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In electronic marketplaces, after each transaction buyers will rate theproducts provided by the sellers. To decide the most trustworthy sellers totransact with, buyers rely on trust models to leverage these ratings toevaluate the reputation of sellers. Although the high effectiveness ofdifferent trust models for handling unfair ratings have been claimed by theirdesigners, recently it is argued that these models are vulnerable to moreintelligent attacks, and there is an urgent demand that the robustness of theexisting trust models has to be evaluated in a more comprehensive way. In thiswork, we classify the existing trust models into two broad categories andpropose an extendable e-marketplace testbed to evaluate their robustnessagainst different unfair rating attacks comprehensively. On top of highlightingthe robustness of the existing trust models for handling unfair ratings is farfrom what they were claimed to be, we further propose and validate a novelcombination mechanism for the existing trust models, Discount-then-Filter, tonotably enhance their robustness against the investigated attacks.

Safeguarding E-Commerce against Advisor Cheating Behaviors: Towards More Robust Trust Models for Handling Unfair Ratings