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A Distributed Trust-based Reputation Model in P2P System

机译:P2P系统中基于分布式信任的信誉模型

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The P2P system is an anonymous and dynamic system, thus, some malicious behaviour can''t be punished. In order to restrict the malicious behaviour in the P2P system, researchers have focused on establishing effective reputation systems. However, the present reputation system can''t avoid the trick of the false reputation feedback. We propose a distributed trust-based reputation model in p2p system (TBRM) to avoid it. The TBRM algorithm differentiated the node''s capability of providing honest quality by the nodes reputation value, and the honest evaluation by the trust value. In our model, the reputation value represented the resource quality of the provider, thus, other nodes would like this node have low reputation value. At this time, the false reputation feedback happened. In this paper, we used the trust value to restrict the false reputation feedback. For the nodes with low trust value was difficult to get the required resource, we punished the false reputation feedback by low their trust value. We show by both theoretical analysis and simulations that the proposed TBRM algorithm can get quick convergence which is 11 times, high equity for both low and high reputation nodes, and can get a high successful rate of file-downloading. When the malicious nodes'' rate is 80%, the proposed TBRM is about 95% successful rate, compared to the algorithm without reputation who is only 27%.
机译:P2P系统是一个匿名和动态的系统,因此,一些恶意行为可能不会受到惩罚。为了限制P2P系统中的恶意行为,研究人员专注于建立有效的声誉系统。但是,目前的声誉系统无法避免虚假声誉反馈的伎俩。我们提出了一种在P2P系统(TBRM)中的分布式信任信誉模型,以避免它。 TBRM算法差异化了节点通过节点声誉值提供诚实质量的能力,并通过信任价值进行诚实评估。在我们的模型中,声誉值表示提供者的资源质量,因此,其他节点希望此节点具有低声誉值。此时,错误的声誉反馈发生了。在本文中,我们使用信任值来限制错误的声誉反馈。对于具有低信任值的节点难以获得所需资源,我们通过低信任价值惩罚错误的声誉反馈。我们通过理论分析和模拟显示,所提出的TBRM算法可以快速收敛,这是低于低声誉节点的11倍,高等权益,并且可以获得高成功的文件下载率。当恶意节点的速率为80%时,建议的TBRM成功率约为95%,与算法相比只有27%的声望。

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