Research on adaptive prediction model of dynamic trust relationship in open distributed systems

In the open systems, trust 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 forecast trust-ship accurately. In this paper, firstly, a new and adaptive trusted decision-making method based on historical evidences window is proposed, which not only can reduce the risk and improve system efficiency, but also can solve trust measurement and forecasting problem when the direct evidences are insufficient. Then, predicting model use a feedback trust information aggregating algorithm based on Direct Trust Tree (DTT). Lastly, a new parameter, feedback factor, is introduced to adjust the weight of feedback trust automatically, which overcomes the shortage of traditional method, in which the weight is set up by subjective manners, and makes the model has a better rationality and a higher practicability. Simulation's results show that, compared to the existing trust metrics, this model has remarkable enhancements in the trust decision's accuracy.