基于加权多属性云的主观信任度量方法

Subjective trust metric based on weighted multi-attribute cloud

针对现有基于云模型的信任度量方法缺乏对信任的多粒度及时效性考虑,提出一种基于加权多属性云的信任度量方法。首先,以多属性信任云对信任度量粒度进行细化,并在实体信任计算中引入时间衰减函数;其次,对信任云的多属性综合与多路径合并得到实体的最终信任云;最后,采用云的相似度比较算法与信任基准云进行比较,得到实体的信任等级。通过网格计算环境下的仿真实验表明,当节点交互100次时,采用加权多属性云的度量方法的交互成功率为80%,比传统方法提升了15%。仿真结果表明,采用加权多属性云的度量方法可以提高信任度量的准确度。

The existing trust metrics based on the cloud model lack of the muhi-granularity and timeliness consideration. For this reason, a trust metric algorithm based on weighted muhi-attribute cloud was proposed. First of all, multi-attribute trust cloud on trust metric was used to refine the grain size, and time decay function was introduced in the entity trust computing; second, multi-attribute comprehensive and multi-path merge was used to get entity ultimate trust cloud. Finally, the trust level of the entity was obtained by comparison with basis trust cloud using cloud similarity comparison algorithm. The simulation results under grid computing environment show that when the node interaction reached 100 times, the interaction success rate of weighted multi-attribute cloud metric was 80%, significantly higher than 65% of the traditional method. The simulation results show that the cloud using the weighted multi-attribute trust cloud metric measurement method can improve the accuracy of trust metric.