考虑信任可靠度的分布式动态信任管理模型

分布式动态信任模型作为适用于云计算环境下的访问管理机制已经得到广泛研究，然而现有的许多信任模型忽视了对信任数据可靠性的评估，导致推荐信任不可靠时出现模型失效.针对这一问题，本文提出了一种新的考虑信任可靠度的分布式动态信任管理模型DDTM-TR.DDTM-TR模型首先使用可靠度对信任进行评估，降低不可靠数据对直接信任、推荐信任、综合信任计算的影响.然后，选择多个待选节点计算它们的综合信任，并以计算出的综合信任为概率，随机选择待选节点进行交互.最后，在交互结束后，根据交互满意度反馈修正节点的可靠度.仿真实验表明，DDTM-TR模型在处理恶意服务、恶意推荐都优于对比模型并且能通过反馈算法进一步降低判断的失败率.

Distributed Dynamic Trust Management Model Based on Trust Reliability

Distributed dynamic trust model, as a new access management mechanism which applies to cloud computing environment, has been studied extensively. However, many existing trust models ignore the reliability of trust data and lead to failure when facing malicious recommendation. To solve this problem, we propose a new model named DDTM-TR (Distributed Dynamic Trust Management Model based on Trust Reliability). Firstly, in order to reduce the bad effects of unreliable data on direct trust, recommendation trust and integrated trust, we evaluate the reliability of trust data. Second, several candidate nodes are selected and their integrated trust values are calculated. And then one of them is selected randomly according to the stochastic decision algorithm based on their integrated trust values. Finally, the node reliability is updated according to the feedback after the interaction. The experiments demonstrate that DDTM-TR model performs better than the comparison models in resisting malicious service, malicious recommendation and the failure rate can be reduced further by the feedback algorithm.