Decentralized Trust Management: Risk Analysis and Trust Aggregation

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 of 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 paper 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.

去中心化信任管理被用作开放协作系统中辅助人类或智能机器决策的参考基准。在任何给定时间段内，每个参与者可能仅与少数其他参与者交互。仅仅依赖直接信任可能经常导致随机组队。因此，信任聚合变得至关重要。它可以利用去中心化信任管理，根据过去的交易经验了解每个参与者的间接信任。本文从三个角度介绍了去中心化信任管理的替代设计及其效率和稳健性。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，我们回顾了具有代表性的信任聚合模型和信任度量指标。第三，我们对这些参考信任聚合方法在有效性和稳健性方面进行了深入分析和比较。我们通过形式分析和实验评估展示了我们的比较研究结果。这项综合研究增进了对当前和未来威胁的不利影响以及不同信任度量指标稳健性的理解。它也可作为在预期风险因素和恶意威胁的情况下研发下一代信任聚合算法和服务的指南。