| 摘要: |
| P2P 网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P 名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名誉计算速度减慢;用数字来表示节点名誉的方式不够自然.实际上,名誉评价的用途是确定节点可信度的相对顺序.因此,提出了一种基于排名反馈的P2P 名誉评价机制RbRf(reputation based ranking feedback).针对RbRf 和其上的恶意攻击进行了数学建模和理论分析,结果表明,RbRf 中非恶意错误的影响随排名反馈的数量指数而衰减;一般恶意攻击对RbRf 的影响随排名反馈数量的多项式而减小;对于有意设计的共谋攻击,由于必须给RbRf 引入正确信息而导致了恶意攻击被有效中和.因此,RbRf 不仅由于不再反馈打分信息而不存在评分反馈引起的名誉评价问题(如不需要对反馈信息的可信度进行二次评价),而且具有更好的抵抗恶意攻击的能力.仿真实验验证了理论分析的结果. |
| 关键词: P2P 网络 名誉评价 排名反馈 共谋攻击 |
| DOI：10.3724/SP.J.1001.2011.03754 |
| 分类号: |
| 基金项目:国家自然科学基金(60803148); 中央高校基本科研业务费专项资助(HIT.NSRIF.2010.047) |
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| Reputation Model with Feedback of Ranking for P2P Systems |
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LI Zhi-Jun, JIANG Shou-Xu, LI Xiao-Yi
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School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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| Abstract: |
| Some peers may receive service and information of low-quality from other peers in peer-to-peer (or P2P) networks. Reputation evaluation is the normal method used to reduce the above phenomena. P2P reputation, based on score feedback, is defective because it can not distinguish the malicious feedback from the erring feedback returned by honest peers. It needs long time to converge the reputation and evaluate feedback. It is inflexible and unnatural to depict the reputation of a peer through a lot of numbers. In fact, the reputation is used to determine the rank of the peers. A reputation model called RbRf (reputation based ranking feedback) based on the rank feedback, is presented in this paper. Mathematical models unfolds in this paper show that the influence of erring feedbacks attenuates with the exponential function of RbRf. The influence of unintended malicious feedbacks is attenuated with the polynomial function in RbRf. The intended collusive feedbacks are counteracted by the correct information introduced by these feedbacks. In summary, the defection of score feedback, such as the need of a second evaluation of the trust of feedback, does not in RbRf any longer because the RbRf uses rank feedback, instead of score feedback, and the RbRf can achieve a better effect when resisting to malicious attacks. All results are verified by experimental data. |
| Key words: P2P network reputation evaluation rank feedback collusion attack |
