The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Exchange-Based Incentive Mechanisms for Peer-to-Peer File Sharing
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Free Riding on Gnutella Revisited: The Bell Tolls?
IEEE Distributed Systems Online
Trusted P2P Transactions with Fuzzy Reputation Aggregation
IEEE Internet Computing
Journal of the American Society for Information Science and Technology
PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing
IEEE Transactions on Parallel and Distributed Systems
Counteracting free riding in Peer-to-Peer networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Future Generation Computer Systems
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Peer-to-Peer (P2P) networking is an alternative to the cloud computing for relatively more informal trade. One of the major obstacles to its development is the free riding problem, which significantly degrades the scalability, fault tolerance and content availability of the systems. Bartering exchange ring based incentive mechanism is one of the most common solutions to this problem. It organizes the users with asymmetric interests in the bartering exchange rings, enforcing the users to contribute while consuming. However the existing bartering exchange ring formation approaches have inefficient and static limitations. This paper proposes a novel cluster based incentive mechanism (CBIM) that enables dynamic ring formation by modifying the Query Protocol of underlying P2P systems. It also uses a reputation system to alleviate malicious behaviors. The users identify free riders by fully utilizing their local transaction information. The identified free riders are blacklisted and thus isolated. The simulation results indicate that by applying the CBIM, the request success rate can be noticeably increased since the rational nodes are forced to become more cooperative and the free riding behaviors can be identified to a certain extent.