Differentially private recommender framework with Dual semi-Autoencoder

To provide much better recommendation service, traditional recommender systems collect a large amount of user information, which, if obtained and analyzed maliciously, can cause incalculable damage to users. Therefore, differential privacy techniques, such as noise injection, have been widely introduced into recommender systems to safeguard users’ sensitive information. However, the introduction of privacy noise will lead to a degradation in recommendation quality. Hence, it is pragmatic to design a system that can furnish high quality recommendation and ensure privacy guarantee. In this article, we design a novel Differentially private recommender system with Dual Semi-Autoencoder recommender framework referred to as DP-DAE, which aims to improve the quality of recommendation while protecting user privacy. Specifically, DP-DAE is a hybrid framework of dual autoencoder and matrix factorization, which can effectively reduce data dimensionality to extract intricate features. In practice, to prevent potential privacy leaks, the differential privacy mechanism is incorporated into DP-DAE via introducing extra noise. Moreover, theoretical analysis certificates that DP-DAE satisfies $ϵ$-differential privacy. We do the experimental evaluation for DP-DAE over FilmTrust, Movielens-1M and Movielens-10M. The experimental results indicate that DP-DAE can provide privacy protection as well as high performance in recommendation tasks.