Trustworthy knowledge diffusion model based on risk discovery on peer-to-peer networks

Knowledge management systems have been inter-networked with each other on distributed environment, e.g., peer-to-peer (P2P) networks. However, as some of users take malicious actions, the corresponding information (or knowledge) on the P2P networks might be contaminated and distorted. In this paper, we propose a robust information diffusion (or propagation) model to detect the malicious peers from which the risks (e.g., information distortion) was originated on P2P networks. Thereby, we want to trace social interactions among peers to identify a recommendation flow and collect them. Given a set of recommendation flows, statistical sequence mining method is exploited to discover a certain social position which provides peculiar patterns on the P2P networks. For evaluating the proposed method, we conducted two experimentations with NetLogo simulation platform for risk discovery on social network.