Privacy Protection for Recommendation System: A Survey

Abstract

Recommendation system have become one of the most well-liked and accepted way to solve overload of information or merchandise. By collecting user's personal data for processing, suitable lists of information or merchandise are provided to the potential consumers. For online business, recommendation systems have become an extremely effective revenue driver and developed rapidly. Although recommendation systems are great beneficial, directly exposing privacy data to the recommender may lead to leakage of privacy and cause risks. Therefore, quality of recommendation and privacy protection are both important metrics in recommendation. In this paper we present a review investigating development in recommendation systems with privacy protection, including the definition of privacy, classification of privacy leakage, taxonomy of privacy, measuring of privacy risk, policies for privacy protection, approaches of privacy protection and models of privacy protection. We also speculate on the future direction.