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Volume 24 Issue 5
September  2009
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Xiao-Yong Li, Xiao-Lin Gui. A Comprehensive and Adaptive Trust Model for Large-Scale P2P Networks[J]. Journal of Computer Science and Technology, 2009, 24(5): 868-882.
Citation: Xiao-Yong Li, Xiao-Lin Gui. A Comprehensive and Adaptive Trust Model for Large-Scale P2P Networks[J]. Journal of Computer Science and Technology, 2009, 24(5): 868-882.
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A Comprehensive and Adaptive Trust Model for Large-Scale P2P Networks

  • Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China

Funds: This work is supported by the National Natural Science Foundation of China under Grant No. 60873071 and the National High-Tech Research and Development 863 Program of China under Grant No. 2008AA01Z410.
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  • Author Bio:

    Xiao-Yong Li is a Ph.D.candidate in Xi'an Jiaotong University in China. As the firstauthor, he has published more than thirty journal papers. In 2009,he is awarded outstanding graduates in Shaanxi Province. His currentresearch interests mainly include networks computing and trustedsystem.

    Xiao-Lin Gui is a professor and a Ph.D. supervisor in Xi'anJiaotong University. He has published more than eighty papers andobtained five patents and three software copyrights. In 2006, he isawarded New Century Excellent Talents in University (NCET). Now he is incharge of a project of the National High-Tech Research and Development 863Program and a project of the National Nature ScienceFoundation of China. Currently, he leads the Trusted ComputingTechnology Research Center (TCT Lab) at Xi'an Jiaotong University.His research interests include networks computing and trustedsystem.

  • Revised Date: May 12, 2009
  • Published Date: September 04, 2009
    Abstract
  • Based on human psychological cognitive behavior, a Comprehensive and Adaptive Trust (CAT) model for large-scale 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.
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