On the robustness of privacy-preserving collaborative filtering schemes

Privacy-preserving collaborative filtering has been receiving increasing attention. There are various algorithms providing accurate recommendations while preserving privacy. Like collaborative filtering algorithms, privacy-preserving collaborative filtering methods might be subjected to shilling attacks. Such attacks are employed by malicious users to increase/decrease the popularity of some target items. They might affect the overall performance of recommendation systems. Therefore, it is imperative to design such attacks with privacy concerns, determine how robust the privacy-preserving collaborative filtering schemes are, how to find out fake profiles, and analyze them. In this dissertation, designing shilling attacks with privacy concerns is studied. Also, robustness analysis of various privacy-preserving collaborative filtering schemes (memory-based, model-based, and hybrid methods) is performed. Determining fake or shilling profiles from perturbed databases is scrutinized. Besides employing the modified existing detection methods, a new shilling attack detection algorithm is proposed. Real data-based experiments are conducted for assessing the overall performance. Empirical outcomes show that designing effective shilling attacks with privacy concerns is possible. Also, existing detection methods can be effectively used to determine fake profiles from masked data. In addition, the novel detection method is successful on filtering out shilling profiles. Compared to memory-based and hybrid schemes, privacy-preserving model-based recommendation algorithms are very robust against shilling attacks.