# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 2 Related Work  
There are several pieces of prior work that suggest the application of blockchain technology to e-commerce payment systems [14; 15] or propose models for deriving reputation scores of the system users [18; 19]. For example in 2015, [PERSON] presented a decentralized and distributed feedback management system built on top of the Bitcoin blockchain without changing the existing Bitcoin protocol [18]. This model, however, has a potential vulnerability of data manipulation, which could be attempted by malicious users giving extremely high or low scores on purpose. The author did suggest that the problem could be mitigated by charging the parties to pay (i.e., transfer money) to give feedback. However in a real world, who would pay just to give feedback? In 2016, [PERSON] et al. suggested blockchain-based trustless reputation system which anonymizes the identity of users [19]. All evaluation data are safely stored in the blocks so that manipulation is nearly impossible, just like our system, and the system expects users to evaluate each other without worrying about retaliations. In real life, however, this approach may bring unwanted side effects. For example, in the Web communities operated on anonymity basis, users may slander others with no proper reason. As a result, the effect expected by using anonymity could be discouraged due to unreliable data generated on the system. There are also trust models for e-commerce systems that are not based on blockchain [20; 21]. E2 CTM (Enhanced E-Commerce Trust Model) [20] suggested a trust model where a trustor uses personal experiences (e.g., purchase transactions) with a trustee and input from other trustors about that trustee to gain strust in trustees and come to a decision about whether to continue transactions with that trustee. Moreover, in the E2 CTM model, if a trustee with a good reputation value abuses his good reputation and performs a fraudulent transaction, all other transactions with other trustors will be affected because it will be reported as soon as possible. PeerTrust [21] proposed a coherent adaptive trust model for quantifying and comparing the trustworthiness of peers based on a transaction-based feedback system, similar to ours. Suggested model introduces three basic parameters and two adaptive trust factors, the transaction context factor, and the community context factor, in computing trustworthiness of peers. Building trust and reputation models suitable for the target application are not an easy task, and knowing the internal operations of a commercial system is also not possible in most cases. [PERSON] et al. [22] analyzed the eBay's reputation system, one of the earliest and best-known Internet reputation systems in e-Commerce, which gathers comments from buyers and sellers about each other after each transaction. The work reports several interesting features from examination of a large data set from 1999. However, there is no way to know the internal operations of eBay, and the work did not propose a new model. The work by [PERSON] and [PERSON] [23] argued that building customer trust is a complex process that involves technology and business practices, as well as movement from initial trust formation to continuous trust development. As will be described later, our model can prevent the manipulation attacks by applying several techniques that carefully considers each user's grading tendency and dilute malicious ratings to transform the raw evaluation data into reliable and objective values. There have been prior works that suggest such standardization approach using various metrics. For example, use of each user's evaluation values' deviation [24], the min-max and \(z\)-score methods [25], and the _preference_ model which takes account of each user's criteria for grading other users [26] has been proposed. Our model builds on top of these prior work, but goes a step further to consider the time, frequency, and human tendency in e-commerce transactions into account.