Restraining False Feedbacks in Peer-to-Peer Reputation Systems

Abstract

The efficiency of reputation system depends on the quality of feedbacks. However current reputation models in peer-to-peer systems can not process such strategic feedbacks as correlative and collusive ratings. Furthermore in them there exists unfairness to blameless peers. We propose a new reputation management mechanism to restrain false feedbacks. Our method uses two metrics to evaluate peers: feedback and service trust. After a transaction both service consumer and provider report the quality of this transaction. According to two ratings, service trust of server and feedback trust of consumer are separately updated. Furthermore the former is closely related to the latter. Besides reputation model we also propose a punishment mechanism to prevent malicious servers and liars from iteratively exerting bad behaviors in the system. However under punishment server is only restrained from providing services and it can continuously send out service requests; consumer is restrained from launching requests while it can provide services. Simulation shows our approach can effectively process aforesaid strategic feedbacks and mitigate unfairness.