Towards a comprehensive testbed to evaluate the robustness of reputation systems against unfair rating attack

Evaluation of the effectiveness and robustness of reputation systems is important for the trust research community. However, existing testbeds are mainly simulation based and not flexible to perform robustness evaluation, and none of them is specifically designed to evaluate the robustness of reputation systems against unfair rating attacks. In this paper, we propose a novel comprehensive testbed by simulating three types of environments (simulated environments, real environments with simulated unfair rating attacks, and real environments with detected unfair ratings). The testbed incorporates sophisticated deception models and unfair rating attack models, and introduces several performance metrics to fully test and compare the effectiveness and robustness of different reputation systems. We also provide two case studies to demonstrate the usage of partial features of our proposed testbed.

[1]  A. Jøsang,et al.  Challenges for Robust Trust and Reputation Systems , 2009 .

[2]  Jie Zhang,et al.  Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach , 2008, Electron. Commer. Res. Appl..

[3]  Chunyan Miao,et al.  iCLUB: an integrated clustering-based approach to improve the robustness of reputation systems , 2011, AAMAS.

[4]  Ee-Peng Lim,et al.  Detecting product review spammers using rating behaviors , 2010, CIKM.

[5]  Nicholas R. Jennings,et al.  TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources , 2006, Autonomous Agents and Multi-Agent Systems.

[6]  Qinyuan Feng,et al.  RepTrap: a novel attack on feedback-based reputation systems , 2008, SecureComm.

[7]  Jie Zhang,et al.  Robustness of Trust Models and Combinations for Handling Unfair Ratings , 2012, IFIPTM.

[8]  Laurent Vercouter,et al.  A specification of the Agent Reputation and Trust (ART) testbed: experimentation and competition for trust in agent societies , 2005, AAMAS '05.

[9]  A. Jøsang,et al.  Filtering Out Unfair Ratings in Bayesian Reputation Systems , 2004 .

[10]  Audun Jøsang,et al.  AIS Electronic Library (AISeL) , 2017 .

[11]  Robin Cohen,et al.  TREET: the Trust and Reputation Experimentation and Evaluation Testbed , 2010, Electron. Commer. Res..

[12]  Munindar P. Singh,et al.  Detecting deception in reputation management , 2003, AAMAS '03.

[13]  Babak Esfandiari,et al.  A Model for a Testbed for Evaluating Reputation Systems , 2011, 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications.