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

TL;DR

This paper evaluates the robustness of existing trust models in e-commerce against unfair rating attacks and introduces a new combination mechanism to improve their resilience.

Contribution

It classifies trust models, develops a testbed for comprehensive robustness evaluation, and proposes a novel Discount-then-Filter mechanism to enhance robustness.

Findings

Existing trust models are less robust than claimed.

The proposed combination mechanism improves robustness significantly.

The testbed enables systematic evaluation of trust model vulnerabilities.

Abstract

In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the reputation of sellers. Although the high effectiveness of different trust models for handling unfair ratings have been claimed by their designers, recently it is argued that these models are vulnerable to more intelligent attacks, and there is an urgent demand that the robustness of the existing trust models has to be evaluated in a more comprehensive way. In this work, we classify the existing trust models into two broad categories and propose an extendable e-marketplace testbed to evaluate their robustness against different unfair rating attacks comprehensively. On top of highlighting the robustness of the existing trust models for handling unfair ratings is far from what they were claimed to be, we further propose and validate a novel combination mechanism for the existing trust models, Discount-then-Filter, to notably enhance their robustness against the investigated attacks.