Trust and Reputation Algorithms for Hierarchically Structured Peer-to-Peer Systems

Abstract

This research focuses on the redesign of trust and reputation algorithms in the context of hierarchically structured Peer-to-Peer (P2P) networks with Chord, a scalable P2P lookup service for Internet applications. Chord, which is an open source project, is an overlay network based on a distributed hash table(DHT), and all peers in Chord are arranged around a circle. In this work, we propose four adapted trust and reputation algorithms for hierarchically structured P2P networks: EigenTrust, PowerTrust, Absolute Trust and NodeRanking. EigenTrust is one of the most well-known trust and reputation algorithms, as well as the most simple. To calculate the reputation, EigenTrust needs to normalize trust and rely on pre-trusted peers. Like EigenTrust, PowerTrust relies on feedback and the use of a distributed ranking mechanism. It chooses a limited number of power nodes with a high reputation. By combining a random walk strategy and the power nodes, it improves accuracy of global reputation. AbsoluteTrust does not require normalization of trust, pretrusted peers or any centralized authority. Weighted average combined with feedback from peers is employed to determine trust. NodeRanking relies on both individual reputation and social relationship to compute the trust value. NodeRanking evaluates reputation using local information. A node's reputation value can be readily determined by the number of references from other nodes in the network. These adapted algorithms are capable of handling a huge number of nodes disseminated in different rings, which improves complexity and reduces the number of malicious nodes in a hierarchical context. Furthermore, we describe the components of the hierarchical model architecture and present and discuss the results from the experiments. These experiments are employed to verify and compare reduction of downloads from malicious peers, load distribution and residual curl in at structured networks and in hierarchically structured networks.