The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
A reputation-based approach for choosing reliable resources in peer-to-peer networks
Proceedings of the 9th ACM conference on Computer and communications security
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Pride: peer-to-peer reputation infrastructure for decentralized environments
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
IEEE Communications Magazine
Cooperativeness prediction in P2P networks
Expert Systems with Applications: An International Journal
RepTrap: a novel attack on feedback-based reputation systems
Proceedings of the 4th international conference on Security and privacy in communication netowrks
Trust beyond reputation: A computational trust model based on stereotypes
Electronic Commerce Research and Applications
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Reputation systems help peers decide whom to trust before undertaking a transaction. Conventional approaches to reputation-based trust modeling assume that peers reputed to provide trustworthy service are also likely to provide trustworthy feedback. By basing the credibility of a peer’s feedback on its reputation as a transactor, these models become vulnerable to malicious nodes that provide good service to badmouth targeted nodes. We propose to decouple a peer’s reputation as a service provider from its reputation as a service recommender, making the reputation more robust to malicious peers. We show via simulations that a decoupled approach greatly enhances the accuracy of reputations generated, resulting in fewer malicious transactions, false positives, and false negatives.