Privacy Preserving Collaborative Filtering with k-Anonymity through Microaggregation

Collaborative Filtering (CF) is a recommender system which is becoming increasingly relevant for the industry. Current research focuses on Privacy Preserving Collaborative Filtering (PPCF), whose aim is to solve the privacy issues raised by the systematic collection of private information. In this paper, we propose a new microaggregation-based PPCF method that distorts data to provide k-anonymity, whilst simultaneously making accurate recommendations. Experimental results demonstrate that the proposed method perturbs data more efficiently than the well-known and widely used distortion method based on Gaussian noise addition.