Privacy Risk in Recommender Systems

Abstract

Nowadays, recommender systems are mostly used in many online applications to filter information and help users in selecting their relevant requirements. It avoids users to become overwhelmed with the massive amount of possible options. To provide an efficient and accurate personalized recommendation, such systems require a large amount of data of user’s personal data which can provide by collecting privacy sensitive data from users such as ratings, consumption histories, and personal profiles. However, the privacy risks in gathering and processing personal data are often underestimated or ignored. The common privacy risks associated with recommender systems are the lack of adequate implementation of privacy protection principles. This review article aimed to evaluate the privacy risks in recommender systems. This paper discusses recommender systems and privacy concepts. Then, it gives an overview of the data that are used in recommender systems and examines the associated risks to data privacy. After that, the paper discusses relevant research areas for privacy-protection techniques and their applicability to recommender systems. The paper discussed various insights of user privacy, in both technical and non-technical environments, privacy design strategies, and privacy engineering approaches for developing a privacy-friendly recommender system. Finally, the paper concludes with a discussion on applying and combining different privacy-protection techniques. The results indicated that better user privacy can be achieved if privacy is considered by design and by default. Moreover, prediction accuracy is not limited by better user privacy when the privacy by architecture is considered alongside the privacy by design.

Keywords: Recommender systems, Privacy risk, Privacy design strategies.