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BaRMS: A Bayesian Reputation Management Approach for P2P Systems

Long, Xuelian and Joshi, James (2011) BaRMS: A Bayesian Reputation Management Approach for P2P Systems. Journal of Information & Knowledge Management, 10 (03). 273 - 283. ISSN 0219-6492

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<jats:p> Current distributed Peer-to-Peer (P2P) applications offer a variety of flexible and convenient services through the Internet to users from different geographic locations and also support enhanced communications and interactions among them. However, security and trust are the key concerns in such applications as users in such an environment are typically unknown to each other. Trust management systems aim to decrease the risks in such applications and protect benign users from malicious users. In this paper, we introduce six attack models and propose a novel Bayesian Reputation Management System (BaRMS) for P2P environments using Bayesian probability and Markov Chain theories. BaRMS handles both positive and negative feedback. Through a case study, we show that this approach is better than the existing EigenTrust framework for P2P systems. Moreover, our simulation results of a P2P file sharing system also show that the proposed algorithm can greatly improve the performance over a system that does not include a trust management service under various attack models. We show that our proposed Bayesian Reputation Computation Algorithm (BaRCA) performs better than the EigenTrust algorithm under various models. </jats:p>


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Long, Xuelian
Joshi, Jamesjjoshi@pitt.eduJJOSHI0000-0003-4519-9802
Date: September 2011
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Journal of Information & Knowledge Management
Volume: 10
Number: 03
Publisher: World Scientific Pub Co Pte Lt
Page Range: 273 - 283
DOI or Unique Handle: 10.1142/s0219649211002985
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0219-6492
Date Deposited: 08 Aug 2012 14:33
Last Modified: 04 Aug 2020 22:55


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