Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
Peer-to-Peer: Harnessing the Power of Disruptive Technologies
Peer-to-Peer: Harnessing the Power of Disruptive Technologies
Valuation of Trust in Open Networks
ESORICS '94 Proceedings of the Third European Symposium on Research in Computer Security
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Managing and Sharing Servents' Reputations in P2P Systems
IEEE Transactions on Knowledge and Data Engineering
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
TrustGuard: countering vulnerabilities in reputation management for decentralized overlay networks
WWW '05 Proceedings of the 14th international conference on World Wide Web
A time-frame based trust model for p2p systems
ICISC'06 Proceedings of the 9th international conference on Information Security and Cryptology
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In peer-to-peer (P2P) systems, peers often must interact with unknown or unfamiliar peers without the benefit of trusted third parties or authorities to mediate the interactions. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of P2P systems. However current P2P reputation mechanism can not process such strategic recommendations as correlative and collusive ratings. Furthermore in them there exists unfairness to blameless peers. This paper presents a new reputation mechanism for P2P systems. It has a unique feature: a recommender's credibility and level of confidence about the recommendation is considered in order to achieve a more accurate calculation of reputations and fair evaluation of recommendations. Theoretic analysis and simulation show that the reputation mechanism we proposed can help peers effectively detect dishonest recommendations in a variety of scenarios where more complex malicious strategies are introduced.