On the modeling of honest players in reputation systems

The effectiveness of reputation-based trust management fundamentally relies on the assumption that an entity's future behavior may be predicted based on its past behavior. Though many reputation-based trust schemes have been proposed, they can often be easily exploited, as an attacker may adapt its behavior and make the above assumption invalid. In this paper, we build a statistic model for honest entities in decentralized systems, which serves as a profiling tool to identify suspicious entities. It can be combined with existing trust schemes to ensure that they are applied to entities whose transaction records are consistent with the statistic model. This approach limits the manipulation capability of adversaries, and thus can greatly improve the quality of reputation-based trust assessment.