Robustness analysis of privacy-preserving model-based recommendation schemes |
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| Affiliation: | 1. Nagoya Institute of Technology, Department of Computer Science, Gokisho, Showa, Nagoya, Aichi, 466-8555, Japan;2. University of the Ryukyus, Department of Electrical Engineering, Nakagami, Nishihara, Okinawa, 903-0213, Japan;1. School of Control Science and Engineering, Dalian University of Technology, Dalian City, PR China;2. Department of Electrical Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Faculty of Phil. and Arts, University of Kragujevac, Jovana Cvijica bb, 34000 Kragujevac, Serbia;2. Faculty of Economics, University of Nis, Trg kralja Aleksandra Ujedinitelja 11, 18000 Nis, Serbia;3. Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac, Serbia;4. Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia;5. College of Applied Mechanical Engineering, Trstenik, Serbia;1. School of Economics and Management, Free University of Bozen-Bolzano, Bolzano, Italy;2. Institute of Mathematics, University of Warsaw, Warszawa, Poland;3. Department “Methods and Models for Economics, Territory and Finance”, Sapienza University of Rome, Rome, Italy |
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| Abstract: | Privacy-preserving model-based recommendation methods are preferable over privacy-preserving memory-based schemes due to their online efficiency. Model-based prediction algorithms without privacy concerns have been investigated with respect to shilling attacks. Similarly, various privacy-preserving model-based recommendation techniques have been proposed to handle privacy issues. However, privacy-preserving model-based collaborative filtering schemes might be subjected to shilling or profile injection attacks. Therefore, their robustness against such attacks should be scrutinized.In this paper, we investigate robustness of four well-known privacy-preserving model-based recommendation methods against six shilling attacks. We first apply masked data-based profile injection attacks to privacy-preserving k-means-, discrete wavelet transform-, singular value decomposition-, and item-based prediction algorithms. We then perform comprehensive experiments using real data to evaluate their robustness against profile injection attacks. Next, we compare non-private model-based methods with their privacy-preserving correspondences in terms of robustness. Moreover, well-known privacy-preserving memory- and model-based prediction methods are compared with respect to robustness against shilling attacks. Our empirical analysis show that couple of model-based schemes with privacy are very robust. |
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| Keywords: | Robustness Shilling Privacy Recommendation Model Collaborative filtering |
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