Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
Robustness of reputation-based trust: boolean case
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
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
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Evidence-based trust: A mathematical model geared for multiagent systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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The problem of encouraging trustworthy behavior in P2P online communities by managing peers' reputations has drawn a lot of attention recently. However, most of the proposed solutions exhibit the following two problems: huge implementation overhead and unclear trust related model semantics. This paper shows that a simple probabilistic technique, maximum likelihood estimation namely, can reduce these two problems substantially when employed as the feedback aggregation strategy. We evaluate the technique in three settings relevant for applications of P2P networks and show that it performs well in all of them. Thus, no complex exploration of the feedback is necessary. Instead, simple, intuitive and efficient probabilistic estimation methods suffice.