# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 5 Evaluation via Real Bitcoin Transaction Dataset  
To evaluate the effectiveness of our model on large-scale real transactions that span over long period of time, we now apply it on Bitcoin's transaction data. Among various cryptocurrencies, Bitcoin was chosen for our evaluation as it is the representative of decentralized digital currency and also has the most number of transactions from 2009 to 2018. Furthermore, Bitcoin has a simple structure, making it relatively easier to analyze its contents than Ethereum which supports more features such as smart-contract [33]. A Bitcoin transaction consists of an input section and an output section [34]. Source addresses and destination addresses are denoted in the input and output sections, respectively, and the amounts of Bitcoin the destination addresses would receive are indicated in the output section. After being verified by mining nodes, transactions are written into a block with a timestamp indicating their mined time, and the block is delivered to the neighbor nodes for network-wide propagation. Eventually, every full nodes in the world (which is around \(\sim\)8500 nodes [29]) will have the same Bitcoin ledger. Thus to conduct the experiment, it was necessary for us to dive into the sea of Bitcoin full nodes filled with undug valuable transactions. Figure 10: Trust of buyer G to two different sellers E and D.  
Figure 9: Change of reputation over time for two different sellers D and E.  
Experiment Set-Up and Data Preprocessing:We installed the Bitcoin Core, an open-source reference software that serves as a node in the Bitcoin network ([[https://bitcoin.org/en/bitcoin-core/](https://bitcoin.org/en/bitcoin-core/)]([https://bitcoin.org/en/bitcoin-core/](https://bitcoin.org/en/bitcoin-core/))). After initialization, it started to download all the past raw blocks from the main Bitcoin network to local storage, and continued to do so as new blocks are generated continuously. We were able to collect all blocks ranging from the genesis block generated on 3 January 2009 to the one in 9 July 2018. The data consist of 531,136 blocks and 326,948,822 transactions with the size of 189 GB in total. As depicted in Figure 11, we split the experimental set-up into two parts: the generator part and the simulator part. In the generator part, we extract and generate, from the raw transaction data of Bitcoin, meaningful data such as transaction amounts and addresses of users. BlockSci [35], an application for analyzing blockchain, is utilized for this purpose which supports three main features: parsing transactions, clustering addresses, and analyzing transactions. We first pass the raw block data into the parser to transform the data to the format used by BlockSci, which resulted in size reduction to 151 GB without loss of original information. Next step is to cluster the addresses. In Bitcoin, users can generate (Using the Bitcoin Core or any client wallet software built for Bitcoin), without limit, a number of Bitcoin _addresses_ that serve as an account number (To be precise, there is no notion of accounts in Bitcoin. By account, we mean the value of the UTXOs for their corresponding keys, and those keys act like account numbers.). Thus, multiple addresses may belong to (and be used by) a person or a team on the Bitcoin's network, resulting in the necessity of clustering them into groups that are likely to be a single user having similar usage patterns. By inserting the transactions into BlockSci's clusterer [35], we extracted 214,219,988 clusters from a total of 947,837,576 addresses discovered in all transactions. After clustering the addresses, we found that there are a lot of addresses which are used only once and never again, the so-called \"single-hit\" addresses. They are not included in any of the clusters due to lack of any usage pattern, and these were unsuitable for evaluating our model. Thus, we then examined which clusters of addresses have done transactions similar to the ones that could happen in real-life.