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Decentralized Trust Management: Risk Analysis and Trust Aggregation

Authors:

Quanliang Jing,

Jingping BiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 1

Article No.: 2, Pages 1 - 33

https://doi.org/10.1145/3362168

Published: 06 February 2020 Publication History

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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 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.

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Decentralized Trust Management: Risk Analysis and Trust Aggregation

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 1

January 2021

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3382040

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 06 February 2020

Accepted: 01 September 2019

Revised: 01 September 2019

Received: 01 July 2018

Published in CSUR Volume 53, Issue 1

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