Open Market or Ghost Town?
The Curious Case of OpenBazaar
James E. Arps and Nicolas Christin
CyLab, Carnegie Mellon University, USA{jarps,nicolasc}@cmu.edu

Abstract. OpenBazaar, a decentralized electronic commerce marketplace, has received significant attention since its development was firstannounced in early 2014. Using multiple daily crawls of the OpenBazaar
network over approximately 14 months (June 25, 2018–September 3,2019), we measure its evolution over time. We observed 6,651 uniqueparticipants overall, including 980 who used Tor at one point or another.
More than half of all users (3,521) were only observed on a single day
or less, and, on average, only approximately 80 users are simultaneously
active on a given day. As a result, economic activity is, unsurprisingly,
much smaller than on centralized anonymous marketplaces. Furthermore,while a majority of the 24,379 distinct items listed seem to be legal offerings, a majority of the measurable economic activity appears to berelated to illicit products. We also discover that vendors are not always
using prudent security practices, which makes a strong case for imposing
secure defaults. We conclude that OpenBazaar, so far, has not gainedmuch traction to usher in the new era of decentralized, private, and legitimate electronic commerce it was promising. This could be due to alack of user demand for decentralized marketplaces, lack of integrationof private features, or other factors, such as a higher learning curve forusers compared to centralized alternatives.
Keywords: Measurement; peer-to-peer systems; electronic commerce.

1

Introduction

OpenBazaar, originally called DarkMarket, is a peer-to-peer electronic commerce
platform that has attracted significant media attention [11,14,15]. It was first envisioned in 2014 as a response to the government takedown of the Silk Road [6]online anonymous (“darknet”) marketplace [11]. However, OpenBazaar developers quickly pivoted away from the darknet marketplace space and toward adecentralized e-commerce platform. The project raised several million dollars inseed funding through a startup called OB1 [14, 15].
While the default OpenBazaar search engine is developed by OB1 and filters
undesirable items such as narcotics [16], the OpenBazaar project itself is opensource and the developers cannot prevent vendors from listing such items using
the platform.

2James E. Arps and Nicolas Christin

Interestingly, the takedown of the Silk Road marketplace failed to curb illicit
activity on centralized anonymous marketplaces. On the contrary, Silk Road’s
successors have been thriving [10,19,22,31]: As of 2017, the leading marketplaces
were grossing hundreds of millions of dollars per year [10], with no decline insight, despite adversarial events such as law enforcement operations and “exitscams,” in which some marketplace operators abruptly shut down their servers,
taking with them any money left in escrow on the platform.
Given that OpenBazaar, thanks to its decentralized architecture, could prevent or mitigate most of these adversarial events, and given the clear economic
demand for such services, we would expect thriving economic activity on a peerto-peer platform such as OpenBazaar. This is what we measure in this paper.
For approximately 14 months (June 25, 2018–September 3, 2019), we havebeen crawling the OpenBazaar network on a near-daily basis to get a sense of thenumber of participants and the size of the overall network. By scraping listings
offered by each vendor and the associated feedback left by buyers (similar totechniques used in related work, e.g., [6, 19, 31]), we also estimate the economic
activity taking place on OpenBazaar. Finally, we can examine the extent of users’
mindfulness about operational security when trading in illicit or illegal products.
Our findings are rather sobering. While there is undeniable network activity,
and a reasonably large number of items available on the OpenBazaar network (atleast 24,379 distinct listings observed during our measurement period), economic
activity remains modest, and appears to be mostly generated by illicit productsales. Furthermore, many vendors appear to misunderstand the security guarantees offered by OpenBazaar or fall prey to some configuration defaults, andpublicly reveal some potentially compromising information about themselves:
Nearly a fifth of the vendors that use OpenBazaar over the Tor network [9] haveaccidentally revealed their IP address at some point in time.
The remainder of this paper is organized as follows. We discuss relevant
background on OpenBazaar in Section 2, describe our collection methodology in
Section 3, and present our results in Section 4. We explain how our work differsfrom related efforts in Section 5, and conclude in Section 6.

2

The OpenBazaar System

Imagine Alice wishes to join the OpenBazaar network and purchase an item.
First, she downloads the OpenBazaar client from https://openbazaar.org, where
she is presented with a “Get Started” page which allows her to edit her profile.
Since all users on the platform can act as both buyers and vendors, she may also
begin creating item listings if she were so inclined. Crucially, if Alice proceedswith the default setup process even once, her public IP address will be leaked to
the network. Indeed, using Tor with OpenBazaar requires an extra configuration
step, which is not immediately evident to first-time users. While the OpenBazaar
client prompts the user if it detects that Tor is already running on the user’smachine, we do not expect this to be common for most users.

Open Market or Ghost Town? The Curious Case of OpenBazaar

3

Once Alice creates her account, she can explore the listings on the platform.
OpenBazaar’s peer-to-peer backbone is the InterPlanetary File System (IPFS),
an open-source protocol which allows content storage and addressing across a
network of distributed nodes [3]. IPFS can be used as an alternative to a traditional client/server architecture – instead of clients requesting data from aserver, here, a node provides a hash of the content it is requesting to its peers,
who are either able to provide the requested content or query their own peersfor its location.
Since IPFS does not support a centralized repository of item listings, search
is implemented through a third-party search engine. At the time of writing,the only search engine enabled by default in the OpenBazaar GUI is createdby the OB1 developers – other known search engines, such as SearchBizarre
and a service operated by BlockStamp, require a manual addition to the client
which may not be intuitive for new users. These search engines operate theirown crawlers which travel the network to index users and listings, where theirresults can then be queried by users who have added their search engine to the
OpenBazaar client. Existing third-party search engines exist mainly to provide
an uncensored view of listings on the platform, as OB1’s search engine does notinclude listings for illicit products.
Listings are stored over IPFS using a Distributed Hash Table (DHT), where
nodes store the hash of a given user, item, or feedback along with their locationon the network. If a node visits a vendor page or views one of its listings, it also
stores the information it receives locally for a certain period of time, allowing
for an added level of redundancy across the network. This enables vendors not
to be perpetually logged in. The system has been designed with the intention
that individual listings will be re-seeded for about a week after their owner waslast seen online [23].
When Alice clicks on a listing she is interested in, her client attempts tofetch the relevant information from her peers by querying the DHT for the item
hash. If Bob, the store owner, is online, Alice will often be able to receive thelisting directly from Bob – otherwise, Bob’s data is typically seeded by other
IPFS peers for a set period of time. If Alice can retrieve the item hash, she isbrought to a listing page which contains information about the item such as its
price, description, shipping details, preferred cryptocurrency payment method,and pictures of the item. Currently, OpenBazaar supports payment in Bitcoin,
Bitcoin Cash, Litecoin, and Zcash [2]. Zcash required a full installation of its
binary for more than half of the duration of our study, which raises usability
concerns; Litecoin was added only in the second half of our measurement interval.
When purchasing an item through OpenBazaar, Alice may opt to directly
send cryptocurrency to Bob to pay him, or to use a moderator service to holdthe funds in escrow until the item is received. Moderators are OpenBazaar users
who volunteer to mediate disputes between other users and decide the eventual
distribution of funds using multisignature transactions, and usually do so in exchange for a small fixed-percentage fee. Moderators receive community feedback,but individual moderators are chosen by the vendor at the time of listing cre-

4James E. Arps and Nicolas Christin

ation. Regardless, Alice sends her payment details and shipping information to
Bob through the platform and the sale proceeds as it would on existing services
such as eBay.

3

Data Collection Methodology

We turn now to our data collection methodology, outlining our objectives first,
before discussing the mechanisms we use and their limitations.
3.1

Objectives

Contrary to traditional dark-web marketplaces [31], data collection on OpenBazaar is encouraged rather than discouraged, which eschews most of the constraints one faces when attempting to scrape a network stealthily. In fact, since
OpenBazaar is not regulated by any central authority, our node cannot be easily
banned from the network. While it could be blocked by individual nodes, we received no indication that this happened at any point during the study. Therefore,
our primary focuses were on data completeness and collection speed.
We elected to scrape data ourselves, despite the existence of several search
providers for OpenBazaar. Some OpenBazaar search providers service the OpenBazaar GUI (i.e., they return JSON parseable by the OpenBazaar client GUI),
while others simply serve their results on public-facing webpages. Regardlessof the method used, we noticed some search engines – in particular, the mostpopular search engine, run by OB1 at http://openbazaar.com – do not display
results for illicit products. As a result, we needed to build our own OpenBazaar
crawling infrastructure to obtain uncensored data.
Ethics of data collection. The data we collect are volunteered by OpenBazaar
participants. In particular, listings, descriptions, and user feedback are all madepublicly available for everyone to see. We previously referred to our Institutional
Review Board (IRB) to determine whether data collected in related work [6, 31]
could be publicly reshared. Our IRB had opined this line of work was not humansubject research. The only difference with previous research is that here, we docollect IP addresses of other clients. But, in doing so, our crawler does not collectany information a regular OpenBazaar node would not collect for operational
purposes. In short, like most peer-to-peer network measurement research (seee.g., [7]), our measurements do not put users at additional risk compared toparticipating in the peer-to-peer network in the first place.
3.2

Crawler design

Our crawler runs over Tor and leverages the OpenBazaar API [25], which is a
modified version of the IPFS API. First, our crawler queries a list of its connected
peers (GET http://localhost:4002/ob/peers/). For each of those peers, we retrieve their closest connected peers (GET http://localhost:4002/ob/closestpeers/

Open Market or Ghost Town? The Curious Case of OpenBazaar

5[peer ID]), and add them to a list, recursively continuing until we no longer findany new peers (in our testing, this usually took between five and seven rounds).
Using each peer’s unique user ID, we can then make separate API calls to retrieve
all of their current listings (GET http://localhost:4002/ob/listings/[peer ID]) as
well as all reviews left for those listings. Users, items, and reviews are all uniquely
identified within OpenBazaar by a 46-character alphanumeric hash, which allows
us to reliably track them across different scrapes. We log the approximate geographic location of peers with public IP addresses with FreeGeoIP (now called
ipstack, [1]).
The crawler makes use of Python’s Requests library [27], and the relatively
small size of the network means that it is very fast (often completing in an houror less). However, to avoid too much redundancy and to reduce strain on the
Tor network, we chose to scrape the network once every two to four hours. This
provided sufficient coverage on a daily basis, and by leaving our OpenBazaar
node running while the scraper was not in use, we also contributed to the overall
health of the network.
3.3

Potential limitations

Coverage

1.0

0.8

0.6

0

25

50

75

100

Number of scrapes
Fig. 1. Estimated coverage as a function of the number of scrapes.

Network coverage One risk of measuring a decentralized marketplace is that
our node could lack a complete view of the network, and it was not uncommon
for our scraper’s requests to the DHT to time out for certain items or users.
Figure 1 depicts the relative coverage percentage of items seen on the network
during April 2019. We observed 6,292 items on the network during this time,and saw 85% of them during the first two days (i.e., the first 24 scrapes), encountering the rest over the following seven. The large number of scrapes required toobserve this proportion of the network is indicative of the high levels of churn we6James E. Arps and Nicolas Christin

discuss in Section 4.1. Non-seller nodes (which make up the majority of network
participants) also have little incentive to leave their OpenBazaar clients open
for long periods of time, as they do not need their listings to be seeded by othernetwork participants. The diminishing returns shown by the curve suggest that
there are few items remaining to be found by the crawler.
While the number of items we observe is less than the number reported by
the OB1 developers [5], we often found through manual inspection that many
items returned in the later pages of a given search result performed through theirservice failed to load in our client. This is likely because the OpenBazaar protocol
attempts to cache data from inactive nodes for approximately a week [23] before
they are “forgotten” by the network. For example, if Alice owns a store anddo not log on for a few days, other nodes should still be able to access Alice’s
store for approximately a week because it will be cached collectively by the othernodes in the network. Since OB1’s search engine returns results indexed by its
crawlers and then stored on its own servers, it is likely that its results containstale listings that are no longer reachable on the network.
We therefore believe that our view of the network is typical of that seen by
an average node; this motivates the need for our crawler to frequently scrape theentire network to increase coverage.

Economic coverage Unlike many other marketplaces, leaving a review is notmandatory after a purchase on OpenBazaar. We were unable to purchase any
items ourselves as a part of this study due to our legal counsel’s concerns about
inadvertently participating in money laundering, but we confirmed this with oneof the OpenBazaar developers. As a result, our sales numbers in Section 4.2 area lower bound, even if we assume complete coverage of the network.
Despite this caveat, we are confident that our results are useful for two reasons. First, social norms on anonymous marketplaces have proven quite strong
over the years: even when leaving feedback is not mandatory, buyers often do so,
especially if they plan on buying from the same seller again in the future [31].
Soska and Christin’s analysis, based on feedback ratings [31], showed numbers
very close to those obtained externally through criminal complaints, when vendors were arrested or through marketplace takedowns, even when leaving feedback was not actively enforced by all marketplaces. The importance of feedbackon those marketplaces was also evident when the original Silk Road changed
the way feedback was tallied (shifting from per-item feedback, to aggregate,
per-vendor feedback) and quickly reversed course in the face of customer complaints [6].
Furthermore, our reported distribution of sales by category is likely valid.
One could think that users purchasing illicit items on OpenBazaar would be lesslikely to leave a public review than users purchasing legal products. As we willsee, our results do not support this hypothesis.

Open Market or Ghost Town? The Curious Case of OpenBazaar

4

7

Results

We next turn to our measurement results. We first describe network-level metrics
such as the size of the peer-to-peer network, or the underlying churn in IP
addresses we see participating in the network, before turning to discussing the
economic activity that appears to take place on OpenBazaar. We then examine
the security and privacy precautions vendors take.
4.1

Network-level metrics

The OpenBazaar peer-to-peer network Over our entire measurement interval,
we have observed 6,651 distinct network participants, using 6,116 distinct IP
addresses.
Table 1. OpenBazaar demographics. Users do not have any product listings; sellers have at least one active listing; active sellers have realized at least one documented
sale.
With Tor Without Tor Total
Users
Sellers
Active sellers

743
197
40

4,487
1,057
127

5,230
1,254
167

Total

980

5,670

6,651

Table 1 breaks down these participants into finer-grained demographics, distinguishing between users, who do not list any product, and are therefore assumed to be solely browsing or buying items; sellers, who list at least one product, but do not have any documented sale – that is no one left any feedback forthem; and active sellers, who have received at least one piece of feedback. We
also break down these participants between those who use Tor and those who
do not.
Figure 2 shows, for each day, the number of OpenBazaar hosts we have encountered during our multiple scrapes on that day. The lighter curve denotes thefraction of hosts that are using the Tor network. We observe that the population
has been relatively steady, at approximately 80 users simultaneously online on
any given day throughout our measurement interval. The few downward spikes
denote measurement issues rather than network instability.
The gaps in the plot denote times during which our measurement infrastructure was disabled, or otherwise unable to properly collect data.
The seemingly decreasing number of Tor users toward the end of the measurement interval might be a slight undercount. Through experimentation with
our own test nodes, we discovered that OpenBazaar nodes in version 2.3.1 andhigher running over Tor, sometimes failed to appear in our crawls, despite being

8

James E. Arps and Nicolas Christin
120

Number of hosts

90

60

All Users
Tor Users30

0
Jul 2018

Oct 2018

Jan 2019

Apr 2019

Jul 2019

Date

Fig. 2. Number of hosts on the OpenBazaar peer-to-peer network. Eachpoint is the cumulative number of different hosts seen over all measurements taken on
a specific day.

online and reachable (that is, if one knew their node ID). This coincides, andmay be due to the backwards-incompatible [24] software upgrade on March 19,
2019 (OpenBazaar 2.3.1), which may have affected some long-term participants
over Tor. Overall, compared to Table 1, this plot seems to indicate that the vast
majority of OpenBazaar users are actually rarely online. To better understandthe dynamics of the OpenBazaar network, we next turn to a survivability analysis. Similar to Christin [6], we estimate vendor “lifespan” by recording the time
we first saw a vendor, and the time we saw them last. They may have left andrejoined in the meantime—here we are looking at the vendor lifetime, regardlessof their transient activity. To account for measurement effects (e.g., vendors still
being present on the last day of measurement), Figure 3 depicts a Kaplan-Meierestimator [17] that shows the probability a given user seen on day 0 will be seen
again after x days, broken down by the categories defined in Table 1.
Churn is high among regular users: more than two thirds of them stay lessthan a day. Vendors, on the other hand tend to stay longer, especially vendorsthat have documented sales (i.e., that have received feedback). Roughly threequarters of all participants do not stay more than a week; a few users remainon the network throughout our measurement interval. Log-rank tests confirm
that visually striking differences between the survival curves for all of these user
categories are statistically significant (p < 0.0001). In short: vendors tend to belong-lived, while regular users are not, and usage of Tor is positively correlatedwith longer presence on the network.
Geographical considerations Figure 4 shows the geolocation of the IP addressesof participants that are not using Tor. OpenBazaar seems to be fairly international, with the usually observed concentration of users in North America andOpen Market or Ghost Town? The Curious Case of OpenBazaar++

Active seller (w/ Tor)
Active seller (w/o Tor)++

Seller (w/ Tor)
Seller (w/o Tor)++

9User (w/ Tor)
User (w/o Tor)Survival probability

1.00

0.75

+
0.50

0.25

0.00

+
+
+
++
+
+
+ +
+
++
+
+
+
+
+
+ +
++
++
+ +
++ +
+
+ + +
0

100

++
+ ++ +
+
200

300

400

Time

Fig. 3. Survivability analysis of OpenBazaar users. Kaplan-Meier estimatorthat shows the probability a given user seen on day 0 will be seen again after x days,
broken down for different types of users. Shaded areas indicate 95% C.I.

Europe. A couple of points with strange locations (e.g., Easter Island) suggest
certain participants use VPNs, some of which are known to advertise implausible
locations that fool geolocation databases [33]. We generally do not observe meaningful differences between different types of users, even though Western Africa,
India and Thailand/Malaysia feature a larger proportion than we expected ofactive sellers – again, it is hard to tell whether this could be due to VPN usage.

4.2

Economic activity

We next turn to a study of economic activity on OpenBazaar. We observed
24,379 distinct item listings during our measurement intervals. The apparent
high average ratio of listings per seller (≈40) is due to the ease of creating
listings (including test listings) and to a few “power users” who have thousandsof listed items on the platform.
Item listing survivability We start with a survival plot in Figure 5 shows theprobability an item seen at time zero will still be available on the network x days
later. The median item stays online for approximately three weeks. One quarterof all items disappears (i.e., are delisted) within a day, which further motivates
the need to repeatedly crawl the network for completeness. A handful of items
were present throughout our measurement interval. The “jumps” observed in
the graph correspond to a large number of items belonging to a given vendor

10James E. Arps and Nicolas Christin●

Active Seller●●● ● ● ● ●
●●
●● ●●
●
● ●
● ●●
●

●
●

●

●
●
●
●
● ●●
●
● ●

●
●
●
●
●●

Seller●●●● ●●●●
●
●
●
●
●
●
●
●

User

●

●

● ●

●

●
●
●

●
●
●

●

●
●

●
●

●
●

Fig. 4. Geolocation of OpenBazaar users. Users are network participants; Sellers
are users with at least one listing; Active sellers are sellers with at least one feedback.

being all delisted on the same day, presumably because the vendor node had
been offline for long enough to have its listings cleared from the IPFS cache.1
Overall, the survivability analysis paints a picture eerily similar to that of
the early days of online anonymous marketplaces [6]: most items are very shortlived. As discussed in prior work [6, 31], short-lived items are usually indicativeof vendors holding low stocks, which in turn suggests that vendors operates
primarily in the retail space, with small product quantities, and (usually) low
sales volumes.
Preliminary economic analysis We next examine economic activity on OpenBazaar, across item categories. We initially attempted to feed each of the 24,379
item listings we observed into the 16-category item classifier proposed by Soska
and Christin [31], who trained their item classification with listings from theAgora and Evolution marketplaces and showed very high (>98%) accuracy whenevaluating a priori unknown listings.
1

Item listings do not automatically disappear when an item is sold out. The seller
needs to either delete the listing, or be offline for a long enough period of time forthe listing to stop being seeded by the network. We differentiate between the twocases by only counting visible reviews for an item as a sale and not considering
disappearing listings as possible sales.

Open Market or Ghost Town? The Curious Case of OpenBazaar

11

Survival probability

1.00

0.75

0.50

+
+
+
++
++
+
+
++
++
+++
+++
+++
+++

0.25

+

+
+

0.00
0

100

++

+

200

300

400

Time

Fig. 5. Survivability analysis of OpenBazaar items. The plot is a Kaplan-Meierestimator that shows the probability a given item seen on day 0 will be seen again
after x days. The median lifetime of an item is approximately 22 days. The curve fully
overlaps the very narrow 95% confidence interval.

We realized, however, that items classified in the “Miscellaneous” category
represented a disproportionately high fraction of all listings, which led us tofurther break this category down into additional categories: Adult Toys, Art,
Clothing, Jewelry, Print Media, and Souvenirs. We took 200 new items fromour OpenBazaar corpus, hand-labeled them,2 and added them five times (i.e.,an extra 1,000 items) to the original training set consisting of 62,989 labeleditems from Evolution and Agora. Our resulting classifier operates on 22 categories. Table 2 shows very good performance metrics, with precision and recall,overall, being over 0.96. This is unsurprising, given that the modified classifier
is very close to the original classifier from Soska and Christin – we merely added
1,000/63,989≈1.6% of new training data to the original classifier. The support,
here, is much larger than the number of items we observed in OpenBazaar, since
we are evaluating with 10-fold cross-validation on the original dataset provided
by Soska and Christin3 [31] and the OpenBazaar data. Certain categories (e.g.,
Adult Toys) do appear very rarely, though. The overall support for the new categories is fairly small (240 items); this again is unsurprising, as the imbalance intraining sets means that an item needs to closely resemble a training example to
be classified as one of the new sub-categories. In other words, our modified classifier closely mimics what Soska and Christin used; in a few “obvious” cases, itwill manage to further identify a subcategory, but will do so very conservatively.
Next, we present the category breakdown for these 24,379 listings in Table 3.
Simply looking at listing counts (columns 2 and 3), close to half of the listings
are in the “Misc.,” “Print Media,” and “Souvenirs” categories, which generally
2

A single researcher was tasked with this labeling, hence we do not report agreement
numbers.
3
The dataset is available from IMPACT, https://www.impactcybertrust.org.

12James E. Arps and Nicolas Christin
Table 2. Classifier performance.
Category

Precision Recall F1-score Support

Adult ToysArt
BenzosCannabis
Clothing
Digital Goods
Dissociatives
Drug Paraphernalia
Ecstasy
Electronics
Jewelry
Misc
Opioids
Prescription
Print Media
Psychedelics
Sildenafil
Souvenirs
Steroids
Stimulants
TobaccoWeapons

1.00
1.00
0.97
1.00
1.00
0.94
1.00
0.97
1.00
0.96
0.76
0.87
0.98
0.95
1.00
1.00
0.98
1.00
0.99
0.98
1.00
0.99

1.00
1.00
0.98
1.00
1.00
0.96
0.99
0.98
0.99
0.94
1.00
0.80
0.98
0.93
0.96
1.00
0.97
0.90
1.00
0.99
1.00
0.97

1.00
1.00
0.97
1.00
1.00
0.95
0.99
0.97
0.99
0.95
0.87
0.83
0.98
0.94
0.98
1.00
0.97
0.95
0.99
0.99
1.00
0.98

3
24
21,132
113,51638
158,162
7,172
15,74049,1845,379
13
47,651
25,511
23,023113
31,02331,22
49
17,04455,555
3,966
5,341

Total

0.96

0.96

0.96

582,761

denote legitimate items. The third largest category, “Digital goods,” contains
a mix of legitimate (e.g., e-books and other guides) and illegitimate (pornographic website account passwords) items. In short, a majority of items listed
on OpenBazaar shops appear to be for legal products.
However, looking at actual sales (columns 4 through 6) paints a very different
picture. To compute sales for a given item listing, using the same technique asin related efforts [6, 31], we add up the price of the item to its total sales at
each time a feedback for a sale of that item is recorded. Feedback that wererecorded prior to our monitoring the OpenBazaar network are counted if thecorresponding item was still listed when we scraped the network, and feedback
was still accessible. As noted before, reviews are not mandatory in OpenBazaar,
so that the sales numbers presented are a lower bound. Even with this caveat,
sales volumes appear to be very modest. We only count around $217,000 in total
sales over our measurement interval. By comparison, sales on Silk Road [6, 31]
and AlphaBay [22], two of the largest online anonymous marketplaces, reachedhundreds of millions of dollars in revenue.
Looking a bit more carefully at the data revealed an interesting outlier: one
vendor, P...A, appeared to be single-handedly responsible for 60% of all sales
on OpenBazaar. In particular, that vendor had three pieces of feedback for a$36,000+ listing for a kilogram of cocaine, which should have accounted formore than $100,000 in sales by itself. When we manually inspected this specificvendor, we discovered that for most of their items, the feedback appeared to beOpen Market or Ghost Town? The Curious Case of OpenBazaar

13

Table 3. Sales observed during our measurement interval. The values incolumns 4–6 exclude vendor P...A, whose feedback seems highly questionable, andlikely fake.
Category

Listings Listings(count) (%)

Adult ToysArt
BenzosCannabis
Clothing
Digital Goods
Dissociatives
Drug Paraphernalia
Ecstasy
Electronics
Jewelry
Misc
Opioids
Prescription
Print Media
Psychedelics
Sildenafil
Souvenirs
Steroids
Stimulants
TobaccoWeapons

182
331
155
1,910
1,881
2,701
21
829
243
1,906
354
6,796
223
289
4,902
242
45
791
69
242
34
233

0.747
1.358
0.636
7.835
7.72
11.079
0.086
3.4
0.997
7.818
1.452
27.8760.915
1.185
20.107
0.993
0.185
3.245
0.283
0.993
0.139
0.956

Total

24,379 100

Sales
Sales
Sales
(count,(USD,(%,
corrected) corrected) corrected)
0
10
8
318
30
139
2
13
10
23
9
333
17
23
6
125
41
60
2
24
1
8

0
975
1,650
22,450
375
1,312
1,050
224
4,895
2,389
208
4,028
2,207
808
123
17,653792
3,587
11
20,835
5
374

0.0
1.13
1.92
26.1
0.437
1.53
1.22
0.261
5.69
2.78
0.243
4.69
2.57
0.941
0.144
20.5
0.921
4.17
0.0128
24.2
0.005
0.436

1,202

85,954100

fake: very closely clustered timestamps, all with highly positive ratings and uninformative messages for highly priced items. This strongly suggested an attempt
at manipulation by the vendor.4 While in centralized marketplaces, padding
feedback with misleading information is prohibited, and frequently results inbanning the vendors engaging in such deceptive practices, the decentralized nature of OpenBazaar makes this kind of enforcement difficult. We do note, though,that OpenBazaar supports moderators that can assist in ensuring transactions
are conducted satisfactorily (see Section 2); unsurprisingly, all of P...A’s listings were unmoderated. We removed this vendor, and the 33 associated sales,from further consideration in columns 4–6, to obtain an hopefully more accurate
picture of sales on OpenBazaar – excluding P...A, the total amount of sales is
actually around $86,000.
4Interestingly, one of their items seemed to have legitimate feedback, which pre-dated
all of the seemingly deceptive feedback discussed here.

14

James E. Arps and Nicolas Christin

As Table 3 shows, the category distribution of items that do sell is very
different from the category distribution of items that are merely listed. Over
25% of all recorded sales are for cannabis-related products (including seeds),
and more than three quarters of all recorded sales are for drugs – prescription
drugs or narcotics.5 This higher economic activity occurs despite the fact that
the OpenBazaar developers have taken active measures to try to make illicit
items harder to find, by excluding them from their built-in search engine search
results, which suggests that the demand for illicit offerings far outpaces that for
legitimate goods available on OpenBazaar.
Table 4. Distribution of feedback ratings. 5 is best, 1 is worst.
Score Count Percent
5
4
3
2
1

1,302
28
34
29
133

85.32%
1.83%
2.22%
1.90%
8.71%

Feedback ratings An alternative hypothesis would be that buyers of legitimate
products are somehow less likely to leave feedback than buyers of illicit goods.
We found no evidence to support that hypothesis. Specifically, we present the
feedback ratings we observed in Table 4. OpenBazaar uses a 5-point rating scale,where higher scores are better, i.e., vendor strive to obtain 5-star feedback. The
ratings we see heavily skew positive, as has been observed in general (legitimate)
e-commerce platforms [28], and feedback distribution presents striking similarities with with that obtained for feedback left by Silk Road patrons [6, Table 3]:
5’s dominate, followed by 1’s, and other ratings are less frequently used.
4.3

Operational security

While much of the core OpenBazaar vendor base tends to connect over Tor, not
all of these users are truly anonymous. Indeed, user IDs are persistent acrosssessions. By comparing the unique user IDs of the 980 nodes seen connecting
over Tor with those seen connecting over public IP addresses, we found that
173 users (17.7%) had revealed an IP address at some point in time. Not all ofthese users may have wished to remain anonymous during the entire collection
cycle, but we did observe some obvious lapses in operational security, such as
US vendors selling marijuana offering global shipping. It appears a version of5

The disproportionate volume of Psychedelics sales seems to be mostly influenced
by one specific vendor, who has been highly successful on various online anonymous
markets, and also operates their own vendor shop, and has presence on OpenBazaar.

Open Market or Ghost Town? The Curious Case of OpenBazaar

15

the OpenBazaar client which is pre-bundled with Tor is in development, whichshould alleviate this problem over time [26].
At the beginning of our measurement timeframe, OpenBazaar nodes weretied to a single cryptocurrency – if a vendor wished to, for example, sell thesame items both with Bitcoin and Bitcoin Cash, they were required to maintaintwo distinct nodes with identical listings. Furthermore, for some time using Zcash
on the platform also required running a full Zcash node. As a result, activity
on the platform was conducted almost entirely in Bitcoin until the release ofa multiwallet feature on January 17, 2019, which added native support for thethree currencies mentioned plus Litecoin. Previous studies (e.g., [21]) have shownthat Bitcoin is highly traceable, meaning that early purchases on the platform
may be able to de-anonymize certain vendors. Since the introduction of themultiwallet, however, there has been large growth in Zcash usage, with at least6
12,441 observed listings accepting payment in the currency.

5

Related work

This work follows a long lineage of peer-to-peer network measurements, which
can be traced back to studies of file-sharing networks such as Napster [30], or
Gnutella [29]; or, beyond file-sharing, of Skype [12]. Later papers focused on
specific metrics to better understand user behavior. For instance, Gummadi etal studied peer churn on Kazaa [13], while others investigated overall peer availability [4,32], or resilience to poisoning attacks [7,20]. A number of papers looked
into the economics of online anonymous marketplaces and have documented theirrise in popularity [6, 10, 31]. In comparison, our analysis of OpenBazaar shows
fairly modest revenues. Finally, also related to the operational security aspects
we outline in this paper are attempts to quantify cryptocurrency traceability –notably efforts to trace Bitcoin [21], Monero [22], and Zcash [18].

6Conclusion

We conducted multiple daily crawls of the OpenBazaar distributed marketplaceover approximately 14 months (June 25, 2018–September 3, 2019). More thanhalf of the 6,651 participants we observed were present only for less than a day,but users relying on Tor tended to be much longer lived, particularly if they
were actively selling items. Economic activity is orders of magnitude smaller
than on centralized anonymous marketplaces, and while most listed items arefor legitimate products, the majority of items that do result in sales are fornarcotics. Finally, vendors are not always using prudent security practices, leaking for instance their IP address despite generally connecting over Tor, whichmakes a strong case for imposing secure defaults—fortunately, the OpenBazaar
developers are already reportedly working on this [26].
6

A bug in our parser code for items with multiple currencies prevented us from precisely computing the number of such listings, but we could recover this lower bound.

16

James E. Arps and Nicolas Christin

We conclude that OpenBazaar, so far, has not gained much traction to usher
in the new era of decentralized, private, and legitimate electronic commerce it
was promising.7 This could be due to a lack of user demand for decentralized
marketplaces, lack of integration of private features, or other factors, such as ahigher learning curve for users compared to centralized alternatives.

7

Acknowledgments

We thank our shepherd, Ben Edwards, and the anonymous reviewers for numerous comments that greatly improved this paper. We are also grateful to
Kyle Soska for extensive discussions about this work. This research was partially
supported by DHS Office of Science and Technology under agreement number
FA8750-17-2-0188.

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