A connection management protocol for promoting cooperation in Peer-to-Peer networks
Computer Communications
EcoRare: An Economic Incentive Scheme for Efficient Rare Data Accessibility in Mobile-P2P Networks
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
A survey of attack and defense techniques for reputation systems
ACM Computing Surveys (CSUR)
FairTorrent: bringing fairness to peer-to-peer systems
Proceedings of the 5th international conference on Emerging networking experiments and technologies
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Distributed and Parallel Databases
TD-trust: a time domain based P2P trust model
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Peer-to-peer indirect reciprocity via personal currency
Journal of Parallel and Distributed Computing
Targeted and scalable information dissemination in a distributed reputation mechanism
Proceedings of the seventh ACM workshop on Scalable trusted computing
FairTorrent: a deficit-based distributed algorithm to ensure fairness in peer-to-peer systems
IEEE/ACM Transactions on Networking (TON)
A Collaborative Replication Approach for Mobile-P2P Networks
International Journal of Handheld Computing Research
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A number of solutions have been proposed to address the free-riding problem in peer-to-peer file sharing systems. The solutions are either imperfect 驴 they allow some users to cheat the system with malicious behavior, or expensive 驴 they require human intervention, require servers, or incur high mental transaction costs. We propose a method to address these weaknesses. Specifically, we introduce a utility function to capture contributions made by a user and an auditing scheme to ensure the integrity of a utility function驴s values. Our method enables us to reduce cheating by a malicious peer: we show that our approach can efficiently detect malicious peers with a probability over 98%