Securing rating aggregation systems using statistical detectors and trust

Authors:
Yafei Yang;Yan Sun;Steven Kay;Qing Yang
Affiliations:
Qualcomm Inc., San Diego, CA;Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI;Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI;Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI
Venue:
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Year:
2009

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Abstract

Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of regular ratings is relatively small and unfair ratings can contribute to a significant portion of the overall ratings. Furthermore, the lack of unfair rating data from real human users is another obstacle toward realistic evaluation of defense mechanisms. In this paper, we propose a set of statistical methods to jointly detect collaborative unfair ratings in product-rating type online rating systems. Based on detection, a framework of trust-assisted rating aggregation system is developed. Furthermore, we collect unfair rating data from real human users through a rating challenge. The proposed system is evaluated through simulations as well as experiments using real attack data. Compared with existing schemes, the proposed system can significantly reduce negative impact from unfair ratings.