Robustness analysis of privacy-preserving model-based recommendation schemes

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.