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.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- Robustness of Trust Models and Combinations for Handling Unfair Ratings
Wee Keong Ng
- Springer Berlin Heidelberg
Neuer Inhalt/© ITandMEDIA