文献类型：期刊文献

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] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2019

卷号：1

起止页码：358

外文期刊名：Proceedings - International Computer Software and Applications Conference

收录：EI(收录号：20194007485186)

语种：英文

外文关键词：Learning systems

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. ? 2019 IEEE.