A Survey on Trust Modeling from a Bayesian Perspective
This work addresses the problem of fragmented understanding in trust modeling for agents in networked computing systems, but it is incremental as it synthesizes existing research rather than introducing new methods.
The paper tackles the lack of a comprehensive analysis of Bayesian trust models by presenting a survey that reviews the literature and highlights a generic Bayesian trust (GBT) modeling perspective, showing that all surveyed models can be cast as special cases of GBT.
In this paper, we are concerned with trust modeling for agents in networked computing systems. As trust is a subjective notion that is invisible, implicit and uncertain in nature, many attempts have been made to model trust with aid of Bayesian probability theory, while the field lacks a global comprehensive analysis for variants of Bayesian trust models. We present a study to fill in this gap by giving a comprehensive review of the literature. A generic Bayesian trust (GBT) modeling perspective is highlighted here. It is shown that all models under survey can cast into a GBT based computing paradigm as special cases. We discuss both capabilities and limitations of the GBT perspective and point out open questions to answer, with a hope to advance GBT to become a pragmatic infrastructure for analyzing intrinsic relationships among variants of trust models and developing novel tools for trust evaluation.