Decentralized Trust Management

Abstract

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.

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

“…the literature. Additionally, older studies[31,27] considered that trust models keep leveraging static thresholds instead of dynamic ones in the model decision making.Following this analysis, it can be concluded that the field of TMM matured in time, has clear definitions and terminology[5,34,6], methods and algorithms[31,32,30,29], and scope in terms of trust collaboration, threats and threat models[27,12,31,11]. Additionally, a pattern frequently observed after breaking down the studies in chronological order, is that the same open issues and gaps are still found and mentioned,VOLUME 11, 2023 …”

“…Our motivation relies on the significant amount of research papers published in the field of TMM, making it of crucial importance to systematise and review the acquired academic knowledge. Multiple previous works [12,6,13,7] already paved the road for a better understanding of how trust is measured, modelled and managed in complex DWNs. Con-sequently, the present work aims to explore and expand this effort through the following contributions:…”

Security and Privacy in Smart Farming: Challenges and Opportunities

“…Alternatively, secure lightweight multi-factor authentication protocols which are offered as a service [13], [59] are more realistic solutions in a smart farming network environment. In fact, an intermediary Certifying Authority (CA) can facilitate the authentication of a connecting device [60]. Such authentication mechanisms do not consume devices' limited resources for authentication processing, but also unauthorized devices will be prevented from connecting and accessing the network in an efficient way [61].…”

An inter-domain routing reputation model based on autonomous domain collaboration

“…信誉值动态更新函数计算如下: [16] 和 ReMSA [17] 中的 信誉聚合算法得到的 AS9498 信誉评价, 如图 5 所示. [30] 提出了 EigenTrust++ [31] , GroupTrust [32] 等方 案. 这些方法引入基于节点直接信誉评价相似度的节点信誉评价可采信度评估、设置信誉传播阈值控 制信誉聚合传播等措施, 以阻止不合理的信誉传播.…”