Leonit Zeynalvand, Tie Luo, Ewa Andrejczuk, Dusit Niyato, Sin G. Teo, and Jie Zhang. 2021. A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '21). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 1707–1708.
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 Blockchain 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.