Deep-Reinforcement-Learning-Based User Profile Perturbation for Privacy-Aware Recommendation
Yilin Xiao, Liang Xiao, Xiaozhen Lu, Hailu Zhang, Shui Yu, H. Vincent Poor
Abstract
User profile perturbation protects privacy in the release of user profiles to receive recommendation services, in which the privacy budget as a privacy parameter can be controlled to effect a tradeoff between the recommendation quality and privacy protection against inference attacks. In this article, we propose a deep reinforcement learning (RL)-based user profile perturbation scheme for recommendation systems. This scheme applies differential privacy to protect user privacy and uses deep RL to choose the privacy budget against inference attackers. Based on an evaluated neural network (NN) and a target NN, this scheme enables a user device to optimize the privacy budget over time based on the sensitivity level of the clicked item, the similarities among the recommended items, and the estimated privacy loss. We provide an upper bound on the privacy protection performance of this scheme in the recommendation game and evaluate its computational complexity. Simulation results for a movie recommendation system show that this scheme increases the user privacy protection level for a given recommendation quality compared with benchmark schemes.