一种细粒度的基于灰色关联度的P2P信任模型①

已有的P2P网络信任模型过于粗糙，对反馈评价进行综合的能力不足。针对这一问题，提出了一种细粒度的基于灰色关联度的P2P信任模型GM—TRUST，根据节点的兴趣和专长将节点化分为不同的域，通过对具体服务各属性评价的综合得出直接信任。引入记忆因子来刻画信任随时间衰减的特性，并利用基于灰色相关度的方法来量化推荐信任的准确度。分析与实验均表明本模型与以往的信任模型相比，能够更准确地评估出节点的信任值，对动态恶意节点和不诚实反馈节点的攻击具有很好的抑制能力。

A Fine-Grained Trust Model for P2P Networks Based on Grey Relation

The present peer-to-peer trust models are too coarse and lack ability of synthesizing the feedback. This paper presents a new fine-grained trust model for P2P networks based on grey relation. The peers are divided into different regions according to interest and speciality, and the direct trust is gained by colligating the evaluation for all attributes of one service. A memory factor is introduced to describe the characteristic that trust trail off along with time. The grey relation is used to measure the accurateness of feedback. ,Analysis and experiments show that compared with existing models, the proposed model evaluates peer＇s trust value more accurately and it has better capability to resist the attack by the dynamic malice peers and peer＇s dishonest feedback.