Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
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Introduction to Reinforcement Learning
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IEEE Internet Computing
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ESORICS '94 Proceedings of the Third European Symposium on Research in Computer Security
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IEEE Transactions on Knowledge and Data Engineering
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P2P '03 Proceedings of the 3rd International Conference on Peer-to-Peer Computing
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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
Trust model based on similarity measure of vectors in p2p networks
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
Modelling autonomic and dynamic trust decision-making mechanism for large-scale open environments
International Journal of Computer Applications in Technology
A comprehensive and adaptive trust model for large-scale P2P networks
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
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WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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WISE'07 Proceedings of the 8th international conference on Web information systems engineering
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WISA'07 Proceedings of the 8th international conference on Information security applications
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Two major challenges regarding peer's trust valuation in P2P systems are how to cope with strategically altering behaviors and dishonest feedbacks of malicious peers efficiently. However, the trust models employed by the existing systems do not provide adequate support to coping with quick changes in peers' behavior and aggregating feedback information, then we present a time-frame based trust model. We incorporate time dimension using time-frame, which captures direct experiences and recommendations' time-sensitivity, we also introduce four trust parameters in computing trustworthiness of peers, namely, trust construction factor, trust destruction factor, supervision period factor and feedback credibility. Together, these parameters are adjusted in time using feedback control mechanism, thus, trust valuation can reflect the dynamics of the trust environment. Theoretical analysis and simulation show that, our trust model has advantages in modeling dynamic trust relationship and aggregating feedback information over the existing trust metrics. It is highly effective in countering malicious peers regarding strategic altering behavior and dishonest feedbacks of malicious peers.