## 2 Theoretical Background
### Common Vulnerabilities of Reputation Systems for E-Commerce  
The rapid growth of e-commerce platforms and their extensive use and dependence on their reputation systems have led to various types of malicious behaviors and threats. [PERSON] [13] identified two problems related to a major approach for deriving users' reputations based on feedback: (1) unfair ratings by buyers and (2) discriminatory seller behavior. In the first case, two types of fraud can occur: Ballot-stuffing fraud, an attack where members positively rate themselves on fake (unfair) transactions in order to inflate their reputation and bad-mouthing fraud as a result of an attack where members misclassify others to deflate their reputation. These attacks are orchestrated in a collusion of a seller, or a group, and buyers. In discriminatory behavior, the seller strategically provides good service to a group of users and bad service to others, in order to gain benefits from that asymmetry of product/service quality. According to [PERSON] [14], the most common threats against reputation systems include ballot-stuffing, bad-mouthing, and traitor attacks. The traitor attack is a type of attack where members exploit their reputation by tricking others until their reputation dissolves. The author also mentions that the reputation systems use a much larger population sample than in the past and this fact may cause greater measurement bias from users who do not leave feedback on their transactions. Following a suggestion by [PERSON], [PERSON] [15] proposed the term _Sybil_ attack in the context of P2P networks. In this type of attack to reputation systems, an entity forges multiple identities in the system, using it in collusion as a means to increase its influence. Whitewashing is another vulnerability in the identity management scope. In this type of behavior, an e-commerce user with a bad reputation can easily create a new identity and continue his activity without any consequence of his past transactions. The architecture of the reputation system has also been subject to criticism due to vulnerabilities when centralized or distributed. According to [PERSON] et al. [16], central authorities can potentially filter, tamper, add, or reject product reviews based on their preference. [PERSON] et al. [17] pointed out the same issues since, potentially, a centralized system can be abused by the central authority. [PERSON] and [PERSON] [18] presented the same arguments, stating that the current centralized systems are silos and non-transparent in the review process. In addition to the lack of transparency, these isolated centralized systemsdo not benefit from the reputation data of each other. Regarding the attacks based on the limitations of identity management, such as whitewashing and Sybil, [PERSON] et al. [19] stated that it was hard to derive a robust model if users did not share information. However, in the case of decentralized systems, nodes could potentially manipulate the data users shared in the network, even if it was encrypted or signed. We consider these issues to be a major problem, regarding the lack of transparency of reputation data, as well as a major difficulty to derive a robust reputation model. These problems are our focus in the present paper and are discussed in the following sections. In the literature, one can find several proposals for classifications and taxonomies of attacks on reputation systems [10, 20, 21, 22]. In these previous works, the known types of attacks and limitations are discussed. However, we should notice that we clearly identify and distinguish two types of attack families. One family of attacks is focused on the vulnerabilities of the reputation model, briefly discussed in the present section, which regards the methodologic approach of calculating the reputation users' scores.