Based on human psychological cognitive behavior, a Comprehensive and Adaptive Trust （CAT） model for largescale P2P networks is proposed. Firstly, an adaptive trusted decision-making method based on HEW （Historical ...Based on human psychological cognitive behavior, a Comprehensive and Adaptive Trust （CAT） model for largescale P2P networks is proposed. Firstly, an adaptive trusted decision-making method based on HEW （Historical Evidences Window） is proposed, which can not only reduce the risk and improve system efficiency, but also solve the trust forecasting problem when the direct evidences are insufficient. Then, direct trust computing method based on IOWA （Induced Ordered Weighted Averaging） operator and feedback trust converging mechanism based on DTT （Direct Trust Tree） are set up, which makes the model have a better scalability than previous studies. At the same time, two new parameters, confidence factor and feedback factor, are introduced to assign the weights to direct trust and feedback trust adaptively, which overcomes the shortage of traditional method, in which the weights are assigned by subjective ways. Simulation results show that, compared to the existing approaches, the proposed model has remarkable enhancements in the accuracy of trust decision-making and has a better dynamic adaptation capability in handling various dynamic behaviors of peers.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.60873071the National High-Tech Research and Development 863 Program of China under Grant No.2008AA01Z410.
文摘Based on human psychological cognitive behavior, a Comprehensive and Adaptive Trust （CAT） model for largescale P2P networks is proposed. Firstly, an adaptive trusted decision-making method based on HEW （Historical Evidences Window） is proposed, which can not only reduce the risk and improve system efficiency, but also solve the trust forecasting problem when the direct evidences are insufficient. Then, direct trust computing method based on IOWA （Induced Ordered Weighted Averaging） operator and feedback trust converging mechanism based on DTT （Direct Trust Tree） are set up, which makes the model have a better scalability than previous studies. At the same time, two new parameters, confidence factor and feedback factor, are introduced to assign the weights to direct trust and feedback trust adaptively, which overcomes the shortage of traditional method, in which the weights are assigned by subjective ways. Simulation results show that, compared to the existing approaches, the proposed model has remarkable enhancements in the accuracy of trust decision-making and has a better dynamic adaptation capability in handling various dynamic behaviors of peers.
