Shilling attack design and detection on masked binary data

Privacy-preserving collaborative filtering methods are effectual ways of coping with information overload problem while protecting confidential data. Their success depends mainly on the quality of the collected data for filtering purposes. Malicious entities might create fake profiles (noise data) and insert user-item matrices of such filtering schemes. Hence, shilling attacks play an important role on the quality of data. Designing effective shilling attacks, developing methods to detect them, and performing robustness analysis of privacy-preserving collaborative filtering methods are receiving increasing attention. In this thesis, six well-known shilling attack models are modified in order to attack binary masked databases in privacy-preserving collaborative filtering methods. Three attack design approaches are proposed. The attack profiles, generated by such schemes, are applied to naïve Bayesian classifier-based collaborating filtering scheme with privacy. A novel shilling attack detection scheme based on classification is proposed to detect fake profiles. Attributes derived from user profiles are utilized for detecting shill profiles. Empirical results show that designing effective shilling attacks is still possible on binary masked data. The proposed detection method is able to successfully detect fake profiles. Keywords: Shilling Attack, Collaborative Filtering, Privacy, Binary Data, Detection, Robustness