest users in the community.
– Rollback of randomness. In Ethereum virtual machine, some functions
(e.g.,require and revertfunction) can be invoked to rollback the trans-
action. Therefore when randomness is involved to determine the result of a
request, the sender can roll back the transaction if the result is not satisfac-
tory. For example, in Cryptozoon [14], the player can buy an egg to hatch a
ZOAN NFT, which will be randomly given a Rarity Level. When initiating
such a transaction, players can decide to roll back it if the obtained level is
not desirable. The solution to this kind of attack can be checking whether
buying function is called by a contract, and prohibiting the contract call.
– Front running. The transparent nature of blockchain makes DApps highly
vulnerable to the front-running attack [65]. For example, players in Polka-
Monster sell their NFTs by setting a price in the marketplace. It is observed
that the seller successfully earned a lot of money by front running. The at-
tacker first set a low price for his NFT to attract a buyer. After that, he
could monitor transactions in the network. When detecting that someone
16 J. Yu et al.
issued a transaction to buy this NFT,6he immediately updated the price
and set a higher gas fee for this updating transaction.7As a result, the price
updating was executed first and the buyer finally paid a much higher price.
The solution to this kind of attack can be letting the buyer set an acceptable
price range or encrypting the transactions.
NFT Content Inaccessibility. In most of the existing P2E projects, the NFT
content is stored in centralized storage, or decentralized storage with improper
incentives. For example, they are stored in AWS, centralized Pinning Service
providers, or IFPS without incentive mechanisms, as mentioned in Section 3. In
this case, the content may be lost [47]. Although we can store the content locally,
in P2E projects, there are various data that need to ensure accessibility, such
as game environments and character designs generated by the entrepreneur. For
example, the costume of a player’s avatar should be accessed by other players
to realize its social function. There will also be content generated by players
that need access when players are playing the game. For example, one of the
use cases of the land in Decentraland is advertising. There is no meaning if the
advertisement can not be accessed publicly.
4.2 Economic Challenges
Token Design. Token designs affect the economy. As mentioned in Section 2.2,
some projects use a single token for multiple purposes, while others use different
tokens for corresponding purposes. If fewer tokens are used, the token design
will be simpler. But on the other hand, the use of multiple tokens allows the
system to respond flexibly to various conditions and participants with different
preferences. For example, the incentive-for-player tokens and funding tokens can
be different in terms of stability and issuance schedule. To attract myopic players
with the network effect, the price of incentive-for-player tokens should be stable.
Otherwise, the decline of the token price may cause collective abandonment of
the game. To maintain a steady user growth rate, the incentive tokens can be
designed to be more valuable when the system suffers slow growth. Instead,
funding tokens can be unstable to reward those visionary investors. In terms of
the issuance method, there can be either a scheduled supply or an unscheduled
supply. Scheduled supply means the total amount of the token is fixed or the
issuance of every period is scheduled. Unscheduled supply is that the issuance
amount will depend on the activity of players in the game universe, e.g., tokens
can be issued every time a player wins a mini-game and in this way, the more
winning events happen, the larger total supply of tokens there will be. Tokens
with scheduled supply are often used for funding, while tokens with unscheduled
6Seehttps://bscscan.com/tx/0x1a31bfc4d3c4a726c931cb784f9e79606d62996b625
1c9ad959e5b2e6621fd9e for this buying transaction, the gas fee of which is 5 Gwei.
7Seehttps://bscscan.com/tx/0xb9ec7e204f186660a377beeee8e9223107f46d871
8314fbfdc4133580311442e for this updating transaction, the gas fee of which is 7.7
Gwei.
SoK: Play-to-Earn Projects 17
supply will be more attractive to those players with a less available cost for
playing.
Irregular Issuance. With more and more players playing the game, more to-
kens will be earned by players, especially those unscheduled utility tokens that
can be easily earned by playing but cannot be used for staking or governance.
As a result, the price of tokens may decrease. To maintain stability, designers
consider reducing those tokens’ supply or increasing consumption. For example,
the utility token of Axie Infinity ( i.e., SLP) experienced a rapider and earlier
price drop than AXS, so Axie Infinity reduces the amount of SLP players can
earn in its season 20 update. In addition, to increase the demand of these tokens,
new events in the universe can be developed. For example, Axie Infinity designs
NFT Runes & Charms which can be crafted by consuming SLP in its new Origin
season.
Therefore, tokenomics should be carefully designed to balance those impacts.
There can be some swap mechanisms to make scheduled and unscheduled tokens
able to be exchanged in the universe. Besides, it can enable these two kinds of
tokens to have similar usage so that players can strategically choose which token
to use, bringing a dynamic adjustment.
Friction When Attracting New Players. High gas fees for the blockchains
and the unfamiliarity of potential players with blockchain technologies can cause
friction when the project wants to attract new players. To solve the problem, the
project can implement the game simultaneously in public chains, private chains,
and centralized servers, and build bridges between them so that players can start
the game with low fees.
Wash Trading. Users of the blockchains can easily lend flash loan [94] and
create multiple accounts. Therefore, anyone can create multiple accounts and
transfer tokens among them, which is called wash trading. For those fungible
tokens, auto market makers (AMM) can prevent them from wash trading, be-
cause the trading price cannot be set directly by the seller. However, there is no
effective mechanism to prevent NFT wash trading nowadays [93].
Wash trading has a significant influence on P2E projects, for it leads to larger
trading volume and price distortion. Some statics websites, such as CoinMark-
Cap, rank P2E projects by the transaction volume, and the higher the volume
is, the more visible the project will be to the potential investors and players [60].
In addition, the price of a set of similar NFTs is calculated by the average of
their sale prices. Therefore, wash trading at a high price can affect the evaluated
value of these NFTs. Wash trading also enables money laundry. For example, the
ICO process of a malicious project may be utilized to become part of a money
laundry process [8].
4.3 Governance Challenges
The P2E projects will go through a centralization phase, a decentralization
phase, and an intermediate transition period. In the decentralization phase, a
proposal-and-majority-voting scheme is widely used, namely, any participant in
the game universe can put forward proposals and vote for them, and their vote
18 J. Yu et al.
is weighted by their tokens like Apecoin [3]. We discuss the challenges in the
decentralization phase in terms of voting security, voting privacy, governance
efficiency, governance fairness, and the tragedy of the commons.
Governance Security in the Centralization Phase. In the traditional joint
equity system, everyone holds the same equity. Therefore, it is reasonable for
a centralized entity that possesses the majority of the equity to conduct the
governance.However,inP2Eprojects,therearetokensthatareonlypossessedby
players, e.g., SLP in Axie Infinity. Since the governor (namely, the entrepreneur)
doesnotholdsuchtokens,hemaymakedecisionsthatharmtheinterestsofthose
token holders. For example, the entrepreneur may update the game universe,
limit the usage of these tokens, and issue new tokens to attract new players but
devalue the original tokens.
In addition, most P2E projects did not identify when and how to enter the
decentralization phase in the smart contract, for it is hard to predict the best
time to do so. Therefore, the entrepreneur can arbitrarily postpone the decen-
tralization phase.
Governance Security in the Transition Period. During the transition pe-
riod from the centralization phase to the ideal decentralization process, the vot-
ing weight of the entrepreneur will decrease while that of players is the reverse.
Their interests may conflict during the shift of duty. For example, before the
voting weight of the entrepreneur is down to less than half, he can make most
of the decisions. Therefore, the interests of the players can not be ensured.
Proposal & Voting Security. After entering the decentralization stage, the
consensus nodes ensure proposal and voting security, which should behave as
neutral entities to collect proposals, tally the votes, and manage the encryption
and delegation of votes [103]. Here, encryption is for voting privacy, and delega-
tionisforgovernanceefficiency,whichwillbediscussedintherestofthissection.
However, the consensus nodes may refuse to include the votes or proposals they
are opposed to, especially for those projects that own their specific consensus.
For example, in some projects, only miners can vote for the proposals. In this
case, the rights and interests of other players that do not run consensus nodes
can not be guaranteed.
Voting Privacy. What an individual voter voted on should not be revealed
to anyone during the voting process or even after the process. Privacy during
the voting process is to mitigate the malicious behavior when tallying the votes,
which may be realized by the commit-and-reveal scheme [74]. The privacy after
the voting process is to prevent bribery and collusion, making the voting pro-
tocol coercion-resistance [64]. To realize the privacy decryption, trustees [61] or
voting committee [103] should be elected for the decryption of the aggregated
votes. However, the privacy of voting is not realized prevalently in the cryp-
tocurrency community, e.g., voting of Bitcoin improvement proposal [10], and
Apecoin DAO Governance [3]. Since the pseudonymity of the blockchain does
not ensure privacy, the voting scheme should be designed carefully.
SoK: Play-to-Earn Projects 19
Governance Inefficiency. The governance inefficiency comes from two facts
players can join or leave the community freely, and not all the voters are always
online.
When players can join or leave the community freely, voters are dynamically
changing. Therefore, different or even conflict policies may be implemented.
Forthevoters’offlineproblem,enoughvotingtimeshouldbesettomakesure
token holders notice the vote has begun and fully discuss the proposals. Other-
wise,governancemaybetakenoverbyanattacker,acentralizedentrepreneur,or
other unexpected token holders. On February 12, 2022, Build Finance DAO en-
counteredaGovernanceTakeoverAttack.Theattackersucceededinthetakeover
by having a large enough vote in favor of the proposal to take control of the
Build token contract [11]. Although the risk can be mitigated by setting a voting
threshold, i.e., how many votes are needed to approve a proposal, or restricting
the amount of token transfer of a single proposal. However, these solutions will
take time and harm the efficiency of the governance. Delegating the votes or
liquid democracy [53,103] can also be a solution to the inefficiency. In liquid
democracy, the delegation can be terminated at any time, having little harm to
decentralization from a long-term perspective.
Governance Fairness. The governance of P2E projects does not adopt the
method that each account has the same voting weight because accounts can be
easily created without any cost. Usually, votes are weighted by project tokens to
realize the fairness that the voting weight is proportional to the time, effort, or
money a player spent on the project. However, for a token allocation proposal
concerninghowtoallocatethetokensinthetreasury,playerswithalargenumber
of tokens may vote for the decision that benefits them. Thus, the proposal that
distributesmoretokenstothosepossessingmorevotingtokenswillbemorelikely
to be accepted. In this way, rich people will become richer, which is opposite to
the sense of fairness. To mitigate the unfairness, we can restrict the distribution
of the governance token. For example, governance tokens can only be obtained
by playing the game and further soulbound [98] to the account that obtains
them, making it difficult to be traded.
In addition, P2E projects usually have multiple governance tokens. When
voting for a proposal, weights are set to each token, and a voter’s voting weight
is the weighted addition of the amount of each token he possesses. The weight
is set either exactly the same as or different from the real-time exchange rate.
If the weight is different from the real-time exchange rate, malicious voters may
exchange for a cheaper token with larger weights. On the other hand, if the
weight is approximately the same as the real-time exchange rate, players pos-
sessing the token with a small market cap will be in an inferior position. For
example, if there are two groups using different tokens in the game universe, all
decisions will be made by the group with tokens of a larger market cap under
the proposal-and-majority-voting scheme. To meet better fairness with multiple
governance tokens, other voting schemes can be used. For example, when decid-
ing on a division of some resources, instead of the majority voting for a proposal
20 J. Yu et al.
of a particular distribution, calculating a distribution based on the preference
aggregation of voters will be fairer [66,79].
Tragedy of the Commons. When governance is conducted by the token hold-
ers, the long-term benefit of the project can not be ensured, because some of
the token holders can be myopic. For example, they can refuse to increase the
budget on the incentives for new players that may have long-term merit on the
value of the tokens. To mitigate this kind of effect, we can: 1) fix the budget
ratio of different categories [103], including marketing, development, and other
necessary components for a project to become sustainable; 2) force the voters
to deposit their stake for a long time to make sure the voters hold the token for
a long enough period before or after voting so that the voters will consider the
long-term return and the financial risk of the attacker will be increased, e.g.,
in stepN, users will get higher voting power by locking GMT [20]; 3) use sub-
DAOs to vote for different issues with proper weights of tokens. For example,
in Yieldguild, issues of different games will be voted for with different subDAO
tokens.
4.4 Ownership of Digital Assets
One of the features of P2E projects is that players can have true ownership of the
assets in the game universe. However, the ownership is not well defined. Since
the entrepreneur that creates the game universe is the most considered central
entity. Whether the universe can be accessed and run without the entrepreneur
or whether the entity that can modify the game is chosen by the community
of players may be one of the rules to test if the project is decentralized. In
many current projects, the hybrid consensus is used, and the codes of the game
universe are not open-sourced. If the entrepreneur becomes malicious and denial
ofservice,thetokensplayersholdinthedecentralizedconsensuswillnothavethe
same value as before. However, anyone can pirate the game if the source code is
revealed. Therefore, the decentralization process can be seen as the entrepreneur
selling the game universe to the decentralized players via the smart contract.
5 Conclusion
In this paper, we discuss the implementation, token models, and governance in
the play-to-earn projects and propose challenges and some solutions in terms of
attacks on the implementation layer, the economic failure that may happen in
player incentive, and the difficulty of governance in the play-to-earn projects.
SoK: Play-to-Earn Projects 21
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A Secure Decentralized Data Storage
In this section, we discuss techniques to store data securely on decentralized
nodes in terms of data structure, upload and download process, and incentives
for nodes that provide storage.
Data Structure. To make sure to get the correct data, data usually break into
blocks and form Merkle DAGs by hashes of each block, e.g., content addressing
in InterPlanetary file system (IPFS) [50]. Therefore, the data will be immutable,
and all the replications of the same data will have the same address. To make the
searching process easier, tags or metadata on data format can be appended to
the hash. Data can also be encoded as erasure code [57] to make the replication
of data more efficient. Privacy and access management [90] can be ensured by
end-to-end encryption as in Storj [99]. Access management for NFT content
may be essential because, in P2E projects, royalties may need to be paid to
NFT creators.
Upload and Download Process. To upload and download the data, storage-
providing nodes can be found by a market or dissemination between nodes.
For example, in Filecoin [16], there is a market matching the data owner and
storage-providing nodes. The data owner can set the price and choose how many
replications are needed for the data. On the other hand, in Arweave [4], nodes
disseminate the data and choose whether to store the data when a data owner
uploadsthedata.Duringthedownloadprocess,nodesalsodisseminatethedown-
load request to neighboring nodes to find the nodes that possess the data.
Incentives. The incentive for nodes to store the data should be set properly.
Otherwise, unpopular files may be trashed, and nodes may deny the service to
the client [91]. The data owner can pay fees either when requesting storage or
requesting accessing the data. When paying for accessing the data, an atomic
exchange is needed between the data and the fee. However, it is impossible
without a trusted third party [84]. Erasure code [99] and payment channels may
be used to mitigate the risk.
To implement proper incentives, proofs are needed. According to different
needs, there can be two kinds of proof, proof of replication/storage of the data
or access to certain data. Proof of replication (PoRep) [51] is dedicating unique
physical storage, which requires the prover to provide processed data, and the
process takes time to prevent the prover from fetching the corresponding data
from another storage provider. On the other hand, proof of access (PoA) lets the
storage provider decide whether to store the data. Therefore, those projects that
attach more importance to data accessibility can choose PoRep, while PoA may
be more economic. Since using whole data for proof and continuous periodical
checking bring too much burden to the network. Several methods can be used
to generate succinct proof, e.g., probabilistic sampling using Merkle tree, non-
interactive scheme [48] [55], zk-snark or other encodings [67]. These proofs can
either check by the users or contracts on the consensus ledger.
B Tables
SoK: Play-to-Earn Projects 27Table 2. The purposes of tokens of the P2E projects. The Require ownership means players should own the tokens to unlock some
features in the game. These tokens can also be seen as paying before playing in rewarded after playing. Exchanging tokens at a fixed
exchange rate is considered a playing process in this paper. Therefore, the tokens that can be gained by a fixed rate exchange are classified
in the Incentive-for-players column.
projects FundingIncentive forPaymentRequireVotingIncentive for Tokenize Incentive for Stake proof
players ownership* investors UGC implementation for consensus
My Neighbor Alice [30] ALICE ALICE ALICE lands&items ALICE ALICE items - -
Wemix Play [45] WEMIXWEMIX, WEMIX,- - WEMIX - WEMIX -GameToken GameToken
Splintershards [39]SPS, card,SPS, DECSPS, DEC,card SPS SPS - SPS -CREDIT CREDIT
Alien Worlds [2] TriliumTrilium,Trilium Core NFTs Trilium TriliumLandowner- -NFTsa-offered NFTs
Gala Games [17] GALA GameTokenGALA-Founder’s- - GALAFounder’s
GameToken Node license Node license
The Sandbox [36] SANDSAND SAND, GEMSLAND SANDSAND, GEMSASSETS- -
ASSETS CATALYSTS CATALYSTS
Decentraland [15] MANA MANA MANA LANDMANA, LAND- UGC - -NAMES
Axie Infinity [6] AXSAXS, SLPAXS, SLP Axie NFT AXS AXS - - -Axie NFT
Illuvium [22] ILV Illuvium NFT ETH [100] Illuvium NFT ILV Promo NFT - - -
Stepn [40] GMTGMT, GSTGMT, GST Sneaker NFT GMT - - - -Sneaker NFT
RadioCaca [33] RACA NFTsbRACA, NFTs Metamon NFT RACA NFTs - - -
UFO Gaming [43] UFO Genesis NFTPlasmaGenesis NFT -UFO,- - -Points Plasma Points
Aavegotchi [1]AavegotchiGHST, NFTscGHST, NFTs Aavegotchi NFT GHST - - - -NFT ,GHST
MOBOX [29] MBOX, GEM MBOX, MECMBOX,MOMO NFT MBOX MBOX - - -GEM, MEC
Mines of Dalarnia [28] DAR, LandDAR, DAR,Land DAR - - - -mineral mineral
Battle world [7] BWO BWO BWO NFTs - BWO - - -
aincluding Core NFTs and Landowner-offered NFTs
bincluding Metamon Egg, portion, and diamond
cincluding Portals and Wearables
28 J. Yu et al.Table 3. The implementations of the P2E projects introduced in their whitepaper or documents.
projects architecture data implementationpublic/ permissioned/ consensusbridgeprogram-specific permissionless protocol
My Neighbor Alice [30] decentralized allaChromia [12] public permissioned BFT -
Wemix Play [45] decentralizedassetbownership Klaytn [26] public permissioned BFTWemix bridgeotherscWemix chain specific permissioned RAFT
Splintershards [39] decentralizedasset ownership WAX [44] public permissionless(DPoS) BFT Splinterlands
game protocoldHive [21] public permissionless(DPoS) - WAX bridge
Alien Worlds [2] decentralizedasset ownership BSC [9] public permissioned(PoSA) - Cross-chain
main game protocol WAX [44] public permissionless(DPoS) BFT reconciling
NFT images IPFS - - - -
Gala Games [17] hybridasset ownership Ethereum [100] public permissionless Nakamoto-other game content Gala Node Ecosystem [18] specific permissionless monitored by Gala Game
The Sandbox [36] hybridasset ownership Ethereum [100] public permissionless Nakamoto -
UGC IPFS [50] - - - -
game protocol AWSe- - - -
Decentraland [15] hybridasset ownership Ethereum [100] public permissionless Nakamoto -
UGC BitTorrent/IPFS - - - -
game protocol User front-end - - - -
Axie Infinity [6] hybridasset ownership Ethereum [100] public permissionless NakamotoRonin bridgeasset ownership Ronin chain [35] specific permissioned BFT
Illuvium [22] hybridasset ownership Ethereum [100] public permissionless Nakamoto -
game protocol AWS - - - -
Stepn [40] hybridasset ownership Solana [37] public permissionless BFT
Energy Bridge asset ownership BSC public permissioned(PoSA) -
asset ownership Ethereum public permissionless Nakamoto
RadioCaca [33]hybrid asset ownership BSC public permissioned(PoSA) --hybrid asset ownership Ethereum public permissionless Nakamoto
UFO Gaming [43] hybrid asset ownership ETH(Immutable X [23]) public permissionless Nakamoto -
Aavegotchi [1] hybrid asset ownership ETH(Polygon [32]) public permissionless Nakamoto -
MOBOX [29] hybridasset ownership BSC public permissioned(PoSA) -
- UGC IFPS - - -
- MOBOX Chainf- - -
Mines of Dalarnia [28] hybrid asset ownership BSC public permissioned(PoSA) - -
Battle world [7] hybrid asset ownership ETH(Polygon) public permissionless Nakamoto -
aincluding asset ownership, UGC, game protocol and its execution
bincluding Wemix token
cincluding Game token [45] ownership, game protocols and their execution
dincluding game protocols and their execution
eAmazon Web Services
funder development(https://faqen.mobox.io/ecosystem/mobox-chain)
|
{
"id": "2211.01000"
}
|
2212.00726
|
Predicting Digital Asset Prices using Natural Language Processing: a
survey
|
Blockchain technology has changed how people think about how they used to
store and trade their assets, as it introduced us to a whole new way to
transact: using digital currencies. One of the major innovations of blockchain
technology is decentralization, meaning that traditional financial
intermediaries, such as asset-backed security issuers and banks, are eliminated
in the process. Even though blockchain technology has been utilized in a wide
range of industries, its most prominent application is still cryptocurrencies,
with Bitcoin being the first proposed. At its peak in 2021, the market cap for
Bitcoin once surpassed 1 trillion US dollars. The open nature of the crypto
market poses various challenges and concerns for both potential retail
investors and institutional investors, as the price of the investment is highly
volatile, and its fluctuations are unpredictable. The rise of Machine Learning,
and Natural Language Processing, in particular, has shed some light on
monitoring and predicting the price behaviors of cryptocurrencies. This paper
aims to review and analyze the recent efforts in applying Machine Learning and
Natural Language Processing methods to predict the prices and analyze the
behaviors of digital assets such as Bitcoin and Ethereum.
|
http://arxiv.org/pdf/2212.00726v1
|
Trang Tran
|
cs.CY, cs.CR
|
cs.CY
|
Predicting Digital Asset Prices
using Natural Language Processing: a survey
Trang Tran
Cornell University, USA
tmt59@cornell.edu
Abstract
The introduction of blockchain technology has changed the way people think about how they
used to store and trade their assets, as it introduced us to a whole new way to transact: using
digital currencies. One of the major innovations of blockchain technology is decentralization,
meaning that traditional nancial intermediaries, such as asset-backed security issuers and
banks, are eliminated in the process. Even though the blockchain technology has been
utilized in a wide range of industries, its most prominent application is still cryptocurrencies,
with Bitcoin being the rst one proposed. At its peak in 2021, the market cap for Bitcoin
once surpassed 1 trillion US dollars. The open nature of the crypto market poses various
challenges and concerns for both potential retail investors and institutional investors, as the
price of the investment is highly volatile and its
uctuations are unpredictable. The rise
of Machine Learning, and Natural Language Processing, in particular, has shed some light
on monitoring and predicting the price behaviors of cryptocurrencies. This paper aims to
review and analyze the recent e orts in applying Machine Learning and Natural Language
Processing methods to predict the prices and analyze the behaviors of digital assets such as
Bitcoin and Ethereum.
Keywords: blockchain; cryptocurrency, natural language processing; price prediction
1 Introduction
In recent years, blockchain technology and its desirable characteristics, such as decentralization and
anonymity, have motivated the industry and academia. Even though the concept of using cryptograph-
1arXiv:2212.00726v1 [cs.CY] 28 Nov 2022
ically secured chain of blocks was rst described by Haber and Stornetta (1990) in their paper "How
to Time-Stamp a Digital Document", the term blockchain started to attract signi cant attention after
the group of developers who go by the pseudonym Satoshi Nakamoto described the blockchain model
their white paper (Nakamoto, 2008) and implemented the peer-to-peer electronic cash system in 2009.
The open and anonymous nature of the market has facilitated new opportunities while introducing new
risks never seen before with established nancial institutions, such as trust, unpredicted
uctuations, and
fraudulent crowdfunding schemes.
The digital age also sees the rise of social media platforms such as Twitter and Reddit, where users
can easily and conveniently express their opinions at any time and from anywhere, in the form of a simple
post or a "tweet." Investment advice and opinions are among the most popular topics discussed online
because of its real-time nature. While Twitter has implemented some forms of veri cations to encourage
people to use their real identities, such as asking for phone numbers or emails, Reddit has done little
to ensure the users' identities are traceable. As a result, the world has witnessed the infamous case of
Reddit's subforum "WallstreeBets" in January 2021, where the explosive power of Internet users and
their speculative investment research can manipulate the investment prices, leading to the short squeeze
of Gamestop's stock (GME) (Boylston, Palacios, Tassev, & Bruckman, 2021). In an open space with less
regulation and more freedom like cryptocurrency, we see a clear opportunity to utilize textual data from
social media posts helping investors make informed investment decisions.
The remainder of this paper is organized as follows. In Section 2, we brie
y covered the background
and terminologies behind blockchain. In Section 3, we similarly presented a brief literature review of the
recent developments in the Natural Language Processing space. We dived deep into the recent works
that use Natural Language Processing to predict the price and behaviours of cryptocurrencies in Section
4, and concluded the paper in Section 5.
2 Background on Blockchain technology
At a high level, a blockchain is a distributed ledger where all transactions are recorded in a chain of
blocks. As new transactions are committed, new blocks will be appended to the chain (Zheng, Xie, Dai,
Chen, & Wang, 2018). A distributed ledger is a public database where all participants can contribute
and maintains a synchronized copy of the data.
2.1 Key characteristics
•Decentralization. A key innovation of blockchain technology is the elimination of traditional in-
termediaries (central banks). Cost and
ow time can be massively reduced since transactions can
2
happen peer-to-peer (P2P). There are no single points of failure, as every network participant has
the same copy of the data.
•Persistency. blockchain is built to be an immutable ledger, meaning it is impossible to alter or
delete the data already recorded in the blocks.
•Anonymity. In the blockchain network, users are not identi ed by real names but by a randomly
generated address, which will be liked to each user's transaction. Even though the transactions
and the user's address (or wallet address) are public, the keys to prove ownership is private to each
user.
•Auditability. Since all participants have a copy of the data; it is possible to verify and trace the
transactions based on the public wallet address and the digital timestamp.
2.2 Types of blockchain network
There are three major types of blockchain networks: Public, Private and Hybrid.
•Public (Permissionless) blockchain. In this network, users can join and leave at any time without
obtaining prior permission.
•Private (Permissioned) blockchain. In this network, only a limited number of users with permissions
can read and write.
•Hybrid blockchain. This is a combination of both public and private blockchains. Transactions and
private information are kept inside the network but can still be veri ed.
2.3 Bitcoin (BTC) and Ether (ETC) and other crytocurrencies
Bitcoin, proposed by Nakamoto (2008), is a digital currency using blockchain technology to facilitate
transactions. It is still the most popular general-purpose cryptocurrency and is considered the rst
cryptocurrency ever created. There are 21 million bitcoins in total, and each bitcoin consists of 100
million smaller units (satoshis). Bitcoin is an example of public blockchain.
Since Bitcoin launched, other alternative cryptocurrencies based on blockchain technology have been
introduced, including Ethereum - developed by Buterin (2014). Its focus is providing a protocol for
building decentralized applications (dApps) deployed on Ethereum virtual machines (EVMs). Users can
create smart contracts to perform various tasks using a built-in Turing-complete programming language
to be executed on the blockchain. Turing complete (or computationally universal) refers to the idea that
a system needs to have the capacity to implement arbitrary computer algorithms. In other words, it needs
3
Figure 1: An example system to predict the price of bitcoin by Pant et al. (2018). The Recurrent
Neural Networks (Section 3.2) take into account two inputs: a sentiment score from a tweet,
and the historical price of bitcoin to predict the next price
to be able to stimulate a Turing machine. Ether (ETH) is the native cryptocurrency on the Ethereum
network. Other popular cryptocurrencies by market capitalization include Tether (USDT), USD Coin
(USDC), Binance Coin (BNB), etc. Both Tether, USD Coin, and Binance Coin were initially built on
the Ethereum network.
2.4 Predicting Prices and Trends of Cryptocurrency
As cryptocurrencies are considered valuable digital assets, various studies have been done to analyze
and protect cryptocurrencies, just like any other type of investment. Past works have used Machine
Learning methods extensively, alongside classical time-series variables (i.e., closing prices), to predict
prices of cryptocurrencies such as Bitcoin, Ethereum, etc. Popular choices for model architecture include
Autoregressive Integrated Moving Average (ARIMA) (Garg et al., 2018; Roy, Nanjiba, & Chakrabarty,
2018), Support Vector Machine (SVM) (Poongodi et al., 2020).
In recent years, with the advancement in Machine Learning and Natural Language Processing, we have
seen broad adoption of language models to support past works in predicting the Prices and Trends of an
investment. Prior research suggested that peers' in
uence and public moods in the form of unstructured
texts have been found to play an important role in investment decisions and market predictions (Farimani,
Jahan, Fard, & Ha ari, 2021; Farrell, Green, Jame, & Markov, 2022; Vo, Nguyen, & Ock, 2019). As a
result, it is common to see an architecture in recent literature comprising two parts: a sentiment analyzer
(to process the textual data such as Tweets from Twitter) and a prediction model (to predict the prices).
4
Figure 2: A biological neuron (left) and its simpli ed mathematical model (right)1
3 Background on Natural Language Processing
To tackle a large amount of textual data, earlier works in the area have focused on gauging the general
sentiment or the polarity of the text and quantifying it using manual labels (i.e., "positive," "neutral,"
and "negative" that are associated with a numeric value). This is generally considered the lexicon-based
approach, in which a sentence will be transformed into a vector via a simple method such as a token
count matrix (S a smaz & Tek, 2021). After that, a simple classi cation model such as Logistic Regression,
Support Vector Machine (SVM), or Naive Bayes classi er. Latest developments in the Natural Language
Processing eld have pushed the frontier in improving the performance of sentiment classi cation system
by allowing the vector representation of a word to have a context. Various recent state-of-the-arts language
models introduced recently include BERT (Devlin, Chang, Lee, & Toutanova, 2018), RoBERTa (Liu et
al., 2019), XLNet (Yang et al., 2019), and ELECTRA (Clark, Luong, Le, & Manning, 2020).
3.1 Arti cial Neural Networks (ANNs)
An arti cial neural network, or a neural network for short, is a mathematical model primarily inspired
by the structure and functionalities of the biological neural system in the brain. Figure 2 showed the
connection between a biological neuron on the left and its mathematical model on the right. At a high
level, they work by taking input signals from their dendrites and output signals along its axon. These
axons eventually connect to the dendrites of other neurons, creating the entire network . Another name
for ANNs is Feed-Forward Neural network because the input information will only be processed in the
forward direction, as seen in Figure 3.
1https://cs231n.github.io/neural-networks-1/
2https://cs231n.github.io/neural-networks-1/
5
Figure 3: Example architecture of a simple 3-layer neural network2
3.2 Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are models for data that involve times or sequences, such as sound,
video, or language. They addressed a few limitations of Feed-Forward Neural networks, such as keeping
track of or "memorizing" some of the inputs to make predictions in a situation where context and historical
information plays an important role.
3.3 Long short-term memory Networks (LSTMs)
Long Short-Term Memory networks or LSTMs are a type of RNN introduced by Hochreiter and Schmid-
huber (1997) to address the long-term dependency problem in the original RNN architecture. The
original RNN cannot make an accurate prediction as the gap between the point containing the relevant
information and the point where the relevant information increases. As a result, LSTMs have become
the popular model architecture with signi cant use cases in Image captioning, Language Translation,
Algorithmic trading, and Text Generation.
4 Applications of NLPs in price prediction
We can categorize the works in this space into two major groups: Corpus-based, and Advanced methods.
4.1 Corpus-based methods
Corpus-based methods are based on the actual, real, and authentic words as they showed up in the
document (Almutairi, 2016). By comparing and contrasting raw words from a document with a dictionary
like the Harvard-IV dictionary (Dictionary with a list of positive and negative words), researchers can
quickly sense the overarching sentiment of a post by aggregating the number of times that a positive
or negative word shows up. Sul, Dennis, and Yuan (2017) utilized this method to show that a trading
strategy incorporating Twitter postings' sentiments could generate an annual return between 10 and 15%.
6
4.2 Advanced NLP methods
Advanced NLP techniques attempt to go beyond relying purely on the count of the words as they occur
in a sentence. These methods gears towards language understanding, as they allow the context to be
incorporated into the vector representation of a word, as discussed in Section 3.
Vo et al. (2019) built a price prediction system using both news data and historical prices of Ethereum
(ETH) to generate a prediction for future prices (Figure 4). The authors created semantic vectors of the
news document using a classic n-gram language model and syntactic vectors utilizing a set of NLP
methods such as Dependency Parser, Coreference Resolution, and Named Entity Recognition. These two
vectors are combined to predict the sentiment score. Eventually, the sentiment score and the historical
cryptocurrency price are used to predict the future price. The text data used in this work is crypto-
related, obtained from NewsNow3, and manually labeled. A label of 1 indicates a positive sentiment
before a rise in the historical price and vice versa. Otherwise, the news will be labeled as 0 (neutral) as
there is no change in the price. Figure 5 showed that the proposed architecture achieved relatively better
performance than the SVM baseline when predicting ETH prices between July 2017 and October 2018.
To leverage the power of transfer learning, Cheuque Cerda (2021) used the pretrained ULMFiT and
BERT model in predicting the stance of a tweet from crypto-in
uencers and used these insights in building
price prediction models using XGBoost and LSTM. The author showed that while there might be a weak
relationship between the asset price and crypto-in
uencers' opinion, this e ect was much stronger and
more causal in the short term.
Huang et al. (2021) proposed using news articles and social media platforms such as Sina-Weibo,
WeChat, and QQ groups to build a crypto-speci c sentiment analysis model based on the Long short-
term memory (LSTM) Recurrent Network Structure (Figure 6). The data used in the training process
was manually labeled as positive (1), neutral (0), and negative (-1). The social media posts were tokenized
using the manually-created crypto word vocabulary speci c for Chinese words and used as the input to
the LSTM Sentiment Analyzer.
Ortu, Vacca, Destefanis, and Conversano (2022) showed via various empirical analyses that the price
of Bitcoin is highly correlated with the sentiment from the discussions on social media forums such as
Reddit. Using dynamic topic modeling and Hawkes models, they were able to explain how public opinions
on social media chatter can in
uence each other and cause price
uctuations.
Ortu, Uras, Conversano, Bartolucci, and Destefanis (2022) compared the performance of four di erent
deep learning models (Multi-Layer Perceptron, Multivariate Attention Long Short Term Memory Fully
Convolutional Network, Convolutional Neural Network, and Long Short Term Memory in predicting
3https://newsnow.co.uk
7
Figure 4: Proposed system to predict the price of Ethereum (Vo et al., 2019)
Figure 5: Result of predicting Ethereum (ETH) prices using di erent approaches (Vo et al.,
2019)
8
Figure 6: Cryptocurrency sentiment analysis and price movement prediction (Huang et al., 2021)
the price movements of Ethereum and Bitcoin between 2017 and 2020, utilizing BERT to extract the
emotions from social media posts. The competitive results showed a lot of promise in detecting the price
movements of Bitcoin and Ethereum.
5 Conclusion
The e ort in using advanced machine methods to better understand cryptocurrencies has proved that
investing in digital currencies is considered an alternative investment. As innovations are introduced in
both the crypto world and the natural language processing eld every day, opportunities and challenges
exist in this niche research area. With cryptocurrency being a new research topic, more work needed to
be done in this area to create an even more robust and accurate monitoring system to better utilize the
massive amount of data available, especially the textual data surrounding the topic.
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11
|
{
"id": "2212.00726"
}
|
2308.02653
|
Incentivizing Gigaton-Scale Carbon Dioxide Removal via a
Climate-Positive Blockchain
|
A new crypto token is proposed as an incentive mechanism to remove CO2 from
the atmosphere permanently at gigaton scale. The token facilitates CO2 removal
(CDR) by providing financial incentives to those that are removing CO2 and an
opportunity to provide additional financial resources for CDR by the public.
The new token will be native to a blockchain that uses a Proof-of-Useful-Work
(PoUW) consensus mechanism. The useful work will be conducted by direct air
carbon capture and storage (DACCS) facilities that will compete with each other
based on the amount of CO2 captured and permanently stored. In terms of energy
consumption, we require that the entire process, comprising DACCS technology
and all blockchain operations, be climate positive while accounting for life
cycle analysis of equipment used. We describe the underlying reward mechanism
coupled with a verification mechanism for CDR. In addition, we consider
security features to limit attacks and fraudulent activity. Finally, we outline
a roadmap of features that are necessary to fully implement and deploy such a
system, but are beyond the current scope of this article.
|
http://arxiv.org/pdf/2308.02653v1
|
Jonathan Bachman, Sujit Chakravorti, Shantanu Rane, Krishnan Thyagarajan
|
cs.CR, cs.CY
|
cs.CR
|
1
Incentivizing Gigaton-Scale Carbon Dioxide
Removal via a Climate-Positive Blockchain
Jonathan Bachman, Sujit Chakravorti, Shantanu Rane and Krishnan Thyagarajan
Abstract
A new crypto token is proposed as an incentive mechanism to remove CO 2from the atmosphere permanently at
gigaton scale. The token facilitates CO 2removal (CDR) by providing financial incentives to those that are removing
CO2and an opportunity to provide additional financial resources for CDR by the public. The new token will be
native to a blockchain that uses a Proof-of-Useful-Work (PoUW) consensus mechanism. The useful work will be
conducted by direct air carbon capture and storage (DACCS) facilities that will compete with each other based on the
amount of CO 2captured and permanently stored. In terms of energy consumption, we require that the entire process,
comprising DACCS technology and all blockchain operations, be climate positive while accounting for life cycle
analysis of equipment used. We describe the underlying reward mechanism coupled with a verification mechanism
for CDR. In addition, we consider security features to limit attacks and fraudulent activity. Finally, we outline a
roadmap of features that are necessary to fully implement and deploy such a system, but are beyond the current
scope of this article.
Index Terms
Direct Air Carbon Capture and Storage (DACCS), distributed consensus, Proof-of-Useful-Work, blockchain, zero-
knowledge proof, crypto token
I. I NTRODUCTION AND BACKGROUND
As realization grows about the impact of climate change, policymakers have considered incentive mechanisms
to reduce the rate of increase of carbon dioxide, CO 2, in the atmosphere. Such incentives include tax credits on
the purchase of electric vehicles and subsidies for renewable energy generation. To reduce net CO 2emissions, one
incentive mechanism enables polluting agents to purchase carbon credits (also called offset credits) to compensate
for their emissions of CO 2and other greenhouse gases. While these credits can fund technologies to reduce the
increase of CO 2in the atomosphere, they have significant limitations. We are particularly concerned that carbon
credits represent an easy option for polluters, removing the incentive to make the concerted changes necessary to
mitigate climate change.
S. Rane, J. Bachman, and K. Thyagarajan are with Palo Alto Research Center (PARC, Inc.). S. Chakravorti is with Alliance for Innovative
Regulation (AIR).
August 8, 2023 DRAFTarXiv:2308.02653v1 [cs.CR] 4 Aug 2023
2
In this article, we propose a mechanism that uses a crypto token to reward carbon dioxide removal (CDR) from
the atmosphere using Direct Air Carbon Capture and Storage (DACCS).1Unlike a carbon credit, that represents
one metric ton of greenhouse gases removed from the atmosphere, our token does not represent an amount of CO 2
equivalent captured and stored. Our tokens are “mined” by the winning DACCS facility and its value is determined
by the market for the token. Furthermore, the DACCS process including life-cycle analysis of the equipment used
must be climate positive, namely capture more CO 2equivalents than greenhouse gasses emitted. The adoption
and usage of the token depends on the demand of two distinct groups: those that are willing to provide financial
incentives for CDR, including individuals, businesses, and governments, and those that remove CO 2from the
atmosphere. Although not exact, one simple way to gauge the demand for CDR and the willingness to pay is to
observe the demand for carbon credits. McKinsey estimates that the annual global demand for carbon credits could
reach up to 1.5 to 2.0 gigatons of CO 2by 2030 and the market size could range from $5 billion to more than $50
billion depending on various factors [2].
To meet the climate goals of the 2015 Paris Agreement, we must substantially reduce greenhouse gas emissions,
generate more energy from renewable sources, and capture and store CO 2from the atmosphere at gigaton scale.
Our proposed solution focuses on the last component. CDR approaches, such as reforestation, biochar, bioenergy
with carbon capture and storage (BECCS) and DACCS, should be a part of the solution to limit global warming
between 1.5°C or 2°C. Smith et al. [1] estimates that novel CDR approaches would have to increase by a factor
ranging from 30 to 540 by 2030 to limit warming between 1.5°C or 2°C.
For the DACCS CDR, we propose a solution that: verifies and records the quantity of CO 2captured and stored
by the winning DACCS facility on an immutable blockchain in real time, rewards DACCS facilities to remove
and store gigatons of CO 2, and offers sufficient security against malicious attacks and fraud. Our results can be
generalized to other CDR technologies as long as measurement of capture and storage can be done accurately and
timely.
We propose collecting telemetric data using tamper-resistant sensors and recording this information on an
immutable climate-positive blockchain. In our case, we define a climate positive blockchain as the sum of CO 2
captured and stored, the amount of CO 2equivalents emitted during the operation of the DACCS facility including
life-cycle analysis of the equipment used, the CO 2equivalents emitted from blockchain operations and storage
of transactions along with life-cycle analysis of the equipment used. The linking of the physical world with the
digital world has its challenges including security especially guarding against adversarial attacks and fraudulent
transactions. We propose a process that is highly automated and secure to provide sufficient reliability and trust in
the data collected in real time. By doing so, we address a major challenge to the current system of carbon offsets,
namely the transparency and verifiability of the amount of CDR and stored [3].
Our proposed blockchain uses Proof-of-Useful-Work (PoUW) consensus mechanism where the useful work is
1CDR technologies do not include Carbon Capture and Storage (CCS) and Carbon Capture and Utilisation (CCU). According to Smith et al.
[1], to count as Carbon Dioxide Removal (CDR) technology, “a method must be an intervention which captures CO 2from the atmosphere and
durably stores it.” CCS and CCU are industrial processes aimed at reducing the emission of CO 2.
August 8, 2023 DRAFT
3
CDR.2The winning DACCS facility would be determined by a weighted lottery draw based on the amount of CO 2
captured and stored. Because we require that each DACCS facility is on net removing CO 2from the atmosphere,
even the non-winning DACCS facility are contributing to the removal of CO 2. Upon verification by independent
validators of the telemetric data, the winning DACCS facility would be financially rewarded with a quantity of
tokens and would add the new block to the chain similar to a crypto miner participating in a PoW consensus
mechanism.
A market would develop where those that wanted to financially contribute to the removal of CO 2could purchase
these tokens from the winning facilities. These tokens represent verified CO 2removal and storage. Clearly, the
DACCS facilities would only expend additional resources to participate if they are able to convert these tokens into
a medium of exchange, such as fiat currency, that could be used to buy goods and services. We also discuss how
these tokens themselves could become a medium of exchange used to buy goods and services directly. In other
words, these tokens would not need to be converted to fiat currency or other media of exchange.
When compared with the enormous amount of energy consumed by the Bitcoin network to operate its Proof-
of-Work-based consensus mechanism, our approach consumes a significantly smaller amount of energy. More
importantly, while the large energy consumption in Bitcoin results in massive amounts of CO 2emissions, our
approach targets a massive net reduction in the amount of atmospheric CO 2.3
In other words, PoUW consensus mechanism that we propose has the advantage that the energy used to “mine” or
add blocks to the blockchain is being used to remove net CO 2from the atmosphere accounting for CO 2equivalents
used in the process. Crucially, all the miners – the DACCS facility that creates the next block, as well as all other
DACCS facilities that do not win the competition to create the next block – contribute to CDR. This should be
contrasted with other PoW cryptocurrencies where the energy consumed by the unsuccessful miners is wasted.
This article is organized as follows. In Section II, we describe the DACCS technology, why this technology is
suitable for a PoUW consensus mechanisms, and identify the telemetry and mass balance equations that are necessary
for the PoUW consensus mechanism. In Section III, we outline the incentive mechanism using a decentralized
framework. In Section IV, we describe the key steps in a notional protocol for establishing consensus in a climate
positive blockchain and discuss some security considerations. In Section V, we explore how the token becomes a
medium of exchange and used to purchase goods and services. In Section VI, we discuss a roadmap for next steps
that were beyond the scope of the current investigation.
II. D IRECT AIRCARBON CAPTURE AND STORAGE (DACCS) AS A BASIS FOR POUW
In this section, we present a high-level description of direct air carbon capture and storage (DACCS) technology,
an argument for why DACCS provides a compelling basis for a PoUW scheme, an outline of the telemetry and
2For general discussion of PoUW consensus mechanisms, see [4], [5], and [6].
3For estimates of the carbon footprint of Bitcoin, the most popular cryptocurrency using a PoW consensus mechanism, see [7], [8], and [9].
For example, according to Digiconomist website on July 7, 2023, the Bitcoin network consumes 102.45 terawatt-hours per year which is similar
to annual power consumption of Kazakhstan and has a carbon footprint of 57.14 million tons of CO 2per year which is similar to the carbon
footprint of Portugal.
August 8, 2023 DRAFT
4
mass flow monitoring that can be used in the PoUW protocol, and finally a discussion on the role of life-cycle
analysis and net CO 2removal.
A. Introduction to DACCS
DACCS is an engineered carbon dioxide removal (CDR) process that involves separating CO 2from air and then
safely and permanently storing it underground. While DACCS is a relatively new technology, there are several scale-
up projects underway to demonstrate its feasibility and effectiveness as a climate mitigation strategy. Government
support for DACCS is growing, with several countries and regions taking an early lead in supporting its research,
demonstration, and deployment. The United States has established several policies and programs to support DACCS,
including the 45Q tax credit that provides up to $180 per ton of CO 2stored. A key feature of DACCS is that it
is not tied to existing energy infrastructure (i.e., fossil-fuel burning power plants), giving it the opportunity to
scale by constructing stand-alone DACCS facilities rather than by retrofit. That said, DACCS deployment does rely
on the availability of low-carbon energy sources and suitable geological storage. The source of energy used will
determine how net-negative the system is and drives overall capture cost, and while the global capacity for storing
CO 2underground is vast, specific site characterization is needed before commissioning a CO 2storage facility.
The growing voluntary market is the primary driver for investment in DACCS. Large companies that are seeking
to deliver on their net-zero commitments can purchase carbon dioxide removal (or a promise for future carbon
dioxide removal) to offset their past, current, or future emissions. However, additional incentives for companies to
conduct DACCS are needed to drive the technology to the gigaton scale.
B. Why use DACCS as the basis for PoUW?
The argument for using DACCS as the basis for a PoUW protocol is based first on its positive climate impact
and second on its ability to be precisely measured and monitored. First, a fundamental requirement for the climate-
positive blockchain is that its operation results in a net positive climate impact, i.e., CO 2removal from the
atmosphere. The net removal efficiency varies by DACCS technology and energy source, with impacts ranging from
–0.36 to –0.94 tCO 2eper 1t atmospheric CO 2captured and stored for a baseline grid mix in 2020 [10]. Second,
when compared to nature-based CDR as well as alternative technology-based CDR, DACCS is unique in that its
rate of carbon removal can be precisely measured. Using well established measurement tools (e.g., temperatures,
pressures, compositions, and flow rates), CO 2removal rates from DACCS processes can be monitored in real-time.
It is this measurement precision that makes DACCS well suited for a carbon negative PoUW scheme.
In our vision, token generation via PoUW can occur in parallel with the voluntary removal purchases to provide
additional revenue to DACCS operators, where DACCS operators can sell carbon removal as usual while also using
their telemetry to mine a fungible cryptocurrency. If there was widespread adoption of this token as a medium of
exchange and store of value, we believe this would greatly accelerate growth in DACCS.
C. Telemetry and Mass Flow Monitoring
The goal here is to propose a set of process value measurements (a.k.a., telemetry) that is general across DACCS
technologies, that can be used to monitor and verify a facility’s rate of carbon dioxide removal, and that can be
August 8, 2023 DRAFT
5
Fig. 1. DACCS facility, process flow diagram, and sensor telemetry for the capture and storage process.
communicated to a network to demonstrate PoUW. DACCS involves a capture process coupled to a storage process.
The capture rate and storage rate can be measured independently at the points of capture and points of storage,
respectively. A process flow diagram and the sensor telemetry that be used to measure and report the CO 2capture
and storage are shown in Figure 1.
Here, we propose a monitoring scheme that involves conducting measurements on the capture process and storage
process during operation, using those measurements to calculate CO 2captured and CO 2stored over a time interval.
Capture is generally conducted in a cyclical or continuous process that involves contacting air with a capture media
that selectively binds CO 2, regenerating the capture media to produce high purity CO 2gas, and reusing the capture
media in the next cycle. There have been several capture technologies deployed with various types of capture media,
wherein the binding can occur via adsorption (CO 2binding to a solid capture media) or absorption (CO 2binding
to a liquid capture media). Common among capture technologies is an operation in which air is flowed across
a capture media, with the concentration of CO 2in the air decreasing from inlet to outlet. There may be several
capture units in parallel operation within the capture facility, so the total capture rate is determined by a sum of
the capture rate from each capture unit. The CO 2concentration and mass flow rate can be measured in a variety of
ways. For example, the CO 2concentration can be measured using an infrared spectrometer and the mass flow rate
can be determined by measuring the volumetric flow rate, temperature, and pressure using an orifice flow meter,
temperature transducer, and pressure transducer, respectively.
After the CO 2is captured, it is compressed and transported to the storage site. The mass flow rate of CO 2can be
measured directly using a Coriolis mass flow meter. Alternatively, the mass flow rate can also be measured using a
thermal mass flow meter or a turbine mass flow meter. The mass fraction of CO 2can be measured using directly
(using an infrad spectormeter) or indirectly. For an indirect measurement of the CO 2mass fraction, the pressure,
temperature, and density can be used to infer the mass fraction of CO 2in the stream. The mass of CO 2stored over
August 8, 2023 DRAFT
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a time interval at the point of storage can be determined using the mass balance equation:
mstored =Zt
t0(CCO2∗˙m)dt
where CCO2is the mass fraction of CO 2and˙mis the mass flow rate. The calculation of net removal from mstored
is discussed in the next section.
D. Life Cycle Analysis and Net Removal
It is important to take into consideration the greenhouse gas emissions associated with constructing and operating
the DACCS facility. This can be done using life cycle analysis, where the amount of CO 2eemitted per ton CO 2
stored is determined. The life cycle analysis takes into account the energy requirement per ton CO 2stored, the
carbon intensity of thermal and electricity sources, the construction and material inputs of the DACCS facility, etc.
The life cycle analysis yields a value assigned to the DACCS facility that represents its net removal per ton CO 2
stored. DACCS facilities which are the most efficient and use the lowest carbon intensity energy sources will have
the greatest net removal per ton stored. The net removal is given by:
Xi=mstored∗Yi
where Xiis the net removal of CO 2by a DACCS facility over some time period (ton CO 2e), and Yiis the life
cycle analysis value assigned to the DACCS facility (ton CO 2e/ton CO 2).
III. T HEINCENTIVE MECHANISM
To incentivize DACCS operators, we propose to create a reward mechanism coupled with a mechanism to verify
a operator’s claims of CO 2capture and storage. We assume that steady state has been reached in terms of the
number of DACCS operators. The verification mechanism is necessary to build trust in the reward mechanism,
which is necessary to drive widespread adoption and to shift public opinion. There are several ways in which a
verification mechanism can be constructed. We do not advocate the use of a trusted entity to drive the verification
mechanism, because such a trusted entity is hard to find or develop in practice.
Instead, in this article, we are concerned with the technological feasibility of a decentralized approach to the
verification mechanism, wherein trust is placed in a network of a large number of mutually untrusting validators. No
single validator is trusted, however, cryptographic mechanisms combined with verification of sensor telemetry from
DACCS facilities can enable a network of validators to verify – i.e., achieve consensus on – a DACCS operator’s
claims of CO 2removal.
Censorship resistance implies that anybody on the planet can set up a DACCS facility and benefit from the
associated reward mechanism. This would require that the technical specifications of a prototypical DACCS facility
and its operating procedures be available to everybody. This is not unlike the censorship resistance achieved in
cryptocurrencies such as bitcoin.
In the subsequent sections, we will describe how to implement this combination of a reward mechanism coupled
with a verification mechanism. For concreteness, the CDR operators in these protocols are DACCS facilities, but
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7
extensions to other approaches are possible. We describe a winner selection protocol, in which every DACCS facility
performs the “useful work” of CO 2removal and wins a reward with a probability proportional to the amount of
CO 2it has (provably) removed.
When the reward is in the form of a crypto token, we describe how the winner selection protocol lends itself
to a blockchain-based cryptocurrency based on PoUW. The verification mechanism in this case, thus establishes
consensus not only on which DACCS facility wins the reward for CO 2storage, but also on the state of the resulting
blockchain. We remark that, unlike popular PoW mechanisms such as bitcoin, the “work” done by allDACCS
facilities (not just the winning DACCS facility) is climate positive. We contend that, if this token is widely adopted
as a medium of exchange, it would provide an additional powerful incentive for CO 2removal. In other words, the
token would not only support CDR activities but economic activity more broadly and would potentially become a
medium of exchange.
In addition, our proposed incentive mechanism takes advantage of a key benefit to issuers of most fiat currencies,
such as the US dollar, called seigniorage. The actual production of these tokens is close to zero but the token
represents monetary value.4The difference between the face value of the token minus the production cost is known
as seigniorage. As the demand for CDR removal increases, so does the monetary value of the token. Thus, the
increase in demand for the coin creates additional monetary incentive to DACCS operators.
IV. D ESIGNING A C ONSENSUS MECHANISM AND REASONING ABOUT SECURITY GUARANTEES
A key technical challenge is to craft a distributed consensus protocol in which mutually untrusting parties can
verify claims of CO 2captured and stored, and for each round, determine a winner based on the amount of CO 2
captured and stored. The role of such a winner can be many-faceted. For example, the process of determining a
winner may trigger the generation of a specified quantity of crypto tokens which are sent to the winner. The winner
– in this case, the winning Direct Air Carbon Capture and Storage (DACCS) facility – could, in turn, distribute
the token amongst several stakeholders, use the token, directly or converting it to medium of exchange, to fund
the DACCS operation, or use it to purchase other goods and services. In a cryptocurrency application in particular,
the winner records the awarded tokens in the block they create and append to the blockchain. We provide below
the key steps in a notional protocol for establishing consensus about the DACCS facility that receives a reward for
storing CO 2in a climate-positive blockchain.
A. Participants
The consensus protocol is executed by a network of mutually untrusting entities. A subset Mof these entities
own or operate carbon capture facilities. For specificity, we will assume that the CO 2capture facilities employ
DACCS technology. We will refer to the DACCS facilities as miners in our context for two reasons: (1) they
are extracting CO 2from the air and storing it, (2) their role is analogous to miners in traditional cryptocurrency
mining [12].
4For discussion of seigniorage, see [11].
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Validator network𝑀!Air𝐶𝑂"𝑀#𝑀"……𝑀$𝑀!∗Miners sending 𝐶𝑂!removal telemetryWinnerSelection𝑀!∗PreliminaryWinnerVerification of sensor telemetryPreliminary Winner SelectionInitial Storage Claim to Validators
SuccessNoYes
Fig. 2. DACCS operators submit sensor telemetry to a network of validators which determines a winning DACCS operator (miner).
Let the set of miners be denoted by M={M1, M 2, . . . , M K}. Another subset Vof the mutually untrusting
entities is tasked with receiving information from the DACCS facilities, verifying the claims of carbon capture and
storage, and determining the winning DACCS facility which earns a reward in the form of a token. We denote the
set of these validators as V={V1, V2, . . . , V N}. Note that N≫K, as any one with a relatively small amount of
computing resources can sign up to be a validator, while setting up a DACCS facility is an expensive proposition.
In some variations, there can be overlaps between the two sets, i.e., a DACCS facility can also be a validator, thus
Vi≡Mjfor some i < N, j < M . For the development below, however, we consider the simpler case in which the
two sets to be disjoint.
B. Winner Selection Protocol
We now describe how a network of untrusted miners and untrusted validators can establish consensus about the
amount of CO 2captured and stored by one of the miners chosen at random (See Fig. 2). If the CO 2-capture and
storage claims are verified, the chosen miner is determined the winner, and a reward – in terms of a quantity of
crypto tokens – is generated and sent to the winner. We defer a discussion of the value of the token and its possible
uses to subsequent sections. Winner selection occurs using the following steps:
1)CO 2Capture: At a designated time-stamp tsagreed upon by the miners and validators, each miner Mi∈ M
starts capturing CO 2. The capture process continues for a designated constant period of time T. The miner
stores the sensor data from the capture process. Let us denote the sensor data captured by MiasCi(t) =
{C(1)
i(t), C(2)
i(t), . . . , C(P)(t)
i}, where ts≤t≤ts+T. Here, C(1)
i(t), C(2)
i(t),etc, are the various physical
variables that constitute the CO 2-capture process.
2)CO 2Storage: At regular intervals after time ts, each miner also performs a process of storing the captured
CO 2. The cadence of capture and storage intervals can differ from miner to miner, depending upon the
equipment and technology used. The storage process can be performed in chunks of time, or can be performed
continuously as more and more CO 2is captured. The miner stores the sensor data from the storage process. Let
us denote the sensor data captured by MiasSi(t) ={S(1)
i(t), S(2)
i(t), . . . , S(Q)
i(t)}, where ts≤t≤ts+T.
Here, S(1)
i(t), S(2)
i(t),etc, are the various physical variables that constitute the CO 2-storage process. The
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sequence of CO 2capture and storage processes, shown here in the context of direct air capture continue until
a time stamp te=ts+T.
3)Initial CO 2-Storage Claim to Validators: Each miner Mi,1≤i≤Mthen sends to each validator Vj,1≤
j≤N, an initial claim of CO 2storage. This communication from Mi, received by each Vjis described
as a set Ai={i, ri, ts, Xi,ZKP(Ci(t),Si(t))}. Here, ZKP (Ci(t),Si(t))is a concise non-interactive zero-
knowledge proof extracted from the capture and storage telemetry.5Xiis the claimed amount of stored CO 2.
riis a random number that will later be used by the validators for selecting a winning miner. To ensure
message authenticity and integrity, Aiis bundled with a digitally signed hash, computed by Mi, given by
Signi(h(Ai)), where h(·)is a standard hash function, e.g., SHA-256. The final message thus takes the form
Bi={Ai,Signi(h(Ai))}.
4)Verification of Initial Claim: Each validator Vj, then verifies that the message received from each Mi
is genuine and unmodified, using the public key of Miand the knowledge of the hash function h(·). Each
validator also verifies the zero-knowledge proof ZKP (Ci(t),Si(t))for each miner. This means that each miner
presents a proof to the validators that they have captured relevant sensor data during capture and storage,
without revealing that data. At this point, a number of mechanisms are possible to enable the validator network
to agree upon a winning miner. We describe one possible mechanism here, but many others are permissible.
5)Preliminary Winner Selection: To each miner Mi, a validator then assigns the length of a subset of the unit
interval [0,1]given by:
ℓi=Xi
ΣM
iXi(1)
To maintain consistency amongst the calculations of the validators, the calculation of ℓiis performed by a
validator Vjif and only if they have received the message Bifor each i∈ {1,2, . . . , M }. If not, the validator
does not participate in this computation. Furthermore, the segments of length ℓiare arranged in ascending
order of i, such that ΣM
iℓi= 1. Let the ithsegment, having length ℓihave endpoints ui−1andui. Thus, the
lengths are given by len (u0, u1) =ℓ1, len(u1, u2) =ℓ2, len(u0, u2) =ℓ1+ℓ2, len(u0, uM) = ΣM
iℓi= 1, etc.
Each validator Vjcomputes a random number, which – by construction – is the same across all validators,
given by:
¯r=MX
i=1ri
The number ¯ris then hashed using a cryptographic hash algorithm, e.g., SHA-256, and mapped to the unit
interval as a number br∈[0,1]. Then each Vjdetermines the winning miner as:
Mi∗≡Misuch that len (u0, ui−1)≤br≤len(u0, ui) (2)
5The zero-knowledge (ZK) proof captures the physical relationships between the sensor readings in the capture and storage processes. We
are not aware of such proofs existing today, but are of the opinion that succinct ZK proof approaches developed in the literature (e.g., [13],
[14]) could be leveraged to construct them. We pose the development of succinct Non-Interactive Zero-Knowledge Proofs (NIZKs) to capture
relationships amongst physical quantities as an interesting and open research area.
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Each Vjbroadcasts or gossips the identity of the winning miner Mi∗to the validator network, along with the
random number brso that other validators can verify that the preliminary winning miner is chosen correctly.
Concretely, Mi∗is considered to be declared as the preliminary winning miner when the number of validators
confirming the choice of i∗crosses a specified (large) threshold.
6)Verification of Sensor Telemetry : When the preliminary winning miner Mi∗is determined, its identity is
broadcast to the network. Mi∗then transmits its actual sensor data, i.e., Ci∗(t)andSi∗(t)to each val-
idator Vj. In addition to the raw sensor data, Mi∗also sends to the validators digitally signed hashes
of the capture and storage data, accompanied by the timestamp ts. We denote this signed, timestamped
data by Sign (ts∥h(C(k)
i∗(ts)∥C(k)
i∗(ts+ 1)∥. . .∥C(k)
i∗(ts+T))for1≤k≤Pon the capture side, and
Sign(ts∥h(S(m)
i∗(ts)∥S(m)
i∗(ts+1)∥. . .∥S(m)
i∗(ts+T))for1≤m≤Qon the storage side. Here again, h(·)is
a standard hash function. What this means is that each sensor on the capture and storage side digitally signs
its telemetry and incorporates a timestamp. Each validator then confirms whether the capture and storage
data are consistent with Xi∗, the claimed amount of CO 2captured.6If the sensor telemetry is determined to
be consistent by each validator, (or a large enough majority of validators), then the winning miner Mi∗is
confirmed. If not, the step of preliminary winner selection is repeated after excluding Mi∗, i.e., after setting
Xi∗= 0 and therefore ℓi∗=len(ui∗−1, ui∗) = 0 . This process is repeated until it is confirmed that the
sensor telemetry received from the chosen miner is consistent with the claimed amount of carbon storage for
ts< t < t s+T.
C. Security and Privacy Considerations
We presented the above protocol with clarity in mind, however, there are alternative implementations that might
be more efficient from the perspective of the protocol’s communication overhead and privacy of the miners. As an
example, it is conceivable that the step representing verification of sensor telemetry in Step 6 might be obviated by
absorbing the verification operations inside a novel zero-knowledge proof in the step representing verification of
the initial claim (Step 4). We do not yet know of an efficient zero-knowledge proof mechanism for this alternative
implementation. Conceptually, it appears to be similar to zero-knowledge middleware boxes (ZKMB) [15] used to
prove compliance with network protocol requirements. In that case, the winner selection process would primarily
be performed in Step 5, with just the mitigations for non-compliant telemetry – i.e., disqualifying miners whose
telemetry has been shown in zero-knowledge in Step 5 to be inconsistent with the claimed CO 2storage values –
being performed in Step 6. In what follows, we will discuss security considerations that apply to the protocol as
described above, wherein the winner selection is done in two steps, a preliminary selection (Step 5) followed by a
verification of sensor telemetry (Step 6). Security considerations include:
6We remark that an alternative approach would refrain from splitting the CO 2storage claims into a preliminary check and a final check.
Concretely, if the zero-knowledge proof can be designed to capture the relationship between Xiand the capture/storage sensor readings, then
the last step of the protocol is not necessary. Again, development of efficient zero-knowledge proofs to accomplish this is posed as an open
research problem.
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1)Data Authenticity and Integrity: The protocol described above ensures authenticity and integrity of the
information being transmitted. In the initial storage claim, each miner Mi,1≤i≤Kdigitally signs the data
sent to the validators. This prevents man-in-the-middle attacks in which the data is intercepted and manipulated
in transit, before it reaches any validator. Furthermore, in the verification of the sensor telemetry, the data
sent from the capture sensors and the storage sensors to the validator network is also digitally signed by the
sensor manufacturer. We assume that the sensors are equipped with a trusted platform module at the time of
manufacture, that allows them to sign their data. The signature verification keys of all the sensors are publicly
available to all validators. This ensures that the miner operating the DACCS facility cannot modify the sensor
data to claim a false amount of CO 2captured or stored. Concretely, a dishonest miner could fabricate sensor
data but would not be able to produce a digital signature on the fabricated data because he does not know
the sensor’s signing key.
2)Replay Attacks: A timestamp is embedded in the signed data sent by each capture and storage sensor. This
ensures that a miner cannot replay sensor data from the past while fooling the network that it has newly
captured or stored CO 2.
3)Faulty Initial Storage Claim Data: Suppose that a validator is not able to verify the zero-knowledge proof
that a particular miner Mihas gathered relevant sensor data. Note that the protocol requires the same ZKP
to be sent to all validators. If some validators discover that they are unable to verify the ZKP sent by Mi,
then it means that Mihas sent a different and incorrect ZKP to some or all validators, so this miner must be
disqualified from the winner selection algorithm. In this case, the validator’s gossip the identity of the miner
Miand exclude that miner from the further steps in the protocol. This is essential because all validators have
to maintain the same state – the same random number ¯rand the same segments of the unit interval in the
preliminary winner selections step – in order to compute the preliminary winner.
4)Sensor Data Incompatible with Storage Claim: Suppose that, in the final step involving verification of
the sensor telemetry, one or more validators discover that the amount of CO 2claimed as stored by Mi∗is
inconsistent with the received sensor data. In this case, at a minimum, Mi∗is removed from consideration,
i.e., the protocol repeats step 5 after setting Xi∗= 0. It is conceivable that stricter measures may be adopted
against Mi∗to discourage dishonest CO 2storage claims by miners, such as disqualification from participation
for a longer time period; We do not consider such measures in detail in this paper.
5)Validators that Intentionally Misreport Results of one or more Protocol Steps: Due to the use of digital
signatures, it is computationally infeasible for a validator to forge the sensor telemetry in both the verification
of the initial claim of carbon capture, and the verification of the full sensor data. Malicious action, in this
case, consists of a collusion of a vast number of validators who coordinate to misreport the result of the
preliminary winner selection algorithm. In essence, this is similar to a malicious attack on a cryptocurrency
where a majority of the nodes refuse to confirm a block transaction, for the purpose of generating a temporary
fork in the blockchain. The larger the size of the set of colluders, the more secure the protocol is from their
actions. At the very least, this number should be set to be greater than 50% of the total number of validators.
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D. Complexity Considerations
The computational complexity incurred at each DACCS facility is dominated by the computation of digital
signatures and zero-knowledge proofs in Step 3. The complexity incurred at each validator is dominated by the
verification of the zero-knowledge proofs in Step 4, and the verification of the sensor telemetry in Step 6. Taken
together, this complexity is minuscule compared to that incurred in conventional PoW mechanisms. For e.g., in
bitcoin, a hash function, such as SHA256, has to be computed billions of times (in general) until the output satisfies
a stipulated mathematical condition. Since these repeated operations have been replaced by CO 2capture and storage
in our approach, the computational complexity of the winner selection protocol is significantly lower than that of
traditional PoW mechanisms.
E. Fault Tolerance Considerations (Missing Initial Storage Claim Data)
Suppose a validator does not receive the initial storage data from some miner Mi. Then, this validator cannot
implement the preliminary winner selection step. We stipulate that any validator that has missing storage data does
not participate further in this round of establishing consensus, i.e., the round corresponding to the time interval
[ts, ts+T]. A key design parameter is the number of validators that must have all the storage claim data presented
by the miners, and must stay online during the entirety of the protocol execution. This number should be large
enough, otherwise the winner selection mechanism would not be credible.
V. L EVERAGING THE CONSENSUS MECHANISM IN A BLOCKCHAIN -BASED CRYPTOCURRENCY
A key motivation for the winner selection protocol is to develop mechanisms that incentivize CO 2capture and
storage. This motivation leads to a reward mechanism and an associated cryptocurrency in a natural way as we
describe below. For the purposes of this article, a cryptocurrency has the additional property versus a token that
it can be used in the broader economy to buy and sell goods and services. To clarify our development below, we
will often refer to the architectural components of Bitcoin [12], the world’s most popular cryptocurrency, by way
of analogy.
A. A Reward in Crypto Tokens
Suppose that the winner selection algorithm of the previous section is executed, and a winning miner Mi∗is
determined. Suppose that, as a result of being the winner, Mi∗is rewarded with a quantity Zof crypto tokens. In
other words, the conclusion of the winner selection algorithm results in the creation of Znew tokens. The winner,
Mi∗may then convert the token into a medium of exchange to purchase goods and services. Alternatively, the
reward could be saved for future use.
For the cryptocurrency application, we remark that the identity of the winning miner Mi∗need not be revealed.
This can be accomplished by using an identifier, such as the miner’s public key PKirather than the index i. For
the winner selection algorithm of Section IV to work with such an identifier, the steps of that protocol can all be
executed by replacing iwithPKi, with one exception: The preliminary winner selection step needs an explicit
ordering amongst the identifiers of the miners, to create the partitions of the unit interval using which the winning
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miner is randomly chosen. With the public key identifiers, we no longer have the naturally ordered identifiers in a
set{1,2, ..., M }. However, we can obtain an ordering of Melements in many ways, the simplest being to order
the identifiers PKi, i∈1,2, ..., M in increasing order of magnitude such that j < i =⇒f(PKj)< f(PKi),
where fis a suitable function. We assume that such an falways exists. With the new ordering, it is again possible
to generate a partition of the unit interval and choose a uniformly distributed random variable over the unit interval
to choose the winning miner. In this case, since the index of the winning miner maps back to a public key identifier
rather than a miner’s identity, it preserves the anonymity - though not the privacy - of the miner. Anonymity can be
improved if the miners use new public-private key pairs in each round, and for each transaction that they engage
in, thus making it difficult to link successive transactions or reward accruals.
B. Crypto-Token Transactions in the Real World
When a crypto token is used to purchase goods and services in the economy, the token serves as a medium of
exchange. The total supply of a cryptocurrency can grow indefinitely such as for ether or be capped such as bitcoin.
In addition, the supply of cryptocurrencies can also decrease by permanently removing coins from circulation. In
this article, we abstract from determining the optimal supply of the new token based on the demand for the token.
As noted before, although, the new crypto token is produced based on competition among DACCS facilities, the
value of the token is not connected to the amount of CO 2captured and stored.
We will now describe an approach in which transactions involving such a token are recorded in a blockchain
that is driven by the climate-positive consensus protocol from Section IV. Consider that Alice and Bob engage
in a transaction in which Alice must pay ztokens to Bob. Assume that Alice and Bob possess digital wallets
similar to the ones available for cryptocurrencies today, and that they are each identified using their public keys.
The transaction can then be reduced to the following quantities (1) the input address, i.e., Alice’s public key, from
which the token is sourced, (2) the amount zof the transaction, (3) the output address, i.e., Bob’s public key, to
which the token will be transferred when the transaction is complete, (4) optionally, an amount ∆z, consisting of
the transaction fees that may be paid to the miner, (5) a time stamp of the transaction.
C. Protocol for Incorporating Transactions in the Blockchain
The protocol for transferring ztokens from Alice to Bob, and recording that transaction in the blockchain would
then follow the steps below (See Fig. 3):
1)Creation: Bob sends his public key PKBto Alice. Alice then creates the transaction file,and signs it with her
private key SKA. Thus, the transaction involves sending z+ ∆ztokens from input address PKAto output
address PKBat some time t.
2) Alice sends the signed transaction to the closest node in the blockchain, which could be a miner or a validator.
From there, it is propagated to the validator network using a suitable gossip protocol [16].
3)Preliminary Verification: The transaction undergoes preliminary verification in which basic requirements
are checked. These include, for example, checking that the input address has a balance greater than z+ ∆z
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𝑀!Air𝐶𝑂"𝑀#𝑀"……𝑀$Miners sending 𝐶𝑂!removaltelemetry& proposed blocks Winner Selection ProtocolBlock proposed by𝑀"∗containing areward for 𝑀"∗Block proposed by𝑀"∗appended to blockchain. Other proposed blocksdiscarded. …
Fig. 3. The DACCS operators can leverage the winner selection protocol to propose candidate blocks of crypto-token transactions that are
appended to a blockchain.
tokens at the time stamp t. Once the transaction clears the preliminary verification, it is transferred to a pool,
analogous to Bitcoin’s Memory Pool, where it awaits a miner.
4) Any miner Mi∈ M can then consider the transaction for inclusion in the next block of transactions to
be mined. We will focus on the activity of a single miner Mi, one of possibly many that will include the
transaction in a candidate block. One criteria for selecting the transaction in the proposed new block is whether
the transaction includes a fee intended for the miner.
5) Each Michooses a specified number, ηof new verified transactions for inclusion in a candidate block that
he will propose to the network. One approach for Mito choose transactions from the Memory Pool for
inclusion into the new block is to select those with the highest transaction fee. The miner also adds the
following information to the proposed block:
a) A cryptographic hash of the most recent block added to the blockchain. This can be a SHA-256 hash,
and it cryptographically chains the blocks.
b) A Merkle tree [17] computed from the hashes of all the transactions included in the block.
c) The amount of CO 2stored during the current time block, indexed by ts, in the winner selection protocol.
This would be indicated by XPK i, rather than Xi, as used in the winner selection protocol.
This additional information allows anyone inspecting the block to determine how much CO 2was stored by
the winning miner. Note that an aggregate of the stored amounts for all blocks is significantly lower than the
total amount of CO 2stored by the blockchain, because the miners that were not chosen as winners have also
performed useful work by contributing to CO 2stored. This is a key beneficial aspect of the climate-positive
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blockchain7.
6) Each Mithen includes a special transaction in which the miner pays into its own digital wallet an amount
given by
θ=Z+ηX
j∆zj
where ∆zjis the transaction fee in the jthtransaction included in the candidate block. The value θis thus
the sum of the mining reward and any transaction fees. This is similar to bitcoin’s coinbase transaction , in
which new crypto tokens are created. Also, similar to bitcoin, this transaction is special in the sense that the
total earnings θcannot be spent unless a specified (large) number of vaildators have confirmed that candidate
block has been appended to the blockchain, thereby confirming that Miwas the winner of that round, i.e.,
(i≡i∗). If, on the other hand, Miis not chosen as the winner in the winner selection algorithm, the new
proposed block of transactions (along with the coinbase transaction) is discarded. Miwill propose a new
block in the next round.
The reader will note that the structure and construction of the blocks resembles that in Bitcoin. The difference
lies in the fact that, in Bitcoin, the block records the block hash as well as nonce that the winning miner used to
successfully mine the block, while in the proposed climate-positive crypto-token, the block records the amount of
CO 2stored by the miner and confirmed by the validator network.
D. Security and Privacy Considerations
Many of the security considerations for this protocol are inherited from those in the winner selection protocol,
as described in Section IV-C.
1)Protocol Security We do not claim that the protocol described here is as secure as bitcoin. This is because
of a fundamental difference: The Bitcoin protocol operates entirely in the digital world and is grounded in
well-understood cryptographic mechanisms that have stood the test of time. Our protocol, on the other hand,
attempts to connect a physical process (CO 2removal) to a digital protocol with cryptographic mechanisms.
Thus, attacks on the proposed protocols can come from both the physical and the digital worlds, and crucially,
at the interface of the physical and digital worlds.
Security mechanisms for preventing subversion of the physical process are a work in progress. We have
described how signing the sensor telemetry can help avoid security problems related to data authenticity and
integrity. Incorporating a time-stamp in the digital signature is used to deter replay attacks. We anticipate
the need to develop further efficient mechanisms (both physical and digital) to improve the security of the
physical process, as well the security of its interface with the digital process. We hope that this work will
highlight the need and catalyze further research in the field.
7Since the CO 2storage amounts are gossiped by the DACCS facilities to the network, the protocol may be modified to include the total
amount of CO 2stored by allminers while mining the previous block in the blockchain. With this modification, aggregating the total CO 2
storage amounts over the entire blockchain provides an estimate of the CO 2stored since the deployment of the blockchain. The caveat is that,
by construction, only the CO 2storage amount of the winning miner has been verified for each block; the storage amounts for all other miners
are unverified, and therefore should only be considered as estimates.
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2)Anonymity of Miners : The protocol, as described, has one key difference compared to consensus mechanisms
based on a cryptographically hard problem. In our protocol, the validator network knows the public key of the
miner when it executes the winner selection protocol. This is slightly different from Bitcoin, in the sense that
(1) the miners who propose a candidate block in the Bitcoin blockchain can do so without easily revealing
their public key (2) the public key of the winning miner can only be accessed by someone who can read the
scriptsig field of Bitcoin’s coinbase transaction, and then the miner can possibly be identified by analyzing
transactions on mining pools. In other words, it is easier to identify the public key of the winning miner in
our proposed mining process, compared to the case in bitcoin. As stated earlier, if the miners refresh their
public/private key pairs for each new mining round, they achieve better anonymity. Like Bitcoin, however,
their identities could still be revealed via analytics on blockchain transactions. This difference has implications
for oversight into the operation of the DACCS operators (miners). The cryptographic machinery of Bitcoin
ensures that it needs no oversight beyond verifying the hash computations. For CDR technologies, the scope
of oversight is broader. While we verify sensor telemetry and ensure data authenticity and integrity in our
protocols by means of cryptographic tools – thus making it difficult for a rogue DACCS operator to fake
telemetry – it may be necessary to supervise other aspects of the DACCS operation. For example, is the
storage being performed in a geologically suitable region using the correct processes so that the stored CO 2
does not escape back into the atmosphere? Are the storage readings taken at the correct stage in the process?
It would be useful to perform such checks at random, sparse intervals. Who should perform this oversight?
These are open questions that need to be addressed.
3)Anonymity of Transacting Entities: We remark that the level of anonymity provided to entities that engage
in actual transactions of our proposed token, is identical to that in Bitcoin. That is because the transacting
entities are only involved in the digital components of consensus mechanism, and have no involvement in the
verification of sensor telemetry.
VI. R OADMAP :SCALING NEEDS ,GENERALIZABILITY AND OPEN QUESTIONS
While novel frameworks such as the one presented here, need a pathway for adoption and scaling, the technologies
behind the platform will also keep improving. In this section, we deliberate on four broad areas: (a) the needs for
scaling such a framework, (b) discussion of tokenomics (c) the generalizability of such a framework to other carbon
capture technologies, and (d) some open unanswered questions.
A. Scaling needs of the framework
The proposed solution considers the steady state situation, where there are sufficient number of DACCS to capture
carbon and likely enough ways of storing or upconverting the captured carbon. However, to scale up from the first
established DACCS to a steady state condition of Mminers, there needs to be a suite of incentive mechanisms in
place for a number of participants in the ecosystem. The following can be viewed as points aiding the scaling of
the framework or also as bottlenecks which might limit scaling possibilities:
August 8, 2023 DRAFT
17
1)DACCS: The ability of a miner to effectively use the awarded crypto-tokens needs the token to be liquid.
This need includes a mechanism to trade it for other forms of hard or cryptocurrency and the ability to handle
cross-currency swap risks. If the tokens display liquidity, adoption of the framework to newer DACCS will
be much faster. Establishing a tokenomics model of the crypto-tokens will be a useful exercise in promoting
the liquidity.
2)Local communities: Setting up a DACCS needs land, which needs to be procured, often from local commu-
nities already established on the land. Although a general concern with DACCS, with an increased monetary
value associated with the facilities as brought about by our framework, there may be several repercussions.
The price of land may go up giving rise to two possible outcomes. Firstly, it may make establishing new
DACCS less attractive given the returns (which may lead to more vertical facilities) thereby driving lower
returns for the farms and local community. Alternately, the price rise may help local communities and create
more efficient DACCS, all while increasing the amount of captured carbon. Noise pollution and the potential
long term safety of stored underground carbon may prove to be a repellent, similar to wind farms. A secondary
market can provide additional leverage and participation in accelerating adoption of the framework.
3)Technology companies: It can be expected that there will be frequent improvements in DACCS technologies
and components including in sorbents, sensors, sequestration and upconversion processes. Being a long term
investment for DACCS, it may come to pass that such technology companies are under beholden contracts
with the facilities to ensure a steady dedicated supply of materials and component devices. To make the
sensors used along various points of the process steps tamper-proof, they may need unique certification by
the manufacturers. This may enable longer term contracts between DACCS and OEMs, thereby making it
attractive to both parties.
4)Local governments: Any modification of the local environment by carbon capture opens the chance of
inadvertent minor engineering of the local climate. This may need the open discussion between local, state
and federal government bodies. If the DACCS are established close to the boundary between two neighboring
countries, it may result in the need for a greater negotiation between multiple parties. If done well, this can
have positive changes to local environments thereby reflective on local economies.
B. Tokenomics
Tokenomics refers to the underlying economics of tokens including factors that determine their optimal token
supply based on the token demand. Although we do not discuss price dynamics of the token in detail, we can offer
some insights. First, as the demand for CDR increases, the price of the token should rise given no or little change
in supply. Second, as the DACCS technology improves, the price of the token falls given little change in demand.
Of course, there could be increases in both supply and demand simultaneously.
Furthermore, decisions regarding whether the total supply of the token will be fixed or unbounded need to be
explored. Discussions about the path of optimal supply of the token over time is beyond the scope of this article.
In other words, are there rules on how supply increases or decreases? Does the community determine when to burn
or take tokens out of supply?
August 8, 2023 DRAFT
18
Initially, the demand of the token will be based on the trust of those that want to participate in DACCS technology
for CDR. What alternatives do these participants have and how does this token rank among those alternatives? If
successful, the demand for this token along with its price will also increase. Once the token is established, it may be
used for a host of other transactions. What token characteristics would be desirable for other types of transactions?
Clearly, the positive impact on the environment resulting from the PoUW would be extremely attractive to users.
Also, as the price rises, these tokens may circulate less because holders of these coins prefer to hold on to them
instead of transacting with them leading to reduced liquidity. Decentralized finance platforms may evolve to increase
liqudity where holders would deposit their tokens to liquidity pools. These discussions are beyond the scope this
article.
As new CDR technologies are adopted, how would DACCS tokens interact with other types of tokens. Would
there be a market-based conversion rate between different types of CDR tokens?
C. Generalizability of the Framework
Thus far, the framework has been described with the DACCS as an example. However, it can be extended to many
other forms of climate positive actions, not restricted to carbon capture. This includes ocean carbon capture, DACCS
+ S (DACCS and sequestration), DACCS + U (DACCS and utilization/upconversion), capture of other greenhouse
gases such as methane ( CH 4), nitrous oxide ( N2O), (with present or future sorbent materials), and ocean de-
acidification, to name a few. Any climate positive action thus, involving the capture, production or modification of
a known parameter along with a relevant set of process steps having periodic measurements with multiple sensors
can be amenable to this protocol/framework.
D. Open Questions
While goodwill is an underlying ethos behind the framework, for improving the future of humanity on earth, it
also provides a financially lucrative reward for doing good. Furthermore, since the consensus mechanism that drives
the reward is achieved algorithmically, it is essential that technological means be developed to ensure correctness
and security of the protocols.
1)Technological Considerations :
a.Sensor Design and Manufacturing: The protocols we describe require sensors to digitally sign the data
they produce, because this preserves authenticity and integrity of the sensor data. To achieve this capability,
it is necessary to augment current sensor technology – for example with hardware enhancements such as
Trusted Platform Modules (TPMs) [18] – to enable them to sign the data. This would increase the cost of
the sensors, and it remains to be seen whether the scale at which such sensors are manufactured will make
the cost manageable in the long run.
b.Protocol Design: As we have described, zero-knowledge proofs can enable DACCS facilities to efficiently
and securely verify their sensor telemetry and their CO 2storage claims. We did not, however, provide an
implementation of the proofs. Developing efficient non-interactive zero-knowledge proofs for verifying sensor
August 8, 2023 DRAFT
19
telemetry and CO 2storage claims can be considered as an open research problem. These proofs constitute a
crucial step in making the climate-positive blockchain feasible, secure and efficient.
c.Adversarial Scenarios: We have described some adversarial situations and provided ways to mitigate them.
Since the proposed approach involves connecting a physical process – carbon capture and storage – to a
digital process – a distributed consensus mechanism driving a blockchain – novel adversarial scenarios arise,
and need to be revealed and mitigated. For example, rogue DACCS facilities may fabricate additional fake
identities and claim larger CO 2storage amounts to increase their probability of winning the mining reward.
Such behavior is difficult to defend against, but it can, in principle, be mitigated using a reputation system in
which validator nodes can reduce the reputation score of a suspected rogue miner. For example, CO 2storage
claims from a fake location, or a location very close to an existing DACCS location, may raise suspicion of
illicit activity. This approach is quite similar to reputation scoring used to prevent malicious behavior in some
proof-of-stake mechanisms.
d.Novel Carbon Capture Mechanisms: Though we have focused on DACCS as the carbon capture mechanism,
the principles underlying the protocols apply to any alternative mechanism. It is an interesting research
question to determine whether several carbon capture mechanisms can interoperate correctly, securely and
fairly in the distributed consensus mechanism, and in its associated applications, such as driving cryptocurrency
transactions.
2)Governance and Oversight Considerations :
a. As we have described, the fact that we are interfacing a physical process (CO 2capture and storage) to
a digital mechanism, creates new challenges of oversight that did not exist in previous (entirely digital)
cryptocurrencies. How this oversight can be performed while preserving the benefits of decentralization and
censorship resistance is a key challenge that needs to be addressed.
b. How can a governing body ensure that before permitting entry into the framework, the users are screened
and determined to be interested investors who align with the larger ‘carbon negative’ mindset?
c. How do you ensure that geographic migration to financially more lucrative operating regions does not make
this a carbon positive endeavor?
d. How can we ensure that there is a standard measure of the life cycle analysis (LCA) of any new added climate
positive action, to be able to compare across processes?
ACKNOWLEDGMENTS
We gratefully acknowledge the support received from the Alliance for Innovative Regulation (AIR) and Palo Alto
Research Center (PARC). Insightful discussions with fellow participants at a January 2023 Tech Sprint organized
by AIR and hosted by AlixPartners at their San Francisco Bay Area offices are also appreciated. In addition, we
thank Dan Boneh and Paula Palermo for critical feedback on previous drafts.
August 8, 2023 DRAFT
20
REFERENCES
[1] Steve Smith, Oliver Geden, Gregory Nemet, Matthew Gidden, William Lamb, Carter Powis, Robert Bellamy, Max Callaghan, Annette
Cowie, Emily Cox, et al. The state of carbon dioxide removal–1st edition. 2023.
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climate challenge. McKinsey , 5, 2021.
[3] Thibaud Clisson. Exploring the carbon offset market: Issues, reforms and the future. Viewpoint, BNP Paribas Asset Management , 2023.
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Proof-of-useful-work: Blockchain mining by solving real-life optimization problems. Symmetry , 14(9):1831, 2022.
[7] Mandy DeRoche, Jeremy Fisher, Nick Thorpe, and Megan Wachspress. The energy bomb: How proof-of-work cryptocurrency mining
worsens the climate crisis and harms communities now. Sierra Club and EarthJustice .
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[11] Joseph H. Haslag. Seigniorage revenue and monetary policy. Federal Reserve Bank of Dallas Economic Review , pages 10–20, 1998.
[12] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system. Decentralized business review , page 21260, 2008.
[13] Nir Bitansky, Ran Canetti, Alessandro Chiesa, and Eran Tromer. From extractable collision resistance to succinct non-interactive arguments
of knowledge, and back again. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference , pages 326–349, 2012.
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Symposium (USENIX Security 22) , pages 4255–4272, 2022.
[16] Ken Birman. The promise, and limitations, of gossip protocols. ACM SIGOPS Operating Systems Review , 41(5):8–13, 2007.
[17] Ralph C Merkle. Protocols for public key cryptosystems. In 1980 IEEE symposium on security and privacy , pages 122–122. IEEE, 1980.
[18] Steven L Kinney. Trusted platform module basics: using TPM in embedded systems . Elsevier, 2006.
August 8, 2023 DRAFT
|
{
"id": "2308.02653"
}
|
2212.01267
|
Understanding Cryptocoins Trends Correlations
|
Crypto-coins (also known as cryptocurrencies) are tradable digital assets.
Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins
are registered on distributed ledgers (i.e., blockchains). Secure encryption
techniques guarantee the security of the transactions (transfers of coins
across owners), registered into the ledger. Cryptocoins are exchanged for
specific trading prices. While history has shown the extreme volatility of such
trading prices across all different sets of crypto-assets, it remains unclear
what and if there are tight relations between the trading prices of different
cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation
indicators to coin owners, suggesting possible acquisitions or sells. However,
these correlations remain largely unvalidated. In this paper, we shed lights on
the trend correlations across a large variety of cryptocoins, by investigating
their coin-price correlation trends over a period of two years. Our
experimental results suggest strong correlation patterns between main coins
(Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting
techniques for time-series modeling in the context of crypto-coins. We release
our dataset and code to reproduce our analysis to the research community.
|
http://arxiv.org/pdf/2212.01267v1
|
Pasquale De Rosa, Valerio Schiavoni
|
q-fin.ST, cs.AI, cs.CR, cs.LG
|
q-fin.ST
|
arXiv:2212.01267v1 [q-fin.ST] 30 Nov 2022Understanding Cryptocoins Trends Correlations
Pasquale De Rosa[0000−0001−9726−7075]and Valerio Schiavoni[0000−0003−1493−6603]
University of Neuchâtel, Switzerland, first.last@unine.ch
Abstract. Crypto-coins (also known as cryptocurrencies) are tradabl e
digital assets. Notable examples include Bitcoin, Ether an d Litecoin. Own-
ershipsofcryptocoinsareregisteredondistributedledge rs(i.e.,blockchains).
Secure encryption techniques guarantee the security of the transactions
(transfers of coins across owners), registered into the led ger. Cryptocoins
are exchanged for specific trading prices. While history has shown the
extreme volatility of such trading prices across all differe nt sets of crypto-
assets, it remains unclear what and if there are tight relati ons between
the trading prices of different cryptocoins. Major coin exch anges ( i.e.,
Coinbase) provide trend correlation indicators to coin own ers, suggesting
possible acquisitions or sells. However, these correlatio ns remain largely
unvalidated.
In this paper, we shed lights on the trend correlations acros s a large va-
riety of cryptocoins, by investigating their coin-price co rrelation trends
over a period of two years. Our experimental results suggest strong cor-
relation patterns between main coins (Ethereum, Bitcoin) a nd alt-coins.
We believe our study can support forecasting techniques for time-series
modeling in the context of crypto-coins. We release our data set and code
to reproduce our analysis to the research community.
Keywords: cryptocoins · correlations
1 Introduction
Cryptocurrencies, also known as crypto-coins, are tradabl e digital assets, backed
by secure encryption techniques to ensure the security of tr ansactions (typically,
the transfer of coins across wallets). Notable examples inc lude Bitcoin [ 14], Ether
(the native cryptocurrency of the Ethereum blockchain [ 8]) or Litecoin (used in
a fork of the original Bitcoin network). Nowadays there exis ts thousands of cryp-
tocurrencies (CoinMarketCap [ 3] lists 10039 coins as of April 2022). Cryptocoins
are designed to be traded as a form of digital money: the first u seful Bitcoin trans-
action was used by a peer-to-peer payment between Satoshi Na kamoto (Bitcoin’s
founder) and one of its early adopters, and dates back to 2009 .1Cryptocoins are
nowadays traded over online (centralized or decentralized )exchanges , including
Coinbase [ 2], Kraken [ 5], Binance [ 1], Uniswap [ 6],etc. With a current estimated
worldwide market-cap of 1.71 Trillion dollars, the cryptoc oins economy roughly
match the GDP of South Korean in 2021 [ 4].
The ownership of cryptocoins is registered on distributed l edgers ( i.e., blockchains),
together with the corresponding transactions (transfers o f coins across wallets).
Cryptocoins are exchanged ( i.e., sold, bought) for specific trading prices. While
1https://www.blockchain.com/btc/block/170© 2022 Springer. Personal use of this material is permitted. Permission from Springer must be obtained
for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,
creating new collective works, for resale or redistributio n to servers or lists, or reuse of any copyrighted component o f this work in other
works. This is the author’s version of the work. The final auth enticated version is available online at doi.org/10.1007/978-3-031-16092-9_3 and
has been published in the proceedings of the 22nd Internatio nal Conference on Distributed Applications and Interopera ble Systems (DAIS’22).
2 Pasquale De Rosa and Valerio Schiavoni
0 0.2 0.4 0.6 0.8 1
Jan
’20Apr Jul Oct Jan
’21Apr Jul Oct Jan
’22Price (normalized)BTC ETH BNB XRP ADAAverage daily price trends for the top−5 coins
Fig.1: Normalized(min-max)averagepricesofthetop-5cryptocoi nssinceJanuary2020.
it is beyond the scope of this work to understand the exact nat ure of those prices,
history has shown the extreme volatility of such trading pri ces across all differ-
ent sets of crypto-assets. For instance, Figure 1shows the normalized average
daily prices of 5 popular cryptocoins ( i.e., BTC, ETH, BNB, XRP, ADA) since
January 2020.
It remains unclear what and if there are tight relations betw een the trad-
ing prices of different cryptocoins. Major cryptocoin excha nges, in particular
given the enormous popularity that such digital assets have grown into the large
public, and further facilitated by the easy access to these m arkets via mobile
apps, started to provide trend correlation indicators to coin/wallet owners. Such
correlation indicators can possibly drive end-users towar ds acquisitions or sells.
Coinbase, among the most popular cryptocoin exchanges, ind icates the price
correlation as the tendency of other asset prices to change at the same time as the
asset shown on the page. In their case, correlation is computed leveraging the
Pearson correlation with USD order books over the last 90 day s.
However, the nature of such correlations, their intensity a s well as the evo-
lution of the correlations through time, remain largely unv alidated.
Thecontributions of this work-in-progress paper are twofold. First, we ex-
tract the trading prices, as well as other exchange metadata (e.g., open and
closing price, market cap, volume), for the top-100 cryptoc oins since the last
two years from a popular cryptocoin monitoring web-site. Se cond, we leverage
this dataset to carry out our preliminary study of the trend c orrelations between
and across crypto-coins. Specifically, we investigate dail y, weekly and monthly
correlation patterns exhibited by two principal cryptocoi ns,i.e., BTC and ETH,
against the remaining set of alt-coins in our dataset. Our analysis show strong
correlations between the observed trends. We will leverage these observations
in our future work, where we plan to exploit the observed corr elations to fore-
cast the future trading trends and by considering the proble m of time-series
forecasting applied to the crypto-coin market.
We follow an open science approach: our datasets will be released and made
available to the research and open-source community.
Roadmap. This paper is organized as follows. Section 2provides background
materials on Bitcoin, Ethereum, as well as general notions o f correlation anal-
ysis. Section 3describes our dataset, as well as our work-in-progress anal ysis.
Understanding Cryptocoins Trends Correlations 3
We briefly cover related work in Section 4, before concluding and presenting our
future work in Section 5.
2 Background
Cryptocoins in a nutshell. Cryptocoins are digitally encrypted assets. They
were typically designed to replace fiat currencies and used m ostly in peer-to-
peer networks. Depending on the incentive natures of the und erlying blockchain,
cryptocoins (or token) are rewarded to nodes in the network. We differentiate
between three main types of cryptocoins: (i)Bitcoin, (ii)alt-coins, and (iii)sta-
ble coins.2Alt-coins are alternative coins to Bitcoin. Notable examples include
Ether (ETH), Cardano (ADA), Litecoin (LTC), or Ripple (XRP) . A stablecoin is
a class of cryptocurrencies that attempt to offer price stabi lity and are backed by
a reserve asset, e.g., gold or the value of the American dollar. Examples include
USDT (Tether) and USDC.
Time series analysis. A time series is an n-tuple of observations collected
sequentially over time. Common examples of time series incl ude trends of interest
rates and stock prices, daily high and low temperatures, the electrical activity
of the heart, etc. The purpose of time series analysis is generally twofold: (i)
to understand the mechanisms and the inner dynamics of an obs erved series,
and(ii)forecast the future values of the series based on the histori cal ones. To
analyze time series as sequences of random variables ( i.e., stochastic processes),
it is common to assume their stationarity: a time series is stationary if the prob-
ability laws that govern its behavior do not change, and its m eanµis constant
over time. The Autoregressive Moving Average is a state-of- the-art stationary
time series modeling approach, which combines an Autoregre ssive (AR) process
of order pand of a Moving Average (MA) process of order q.
Real applications do not expose stationary trends. The Auto regressive Inte-
grated Moving Average model (ARIMA) differentiates a nonsta tionary process a
numberdof times, until it becomes stationary. It is common to observ e the pres-
ence of a seasonality in the trend of a time series, especiall y in applications where
cyclical tendencies are very common (like business or econo mics). To handle pe-
riodical components, a common model is the Seasonal Autoreg ressive Integrated
Moving Average (SARIMA), that can be mapped to a standard ARI MA model
in absence of seasonality [ 10].
A significant progress in time series modeling was introduce d bytemporally-
aware ML models, i.e., Recurrent Neural Networks (RNNs) (see Figure 2). In
those, the behaviour of hidden neurons is not only determine d by the activations
in previous hidden layers, but also by the activations at ear lier times. The acti-
vation function for every hidden layer of a RNN is: h(t)=f(h(t−1),x(t),θ). There,
the hidden layer at the time t,h(t), is a function of the previous status, h(t−1),
of the current input x(t)and of the activation function adopted, θ.
2Some characterizations define stable coins as sub-classes o f alt-coins, together with
secure tokens, utility tokens, and more. We leave as future w ork to study in-depth the
correlations between such sub-types of alt-coins.
4 Pasquale De Rosa and Valerio Schiavoni
RNN LSTM
GRU σx+htQt
σx
tanh σtanh
x
htCt
ftitt-1
t-1
xtOttanh t-1
xtht
xttanh ht-1
xht
zt1-
σ+
xhtx
σ
Fig.2: ML-based time series forecasting approaches: RNN, LSTM and GRU.
Coin Open High Low Close Volume Market Cap
BTC 29.39/19.55 30.18/20.07 28.50/18.92 29.42/19.53 39.63/20.41 550.41/368.54
ETH 1.57/1.44 1.63/1.49 1.51/1.39 1.57/1.44 20.48/11.05 184.19/170.90
BNB 0.20/0.21 0.21/0.22 0.19/0.20 0.20/0.21 1.63/1.91 33.18/35.39
XRP 5.64e-4/3.94e-4 5.90e-4/4.17e-4 5.36e-4/3.68e-4 5.64e-4/3.94e-4 4.59/4.91 25.70/17.96
ADA 7.97e-4/8.21e-4 8.33e-4/8.55e-4 7.60e-4/7.84e-4 7.99e-4/8.21e-4 2.29/2.77 25.67/26.77
Table 1: Mean/standard deviation for the top-5 cryptocoins since Ja nuary 2020 (Open,
High, Low and Close expressed in 1K US dollars, Volume and Mar ket Cap in 1B US
dollars).
The training process of RNNs is usually complex, due to the unstable gradi-
ent problem : the gradient of the adopted cost function tends to get small er or
bigger as it is propagated back through layers, resulting in a final vanishing or
exploding effect, respectively. RNNs are unable to model long term dependencies ,
lacking predictive ability when dealing with long sequence s of data. To solve this
problem, more effective sequence models are adopted in pract ical applications,
such as Long Short-Term Memory (LSTM) and networks based on t he Gated
Recurrent Unit (GRU). Such gated RNN architectures allow the network to ac-
cumulate information over a long time period, learning to de cide how to forget
the old states once that information has been used and proces sed [11].
3 Preliminary Evaluation
We describe here our experimental evaluation of the correla tions between cryp-
tocoins. First we describe our dataset, and then we show seve ral correlation
patterns.
Dataset. We collected our dataset from CoinMarketCap [ 3], a leading ag-
gregator of cryptocurrency market data. It contains record s (High, Low, Open,
Close, Volume and Market Capitalization) for 68 coins regis tered during a time
frame of 25 months, namely from 24.12.2019 to 24.01.2022. "H igh" and "Low"
are the highest and lowest prices reached by the asset during the considered time
frame; "Open" and "Close" the opening and closing market pri ces; "Volume"
the measure of how much it was traded in the last period. Final ly, "Market
Capitalization" indicates the total market value of its cir culating supply. The
dataset includes a total of 51884 observations. The resulti ng time series for each
coin trend includes 763 steps. Table 1reports mean and standard deviation for
the gathered records and across the top-5 cryptocoins in our dataset.
Understanding Cryptocoins Trends Correlations 5
Jan
2020Apr Jul Oct Jan
2021Apr Jul Oct Jan
2022Open
0.00.51.0Price (normalized)BT C
ETH
Jan
2020Apr Jul Oct Jan
2021Apr Jul Oct Jan
2022Close
0.00.51.0Price (normalized)BT C
ETH
Jan
2020Apr Jul Oct Jan
2021Apr Jul Oct Jan
2022High
0.00.51.0Price (normalized)BT C
ETH
Jan
2020Apr Jul Oct Jan
2021Apr Jul Oct Jan
2022Low
0.00.51.0Price (normalized)Daily price trends BTC vs ETH
BT C
ETH
Fig.3: Trend of Bitcoin and Ethereum prices during the last 2 years.
The two major coins in terms of Volume and Market Capitalizat ion are Bit-
coin (BTC) and Ethereum (ETH), that we selected as the benchm arks for our
subsequent study. The price trend of those cryptocoins over the past two years
(shown in Figure 3) showed on average a high positive correlation (with a Pear-
son coefficient ≈0.9).
Correlation Patterns. The aim of the present study is to identify and an-
alyze the presence of cross-correlation patterns in crypto currency trends. To do
so, we analyze the correlations of 66 alt-coins present in ou r dataset against BTC
and ETH, and for three different time frames: daily, weekly an d monthly. For
weekly and monthly correlations we define the sequence segme nts adopting a
sliding window approach, where observations are grouped wi thin a window that
slides across the data stream. The daily observations for ea ch coin are averaged
over sliding partitions of 7 and 30 days respectively, and th en the correlations
with other coins are computed on the resulting aggregated va lues. Note that we
postpone the study of thumbing windows, where there is no overlapping of data
clusters, to future work. We represent those correlations, averaged among all the
studied variables ( i.e., High, Low, Open, Close, Volume and Market Cap), as a
series of "cross-correlograms" of coins (Figure 4). The radius of each circle repre-
sents the strength of the relation (in terms of Pearson coeffic ient) between each
of the considered alt-coin and BTC (Figs. 4a/c/e) or ETH (Figs. 4b/d/f). The
color identifies the sign of the correlation (green if positi ve, red otherwise). The
analysis of the cross-correlogram clearly shows how the vas t majority of consid-
ered alt-coins are strongly correlated with and follows the same trend of the two
market leaders. Their average values of the Pearson coefficie nt very close to 1.
Not surprisingly, the only visible exceptions are represen ted by the stablecoins
available in our dataset ( i.e., USDP, TUSD, DAI, BUSD, USDC, USDT), that
are pegged to the US dollar and follow standalone trends with total independence
from the rest of the coins in the market.
6 Pasquale De Rosa and Valerio Schiavoni
bnbusdtusdcadaxrplunadogematicbusdcrolinkwbtcltcdaialgoatomftmbchxlmtrxfttmanavethbarbtcbfilthetaetconexmrxtzleomiotaeosstxbttksmruneenjmkrbsvlrcqntzecneobatkcschzokbwaveshtdashtusdnexoxdchotxemrvniotxusdptfuelvlxomgbntboradcr
openhighlowclosevol.m.cap(a) Daily Corr. BTC
openhighlowclosevol.m.cap(b) Daily Corr. ETH
openhighlowclosevol.m.cap(c) Weekly Corr. BTC
openhighlowclosevol.m.cap(d) Weekly Corr. ETH
openhighlowclosevol.m.cap(e) Monthly Corr. BTC
openhighlowclosevol.m.cap(f) Monthly Corr. ETH
Fig.4: Daily, weekly and monthly cross-correlations between the a lt-coins and
BTC/ETH.
4 Related Work
There exists studies which analyzed co-movement and cross- correlation phenom-
ena in cryptocurrency market trends. Similar to our study, K atsiampa [ 12] in-
vestigated the volatility dynamics of the two major cryptoc urrencies, Bitcoin
and Ethereum, finding evidence of interdependencies betwee n the two and price
responsiveness to major news in the market. Aslanidis et al. [7] showed that
cryptocurrencies exhibit similar mean correlation among t hem, with an unsta-
ble trend over time; in addition, the authors computed coins correlation against
more traditional assets, detecting an independent behavio r respect to other fi-
nancial markets. In [ 13], Bitcoin is identified as the leader in the cryptocurrency
market using wavelet-based methods, showing how other coin s trends are de-
pendent from BTC price movements: as a result, Bitcoin price drops are im-
mediately reflected in other cryptocurrency prices. Finall y, [9] studied the col-
lective behaviour for the cryptocurrency market discoveri ng distinct and not
time-persistent community structures characterized by cr oss-correlation.
Understanding Cryptocoins Trends Correlations 7
5 Conclusion and Future Work
Cryptocoins present very volatile trends on public exchang es. In this work-in-
progress paper, we presented our preliminary evaluation of the correlations be-
tween BTC, Ether and 66 other alt-coins. Our analysis shows s trong correlations,
suggesting alt-coins follow closely the trends of the two ma in ones. Following
this initial study, we will further investigate the cross-c orrelation between the
two market leaders and the alt-coins, in the perspective to f orecast their price
trends by using the time-series techniques from § 2. We believe that our work
could represent a significant starting point for further ana lyses in co-movement
behaviors within the cryptocoin markets and in modeling and forecasting trends
of the asset prices.
Metadata, analysis data, tools and code for reproducibilit y are available
to the research community at https://github.com/quapsale/cryptoanalytics/ .
8 Pasquale De Rosa and Valerio Schiavoni
References
1. Binance Exchange. https://www.binance.com
2. Coinbase Exchange. https://www.coinbase.com
3. CoinMarketCap Web Service. https://coinmarketcap.com/
4. International Monetary Fund. https://www.imf.org/en/Publications/WEO
5. Kraken Exchange. https://www.kraken.com
6. UniSwap Decentralized Exchange. https://uniswap.org/
7. Aslanidis, N., Bariviera, A.F., Martínez-Ibañez, O.: An analysis of cryp-
tocurrencies conditional cross correlations. Finance Res earch Letters 31,
130–137 (2019). https://doi.org/https://doi.org/10.1016/j.frl.2019. 04.019 ,
https://www.sciencedirect.com/science/article/pii/S 1544612319302168
8. Buterin, V., et al.: Ethereum white paper. GitHub reposit ory1, 22–23 (2013)
9. Chaudhari, H., Crane, M.: Cross-correlation dynamics an d community
structures of cryptocurrencies. Journal of Computational Science 44,
101–130 (2020). https://doi.org/https://doi.org/10.1016/j.jocs.2020 .101130 ,
https://www.sciencedirect.com/science/article/pii/S 1877750320304312
10. Cryer, J., Chan, K.S.: Time Series Analysis. Springer, N ew York, NY, USA (2008).
https://doi.org/https://doi.org/10.1007/978-0-387-7 5959-3
11. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learnin g. MIT Press (2016),
http://www.deeplearningbook.org
12. Katsiampa, P.: Volatility co-movement between bit-
coin and ether. Finance Research Letters 30, 221–227
(2019). https://doi.org/https://doi.org/10.1016/j.frl.2018. 10.005 ,
https://www.sciencedirect.com/science/article/pii/S 1544612318305580
13. Kumar, A., Ajaz, T.: Co-movement in crypto-currency mar -
kets: evidences from wavelet analysis. Financial Innovati on33
(2019). https://doi.org/https://doi.org/10.1186/s40854-019- 0143-3 ,
https://jfin-swufe.springeropen.com/articles/10.118 6/s40854-019-0143-3
14. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash s ystem. Decentralized
Business Review pp. 212–260 (2008)
|
{
"id": "2212.01267"
}
|
1508.04364
|
Trends in crypto-currencies and blockchain technologies: A monetary
theory and regulation perspective
|
The internet era has generated a requirement for low cost, anonymous and
rapidly verifiable transactions to be used for online barter, and fast settling
money have emerged as a consequence. For the most part, e-money has fulfilled
this role, but the last few years have seen two new types of money emerge.
Centralised virtual currencies, usually for the purpose of transacting in
social and gaming economies, and crypto-currencies, which aim to eliminate the
need for financial intermediaries by offering direct peer-to-peer online
payments.
We describe the historical context which led to the development of these
currencies and some modern and recent trends in their uptake, in terms of both
usage in the real economy and as investment products. As these currencies are
purely digital constructs, with no government or local authority backing, we
then discuss them in the context of monetary theory, in order to determine how
they may be have value under each. Finally, we provide an overview of the state
of regulatory readiness in terms of dealing with transactions in these
currencies in various regions of the world.
|
http://arxiv.org/pdf/1508.04364v1
|
Gareth W. Peters, Efstathios Panayi, Ariane Chapelle
|
cs.CR, cs.CY
|
cs.CR
|
Trends in crypto-currencies and blockchain technologies: A
monetary theory and regulation perspective
Gareth W. Peters z? Efstathios Panayi y
Ariane Chapelley
zDepartment of Statistical Science, University College London
?Associate Fellow, Oxford Mann Institute, Oxford University
Associate Fellow, Systemic Risk Center, London School of Economics.
yUCL, Department of Computer Science, WC1E 6BT, London, UK
August 19, 2015
Abstract
The internet era has generated a requirement for low cost, anonymous and rapidly veri-
able transactions to be used for online barter, and fast settling money have emerged as a
consequence. For the most part, e-money has ful lled this role, but the last few years have
seen two new types of money emerge. Centralised virtual currencies, usually for the purpose
of transacting in social and gaming economies, and crypto-currencies, which aim to eliminate
the need for nancial intermediaries by o ering direct peer-to-peer online payments.
We describe the historical context which led to the development of these currencies and
some modern and recent trends in their uptake, in terms of both usage in the real economy and
as investment products. As these currencies are purely digital constructs, with no government
or local authority backing, we then discuss them in the context of monetary theory, in order
to determine how they may be have value under each. Finally, we provide an overview of the
state of regulatory readiness in terms of dealing with transactions in these currencies in various
regions of the world.
1 Introduction
It has been 20 years since Bill Gates opined: `Banking is essential, banks are not'. The early
21st century has seen a proliferation of ntech ( nancial technology) rms, providing a wide and
varied array of services, from payments and local and international money transmission through to
nancing through peer-to-peer lending and crowdfunding. Venture capital funding in the UK for
ntech related business has increased to over $500 million in 2014, while the sector is estimated to
contribute more than GBP 20 billion to the economy1. A number of countries have stated their
intention to create an eco-system in which such businesses can grow, which can only mean the
continued growth of the sector in the foreseeable future.
In parallel to these innovations, which aim at reducing the friction of making payments and
transfers in at currency, which have been facilitated by electronic money (\e-money"), there has
also been a rise in the use of virtual and crypto-currencies. While the former have traditionally
been utilised in virtual economies, such as those of an online game or community [Lehdonvirta &
1Investment Trends in Fintech report by SVB, available at http://www.svb.com/News/Company-News/
2015-Fintech-Report--Investment-Trends-in-Fintech/?site=uk
1arXiv:1508.04364v1 [cs.CR] 18 Aug 2015
Figure 1: Location and industry for 318 startups in Bitcoin. Source: http://www.creandum.com/
318-records-and-counting-the-bitcoin-database-is-now-available-for-everyone-2/
Castronova, 2014], the latter has entered into the real economy also, see discussion in Peters et al.
[2014]. The goal of the most successful crypto-currency thus far, Bitcoin, is in fact in line with that
of the companies mentioned above, i.e. reducing transaction costs, but with the additional aim of
completely eliminating the need for nancial intermediaries.
While one of the objectives of Bitcoin was to become a form of electronic cash for online pay-
ments, its main use thus far has been for speculation. However, this is beginning to change, and
there are numerous emerging intermediaries that are beginning to operate within the Bitcoin net-
work, which include exchanges, merchant processes and money transmitters. In fact, Bitcoin has
been traded in various exchanges since at least 20102, and it has experienced various boom-bust
cycles in this time with regard to its exchange to the US dollar, UK pound, Euro and other impor-
tant at currencies. This price volatility is seen as an impediment to its more widespread use as a
medium of exchange, and there have already been suggestions (e.g. by Brito et al. [2014]) for the
creation of nancial instruments to aid in the reduction of volatility. Section 3 will highlight trends
in price and trading volumes for Bitcoin over the past two years.
The main innovation of crypto-currencies such as Bitcoin has been introducing technologies
such as the blockchain, a ledger containing all transactions for every single unit of currency. It
di ers from existing ledgers in that it is decentralised, i.e. there is no central authority verifying
the validity of transactions. Instead, it employs veri cation based on cryptographic proof, where
various members of the network verify `blocks' of transactions approximately every 10 minutes.
The incentive for this is compensation in the form of newly `minted' Bitcoins for the rst member
to provide the veri cation. The distributed ledger at the heart of the network could, of course be
used for a number of other use-cases, such as smart property and smart contracts, and regulators
have looked at such applications much more favourably than crypto-currencies, though this is also
beginning to change. We provide more details of such use-cases and the potential of the blockchain
2Mt. Gox was launched in July 2010, and was responsible for the vast majority of Bitcoin trading until 2013.
2
in Section 3.4.
Bitcoin in particular has had a fair amount of criticism questioning why its digital tokens,
produced as a result of solving a computational problem, should have any value, particularly when
they are not backed by any authority i.e. not at currencies. In Section 4 we discuss this question in
more detail from both the traditional metalist views on currency value generation and more recent
(and perhaps less orthodox) monetary theories, such as the Modern Monetary Theory (MMT) in this
context. We discuss issues relating to monetary theory and resultant economic policy implications
that may arise under each of these frameworks, if crypto-currencies were to interact more widely
with the real economy.
In this environment of fast-paced technological evolution, nancial innovation is running ahead
of regulation. For example, the transaction anonymity provided by transacting in the Bitcoin
network is a clear driver for several operational risks, money laundering, fraud and legal risk, as
discussed at length in Peters et al. [2014]. Government responses have been mixed, and while
they want to be careful not to overburden the budding sector of nancial innovation with excessive
regulation and curtail growth in the area, there is a need to ensure that the new services are not
used to circumvent regulation in traditional banking services. Section 5 will summarise regulatory
interventions in some major economies.
2 Physical and electronic forms of money, and the devel-
opment of crypto-currencies
In this section we provide a brief overview of the historical context in which crypto-currencies
have emerged. We touch upon government-backed and commodity backed currency and discuss
the development of cryptographic protocols that enabled e-money. Finally, we describe the online
communities which were rst exposed to virtual currency and the di erences between the afore-
mentioned forms of money and crypto-currency.
2.1 Fiat currency and e-money
We start with a brief de nition of a at currency. The European Central Bank de nes at currency
as any legal tender designated and issued by a central authority that people are willing to accept
in exchange for goods and services because it is backed by regulation, and because they trust this
central authority. Fiat money is similar to commodity backed money in this regard with respect
to its usage, but di ers in that it cannot be redeemed for a commodity, such as gold. The most
common form of at currency backing is at the sovereign state's government level, but there have
also been localised currencies or private monies, see discussion in Peters et al. [2014] for their use
in local communities in the UK and Germany.
While one is most commonly accustomed to thinking about money in its physical form, only a
very small fraction of a country's total money supply is typically in the form of notes and coins.
In the UK, this percentage is 2.1% of the 2.2 trillion GBP total money supply [Lipsey & Chrystal,
2011]. This then motivates the discussion of electronic money, or e-money, de ned by Al-Laham
et al. [2009] as a
oating claim on a private bank or other nancial institution that is not linked to
any particular account. Under this rather general de nition one can consider many di erent forms
of e-money such as bank deposits, electronic fund transfers, direct deposits, and payment processors
(including micro-payments).
Instead we put forward the rather more narrow de nition of the UK regulator de nes electronic
money as follows (see Halpin & Moore [2009]):
\Electronic money (e-money) is electronically (including magnetically) stored monetary value,
represented by a claim on the issuer, which is issued on receipt of funds for the purpose of making
3
payment transactions, and which is accepted by a person other than the electronic money issuer.
Types of e-money include pre-paid cards and electronic pre-paid accounts for use online. "
Typically, e-money is stored in the same unit of account as the at denomination used to obtain
the e-money.
2.2 Cryptographically secure e-money
In the case of early forms of e-money one may go back to the early 1980's where David Chaum (see
Chaum [1988, 1985, 1992]) developed the concept of electronic cash under the view that for it to be
useable in the real world economy it would require a token of money that would emulate physical
currency, and most importantly, privacy feature to enable safely and securely anonymous payments.
He developed such a digital cash as an extension to the RSA encryption protocol used for most
security purposes on the web at present which led to the creation of the company DigiCash. Due
to complications that arose with the central bank in Amsterdam where DigiCash was founded, it
was decided that such currency would only be sold as a product to banks. This e-money attempt
had a lot of promise, but it was unable to gain mainstream uptake in the end due more to political
and business related issues3.
Following DigiCash there was an explosion of small venture capital rms established to develop
e-money systems, leading to the release of a key initial regulatory response to such e-money, the
1994 EU Report by the Working Group on EU Payment Systems which was made to the council
of the European Monetary Institute. After the release of this report there were three notable front
runners that emerged: PayPal, Liberty Reserve and E-gold which was incidentally started by Nick
Szabo, a former DigiCash employee and e-contract innovator.
Whilst PayPal was careful to negotiate and avoid the challenges faced by integrating into the
monetary system in a manner deemed acceptable by central banks and regulators, the other two
eventually ran foul of authorities in the US due to the the suspected nature of some clients that
may have taken up these services for activities related to money laundering and criminal enterprise.
These three early e-money systems primarily operated as centralized systems.
The impact of e-money on physical forms of currency has been discussed by Drehmann et al.
[2002], while Sifers [1996] discusses policy concerns and regulatory issues. We will now be focusing
on other electronic forms of money, which in contrast to e-money are not digital representations of
at money, but rather new forms of currency altogether.
2.3 Virtual currencies to facilitate online gaming economies
The 1990s saw the emergence of virtual currencies, typically currencies that were also centralized but
restricted, at least in their early forms, to use in online messaging and virtual gaming environments.
An early example was the Q coin, which could be purchased from brick and mortar shops in China
for use on Tencent's online messaging platform Lehdonvirta & Castronova [2014]. Virtual currencies
are now prevalent in massively multiplayer online games (e.g. World of Warcraft) or life simulation
games (e.g. Second Life).
Where these currencies are used as the medium of exchange in an online virtual economy, they
have similarities with their at currency counterparts. To start with, the currencies are typically
used by the participants in the economy for the purchase of virtual goods and services. Secondly, the
currencies feature a central authority, which similar to a country's central bank4, can regulate the
money supply in order to attain particular goals, such as controlling in
ation or promoting economic
3http://globalcryptonews.com/before-bitcoin-the-rise-and-fall-of-digicash/
4The Money Supply, New York Federal Reserve, accessed 10 August 2015, available at http://www.newyorkfed.
org/aboutthefed/fedpoint/fed49.html
4
growth. In particular, some platforms actively manage the monetary supply, increasing money
supply through in game features, or reducing money supply through in game \sinks", or desirable
consumption items that remove money from the online environment Lehdonvirta & Castronova
[2014].
The limited interaction of virtual currencies with the real economy stems from the fact that for
many of these virtual currencies, the
ows between at and the virtual currency are uni-directional,
i.e. one can only purchase, but not sell the virtual currency [Peters et al. , 2014]. For some
environments, such as World of Warcraft, the developer Blizzard Entertainment actively monitors
and polices the use of their virtual currency to restrict its use within the virtual economy and thus
avoid any legal issues that may arise. There are a minority of cases, however, such as Second Life,
whose developer Linden Labs does not oppose actively the exchange of the Linden dollar with real
at currency. This has led to a bi-directional cross over between the virtual currency and real at
currencies.
Virtual currencies cannot be fully considered as e-money since, as although they share some of
its attributes, there is currently no legal founding to enforce the link between at physical money
and virtual currencies as there is in regulated electronic money transactions. In addition, virtual
currencies are not stored in the same unit of account as any at currency that would preserve their
worth.
2.4 Crypto-currencies
Unlike such virtual currencies which are centrally controlled by a game designer or online platform
operator, the development of crypto-currencies has been such that they are typically not operated
in a centralized manner. By far the most widely known crypto-currency is Bitcoin, introduced by
Nakamoto [2008]. It is a `decentralized' currency, in that one does not need nancial intermediaries
in order to perform electronic transactions and it does not have a central bank or other authority
in control of monetary policy.
Simply put, Bitcoin can be described as a decentralised ledger of transactions. The role of the
verifying third party found in centralised systems is played by the Bitcoin network participants, who
contribute computational power and are rewarded in the form of new amounts of crypto-currency.
Designed to be a currency for the internet, Bitcoin is not localized to a particular region or country,
nor is it intended for use in a particular virtual economy. It is not backed by any local government
or private organisation and is being circulated in the real economy on an increasing scale. Because
of its decentralized nature, this circulation is largely beyond the reach of direct regulation, monetary
policy, oversight and money supply control that has traditionally been enforced in some manner
with localized private monies and e-money.
Bitcoin is certainly not the only crypto-currency, and there are numerous papers discussing both
identi ed weaknesses of the current protocol, as well as possible improvements to both centralised
and decentralised currency architectures, see discussions in Eyal & Sirer [2014]; Barber et al. [2012];
Carroll & Bellotti [2015] and references therein. Other examples of decentralized crypto-currencies
include Litecoin, which was originally based on the Bitcoin protocol, and has a faster veri cation
time, Ripple which is a monetary system based on trust networks, Dogecoin, Monero and Nxt.
2.5 The distinct nature of crypto-currencies
To distinguish between centralized and decentralized currencies, one can consider for instance the
de nition from the central bank of Canada5`Decentralized e-money is stored and
ows through
a peer-to-peer computer network that directly links users, much like a chat room. No single user
controls the network.'
5http://www.bankofcanada.ca/wp-content/uploads/2014/04/Decentralize-E-Money.pdf
5
The ECB report on virtual currencies6classi ed these currencies based on their interaction with
at money and the real economy. Peters et al. [2014] proposed to extend this classi cation to include
the existence of a central repository and a single administrator, where the absence of both means that
the currency is operated via a decentralised network consensus-type administration. Decentralised
virtual currencies are then termed crypto-currencies, as the operation of these currencies is usually
based on cryptographic proof provided by a network, rather than the existence of a trusted third
part to verify transactions.
Di erentiating between the di erent forms of virtual currencies is non-trivial as they are mul-
tifaceted in their attributes and interactions in the real economy. Several di erences between cen-
tralised virtual currencies and crypto-currencies were identi ed in Peters et al. [2014] and we brie
y
summarise some of these below:
In terms of changes to their speci cation. In centralised virtual currencies the speci cation
can be altered by the controlling company, whereas in crypto-currencies the speci cation is
agreed by cryptographic consensus.
In terms of their purpose and geographic area of operation, i.e. for use within an online
community in the case of centralised virtual currencies, or in the wider economy, in the case
of crypto-currencies.
In terms of the existence of a centralised authority to exert control over issuance, monetary
policy and administration of currency balances. In centralised virtual currencies, a central
authority can step in to control money supply and reverse transactions at will. In crypto-
currencies, the absence of a centralised authority means that users control these aspects ac-
cording to the computational power they contribute to the network. In addition, transactions
are generally irreversible, as there is no authority to appeal to.
In terms of the
ow of currency between users and the currencies' exchangeability with at.
In terms of the value generation mechanism, which will be discussed in detail in Section 4.
The distinct nature of crypto-currency is apparent in its comparison to centralised virtual cur-
rency above, but also, as we will see here, to e-money. The issuance mechanism in Bitcoin is xed,
with the coin generation process and nal available currency dictated by a mathematical protocol.
E-money is intrinsically linked to the underlying at currency, whose issuance is controlled by a
central banking authority. In addition, in the current absence of the requirements of `know your
customer' that e-money transactions tend to require, one can have a more anonymous interaction
with crypto-currency. In general it is acknowledged that anonymity is perhaps greater with crypto-
currencies, as not all companies directly follow the Financial Action Task Force standards with
regard to customer identi cation.
Another key point that can distinguish the utility of crypto and virtual currencies relates to the
environments they operate in. This is becoming an important feature in terms of accessibility, at
present Bitcoin is limited to people with internet connections. This turns out to be signi cant as
it precludes its widespread uptake in the third world and developing countries, where e-money has
been very popular in mobile and paging service networks.
To conclude this section on the distinct nature of crypto-currency, we also observe the comments
made by Maurer et al. [2013] that in the case of Bitcoin, it is its code that is its core. They state
succinctly: \...the currency functions based on the trust its community of users place in the code
and, as with all free and open-source projects, the trust they place in their collective ability to
review, e ectively evaluate, and agree as a group to changes to it". This is clearly di erent from
6https://www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemesen.pdf?
fe92070cdf17668c02846440e457dfd0
6
e-money which involves trust in the central authority, government or state that backs the at
denomination underlying the e-money.
2.6 Ful lling the functions of money
Having described the historical context in which crypto-currencies emerged, as well as the di er-
ences with other forms of electronic money, we now analyse whether these currencies can ful ll the
traditional role of money in an economy. A widely held view is that money should serve three
distinct functions:
1. It should be generally accepted as a medium of exchange.;
2. It should be a unit of account so that we can compare the costs of goods and services over
time and between merchants.; and
3. It should be a store of value that stays stable over time.
Both the Bank of England7and the central bank of Canada8, using Bitcoin as a case study,
found that crypto-currencies do not currently ful ll these functions in the way that at currencies
and e-money do. However, it is of course possible that in the future, a more widespread uptake in a
particular crypto-currency may lead it to it satisfying this criteria. This is not necessarily the view
held in all jurisdictions throughout the world, we will discuss recent changes proposed to this view
in for instance Australia, in Section 5.
Separate from the functions of money, one can also explore particular qualities of money that
make it suitable for facilitating transactions. In the case of commodity money, these include dura-
bility, value per weight unit (portability), and scarcity, and Graf [2015] argues that Bitcoin evaluates
well on each characteristic. As these currencies were primarily oriented towards direct, online trans-
actions, we can additionally consider the following qualities in the context, e.g. of online commerce
[Drehmann et al. , 2002]:
They should be low cost;
they should provide reliable security; and
they should o er a degree of privacy in transactions.
See further discussions on these points in Maurer et al. [2013].
Two further distinctive feature of crypto-currency like Bitcoin, which are not readily replicated
in at e-money, relate to its divisibility and fungibility, see discussion in Barber et al. [2012]. They
note that one of the key practical appeals of for instance Bitcoin is \...the ease with which coins
can be both divided and recombined to create essentially any denomination possible. This is an
Achilles heel of (strongly anonymous) e-cash systems, because denominations had to be standardized
to be unlinkable, which incidentally makes the computational cost of e-cash transactions linear in
the amount. In Bitcoin, linkage is inherent, as it is what prevents double spending; but it is the
identities that are anonymous."
We note that such crypto-currencies as Bitcoin do not however have, compared to conventional
at backed e-money payment systems, a strict governance structure other than its underlying soft-
ware. The implications of this are discussed recently by both Peters et al. [2014] and B ohme et al.
[2015]. Without the lack of governance a orded by traditional at e-money payment systems, the
Bitcoin network is unable to impose any obligation on a nancial institution, payment processor, or
other intermediary to verify a users identity or cross-check with watch-lists or embargoed countries.
7http://www.bankofengland.co.uk/publications/Documents/quarterlybulletin/2014/qb14q302.pdf
8http://www.bankofcanada.ca/wp-content/uploads/2014/04/Decentralize-E-Money.pdf
7
The implications of this for money laundering and money transmitter regulations are discussed in
Brito et al. [2014]. Finally, it is clear that without central governance, one cannot impose any form
of prohibition on sales of particular items, this point is discussed by MacCarthy [2010], where they
point out that traditional e-money and credit card payment systems regularly monitor and disallow
a range of transactions which are deemed unlawful in the place of sale.
3 Trends in the usage of crypto-currencies in the economy
The discussion in the previous section should highlight the much greater potential of crypto-
currencies for entering the real economy, compared to virtual currencies. We present in this section
summary statistics for the uptake of Bitcoin, the most popular crypto-currency. We also discuss
associated investment products, as well as views about the currency's potential use for facilitating
criminal transactions.
3.1 Bitcoin trading by exchange and currency
Bitcoin is by no means the only crypto-currency. Coinmarketcap9lists 590 currencies, with a total
market capitalisation of $4.5 billion. As Bitcoin accounts for more than 80% of this amount, we
will focus on it to exhibit trends in crypto-currency activity. Figure 2 shows the evolution of price,
as well as traded volumes over a 2-year period. It is interesting to note that while trading in
Bitcoin was predominantly in US dollars, it has now moved to being predominantly in Chinese
Yuan. This highlights Bitcoin's nature as both a highly speculative investment and as a tool for
evading currency controls10.
The Bitcoin network relies on `miners', or members that contribute computational power to
solve a complex cryptographic problem and verify the transactions that have occurred over a short
period of time (10 minutes). These transactions are then published as a block, and the miner who
had rst published the proof receives a reward (currently 25 bitcoins). The maximum block size is 1
MB, which corresponds to approximately 7 transactions per second. In order to ensure that blocks
are published approximately every 10 minutes, the network automatically adjusts the diculty of
the cryptographic problem to be solved.
Bitcoin mining requires specialised equipment, as well as substantial electricity costs, and miners
thus have to balance their technology and energy investment so that their activities are pro table.
As the price of Bitcoin increased, miners invested in more hardware, increasing their computational
capability. However, the Bitcoin network then increased the diculty of the cryptographic problem,
in order to keep blocks published in regular intervals. Figure 3 shows the evolution in both the
diculty of the cryptographic problem over time, as well as the block size. We note the exponential
increase in the diculty for a sustained period of time. As Bitcoin prices had been steadily declining
in the latter part of this period, it is likely that mining became less pro table, which explains the
plateau in diculty.
With regards to the increase in blocksize, this corresponds to an increase in Bitcoin transactions
over time. A blocksize of 0.4 MB corresponds to approximately 3 Bitcoin transactions per second.
A summary of other Bitcoin related trends is also provided in reports such as by B ohme et al.
[2015].
3.2 Crypto-currency real world usage
The projected future use of crypto-currencies like Bitcoin is discussed at length by Brito et al.
[2014], with regard to securities, options, swaptions, forwards, bonds that may be developed going
9http://coinmarketcap.com/all/views/all/ , accessed 30/06/2015.
10http://www.ft.com/fastft/289502/bitcoin-still-gaining-currency-china
8
05001000
Jul−2013
Oct−2013
Jan−2014
Apr−2014
Jul−2014
Oct−2014
Jan−2015
Apr−2015
Jul−2015Price (US $)Exchange
anxbtc
bitfinex
bitquick
bitstamp
btce
campbx
hitbtc
itbit
localbitcoins
others
0246
Jul−2013
Oct−2013
Jan−2014
Apr−2014
Jul−2014
Oct−2014
Jan−2015
Apr−2015Weekly volume (Million BTC)Exchange
anxbtc
bitfinex
bitstamp
btcchina
btce
huobi
lakebtc
mtgox
okcoin
others
0246
Jul−2013
Oct−2013
Jan−2014
Apr−2014
Jul−2014
Oct−2014
Jan−2015
Apr−2015Weekly volume (Million BTC)Currency
AUD
CAD
CNY
EUR
GBP
HKD
JPY
PLN
USD
othersFigure 2: Top: Price
uctuation of Bitcoin over time. Bottom: Traded volumes by exchange (left)
and by currency (right).
0e+001e+102e+103e+104e+105e+10
Jul−2013
Oct−2013
Jan−2014
Apr−2014
Jul−2014
Oct−2014
Jan−2015
Apr−2015Difficulty
0.00.10.20.30.4
2011 2012 2013 2014 2015Blocksize (MB)
Figure 3: Diculty
forward based on virtual currencies such as Bitcoin. The European Central Bank, in its second
9
report11, presents both an overview of the actors, the di erent modes of operation and the di erent
business models that originate from virtual currencies schemes. Measures of current usage for
Bitcoin shows between 60,000 and 70,000 transactions daily, for a total transacted volume of between
e15 and e30 million, numbers which are somewhat insigni cant compared to activity with existing
payment solutions12. However, the ECB report highlights speed, cost and facilitation of cross-border
payments as a major advantages of virtual currencies.
The European Securities and Markets Authority (ESMA) has published a call for evidence on
virtual currency investment products, as well as blockchain investment applications not involving
virtual currencies13. This interest of ESMA is much more narrow than that of other stakeholders,
in that it does not seek to express a view of the desirability of using virtual currency in a payment
system. Instead, it focuses on collective investment schemes (CIS) and virtual currency derivatives.
In its preliminary work, ESMA has obtained data from 6 of 13 virtual currency CIS, which had
approximately e246 million, with the largest accounting for almost half of this gure. Besides
these schemes, ESMA also identi ed regulated European companies o ering contracts for di erence
(CFDs) in Bitcoin and Litecoin, as well as binary options on either.
3.3 Crypto-currency as a means of facilitating crime
In its infancy, Bitcoin was associated with criminal activity through the online marketplace `Silk
road', which operated on the Dark Web. Analysing 8 months of data from this marketplace,
Christin [2013] found that the majority of the 24,400 items sold on the market place were controlled
substances and narcotics, with 112 sellers active throughout this interval. The total revenue from
public listings in this time was approximately $10 million. Silk road was shut down by the FBI in
2013, while also seizing $28.5 million in Bitcoin and arresting the marketplace's operator14.
Moser et al. [2013] provided the rst thorough study of the potential for Bitcoin to be used as
a money laundering tool. In particular, they investigated companies which provided anonymising
services for a fee, by `mixing' Bitcoin inputs from several participants, and generating new Bitcoin
addresses to hold the outputs. They determined that some services were indeed e ective for this
purpose and concluded that because of this, it is unlikely that a Know-Your-Customer principle
can be enforced in the Bitcoin system.
In terms of real-world use in this context, an assessment of the National Crime Agency in the
UK found that the majority of transactions for illicit purposes where actually of low value, and
there was little to suggest that digital currencies have been widely used in the context of money
laundering. Although anonymity was identi ed as a potential facilitator of criminality, in reality
to use many of the available digital currency services, users would have to register an (eponymous)
account.
3.4 Other distributed ledger technologies
While HM Treasury and the Euro Banking Association (EBA) have been ambivalent towards Bitcoin
in their recent reports, they have both recognised the potential of cryptotechnologies for other use
cases. In particular, they have identi ed the distributed ledger at the core of the Bitcoin protocol,
which achieves governance by consensus. While few concrete examples exist at present, Swan [2015]
cites several examples of transnational groups which could use such a governance structure, such as
the Internet Standards group ICANN and DNS, thus avoiding the in
uence (political and otherwise)
11www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemesen.pdf
12Existing payment solutions include Visa, MasterCard, Paypal etc, and the ECB puts current daily non-cash
payment transactions at 274 million.
13http://www.esma.europa.eu/system/files/2015-532_call_for_evidence_on_virtual_currency_
investment.pdf
14http://www.forbes.com/sites/andygreenberg/2013/10/25/fbi-says-its-seized-20-million-in-bitcoins-from-ross-ulbricht-alleged-owner-of-silk-road/
10
of certain groups that would occur when registering in particular jurisdictions. A more ambitious
example is that of smart property, where potentially every asset could be encoded onto this ledger
with a unique identi er, and thus all asset transactions could be con rmed and tracked via the
blockchain.
As noted in Barber et al. [2012], the notion of scripting o ered by crypto-currencies like Bitcoin
is a highly useful and very innovative feature. It allows users to embed scripts in their Bitcoin
transactions, this key feature is only just being recognised as a utility in its own right. It has
been realized that at least in theory, as noted in Barber et al. [2012] that this can lead to \...
rich transactional semantics and contracts through scripts, such as deposits, escrow and dispute
mediation, assurance contracts, including the use of external states, and so on."
The Bitcoin use-case is one where the blockchain used is permissionless. `Permission' refers
to the veri ers on the network, and in the case of Bitcoin, miners do not have to be authorised
by a central authority before performing their mining activities. This is not the only model for a
blockchain, however, and indeed the actors on the network who verify transactions can be subject
to authorisation, as well as legal accountability. The applications outlined in this section span both
modes of blockchain operation.
In its report, the EBA15presents an analysis of cryptotechnologies in four application areas,
presented also in Figure 4:
Currencies such as Bitcoin, Litecoin etc.
Asset registries : Similar to the smart property example mentioned earlier, ownership details
would be recorded in the blockchain, and while physical assets could always be lost or stolen,
the holder of an asset would not be able to claim ownership until it has been transferred
via a blockchain transaction. However, because of the potentially large number of assets and
associated details that could be recorded on the blockchain, this could create a large amount
of trac on the network. Bitcoin's 1MB block size caps the number of transactions at an
average of 7 per second, and it is clear that a much higher number would be needed for the
purpose of asset registration in certain areas (e.g. nancial). A good example of a use-case
is that of Everledger16, a ledger for the certi cation and transaction history of diamonds. A
laboratory rst takes measurements of cut, clarity, size and other information and this is all
stored on the blockchain.
Application stacks : This application area aims to provide a platform for the execution
of `complete applications on top of decentralised networks'. Examples include the smart
contracts proposed by by Eris Industries17, which can automatically verify the interactions
between the parties to the contract. With such contracts, there is the possibility of creating
derivatives that settle automatically and reduce counterparty risk, such as the blockchain
derivatives developed by Hedgy18. There are several caveats to this application area also,
however, as smart contracts will always be limited to the ability of the data to describe these
interactions.
Asset-centric technologies : These focus on digital representation of real assets on a shared,
but not public, ledger.
15Available at https://www.abe-eba.eu/downloads/knowledge-and-research/EBA_20150511_EBA_
Cryptotechnologies_a_major_IT_innovation_v1.0.pdf , accessed 29/05/2015
16http://www.everledger.io/
17https://erisindustries.com/
18http://hedgy.co/
11
Figure 4: 4 categories of cryptotechnologies. Reproduced from the EBA Cryptotechnologies report.
4 Value generation in crypto-currency
At rst glance, it may be dicult to comprehend why crypto-currency, as a purely arti cial digital
construct produced as a result of solving a computational problem, with no backing from a central
authority, should have any value in the real economy. In this section we will refer to a number of
economic principles followed by associated monetary theories, in order to determine any elements
which could explain the value of this digital resource. We note that we do not advocate one
particular school of economic thought over another, but will rather discuss issues that may arise
under a range of these di erent prospective analytical frameworks, if crypto-currencies were to
interact more widely with the real economy.
4.1 Crypto-currencies as scarce economic goods and the potential of a
`De
ationary Spiral'
Graf [2015] suggests that bitcoin `meets key characteristics of a good, as de ned in relation to action
and choice'. It is in fact a scarce digital good, produced through a predetermined issuance process,
and guaranteed not to exceed a certain quantity, as its protocol has a hardcoded upper limit of 21
million coins, a kind of asymptotic upper bound. While one is accustomed to think about goods
and scarcity in a material sense, this of course does not have to be the case.
Consequently, it is then worth considering what will be the nal means of value generation
when the money supply for instance in Bitcoin is complete, either by means of exhausting the
computational e ort one is willing to expend in mining more coins or the actual total number
of Bitcoins is produced. Unlike physical metal commodities, which are in unknown total supply,
we argue that the knowledge of the total amount available will change the perceived value of the
currency. Though physical metals may be scarce, the lack of knowledge of their total supply leads
an ever more involved and expensive search for more, maintaining or increasing the worth of those
12
currently in circulation, this will not be the case with Bitcoin. At which point the argument of
value maintenance for such a crypto-currency must change to a di erent perspective.
Some economists, such as Paul Krugman19observed the following possibility of de
ationary
pressure in crypto-currency networks. Bitcoin's capped total money supply could be viewed as
a variation on Milton Friedmans \k-percent rule" [Friedman, 1960]. This theory states that an
optimal way to control in
ation over the long term is for the central bank to grow the money
supply by a xed amount of k% each year, irrespective of the cyclical state of the economy. In
particular, one should set the growth variable of k% at a rate equal to the growth of real GDP each
year. This connection between Milton Freidman's Nobel prize winning theory and Bitcoin practice
was highlighted recently in B ohme et al. [2015] who argue that one can consider Bitcoin as a type
of \... proposal to x the annual growth rate of the money supply to a xed rate of growth." At the
end of the mining process, when the total Bitcoin money supply is created, this would be equivalent
to ak= 0 or perhaps a negative kif a large loss of money supply occured due to theft, electronic
storage corruption or damage to physical storage of a non-trivial portion of the total money supply.
Hence, one needs to consider what is applicable monetary policy to deal with the situation that
the size of an economy grows at a di erent rate to the quantity of money in that economy, in this
case Bitcoins. B ohme et al. [2015] reiterate the views of Paul Krugman that \... the xed slow
growth rate of Bitcoin creates the possibility of de
ation if Bitcoin was to be used widely...". They
also note that there have been other crypto-currency extensions of Bitcoin proposed to overcome
such potential problems, see discussion by for instance King [2013] which introduces Primecoin with
in nite money supply or the introduction of Peercoin which keeps k% around 1-2.
Barber et al. [2012] also discuss such issues, talking about a de
ationary spiral that may arise
from the capped money supply. We rst brie
y recall what a de
ationary spiral is before discussing
this in the context of Bitcoin.
A de
ationary spiral refers to an economic development where rampant de
ation can eventually
lead to the collapse of the currency. In general de
ation can be considered as a decline in the general
price level. It can occur when the price of goods and services, as measured relative to a speci c
measure, begin to decline. This may not be due to the fact that the value of the goods and services
themselves reduced, instead it can simply occur due to the fact that the value of the currency itself
increased. So one can consider the spiral of de
ation as arising in the situation that the value of
a currency, relative to the goods in an economy, increases continually as a result of hoarding. In
response, as the value of the currency relative to the goods in the economy increases, people are
given an incentive to hoard the currency. This incentive arises from the fact that by retaining the
currency, they aim to be able to purchase more goods for less money in the future, this becomes
a vicious cycle as the lack of available currency in the economy causes prices of goods to decrease
and this results in yet further hoarding.
Such an e ect is a real condition that a ects the at backed fractional reserve banking system.
There are two schools of thought as to whether such a de
ationary spiral may occur for Bitcoin.
One view is that it is not likely to occur in the case of Bitcoin, since it is argued that users in the real
economy may not foresee a xed cost (unit amount) that they must pay with Bitcoin. Therefore,
if the value of the Bitcoins that they own increases, then one may expect that any future cost will
take a proportionally smaller amount of Bitcoins. A consequence of this view is that there would
however by no real xed incentive to hold Bitcoin other than pure speculation. In addition, if the
real economy that allows Bitcoin grows, then one would also expect the per-unit value of Bitcoin in
such a perspective to proportionally increase. This view e ectively perceives Bitcoin not as a debt
but as an asset, and as such under such a perspective one would expect that Bitcoins would only
de
ate in value when the Bitcoin economy is growing.
In Barber et al. [2012] they take this perspective and they postulate on a setting in which
Bitcoin usage has matured in the real economy, considering for instance a stable 1% of US GDP
19http://krugman.blogs.nytimes.com/2011/09/07/golden-cyberfetters/
13
transactions in Bitcoins and 99% in USD. They then argue that in such a setting one may expect
that the purchasing power of Bitcoin would still increase over time. The reason is that each coin
will increasingly capture a correspondingly constant fraction of the countrys growing wealth. They
acknowledge that such a de
ationary spiral may occur for bitcoins and discuss potential for hoarding
of such crypto-currency. They argue that their appreciation potential will result in a user tendency
to accumulate Bitcoins rather than spend them in the real economy. The consequence of this is
that the incentives o ered to groups that verify and validate Bitcoin transactions on the blockchain
will reduce as there will be less Bitcoins in circulation, hence transaction volumes naturally reduce
resulting in a less pro table operating environment for veri cation of transactions. They aptly term
this condition \bit rot".
The alternative economic perspective on how de
ationary spirals may manifest is given by the
argument that they occur when there is an incentive to hoard because of declining prices. The
decline in prices will result in less available currency in the market place, which further perpetuates
a decline in prices, and the de
ationary cycle emerges. The website https://en.bitcoin.it/wiki/
Deflationary_spiral discusses mechanisms under which a non-traditional de
ationary spiral may
arise in the Bitcoin network. It argues that once Bitcoin value stabilizes there will always be the
knowledge that the number of Bitcoins in the market is limited. Consequently, if the total value
of all Bitcoin transactions completed increases in "real" terms, then there will continue to be price
de
ation. From this view, there can be an expectation of future de
ation which will result in a
discrepancy in perceived values of Bitcoins depending on ones investment horizon. In the short
term under this scenario, there would be an apparent over-pricing of Bitcoin, which may encourage
alternative competition.
4.2 The metalist view
A range of authors have alluded to the metalist perspective on understanding the value generation
mechanism for the Bitcoin crypto-currency, see discussions in Maurer et al. [2013]; Ingham [2004];
Blanchette [2011]. For instance, Maurer et al. [2013] discuss Bitcoin and the embracement of its
users in a form of monetary pragmatism, and state \... Bitcoin enthusiasts make the move from
discourse to practice in their insistence that privacy, labor, and value are built into the currencys
networked protocols. This semiotics replays debates not just about privacy and individual liberty,
but about the nature of money, as a material commodity or chain of credits.". They argue that
Bitcoin embodies a form of \practical materialism" which is manifest in the form of a modern day
digital metallism, an extension of the ideas of Ingham [2004] and his perspectives on \practical
metallism".
Both Blanchette [2011] and Maurer et al. [2013] argue for a form of metalist monetary per-
spective on Bitcoin. The latter stating \... Despite the supposed immateriality of digital bits of
information, matter itself is very much at issue with Bitcoin, both in how it is conceptualized and
in how individual Bitcoins are mined...."
Under the premise of a \metalist's" view of the value derivation of money, many would argue
that value of crypto-currencies may at present be derived from physical commodities consumed in
the mining process utilised to obtain this increasingly scarce resource. For instance several studies
have argued that the price of crypto-currency Bitcoin is related to the cost of maintenance, storage
and electricity consumption required for the large server farms \virtual mines" utilised to create the
bitcoin currency, see discussions in O'Dwyer & Malone [2014]. In J.P. [2011] they argue that the
material value of Bitcoin is not limited to the privacy feature o ered by the crypto-currency, they
argue that it nds another feature that provides its value, the process of producing new Bitcoins
known as mining which \mimic[s] the extraction of minerals [...]. As the most readily available
resources are exhausted, the supply dwindles."
If one then continued the perspective of a metalist monetary theory for crypto-currency such
14
as Bitcoin, then one could argue based on ideals expressed in Ingham [2000], where they consider
money to be the consequence of rational agents that prefer to work with money that is the most
tradeable commodity in the current real economy. Under this perspective there is some notion
that virtual and crypto-currencies especially could maintain value after the mining process. For
instance, if rational agents in the economy began to prefer or value them more than other at
backed e-money substitutes. This could happen in a number of ways, for instance rational agents
may prefer the privacy features such virtual and crypto-currencies may o er in the digital economy
more than other at based e-money competitors. Another possibility may be that the block-chain
technologies that act as ledger, for instance in Bitcoin, may nd wide-spread uptake as a means of
virtual contract construction between di erent economies, or as a third perspective, if virtual and
crypto-currencies found a wider market base in third world countries by moving beyond internet
based services to mobile services, this may also maintain their value in the real economy.
4.3 The chartal view
Next we discuss some alternative monetary theory perspectives on cryto-currencies such as Bitcoin.
In particular, we consider the case of Bitcoin when the mining process is completed and all the
money supply has been created. We then consider the chatalist perspective of where Bitcoin may
derive its value, this is an alternative perspective to that of the metalist views expressed above that
has not been discussed previously in the context of Bitcoin. Therefore, we nd it interesting to
open up this avenue of thought to more debate.
An alternative view to the metalist perspective can also be considered, where the value of
Bitcoins may continue to be maintained. This alternative view would be based on a transition
from the metalist perspective, post mining completion, to a chartalist's view. This view posits
that money should not be studied in isolation from the powers of the state, i.e. the country that
\created" and \controls" the money. In particular, under this perspective, money in its general
sense is a unit of account created by a central (government) authority for the legal structuring of
its social debt obligations.
Well before crypto-currencies were conceived of, for instance Knapp [1924] argued that all monies
are chartal, and this can include crypto-currencies, since all payments in the form of tax to the state
or governing powers are measured in some unit of value. Furthermore, the state makes a decision
\that a piece of such and such a description shall be valid as so many units of value", it is then
irrelevant what this token or money manifests as since it is only a \sign-bearing" object that a state
\gives a use independent of its material".
4.4 How do 'outside monies' like virtual and crypto-currencies t into
the chartal and modern monetary theory perspectives ?
In this section we delve in more detail into the importance of thinking about the role of such virtual
and crypto-currencies in aspects of monetary theory and monetary policies if they become more
prevalent in the real economy. We contrast views formed based on at backed e-money with the
how they may be a ected in a real economy with both at and virtual or crytpo-currencies. In
general we will tend to raise more questions, than we pro er solutions. Though this is useful to
open dialogue and ways of thinking about the challenges that may lie ahead.
In particular we rst recall that monetary theory is developed with the aim of understanding the
most suitable approaches to monetary policy and how it should be conducted within an economy. It
is suggested by such theories that a variety of di erent monetary polices may be employed to bene t
countries, depending on their economy and resources. For most monetary theories, the core ideals
relate to factors such as the size of the money supply, price levels and benchmark interest rates and
how they all a ect the economy through in
ation, taxation, wage growth and unemployment levels.
15
It is then the realms of economists and central bankers to execute the outcomes of such theories in
practice. As stated, we would like to initiate some exploration of how virtual and crypto-currencies,
when mixed with at currency in the real economy, may alter traditional outcomes on policy
decisions compared to at backed money supplies.
There are many forms of monetary theories that have been developed by economists, indeed
we have seen brief discussions on metalist and chartal views already above. These include ideas of
Fiat Debt-Free Money Reformers, Modern Monetary Theorists, Modern Monetary Realists, Post
Keynesian Reformers, Islamic Banking Advocates, Social Credit Reformers, Land Reformers, Hard
Money Reformers and Competing Currency Reformers. Recent, some would say unorthodox ver-
sions of such theories Tcherneva [2006], include variants such as Modern Monetary Theory (MMT)
Wray [1998b] and Modern Monetary Realism (MMR) which were developments from early forms of
Chartalism Wray [1998a] and prior ideas from Knapp [1924]; Forstater [1999] and functional nance
theories of Lerner [1943]. Such theories also are termed neochartalist approaches and \tax-driven"
money, see discussion in Wray [2000]. All these theories revolve around the procedures and conse-
quences of utilization of government issued units of money often called at money, in the sense of
the de nition o ered earlier.
A key premise of theories like MMT and the consequences of monetary policy that
ows from
these theories is the notion that governments have some level of control over the money supply and
elasticity of money. So we wonder, what happens to such controls when other forms of currency,
created outside of any sovereign state, starts to interact in a given economy. Does this reduce the
power of the state to enact policies based on the assumption of ultimate control of money supply,
or does it act as a friction or damping factor on the utility of resulting policy levers when enacting
policies assuming ultimate money supply controls are still relevant.
One can view money, in its general sense, as a unit of account created by a central (government)
authority for the legal structuring of its social debt obligations. For instance, this may manifest
between a population and a governing central gure in the form of taxation liabilities. In this
setting it is conceived by chartalists and many modern monetary theories that money then arises
from the state as a form of tax credit that can nullify these taxation debts. This is in rm contra-
diction to other orthodox theories that followed from commodity based currency views such as gold
standards which view money more as naturally arising as a medium of exchange from the attempts
of enterprising individuals to minimize transactions costs in barter economies.
No matter which view one prefers, it is interesting to question what implications may arise from
interactions in such economies of non-government controlled currencies which are non- at such as
virtual currencies and crypto-currencies acting as truly \outside" monies. Before embarking on
developing such questions for future consideration we summarise a few key ideas from chartalist,
MMT and MMR thinking, based on the account provided in Tcherneva [2006] where it is observed
that in general the following principles are considered by these theories. With each concept, we
brie
y pose questions relating to their applicability in the setting of an economy which admist both
at currency as well as virtual and crypto-currencies.
Dismissal of the view that money emerges naturally as a medium of exchange that enables
the minimization of transaction costs among utility maximizing rational agents in the real
economy, due to their view that such notions lack historical support.
{Is this view now valid for crypto-currencies. Some would argue one of the key reasons
crypto-currencies are being adopted in the real economy at present is due to the very fact
that they are providing a reduction in transaction costs for some agents in comparison
to other at backed e-money payment services such as Paypal, see discussions in Brito
et al. [2014]. Perhaps, therefore there will be some historical precedent for questioning
this perspective further in the case of virtual and crypto-currencies.
16
One should study money in the context of institutions and culture with special consideration
given to political and social considerations.
{Certainly, the role of virtual and crypto-currencies may t into this perspective, in the
sense that the context of their uptake in the real economy has historically certainly been
a function of institutional in
uence from governments in the form of regulations and
central bank policies. The role of virtual and crypto-currencies has also been in
uenced
by cultural and social considerations. To see this one may consider for instance the rapid
uptake of some virtual and crypto-currencies in the U.S. and more recently in China,
where in some cases they are used as alternative means for transmission of assets with
enhanced anonymity from central government oversights.
Money is by its nature a credit-debt social construct. Furthermore, chartalists argue that
social debt relationships may be ordered with the top of the hierarchy being the liability of the
central authority which they deem the most reliable. Neochartalists also argue that modern
currencies are contained in a context of certain governing central or state controls: the ability
to levy taxes on the population and economy; and the ability to decide what is acceptable for
payment of tax liabilities. In this context tax should be understood in a broader context of
modern income tax, estate and commercial tax as well as any non-reciprocol obligation to the
state such as nes and fees.
{We will address this point in Section 4.5
Money functions as an abstract unit of account which is then used as a means of payment
and debt settlement. Unlike orthodox monetary theories, charatalists distinguish between
money-of-account and money in the real economy, perhaps summarised by Keynes [1930] who
argued that \money-of-account is the description or title and the money is the thing which
answers the description." With this view, chartalists see money's function in the real economy
as a medium of exchange is incidental to and contingent on its primary function as a unit of
account and a means of payment of liability. Neochartalism generally views taxation not as a
form of nancing government spending but instead as a mechanism to create demand for the
currency.
{We will address this point in Section 4.5
Neochartalists believe that given the view that modern states or countries or unions have
the monopoly power over the issue of their currency, i.e. sovereign currency control with no
xed exchange rates, dollarization, monetary unions or currency boards, they they will not
face operational nancial constraints, though they could face political constraints. Further-
more, they consider that such states should consider borrowing as an ex ante interest rate
maintenance operation, arguing that instead the taxation system is established as a means to
creating demand for currency rather than nancing of government spending.
Their perspective is such that, no entity with the power to create and destroy money as
they require will need anyone else to assist in the ability to `fund' spending. However, even
though de cits for the economy are not nancially constrained in the typical sense, they
are still subject to potential pressures from in
ation rates and exchange rates, as well other
considerations such as access to available resources, capacity utilization, labour availablility,
and external balance.
{Firstly, we discuss the issue of monopoly power over currency supply. To address this
consideration, the question that may arise is whether or not the central bank or gov-
ernment can control the money supply and elasticity of such decentralised virtual or
17
crypto-currencies perhaps through accumulation of stored reserves raised through taxa-
tion? This would ofcourse be assuming they were eventually allowed by governments as
alternative forms of payment for tax liabilities along side traditional at currency. If this
were the case, then one would need to be very careful in the money supply management,
since as noted previously too greater hoarding of these currencies, which are of bounded
total money supply, may result in a de
ationary spiral.
{An alternative perspective, which avoids the need for reserving of virtual or crypto-
currencies, in order to achieve control of the money supply may also be possible for some
types of virtual and crypto-currencies. For instance, in the case of Bitcoin, instead or
accumulating reserves, a government may alternatively take greater stakes in the network
mining and transaction validation activities. A governments access to vast computing
power, relative to most agents in the economy, puts them at a distinct advantage to gain
sucient computational power within such networks that any virtual or crypto-currency
with consensus network type protocol embedded in its code may be able to have its core
attributes modi ed by governments who earn sucient voting rights. For instance, a
government may gain sucient control of the currencies network to alter core features
of the code such as the nite money supply aspect, the mining rates and other key
features related to the money supply. Perhaps it may be argued that in e ect this is the
crypto-currency equivalent of state central power over money supply.
{Secondly, we consider the issue of whether virtual and crypto-currencies would result
in a form of operational nancial constraint for states and governments. In the case of
decentralised virtual or crypto-currencies, the operations required to gain some form of
control or assert some form of management of the money supplies in the real economy
may not in general be free from operational nancial constraints. For instance the ac-
tions mentioned above such as reserving of virtual or crypto currencies, or more active
control/'voting power' with in the virtual network, through enhanced mining or trans-
action processing activities will be potentially expensive for the state to maintain and
can be considered as a operational nancial constraint on the actions they may wish to
enact in their monetary and scal policies.
Neochartalists also consider that when a state has a monopoly over the currency, it also has
the power to set prices, including interest rates and how currency will be exchanged for other
goods and services.
{So if one assumes that the state only has partial power over some aspect of a virtual or
crypto-currency through such means as discussed in the previous bullet points above, then
an interesting question to raise is what implications does this have for the perspectives
held by Neochartalists views on the ability of a state to set prices, interest rates and
exchange rates? These views are based on the premise that the state has monopoly power
over the currency, and ofcourse they will still maintain this over their at denominations.
So the point of consideration is more whether an increased growth and uptake in the
economy of virtual and crypto-currencies, for which the state does not have monopoly
control over the money supply attributes, will create a friction in their ability to set
prices, interest rates and exchange rates ?
4.5 Acceptance and Legal Tender
Many have argued against various aspects of MMT and related theories from a chartalist root.
One of the key aspects they point to relates to the notions of legal tender. For instance, ?and?
emphasized legal tender laws as critical, where the state or government would issue a currency in
18
terms of a unit of account and then pass laws to require adoption of that currency in designated
public and private payments. This is a jurisprudence perspective of how currency can become
valuable in a real economy, however chartalists like Knapp [1924] took an alternative view that
such laws would not suce and that the state or government e ectively establishes the money of
account when it determines what will be \...accepted at public pay oces...", rather than through
legislation.
Hence, we see that an important point to note that is directly of consequence to understanding
a chartalist's view of virtual currency and crypto-currency is to observe that the chartal nature of
money and its acceptance in the real economy lies not in its acceptance in the form of a legal tender
status but instead on its place in the heirarchy order of social debt relationships. This derives
instead from the states power to delegate taxes and dictate how and in what form of money such
accounts will be paid.
Therefore, under a chartalists view on monetary theory it is not a question of whether at
currency is in direct competition with virtual or crypto-currencies, but instead whether there will
be sucient demand from the public that will enforce the will of the public to push the state to
accept such currency forms as means of payment of liabilities owed to the government. Should this
occur, there will then be an interesting circumstance arising where one unit of account is established
in a at currency which is under the control of the government, however a second unit of account
is from a decentralised money supply mechanism in the form of crypto-currency. We point out
that has potential to change dynamics in the supply and demand of at currency and should be
considered further.
4.6 Competition between virtual/crypto-currencies and at backed cur-
rencies
Another interesting point to make that arises naturally from a chartalist view and relates to virtual
and crypto-currencies in regards to the concern some have raised about such monies competing and
perhaps becoming a dominant unit of barter in an economy is that agents can never simply refuse
to take a sovereign's money. That is, at currency is the key money to make payment for taxation
liabilities, so long as there is always taxation present in the economy, which in some form relies
upon the at currency more than the virtual or crypto-currency. In this case, the at currency
will always remain at the top of the hierarchy of social order in terms of debt relationships, see
further discussion on this general view in Tcherneva [2006]. The only issue arising in such cases
is again the fact that when virtual and crypto-currencies are allowed into the economy to pay tax
they diminish the power of the state to posses and maintain unconditional control of the currency,
that they would maintain if they only allowed for receipt of tax credit their own unit of money or
at currency.
Consequently another issue arises here that potentially complicates the above considerations, this
is the one pointed out by Innes Innes [2004] where it is argued that it is not only the requirement to
pay taxes in any particular state mandated monies, but also the diculty in obtaining these monies
that provide the monies worth. To understand where this may pose a challenge to at currencies,
one needs to consider the situation in which at money and virtual and crypto-currencies are allowed
in the economy (perhaps not as legal tender) but to settle tax debt in government oces. In this
case, if it is perceived by the public that certain attributes, for instance privacy features of virtual
currencies or crypto-currencies are more valued that those of at denominated e-money, then it
may be conceivable that these would have preference in the economy. Now add to this the scarcity
of such bitcoin monies in terms of the hard limit on their physical creation, unlike government
money which is only really limited by in
ationary pressures in the given economy and one has an
interesting question to postulate relating to which form of currency and in what conditions would
maintain the top heirarchy in terms of social debt settlement unit.
19
4.7 Not high powered money and yet somehow explicitly liability free
?
Consider the context of a modern economy with a fractional banking system in place. In such an
economy, a bank recognizes that it is safe to issue deposits to an amount that is some multiplier
of its actual physical reserves since it may be reasonable to expect that only a small fraction of
depositors will try to "cash-out" deposits, redeeming them for reserves. Then, under the setting in
which a reasonably stable deposit multiplier is established as a function of the ratio of reserves held
against deposits, the supply of deposits will then be determined by the quantity of loans demanded
and the quantity of reserves supplied. One can then consider the role of governments in controlling
this process, they are e ectively able to exert some measure of control by deciding what should
form the basis of reserves and also by establishing a legally required reserve ratio. At some stage
this corresponded to the gold standard and now days has moved instead to government at money
sometimes known as a form of High Powered Money. Since the government then has the ability
to control the at money supply i.e. a seigniorage in the real economy, they then naturally obtain
a level of control in the economy since banks will continue to have a demand for such currency in
order to increase the value of their loan books, which is constrained by their ability to accumulate
reserves and a reserve ratio condition on lending.
Hence, a modern economy revolves around a money supply that consists of bank deposits plus
the portion of high powered money created by government that is not held by banks as reserves.
Even though the banks may exert some level of control on the amount of at money help by the
general public by adjusting interest rates on deposits to induce them to deposit or spend at money,
however the government with its control of high powered money supplies to banks and it setting of
reserve ratios, exerts exogenously a pressure on banks and ultimately the money supply.
Hence, another point worth questioning is the role of these exogenous currencies like virtual
and crypto-currencies which are not created by central banks or private banks. Somehow they
are liability free in some sense and yet they may not be considered in the Neo-Chartalists view as
High Powered Monies, issued by central banks for spending in the private sector to fuel taxation
generation and value creation in at currency. Unlike the view that although banks can also create
money, their creation is a \horizontal transaction" since such created credit or money does not
increase net nancial assets since these assets are o set by liabilities. However, this is not the case
with virtual and crypto-currencies, in addition, if they were allowed as monies to make payment for
taxes and nes from a given government, then their legal power to discharge debt would increase
their worth. This may cause a friction with the at denominated e-money system, since unlike at
e-money which is issued or controlled by the government where it can issue its own currency at
will subject to a public liability in the countries accounts appearing as a de cit in the countries
accounts, it has no control over the issuance of the virtual or crypto-currencies except that which
it may exert should it store signi cant reserves of such currencies in the central bank. This may
therefore in principle, should virtual currencies become more mainstream act as a problem for the
universality of the policy tool governments have utilised for years based on their universal monopoly
of money creation that regulates in
ation and unemployment.
In continuation of these above lines of questioning, one would wonder about the government or
states ability to utilise money creation and taxation to control the rate of spending in the economy
and therefore the ability to ful ll, as Lerner [1943] puts it, \...to ll its two great responsibilities, the
prevention of depression, and the maintenance of the value of money". If virtual currency or crypto-
currency were to be admitted as viable tender to pay tax to the government, then such currencies
may diminish the standard monetary controls available to the government, since currency creation
is no longer the sole mandate of the government, it would therefore require some form of symbiotic
relationship with the at money supply and the virtual or crypto-currency supply to maintain the
status quo. A fact that has not been lost on central banks over the years as early forms of e-money
20
and non- ate monies arose.
One last point to make about the notion of liabilty in the case of virtual and crypto-currencies
is perhaps that they are implicitly creating liabilities. This can be seen in the case that in the
creation of such currencies through the mining process, the mines require utilisation of resources,
loans/credit agreements with banks in creation of resources required to run and setup such mines,
meaning the implicitly the creation of such currency, though explicitly it seems liability free, is
actually implicitly not free of liability.
5 Views on crypto-currency from a regulation perspective
Given the importance of understanding the role of crypto-currencies in the monetary system high-
lighted above, we now turn to another core element that must be considered should such currencies
be utilised increasingly in the real economy, the role of regulation. A detailed account of several
aspects of regulation response to crypto-currencies can be found in Peters et al. [2014].
Even before the advent of crypto-currencies, there have been concerns about how centralised
virtual currencies may limit a country's ability to control in
ationary pressures. The Chinese Q-coin
was adopted widely as a form of payment by online entrepreneurs, i.e. outside the online messaging
environment which it was created for. The Chinese central bank, citing concerns about an increased
money supply outside of its control, as well as a diculty in imposing taxation, enacted limits in
the issuance of these currencies Lehdonvirta & Castronova [2014].
A number of regulators around the world have been devoting an increasing amount of attention
to virtual and crypto-currencies in recent years. Mitchell [2014] outlines the responses of several
regulators, from which one can observed that there are both varied interpretations of crypto-currency
(e.g. as e-money, private money20, as a commodity or private property, or as a private unit of
account), which informs their treatment from a taxation perspective also. In most regulatory
responses to virtual currencies in Europe, Bitcoin has not been found to ful ll the criteria/de nitions
of a currency. Sweden however has required virtual currency exchanges to register with the nancial
supervisor, while Germany and France have declared that certain Bitcoin related activities are
subject to authorisation. There is no uni ed approach to regulation of such virtual currencies as
payment services within the EU, and the European Central Bank (ECB) has not expressed any
intention to amend the current legal framework to incorporate such considerations. We will discuss
in a little more detail the recent responses of the ECB and the UK's HM Treasury, who have both
conducted surveys about the use, bene ts and risks of virtual currencies, as well as the New York
Federal Reserve's recently released detailed regulatory framework.
In November 2014, HM Treasury in the UK issued a call for information, attracting over 120
responses from diverse participants, including banks, payment service providers and digital currency
developers. Results were published in March 201521. Bene ts of digital currencies include lower
costs and faster, 24 hour processing availability, particularly for cross-border transactions. The
risk side of these advantages are limited controls over transactions, theoretically allowing very large
international transfers, with no capacity for the authorities to freeze or reverse payments, given the
irreversibility of transactions in virtual currencies.
The ECB has been actively considering monetary policy implications resulting from the intro-
duction centralised virtual currencies and decentralised crypto-currencies since at least 2012. In its
rst report22it noted that both virtual currencies and crypto-currencies fall under the responsibility
20Bitcoin has been recognized by the German Finance ministry as a unit of account, and is
thus treated as a type of private money. http://www.spiegel.de/international/business/
germany-declares-bitcoins-to-be-a-unit-of-account-a-917525.html
21https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/414040/digital_
currencies_response_to_call_for_information_final_changes.pdf
22https://www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemes201210en.pdf
21
of central banks, due to the characteristics shared with payments systems, it highlighted the lack
of supervision and concluded that they did not pose a risk to nancial stability. In its more recent
study23,, it suggested thatd ue to its high price volatility and low acceptance rate, the Bitcoin could
not be, yet at least, regarded as a full form of money from an economic perspective. The ECB
revised its de nition of virtual currency as `a digital representation of value, not issued by a central
bank, credit institution or e-money institution, which, in some circumstances, can be used as an
alternative to money'.
Despite the slow uptake of virtual currencies, the ECB also has stated its intention to monitor
possible threats to monetary policy and nancial stability, in the case where virtual currencies gain
mainstream acceptance. It suggests that this would be possible for a new generation of virtual
currencies which address current technical weaknesses and are geared towards a more mainstream,
less technologically minded audience.
With regards to enacting regulation, the UK govenment has thus set out a series of steps, which
will include AML regulation pertaining to digital currency exchanges in the UK, to ensure that
law enforcement bodies have the capabilities required to combat criminality in the digital currency
space. More interventionist maybe than its European counterparts, the New York Department
of Financial Services (NYDFS) has recently released the BitLicense regulatory framework, after
approximately 2 years of consultation24The regulation sets out de nitions for virtual currencies
activities, which include:
receiving virtual currency for transmission or transmitting virtual currency;
storing, holding, or maintaining custody or control of virtual currency on behalf of others;
buying and selling virtual currency as a customer business;
performing exchange services as a customer business; or
controlling, administering, or issuing a virtual currency.
Any individual or corporation engaged in the aforementioned activities is required to obtain a
license to do so. This entails the completion of a lengthy application form25and a $5000 fee. The
regulation is far-reaching and there have already been rms that have either withdrawn their New
York operations, or shut down altogether, citing excessive compliance burdens26.
The Law Library of Congress has compiled a list of regulatory responses besides the ones detailed
above27. Outside of the EU and the US, regulatory activity regarding crypto-currency usage has
mostly been limited to warning about its nature as a non-state-backed currency and its price
volatility. There are a number of exceptions however, as China has banned nancial institutions
from handling bitcoin, while Japan has stated that `due to their intangible nature and reliance on
third parties', bitcoins are e ectively not subject to ownership, and thus are not covered by existing
regulation28. On the other hand, the Australian Senate will e ectively put forward recommendations
to treat Bitcoin as money, as treating Bitcoin as a tradeable commodity would have created a double
taxation e ect29.
23www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemesen.pdf
24The nal text of the regulatory framework is available at http://www.dfs.ny.gov/legal/regulations/
adoptions/dfsp200t.pdf .
25http://www.dfs.ny.gov/legal/regulations/vc_license_application.pdf
26http://cointelegraph.com/news/114623/bitlicense-doing-its-job-eobot-becomes-3rd-firm-gone-from-new-york
27http://www.loc.gov/law/help/bitcoin-survey/regulation-of-bitcoin.pdf
28http://www.japantimes.co.jp/news/2015/08/06/national/crime-legal/bitcoins-lost-in-mt-gox-debacle-not-subject-to-ownership-claims-tokyo-court-rules/
\#.VctCRLwy3CK
29http://www.reuters.com/article/2015/08/05/us-australia-bitcoin-idUSKCN0QA0TS20150805
22
A common theme in recent regulatory responses is that they have identi ed that more promising
perspectives of virtual currencies may actually lie in the technology they use, i.e. the distributed
ledger technologies introduced in Section 3.4. The term `virtual currency scheme' also encom-
passes the technologies and mechanisms used for the operation of transactions in the currency.
The UK government, whilst identifying barriers that would prevent digital currencies from gaining
widespread acceptance, has also identi ed the associated blockchain, or distributed ledger technol-
ogy as having promise for the future of payments. Following the survey of HM Treasury, it has set
out a series of recommendations to provide funding to research bodies to explore opportunities for
digital currency technology.
6 Conclusions
Our report highlights current trends in the virtual and crypto-currency space, from a number of
di erent perspectives. The rst is the emergence of such currencies, given the historical context of
at money and the advent of cryptographic protocols that enabled e-money. We show that from
this perspective, virtual currencies emerged to serve the need of particular niches of online gaming
and social communities, while crypto-currencies sought to have a wider reach, and become the de
facto currencies of the internet.
Given these goals and the much greater probability for decentralised crypto-currencies to start
entering the real economy, we focus on these to present current usage trends. Though to date,
even the most popular crypto-currency, Bitcoin, has not gained widespread acceptance, while its
use as an investment product has also remained low. It is believed that this will change as a
greater understanding of these crypo-currencies occurs by regulators, exchanges and businesses in
the economy. We hope to have contributed to this discussion by highlighting several aspects of
monetary theory and the role of virtual and crypto-currencies in such theories.
Finally, we summarised current regulatory responses, showing the varied reaction to Bitcoin,
from outright bans in China to e ective treatment as money in Australia. The decentralised nature
of the currency means that there is limited e ect any single jurisdiction can have on the operation
currency itself, and the focus is on companies providing services in the eld. Given the border-
less nature of Bitcoin, however, it is dicult to see how regulators can prevent companies taking
advantage of regulatory arbitrage, by setting up in jurisdictions with less restrictions.
Funding
The support of the Economic and Social Research Council (ESRC) in funding the Systemic Risk
Centre is gratefully acknowledged [grant number ES/K002309/1].
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25
|
{
"id": "1508.04364"
}
|
2007.11877
|
Proposal for a Comprehensive (Crypto) Asset Taxonomy
|
Developments in the distributed ledger technology have led to new types of
assets with a broad range of purposes. Although some classification frameworks
for common instruments from traditional finance and some for these new, so
called cryptographic assets already exist and are used, a holistic approach to
integrate both worlds is missing. The present paper fills this research gap by
identifying 14 attributes, each of which is assigned different characteristics,
that can be used to classify all types of assets in a structured manner. Our
proposed taxonomy which is an extension of existing classification frameworks,
summarises these findings in a morphological box and is tested for
practicability by classifying exemplary assets like cash and bitcoin. The final
classification framework can help to ensure that the various stakeholders, such
as investors or supervisors, have a consistent view of the different types of
assets, and in particular of their characteristics, and also helps to establish
standardised terminology.
|
http://arxiv.org/pdf/2007.11877v1
|
Thomas Ankenbrand, Denis Bieri, Roland Cortivo, Johannes Hoehener, Thomas Hardjono
|
q-fin.GN
|
q-fin.GN
|
Proposal for a Comprehensive
(Crypto) Asset Taxonomy
Thomas Ankenbrand & Denis Bieri, Lucerne University of
Applied Sciences and Arts;
Roland Cortivo & Johannes Hoehener, Swisscom AG;
Thomas Hardjono, MIT Connection Science
July 24, 2020
Abstract —Developments in the distributed ledger technology have
led to new types of assets with a broad range of purposes.
Although some classification frameworks for common instru-
ments from traditional finance and some for these new, so-
called cryptographic assets already exist and are used, a holistic
approach to integrate both worlds is missing. The present paper1
fills this research gap by identifying 14 attributes, each of which is
assigned different characteristics, that can be used to classify all
types of assets in a structured manner. Our proposed taxonomy,
which is an extension of existing classification frameworks,
summarises these findings in a morphological box and is tested
for practicability by classifying exemplary assets like cash and
bitcoin. The final classification framework can help to ensure that
the various stakeholders, such as investors or supervisors, have a
consistent view of the different types of assets, and in particular
of their characteristics, and also helps to establish standardised
terminology.
I. I NTRODUCTION
Since the inception of the Bitcoin network in the year 2009,
the space for cryptographic assets has developed rapidly.
The continuing technological innovation in the underlying
distributed ledger technology could consequently lead to an
increasing transformation of traditional financial markets into
crypto-based markets. Although different asset classification
frameworks exist for both worlds, a holistic approach merging
both traditional finance and the crypto economy is still lacking.
This poses a challenge to the various stakeholders such as
investors or regulators in retaining an overview of existing
assets of different types and, in particular, of their design and
individual characteristics. In order to fill this lack of research,
we propose a taxonomy for the systematic classification of all
types of assets, be it of physical, digital or tokenised nature.
II. L ITERATURE REVIEW
The characteristics and properties of the most common types
of financial instruments such as stocks, bonds, and derivatives
have been the subject of research for some time, not only
in the academia, but also in the industry. Therefore, a wide
range of publications exist that deal with the functioning of
these different instruments in a structured way.
One framework defining the structure and format for the
classification of financial instruments (CFI) was first proposed
by the International Organization for Standardization (ISO)
in the year 1997. The last revised version of the framework is
1Our research is part of the FinTech programme supported by Finnova,
Inventx, Swiss Bankers Prepaid Services, and Swisscom.called ISO 10962:2019 and was published by ISO in 2019. It
seeks to provide a standard for identifying the type of financial
instrument and its main high-level features in the form of
specific codes consisting of six alphabetical characters, and
should thus help to standardise country and institution-specific
terminology in relation to financial instruments [1]. The first
character of the CFI code indicates the main category of
financial instruments. These include equities, collective
investment vehicles, debt instruments, different types of
derivatives, and others.2The second character of the CFI code
indicates multiple subclasses in a given main category, called
groups. Equities, for example, are divided into the groups
common/ordinary shares, preferred/preference shares, and
common/ordinary convertible shares, among other groups.
The last four characters of the CFI code define the specific
attributes of a financial instrument and depend on the group
to which the asset is allocated. For financial instruments
in the group common/ordinary shares from the “equities”
main category, relevant attributes include voting rights,
ownership, payment status, and form. These attributes come
with predefined possible values that determine the final code
of a financial instrument [1]. For other groups such as bonds
from the “debt instruments” category, alternative attributes,
e.g., the type of interest or guarantee, are of relevance.
A second framework for classifying financial instruments is
proposed by Brammertz and Mendelowitz [2]. Their so-called
ACTUS taxonomy is based on the specific nature of financial
contracts and in particular on their cash flow profiles and seeks
to create a global standard for the consistent representation
of financial instruments. It distinguishes between financial
contracts, which in turn are split into the subcategories
of basic contracts and combined/derivatives contracts on
the one hand, and credit enhancement on the other. Basic
contracts consist of fixed income and index-based products,
whereas combined/derivative contracts comprise symmetric
financial products, options, and securitisation products.
The second main category of the ACTUS taxonomy, i.e.,
credit enhancement, includes guarantee contracts, collateral
contracts, margining contracts, and repurchase agreements.
The standard is implemented on the SolitX platform with
a technical API layer and DLT adapter for transaction
systems and accounting, and in the AnalytX architecture for
risk management analysis, simulations, asset and liability
management, and business planning [3].
The standards proposed by [1] and [2] show that sophisticated
classification frameworks for traditional financial assets exist,
which are used in practice. For cryptographic assets, on
the contrary, the characteristics of many tokens in various
respects, for example in terms of regulation, utility or
valuation, were and are still largely ambiguous and hard to
measure. Several initiatives from governments, the academia,
and the industry have sought to reduce these uncertainties
2For a detailed description of each category, see [1].
1arXiv:2007.11877v1 [q-fin.GN] 23 Jul 2020
by systematically structuring the hundreds of existing tokens
based on predefined criteria.
The Swiss Financial Market Supervisory Authority (FINMA),
for example, issued guidelines for enquiries regarding the
regulatory framework for initial coin offerings in early 2018,
in which it distinguishes between three types of tokens, i.e.,
payment tokens, utility tokens, and asset tokens, based on
the underlying economic purpose [4]. Whether a particular
token is a financial instrument and thus would be subject
to certain laws and regulations depends on its economic
function and the rights associated with it. Other jurisdictions,
such as the European Union, Israel, Malta, and the United
Kingdom, follow a similar classification approach, although
their terminologies differ to some extent [5]. Additionally,
some jurisdictions follow the approach that the three main
types of tokens are not necessarily mutually exclusive. Rather,
there are also hybrid forms that share characteristics of two
or three main types. Accordingly, particular cryptographic
assets could thus, for example, have certain characteristics of
both payment and utility tokens.
In April 2019, the U.S. Securities and Exchange Commission
(SEC) through its strategic hub for financial innovation,
FinHub, published guidelines to determine whether a digital
asset, which may be a cryptographic asset, is an investment
contract, i.e. an agreement whereby one party invests money
in a common enterprise with the expectation of receiving a
return on investment. This assessment is done by applying
the so-called Howey test. If an investment agreement exists,
the digital asset is classified as a security and therefore U.S.
federal securities laws apply and must be considered by issuers
and other parties involved in, for example, the marketing,
offering, sale, resale or distribution of the respective asset [6].
Other jurisdictions, e.g., Ireland, follow a similar approach of
classifying cryptographic assets based on their qualification as
a security [7]. However, the Howey Test is to be understood
less as a classification framework but more as a decision aid
as to whether a cryptographic asset represents a security or not.
An academically based classification framework for
cryptographic assets, which goes beyond the legal perspective
and also takes technological and economic aspects, among
others, into account was carried out by Oliveira et al. [8]. By
applying a design science research approach, including 16
interviews with representatives of projects with blockchain-
based token systems, the paper derives a token classification
framework for cryptographic assets that can be used as
a tool for better informed decision making when using
tokens in blockchain applications. Their final classification
framework consists of the 13 attributes class, function,
role, representation, supply, incentive system, transactions,
ownership, burnability, expirability, fungibility, layer, and
chain, each of which include a set of defined characteristics.
A similar framework was developed by Ballandies et al.[9]. The authors established a classification framework
for distributed ledger systems consisting of a total of 19
descriptive and quantitative attributes with four dimensions
(distributed ledger, token, action, and type). The attributes
comprise the distributed ledger type, origin, address
traceability, Turing completeness, and storage in the
distributed ledger dimension, underlying, unconditional
creation, conditional creation, transferability, burn, and supply
in the token dimension, action fee, read permission, and
actor permission in the action dimension, and fee, validate
permission, write permission, proof, and type in the consensus
dimension. The framework was derived from feedback from
the blockchain community.
Three further classification frameworks for cryptographic
assets that were strongly driven by the industry are those
proposed by the consulting firm MME, the International
Token Standardization Association (ITSA), and the Ethereum
Enterprise Alliance (EEA).
The framework by MME was published in May 2018 and
focuses on the legal properties and risk assessment of
cryptographic assets. The paper’s resulting classification is
based on a token’s function or main use, alongside other
criteria such as the existence of a counterparty, as well as its
type and/or the underlying asset or value. The final archetypes
of cryptographic assets are native utility tokens, counterparty
tokens, and ownership tokens, which are each subject to
additional subcategories of token types [10].
The International Token Classification (ITC) framework by
the ITSA comprises an economic, technological, legal, and
regulatory vertical each containing a set of subdimensions
with different attributes. The economic and technological
verticals include three subdimensions each, which refer to
a token’s economic purpose, its target industry, and the
way of distribution, and the technological setup, consensus
mechanism, and technological functionality, respectively. The
legal vertical includes the two subdimensions legal claim
and issuer type, whereas the regulatory vertical focuses
on assessing a tokens regulatory status in the US, China,
Germany, and Switzerland. Over all verticals, a total of
twelve subdimensions are defined, though ITSA plans to
define further subdimensions in the future. Concerning
the evaluation of these individual subdimensions, as of
September 2019, the ITC framework already provided
detailed information on four of the twelve subdimensions,
namely for the economic purpose, industry, technological
setup, and legal claim. The classification into these four
subdimensions was compiled in a database covering more
than 800 cryptographic tokens. Besides the classification
framework and the corresponding database, the ITSA also
introduced a nine digit unambiguous identifier for each token,
the so-called International Token Identification Number, short
ITIN [11].
2
The third industry-driven framework for classifying crypto-
graphic tokens was published by the EEA in November 2019.
Their proposed Token Taxonomy Initiative (TTI) distinguishes
between five characteristics a token can possess. The first
characteristic is the token type and refers to whether a token
is fungible or non-fungible. The second characteristic, the
token unit, distinguishes between the attribute of being either
fractional, whole or singleton and indicates whether a token is
subdivisible or not. The value type, as the third characteristic,
can assume the attribute of being either of an intrinsic value,
i.e., the token itself is of value (e.g., bitcoin), or a reference
value, i.e., the token value is referenced elsewhere (e.g.,
tokenised real estate). Characteristic four, the representation
type, comprises the attribute of being common or unique.
Common tokens, on the one hand, share a single set of
properties, are not distinct from one another and are recorded
in a central place. Unique tokens, on the other hand, have
unique properties and their own identity, and can be traced
individually. The fifth and last characteristic is the template
type and classifies tokens as either single or hybrid and refers
to any parent/child relationship or dependencies between to-
kens. Unlike single tokens, hybrid tokens combine parent and
child tokens in order to model different use cases. In addition,
the TTI provides measures in order to promote interoperability
standards between different blockchain implementations [12].
III. T HE(CRYPTO ) ASSET TAXONOMY
Building on the literature review in Chapter II, this chapter
proposes a holistic framework for the classification of
assets. Unlike existing classification frameworks, our asset
taxonomy aims to classify all existing types of assets, i.e.,
assets from both traditional finance as well as the crypto
economy, based on their formal characteristics. Furthermore,
the taxonomy introduces a terminology that is suitable for
both traditional and the crypto assets. A morphological
box is chosen as the methodological approach in order to
be able to take the multi-dimensionality of the matter into
account. The taxonomy is illustrated in Appendix A. In
total, we identify 14 different attributes based on which
all types of assets can be classified. They include claim
structure, technology, underlying, consensus/validation
mechanism, legal status, governance, information complexity,
legal structure, information interface, total supply, issuance,
redemption, transferability, and fungibility, with each attribute
comprising a set of at least two characteristics. Note that
certain attributes in the frameworks discussed in Chapter
II subsume some of the attributes presented here. Hence,
our 14 attributes factorize these superordinate attributes to
make them universally applicable. Table I breaks down the
14 attributes in terms of their inclusion in the publications
discussed in Chapter II. The first column shows the attribute
labels of the taxonomy we propose. Column two to ten refer
to the publications discussed, where an “x” indicates that
the corresponding attribute is either explicitly or implicitly
considered in the classification framework given in row one.
Note that the terminology regarding a particular attributediffers across these publications, for example, because they
focus on different types of assets. The terminology we propose
generalises these terms to ensure compatibility across all types
of assets, thus creating a common linguistic understanding.
Also note that due to the extension of the taxonomy to
traditional assets, some DLT-specific attributes/characteristics
in the publications discussed are summarised or generalised,
while new attributes/characteristics were added in order to
enable the mapping of traditional asset types. Overall, Table
I shows that each of the existing frameworks covers certain
attributes determined by the specific focus or objective of
the publication. The framework of FINMA [4], for example,
focuses on regulatory aspects, and thus predominantly
includes corresponding attributes, i.e., claim structure, legal
status, and legal structure. Other frameworks, for example the
one published by the EEA [12], focus more on technological
aspects or the design of token features. Overall, none of the
frameworks discussed in Chapter II covers the full range of
formal attributes identified in our taxonomy. However, our
taxonomy is generally confirmed by the existing literature,
as each attribute is considered in at least one of the existing
classification frameworks. The degree of agreement with
the classification framework we propose varies, however.
While the publication of ISO [1] covers four attributes of our
taxonomy, the publications of Oliveira et al. [8] and Ballandies
et al. [9] cover ten. There are also differences in coverage
from an attribute perspective. While the underlying of an
asset is of relevance in all frameworks analysed, the attributes
information interface and fungibility are only covered by
two. The taxonomy we propose therefore goes further than
the existing classification frameworks, firstly because it is
independent of the type of assets to be classified and secondly
because it contains additional attributes and characteristics.
Since some of these attributes and characteristics are not
intuitively clear, they are explained in more detail in the
following:
Claim structure: Does the asset represent a claim, i.e., a
demand for something due or believed to be due [13]?
– No claim(s): The asset does not represent any kind of
claim.
– Flexible claim(s): The asset represents certain claims, the
possession or exercise of which can depend on certain
conditions (e.g., catastrophe bonds).
– Fixed claim(s): The asset represents claims which can
neither be restricted nor restrained under any condition
(e.g., fixed income).
Technology: Which technology is the asset based on?
– Physical: The asset exists in a physical form (e.g., gold
bullion).
– Digital: The asset exits in a digital form, but is not based
on the distributed ledger technology (e.g., electronic
share).
– Distributed ledger technology: The asset is based on the
distributed ledger technology, structured either as a native
3
Table I: Coverage of the 14 attributes in existing classification frameworks
Attribute ISO
[1]B.&M.
[2]FINMA
[4]O. et al.
[8]B. et al.
[9]MME
[10]ITSA
[11]EEA
[12]
Claim structure x x x x x x
Technology x x x x x x
Underlying x x x x x x x x
Consensus/Validation mechanism x x x
Legal status x x x x
Governance x x x
Information complexity x x x
Legal structure x x x x
Information interface x x
Total supply x x x x
Issuance x x x x
Redemption x x x x
Transferability x x x x x
Fungibility x x
token, i.e., a token that is native to a specific blockchain,
or as a protocol token, i.e., a token issued on an existing
blockchain protocol [8] such as, for example, ERC-20 or
ERC-721 tokens for the Ethereum blockchain.
Underlying: Which underlying or collateral is the asset’s
value based on?
– No underlying: The asset’s value is not a derivative of an
underlying asset (e.g., bitcoin).
– Company: The asset’s value represents a stake in a
company (e.g., equity).
– Bankable asset: The asset’s value represents a bankable
asset, i.e., an asset that can be deposited in a bank or
custody account (e.g., fiat currencies).
– Cryptographic asset: The asset’s value represents a cryp-
tographic asset, i.e., an asset based on the distributed
ledger technology (e.g., derivative of a cryptographic
asset).
– Tangible asset: The asset is in a physical form [14] (e.g.,
real estate).
– Contract: The asset’s value represents a contract (e.g.,
license agreement).
Consensus-/Validation-mechanism: How is the agreement on
the finality (e.g., property rights or ownership transfer) of the
asset reached?
– Instant finality: Consensus is final. Mechanisms that
typically, but not necessarily, belong to the deterministic
type are, for example, notary services or qualified written
form.
– Probabilistic finality: Consensus is not final, but reached
with a certain level of confidence. Mechanisms that
typically, but not necessarily, belong to the probabilistic
type are, for example, proof-of-work or proof-of-stake.Legal status: What is the regulatory framework governing the
asset?
– Regulated: There are regulatory requirements for the
issuance, redemption and governance of the asset.
– Unregulated: There is no specific regulatory framework
for the issuance, redemption and governance of the asset.
Governance: In which way is the asset governed?
– Centralised: The asset is governed by an authoritative
party or consortium.
– Decentralised: The asset is governed without centralised
control (e.g., certain types of cryptographic assets such
as bitcoin).
Information complexity:3What type of information complex-
ity is associated with the asset?
– Value: The asset represents a specific value (e.g., curren-
cies).
– Contract: The asset encompasses conditional information
in addition to its value (e.g., coupon bonds or DLT-based
smart contracts4).
– Turing completeness: The asset is based on a Turing-
complete («universally programmable») computational
model (e.g., Ethereum).
Legal structure: What is the legal form of the asset?
– No legal structure: There is no legal structure governing
the asset.
– Foundation: The asset is governed by a foundation/ trust
structure.
3Note that the characteristics of this attribute build on each other, i.e., each
characteristic contains additional information compared to the previous one.
4Note that such (smart) contracts, as in the case of bitcoin, are not
necessarily based on a Turing-complete system.
4
– Note/bond: The asset is structured as a note or bond.
– Share: The asset is structured as a share.
– Other5: The asset has an alternative legal structure (e.g.,
central bank money).
Information interface: How does the asset receive and/or
send relevant information?
– No interface: The asset has no kind of information
interface.
– Qualitative: The asset manages relevant information in-
directly through an authorised instance (e.g., general
assembly).
– Quantitative: The asset manages relevant information
from authorised sources automatically (e.g., IoT sources
or oracle interfaces in the case of DLT-based smart
contracts).
Total supply: To which limit can the asset be generated?
– Fixed: The total supply of the asset is fixed.
– Conditional: The total supply of the asset is dependent
on predefined conditions.
– Flexible: The total supply of the asset is managed flexibly
by authorised parties.
Issuance: How is the asset generated?
– Once: After an initial issuance, no additional units of the
asset are issued.
– Conditional: Additional units of the asset are issued
once predefined conditions are met (e.g., newly issued
cryptographic assets through mining).
– Flexible: Additional units of the asset can be issued
flexibly by authorised parties (e.g., increase in share
capital).
Redemption: How is the number of outstanding assets re-
duced?
– No redemption: The number of outstanding assets cannot
be reduced.
– Fixed: The reduction of the number of outstanding assets
follows a predefined protocol.
– Conditional: The reduction of the number of outstanding
assets is initiated once predefined conditions are met.
– Flexible: The reduction of the number of outstanding
assets can be carried out flexibly by authorised parties
(e.g., share buyback).
Transferability: Can the asset’s ownership be transferred to
another party?
– Transferable: The asset’s ownership can be transferred to
another party.
– Non-transferable: The asset’s ownership cannot be trans-
ferred to another party, for example, by sale or giveaway
(e.g., some types of registered securities).
5The characteristic “Other” subsumes the broad range of alternative legal
structures for reasons of simplicity and practicability.Fungibility: Can the asset be interchanged with another asset
of the same type?
– Fungible: The asset is substitutable with another asset of
the same type.
– Non-fungible: The asset is not substitutable with another
asset of the same type (e.g., artwork).
IV. C LASSIFICATION EXAMPLES
This subchapter seeks to test the above-mentioned taxonomy
with selected examples. First, the taxonomy is used to compare
cash to bitcoin, as both are intended means of payment6.
This comparison is followed by the classification of Ether,
a utility token, Crowdlitoken, an asset token, CryptoKitties,
and a traditional share.
A. Comparison between Cash and Bitcoin
As both cash and bitcoin follow the purpose of a means of pay-
ment, both assets share certain similarities (see Appendix B).
Neither cash, in the case of a fiat money system, nor bitcoin
have a direct underlying asset. The value of the two assets is
rather based on the public’s trust in the issuer of the currency
or in the underlying technological protocol, respectively. There
is also no oracle interface, i.e. no specific source that interacts
(e.g., directly provides information) with cash or bitcoin. Since
both assets are designed as cash equivalents, their units are
transferable from one party to another and individual units are
interchangeable. Besides these commonalities there are some
significant differences. While cash represents a certain value
which depends on the denomination, bitcoin is of contractual
type as it is transferred via smart contracts in the Bitcoin-
script which is not Turing-complete. Bitcoin is furthermore not
subject to any type of legal claim and has no legal structure.
In contrast, cash is regulated as legal tender under national
law. Since cash is of physical form, consensus on its state
is given deterministically by the owner of the asset. Bitcoin,
on the contrary, is a digital representation of value based on
the distributed ledger technology. It is the native token of the
Bitcoin blockchain, the consensus of which is based on the
proof-of-work mechanism and thus finality of the system is not
guaranteed but only probabilistic. This implies a decentralised
governance of the asset, which is in contrast to the centralised
governance of cash by central banks. Both assets also differ
in terms of their total supply as well as in their ways to
manage the number of outstanding units. While the maximum
supply of bitcoin is fixed at 21 million units, there is no such
restriction for cash. The issuance of additional units of bitcoin
is conditional on the mining of new blocks and reducing the
number of outstanding units is not possible7. The issuance
and redemption of cash, on the contrary, is handled flexibly
by central banks.
6Bitcoin is often considered to be a store of value, but the original intention
is to provide an alternative means of payment.
7It is possible to send units of bitcoin, or other cryptographic assets, to
an address without a known private key, so that these units are no longer
accessible. However, this does not reduce the number of total units in the
system.
5
B. Ether
Ether (see Appendix C), which is classed as a utility token, is
the native token of the Turing-complete Ethereum platform
which is governed by the Ethereum Foundation located in
the Crypto Valley. The token itself is unregulated. Although
multiple decentralised systems which can act as a quantitative
oracle interface for the platform exist, there are no legal claims
and no underlyings associated with the token. Consensus on
the Ethereum platform is, at the time of writing, achieved
based on the proof-of-work mechanism, and therefore is of
a probabilistic nature. As a consequence, the governance of
the token is decentralised. Like with bitcoin, the issuance of
Ether tokens is conditional on the creation of new blocks, i.e.,
when miners get awarded with newly mined units, and the
destruction of existing units is not possible. However, currently
the total supply of Ether is not limited. All Ether tokens are
transferable between parties and are fungible.
C. Crowdlitoken
Crowdlitokens (see Appendix D) are classed as asset tokens
and are tokenised real estate bonds, regulated under the
existing law. They are issued on the Ethereum Blockchain
under the ERC-20 standard and represent a contract including
fixed claims (e.g., voting and interest payment). The token
value is derived from the fundamental value of the issuing
company, and only indirectly by its real estate portfolio. Due to
the underlying distributed ledger technology, consensus on the
state of the tokens is not final but only probabilistic. Crowdl-
itokens are structured as notes/bonds. They are governed in a
centralised manner through a qualitative oracle interface since
token holders are allowed to vote on changes proposed by the
management. They can be issued and burnt (e.g., through token
buybacks) flexibly by the corresponding company, implying a
flexible token supply. The Crowdlitoken is both transferable
and fungible, whereby only persons who have successfully
completed the KYC/AML audits can subscribe to the bonds
and exercise all rights relating to them.
D. CryptoKitties
CryptoKitties (see Appendix E), as the last example from
the crypto space, are collectible digital representations of cats
created on the Ethereum blockchain. The corresponding smart
contracts can generate over four billion variations of phe-
notypes and genotypes (CryptoKitties, 2019). CryptoKitties
neither represent claims against a counterparty, nor a specific
underlying. They are non-fungible - every cat is unique - but
transferable ERC-721 tokens, without any regulatory or legal
governance. Although the front-end as a traditional web app
is managed by the development team, the token’s governance,
e.g., ownership, is decentralised. Since consensus of the un-
derlying Ethereum protocol is reached via a proof-of-work
mechanism, the finality of the state of a CryptoKitties token
is probabilistic. Also, there is no oracle interface related to
CryptoKitties tokens. The creation of additional units is done
by breeding two CryptoKitties, resulting in a new unique kitty,
represented by a newly issued unique token, while destroyinga unit is not possible. The corresponding smart contract allows
for a total limit of around four billion cats that can be bred,
implying a fixed total supply.
E. Traditional Share
Traditional shares (see Appendix F), as the one example from
traditional finance, are either physical or digital in nature and
represent a contract including fixed claims (e.g., voting and/or
profit participation) against a counterparty, with its fundamen-
tal value also representing the underlying of the asset. Shares,
as a legal form, are governed in a centralised manner and
are subject to the existing law (e.g., national corporate law),
with the general assembly of shareholders being the supreme
organ of a stock corporation, i.e., acting as a qualitative oracle
interface. Consensus on the state of a share is deterministically
given by the share registry. The creation of new shares as well
as the reduction in share capital, for example through share
buybacks, is left to the general assembly of the corporation.
As a consequence, the total supply of traditional shares is
flexible. Shares are typically transferable, with exceptions such
as restricted shares, and fungible, i.e., substitutable with other
shares of the same company.
V. C ONCLUSION
Various classification frameworks for traditional and cryp-
tographic assets already exist and are applied in practice.
However, a universal approach linking the two worlds has
not yet been developed. In this paper we fill this research
gap by proposing a taxonomy that extends existing classi-
fication frameworks. We identify 14 different attributes that
are supported by the existing literature and by which each
type of asset can be properly classified. These attributes
include the claim structure, technology, underlying, consensus-
/validation mechanism, legal status, governance, information
complexity, legal structure, information interface, total supply,
issuance, redemption, transferability, and fungibility. With the
help of a morphological box, various possible characteristics
that an asset can have are identified and assigned to these
attributes. In this way, our taxonomy bridges the gap between
physical, digital, and cryptographic assets, where sometimes
the same asset can appear in all three forms, thus creating
clear terminology. Thanks to the methodical approach, the
individual attributes can be expanded or broken down at any
level of detail without changing the overall framework. The
classification of selected assets, such as cash and bitcoin,
has also shown that the proposed taxonomy is applicable in
practice. In a next step, the robustness and practical relevance
of the taxonomy could be further tested, for example by
interviewing experts in the field.
6
APPENDIX A
ASSET TAXONOMY
APPENDIX B
CLASSIFICATION OF CASH (GREEN )AND BITCOIN (ORANGE )
7
APPENDIX C
CLASSIFICATION OF ETHER
APPENDIX D
CLASSIFICATION OF A CROWDLITOKEN TOKEN
8
APPENDIX E
CLASSIFICATION OF A CRYPTO KITTIES TOKEN
APPENDIX F
CLASSIFICATION OF A TRADITIONAL SHARE
9
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10
|
{
"id": "2007.11877"
}
|
2410.01107
|
Count of Monte Crypto: Accounting-based Defenses for Cross-Chain Bridges
|
Between 2021 and 2023, crypto assets valued at over \$US2.6 billion were
stolen via attacks on "bridges" -- decentralized services designed to allow
inter-blockchain exchange. While the individual exploits in each attack vary, a
single design flaw underlies them all: the lack of end-to-end value accounting
in cross-chain transactions. In this paper, we empirically analyze 10 million
transactions used by key bridges during this period. We show that a simple
invariant that balances cross-chain inflows and outflows is compatible with
legitimate use, yet precisely identifies every known attack (and several likely
attacks) in this data. Further, we show that this approach is not only
sufficient for post-hoc audits, but can be implemented in-line in existing
bridge designs to provide generic protection against a broad array of bridge
vulnerabilities.
|
http://arxiv.org/pdf/2410.01107v2
|
Enze Liu, Elisa Luo, Jian Chen Yan, Katherine Izhikevich, Stewart Grant, Deian Stefan, Geoffrey M Voelker, Stefan Savage
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cs.CR
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cs.CR
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Count of Monte Crypto: Accounting-based Defenses for Cross-Chain Bridges
Enze Liu, Elisa Luo, Jian Chen Yan, Katherine Izhikevich, Stewart Grant,
Deian Stefan, Geoffrey M V oelker, Stefan Savage
UC San Diego
Abstract —Between 2021 and 2023, crypto assets valued at
over $US2.6 billion were stolen via attacks on “bridges”
— decentralized services designed to allow inter-blockchain
exchange. While the individual exploits in each attack vary, a
single design flaw underlies them all: the lack of end-to-end
value accounting in cross-chain transactions. In this paper, we
empirically analyze over 10 Mmillion transactions used by key
bridges during this period. We show that a simple invariant
that balances cross-chain inflows and outflows is compatible
with legitimate use, yet precisely identifies every known attack
(and several likely attacks) in this data. Further, we show
that this approach is not only sufficient for post-hoc audits,
but can be implemented in-line in existing bridge designs to
provide generic protection against a broad array of bridge
vulnerabilities.
1. Introduction
Careful accounting is key to the correct management of
assets in virtually all financial systems. Indeed, since Paciloli
popularized double-entry bookkeeping in the 14th century,
it has been standard practice to separately track inflows
(credits) and outflows (debits) to establish a net position
— a balance sheet.
Such accounting is implicit in blockchains as each and
every transaction is recorded, immutable and in possession
of implicit integrity. Any change in ownership for a given
token is explicitly recorded via some past transaction and
thus the net position for assets in a blockchain is generally
consistent and well-known. However, these integral proper-
ties only hold within a single blockchain. As soon as one
wishes to engage in transactions between chains (e.g., trad-
ing Ethereum on Solana), it requires building a system that
steps outside these isolated environments and implements
its own financial calculus between them, including its own
accounting.
Today, one of the principal mechanisms for such trans-
actions is the cross-chain bridge — a service using a
pair of “smart contracts” (immutable programs stored on
a blockchain) to synthesize inter-chain transactions that are
not possible to express natively on a single chain.1Such
bridges are an extremely popular component of what is
1. Some newer blockchain ecosystems, such as Cosmos and Avalanche,
are themselves composed of multiple blockchains and offer some support
for bridging across their own chains, but not with external chains.commonly referred to as “decentralized finance” (DeFi),
and they routinely manage transaction volumes with value
equivalent to $US8–10 billion per month [11].
However, bridges are just code. They can have bugs
in their implementations, in the other services they depend
upon, or in the mechanisms used to guard the integrity of
their cryptographic secrets. As a result, attackers can, and
do, exploit such vulnerabilities to extract significant value
from such systems. For example, between 2021 and 2023,
crypto assets valued at over $US2.6 billion were stolen in
an array of attacks on bridges.
The scale of these hacks have motivated a range of
research focused on automating the detection of such vul-
nerabilities and attacks, through the analysis of low-level
implementation details (e.g., using static analysis of smart
contracts [23], [43], predefined anomaly rules [48], or the
features of specific attacks [24]). However, virtually all
of these efforts either require detailed modeling of each
contract and bridge infrastructure (e.g., the complex protocol
bridges run off-chain to relay messages between on-chain
contracts), or are specialized to particular sets of vulnera-
bilities (e.g., fake deposit events [24]); they require continual
updating, tuning and re-evaluation as they are tied to specific
implementation artifacts.
In this work, we propose a complementary approach for
detecting bridge attacks, by monitoring violations of the bal-
ance invariant — a measure of the difference between value
inflows and value outflows at a bridge. Unlike prior work,
this approach is extremely simple to reason about because
it focuses purely on detecting potential negative financial
outcomes instead of focusing on the precise mechanism
of attack that would lead to those outcomes. Indeed, the
power of the approach arises from its technical simplicity.
Because it abstracts away the complicated implementation
details of bridge contacts (and the off-chain code interfacing
with these contracts), the bridge invariant naturally captures
attacks independent of the details of the vulnerabilities they
target. We demonstrate this effectiveness by comprehen-
sively surveying the twelve largest attacks (each $US1 M
or more) between 2021 and 2023, and showing that while
the details of their vulnerabilities vary considerably, all of
them share the property that transactions are allowed to
be “unbalanced” (i.e., that outflow can exceed inflow, less
costs). We then empirically demonstrate, by auditing over
10Mtransactions from 11 different bridges on across 21
blockchains, that auditing transactions for this property isarXiv:2410.01107v2 [cs.CR] 15 Nov 2024
sufficient to retrospectively and automatically identify each
of these past attacks.
We further show that such audits can be performed in
real-time — either to alert bridge operators about potential
fraud, or prevent such unbalanced transactions from com-
pleting altogether (i.e., thus preventing any such loss).
Concretely, our paper makes three main contributions:
▷Balance Invariant . We introduce the notion of a
simple balance invariant, outflow = inflow - costs ,
which can be calculated automatically from the
transaction formats used by current bridges. We
hypothesize that this invariant should naturally hold
for benign transactions, but not for those exploiting
bridge vulnerabilities.
▷Retrospective validation . We validate our hypothesis
by empirically auditing over 20 million past transac-
tions (across more than 20 blockchains, between 11
different bridges) and show that the balance invariant
is sufficient to identify each and every attack for
which we have collected ground truth (and addi-
tional transactions that should have been flagged).
Moreover, we identify vanishingly few other trans-
actions that violate this invariant, virtually all of
which deserve further scrutiny. Some are clearly
new, unreported thefts (separate from the 12 attacks
we study), and others capture bridge behaviors that
are deserving of considerably more transparency
(e.g., unilateral issuance of millions of dollars worth
of uncollateralized tokens).
▷Real-time auditing . We show our approach is not
only useful for retrospective analysis, but can be
used to audit bridge transactions and detect new
attacks in real-time. We implement and demonstrate
an online audit of the Wormhole bridge and show
that it automatically detects every attack we in-
ject. Further, we introduce and implement a proof-
of-concept for a new bridge architecture, called
announce-then-execute , that incorporates such au-
diting into the transaction flow itself — thereby
preventing unbalanced malicious bridge transactions
from ever completing. Our approach treats the most
complicated components of bridges (e.g., verifying
relayed messages) as black boxes, adds no new at-
tack surface for theft, and requires minimal changes
to existing codebases — a benefit which we demon-
strate by implementing a modified version of the
Wormhole bridge.
We argue that this approach is powerful both due to its
simplicity (in a legitimate financial transaction, the value
paid should be equivalent to the value received, less costs)
and its independence from the vagaries of smart contract
details. Moreover, it represents a straightforward mechanism
that can prevent an entire class of existing attacks on crypto
bridges.
Our work directly inspirited the design and implementa-
tion of the Bascule drawbridge security system, which has
been used by Lombard—the largest Bitcoin liquid stakingprotocol—over the last three months to secure the bridging
of (over $1B as of today) BTC deposited on Bitcoin to
LBTC on Ethereum [8].
2. Background
We first give a brief introduction to smart contract
blockchains and cross-chain bridges. We then describe how
bridges, under attack, collapse in practice.
2.1. Smart-contract Blockchains
Modern DeFi protocols (e.g., decentralized exchanges
and lending protocols) are built on top of “smart-contract”
blockchains, like Ethereum. At their core, these chains,
like the original Bitcoin blockchain, are distributed ledgers
that manage accounts (public keys) and their balances (in
native tokens like Ethereum’s ETH). Users interact with
these chains by signing and broadcasting (to the distributed
nodes that make up the chain network) transactions that, for
example, transfer funds from their account (using the cor-
responding account private key) to another user’s account.
Ethereum, and the smart-contract chains it inspired since
its release in 2015, differ from Bitcoin by extending the
“simple” distributed ledger with a smart contract execution
layer. A smart contract on Ethereum is an internal account—
and, like a normal, externally owned account (EOA), it has a
balance—that has associated code (EVM bytecode), which
implements the smart contract’s program logic, and storage ,
which persists the program’s state across executions. Users
interact with (i.e., execute) smart contracts much like they
do when transferring funds from their account: they sign
a transaction that encodes the smart contract to call (i.e.,
the contract address), the particular function to execute, and
the arguments to call the function with. Instead of simply
transferring funds from the user’s account, then, executing
such a smart-contract call transaction amounts to executing
the smart contract bytecode—and any smart contracts the
contract itself calls.
2.2. ERC-20 Tokens
One of the immediate applications of smart contracts—
and to date still one of most popular—is to create custom
tokens (or coins). To launch a new token, an organization no
longer needs to launch a new blockchain; they can instead
deploy a new contract that implements the ERC-20 token
standard interface on a chain like Ethereum and take advan-
tage of existing on-chain infrastructure like decentralized
exchanges that make it easy to, for example, trade one kind
of token for another—both native tokens (e.g., ETH) and
other tokens.2
Figure 2 shows a simplified variant of one such to-
ken contract—the USDC stablecoin contract. This contract
2. Most EVM chains, i.e., chains that use Ethereum’s execution layer,
follow Ethereum standards like ERC-20. Most non-EVM chains like Solana
(which have different execution models) have similar standards (e.g., SPL
tokens in Solana’s case).
1
Smart Contract
3Emit Event2Process Deposit SenderDeposit X
96 Withdraw X
Recipient
4
5Sign
Store7Submit
Submitter
Smart Contract
Process Withdraw8Relayer Source Blockchain Destination Blockchain
SC
EventX
SC
EventX'
Read Auth +
Figure 1: Cross-chain token bridging and the different steps attackers can exploit to withdraw unbacked deposits.
bridges) and show that the balance invariant is sufficient
to identify each and every attack for which we have col-
lected ground truth. Moreover, we identify vanishingly
few other transactions that violate this invariant, virtu-
ally all of which deserve further scrutiny. Some are clear
contract implementation errors, others are likely new, un-
reported thefts, and yet others capture bridge behaviors
that are deserving of considerably more transparency
(e.g., unilateral issuance of millions of dollars worth of
uncollateralized tokens).
•Real-time auditing . We show our approach is not only
useful for retrospective analysis, but can be used to audit
bridge transactions and detect new attacks in real-time.
We implement and demonstrate an online audit of the
Wormhole bridge and show that it automatically detects
every attack we inject. Further, we introduce and imple-
ment a proof-of-concept for a new bridge architecture,
called announce-then-execute , that incorporates such au-
diting into the transaction flow itself — thereby prevent-
ing unbalanced malicious bridge transactions from ever
completing. Our approach treats the most complicated
components of bridges (e.g., verifying relayed messages)
as black boxes, adds no new attack surface for theft, and
requires minimal changes to existing codebases — a
benefit which which we demonstrate by implementing a
modified version of the Wormhole bridge.
We argue that this approach is powerful both due to its
simplicity (in a legitimate financial transaction, the value paid
should be equivalent to the value received, less costs) and
its independence from the vagaries of smart contract details.
Moreover, it represents a straightforward mechanism that can
prevent an entire class of existing attacks on crypto bridges.
2 Background
In this section we first give a brief introduction to smart con-
tract blockchains and cross-chain bridges. We then describe
how bridges, under attack, collapse in practice.
2.1 Smart-contract blockchains chains
Modern DeFi protocols (e.g., decentralized exchanges and
lending protocols) are built on top of “smart-contract”
blockchains, like Ethereum. At their core, these chains, like
the original Bitcoin blockchain, are distributed ledgers that
manage accounts (public keys) and their balances (in native to-
kens like Ethereum’s ETH). Users interact with these chains
by signing and broadcasting (to the distributed nodes that
make up the blockchain network) transactions that, for exam-
ple, transfer funds from their account (using the corresponding
account private key) to another user’s account.
Ethereum, and the smart-contract chains it inspired since its
release in 2015, differ from Bitcoin by extending the “simple”distributed ledger with a smart contract execution layer. A
smart contract on Ethereum is an internal account—and, like
a normal, externally owned account (EOA), it has a balance—
that has associated code (EVM bytecode), which implements
the smart contract’s program logic, and storage , which persists
the program’s state across executions. Users interact with (i.e.,
execute) smart contracts much like they do when transferring
funds from their account: they sign a transaction that encodes
the smart contract to call (i.e., the contract address), the partic-
ular function to execute, and the arguments to call the function
with. Instead of simply transferring funds from the user’s ac-
count, then, executing such a smart-contract call transaction
amounts to executing the smart contract bytecode—and any
smart contracts the contract itself calls.
contract USDC {
mapping (account =>amount )_balances ;
event Transfer (from ,to,value );
function transfer (to,amount ){
address from =msg.sender ;//user
//... validate user has enough balance
_balances [from ]- = amount ;
_balances [to]+ = amount ;
emit Transfer (from ,to,amount );
}
function mint (to,amount ){
_balances [to]+ = value ;
emit Transfer (address (0), to,amount );
}
function burn (from ,amount ){
//... validate user has enough balance
_balances [from ]- = amount ;
emit Transfer (from ,address (0), amount );
}
function safeTransferFrom (from ,to,amount ){
//... transfer tokens ;revert iffailed
}
}
2.2 ERC-20 Tokens
One of the immediate applications of smart contracts—and
to date still one of most popular—is to create custom tokens
(or coins). To launch a new token, an organization no longer
needs to launch a new blockchain; they can instead deploy
a new contract that implements the ERC-20 token standard
interface on a chain like Ethereum and take advantage of
existing on-chain infrastructure like decentralized exchanges
that make it easy to, for example, trade one kind of token for
another—both native tokens (e.g., ETH) and other tokens.2
Figure 1shows a simplified variant of one such token
contract—the USDC stablecoin contract. This contract tracks
how many USDC tokens an account has and governs the
2Most EVM chains, i.e., chains that use Ethereum’s execution layer,
follow Ethereum standards like ERC-20. Most non-EVM chains like Solana
(which have different execution models) have similar standards (e.g., SPL
tokens in Solana’s case).
2
Figure 2: Simplified USDC ERC-20 Token Contract.
tracks how many USDC tokens an account has and governs
the spending of these tokens (much like a bank governs
bank notes). For example, the contract’s mint function
lets Circle (the company that owns the USDC contract)
mint new tokens into a user’s account—e.g., after receiving
the corresponding payment from the user off-chain (in US
dollars, as USDC tokens are pegged to the US dollar). The
contract exposes the ERC-20 interface that lets users (and
smart contracts) transfer tokens from their account by simply
calling functions like safeTransferFrom , and, in turn,
use the tokens in any DeFi protocol (e.g., lending the USDC
to different markets, exchanging USDC for ETH, etc.).
Finally, the contract’s burn function “burns” a token out
of circulation—and emits an event (Figure 3) that Circle’s
off-chain code looks for before allowing a user to withdraw
the corresponding USD fiat off-chain.
Figure 3: Event Emitted by an ERC-20 Token (USDC).
2.3. Cross-Chain Token Bridges
While smart contracts make it easy to launch new tokens
without spinning up new chains, there is no real shortage of
new blockchains being deployed (almost weekly). Indeed,
the modern blockchain ecosystem is a many-chain ecosys-
tem. Blockchains like Avalanche, Base, and Solana have
different design points—from cheaper “gas” execution costs,
to higher throughput, lower latency, and different permission
models—that make them better suited for different classes of
application. This situation has resulted in applications that
span many chains and cross-chain infrastructure that ulti-
mately (try to) allow users to, for example, buy Dogwifhat
NFTs on Solana using USDC tokens on Ethereum.
Core to these applications and infrastructure—and the
focus of our work—is the cross-chain token bridge . At a
high level, a cross-chain token bridge makes it possible for
a user to “transfer” their tokens from one chain (e.g., their
USDC on Ethereum) to another chain (e.g., Solana) and then
use the transferred tokens on the destination chain (e.g., on
a Solana exchange trading USDC and Dogwifhat).3Since
smart contracts cannot make network requests or otherwise
access sate outside their own storage, a cross-chain token
bridge consists of smart contracts on both the source and
destination chains, and off-chain infrastructure that serves
to relay the “transfer” call across the two contracts.
3. While some cross-chain bridges dolet users transfer one kind of token
(e.g., USDC on Ethereum) for a completely different kind of token on the
destination chain (e.g., Dogwifhat on Solana), these bridges essentially fuse
the cross-chain token bridge with an exchange (or swap). We focus on token
bridges not only because they are fundamental to other kinds of bridges,
but also because they have higher volume, more liquidity, and more attacks.
As Figure 1 shows, a typical cross-chain token transfer,
i.e., a bridge transaction, consists three phases:
1. Deposit (on source chain). To initiate a cross-chain
token transfer, the user first calls the bridge’s contract on
the source chain:
deposit( address token, uint256 val, address to) {
address from = msg.sender; // user
address to = address (this ); // bridge contract
// ... validate the ERC-20 token contract
token.safeTransferFrom(from, to, val);
emit Deposited(id++, token, from, to, val);
}
This contract function—a simplified version of the Qubit
bridge deposit function—processes the user’s deposit (step
2) by validating the transfer request, e.g., against a list
supported tokens, and then transferring the user’s ERC-20
tokens to the bridge contract—recall contracts are accounts
with balances. Then, the function emits an event (step 3)
recording the user’s deposit details (including the deposit
ID, the ERC-20 token contract address, the recipient on the
destination chain, and value).
2. Off-chain relay. The emitted event is observed by an
off-chain relayer in (step 4), which constantly monitors the
source blockchain. The relayer first verifies the authenticity
of the deposit event. If the event is authentic, the relayer then
produces a signed receipt endorsing the deposit (step 5) and,
typically, stores the receipt off-chain (step 6). Finally, this
signed receipt is sent to the bridge’s withdrawal contract on
the destination blockchain (step 7). Who submits the receipt
varies across bridges—some bridges submit the receipt on
the user’s behalf (in these cases, the signed receipt is simply
a signed contract-call transaction), while others give users
(and anyone willing to pay gas) the signed receipt and they,
in turn, submit the receipt to the withdrawal contract to
complete the transfer.
3. Withdraw (on destination chain). The withdrawal
contract on the destination chain first processes the withdraw
request by verifying the receipt and transferring the tokens
to the recipient (step 8). In (the simplified) Chainswap’s
withdraw case, for example, users call:
withdraw( uint256 id, address token, address to,
uint256 val, Signature[] sigs) {
_chargeFee();
// verify receipt
require (received[id][to] == 0, ’withdrawn’);
for(uint i=0; i < sigs.length; i++) {
verify_receipt(sigs[i], id, token, to, val);
}
received[id][to] = val; // mark as withdrawn
token.safeTransferFrom( address (this ), to, val);
emit Withdraw(id, token, to, val);
}
This contract function first charges the caller a fee, then
verifies the deposit details against receipt—both that the
deposit was not already withdrawn and that the receipt
signatures are valid—and finally transfers the tokens to the
intended recipient. We consider a cross-chain transaction
complete when the asset is released to the recipient (step 9).
We expect every cross-chain bridge transaction to uphold
the balance invariant: the value (and kind) of the tokenswithdrawn—the outflow—should equal the value (and kind)
of the tokens deposited—the inflow—minus the charged
fees. In practice, they do not.
2.4. How Bridges Collapse
In practice, attackers exploited bugs in all three
components—the deposit contract, the relayer, and the with-
draw contract—and stole signing keys to siphon hundreds of
thousands of dollars. Figure 1 highlights the precise steps in
the cross-chain token transfer that attackers have historically
exploited, including:
▷Bugs in the deposit contract. In step 2, the bridge
contract verifies that it has received the correct amount of
assets before emitting an event. Bugs in this verification
logic could allow an attacker to deposit a smaller amount
of assets than what is recorded by the bridge in the
event (step 3). For example, Qubit’s deposit function
(see above) did not properly validate the token address.
This bug allowed an attacker to pass 0for the token
address, so the contract function did not actually transfer
any funds from the attacker’s account but still emitted a
Deposit event which allowed the attacker to withdraw
actual tokens on the destination chain [35].
▷Bugs in the off-chain deposit verification. In step
5, the relayer verifies the authenticity of the deposit
event emitted by the bridge contract, including whether
the event is emitted by the bridge’s designated contract.
Bridges that do not correctly verify deposits would allow
attackers to withdraw assets that are never deposited.
▷Stolen relayer (or submitter) keys. In step 5, the re-
layer signs the deposit receipts which are then submitted
to the withdrawal contract as evidence of a valid deposit.
If the relayer key is compromised (e.g., as with the
Ronin bridge [41]) the attacker can forge a valid receipt
and then withdraw assets that were never deposited by
calling withdraw with the forged receipt.
The same is true for bridges that submit receipts on
behalf of users—and essentially restrict the withdraw
callers to privileged submitter accounts. The bridge sub-
mitter keys (step 8) have similarly been compromised
(e.g., as with AnySwap [7]) and used to withdraw un-
backed deposits.
▷Bugs in the withdraw verification. In step 8, the
bridge contract verifies that the messages are signed by
the relayer and have not been replayed. Bugs in this
verification logic (e.g., as we saw with Wormhole [4])
have allowed attackers to supply “valid” payloads that
were not signed by the relayer and replay withdrawal
requests with valid deposit receipts that have already
been withdrawn.
In this paper, we assume an attacker can exploit any
of the aforementioned components or otherwise control the
relayer (or submitter) keys. In the next section, we show
that this attacker model and our simple balance invariant
checking captures the largest attacks on cross-chain bridges
that have happened in the past. In Sections 5 we show
that monitoring withdrawals and deposits on the source and
destination chains can be used to detect similar attacks in the
future. Finally, in Section 6 we describe an announce-then-
execute bridge design that enforces this invariant to prevent
attacks before they happen.
We note that while our threat model captures a wide
variety of vulnerabilities, it is not exhaustive. As with any
detection system, an attack that violates one of our assump-
tions (e.g., avoids violating the balance invariant by transfer-
ring funds off-bridge or not having a withdrawal, subverts
the transaction data used to validate the invariant, etc.)
might succeed. We similarly consider other smart contract
bugs (beyond bugs in deposit and withdrawal functions) and
account key compromises out of scope—and instead focus
on the cross-chain bridging aspects which are relatively less
well understood. As we show later, our model captures the
largest attacks on cross-chain bridges that have happened in
the past and systems can use it to prevent similar attacks in
the future.
3. Approach
The core hypothesis of our work is that value should
be conserved within cross-chain transactions. That is, that
the value of the asset inflow in such a transaction (i.e.,
the deposit) should equal the value of the asset outflow
(i.e., the withdrawal). In token transfer bridges, this in-
variant corresponds to a balancing of the inflow tokens
and the outflow tokens (less any fees or transaction costs
incurred by the bridge itself). When this balance invariant
does not hold it allows a range of opportunities for fraud,
all of which involve greater outflows (withdrawals) than
inflow (deposits). Figure 4 illustrates such an outcome by
graphing the aggregate difference between bridge inflow
(on Ethereum) and outflow (on Solana) leading up to and
during the February 2022 attack on the Wormhole bridge.
The net difference is consistently near zero, with only short
positive deviations (representing delayed withdrawals) until
the attack in January, at which point there are significant
withdrawals without matching deposits—producing a large
negative difference.
Testing this invariant on a per transaction basis is
straightforward in principle. However, since bridge trans-
action formats are not standardized, it requires a range of
per-bridge and per-chain parsing in practice. Our method-
ology for normalizing this information focuses on two
key pieces of information: a) identifying each bridge
transaction—a composite of a deposit (inflow) transaction
on a source blockchain and a withdrawal (outflow) transac-
tion on another—and b) identifying the value transferred in
each such bridge transaction.
3.1. Identifying Bridge Transactions
For almost all bridges, identifying their component (per-
chain) inflow and outflow transactions is straightforward —
Oct 01 2021 Oct 15 2021 Nov 01 2021 Nov 15 2021 Dec 01 2021 Dec 15 2021 Jan 01 2022 Jan 15 2022 Feb 01 2022
Date125000
100000
75000
50000
25000
0250005000075000100000Diff AmountT otal Inflow (on Ethereum) - T otal Outflow (on Solana)Figure 4: Total Inflow (on Ethereum) - Total Outflow (on
Solana) Over Time: Wormhole Attack in Feb 2022.
bridges typically use explicit events on each chain to signal
if a given transaction is a deposit or withdrawal.4
Pairing these component transactions (i.e., matching
deposits on one chain to withdrawals on other) can be
performed in several different ways. The easiest, and most
common, is via a unique transaction identifier. Such IDs are
typically generated by the bridge on the source chain during
a deposit transaction and then copied into the withdrawal
transaction on the destination chain.5However, instead of
an explicit ID, some bridges use a hash of the deposit
transaction for the same purpose (e.g., for Anyswap, each
withdraw transaction will include the hash of its correspond-
ing deposit transaction).6Finally, in a handful of cases there
are no “inband” identifiers that can be used to associate
transactions. We believe this is a poor design choice that is
fundamentally in conflict with auditability. However, even
in these cases, for the purpose of our analysis we have been
able to pair transactions using explicit query APIs provided
by the affected bridges services.7
At the end of this process, we identify a comprehensive
collection of “bridge transactions” (a pair of transactions
from two different blockchains that were used by the bridge
to transfer value across them). While the vast majority
of bridge transactions are pairs of deposit and withdrawal
transactions, a handful of bridge transactions are either
4. One key exception to this rule is the Wormhole bridge which, until
late 2023, did not emit specific events when executing a withdrawal
transaction. In this case, we infer that a withdrawal took place by looking
for transactions that invoke functions designated for performing withdrawal
operations (e.g., completeTransfer ). It also appears to be widely understood
today that emitting explicit events is a best practice.
5. These unique IDs are commonly simple global variables incremented
with each new transaction.
6. We note that to prevent potential replay attacks, bridge implementers
should also include an ID that uniquely identifies individual deposit events
within a transactions. Sadly, this is often not the case.
7. For example, when a bridge transaction on the Poly Network bridge
includes a withdrawal from Curve (a kind of liquidity pool) or when a
bridge transaction on the Binance Token Hub includes a Binance Smart
Chain (BSC) withdrawal, it is not possible to identify the partner deposit
from blockchain data alone and we must make use of “out of band” data
available through their respective bridge query APIs.
withdrawal-only (e.g., in the case of attacks) or deposit-only
(e.g., if the user chooses to delay their withdrawal).
3.2. Identifying the Value Transferred
Many bridge transaction event formats explicitly identify
the amount and kind of tokens transferred. However, some
bridges do not emit this information and others can be
unreliable.8In such cases, we can frequently make use of the
ERC-20 “Transfer” event (see Figure 3 for an example) that
is emitted when the contract transfers the tokens from the
user’s account to the bridge’s account. This event contains
the number of tokens transferred as well as the sender and
recipient addresses, which we use to identify the number
of tokens transferred to or from the bridge. Because the
Transfer event is adjacent to its associated bridge-generated
event, it is easy to identify and thus establish the number
of tokens transferred.9In a few implementations, multi-
ple related Transfer events can be emitted at once, and
in these cases we have manually inspected their contracts
and constructed implementation-specific logic to account for
this behavior. Another special case is caused by so-called
“reflection tokens” in which the number of tokens logged in
the event (the value field in Figure 3) is dynamically adjusted
based on a combination of the intended number and the total
token supply. For such cases, we either rely on bridge events
which capture the intended number of tokens transferred
or implement token-specific logic to recompute the value
accordingly. Finally, native tokens have no Transfer event
(since they are not ERC-20 tokens), but thus far we have
either been able to recover the number of tokens transferred
from bridge events or so-called “internal transactions”.
To summarize, while it is certainly possible to create a
bridge transaction protocol that records insufficient data to
match deposits and withdrawals, or for which the number
of tokens transferred might be ambiguous, our empirical
experience analyzing 11 bridges and 21 blockchains is that
such reconstruction has always been possible.
3.3. Checking the Balance Invariant
Once we have identified both sides of the bridge trans-
action and the number of tokens transferred by the bridge,
we can verify if the balance invariant holds: for each bridge
transaction (including those that are withdrawal-only), does
the number of tokens transferred from the bridge—the with-
drawal amount—match the number of tokens received by the
bridge—the deposit amount?
8. For example, as mentioned earlier, pre-2024 Wormhole does not emit
an event for withdrawal transactions. Other examples include the Meter
bridge and Qubit bridge, which attempted to verify the number of tokens
received but had exploitable bugs.
9. As a sanity check, we also require that the sender and recipient
addresses in the Transfer event are consistent with the bridge’s defined
behavior: for withdrawal transactions, we require that the sender is either a
mint address (i.e., all-zero address) or a bridge-controlled address, while for
deposit transactions, we require that the recipient is either a burn address
(i.e., all-zero address) or a bridge-controlled address.However, a key complication is that bridges can charge
fees and, while these fees can sometimes be paid “out of
band”, it is not uncommon for them to be subtracted from
the tokens received on deposit. To account for this behavior
(i.e., outflow = inflow - costs), we must be able to determine
such costs on a per-transaction basis. In most cases, fees can
be accounted for in a straightforward manner: they either are
made explicit in bridge-generated events (and can thus be
accounted for directly) or can be calculated based on either
published fee schedules or inferred fee schedules (i.e., since
all transactions are typically subject to the same fixed or
percentage fees).
In some cases, the precise value of fees may be difficult
to determine retrospectively because they depend on some
external contemporaneous value not recorded in the trans-
action (e.g., fees valued in US dollars implicitly depend on
the exchange rate of a given token at that time). While such
ambiguity could be further minimized with additional data,
in our work we manage this issue by defaulting to a simple
rule that the number of tokens withdrawn should not exceed
the number deposited.
4. Retrospective Analysis
The basic reasoning motivating the balance invariant is
simple: that legitimate bridge transactions should conserve
value. However, this assumes a particular model for how
bridges are used and operated which may or may not hold
in practice. To validate our hypothesis, we applied the
balance invariant analysis retrospectively across a large set
of past transactions which, while primarily benign, contain
the largest known bridge attacks during our period of study.
Our goal is both to show that all real attacks are identified
(detection), but also that the invariant does not alert on large
numbers of benign transactions (bridge compatibility). Later,
in Sections 5 and 6, we will describe systems for live bridge
monitoring as a third party and a new implementation for
preventing unbalanced transactions from being committed.
4.1. Data Set
To perform a retrospective analysis we need data for
which (at least some) of the ground truth is known. In this
section we describe how we chose the bridge attacks we
study, the blockchains and smart contracts involved, and how
we collected the historical transaction data.
Attack Selection. We compiled a comprehensive list
of attacks on cross-chain bridges that occurred between
January 2021 and December 2023. We used both academic
papers surveying cross-chain bridge attacks [21], [49], [51]
as well as industry blog posts that collect and characterize
attacks over time [1], [10], [37], [44], [45]. As this pa-
per focuses on end-to-end auditing of bridge transactions,
we filter out attacks involving blockchains lacking smart
contracts such as Bitcoin (e.g., the pNetwork attack in
2021 [33]), attacks on swap bridges (e.g., the Thor bridge
attacks [39], [40]), attacks that do not involve individual
Blockchain Bridges that Operate on the Chain
Arbitrum Anyswap, Poly Network, Wormhole
Avalanche Anyswap, Poly Network, Meter, Nomad, Wormhole
Binance Beacon Binance Token Hub
Binance Smart Anyswap, Binance Token Hub, Chainswap, Harmony, Meter, Omni, Poly Network, Qubit, Wormhole
Celo Anyswap, Poly Network, Wormhole
ETH Anyswap, Chainswap, Harmony, HECO, Meter, Nomad, Omni, Poly Network, Qubit, Ronin, Wormhole
EVMOS Anyswap, Nomad, Omni
Fantom Anyswap, Poly Network, Wormhole
Gnosis Omni, Poly Network
Harmony Anyswap, Harmony Bridge, Poly Network
HECO Anyswap, Chainswap, HECO, Poly Network
Meter Meter
Metis Anyswap, Poly Network
Milkomeda Nomad
Moonbeam Anyswap, Meter, Nomad, Wormhole
Moonriver Anyswap, Meter
OKT Chain Anyswap, Chainswap, Poly Network
Optimism Anyswap, Poly Network, Wormhole
Polygon Anyswap, Chainswap, Poly Network, Wormhole
Ronin Ronin
Solana Wormhole
TABLE 1: The blockchains CrossChecked supports and the cross-chain bridges that operate on them. While a particular
attack on a bridge involves two chains, we collect deposit and withdrawal transactions for a bridge on all chains that the
bridge supports. Anyswap is also known as Multichain.
bridge transactions (e.g., the evoDefi attack [3]), and at-
tacks that withdraw funds through other means (e.g., direct
transfer from a vault that stores assets for a bridge such as
the Multichain 2023 attack [27]). Moreover, given the large
number of attacks during this period, we focus on major
attacks with claimed losses greater than $1 million USD
and exclude the remaining (the sum of losses from these
excluded attacks are a small fraction of those in our scope).
This process yielded 12 attacks on 11 distinct bridges. We
note that this list includes the top five attacks on cross-chain
bridges in history, which collectively resulted in more than
$2.6 billion USD in claimed losses.
Blockchain Selection. Validating bridge transactions re-
quires access to transaction data from both the source and
destination chains. We support every blockchain involved in
the bridge transactions associated with the 12 attacks, either
as the source (or claimed source) or destination chain. As a
result, we support a total of 21 blockchains that together
cover a broad range of designs, including many of the
most popular such as Ethereum, Binance Smart Chain, and
Solana. Table 1 lists the blockchains and the bridges in our
retrospective study that operate on them.
Smart Contract Selection. For each bridge, we com-
prehensively collected the results of all versions of its bridg-
ing smart contracts on every blockchain we considered. In
particular, we collected deposit and withdrawal transactions
created by every contract the bridge deployed on every
blockchain we support (typically many more blockchains
than the ones involved in a bridge attack) as well as all
versions of the smart contract implementation for the bridge
(bridges evolve their implementations over time, such as in
response to an attack). Since the transactions we collected
Jun 01 2020 Oct 01 2020 Feb 01 2021 Jun 01 2021 Oct 01 2021 Feb 01 2022 Jun 01 2022 Oct 01 2022 Feb 01 2023 Jun 01 2023 Oct 01 2023
DateHECOQubitMeterChainswapBSC T oken HubNomadPolyNetworkHarmonyWormholeRoninAnyswapBridge Name
Reported AttackFigure 5: The lifetime of the bridges in our retrospective
study. Lines start with the bridge’s first valid transaction and
end with the last valid transaction in our data, corresponding
to the bridge’s closure or November 2023 if the bridge was
still operating at the end of our data set. Diamonds indicate
the dates of attack.
from this set of smart contracts are much broader than those
just involved in the top 12 attacks we consider, including
them further reinforces our findings that the alerting work-
load is very small (Section 4.2).
Data Collection. Collecting smart contract transaction
data—the verbose record of what each contract did—across
many chains constitutes the most time-intensive aspect of
performing a retrospective analysis.
For each bridge, we collected deposit and withdrawal
transactions for each of the blockchains it operates on (for
the 21 chains we support) using commercial RPC services
Name Date Loss (USD) Analyzed Reported New Test Error Suspicious
Ronin Mar 2022 624 .0M 3.0M 2 - - - -
PolyNetwork/2021 Aug 2021 611 .0M 292 K 18 - - 1 -
BSC Token Hub Oct 2022 587 .0M 2.0M 2 - - - -
Wormhole Jan 2022 360 .0M 642 K 1 - - 2 -
Nomad Aug 2022 152 .0M 37K†962 - - - -
Harmony Jun 2022 100 .0M 336 K 15 - - - 43
HECO Nov 2023 86.0M 23K 8 - - - 73
Qubit Jan 2022 80.0M 260 16 - 114 - -
Anyswap Jul 2021 7.9M 3.4M 4 24 - 806 7
PolyNetwork/2023 Jun 2023 4.4M 290 K 136 - - 1 27
Chainswap Jul 2021 4.4M 53K∗1136 - 15 4 -
Meter Feb 2022 4.3M 14K†5 - - - -
Total 2.6B 10.1 M 2,305 24 129 814 150
TABLE 2: List of top attacks on cross-chain bridges in the retrospective analysis, ordered by amount stolen. CrossChecked
analyzed over 10 Mbridge transactions (20 Mcomponent deposit and withdrawal transactions) and identified all bridge
transactions previously reported as having been associated with the attacks (Reported). It also identified bridge transactions
that violated the invariant that were a previously unidentified attack (New), test transactions (Test), transactions reflecting
bugs in implementations or use (Error), and suspicious transactions that employ manual signing (Suspicious).†CrossChecked
identified slightly more transactions than were reported by the Nomad [28] and Meter [30] bridges for their attacks (+2 for
Nomad, +1 for Meter).∗The Chainswap attack only has reports of the malicious deposit address [25], which matches the
one identified by CrossChecked.
which charge for queries. We primarily used five com-
mercial services,10as no single service supports all of the
blockchains and functionality we need. For each bridge and
blockchain combination, by default we collected all of the
historical transactions generated by its smart contracts from
the genesis of its deployment to the end of November 2023
or the end of its life, whichever came first.
The two exceptions to this rule are the Binance bridge,
for which we limited data collection to a year (six months
prior to its attack and six months after) because of the
sheer volume of transactions over its lifetime (30 million
transactions) and the Harmony bridge, whose contract was
reused by another bridge (LayerZero) after its attack (since
LayerZero was not attacked itself, we only consider trans-
actions that were directly associated with Harmony).
Figure 5 illustrates these bridge lifetimes with each line
starting at a given bridge’s first transaction and ending with
its last in our data set. Diamonds indicate the dates of
individual attacks, emphasizing that many of the bridges
closed shortly after their attacks.
4.2. Analysis
We developed a tool called CrossChecked that pairs
deposit and withdrawal transactions from blockchains into
bridge transactions, and applies the balance invariant and
consistency checks on them. Using the transactions we
collected for the 12 attacks across 11 bridges and 21 chains,
CrossChecked analyzed over 10 Mbridge transactions (20 M
individual deposit and withdrawal transactions), identifying
more than 2.3 Kbridge transactions associated with the
attacks and 1.1 Kmore that violated the balance invariant.
10. GetBlock, QuickNode, ChainStack, GoldRush, and Ankr.Table 2 summarizes our results. For each attack, it shows
the bridge involved, the date of the attack, the claimed loss
in USD, the number of transactions CrossChecked analyzed,
and the number and kinds of transactions that violated the
balance invariant. Below we discuss these two categories
of transactions in more detail. For further context on each
of the attacks, Appendix A provides a descriptive account
summarizing how the attacks transpired.
Looking forward to Sections 5 and 6, altogether Cross-
Checked identified 3,423 bridge transactions (0.03%) that
violated the balance invariant out of more than 10 Mbridge
transactions analyzed. If the invariant is used by a third-party
auditing or protection system, we note that such an alert
workload has a negligible overhead for manual inspection,
typically raising no more than one alert (or one batch of
alerts) every few weeks.
4.2.1. Reported. For each of the 12 significant cross-chain
attacks, Section 4.1 describes how we gathered the historical
transactions that correspond to the attacks using external
sources. We use these identified transactions as ground truth
for evaluating the ability of CrossChecked to identify attack
transactions. As shown in Table 2, when processing the
transaction histories of the chains involved, CrossChecked
successfully identified all 2,305 bridge transactions on the
source and destination chains associated with the attacks
(including a few extra ones that are missed by the public
reports).
Since we designed CrossChecked to specifically identify
such attacks, these results may not be surprising. However,
they are useful for confirming that the approach of checking
a simple, well-defined invariant works well. Moreover, the
approach works well across a variety of models, including
bridges that specify fees in a fiat currency, tokens that use
a reflection mechanism, etc.
4.2.2. Other Violating Transactions. Equally compelling
is the extent to which other, non-attack transactions violate
the balance invariants. If the attack transactions are domi-
nated by many false positives, then the approach becomes
less effective. As shown in Table 2, CrossChecked finds
significantly fewer (1,117 compared to 2,305) bridge trans-
actions that were not previously identified as attacks. Given
the nature of these additional transactions, though, they do
not undermine the effectiveness of the invariant approach.
By violating the invariant something highly unusual is taking
place. As a result, we argue that such transactions should be
flagged for further scrutiny and perhaps even blocked from
executing (particularly transactions in the New category and
the large transactions in the Suspicious category below).
To ensure that we have not missed benign explanations
for a violation, we manually inspected violating bridge
transactions in at least of one of the following ways: (1)
using blockchain explorers to verify that the funds have been
created by the withdrawal (and if so, whether they have been
moved); (2) searching online for any additional information
about the transaction and addresses involved; (3) checking
that if claimed deposit exists; and (4) examining unredeemed
deposits on the source chain that potentially could have
been used to back the withdrawal (e.g., because the smart
contract implementation changed between the deposit and
withdrawal). If we can manually reconcile a bridge transac-
tion, we consider it benign and do not consider it further.
We group the remaining bridge transactions that violate
the invariant into four categories, which we describe below.
For reference, we also list some of these bridge transactions
in Table 3 in the appendix to provide specific examples with
more detail.
New. We believe that we have identified previously unre-
ported bridge transactions involved in two new, unreported
attacks on Anyswap. The first group of three transactions
executed once Anyswap reopened after the attack but before
Anyswap patched its smart contract (Anyswap transactions
#1–3 in Table 3 in the appendix). These transactions were
three days later and involved different deposit and with-
drawal addresses than the July 10, 2021 attack (yet utilizing
the same compromised key). The second group of 21 trans-
actions on November 18, 2021, were all withdrawing on
Avalanche. These transactions either referenced deposits that
had already been redeemed months previously (Anyswap #6
in Table 3) or referenced non-existing deposits (Anyswap
#7 in Table 3). Manually inspecting the smart contract
used, the attackers were exploiting a bug in Anyswap’s
implementation (the access control code was commented out
and thus ineffective) and one of the addresses was labeled
as “KyberSwap Exploiter”.
Tests. Two bridges had test transactions that violated the in-
variant. Chainswap had 15 transactions that withdrew tokens
labeled as test tokens (e.g., tokens labelled as “testtoken” or
“startertoken”). Similarly, Qubit had 114 transactions that
minted test tokens (e.g., “xTST”) without backing deposits.
While all these transactions did not have correspondingdeposits, we surmise that they were likely benign given the
tokens involved (tokens that have no real value).
Error. Four bridges had transactions that suggest bugs in
either their implementation or their invocation.
PolyNetwork/2021 had one bridge transaction where
the withdrawal amount matched the deposit amount, but
the withdrawal moved the funds to the wrong destination
chain (CrossChecked flagged the mismatch in destination
chain specified in the deposit and withdrawal transactions).
Anyswap had two groups of bridge transactions in this
category. The first consists of 800 withdrawal transactions
that pointed to just a few deposits. Manually sampling a
few, the deposit transaction hash was not set correctly in the
withdrawal transactions (we found their matching deposits),
suggesting a program error. The second Anyswap group
had four double-spend transactions (referencing the same
deposit) that are also likely program errors: the deposit
and withdrawal amounts are the same, and the withdrawals
occurred within a few hours of each other. Wormhole
had two bridge transactions that violated the invariant: 0.5
wrapped Solana on Polygon →650 USDC on Solana, and
4.3KMATIC on Polygon →2.3Kon Avalanche. The small
amounts and close proximity of the dates of the transactions
suggest they are also likely errors.
Finally, three bridges had transactions that had no ap-
parent effect, suggesting invocation errors or undocumented
behaviors. PolyNetwork/2023 had one bridge transaction,
and Chainswap had four, where the deposits were non-zero
but the withdrawal amount was zero. And Anyswap had two
withdrawals referencing deposits that did not specify a recip-
ient, yet the deposit amounts covered the withdrawals. While
technically not balance invariant violations, CrossChecked
flagged them because of their unusual circumstances.
Suspicious. The final category of bridge transactions sug-
gests the manual, intentional use of private keys in signing
transactions that effectively bypass verification — precisely
the kind of transactions that warrant auditing.
Many of these cases involved highly suspicious un-
backed bridge transactions involving very large withdrawals
without corresponding deposits. For example, Anyswap had
seven transactions totaling more than $1.5 Mpointing to non-
existent or already-redeemed deposits (e.g., Anyswap #4
and #5 in Table 3 in the appendix). PolyNetwork/2023 had
27 withdrawals referencing a non-existent deposit address
totaling more than $20 M(PolyNetwork/2023 #1 in Table 3).
The HECO bridge had 73 unusual transactions. One was
a very suspicious unbacked bridge transaction minting $5 M
of USDT on the HECO chain (HECO #1 in Table 3). The
remaining 72, totaling over $36 M, involved withdrawals to
an address labeled “HECO recovery”. The label suggests
benign intent such as rescuing funds trapped in the bridge,
but the activity is also consistent with a rug pull.
Other cases suggest seemingly careless operational prac-
tices. In particular, Harmony had 43 bridge transactions that
violated the invariant in a variety of different ways. Eight
double-spending bridge transactions (e.g., Harmony #1 in
Human
InvestigatorBlockchain
Monitor
Auditor
Live Auditing System Blockchains
TX Data
Violating TX
Database
Figure 6: Live auditing system which consists of three
components.
Table 3) used the same deposit to release tokens twice
(though only resulting in a profit of a few hundred USD).
Thirty-two had indecipherable data: it was not possible to
decode the deposit function name, function input, and some
events (thus preventing verification of the deposit). One
bridge transaction minted piggybankone tokens on Harmony
chain referencing a non-existing deposit. And two had with-
drawal amounts that were smaller than the deposits, perhaps
caused by undocumented fees or errors. Considering the
specific mechanism used by the Harmony bridge, where a
privileged submitter key was submitting transactions that
it should not have, and the fact that some double-spending
transactions had amounts different than what was deposited,
these incidents suggest that Harmony had issues operating
securely and correctly.
5. Live Auditing
Section 4 showed the effectiveness of applying the bal-
ance invariant on restropective data to accurately identify
past bridge attacks. Incorporating the logic used in the
retrospective analysis, we implemented a real-time auditing
system that uses the balance invariant to monitor ongoing
transactions on a bridge.
Since most of the bridges in our retrospective analysis
are shut down at the time of writing (Figure 5), we focus
on the Wormhole bridge. Wormhole is still operational, sup-
ports a wide variety of blockchains, and is among the most
popular bridges in terms of transaction activity [49]. This
live system monitors thousands of withdrawal transactions
per day across ten blockchains, which together account for
60% of all withdrawal transactions on Wormhole. Extending
the system to support other bridges is straightforward.
5.1. System Overview
Figure 6 depicts the architecture of our live auditing
system. The system consists of three main components.
The Blockchain Monitor tracks the blockchain network and
retrieves the latest deposit and withdrawal transactions. The
Auditor checks extracted transactions for invariant viola-
tions. The Database component stores all the transactions
and auditing results.Blockchain Monitor. The Blockchain Monitor obtains
deposit and withdrawal transactions by periodically retriev-
ing the latest finalized blocks. The Monitor uses the same
commercial RPC services for collecting Wormhole deposits
and withdrawals as we used for the retrospective analysis
(Section 4.1).11
The Monitor only retrieves finalized blocks since un-
finalized blocks may be reverted or reordered. Since dif-
ferent blockchains have different finality times, we use the
timestamp of the latest finalized block of Ethereum as the
synchronization time since Ethereum has the slowest finality
time (around 12 minutes) [19]. After retrieving the blocks,
the Monitor extracts deposit and withdrawal transactions for
Wormhole and saves them to a local database.
The polling interval is configurable and the Monitor
polls chains every 1 minute by default. As a result, the live
auditing system is as recent as the polling interval combined
with the finality time — a situation similar to bridges due to
the polling nature of distributed off-chain communication.
Auditor. For every new withdrawal transaction extracted
by the Blockchain Monitor, the Auditor attempts to find the
corresponding deposit transaction in its local database. In
most cases it will find the deposit, but if it cannot find
it (e.g., because the withdrawal references a non-existent
transaction), the Auditor sends an email alert inviting man-
ual inspection. If it finds a deposit, it examines the deposit
and withdrawal transactions, and sends an alert if either the
deposit has already been withdrawn (the new withdrawal
is double-spending) or the bridge transaction violates the
balance invariant.
Database. The system uses a PostgreSQL database to
store all information regarding deposit and withdrawal trans-
actions. This information includes the transaction hash, the
blockchain, the amount, and the timestamp. It also stores
the auditing results, including whether the deposit has been
withdrawn and whether the invariant is violated.
5.2. Deployment
We have deployed our live auditing system for the
Wormhole bridge for a month. The system audits the deposit
and withdrawal transactions between 10 blockchains, which
collectively account for around 60% of all withdrawal trans-
actions on Wormhole. For the four weeks that our system
has been operational, it has audited over 60,000 transactions
in total (averaging more than 2,000 transactions per day).
The system currently refreshes every minute (configurable),
and sends email alerts if it observes a transaction violating
the balance invariant or another auditing property (e.g., the
destination chain in the deposit does not match the chain in
the withdrawal).
In the time that our system has been operational, it
has alerted on 22 transactions in six batches over four
weeks. Upon manual inspection, we confirmed that all alerts
were caused by previously unseen tokens (recall that in
11. A fully operational deployment would manage nodes participating
in each of the blockchains to obtain transactions in block data directly.
some cases we need to manually account for token-specific
logic) or bugs in our code. Consistent with our retrospective
analysis, the alert rate is very low at 0.03% and raises fewer
than one alert per day. This rate is on-par with other systems
that have an alert budget (e.g., five alerts per day in the
context of a lateral movement monitoring system [16]).
In addition, while we have not observed any attacks
during our monitoring period, we simulated three attack
scenarios to confirm that the system alerts when expected.
These three transactions represent three types of attacks: (1)
a double-spend attack, (2) an unbacked withdrawal attack,
and (3) an attack where the deposit and withdrawal amounts
do not match. The system successfully alerted on all three.
6. Protecting Bridges
Sections 4 and 5 showed how applying the balance
invariant can identify attacks retrospectively and monitor
ongoing transactions as an external third party without any
changes to existing bridge infrastructure. However, the live
monitoring system only alerts on violations of the invariant
and does not prevent violating bridge transactions from
being processed.
In this section, we describe an approach for extend-
ing an existing bridge implementation to prevent malicious
transactions from executing. Our goal is to present a proof-
of-concept implementation to demonstrate the feasibility of
applying the balance invariant in the workflow of bridge
transactions as an active defense against malicious transac-
tions. We acknowledge that several practical issues remain
for a complete operational system, presenting an opportunity
for interesting future work.
We start by proposing a new model for the workflow of
bridge transactions. Then, using Wormhole as an example,
we show how to modify an existing bridge implementation
to prevent malicious transactions from being processed with
fewer than 30 lines of code changes. Finally, we evaluate
the correctness and overhead of this initial implementation.
6.1. The Announce-then-Execute Model
To guide the design of a new model, we first highlight
where the attack surfaces exist. As shown in Figure 1, a
vulnerability can exist in as early as the second step (verify
lock amount) to as late as the next-to-last step (verify relayed
transaction). Thus, a natural place to introduce a protection
mechanism is just before the final step, which executes the
relayed transaction.
Using this insight we propose an announce-then-execute
model. Figure 7 depicts the entities and steps involved in
the new model, focusing on the destination chain (replacing
the right-most box of Figure 1). The new model employs
two steps to process a withdrawal transaction: (1) verify
and announce the withdrawal transaction on the destination
blockchain (steps 8 and 9) and (2) execute the withdrawal
transaction (steps 11 and 12). Of particular note, this new
model introduces an additional entity, the Approver , which
is responsible for approving the relayed transaction before
Approver
Recipient
Submitter
Withdraw X'
7
Smart Contract
Process
Withdraw
Announce8
9Approve
121110
Execute
Approve Announce SC X'
Destination Blockchain1-6 From
Figure 2Figure 7: Announce-then-execute model for bridges. The
approver verifies the relayed transaction before its execution.
The approver can be implemented differently (e.g., a set of
third-parties), depending on the desired security properties.
it is executed. The Approver can be a bridge operator, a
trusted or trustless third-party (e.g., Bascule [8], inspired
by our work, uses trusted execution environments instead
of consensus to ensure the safety of the approver), or a
set of third-parties (e.g., using multisig for decentralized
control). It is responsible for verifying that the announced
withdrawal transaction satisfies the invariant by validating
the same conditions as the Auditor in the live monitoring
system (Section 5). If the relayed transaction violates the
invariant, the Approver can reject the transaction to prevent
it from being executed.
6.2. Threat Model, Assumptions, and Trade-offs
We assume that the approver is able to independently
verify the relayed transaction and the correct amount of to-
kens to be transferred. Under this assumption, the announce-
then-execute model prevents any implementation bug in any
existing bridge components, from step 2 (e.g., the bridge
fails to compute the correct deposit amount) to step 8 (e.g.,
the bridge fails to verify the signature of a relayed transac-
tion). Further, if the approver uses secure hardware (namely,
HSMs) or a decentralized third-parties (e.g., with multisig),
the model can provide additional protection against insider
threats and key theft (e.g., up to the multisig threshold). Of
course, if the bridge is the only approver, the model does
not provide any additional protection against such insider
threats or key theft attacks.
The announce-then-execute model has a few advantages.
First, it explicitly separates the verification and execution
of the relayed transaction, thereby providing an opportunity
to reject transactions that violate the balance invariant. In
comparison, many bridges (e.g., Wormhole) couple the last
two steps tightly — the same function call performs both
the verification and execution of the relayed transaction. As
a result, if the verification step has a bug, it is not possible
to stop violating transactions from being executed.
Second, the announce-then-execute model allows any
external party, that has visibility into both blockchains, to
determine if the relayed transaction violates the invariant. A
long-standing challenge with bridges is the oracle problem:
the destination blockchain does not have visibility into the
source blockchain. The announce-then-execute model side-
steps the oracle problem by allowing an external party to
verify the relayed transaction before it is executed. This
external party can be the bridge operator or a group of third-
parties. Moreover, the announce-then-execute model is com-
patible with existing bridges, as it only requires modification
to the last step and treats the relaying infrastructure as well
as the verification code as a black box. In fact, Wormhole’s
implementation on Solana already separates the process
of verifying and executing the relayed transaction, making
it even easier to adopt the announce-then-execute model.
Had they implemented the announce-then-execute model,
the Wormhole bridge would have been able to prevent the
$360M attack on the Solana network [4].
Lastly, the announce-then-execute model does not intro-
duce any new attack surfaces — even if the approver naively
approves all transactions, an attacker still has to exploit other
vulnerabilities in the bridge to profit.
6.3. Implementation
Continuing our focus on Wormhole from Section 5,
we modified one of Wormhole’s open-source contracts to
implement the announce-then-execute model. Wormhole’s
current withdrawal function performs two tasks: it verifies
the signatures that authorize the withdrawal transaction,
and then executes it. We split the withdrawal function
into two separate functions: announceWithdraw and
approveWithdraw . The announceWithdraw func-
tion handles signature verification and announces the with-
drawal transaction, while the approveWithdraw func-
tion executes the withdrawal transaction. This modification
required fewer than 30 lines of code changes.
For simplicity, we opted for a single approver with one
key in our implementation. However, our implementation
can be easily extended (e.g., to use multisig). In addition,
while our implementation has not been adopted by the
Wormhole team (as we are a research group not affiliated
with Wormhole), our solution has inspired other industry
projects to adopt a similar approach [8].
6.4. Evaluation
As a final step we performed a high-level evaluation of
the correctness and overhead (in terms of gas usage) of the
announce-then-execute implementation. We deployed our
bridge implementation on the Binance and Fantom testnets
due to the availability of free faucets for acquiring testnet
tokens for them. We then executed a series of benign and
malicious transactions using Binance as the source chain
and Fantom as the destination chain.
Correctness. For evaluating correctness we disabled
the security checks in the original implementation (e.g.,
signature verification) to allow malicious transactions to
flow through the contract implementation (e.g., simulating
key compromise). We then executed 100 pairs of depositand withdrawal transactions to validate that the implementa-
tion correctly identifies and protects itself against malicious
transactions that violate the balance invariant.
All but three of the 100 paired transactions were benign,
and used randomly generated values as transaction inputs.
The remaining three paired transactions were malicious and
were randomly placed in the transaction sequence. The
malicious transactions represented the three kinds of bugs
underlying the large attacks in Section 4: (1) a bug that
allows a user to withdraw more tokens than they deposited,
(2) a bug that allows a user to withdraw tokens without
depositing any (simulating key compromise), and (3) a bug
that allows a user to withdraw twice from the same deposit
(double spending).
The bridge implementation correctly executed the benign
transactions to completion and rejected the three malicious
transactions. The results are the same independent of where
the malicious transactions randomly appear in the sequence.
Overhead. The additional steps add overhead to im-
plementing bridges. For this experiment, we measured the
gas usage for executing Wormhole’s original implementation
(with its security checks) and the gas usage of the announce-
then-execute implementation (also with the original security
checks plus our added code). On average, our proof-of-
concept implementation consumed 110 Kmore gas than the
original implementation (370 Kvs. 260 Kgas) when execut-
ing the benign transactions, which translates to a roughly
$1 increase for a typical withdrawal on Ethereum.
We note that our implementation is not optimized for
gas usage, and these results indicate that an operational
deployment will likely want to reduce the gas overhead of
the additional work (e.g., narrowing to transactions above
a threshold amount of funds). As discussed further in Sec-
tion 9, such optimizations and other practical issues of a
real deployment are interesting future work.
7. Related Work
Blockchains are, by their nature, public and thus sup-
port direct empirical analysis of past transactions. This
property has engendered a rich literature quantifying and
characterizing a range of quasi-adversarial activities on in-
dividual blockchains including arbitrage [26], [34], sand-
wich attacks [34], [52], frontrunning [9], transaction re-
plays [34], and a range of smart contract vulnerabilities
and attacks [13], [14], [32], [36], [46], [50]. While our
work focuses on the cross-chain context, a number of the
vulnerability classes identified in such work are directly
implicated in the attacks we analyze.
In the cross-chain context, there are several different
streams of related work. First are the efforts to improve the
security and performance of cross-chain bridge designs —
particularly in managing cross-chain consensus concerning
state changes — using zero-knowledge [47] or commit-
tees of validators [20], [22]. Some many-chain blockchain
ecosystems (e.g., Avalanche) are extending their chains with
built-in bridges (across different chains). We consider our
work orthogonal to these efforts as we focus on unbalanced
bridge transactions, independent of the particular security
violation that allowed such an outcome to take place. Indeed,
some of the compromised bridges did use sets of validators.
Another line of work has reviewed real-world attacks
on cross chain bridges [21], [29], [49], [51] — ranging
from just a few such events to an analysis of over 30
attacks. Our work has directly benefited from the insight
and documentation these authors provide, but our respective
focus is distinct. While these efforts have concentrated on
identifying the vulnerabilities and mechanisms of attack, our
work is agnostic to these details and focuses on the financial
side-effects those actions.
Yet a third research direction seeks to automatically
discover new vulnerabilities in cross-chain bridges. Some
examples of this work use machine learning such as Chain-
Sniper [42], which trains models to identify vulnerable smart
contracts, and Lin et al. [24], who train models to detect fake
deposit events. Other examples use static analysis such as
XGuard [43], which statically analyzes bridge contracts for
inconsistent behaviors, and SmartAxe [23], which analyzes
the control-flow graph of smart contracts and identifies
access control and semantic vulnerabilities.
The papers philosophically closest to ours are those that
consider the security properties of cross-chain bridges at a
higher-level of abstraction. For example, Belichior et al. [2]
build and evaluate a synthetic bridge designed to allow high-
level monitoring of bridge behavior — including variations
in financial state. In a more empirical context, Huang et
al. [17] characterize the transactions of the Stargate bridge,
and highlight the correlation between large or unusual trades
and individual attacks. Finally, perhaps the closest work
to our own is Zhang et al.’s XScope [48] which models
real-world bridge bugs using a set of pre-defined rules and
applies these empirically to identify several attacks retro-
spectively. Like our work, they focused on invariant patterns
rather than the low-level details of smart contract bugs. That
said, the rules considered by XScope are still considerably
lower-level and more granular than our balance invariant as
they are seeking to identify the cause of the detected attacks
that have taken place. While the two efforts are comple-
mentary, we believe our work is simpler to understand and
implement, more clearly robust (we have tested against a far
wider range of bridges and blockchains), and, as a result, is
likely more attractive for inline deployment.
Finally, a range of blockchain companies advertise
tools that actively monitor cross-chain bridge transactions
for anomalies. These include Hexagate [15], Hyperna-
tive [18], PeckShield [31], Slowmist [38], Certik [5] and
ChainAegis [6] (among others), all of which claim various
levels of automated alerting for suspicious transactions.
However, without clear information on how these systems
operate, or empirical results on their efficacy, it is hard to
relate our work to those tools beyond our shared goals.
8. Ethics
We believe our work, which deals with public data, no
identified individuals and a simple means for identifyingattacks on crypto token transfer bridges (and potentially
preventing such attacks in the future), has very low ethical
risk and significant upside. Moreover, we have attempted to
disclose significant suspicious bridge transactions to appro-
priate bridge operators (yet with limited success as most of
the bridges have ceased operations). Lastly, we will make
our code and data available upon publication.
9. Discussion and Future Work
This paper is a “first cut” at identifying, validating and
implementing a generic mode of theft protection into cross-
chain transactions. We show that a simple balance invariant
is sufficient to detect and prevent most bridge attacks. Our
effort also highlights the need for better auditability and au-
diting in the bridge ecosystem, and for new bridge designs.
Indeed, deploying a cross-chain token bridge with an in-line
balance invariant monitor is an open challenge.
As mentioned in Section 2, in this paper we focus
on cross-chain token bridges and thus our approach does
not directly apply to bridges that have built-in exchanges
and swaps. Still, similar invariant-checking techniques likely
extend to such cross-chain exchanges and similar protocols
(e.g., Automated Market Makers which trade between a
range of cryptocurrencies in decentralized exchanges). Such
extensions will require incorporating contemporaneous price
oracle data into our framework and addition reasoning.
The larger opportunities opened up by this work are
more architectural. Today, when a crypto platform declares
that their system has been audited, they typically are refer-
ring to a third-party that inspects their smart contracts for
common logic errors or deficiencies in their process. Any
validation of overall financial safety is inherently entwined
with the totality of the implementation. However, such code-
oriented audits are inherently challenging and ill-suited for
overarching questions of financial risk. They must evaluate
all contracts and bridge code that directly or indirectly
have access to keys controlling assets and then must ensure
that no combination of inputs or invocations might lead
to a loss (itself an outcome that is rarely well defined a
priori ). Unsurprisingly, multiple bridges in our study had
been previously audited by third parties before their attacks.
Our work suggests that by separating overall financial
safety invariants from the intricate details of every given
contract or trade, we can dramatically simplify the de-
fender’s job and their ability to reason about risk. Similar
to so-called “circuit breakers” implemented in traditional
securities exchanges, or the atomic repayment guarantees
embedded in single-blockchain “flash loan” services, the
ability to script overarching financial constraints indepen-
dent of transaction details can be extremely powerful. For
example, one might extend this work beyond a simple cross-
chain balance invariant to enforce limits on liquidity losses
or to place limits on collateralization risk for tokens backed
by stablecoins. We hypothesize that by centralizing the
specification and enforcement of overall financial constraints
in one place, they will be far easier to audit and reason about.
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Appendix
In this section we provide a descriptive account of each
of the attacks listed in Table 2 in Section 4, giving additional
background on the bridge, how the attack transpired, and
more details on our results using CrossChecked.
1. Ronin Bridge
Background. Ronin bridge operates between Ethereum
and the Ronin chain. It was hacked in March 2022. The
attacker compromised the bridge’s private keys, allowing
them to mint arbitrary amounts of assets. The attacker
carried out two transactions, minting around $624 million
USD worth of assets on Ethereum.
Results. CrossChecked analyzed over 3 million bridge trans-
actions and alerted on both attack transactions.
2. Poly Network Bridge (2021)
Background. PolyNetwork is a cross-chain bridge that
supports asset transfers between multiple blockchains (e.g.,
BSC, ETH, and Polygon). It was hacked on August 10th,
2021. The attacker exploited a bug in the bridge’s verifi-
cation code, allowing them to insert their own keys and
verify any malicious payload. Overall, the attacker stole
around $600 million USD worth of assets on BSC, ETH,
and Polygon.
Results. In total, CrossChecked analyzed over 292 Kbridge
transactions between ETH, BSC, Polygon and Poly Net-
work’s liquidity pool. CrossChecked alerted on all 18 bridge
transactions in the attack. In addition, CrossChecked also
flagged one withdrawal that seemingly was relayed to the
wrong destination chain.
3. Binance Token Hub
Background. Binance Token Hub facilitates asset trans-
fers between Binance Beacon Chain and Binance Smart
Chain. It was hacked on October 7th, 2022. The attacker
exploited a bug in Binance Bridge’s verification code, al-
lowing them to verify carefully crafted malicious payloads.
The attacker carried out the attack in two transactions, each
minting 1,000,000 BNB on Binance Smart Chain (approxi-
mately $580 million USD in total).
Results. CrossChecked analyzed over 2M bridge transac-
tions between Binance Beacon Chain and Binance Smart
Chain. CrossChecked only alerted on the two attack trans-
actions, citing the discrepancy between the amount given out
and the amount received by the bridge. We end by noting
that the partner deposit transactions returned by the Binance
Token Hub’s API for two attack transactions are different
than the ones suggested in some blogposts [12]. However,
the attack transactions will be flagged regardless of which
partner deposit transactions is used.4. Wormhole
Background. Wormhole is a general-purpose cross-
chain bridge that currently supports around 35 blockchains.
It was hacked on February 2nd, 2022. Specifically, the
attacker exploited a bug in Wormhole’s smart contract on
Solana that allowed them to verify arbitrary unauthorized
payload. The attacker executed one transaction and minted
120,000 wETH (around $350 million) on Solana.
Results. In total, CrossChecked analyzed over 642k transac-
tions across ten blockchains. It alerted on three transactions,
one of which was the attack transaction.
5. Nomad Bridge
Background. Nomad bridge support asset transfers
across six blockchains. It was hacked on August 1st, 2022.
The attacker exploited a bug in the bridge’s verification
code, allowing them to verified any payload that had not
been verified before. Shortly after the first a few attack
transactions, a group of copycats joined the crusade of
draining the bridge. In total, the reported loss was around
$190 million.
Results. CrossChecked analyzed over 37k transactions, and
alerted on 962 transactions that transferred assets to 561
unique addresses. We found one dataset that reported 561
addresses, which matched exactly with the addresses we
identified. We also found one Github repository that men-
tioned identifying 960 transactions [28]. As mentioned ear-
lier, we identified two more transactions. Upon manually
inspection, we confirm that the two transactions were indeed
malicious.
6. Harmony Bridge
Background. Harmony bridge operates between ETH,
BSC and Harmony. It was hacked on June 24, 2022. The
attacker compromised two of the signing keys of the bridge,
allowing them to mint arbitrary amounts of assets. In total,
the attacker minted around $100 million worth of assets on
BSC and ETH in 15 transactions.
Results. CrossChecked analyzed over 336k transactions and
alerted on 58 transactions, including all 15 attack transac-
tions.
7. HECO Bridge
Background. HECO bridge allows users to transfer
assets between Huobi ECO Chain (HECO) and Ethereum.
It was hacked on November 11th, 2023. The attacker
compromised the bridge’s private keys, allowing them to
sign arbitrary transactions. The attacker carried out eight
transactions, minting around $86 million worth of assets on
Ethereum.
Results. CrossChecked analyzed over 23k transactions. All
eight attack transactions were flagged by CrossChecked.
8. Qubit Bridge
Background. Qubit bridge allows users to transfer assets
between ETH and BSC. It was hacked on January 27th,
2022. The attacker exploited a bug in the deposit function,
which allowed them to trick the bridge into believing that a
deposit had been made when it had not. The attacker carried
out 16 transactions, stealing around $80 million worth of
assets.
Results. CrossChecked analyzed over 260 transactions and
alerted on all 16 transactions.
9. Anyswap Bridge
Background. Anyswap bridge supported moving assets
across many blockchains at the time of the attack. It was
hacked on July 10, 2021. The attacker exploited a bug in the
bridge’s verification code, allowing them to verify any ma-
licious payload. The attacker carried out three transactions,
minting around $7.9 Mworth of assets on Ethereum.
Results. In addition to the previously reported transactions
from the July 10, 2021, attack, Anyswap had more than 800
additional transactions that violated the balance invariant.
10. Poly Network Bridge (2023)
Background. Shortly after the hack in 2021, PolyNet-
work switched to a new set of smart contracts. It was,
however, hacked again in August 10th, 2021. The attacker
exploited a bug in the bridge’s verification code, allowing
them to verify arbitrary payload. Overall, there were 136
reported transactions.
Results. CrossChecked analyzed over 290k transactions and
flagged all 136 transactions. CrossChecked also flagged 27
transactions that pointed to the same non-existent deposit
transaction hash ( 0x0101... with 01 repeated 32 times).
All of these transactions occurred on Aug 22, 2023 and
withdrew funds from the bridges without backing deposits.
In total, over $20 million worth of assets were withdrawn
in these transactions.
11. Chainswap Bridge
Background. Chainswap bridge support token transfers
between five bridges and was hacked on July 10, 2021.
The attacker exploited a bug in the bridge’s verification
code, allowing them to verify any malicious payload. The
attacker stole $4.4 million worth of assets on Ethereum and
BSC using one address. Of particular note, unlike other
bridges, Chainswap did not publicly disclose the list of
bridge contract addresses. As such, we programmatically
identified all potential bridge contract addresses by search-
ing for the specific event signatures that known Chainswap
bridge contracts emit.
Results. CrossChecked analyzed over 53k transactions.
CrossChecked alerted on 1136 transactions, all of whichwere executed by the single malicious address. We note that
we are unable to find any public information on the list of
transactions that were part of the attack, preventing us from
verifying individual transactions.
12. Meter Bridge
Background. Meter bridge allows users to transfer be-
tween Meter’s own chain and a few EVM-based chains. It
was hacked on February 5, 2022. The attacker exploited
a bug in the bridge’s deposit function, where the attacker
tricked the bridge into believing that a deposit had been
made. In total, the attacker carried out 5 transactions, steal-
ing around $4.3 million worth of assets.
Results. CrossChecked analyzed over 14k transactions,
alerting on all five attack transactions.
Table 3 provides additional details on specific example
bridge transactions that violate the balance invariant and
have otherwise not been previously reported. For each ex-
ample, the table lists the hashes of the paired deposit and
withdrawal transactions, the blockchain, and the number of
tokens transferred. If a deposit transaction does not exist or
is otherwise invalid, we mark its chain and token as N/A.
Bridge (Claimed) Deposit Transaction HashChain &
TokenWithdraw Transaction HashChain
& Token
Wormhole
#10x8bbb7befd198a5e90297f451fc43a9e9
0de083289a041c8af94116c785cf496dPolygon
0.5
WSOL5AiesW9pKrZvCCJM8QPWmqx...dkadV1wa
PVLWfsnCMVQmyYaciLxEoSOL
650
USDC
Wormhole
#20x55d1e486a8e2102e07fd6270a03f05bb
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{
"id": "2410.01107"
}
|
2105.11761
|
The Giving Game
|
This paper describes a basic model of a gift economy in the shape of a Giving
Game and reveals the fundamental structure of such a game. Main result is that
the game shows a community effect in that a small subgroup of players
eventually keeps all circulating goods for themselves. Example applications are
where computers are sharing processing power for complex calculations, or when
commodity traders are making transactions in some professional community. The
Giving Game may equally well be viewed as a basic model of clientelism or
corruption. Keywords in this paper are giving, gift economy, community effect,
stabilization, computational complexity, corruption, micro-economics, game
theory, stock trading, distributed computing, crypto currency, blockchain.
|
http://arxiv.org/pdf/2105.11761v1
|
Peter Weijland
|
econ.TH
|
econ.TH
|
1 The Giving Game
Dr. W.P. Weijland
Delft University of T echnology
May 2021
Keywords: Giving, gift economy, community effect, stabilization, computational complexity,
corruption, micro -economics, game theory, stock trading, distributed computing, crypto
currency, block chain .
Abstract. Assume you are with N players in a room. One player has a ball. Once you have
the ball you must choose a player – not being yourself – and pass the ball to her /him . The game
repeats itself from then onwards . Every time you receive the ball you get a point. Question:
which strategy is optimal to receive as many points possible?
One strategy would be to play the ball to the player from whom you received the ball most times
in the past, assuming this creates the best chance to receive the ball in return. Receiving the ball
creates an increase of preference for the submitting player who has just granted you a point.
This seems rational to do.
A game described above is called a Giving Game . This paper revea ls the fundamental structure
of such a game and how the chosen strategy works out. Main result is that the game necessarily
stabilizes into a repetitive pattern within a subgroup – in this case: a pair – of players.
Furth ermore, I will show that the path towards stabilization consists of a series of elementary
cycles, each further enhancing the preferred pair.
Example applications are where computers are sharing processing power for complex
calculations, or when commodity traders are making transactions in some professional
community. The name ‘Giving Game ’ has a positive connotation. However, it may e qually well be
viewed as a basic model of clientelism or corruption.
INTRODUCTION
Assume we play the following game. We have N agents and one token. Initially, the token
is assigned to one of the agents: the initial agent. The game proceeds to the next step as
follows: th e agent with the token in possession submits its token to some other player,
thus not being itself. Aim of the game is to maximize the number of tokens received over a certain period.
We call this game The Giving Game.
Once holding the token a strategy fo r any agent c ould be to assign the token to the agent
from whom it has received the ball the most times in the past . The idea is to stimulate
loyalty of those agents that are inclined to transfer the token to you. We will discuss how
this strategy works ou t.
Firstly, imagine we play the Giving Game without any history: no one has received any
token from anyone else in the past. The initial agent passes its token to some other agent. This agent now has a preference for the initial agent and will return the b all to it.
After the token has returned to the initial agent both agents have a mutual preference for each other: both have received a token from the other in the past. So, the token will be passed back and forth between the two players endlessly. T his ins tance of the Giving
Game – with out history – is called the trivial game .
2 The trivial game immediately alternates between two agents , one of which is the initial
agent. We refer to such pair of agents as a stability pair . The term ‘stability’ refers to the
fact that the path eventually repeats itself in a forced loop (in this case between two
agents) .
The question rises what happens when the agents do have a history. For each agent such
history consists of a count of tokens received from each of the other agents. The
remainder of this paper is devoted to proving that in such cases the game still stabilizes into a stability pair, be it after some steps (Stabilization Theorem II.5) . We also analyz e
which pairs of agents are potential stability pairs and how su ch stability pair emerges
from the initial steps in the game (Cycle Theorem VI .6).
I DEFINITION
Let us further formalize the definition of a Giving Game, starting from N agents A, B, C, …
and one token. We assume each agent keeps track of a list of N -1 pre ference values
(non-negative integer numbers) assigned to every other agent. The game proceeds to
the next step as follows:
(1) the agent with the token in possession (the submitting agent ) submits its token to
some other agent (the receiving agent ) with maximal preference value in the list;
(2) the receiving agent increments the preference value of the submitting agent in its
list, and becomes submitting agent of the next step.
If we view the preference list of each agent as a column of values then these columns
together form a preference matrix . Such matrix M may look as follows:
Figure 1. A preference matrix.
Submitting agents are in the top row. Receiving agents in the left column. Note that the diagonal cells have no preference values reflecting the fact that submitting agents cannot submit tokens to themselves .
N
OTATION Throughout this article we will write (X, Y) for the matrix cell at the
intersection of column X and row Y .
As a helpful tool, at the start of any step in the g ame we color the submitting agent in
red, cells with maximum column value in blue and all other cells white, such as in :
3
Figu re 2. A colored preference matrix.
In this particular example submitting agent C has a maximum column value at row D –
i.e. at cell (C, D) ) – and will select agent D to submit the token to . The matrix is then
updated by incrementing the value of cell (D, C) with 1 , and D becomes the next
submitting agent.
NOTATION We write | (X, Y) | for the value assigned to a cell, though we will also use
abbreviations like ‘ (X, Y) = 2’, ‘ (X, Y) is incremented by 1’, or ‘ (X, Y) is maximal in the
column of X ’ if that does not cause ambiguity . (Y, X) is referred to as the twin cell of
(X, Y)1. |(Y, X) | is the twin value of (X, Y) and the color of (Y, X) twin color of (X , Y).
Following the notions in the above, a giving game is defined by some initial pair ( A0, M 0)
consisting of the initial agent and the initial preference matrix. From this initial pair the
game develops into a game sequence ( Ai, Mi)i≥0 where each next pair fits with the rules of
the game. To be more precise:
DEFINITION I.1 Give n a finite number of N agents, a Giving Game is defined by a pair
(A0, M 0), where A0 is some agent – the initial agent – and M 0 is so me preference
matrix : an N xN matrix with non- negative integer values assigned to each cell except
for those on the diagonal.
DEFINITION I.2 Let G =(A0, M0) be a Giving Game. A game sequence of G consists of
an infinite sequence ( Ai, M i)i≥0 where all A i are agents and all M i are preference
matrices, and for all i :
a) (Ai, M i)=G for i=0.
b) |(Ai, Ai+1)| is maximal in the column of A i in M i, i.e.: (A i, Ai+1) is blue in M i.
c) |(Ai+1, Ai)| in M i+1 has incremented by 1 compared to its value in M i.
d) All other correspondi ng cell values in M i and M i+1 are equal.
Agent A i is referred to as the submitting agent of step i , Ai+1 as its receiving agent , and cell
(Ai, Ai+1) as the selected cell of step i. Note that in Definition I.2 the cell (Ai+1, Ai) is the twin
cell of t he selected cell . In other words: the selection of a blue cell (A i, Ai+1) at step i
causes its twin cell to be incremented by 1.
A Giving Game G is deterministic if it has only one game sequence. Otherwise it is non-
deterministic, implying that at some step n≥0 of some game sequence the submitting
agent has a choice between two potential receiving agents (i.e. has at least two blue cells
in its matrix column).
A handy notion related to a game sequence is that of a game path :
DEFINITION I.3 A (finite or infinite) sequence ( Ai)i≥0 is called a game path of G if all
Ai are subsequent submitting agents of some game sequence of G. If A, B, C, D,… are
agents we may also write ABCD… as a shorthand for a (finite segment of) a game path.
1 So the twin of a cell is equal to its diagonal mirror image in the matrix.
4 Clearly , not every sequence of agents is a viable game path since the initial matrix M 0
(and thereby all subsequent preference matrices) puts restrictions on whether such
sequence fits with Definition I.2 .
II STABILITY
Let us go back to the trivial game in the introduction. It is easy to see that this is
equivalent to the Giving Game with all values in the initial preference matrix put at zero.
In this section we will generalize this result to hold for any Giving Game.
NOTATION Let S = (Si)i≥0 be a sequence then for n ≥0 we use the following
abbreviations:
a) n|S = (Si)i≥n , hence: 0| S = S ;
b) S|n = (Si)0≤i<n and S |0 = ∅, i.e.: the empty sequence.
So S |n is the segment of the first n items in the sequence. n|S is the remaining infinite tail
of the same sequence S .
DEFINITION II.1 A pair of agents { A, B} is a stability pair of game G if for some game
path S of G there exists n such that either n |S = ABABAB… or n |S = BABABA…
Furthermore, a pair of agents { A, B} is a stability pair of a preference ma trix M if it is a
stability pair of G =(C, M ) for some agent C.
LEMMA II.2 Let G =(A, M) be a Giving Game. Suppose (A, B) and (B, A) are both
blue cells in M . If at the initial step A selects B then the game path is equal to ABABAB…
PROOF Since (A, B) is bl ue, B is maximal in the column of A and according to Definition
I.2b agent A may choose B at step 0. Following Definition I.2c the value of (B, A) will be
incremented. Since it is blue in M it will become single maximum in the column of B . But
then in the next step B must choose A thereby increasing the value of (A, B) which in
turn becomes single maximum in the column of A . By induction the game path is equal to
ABABAB… □
Note that this Lemma is a generalization of the argument for the trivial game at the introduction.
L
EMMA II.3 Let G be a Giving Game. A pair of agents { A,B} is a stability pair of G if and
only if there exists a game path of G containing ABA.
The proof resembles the proof of Lemma II.2 and is left to the reader. Later we will see
that the Lemma can be further generalized to “…containing both steps AB and BA ”. Also
this proof is left to the reader.
LEMMA II.4 Let G =(A, M) be a Giving Game. If (A,B) and (B,A) are both white
cells in M then ABA is not a segment of a game path of G.
PROOF It follows from Definition I.2 that a cell value can only be incremented if its twin
cell is blue. Thus the values of (A,B) and (B,A) remain unchanged, and since cell values
cannot decrease none of these cells can ever turn blue. □
THEOREM II.5 (STABILIZATION ) Every game path eventually alternates between two agents.
PROOF At every step in the game path a blue cell is selected in some column of a game
matrix. If its twin ce ll is also blue, then Lemma II.2 applies. So assume at all steps twin
cells are white. Let MAX be the maximum of all values in the initial matrix M . In each
step a white cell is incremented and since the value of that white cell is smaller than
5 MAX, the ma ximum value in any subsequent matrix is also equal to MAX. Now if we have
N agents, we have N.(N -1) cells in the matrix. So after N.(N -1).MAX+1 increments , at
least one of the twin cells must contain a value ≥MAX. But then it cannot be white.
Contradiction. □
III COLORED PAIRS
From the Stabilization Theorem II.5 we know that every game path stabilizes eventually.
We may wonder how this stability pair is ultimately created in the game. We refer to the
long alte rnating tail of a game path as the stability phase of that path. The initial part of a
game path until the stability phase is referred to as the erratic phase emphasizing its
seemingly hectic nature. It is where choices of individual agents in the game determine
which stability pair eventually emerges.
Before we formally define the stability and erratic phase, let us take a closer look at how
the Giving Game is played.
EXAMPLE Let us look again at the matrix in Figure 2:
• Starting from C the game immediately stabilizes into CDCDCD… since C has only one
maximum to choose from (i.e.: at row D ), so we get CD… . Since both (C,D) and (D,C)
are blue cells Lemma II.2 applies.
A twin pair of blue cells is called a blue pair . Lemma II.2 states that if the submitting
agent selects a cell from a blue pair, then the game stabilizes immediately .
• Starting from B the game proceeds as BCDCDCD… . Note that while B selects C, the
cell value of (C,B) is incremented with 1 (increments from 2 to 3). However, since the
maximum value of C’s column is 4, the color of (C,B) remains white.
A twin pair of which one cell is blue and the other is white is called a turquoise pair .
Of such pair only the blue cell can be selected by the submitting agent (by def inition)
and its twin cell may stay white (as in this case) or turn blue (for example if the
maximum value in C’s column would have been 3), thus creating a blue pair in the next step’s preference matrix.
• Starting from A things are getting slightly more co mplex. The game proceeds in
either of two ways: AB… or AD… . Both cells (A,B) and (A,D) are blue and (B,A) and
(D,A ) are white, so in either case A selects a turquoise pair.
The two cases develop as follows:
1. In the case of AB… note that (B,A) is incremented with 1 and turns blue. Now B
chooses either A (with which it then forms a blue pair) to stabilize at ABABAB…
(stability pair { A,B}), or C (with which it forms a turquoise pair) to stabilize at
ABCDCDCD… (stability pair { C,D}).
6 2. In the case of AD… we find that the value of (D,A) increments from 0 to 1 thereby
turning blue. Now D has a choice between A and C both of which form a blue pair
with D . So the game stabilizes either as ADADAD… or as ADCDCDC… .
We count four stability pairs of this game out of six possible combinations of two
agents. Note that { A,C} and { B,D} are the pairs missing: they are twin pairs of white
cells, referred to as white pairs , and by Lemma II.4 they are no stability pair s.
[END EXAMPLE ]
From the Lemmas II.2 and II.3 three we learn that blue pairs are stability pairs whereas white pairs are not. The kind missing are the turquoise pairs. We will use this
observation in a few moments.
IV FRAMES
Before we continue let us present a useful simplification of the Giving Game that helps
us find stability pairs. We introduce the concept of a frame as a preference matrix
without values. Such matrix inherits the structure of the preference matrix, with the
same agents and the same cell colors but with no cell values to increase. It is referred to
as the frame of a preference matrix and looks as follows:
Figure 3. The frame of the preference matrix in Figure 2.
We can generalize the concept of a game path to that of a frame path as follows:
DEFINITION IV.1 Let F be the frame of some preference matrix . A frame path of F
consists of an infinite sequence ( Ai)i≥0 where all A i are agents in F , and (A i, Ai+1) is
blue in the column of A i in F . A finite frame path is a finite segment of a frame path.
Note that the frame F remains unchanged along its frame paths . For that reason the
concatenation of two ( finite) frame paths is again a frame path.
DEFINITION IV.2 Let F be a frame . B is reachable from A – notation: A ⊒F B – if there
exists some finite frame path A…B of F.
DEFINITION IV.3 A and B are equivalent – notation: A ≡F B – if A ⊒ F B and B ⊒ F A.
LEMMA IV.4 ≡F is an equivalence relation.
The proof is left to the reader. The equivalence relation ≡F creates equivalence classes
A, B, C, … (sets of equivalent agents) that are partially ordered as follows:
DEFINITION IV.5 Let F be a frame and A and B sets of equiv alent agents in F , then we
define: A ⊒F B if for some agents A ∈A, B∈B: A ⊒F B.
For each agent A ∈A we thus obtain a tree structure with A at the top node and classes of
reachable agents a t lower nodes . Along each path in the tree classes are mutually
exclus ive. Edges in the tree are one direction only: lower nodes are reachable from the
7 higher but not the other way around . For example, starting from A the structure of the
frame of Figure 3 looks like:
Figure 4. Equivalence classe s in Figure 3 in a tree.
Note that if we would put { C,D} at the top of the tree we would just have one single node,
since no other classes are reachable from { C,D}. Instead of a tree, we can also use the
structure of a directed graph as in Figure 5
Figure 5 . Equivalence classes in Figure 3 in a directed graph.
Such directed graph consists of all equivalence classes of F , not only those reachable
from the top.
DEFINITION IV.6 A finite game (frame) path is elementary if all its agents occur only
once.
LEMMA IV.7 Let F be a frame. If there exists a frame path from A to B (i.e.:
A ⊒F B) then there also exists an elementary frame path from A to B .
PROOF We provide an intuitive proof. Consider a frame path A…B . Suppose this fr ame
path has a multiple occurrence of some agent C, th en we have: A…C…C…B . Since F
remains unchanged along the path, at each step each agent has the same choice options
to continue. But then we can shortcut the path to obtain A…C…B , reducing C…C to a
single occurrence C. By repeating this argument for every multiple occurrence of some
agent we find an elementary p ath A…B . □
The segment C…C that is eliminated from the path is referred to as a cycle . Cycles play an
important role in the creation of new stability pairs as we will see later.
DEFINITION IV.8 Let F be a frame. A cycle is a finite frame path from some agent to
itself.
It follows directly from Lemma IV.7 that each cycle can be reduced to an elementary
cycle starting from the same agent.
8 V ERRATIC PHASE AND STABILITY PHASE
In section III we already spoke of the concept of erratic and stability phase. We formalize
these notions in a definition.
DEFINITION V.1 Let G =(A,M) be a Giving Game and S =(Ai, M i)i≥0 be a game sequence
of G . Let n≥0 be the smallest n such that (A n+1, An) is blue in M n, then the subsequence S |n
is the erratic phase of S and n |S is the stability phase of S .
By Theorem II.5 that such n exists for every game sequence S , so the definitio n is sound.
It says that the stability phase starts at the first step n in S with a blue twin. It follows
from Lemma II.2 that from this step n all pairs selected thereafter are blue since all cells
(An, An+1) are blue (by Definition I.2). As a consequence we have:
COROLLARY V.2 In the stability phase all selected pairs are blue and in the erratic
phase all selected pairs are turquoise (see Section III) .
In the erratic phase every step consists of selecting a blue cell from the column of the
submitting agent such that its twin cell is white.
LEMMA V.3 Let S =(Ai, M i)i≥0 be a game sequence. If for some n cell ( An, An+1) is
blue in M n and white in M0 then step n is in the stability phase of S .
PROOF Let (B, C) be blue in Mn and white in M 0. Then for some k <n cell ( B, C) must turn
blue for the first time: it is white in M k and blue in Mk+1. In that case the value of cell
(B, C) must have incremented at step k, which only results from C selecting B . So ( C, B)
must be blue in M k (in order to select B ) and it wil l stay blue in M k+1, since (B, C) and
(C, B) cannot change color at the same step. Now both (B, C) and (C, B) are blue in M k+1.
By Definition V.1 step n is in the stability phase . □
This lemma implies that during a game sequence blue cells from the initial matrix
cannot change color until the sequence has reached its stability phase. This leads to the
following insight:
LEMMA V.4 The erratic phase of a game path of M is also a fram e path of M .
PROOF By Lemma V.3 we know that no blue cells from the initial matrix M turn white
during the erratic phase. All newly created blue cells originate from white twin cells that
are incremented at some step . At the step they are created they form a blue pair with the
(blue ) selected cell. By Definition V.1. this step cannot lie in the erratic phase. □
In summary we look at the following. At each step of the game the submitting agent
selects a receiving agent by picking a blue cell from its column while updating its twin
cell (i.e.: adding 1 to its value) before the next step begins. If the twin of this blue cell is
also blue, then we are in the stability phase ( Definition V.1). As long as it is white we are
in the erratic phase. While the game progresses in the erratic phase, white cells may turn
blue but never the other way around: blue cells cannot turn white, unless in the stability
phase (Lemma V.3 ).
VI CYCLES
Suppose we put ourselves the task to find out w hether { A, B} is a stability pair , given
some initial matrix M . How would we be able to decide? Some basic cases are trivial. For
instance, if { A, B} is a blue pair in M then it is a stability p air starting from initial agent A
or B. It is also a stability pair starting from some other initial agent C provided A or B is
reachable from C (Definition IV.2). If {A, B} is a white pair, we know that it cannot be a
9 stability pair regardless the initial agent (Lemma II.4) . So the only case left is when { A, B}
is a turquoise pair in M .
(*) In this section let us assume (A, B) is a blue cell in M and twin cell (B, A) is white.
We are looking for game paths stabilizing at ABABAB… or BABABA… Note that any such
game path must have some step where (B, A) turns blue (since otherwise step BA would
not be valid). This implies that the value in cell (B, A) has ‘caught up with’ the maximum
of column B during the err atic part of the game path. Values of blue cells can only change
in the stability phase since they are the twin of some other blue cell and blue pairs only occur in the stability phase (Corollary V.2) . So the maximum value that (B, A) catches up
with is al so a maximum of column B in M .
D
EFINITION VI.1 The gap of a cell (X, Y) in a game matrix M – notation: gap M(X, Y) – is
equal to the difference between the maximum value in column X of M and the cell value of (X, Y) .
The gap of cell (B, A) can be closed ( i.e.: become zero) during the game by selecting (A, B)
sufficiently many times. In fact, since at every selection of (A, B) the gap decreases with 1
we need to select cell (A, B) precisely gap
M(B, A) many times in order make cell (B, A)
turn blue. This leads to a simplification of the original question:
COROLLARY VI.2 A turquoise pair is a stability pair of a game ( C, M) if and only if
there exists a game path of ( C, M ) with at least gap M(B, A) many occurrences of step AB .
EXAMPLE Let us look again at the matrix in Figure 2:
Pair { A, B} is a turquoise pair with (A, B) blue and (B, A) white. Cell (B, A) has value 0. The
maximum in column B is 1 at cell (B, C) . So the gap of (B, A) = 1 – 0 = 1. So we are looking
for game paths w ith at least one occurrence of step AB . Obviously, in ( A, M) we find such
path: AB itself. However, in the games ( B, M), (C, M ) and ( D, M) such path does not exist.
[END EXAMPLE ]
So let us zoom in further on the structure of a game path with stability pair {A, B}. Such a
path starts with an initial agent – say: C – and has at least gap M(B, A) many occurrences
of AB . So it looks like: C…AB…AB …AB…ABABAB… In the following we will define a
reduction system to reduce any game path with stability pair { A, B} to a ‘short er version’
of the same path. Such reduction makes use of reduction rules as follows .
REDUCTION SYSTEM VI.3 Let M be an initial matrix. Consider a game path of M with
arbitrary initial agent and with stability pair { A, B}. We look at the following two -rule
reduction system:
(1) If the initial segment of the path until the first occurrence of step AB is not empty,
then we cut it off making the reduced game path s tart with AB while leaving the rest
of the path unchanged .
10 (2) If between any two consecutive steps AB …AB some agent ( including A or B ) has a
multiple occurrence of C, as in: AB…CDEFC…AB , then delete CDEF to produce the
reduced form AB…C…AB .
[END REDUCTION SYSTEM ]
Obviously, reduction rule (1) can only apply once to a game path . The second, however,
can be applied as many times until all multiple occurrences of agents between
consecutive steps AB have been eliminated .
LEMMA VI.4 Reduction system VI.3 preserves the following properties:
(i) the reduced game path is again a valid game path;
(ii) the reduced game path maintains stability pair { A, B}.
PROOF Let S be a game path of ( C, M) with stability pair { A, B}. Suppose S’ is the erratic
phase of S and S its stability phase. Hence: S = S’ S. By Lemma V.4, S’ is also a frame path
of M . The proof consis ts of five claims:
CLAIM 1: Reduction system VI.3 only applies to segments of S’.
Rule ( i): If any step before the first occurrence of AB has a blue twin, it is in the stability
phase of S (Corollary V.2). Then it must be either AB or BA . Step AB cannot o ccur before
its first occurrence. S ince (B, A) is white in M (by (*)) step BA can not be a valid step
before the f irst occurrence of AB . Contradiction.
Rule ( ii): The stability phase has only occurrences of AB with no other agents between
them. So rule (2) cannot apply outside the erratic phase of S .
So let us assume Reduction system VI.3 reduces S’ to S’’.
CLAIM 2 : S’’ is a turquoise frame path of M .
Rule ( i): Trivial .
Rule ( ii): If we cut out a cycle segment from S’ the resulting path S’’ only consists of steps
from S’ . So S’’ remains a turquoise frame path of M 2.
CLAIM 3 : S’’ is a turquoise game path of M .
Since S’’ is a turquoise frame path it only selects blue cells in M with white twin cells.
Suppose that after some steps in S’’ one of th ese twin cell s – say: (Y, X) – has turned blue.
Then blue cell (X, Y) must have been selected at least gap M(Y, X) many times. The
number of occurrences of (X, Y) in S’’ cannot be higher than that in S ’ so (Y, X) must also
turn blue at some step to form a blue pair in S ’, contradicting that S’ is erratic.
CLAIM 4: The last agent of S ’’ is equal to that of S’ , or S’ is fully deleted.
Rule ( i): Trivial.
Rule ( ii): Any cycle C…C deleted from the erratic phase is replaced by C and therefore the
last agent of the erratic phase remains in place.
CLAIM 5: S’’ S is a game path of M with stability pair { A, B}.
S has stability pair { A, B} so its erratic phase S’ contains precisely gap M(B, A) many
occurrences of step AB (Corollary VI.2). Since these occurrences are preserved by
Redu ction system VI.3 we find equally many occurrences of AB in S’’. Therefore {A, B} is
a blue pair after the last step of S’’. The last agent of S’ – say C – is unaffected by
Reduction system VI.3. Since it is in the erratic path S’’ no blue cell in C’s colu mn has
change color, so C may choose A or B , whichever is first in the stability phase. So the
concatenation S’’S is a game path of M with stability pair { A, B}. □
2 Note: if we would cut out non- cyclic segment s from S’ this may no longer hold, since we t hen may create
steps in S’’ that are not present in S’ .
11 DEFINITION VI.5 A game path of M with stability pair { A, B} is in normal form if none
of the reduction rules in VI.3 apply.
The following Corollary follows immediately from the construction of Reduction system
VI.3:
COROLLARY VI.6 (C YCLE THEOREM )
1. {A, B} is a stability pair of M if and only if some game path in normal form stabilizes
at {A, B}.
2. A normal form path starts with its stability pair as the first step.
3. The erratic phase of a normal form is built from the concatenation of a series of
elementary cycles AB…A .
All of the elementary cycles from Corollary VI.6.3 are present in the frame of M , so we do
not have to look further than the initial matrix to identify them. Also note that agents in a cycle are equivalent (Definition IV.4) and so a normal for m game path has only agents
from one single equivalence class, as they all contain A and B.
It is not difficult to see that if we would change the order of occurrences of the
elementary cycles in Corollary VI.6. 3, then the sequence remains a valid game path
stabilizing at { A, B}. So in the process of finding a game path stabilizing at { A, B} we need
to focus on which cycles are involved and how many times each of them occur s (the
‘power’ of the cycle) .
E
XAMPLE A Giving Game ‘ produces ’ new stability pairs ( i.e.: not blue in the
initial matrix) by running cycles. In Figure 6a below we illustrate how this works.
Figure 6 a: All agents in one cycle.
Note that the frame of this matrix is fully deterministic: it does not leave choices open to agents. Also note that the matrix has no blue pairs. So any stability pair must be created through out the game.
Further, check that ABCDA… is a cycle. Following the steps in this cycle the twins of the
selected cells are incremented by Definition II.2. These incremented values are marked
red in the left- hand matrix in Figure 6b :
Figure 6b: All agents in one cycle (cont ’d).
12 Still, blue cells remain blue and white cell s remain white. In the second run of the same
cycle we obtain ABCDABCDA… – in shorthand: ( ABCD )2 A… – and we arrive at the second
matrix where the red twin cells are incremented again. At the third run we have
(ABCD )3 A… and we obtain the t hird matrix with cell (A, D) turned blue reflecting the fact
that its value now matches the maximum value in its column. Now , A has two options to
choose from: either AB… or AD… . The first option AB … leads to a fourth run of the same
cycle, so we have ( ABCD )4 A… to ar rive at the right- hand matrix. This time A only has
one option to continue by choosing D. So the path stabilizes at … ADADAD… to form
(ABCD )4 ADADADA… . It is easy to check that the second option AD… leads to the s ame
stability pair to form (ABCD )3 ADADADA… .
[END EXAMPLE ]
So, to decide whether { A, B} is a stability pair of a matrix M , we – theoretically – may
wish to list all elementary cycles in M and see if we can add powers to them such that:
(1) {A, B} occurs precisely gap M(B, A) many times ;
(2) No pair (X, Y) occu rs more than gap M(Y, X) many times.
Let us take a closer look at the matrix in Figure 6a. The matrix has one elementary cycle.
The steps in the cycle are AB, BC, CD, DA and the values i n their twin cells (B, A), (C, B),
(D, C) and (A, D) are 2, 0, 1, 1 respectively. The maximum values in their respective
columns B, C, D and A are : 7, 5, 9 and 4. So the respective gaps (Definition VI.1) of the
twin cells (‘twin gaps’) are 5, 5, 8, 3: the lowest being 3. This explains that after three runs of the cycle the first twin cell to turn blue is (D, A).
This little example also shows that – in order to make { A, B} a blue pair – we cannot
simply run the cycle 5 times: if we do, at the fourth run { A, D} would irreversibly deviate
into stability and the game will never get back to B . We cannot run any cycle more times
than its minimal gap of twin cells, unless it is { A, B} itself that has the minimal twin gap .
In such case it is at pole position in th e cycle. The minimal twin gap is the order of the
cycle.
Lemma VI.7 If {A, B} is at pole position in some elementary cycle of M , then it is
a stability pair.
If {A, B} is not in pole position at any elementary cycle th en to determine whether it is a
stability pair gets more tricky . One could think that we simply had to run each
elementary pair AB…A as often as its order . Since all cycles contain AB we only need to
check whether the sum of all those orders is at least gap
M(B, A). This would work indeed
if all cycles would be disjoint in steps (apart from step AB , ofcourse). If not, running
cycle 1 may lower the gap of steps in cycle 2 and thereby its order . In other words:
orders of cycles are not mutually independent unless the cycles are disjoint. The tric ky
thing now is that we must find out which cycle to run how many times so as to maximize
the overall number of runs.
I have not found an efficient algorithm to achieve this. I leave the question open here
whether an algorithm exists with less than exponential complexity in relation to N .
Part of the complexity of the algorithm comes from the number of elementary cycles in a matrix M . This determines how many elementary cycles in a game we must evaluate. We
have bad news:
Lemma VI.8 The number of elementar y cycles may grow exponentially with N .
P
ROOF Consider the matrix in Figure 7. Let us say M6 is the smaller matrix with six
agents A…F . If we add column and row G , then we create a larger matrix M7 , and also M8
13 by adding H . This way we build a series of m atrices M k for any natural num ber k. All
matrices only have turquoise pairs. Every next matrix inherits all cycles from the
previ ous one . CLAIM: F or all even k and every elementary cycle AB…A in M k there is a
unique elementary cycle in M k+1 that does not exists in M k. Thus we prove that M k+1
has at least twice as many elementary cycles as M k.
Figure 7 : Building matrices with only turquoise pairs .
We will prove the CLAIM for k =6 and leave the general proof by induction to the r eader.
So we will prove that M7 (with agents A,…,G ) has at least twice as many elementary
cycles as M6 . Now let us say w e have odd agents A, C, E, G,… and even agents B, D, F , H…
Note that:
I. Agent B can only select odd agents .
II. In M7 agent G can select all odd agents .
Assume we have an elementary cycle AB …A in M 6. The cycle cannot be ABA since all
pairs in M 6 are turquoise. L et X be such that the cycle in M 6 is equal to ABX…A . Clearly,
ABX…A is also a cycle in M 7. X is an odd agent, since it is selected by B (property I ). In
M7 agent G can select X , since X is odd ( property II). As G is odd, B can select G ( property
I). So ABGX…A is an elementary cycle in M7 .
So each elementary cycle AB…A in M6 has two unique images in M7 : AB…A and ABG…A .
But then M7 has at least twice as many elementary cycles AB…A as M6 . □
So simply listing all elementary cycles and find the optimal combination of powers is not
the way to go for large N unless the frame of the matrix in which agents are ordered has
some regular pattern to it.
VII AFTERMATH
Back in 1980 my friend Jan Wielemaker – fellow member of the student society SSRE –
revealed to me how social relations really work : ‘every interaction with someone else
leads to a social credit/ debet that adds to a net positive or negative balance of favours’.
The fact that I still remember his words means they made an impression, which –
however – did not materialize in action until 2014, when t he original idea of the Giving
Game struck me as an rudimentary model of the phenomenon he pointed out.
At the time, I was working on a mathematical model to restore some equilibrium result
between supply and demand in the context of a ‘gift economy’ ([4]). An old pub lication
by Kiyotaki & Wright [6 ] inspired me to l ook at game theoretical models of exchange of
goods. Thus the trivial model (section I) emerged as a rudimentary form of a giving
14 game that provides stabilization, a property comparable to a supply and demand
equilibrium. Non- trivial extension of this model provided t he subject of this publication.
The concept of ‘giving’ in the sense of asynchronous transactions – comparable to
promises [1], altruistic forms of giving at the benefit of a ‘warm glow’ [2, 3] , or the
‘favours’ of Jan Wielemaker – can be posed opposite to a ‘tit for tat’ exchange trade
economy where every transaction consists of an economically neutral transfer of assets. The name ‘ Giving G ame ’ has a positive connotation. H owever, it may e qually well be
viewed as a basic m odel to describe the mechanics of clientelism or corruption.
The Giving Game as an attempt to understand the notion of ‘giving’ in the economical
sense . It allows for many topics of f uture research. Let me briefly mention some:
Stability and Community Effect The standard game presented in this paper has the pivotal stabilization theorem (Theorem II.5) as a center characteristic. The resulting stability pair may be looked at as a ‘commu nity’ that eventually holding the token for itself. My vision is that this concept
can be extended to more complex games while still preserving the existence of communities as objects of stabilization. Such complex games may have added features,
such as fo r example:
- Multiple tokens , possibly each with a different ‘weight’ reflected by different
increments of cell values ;
- Introduction of non- durable tokens in this game, for example by adding a decay
period of some fixed number of steps in the game;
- A ‘hold’ option for submitting or receiving agents: they may be temporarily
unavailable for receiving (saturation) or submitting (keep stock) tokens.
One aim of my research is to identify under which conditions we can restore the
Community Effect in the form of a community of agents circulating certain tokens
amongst themselves (a stability pair is a simplified version of such community).
Economic modelling
The game extensions in the previous paragraph already create a micro -economic model
of value transactions. A way to look at the p reference matrix is like that of a historical
balance of transactions that have taken place in the past (see F igure 6 ). Column values
represent tokens received / consumed whereas row values represent tokens submitted / produced . So, for each agent r ows represent the distribution of production units across
all agents. C olumns represent the distribution of units consumed across all agents.
Figure 6 : Preference matrix as a scheme of
production and consumption .
15 In Figure 6 total production and consumption are in balance, which in general may not
be the case.
Game Theory
In The Giving Game we have introduced a decision rule for submitting agents to select a
receiving agent. In what sense can this rule be regarded as an ‘optimal strategy’ in the
game theoretical sense of the word? Can we define a game space of potential strategies where such decision rule forms a Nash Equilibrium, i.e.: a strategy that no individual
agent can improve?
Computational Complexity
I have shown that any game path leading to a stability pair in the game has a normal
form ( Corollary VI.6). Can we establish some efficient (polynomial) algorithm to
calculate that such path exists , given a matrix M and a pair { A, B}? Or can we prove it
equival ent to some well -know problem with exponential complexity and prove the
opposite ?
Crypto Currency
A preference matrix is, in fact, a simplified accumulated version of a ledger of all past transactions [7, 8] . See also Figure 6. Such ledger i s a common e lement of many – if not
all – crypto currencies. Can we relate b lock chain technology to the Giving Game
described here?
Applications
I imagine several applications of the theory in this paper . I mention two:
- Firstly, in a network of distributed computing systems individual processors are at
continuous search of idle processing power elsewhere in the network to commission
partial tasks of computation to them [5]. A strategy of mutual preference – based on
historical exchange of processing power – may enhance their chances to succeed their tasks in the future.
- Secondly, think of professional commodity traders trading vast amounts of stock with
other traders every day. Their individual mission is to buy at the lowest price and sell at
the highest. However, at a higher level patter ns of preference may emerge between
traders that share a track record of positive mutual benefits (e.g.: “ he saved my ass the
last few times”) causing a preference bias towards each other at a lower than ‘best price’ level. This may cause inefficiency in a – relatively small – community of traders worldwide.
VIII. REFERENCES
1. J.A. BERGSTRA AND M. BURGESS , A Static Theory of Promises , ArXiv 0819.3294v3, 2013.
2. J. A NDREONI , Giving with Impure Altruism: Applications to Charity and Ricardian
Equivalence, The Jour nal of Political Economy Vol 97, Issue 6, 1989, 1447 -1458.
3. R. B EKKERS AND P. WIEPKING , A Literature Review of Emperical Studies of Philanthropy:
Eight Mechanisms that Drive Charitable Giving, Sage Publications 2010.
4. W.P. Weijland, Mathematical Foundations for the Economy of Giving , ArXiv
Categories: q -fin.GN, Report 1401.4664, 2014.
5. A. Karadimas, Relating Economy of Giving to Peer- to-Peer File Sharing Technology ,
University of Amsterdam, 2014.
16 6. N. Kiyotaki & R. Wright, On Money as a Medium of Exchange, Journ al of Political
Economy , vol. 97, no. 41 K 1989, The University of Chicago , 1989.
7. A. Oberhauser, Decentralized Public Ledger as Enabler for the Gift Economy at Scale ,
VU University Amsterdam, 2014.
8. J.A. Bergstra and W.P. Weijland, Bitcoin: a Money -like Inf ormational Commodity , ArXiv
categories: cs.CY, Report 1402.4778, 2014.
|
{
"id": "2105.11761"
}
|
2403.03504
|
Graph Visualization for Blockchain Data
|
In this report, we introduce a novel approach to visualize extremely large
graphs efficiently. Our method combines two force-directed algorithms,
Kamada-Kawai and ForceAtlas2, to handle different graph components based on
their node count. Additionally, we suggest utilizing the Fast Multipole method
to enhance the speed of ForceAtlas2. Although initially designed for analyzing
bitcoin transaction graphs, for which we present results here, this algorithm
can also be applied to other crypto currency transaction graphs or graphs from
diverse domains.
|
http://arxiv.org/pdf/2403.03504v1
|
Marcell Dietl, Andre Gemünd, Daniel Oeltz, Felix M. Thiele, Christian Werner
|
cs.DS, cs.DM
|
cs.DS
|
Graph Visualization for Blockchain Data
Marcell Dietl2, Andre Gem¨ und1, Daniel Oeltz1, Felix M. Thiele1,
and Christian Werner2
1Fraunhofer Institute for Algorithms and Scientific Computing
SCAI, Schloss Birlinghoven, 53757 Sankt Augustin, Germany.
2frontmark GmbH, Taunusstraße 63, 65183 Wiesbaden, Germany.
February 2024
Abstract
In this report, we introduce a novel approach to visualize extremely
large graphs efficiently. Our method combines two force-directed algo-
rithms, Kamada-Kawai and ForceAtlas2, to handle different graph com-
ponents based on their node count. Additionally, we suggest utilizing the
Fast Multipole method to enhance the speed of ForceAtlas2. Although
initially designed for analyzing bitcoin transaction graphs, for which we
present results here, this algorithm can also be applied to other crypto
currency transaction graphs or graphs from diverse domains.
1 Introduction
Blockchain technology is gaining increasing importance across various fields such
as healthcare [8], supply chain management [18], finance [17], energy [1], voting
systems [11] and more [22]. As the significance of blockchain technology contin-
ues to expand, there is a corresponding rise in the demand for methodologies to
analyze blockchain data. A crucial aspect of such methodologies is the develop-
ment of algorithms that facilitate data visualization to enable users to discern
underlying patterns and structures with greater clarity. In this report, we dis-
cuss an approach to visualize so-called transaction graphs that typically arise in
the context of crypto currency data. Here, in contrast to other approaches, we
put special focus on the efficiency of our method w.r.t. the number of nodes and
edges in the graph to handle the massive amount of transaction data. It is worth
noting that the algorithm proposed herein is not limited to transaction graphs
but can be employed in scenarios where large graphs require visualization. Such
scenarios may also occur within the domains mentioned earlier.
1arXiv:2403.03504v1 [cs.DS] 6 Mar 2024
Figure 1: Number of bitcoin transactions and addresses on a monthly basis.
2 Bitcoin Transaction Graphs
Bitcoin is a digital currency that has experienced significant growth and pop-
ularity since it was launched in 2009. The number of bitcoin transactions and
new addresses has significantly increased over time, as we can see in Figure 1,
which shows the history of the number of transactions and new addresses on
a monthly basis. Bitcoin is not issued by any central organization. Instead,
it operates with a public ledger, also known as the blockchain [16]. Therefore,
bitcoin, among other crypto currencies, provides a great framework for studying
transaction behavior. The transaction graphs that arise typically become very
large and, thus, hard to process. Bitcoin processes around seven transactions
per second, which sums up to around half a million transactions a day. Even
worse, each transaction can contain many participating entities.
The bitcoin transaction data is encoded in the blockchain. The blockchain
consists of many blocks organized in a linear ordering over time, see Figure 2
for a schematic illustration of the chain.
Each block has two main parts: the header section and the list of transac-
tions. The header section contains general information about the block, such
as the time it was created and a reference to the previous block. The list of
transactions, on the other hand, is composed of inputs and outputs. Inputs re-
fer to entities that send value, while outputs refer to entities that receive value.
Each output contains a value to be received and a script that must be solved to
authorize the spending of the value. Each input, on the other hand, consists of
a hash of a previous transaction and a script that solves the problem to one of
the outputs, thereby authorizing the spending of its value. It is worth noting
that no value is required as an input since it spends the entire amount of a
2
Figure 2: Schematic illustration of a blockchain.
previous output.
In most cases, both the input and output scripts follow one of a couple of
standardized formats, where the output problems, containing a public key, can
easily be solved with the knowledge of a private key. The public keys can be
interpreted as addresses. From this, we can build a first transaction graph, the
vertices being the addresses and the transactions being directed hyperedges.
Working with hyperedges, albeit structurally encoded into the bitcoin trans-
action format, is not very practical. Following [3], we will define the bitcoin
transaction graph as follows. The transaction graph is a bipartite graph com-
posed of two sets of vertices: addresses and transactions. An edge exists between
an address and a transaction if the former served as an input in the transaction.
Conversely, an edge exists between a transaction and an address if the latter
was an output in the transaction.
Note that every user usually corresponds to multiple addresses in this re-
sulting graph. The work of [21] suggests to cluster addresses used as inputs to
the same transaction. Another heuristic in [21] tries to abuse the fact that, by
definition, the input address always spends all of its value in a transaction, so
a user wanting to spend some partial value of an address might create a new
address to receive the remaining value of the input address.
Better clustering is closely related to the anonymity of the blockchain [19],
[6], [20].
Our goal is to visualize this bitcoin transaction graph in certain time frames,
for example, the visualization of transactions of one block or one day.
3 Force-Directed Algorithms
In this section, we describe the two force-directed algorithms we use as building
blocks for the method described in this report. Force-directed algorithms are
inspired by physical particle systems where the minimization of a certain en-
ergy functional w.r.t. the node locations leads to the layout. Here, the energy
functional is induced by one or multiple forces defined between the nodes of the
graph.
3
3.1 Algorithm from Kamada and Kawai
In this section, we describe the algorithm from Kamada and Kawai [13] to
visualize graphs. The algorithm computes coordinates pi∈R2for each vertex
vi, 1≤i≤N, in the graph by minimizing the energy norm
NX
i=1NX
j=i+11
d2
ij(|pi−pj| −lij)2.
Here, dijdenotes the length of the shortest path between vertex iandjand
lij:=l·dijfor some rescaling constant l >0. To minimize the energy, we use
the Newton-Raphson method. Note that to find the shortest path between each
pair of vertices, we use the Floyd–Warshall algorithm [4], which has a runtime
ofO(|V|3) and needs O(|V|2) storage for the pairwise distances. One may
further reduce the runtime for our sparse transactions graphs to O(|V|2log|V|+
|E||V|) by using Johnson’s algorithm [12]. However, in both cases, typical daily
bitcoin transaction graphs with the number of nodes in the range of hundreds of
thousands or even millions are too large to be directly handled by this approach.
One can further speed up this algorithm by clustering portions of the graph
to reduce the number of vertices, rendering the layout, and finally, declustering.
Examples of such clustering/hierarchical methods are:
1. Contracting nodes with only one edge to their neighbours. Then declus-
tering can be done by locating this node using the given distance of the
edge to the neighbour away from the center of gravity of the layout.
2. Removing nodes with only two edges and replacing both edges with a
single edge. To decluster, simply replace the new edges with two edges
with a node in the center.
Compare also [14] for a discussion about handling large graphs to speed up
the algorithm. However, we apply a different method in the case of components
with many edges or vertices that lead to good results in a lot of examples, which
we will describe in the following section.
3.2 ForceAtlas2 with Fast Multipole
The approach presented here belongs to a line of approaches simulating a phys-
ical system where nodes repulse each other and edges attract the incident nodes
[5, 15, 9]. It is closely related to the ForceAtlas2 algorithm [10] while we are
replacing the Barnes-Hut algorithm with the Fast Multipole method, see sub-
section 3.2.1.
In each iteration, we calculate several forces that apply to the different nodes.
For each edge, we calculate an attraction force on the incident nodes, which is
proportional to the distance between incident nodes. We further have a gravity
force pulling all nodes to the center, proportional to the distance to the center.
Finally, we have repulsion forces between all node pairs proportional to one over
their distance.
4
The next step is to apply the calculated forces to each node. We follow [10]
on how to choose the step size. For this, in each iteration t, we define two values,
the swing:
swg=X
n|Ft(n)−Ft−1(n)|
and traction:
trc=X
n|Ft(n) +Ft−1(n)|
where Ft(n) are the aggregated forces of a node nin iteration t. Note that a big
swing signals a large variance in the forces between iterations and, therefore,
much more erratic movement. On the other hand, if the traction is large, this
signals progress in some sense as we continue to push nodes in the same direction.
Now, we iteratively adjust the step size to keep the ratiotrc
swgin some tolerated
interval.
3.2.1 Fast Multipole Algorithm
In each iteration of the force-directed algorithm, the forces on each node need
to be calculated. As the bitcoin transaction graphs are not very dense, we can
efficiently calculate the forces generated by the edge attraction and also the
gravity attraction. However, computing the repulsion forces between each pair
of nodes in a straightforward way is of quadratic complexity in the number
of nodes, which may be very time-consuming considering the number of nodes
of a typical transaction graph. Here, the Barnes-Hut algorithm [2], developed
initially to speed up the computation for physical systems, reducing the com-
plexity to O(Nlog(N)), has been successfully applied in the context of graph
visualization, see [10]. However, we can further improve complexity to O(N)
by using the Fast Multipole algorithm [7], which we will briefly describe in the
following.
In a first step, the algorithm computes a quad tree structure of the given
particles. Here, we start with a square that covers all the particles. If a square
contains more than a certain number of particles, we divide it into four new
squares. Each of these squares is called a cell. This generates a tree structure of
cells, a cell being the parent of another if it was generated by one subdivision of
the parent. For each cell, we define the neighbouring cells to be those adjacent
cells that have minimal size but are not smaller than the original cell. Further,
we define interacting cells of a cell to be the minimal cells that are neighbouring
the parent of the cell or are children of a cell neighbouring the parent.
The trick of the Fast Multipole method is to calculate two Taylor expansions
of a chosen degree for each cell around its center. The first approximation is
for the force that nodes act on particles that are far away, called the outgoing
expansion. The second approximation is for the force that nodes in the cell feel
from particles that are far away, called the incoming expansion. Here, the term
“far away” refers to the nodes not located in neighbouring cells.
The idea is to calculate these expansions recursively. The recursive calcula-
tion of the expansions is performed by first computing the outgoing expansions.
5
Figure 3: Illustration of the overall algorithm for blockchain data visualization.
The method starts by computing the outgoing expansion for each leaf cell. Sub-
sequently, the quad tree is traversed upwards, and the outgoing expansions of
the four children of a cell are used to calculate its outgoing expansion. This
is accomplished by shifting the center of the children’s outgoing expansions to
the center of the new cell and adding them up. The quad tree is then traversed
in reverse order to calculate the incoming expansions. This is accomplished
by applying a center shift of the incoming expansion of the parent and adding
all outgoing expansions of the interaction neighbours reformulated as incoming
expansions of the cell to it.
Finally, we can calculate the force that applies to each single node. For each
node, let the corresponding leaf cell be the minimal cell containing the node.
Now, for every node, we apply the incoming expansion of its leaf cell to the node
to get the force from far away nodes and additionally add all the forces from
nodes in neighbouring cells of the leaf cell and the leaf cell itself to the node.
4 Final Algorithm
The final algorithm that we apply to visualize the transaction data now com-
bines the algorithms above to optimize the tradeoff between quality and com-
putational time.
Here, we first split the given graph into its components (ignoring the direc-
tions of the edges). Note that the typical transaction graph for a certain time
period most often consists of many components, where most of the components
consist of only a few nodes. Now, for each component, we calculate a separate
layout. In cases with a few nodes, we opt for the Kamada-Kawai algorithm,
6
which typically generates more expressive layouts. For components containing
a high number of nodes, we employ the force-directed algorithm based on the
Fast Multipole method described above.
These component layouts are then rescaled to have a similar density. Next,
we assemble the separate component layouts into a single figure. To accomplish
this, we construct a new graph with the components represented as nodes,
connecting them with edges to form a tree structure. These edges are determined
by selecting a random order and sequentially linking the next component to the
largest previous component. We choose the edge sizes to be half of the diameters
of both components summed up plus some constant. If this resulting graph is
small enough, we apply the Kamada-Kawai algorithm. Otherwise, we divide it
into several components and apply the Kamada-Kawai algorithm individually
to each component and reassemble them. Figure 3 shows an illustration of the
overall algorithm.
Figures 4 and 5 show the resulting visualization of the transaction graphs at
two different points in time.
Figure 4: A visualization of the transaction graph on April 19, 2011. Transaction
nodes are red, address nodes are blue. Contains around 1 .5·104nodes.
7
Figure 5: A visualization of the transaction graph on December 12, 2013. Trans-
action nodes are red, address nodes are blue. Contains around 2 ·105nodes.
5 Summary
In this report we described an algorithm to efficiently visualize graphs with a
very high number of nodes and edges. In our experiments, this algorithm pro-
duced meaningful graphs within a reasonable amount of computing time, while
other algorithms were not applicable due to their computational complexity.
Although developed for analyzing bitcoin blockchain data, it may be worth
applying this algorithm to other blockchain transaction graphs or large graphs
from other domains.
We provide the source code of this algorithm in the public GitHub repository
athttps://github.com/frontmark/research .
8
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10
|
{
"id": "2403.03504"
}
|
1811.00742
|
Rationality-proof consensus: extended abstract
|
Blockchain systems benefit from lessons in prior art such as fault tolerance,
distributed systems, peer-to-peer systems, and game theory. In this paper we
argue that blockchain algorithms should tolerate both rational
(self-interested) users and Byzantine (malicious) ones, rather than assuming
all non-Byzantine users are altruistic and follow the protocols blindly. Such
algorithms are called BAR-tolerant [1]. To design a BAR-tolerant system, one
can follow these three steps: clearly define the utility function for the
rational users, prove the algorithm is such that there is no benefit from
unilaterally deviating (that is, it's a Byzantine Nash Equilibrium), then prove
the algorithm correct assuming the rational actors follow the protocol. We
present an example attack by rational users: the gatekeeping attack, where
members of a system selfishly decide to prevent newcomers from joining. This
attack may affect any stake-based system where the existing members prevent
newcomers from making a stake, and essentially form a cartel. We then sketch a
BAR-tolerant consensus protocol for blockchain that can defend against this
attack. It relies on a strict order to decide who gets to propose a new block
(so there's no need to race to solve a crypto puzzle) and it relies on hardware
ID tokens to make sure every computer is only represented at most once as a
block proposer to mitigate Sybil attacks. It also defends against the
gatekeeper attack. The BAR-tolerant approach is naturally also applicable to
other blockchain algorithms.
|
http://arxiv.org/pdf/1811.00742v1
|
Jean-Philippe Martin, Eunjin, Jung
|
cs.DC
|
cs.DC
|
Rationality-proof consensus: extended
abstract
Jean-Philippe Martin (self) and Eunjin (EJ) Jung (RationalMind)
Abstract
Blockchain systems benefit from lessons in prior art such as fault tolerance, distributed systems,
peer-to-peer systems, and game theory. In this paper we argue that blockchain algorithms
should tolerate both
rational
(self-interested) users and
Byzantine
(malicious) ones, rather than
assuming all non-Byzantine users are
altruistic
and follow the protocols blindly. Such algorithms
are called
BAR-tolerant
[1]. To design a BAR-tolerant system, one can follow these three steps:
clearly define the utility function for the rational users, prove the algorithm is such that there is
no benefit from unilaterally deviating (that is, it's a Byzantine Nash Equilibrium), then prove the
algorithm correct assuming the rational actors follow the protocol. We present an example
attack by rational users: the
gatekeeping
attack, where members of a system selfishly decide to
prevent newcomers from joining. This attack may affect any stake-based system where the
existing members prevent newcomers from making a stake, and essentially form a cartel. We
then sketch a BAR-tolerant consensus protocol for blockchain that can defend against this
attack. It relies on a strict order to decide who gets to propose a new block (so there's no need
to race to solve a crypto puzzle) and it relies on hardware ID tokens to make sure every
computer is only represented at most once as a block proposer to mitigate Sybil attacks. It also
defends against the gatekeeper attack. The BAR-tolerant approach is naturally also applicable
to other blockchain algorithms.
Introduction
Blockchain algorithms have captured the world's imagination, and are considered for
applications beyond payment services. As they grow, we want to make sure we design these
algorithms carefully so they can work reliably even at a large scale. Reliability and scalability are
not new challenges in computer science so naturally we draw from prior art including distributed
systems, peer-to-peer, and game theory. The latter especially in the context of incentive
compatibility, a key property for Bitcoin or similar systems where software isn't run by a trusted
central authority: the participants might write a modified version of the software, one that
deviates from the specified algorithm in order to gain some unfair benefit to the user. This
selfish behavior is happening already: some Bitcoin miners skip the validation of transactions
even though this could result in including invalid transactions in the ledger. Research shows that
consensus in Bitcoin will be difficult to achieve when the block reward becomes so small that
the transaction fees are the main source of incoming for miners [6].
1
BAR tolerance [1] is the property of protocols that work despite both selfish users (called
rational
) and some number of malicious users (called
Byzantine
). We argue that BAR-tolerance
is a desirable property for blockchain systems. In this paper, we discuss an example attack on
the system by rational users and show how a BAR-tolerant blockchain protocol might defend
against it. We sketch this protocol to show how one would go about designing a BAR-tolerant
algorithm; our protocol is based on the consensus from [1].
A
gatekeeping
attack is an example attack by rational users in blockchain systems that requires
a stake (security deposit). More precisely, this attack is applicable to any system that requires a
user to make a deposit before participating in any protocol that gives rewards proportional to the
stake. Many Proof-of-Stake consensus algorithms, including those used in Ethereum Casper
FFG and Tendermint, use deposit-slashing [5] as a solution to the Nothing-at-Stake problem [4],
and require a stake from a user before participating in consensus. For a new user to become a
block producer and earn the block rewards, the new user has to deposit a stake first to a special
address, which is a transaction in the chain’s native currency. For the existing block producers,
the expected block reward they could earn decreases as the newcomer joins. If a block
producer is chosen with a probability proportional to its own stake over the total stake in the
system, then as a new comer joins, the total stake increases and the probability of being elected
decreases. If the block producers vote on candidates and get the reward proportional to its own
stake over the total stake, then as a new comer joins, the total stake increases and the amount
of reward for each block decreases. Thus it is a rational behavior for the existing block
producers to not include the deposit transaction in their blocks, which prevents a newcomer
from becoming a block producer.
Related Work
Blockchain systems experience attacks from Byzantine users. Bitcoin Gold suffered from double
spending attack [9]: The hacker with the majority of the computing power was able to deposit
the same Bitcoin Gold to an exchange and also to its own wallet. The attacker may have stolen
$18.6 million from the exchange. Monacoin also experienced a selfish-mining attack that cost an
exchange $90K [7]. Software bug could also cause a Byzantine error in the system: overflow in
sum created more bitcoins for block reward than what the protocol dictates and also two
addresses received 92.2 billion bitcoins each [3].
While it is less evident than Byzantine attacks, the community has been speculating that the
rational users are the majority of the system and may deviate from a desirable behavior. For
example, some bitcoin miners generated invalid blocks [2]: they would rather spend the time to
check the validity of transactions on solving the hash puzzle. Researchers in [6] showed that it
may be rational to fork the chain when there are not many transactions left, and the transaction
fees form the majority of the reward.
2
Approach
We need to tolerate both malicious and self-interested (rational) actors.
Since some actors may be malicious, we need to use a Byzantine-tolerant algorithm. These
algorithms can function despite arbitrary behavior from some bounded fraction of the
participants (the exact fraction depends on the model. It's typically 1/3, but can be up to 100% if
sufficiently strong assumptions can be made). The key requirement is to bound the fraction of
malicious actors (otherwise they may be able to evict honest actors, prevent correct transactions
from entering the blockchain, etc.). Instead of using a proof of work (that is, relying on the
assumption that the aggregate computing power bought by the honest actors is larger than that
of the malicious actors), one can use modern hardware features such as the TPM chip present
in most motherboards today. The manufacturer guarantees actors cannot pretend to own more
motherboards than they genuinely do. So long as the aggregate buying power of the honest
actors is larger than that of malicious actors, the majority vote of the system will be in honest
hands - without needing to continually consume vast amounts of power. These hardware tokens
prevent Sybil attacks. Remains only to pick who can add the next block to the blockchain: this
could be done at random, or simply by taking turns.
Since some actors can be rational (perhaps everyone who is not malicious), we need an
incentive-compatible algorithm. To tolerate both Byzantine and rational actors we use the BAR
approach [1]. The idea is to design a protocol that is a Byzantine Nash Equilibrium. This means
that it is in the best interest of each rational actor to follow the protocol as specified. In this
methodology one first needs to specify what the rational actors will consider
costs
and
benefits
when computing their utility function. A protocol is a Byzantine Nash Equilibrium if rational
actors see no increase in their utility from unilaterally deviating from the protocol. We assume
that they consider the Byzantine actor's worst possible behavior when estimating the utility they
would get from following a particular sequence of actions. To prove a BAR-tolerant protocol
correct, we show
1.
That the algorithm is a Byzantine Nash Equilibrium, and then
2.
That the algorithm has the desired properties, under the assumption that the rational
actors obey the protocol.
The BAR model allows (but does not require) some of the actors to be altruistic, meaning that
they follow the protocol even if it weren't in their personal best interest to do so. In this paper we
do not require (nor take advantage of) altruistic actors. The model also assumes that rational
actors do not collude. This doesn't mean that no collusion happens, it just means that if some
actors collude then they count against the limit on Byzantine actors. When designing a
BAR-tolerant protocol, the author chooses what counts as costs or benefits. This is a trade-off:
including more things as costs or benefits means it applies to more individuals in real life, but it
also makes for a craftier rational actor and the protocol needs to be correspondingly stronger
3
(and its correctness argument gets correspondingly longer). Including only a few things (for
example, perhaps the rational actor is only trying to optimize how many tokens they earn) keeps
the algorithm and proof simpler, but some individuals may not fit this model. This isn't as bad as
it sounds: those actors that act rationally but have different incentives than those the system
was designed to resist will still be tolerated, they will just count against the Byzantine tolerance
threshold.
We believe that BAR-tolerance or similar approaches are a good fit for blockchain algorithms
because the high stakes make the temptation to write a modified client harder to resist (so
rational behavior may be seen in practice), and the same stakes also mean that it's important to
rely on as few assumptions as possible (so tolerating arbitrary behavior from some actors is
beneficial).
Key benefits
We sketch a blockchain algorithm that can (a) tolerate malicious actors without requiring a
wasteful proof of work, and (b) tolerate rational actors, acting in their individual self-interest. We
hope it serves as an illustration of how one may design a BAR-tolerant algorithm in the context
of a blockchain.
Proofs of work in Bitcoin waste large amounts of electricity, creating unnecessary pollution.
They defend against
Sybil attacks
, where a single bad actor takes on multiple identities in order
to have more voting power and subvert the protocol. Proof of stake has been proposed as an
alternative; our approach is similar but with small stakes, allowing everyone to join and be
equally rewarded for participating in the chain. Hardware features (e.g. TPM) can be used to
hinder bad actors trying to pretend to be more than one person: they have to buy real hardware
for each voter so it's not as easy as just sending another network message.
Another function of Bitcoin's proof of work is to act as a lottery, to pick a leader. Here instead we
use a round-robin approach, taking turn. We rely on a BAR-tolerant protocol to ensure that we
can reach consensus on which block to add to the chain, without giving rational actors room to
deviate for gain.
Components
The protocol is built modularly, out of multiple component parts. Going in approximate
chronological order, a user can first read and write transactions in the chain. Then if they want
to produce blocks (and be rewarded for it), they go through a joining and registration step. Then
they follow the consensus subprotocol to wait for their turn and finally produce a block.
Keeping the protocol modular helps keep the design simple and the proofs manageable, though
a few of the arguments still need to be over the system as a whole. It also makes it easier to
4
evolve the system over time and to add features. For example in this paper we are not touching
on smart contracts or other high-level concepts, but of course they can also benefit from being
Byzantine and rational tolerant.
Joining and Registration
Any user of the system can submit transactions to the blockchain without having to register or
join, but they need to do both in order to produce blocks. Joining is done by submitting a
transaction that transfers a small amount of "coin" to a special address for members (this
address may be managed by a smart contract, or be hardcoded into the consensus protocol
itself). The user can get this deposit back when they're done producing blocks (this may be
done automatically when the user closes the mining program). The deposit is forfeit if the user is
found deviating from the protocol.
The registration protocol is part of the same interaction, but with the purpose of making it harder
for a single malicious person to create multiple accounts in an attempt to manipulate voting or
other aspects of the system, i.e. mitigating Sybil attack. Registration can be done by reading a
unique hardware ID from the user's machine, so that each user is only allowed a single identity
in the system. For example one may use the key pair embedded in a TPM, or SGX's linkable
quotes to ensure that a given computer cannot register more than once. Another approach is to
ask the user to pay a one-time fee of some sort. The fee could be a computation (compute
some hashes), human endeavor (mail in a postcard), or paying a token amount. Something that
is small enough that users don't mind doing it once, but that would become a burden if it had to
be done thousands of times.
One of the subtleties of the joining component is that we want it to work even if an existing block
producer benefits from preventing a particular user from joining. This works out with a bit of care
as each block producer in turn gets the power to admit the new user. In our model, a rational
user assumes that all other rational users follow the protocol, so it expects other rational block
producers would let this particular user join. In other words, as block producers take turns
admitting new users, a rational block producer can delay a particular user from joining when it is
its own turn to admit new users, but cannot prevent it indefinitely. Another subtlety is the
question of whether adding a block producer reduces everyone's reward, and so everyone may
be incentivized to delay new users joining the block producers group. This is a rational behavior
not only for our consensus protocol but also for any Proof-of-Stake consensus algorithm where
the staking (join) operation needs to be included in a blockchain, such as Ethereum Casper
FFG as of May 2018 or Tendermint. One way to prevent delayed joins is to make sure
everyone's reward remains the same as others join. Since with more participants there is a
longer wait between turns, that means we'd have to keep on increasing the individual reward,
which will lead to inflation. Instead, the protocol can detect attempts at delaying a join, and
punish that behavior. The rational choice is then to accept the joiner since the punishment is
worse than the benefit from delaying the joiner.
5
We incentivize the block producers to include a join operation by both rewards and
punishments. The reward is that the join transactions do not count toward the block size limit, so
the block producers can include as many join transactions as they wish to maximize their gain
from the transaction fees. The punishment happens when a block producer does not include the
join transaction in a new block it is proposing even though the transaction was sent to it in
advance. Participants who wish to join must send their request to multiple actors, and have
them forward the request to a proposer. Since joins do not count against the block size limit, this
proposer is always able to include the join transaction. If they do not, each of the relay actors
will sign a SUSPECT message and send it to the next leader. This next leader will strip the
faulty proposer of their block reward if they have recent SUSPECT messages from f+1 distinct
actors (so Byzantine actors by themselves are not able to force punishment). A participant might
choose to send their join request to only a single proposer at a time instead of multiple,
choosing to accumulate SUSPECT messages over time instead. Either way, it is in the block
proposer's best interest to publish the join transaction since the cost of the punishment is larger
than the benefit from delaying that particular user from joining.
Block Production
Block producers have the job of taking in transaction requests from users, putting them into a
block, and linking it to the blockchain. When things go well, the producers simply take turns.
When things don't (for example in case of failure or asynchrony), the BAR-tolerant consensus
protocol ensures that a new producer is picked and that every non-malicious actor still agrees
on a unique block to append to the chain.
The other facet of block production is making sure that all user transactions are included. We
can use a similar mechanism as for join: producers can be suspected of misbehavior and
punished if they ignore a user for too long. This ensures that rational users are disincentivized
from trying to delay a user's transaction.
Shared State
In order for the block producers to take turns, as described in the previous section, we need
them to agree on who the block producers are. For this, we use blockchain order of the "join"
and "leave" transactions. Both transactions (paying to join or withdrawing deposits to leave) are
recorded in the blockchain. Since the blockchain implements an append-only log, everyone
agrees on the sequence of membership transactions (joins and leaves) so they agree on who
the block producers are and in which order they were added. Thus, everyone agrees on a
queue of block producers: they take turns producing a block, so for each block everyone knows
who the next block producer should be.
Producers can be kicked out if they misbehave and this is also done as a transaction in the
blockchain so we can leverage the same mechanism to keep track. Rational block producers
6
have an incentive to keep track of this state since it's necessary to follow the protocol correctly
and avoid punishment or expulsion (or just missing their turn).
It may be beneficial to share more state, such as the balance of every user if the blockchain is
used to keep track of a cryptocurrency balance. A similar mechanism can be used, but other
variants are possible as well such as having a different group of actors keep track of the state,
or offering a "summary" service that allows one to quickly catch up to the present state of the
system.
Consensus
The consensus protocol ensures that a unique block is added to the chain. More formally:
Each participant ("block producer") may propose a value ("block"). At the conclusion, each
participant that follows the protocol ("correct participant") will decide on a block, and
●
the block was proposed by a participant, and
●
all correct participants decide the same block.
In a long enough period of synchrony, the protocol will always conclude. In periods of
asynchrony it may or may not conclude. If it does, the guarantees hold.
It uses the shared state described above to determine which block producer is the "leader", in
charge of publishing a block. If this leader is too slow then it switches to the next leader. The
algorithm ensures that even in periods of asynchrony, no two different values are decided.
We run an instance of consensus for each block to add to the chain, keeping track of the current
leader so everyone gets their turn. Blocks are linked together in the traditional way, with a hash,
but each block also includes signed messages from the protocol participants that vouch for its
correctness. This prevents a malicious participant from forging a block.
As an additional consideration, instead of taking turns in a predictable order (1, 2, 3, ...) we can
use a VRF-like function [8] to pick the next leader based on the contents of the last block. This
still gives everyone the same number of turns on average, but it makes it difficult for an
adversary to run a denial of service attack against the next leader since its identity is only
revealed at the last minute.
Details about consensus
To make things concrete, let's flesh out the consensus part. At a high level, block producers
take turns and a timeout mechanism ensures eventual progress. We start by recapitulating our
assumptions.
7
Model
We assume partial synchrony: the system has asynchronous periods where there is no bound
on message delivery or processing time, and it also has synchronous periods with a bound on
both. We pick a timeout T that is long enough for consensus to finish in synchronous periods.
To formalize the notion that "synchronous periods are long enough to make progress" we prove
that the algorithm works if the system is eventually forever synchronous. In addition, we assume
clock rates are similar, so that at the scale of a single consensus instance, if two users have
clocks t1 and t2, their ratio is bounded by a constant r: 1/r <= t1/t2 <= r.
We assume that if messages are sent infinitely often, they are eventually delivered. Using
acknowledgements and resends this can implement reliable links between actors that follow the
protocol.
We have Byzantine and rational actors, as per the BAR model. We make no assumption about
the behavior of malicious actors. For the rational actors we assume that they are not forced to
obey the protocol as it was presented to them but instead they can follow any different algorithm
of their choosing. However, they only do so if it increases their net utility from participating in the
system. This utility function accounts for the costs and benefits from using the system (e.g.
coins earned and spent, but also the ability to make transactions at all). For this protocol we are
only considering the following for the utility function: coin balance, the ability to exclude specific
transactions, the ability to force their own transactions to be included in the blockchain, and the
ability to include transactions that would normally be rejected (such as double-spending).
Naturally, while the rational actor would see double-spending their own money as a benefit, the
protocol guarantees this cannot happen.
We assume there are at most one third of malicious actors. The rest are rational. We use "f" for
the number of malicious actors and "n" for the total number of block proposers, so n>3f. We are
not assuming that any of the actors are altruistic (meaning they follow the protocol blindly),
although of course if some are the system still works.
Structure
The protocol follows the classical three-phase commit approach and has "rounds". In round R,
actor (R modulo n) is the leader. In each round, either the leader sends its value to everyone
(who then eventually decide it), or no value is decided. If no value is decided then the next
round starts. This round first "freezes" the previous round, preventing it from making further
progress. The freeze process also determines whether a block might have been decided in the
previous round. If so, the new leader must propose that block and finish the job. This new leader
will be able to propose their own block in the instance of consensus that follows immediately.
In a round, the communication pattern goes like this:
8
●
Leader sends an AGREE message to everyone. This message is signed and includes
the proposed block. The leader then collects signed acknowledgments for the hash of
the block.
●
Leader sends a WRITE message to everyone. This signed message includes 2f+1 of the
previous acknowledgments for the same hash. Since users ack only one message, this
guarantees that the leader cannot send a block to some users and a different one to
some other users. The users store the WRITE message and acknowledge.
●
Leader sends a DECIDE message to everyone. This signed message includes 2f+1
signed acks from the previous round. Users decide when they see this message.
If the current leader is too slow (perhaps it's Byzantine, or has crashed), we start the next round.
This happens after f+1 actors suspect the current leader of being too slow and send a signed
TIMEOUT message to the next leader. To ensure this next round R doesn't conflict with the
previous one, we "freeze" round R-1 before doing so. The freezing algorithm is a single
exchange:
●
New leader sends FREEZE to everyone. This signed message includes the f+1
TIMEOUT messages. The users that have not yet seen a WRITE from the leader
acknowledge this message and will ignore the previous leader's message. Those who
have will forward this block to the new leader.
●
New leader starts its round with an AGREE message as before, but they also include
2f+1 signed acks for their corresponding FREEZE. If any of these acks include a WRITE
block then the new leader must propose that block. Otherwise, it proposes its own.
Correctness Sketch
To prove a BAR-tolerant algorithm correct, the first step is to show that no individual rational
actor can improve their utility by deviating from the protocol, under the assumption that the other
rational actors are following the protocol.
There are several things the rational actor may care about. First, there is a coin reward for
having one's block added to the chain. Actors can't do much to influence the reward. They can
follow the protocol so it's back to their turn in a timely manner and they get the reward (that's
good). They can't jump the queue - we enforce a strict round-robin rule: everyone gets their turn.
They can try to prevent other actors from joining - this is a topic on its own and we discuss it
elsewhere. Second, preventing blocks from being added is something a rational actor might
cheat for. Sending fake FREEZE messages doesn't work because it requires signatures from
other actors. The actor could try sending fake TIMEOUT messages to speed up eviction of a
leader that is being slow (or perhaps in a period of asynchrony). This also requires others to
cooperate but the argument is more subtle because some non-malicious actors may genuinely
observe a leader being slow. We use the clock rate assumption here: the next leader will reject
TIMEOUT messages if they arrive too early. Since the clock ratio is r, a message that arrives
before T/r is too early. If a rational actor sends at time T then it's guaranteed that the new leader
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will accept it. In fact this is the earliest time the new leader is guaranteed to accept it, so it's the
optimal time to send (hence the algorithm is at equilibrium). Conversely, one might imagine
rational actors trying to prevent others from proposing a block. However, actors take turns and
they cannot end this round without suspect messages from other actors.
Once we've shown the algorithm is a Byzantine Nash Equilibrium, for the rest of the argument
we can safely assume that rational actors will follow the protocol. The key point of the freezing
algorithm is that it will not result in two different blocks being decided because (a) if a block
could be decided by the previous round, then that block is discovered by the freezing algorithm
and proposed in the next round. (b) otherwise, the previous round will never decide a block. The
first point follows from the fact that the DECIDE message need 2f+1 signatures: at least one of
the signatories will be uncovered by the freezing algorithm and the block will be forwarded to the
next round. The second point follows from the symmetric argument that FREEZE is sent to 2f+1
actors. If all accept it before seeing a WRITE, then there are not enough left to sign the DECIDE
message so the previous round will not decide.
This algorithm allows the system to pick a block and prevents malicious or rational actors from
preventing consensus or interfering with the turn order.
Conclusion
In this paper we argue for algorithms that tolerate both rational (self-interested) actors, and
malicious (Byzantine) ones. These algorithms are called BAR-tolerant. When writing one, follow
these three steps: clearly define the utility function for the rational actors, prove the algorithm is
such that there is no benefit from unilaterally deviating (that is, it's a Byzantine Nash
Equilibrium), then prove the algorithm correct assuming the rational actors follow the protocol.
We explain the gatekeeper attack, where members of a system selfishly decide to prevent
newcomers from joining. We sketch a BAR-tolerant blockchain protocol. It relies on a strict order
to decide who gets to propose a new block (so there's no need to race to solve a crypto puzzle)
and it relies on hardware ID tokens to make sure every computer is only represented at most
once as a block proposer. It also defends against the gatekeeper attack. The BAR-tolerant
approach is naturally also applicable to other blockchain algorithms.
References
[1] Amitanand S. Aiyer, Lorenzo Alvisi, Allen Clement, Mike Dahlin, Jean-Philippe Martin, and
Carl Porth. 2005. BAR fault tolerance for cooperative services. SIGOPS Oper. Syst. Rev. 39, 5
(October 2005), 45-58. DOI:
https://doi.org/10.1145/1095809.1095816
[2] Bitcoin.org. 2015. Some Miners Generating Invalid Blocks.
https://bitcoin.org/en/alert/2015-07-04-spv-mining
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[3] Bitcoin Wiki. Value overflow incident.
https://en.bitcoin.it/wiki/Value_overflow_incident
[4] Vitalik Buterin. 2014. On Stake.
https://blog.ethereum.org/2014/07/05/stake/
[5] Vitalik Buterin. 2014. Slasher: A Punitive Proof-of-Stake Algorithm.
https://blog.ethereum.org/2014/01/15/slasher-a-punitive-proof-of-stake-algorithm/
[6] Miles Carlsten, Harry Kalodner, S. Matthew Weinberg, and Arvind Narayanan. 2016. On the
Instability of Bitcoin Without the Block Reward. In
Proceedings of the 2016 ACM SIGSAC
Conference on Computer and Communications Security
(CCS '16). ACM, New York, NY, USA,
154-167. DOI:
https://doi.org/10.1145/2976749.2978408
[7] Dave Gutteridge, Japanese Cryptocurrency Monacoin Hit by Selfish Mining Attack, CCN
https://www.ccn.com/japanese-cryptocurrency-monacoin-hit-by-selfish-mining-attack/
(retrieved
2018-10-16)
[8] S. Micali, M. Rabin, S. Vadhan. 1999. Verifiable random function. In
Proceedings of the 40th
Annual Symposium on Foundations of Computer Science.
[9] Josiah Wilmoth, Bitcoin Gold Hit by Double Spend Attack, Exchanges Lose Millions, CCN
https://www.ccn.com/bitcoin-gold-hit-by-double-spend-attack-exchanges-lose-millions/
(retrieved
2018-10-16)
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|
{
"id": "1811.00742"
}
|
Subsets and Splits
Top 100 Blockchain Crypto Papers
Retrieves the first 100 entries sorted by primary category and title, providing a basic view of the dataset structure but limited analytical insight.