Probabilistic Estimation of Peers’ Quality and Behaviors for Subjective Trust Evaluation

The management of trust and quality in decentralized systems has been recognized as a key research area over recent years. In this paper, we propose a probabilistic computational approach to enable a peer in the system to model and estimate the quality and behaviors of the others subjectively according to its own preferences. Our solution is based on the use of graphical models to represent the dependencies among different QoS parameters of a service provided by a peer, the associated contextual factors, the innate behaviors of the reporters and their feedback on quality of the peer being evaluated. We apply the EM algorithm to learn the conditional probabilities of the introduced variables and perform necessary probabilistic inferences on the constructed model to estimate peer's quality and behaviors. Interestingly, our proposed framework can be shown as the generalization of many existing trust computational approaches in the literature with several additional advantages: first, it works well given few and sparse feedback data from the reporting peers; second, it also considers the dependencies among the QoS attributes of a peer, related contextual factors, and underlying behavioral models of reporters to produce more reliable estimations; third, the model gives outputs with well-defined semantics and useful meanings which can be used for many purposes, for example, it computes the probability that a peer is trustworthy in sharing its experiences or in providing a service with high quality level under certain environmental conditions.