Privacy Preserving Recommender Systems

Abstract

Recommender systems plays an important role today and are widely applied to a wide range of domains. Similar to most machine learning algorithms, recommendation systems rely on users’s data to train and to generate recommendations .Often, these data can be private and in case of a leakage, it could seriously cause harm to the users, making it a crucial mission to investigate privacy threats in recommender systems and implement defensive techniques to address them. In this work we highlight various recommendation systems types and techniques, then we present popular privacy preserving machine learning techniques. Finally, we summarize the different proposed techniques to preserve privacy in recent works, to provide a better insights and directions for future research