# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 1 Introduction  
Advancements in e-commerce technology over the past few years have enabled customers to easily purchase products or services, or even transfer money, at the end of their fingertips over the Internet. As a result, e-commerce market has expanded with a very rapid growth rate, which is reflected by the number of transactions taking place all around the world for a day. Usually when a customer shops for a product, he or she considers various factors such as price, quality, delivery time, etc. When different sellers offer a same product at similar prices or conditions, then the next thing that customers pay attention to are the seller's _reputation_ or the degree of _trust_ to those sellers. Many existing online payment systems often do provide such information, but information such as reputation are prone to manipulation by malicious entities, which include the advertisers or owners themselves who may give extremely high or low ratings on purpose. Furthermore, as each individual has different evaluation criteria, the value of reputation could be non-objective and unreliable. Therefore, it is difficult for customers to distinguish between good and bad products, or between reliable and unreliable sellers. Thus, as online markets start to play important roles in our daily life, building an online payment system that are trustworthy has become an important issue. The question is, how? In 2008, [PERSON] presented Bitcoin [1], a decentralized digital cryptocurrency based on blockchain technology. Blockchain keeps data in a list of blocks in which blocks are sequentially linked(pointing to the previous block), time-consuming to create, and almost impossible to overtake the competitors by several blocks. This chain is created in a distributed manner by numerous competing peers over the network. Thus the data stored in blocks eventually become invulnerable to potential attacks that might try to manipulate them. Due to these unique characteristics, and also in line with Bitcoin's recent success, there has been growing interest in applying blockchain technology to various domains, such as digital rights management [2; 3; 4], health care [5; 6; 7; 8], Internet of things [9; 10; 11; 12; 13] and e-commerce [14; 15]. In particular, applying blockchain's immutability to online payment systems can make it difficult for malicious entities to manipulate transaction history such as the amounts or ratings, which will result in better reliability, security, and trustworthiness of the systems. To explore the design of a trustworthy online e-commerce system, we implemented a blockchain-based electronic payment system where not only the transaction data, but also the mutual and bi-directional \"rating\" information is recorded into the blockchain. For the ratings, the parties of the transactions evaluate each other in the range from 1 to 10, or equivalently from one to five stars with half star granularity, based on their satisfaction after every transaction. For instance, say a consumer [PERSON] purchases a product or a service from a provider [PERSON]. After the transaction is validated, [PERSON] grades the quality of the product/service. Similarly, [PERSON], the seller, also evaluates [PERSON] based on his own criteria. For example, if [PERSON] frequently asks for a refund without any specific reason, [PERSON] might give her low evaluation, which may mean defining her as a black consumer. Then, these ratings are linked to their corresponding transactions within the blockchain after every transaction gets validated, as if it is a transaction of its own in its virtual ratings currency. The goal is to build a reliable method for measuring trustworthy _reputation_ and degree of _trust_ of entities participating in e-commerce transactions based on their immutable history. In general, the concepts of _reputation_ and _trust_ may look similar but they are fundamentally different in the way they work. Reputation is formed based on the word-of-mouth (WoM) mechanism [16].