摘要
为了有效地评估P2P网络中节点的信誉,提出了一种基于激励与惩罚机制的信誉计算模型。该信誉模型的计算数据源包括节点的直接交易经历和其他节点的推荐数据,推荐数据包括推荐节点与目标节点的直接交易经历以及它们在交易前曾经查询的数据。通过赋予这些数据不同的权重,使用相对的激励与惩罚机制综合计算节点的信誉。实验结果表明:该模型不仅可以遏制恶意节点,而且可以保证较高的交易成功率。
For efficient evaluating the reputation of the target peers in Peer-to-Peer environments,an encourage-and-punish-based computational reputation model is proposed.Th proposed model considers not only the direct transactions,but also the recommending data of other peers,which includes their direct transactions with target peers and their querying results before their transactions.By different weights on these data and the measure of relative encouragement and punishment,the integrated reputation of peers may be computed.The experimental results show that the present model can not only hold back malicious peers,but also ensure higher transaction rate.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2010年第6期801-806,共6页
Journal of East China University of Science and Technology
基金
国家自然科学基金(60473055
60773094)
上海市曙光计划(07SG32)
关键词
P2P
信誉
推荐
激励
惩罚
P2P
reputation
recommendation
encouragement
punishment
