Privacy Preserving Collaborative Filtering with k-Anonymity through Microaggregation
Fran Casino, Josep Domingo‐Ferrer, Constantinos Patsakis, Domènec Puig, Agustí Solanas · 2013
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 micro aggregation-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.