A Survey of Privacy-Preserving Collaborative Filtering Schemes

Abstract

With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while providing predictions, various schemes have been proposed to estimate recommendations without deeply jeopardizing privacy. Such methods eliminate or reduce data owners' privacy, financial, and legal concerns by employing different privacy-preserving techniques. Although there are considerable numbers of studies focusing on privacy-preserving collaborative filtering schemes, there is no comprehensive survey investigating them with respect to different directions. In this survey, we mainly focus on studying various privacy-preserving recommendation methods according to the data partitioning cases and the utilized techniques for preserving confidentiality. We also review privacy in general and examine in collaborative filtering scenarios. We discuss the proposed schemes in terms of their limitations and practical implementation challenges. Moreover, we give an overview of evaluation of such schemes. We finally provide a comprehensive guideline for studying in this area and propose future research directions.