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Heuristic Discovery of Role-Based Trust Chains in Peer-to-Peer Networks

机译:对等网络中基于角色的信任链的启发式发现

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Abstract: Credential chains are needed in trusted peer-to-peer (P2P) applications, where trust delegation must be established between each pair of peers at specific role level. Role-based trust is refined from the coarse-grained trust model used in most P2P reputation systems. This paper offers a novel heuristic-weighting approach to selecting the most likely path to construct a role-based trust chain. We apply history-sensitive heuristics to measure the path complexity and assess the chaining efficiency. We discover successive edges of a trust chain, adaptively, to match with the demands from various P2P applications. New heuristic chaining algorithms are developed for backward, forward, and bi-directional discovery of trust chains. Our heuristic chain discovery scheme shortens the search time, reduces the memory requirement, and enhances the chaining accuracy in scalable P2P networks. Consider a trust graph over N credentials and M distinct role nodes. Our heuristic trust-chain discovery algorithms require O(N2logN) search time and O(M) memory space, if the secondary heuristics are generated off-line in advance. These are improved from O(N3) search time and O(NM) space required in non-heuristic discovery algorithms by Li, Winsborough, and Mitchell (2003). Our analytical results are verified by extensive simulation experiments over typical classes of role-based trust graphs.
机译:摘要:受信任的对等(P2P)应用程序中需要凭证链,其中必须在特定角色级别的每对对等之间建立信任委托。基于角色的信任是从大多数P2P信誉系统中使用的粗粒度信任模型中提炼出来的。本文提供了一种新颖的启发式加权方法,以选择最可能的路径来构建基于角色的信任链。我们应用对历史敏感的启发式方法来测量路径复杂度并评估链接效率。我们自适应地发现信任链的连续边缘,以适应各种P2P应用程序的需求。开发了新的启发式链接算法,用于向后,转发和双向发现信任链。我们的启发式链发现方案缩短了搜索时间,减少了内存需求,并提高了可扩展P2P网络中的链接精度。考虑在N个凭据和M个不同角色节点上的信任图。如果辅助启发式方法是离线生成的,我们的启发式信任链发现算法需要O(N2logN)搜索时间和O(M)内存空间。 Li,Winsborough和Mitchell(2003)改进了非启发式发现算法所需的O(N3)搜索时间和O(NM)空间。我们的分析结果已通过对典型类别的基于角色的信任图进行的广泛模拟实验验证。

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