学术
一种基于推荐证据的有效抗攻击P2P网络信任模型
田春岐[1,2] 邹仕洪[2] 王文东[2] 程时端[2]
[1]同济大学嵌入式系统与服务计算教育部重点实验室,上海201804 [2]北京邮电大学网络与交换技术国家重点实验室宽带网研究中心,北京100876
摘 要:
提出一种基于推荐证据的对等网络（Peer-to—Peer，P2P）信任模型RETM（Recommendation Evidencebased Trust Modelfor P2P networks），解决了基于推荐的信任模型中普遍存在的在汇聚推荐信息时无法处理不确定性信息以及强行组合矛盾推荐信息引起的性能下降问题，同时，RETM采取推荐证据预处理措施，在合成之前有效过滤了无用的以及误导性的推荐信息，使得该模型具有一定的抗攻击性能．在推荐信息的查找问题上，RETM提出了基于反馈信息的概率查找算法，该算法在降低了网络带宽开销的情况下，提高了信息查询的准确率．实验证明RETM较已有的信任机制在系统成功交易率、模型的安全性等问题上有较大改进。[著者文摘]
文章出处:
《计算机学报》-2008年31卷2期 -270-281页
栏目信息:
分 类 号:
A New Trust Model Based on Recommendation Evidence for P2P Networks
TIAN Chun-Qi ZOU Shi-Hong WANG Wen-Dong CHENG Shi-Duan（Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804;Broadband Network Research Center, State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing 100876）
Abstract:
A novel trust model for P2P networks, namely RETM, a Recommendation Evidence based Trust Model, is presented in this paper. It solves some problems, for instance, not invalidly aggregating incompatible recommendation information and dealing with uncertainty of information in the reputation-based P2P trust model. Before combining the evidences, RETM will filter out noisy recommendation information, and moreover the method makes RETM more robust. In addition a feedback-based probabilistic searching algorithm is proposed to find the recommendation information, which improves the searching success rate and lowers the network traffic. Theoretical analyses and experimental results show that, compared to the current some trust models, the proposed model RETM has advantages in modeling dynamic trust relationship and aggregating recommendation information, moreover, is more robust on trust security problems and more advanced in successful transaction rate.[著者文摘]
Key words:
D-S theory of evidence; P2P; trust; reputation; local trust value
基金资助:
本课题得到国家“九七三”重点基础研究发展规划项目基金（2003CB314806）和国家自然科学基金（60603060,60502037,90604019）资助.
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