An efficient privacy-preserving recommender system in wireless networks

Abstract

AbstractRecommender systems have been widely used for implementing personalised content on many mobile online services to reduce computational overload and preserve wireless data for users. The underlying mechanisms used for building recommender systems analyse data collected from users to make recommendations. This poses concerns over the privacy of data from users as both service providers and the cloud will have access. Privacy-preserving recommender systems protect user information by incorporating various cryptographic mechanisms to prevent accessing the data. However, existing works are not practical due to the use of heavy cryptography. In this paper, we propose an efficient privacy-preserving recommender system that takes advantage of clustering to improve efficiency. Using a secure clustering mechanism, user data are assigned to multiple clusters before being fed into the recommendation. Our proposed protocols are privacy-preserving and do not leak information that could be used to identify a data subject. The experiments show that our system is efficient and accurate.