P2P reputation management: Probabilistic estimation vs. social networks

Abstract

Managing trust is a key issue for a wide acceptance of P2P computing, particularly in critical areas such as e-commerce. Reputation-based trust management has been identified in the literature as a viable solution to the problem. The current work in the field can be roughly divided into two groups: social networks that rely on aggregating the entire available feedback in the network in hope achieving as much robustness against possible misbehavior as possible and probabilistic models that rely on the well known probabilistic estimation techniques but use only a limited fraction of the available feedback. In this paper we provide first an overview of these techniques and then a comprehensive comparison of the two classes of approaches. We test their performance against various classes of collusive peer behavior and analyze their properties with respect to the implementation costs they incur and trust semantics they offer to the decision makers.