Adaptive Dynamic Trust Measurement and Prediction Model Based on Behavior Monitoring

Abstract

In the large-scale distributed systems,trust relationship model is one of the most complex concepts in social relationships,and it also is an abstract psychological cognitive process,involving assumptions,expectations,behavior and the environment,and other factors.So,it is very difficult to quantify and predict trust relationship accurately.In this paper,rough set theory and information entropy theory are combined and applied to the study of distributed dynamic trust measurement and prediction model based on behavior data.The new model works through analysis monitored behavior data by sensors directly,changes the traditional modeling thoughts,brakes away from the fetter of various subjective assumptions in traditional modeling methods,and overcomes the problem of inadequate handling capacity for multi-source behavior data in the traditional trust model.Simulating results shows that the new model can accurately implement trust measurement and prediction process between entities in open and complex distributed environment,and has a better scalable capacity of behavior data.