Leveraging node properties in random walks for robust reputations in decentralized networks

Reputation systems are essential to establish trust and to provide incentives for cooperation among users in decentralized networks. In these systems, the most widely used algorithms for computing reputations are based on random walks. However, in decentralized networks where nodes have only a partial view of the system, random walk-based algorithms can be easily exploited by uncooperative and malicious nodes. Traditionally, a random walk only uses information about the adjacency of nodes, and ignores their structural and temporal properties. Nevertheless, the properties of nodes indicate their reliability, and so, random walks using much richer information about the nodes than simple adjacency may achieve higher robustness against malicious exploitations. In this paper, we introduce the properties of nodes that are indicative of their reliability, and we propose a scheme to integrate these properties into the traditional random walks. Particularly, we consider two common malicious exploitations of random walks in decentralized networks, uncooperative nodes and Sybil attacks, and we show that integrating node properties into random walks results in much more robust reputation systems. Our experimental evaluation in synthetic graphs and graphs derived from real-world networks covering a significant number of users, shows the effectiveness of the resulting biased random walks.