文献类型：会议论文

英文题名：Recommender Systems Based on Autoencoder and Differential Privacy

作者：Ren, Jiahui[1];Xu, Xian[1];Yao, Zhihuan[1];Yu, Huiqun[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

会议论文集：43rd IEEE-Computer-Society Annual International Computers, Software and Applications Conference (COMPSAC)

会议日期：JUL 15-19, 2019

会议地点：Marquette Univ, Milwaukee, WI

主办单位：Marquette Univ

语种：英文

外文关键词：differential privacy; recommender system; autoencoder

摘要：Recommender systems are widely applied in practice. However the process of recommender involves the users' sensitive and privacy information inevitably. The privacy protection of recommender systems must be taken into account. In this paper, a recommender system model based on autoencoder and differential privacy is proposed. Two methods of applying differential privacy to autoencoder are designed: input perturbation and objective function perturbation.Both theoretical analysis and experimental results show that the proposed methods, as well as related algorithms, can provide reliable privacy preservation while maintaining high prediction accuracy.