Towards the Design of Robust Trust and Reputation Systems

Our research is within the area of artificial intelligence and multiagent system. More specifically, we are interested in addressing robustness problems in trust and reputation systems so that buying agents are able to accurately model the reputation of selling agents even with the existence of various unfair rating attacks from other dishonest buyers (called advisors) [Zhang and Cohen, 2008]. In multiagent-based e-marketplaces, trust and reputation systems are designed for buyers to model seller reputation based on ratings shared by advisors. However, unfair rating attacks from dishonest advisors render trust and reputation systems ineffective to mislead buyers to transact with dishonest sellers [Jøsang, 2012]. Typical unfair rating attacks include Constant where dishonest advisors constantly provide unfairly positive/negative ratings to sellers; Camouflage where dishonest advisors camouflage themselves as honest advisors by providing fair ratings to build up their trustworthiness first and then gives unfair ratings; Whitewashing where a dishonest advisor is able to whitewash its low trustworthiness by starting a new account with the initial trustworthiness value; Sybil where a dishonest buyer creates several accounts to constantly provide unfair ratings to sellers; Sybil Camouflage (the combination of Sybil and Camouflage) where a number of dishonest advisors perform Camouflage attacks together; and Sybil Whitewashing where a number of dishonest advisors perform Whitewashing attacks together. The challenge is thus how to design robust trust models to against various strategic attacks. Various trust models [Zhang and Cohen, 2008; Jøsang, 2012] have been proposed to cope with unfair ratings. However, these models are not completely robust against various attacks. In particular, when dishonest advisors occupy a large proportion in e-marketplaces (i.e., Sybil), BRS becomes inefficient and iCLUB is unstable because they both employ the “majority-rule”. When dishonest advisors adopt strategic attacks, TRAVOS does not work well because it assumes an advisors’ rating behavior is consistent. ReferralChain assigns trust value 1 to every new buyer (advisor) which provides a chance for dishonest advisors to abuse the initial trust (i.e., Whitewashing). Personalized is vulnerable when buyers have insufficient experience with advisors and the majority of advisors are dishonest (i.e., combination of Whitewashing and Sybil). Thus, we need more robust trust models. 2 Progress to Date