COMPARS: toward an empirical approach for comparing the resilience of reputation systems

Abstract

Reputation is a primary mechanism for trust management in decentralized systems. Many reputation-based trust functions have been proposed in the literature. However, picking the right trust function for a given decentralized system is a non-trivial task. One has to consider and balance a variety of factors, including computation and communication costs, scalability and resilience to manipulations by attackers. Although the former two are relatively easy to evaluate, the evaluation of resilience of trust functions is challenging. Most existing work bases evaluation on static attack models, which is unrealistic as it fails to reflect the adaptive nature of adversaries (who are often real human users rather than simple computing agents). In this paper, we highlight the importance of the modeling of adaptive attackers when evaluating reputation-based trust functions, and propose an adaptive framework - called COMPARS - for the evaluation of resilience of reputation systems. Given the complexity of reputation systems, it is often difficult, if not impossible, to exactly derive the optimal strategy of an attacker. Therefore, COMPARS takes a practical approach that attempts to capture the reasoning process of an attacker as it decides its next action in a reputation system. Specifically, given a trust function and an attack goal, COMPARS generates an attack tree to estimate the possible outcomes of an attacker's action sequences up to certain points in the future. Through attack trees, COMPARS simulates the optimal attack strategy for a specific reputation function f, which will be used to evaluate the resilience of f. By doing so, COMPARS allows one to conduct a fair and consistent comparison of different reputation functions.