A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence
The prevalence of e-commerce applications poses new trust challenges that render traditional Trust and Reputation Management (TRM) approaches inadequate. The first challenge is that TRM is built on evidence (direct or indirect observations) but evidence is becoming increasingly sparse because nowadays users have many more venues to share information. This makes it hard to derive trust models that are robust to attacks such as whitewashing and Sybil attacks. Second, the cost of attacks has reduced significantly due to the widespread presence of bots in e-commerce applications, which tends to invalidate the traditional assumption that majority users are honest. In this paper, we propose a new TRM framework called BEQA, which uses Block chain to transform multiple disjoint and sparse sets of evidence into a single and dense evidence set. To address the second challenge, we introduce and formulate the cost of Sybil attacks using Blockchain transaction fees. In addition, we make a key observation that existing trust models have overlooked publicity (evidence originating from influencers) that exist in e-commerce applications. Thus, we formulate publicity as a whitewashing deposit such that a higher level of publicity will impose higher cost on Sybil attacks.
L. Zeynalvand et al., "A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence," Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (2021), vol. 3, pp. 1695 - 1696, Association for Computing Machinery (ACM), May 2021.
The definitive version is available at https://doi.org/10.5555/3463952.3464208
20th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '21 (2021: May 3-7, Virtual)
Keywords and Phrases
Blockchain; Sybil Attack; Trust Management; Whitewashing Attack
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Article - Conference proceedings
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07 May 2021