Decentralized Trust Management
Decentralized Trust Management
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DOI：
10.1145/3362168
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Xinxin Fan;Ling Liu;Rui Zhang;Quanliang Jing;Jingping Bi
中科院分区：
文献类型：
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作者：
Xinxin Fan;Ling Liu;Rui Zhang;Quanliang Jing;Jingping Bi
分散式信任管理被用作开放协作系统中人类或智能机器辅助决策的参考基准。在任何给定的时间段内，每个参与者只能与少数其他参与者交互。仅仅依靠直接信任可能会经常诉诸于随机的团队组建。因此，信任聚合变得至关重要。它可以利用分散的信任管理，根据过去的交易经验了解每个参与者的间接信任。本文从三个方面介绍了分散式信任管理的替代设计及其效率和鲁棒性。首先，我们研究了六种常见威胁模型的风险因素和不利影响。其次，我们回顾了代表性的信任聚合模型和信任度量。第三，我们提出了一个深入的分析和比较这些参考信任聚合方法的有效性和鲁棒性。我们通过形式分析和实验评估来展示我们的比较研究结果。这项全面的研究推进了对当前和未来威胁的不利影响以及不同信任度量的鲁棒性的理解。它也可以作为研究和开发下一代信任聚合算法和服务的指导方针，在预期的风险因素和有害的威胁。
Decentralized trust management is used as a referral benchmark for assisting decision making by human or intelligence machines in open collaborative systems. During any given period of time, each participant may only interact with a few other participants. Simply relying on direct trust may frequently resort to random team formation. Thus, trust aggregation becomes critical. It can leverage decentralized trust management to learn about indirect trust of every participant based on past transaction experiences. This article presents alternative designs of decentralized trust management and their efficiency and robustness from three perspectives. First, we study the risk factors and adverse effects of six common threat models. Second, we review the representative trust aggregation models and trust metrics. Third, we present an in-depth analysis and comparison of these reference trust aggregation methods with respect to effectiveness and robustness. We show our comparative study results through formal analysis and experimental evaluation. This comprehensive study advances the understanding of adverse effects of present and future threats and the robustness of different trust metrics. It may also serve as a guideline for research and development of next-generation trust aggregation algorithms and services in the anticipation of risk factors and mischievous threats.