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On Studying Front-Peer Attack-Resistant Trust and Reputation Mechanisms Based on Enhanced Spreading Activation Model in P2P Environments

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Advances in Web and Network Technologies, and Information Management (APWeb 2007, WAIM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4537))

Abstract

In this paper, we proposed two enhanced trust propagation and reputation ranking approaches based on spreading activation model to mitigate the effect of front peer. By front peer it means that these malicious colluding peers always cooperate with others in order to increase their reputation, and then provide misinformation to promote actively malicious peers. One approach is to use adaptive spreading factor to reflect the peer’s recommendation ability according to behaviors of the peer’s direct/indirect children in trust graph; another way is to investigate the feasibility of propagating distrust to effectively combat front peer. Preliminary simulation results show that those approaches can identify and mitigate the attack of front peer.

Research supported by the NSFC Grants 60472067, JiangSu education bureau (5KJB510091) and State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (BUPT).

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Kevin Chen-Chuan Chang Wei Wang Lei Chen Clarence A. Ellis Ching-Hsien Hsu Ah Chung Tsoi Haixun Wang

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© 2007 Springer-Verlag Berlin Heidelberg

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Wang, Y., Hori, Y., Sakurai, K. (2007). On Studying Front-Peer Attack-Resistant Trust and Reputation Mechanisms Based on Enhanced Spreading Activation Model in P2P Environments. In: Chang, K.CC., et al. Advances in Web and Network Technologies, and Information Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72909-9_22

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  • DOI: https://doi.org/10.1007/978-3-540-72909-9_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72908-2

  • Online ISBN: 978-3-540-72909-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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