一种对等网中基于相互信任的两层信任模型

在P2P 系统中,声誉模型是建立节点间信任关系的重要方法之一,但现有的P2P 声誉模型几乎都是纯分散式的,具有信任收敛慢、信任管理复杂和网络开销大等缺点.在TLT(two-level trust)中,节点自发组织为信任簇,信任评价以簇为单位.每个簇由簇首和成员节点组成,簇首和成员节点之间是一种相互信任的关系:簇首为了提高自身的簇间服务信任,利用簇内服务信任观察成员节点的服务性能,过滤恶意的成员节点;成员节点为了提高服务声誉和接受更好的服务,利用代理信任考察簇首的管理能力.分析和仿真结果表明:在TLT 中,节点的信任值收敛快,恶意行为能够被快速识别;TLT 可扩展性好,如信任管理简单和网络开销小.

Two-Level Trust Model Based on Mutual Trust in Peer-to-Peer Networks

The reputation model is one of the most important methods that can be used to construct trust between peers in peer-to-peer systems. However, almost all reputation models for P2P applications are purely decentralized.They have many defects such as slow convergence speed of trust in node, complicated trust management and overwhelming network cost. So to solve these problems TLT (two-level trust), a two-level trust model, is proposed in this paper. In TLT a series of trust clusters are spontaneously formed that are the minimum unit of trust evaluation. Every trust cluster includes some members and a cluster header. There is a mutual trust relationship between the cluster header and member node. For example, in order to increase inter-cluster service trust the cluster header checks the service performance of members and eliminates malicious members by using the concept of intra-cluster service trust; while member nodes, aiming to heighten the service reputation and receive good quality ervices, also examine the management capability of the cluster header and isolate the malicious cluster headers by employing the concept of proxy trust. Analyses and simulations show that malicious behaviors can be quickly identified in TLT because of the fast convergence speed of trust value and TLT is scalable because of its simple trust management and small network overhead.