A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 11142-11166 RESEARCH ARTICLE A differential privacy-preserving deep learning caching framework for heterogeneous communication network systems Huanhuan Wang, Huanhuan Wang Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorXiao Zhang, Xiao Zhang Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, ChinaSearch for more papers by this authorYoubing Xia, Corresponding Author Youbing Xia 110403@njucm.edu.cn Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China Correspondence Youbing Xia and Xiang Wu, Institute of Medical Information Security, Xuzhou Medical University, 221000 Xuzhou, China. Email: 110403@njucm.edu.cn and wuxiang@xzhmu.edu.cnSearch for more papers by this authorXiang Wu, Corresponding Author Xiang Wu wuxiang@xzhmu.edu.cn orcid.org/0000-0003-3126-5768 Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China Correspondence Youbing Xia and Xiang Wu, Institute of Medical Information Security, Xuzhou Medical University, 221000 Xuzhou, China. Email: 110403@njucm.edu.cn and wuxiang@xzhmu.edu.cnSearch for more papers by this author Huanhuan Wang, Huanhuan Wang Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorXiao Zhang, Xiao Zhang Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, ChinaSearch for more papers by this authorYoubing Xia, Corresponding Author Youbing Xia 110403@njucm.edu.cn Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China Correspondence Youbing Xia and Xiang Wu, Institute of Medical Information Security, Xuzhou Medical University, 221000 Xuzhou, China. Email: 110403@njucm.edu.cn and wuxiang@xzhmu.edu.cnSearch for more papers by this authorXiang Wu, Corresponding Author Xiang Wu wuxiang@xzhmu.edu.cn orcid.org/0000-0003-3126-5768 Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China Correspondence Youbing Xia and Xiang Wu, Institute of Medical Information Security, Xuzhou Medical University, 221000 Xuzhou, China. Email: 110403@njucm.edu.cn and wuxiang@xzhmu.edu.cnSearch for more papers by this author First published: 26 August 2022 https://doi.org/10.1002/int.23036Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Caching and artificial intelligence technology are means to improve the content transmission rate and solve the delay problem in heterogeneous communication networks, which provide an important technical basis for telemedicine diagnosis, disease monitoring, and other medical and healthcare fields. However, since the data used for caching recommendations and mining usually includes a lot of sensitive information, collecting these data will inevitably cause users to worry about personal privacy security. This paper proposes a differential privacy-preserving deep learning caching framework (DP-DLCF) for heterogeneous communication networks. First, the multivariate randomized response mechanism is used to perturb the mobile client's sensitive information while introducing the least square method to balance the data privacy and utility, and realize the adaptive allocation of the privacy budget. Then use selective integrated learning methods to further improve the local cache accuracy of the neural network training model. Therefore, the framework not only improves the prediction accuracy of the cache network but also solves the problem of privacy leakage in data sharing. Finally, four simulation examples verify the DP-DLCF framework's effectiveness. CONFLICT OF INTEREST The authors declare no conflict of interest. Citing Literature Volume37, Issue12December 2022Pages 11142-11166 RelatedInformation