Tree-trust: a Novel and Scalable P2p Reputation Model Based on Human Cognitive Psychology

The key to the success of a P2P reputation system is the feedbacks aggregating mechanism. Based on human cognitive psychology, Tree-Trust, a novel reputation model is introduced, in which the concept of direct trust tree (DTT) is presented innovatively. The main contributions include: (1) in Tree-Trust, feedbacks are searched by using DTT instead of in broadcast way of other work, which makes the proposed model have a better scalability than exiting approaches; (2) two new parameters, quality factor and distance factor, are introduced to adjust the peers’ scale of aggregation computing automatically; (3) a novel self-feedback mechanism is used to integrate peers’ direct trust degree into reputation evaluation, which can overcome the difficulty of subjective assigning method for weights of the trust decision factors. Simulation’s results clearly show that, compared to the existing models, the proposed model is more robust on dynamic adaptability, and has remarkable enhancements in the scalability of system-scale.