GGRA: a grouped gossip-based reputation aggregation algorithm

Abstract

An important issue in P2P networks is the existence of malicious nodes that decreases the performance of such networks. Reputation system in which nodes are ranked based on their behavior, is one of the proposed solutions to detect and isolate malicious (low ranked) nodes. Gossip Trust is an interesting previously proposed algorithm for reputation aggregation in P2P networks based on the concept of gossip. Despite its important contribution, this algorithm has deficiencies especially with high number of nodes that leads to high execution time and low accuracy in the results. In this paper, a grouped Gossip based Reputation Aggregation (GGRA) algorithm is proposed. In GGRA, Gossip Trust is executed in each group between group members and between groups instead of executing in the whole network. Due to the reduction in the number of nodes and using strongly connected graph instead of a weakly one, gossip algorithm in GGRA is executed quickly. With grouping, not only reputation aggregation is expected to be more scalable, but also because of the decrement in the number of errors of the gossiped communication, the results get more accurate. The evaluation of the proposed algorithm and its comparison with Gossip Trust confirms the expected results.