A Collusion Mitigation Scheme for Reputation Systems

Abstract

Reputation management systems are in wide-spread use to regulate collaborations in cooperative systems. Collusion is one of the most destructive malicious behaviors in which colluders seek to affect a reputation management system in an unfair manner. Many reputation systems are vulnerable to collusion, and some model-specific mitigation methods are proposed to combat collusion. Detection of colluders is shown to be an NP-complete problem. In this paper, we propose the Colluders Similarity Measure (CSM) which is used by a heuristic clustering algorithm (the Colluders Detection Algorithm (CDA)) to detect colluders in O (n2m + n4) in which m and n are the total number of nodes and colluders, respectively. Furthermore, we propose architecture to implement the algorithm in a distributed manner which can be used together with compatible reputation management systems. Implementation results and comparison with other mitigation approaches show that our scheme prevents colluders from unfairly increasing their reputation and decreasing the reputation of the other nodes.