Generative adversarial networks enhanced location privacy in 5G networks
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. RESEARCH PAPER .
December 2020, Vol. 63 220303:1–220303:12 https://doi.org/10.1007/s11432-019-2834-x
Special Focus on Challenges and New Insights for Network Security in 5G-and-Beyond
Generative adversarial networks enhanced location privacy in 5G networks Youyang QU1 , Jingwen ZHANG2 , Ruidong LI3 , Xiaoning ZHANG4 , Xuemeng ZHAI4 & Shui YU5,2* 1 School of Information Technology, Deakin University, Burwood VIC 3125, Australia; School of Computer Science, University of Technology Sydney, Ultimo NSW 2007, Australia; 3 National Institute of Information and Communications Technology (NICT), Tokyo 1840015, Japan; 4 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610051, China; 5 Center of AI and Big Data, Southeast Digital Economic Development Institute, Quzhou 324000, China 2
Received 21 October 2019/Accepted 12 March 2020/Published online 4 November 2020
Abstract 5G networks, as the up-to-date communication platforms, are experiencing fast booming. Meanwhile, increasing volumes of sensitive data, especially location information, are being generated and shared using 5G networks for various purposes ceaselessly. Location and trajectory information in the published data has always been and will keep courting risks and attacks by malicious adversaries. Therefore, there are still privacy leakage threats by simply sharing the original data, especially data with location information, due to the short cover range of 5G signal tower. To better address these issues, we proposed a generative adversarial networks (GAN) enhanced location privacy protection model to cloak the location and even trajectory information. We use posterior sampling to generate a subset of data, which is proved complying with differential privacy requirements from the end device side. After that, a data augmentation algorithm modified from classic GAN is devised to generate a series of privacy-preserving full-sized synthetic data from the central server side. With the synthetic data generated from a real-world dataset, we demonstrate the superiority of the proposed model in terms of location privacy protection, data utility, and prediction accuracy. Keywords
5G, privacy preservation, generative adversarial nets, differential privacy
Citation Qu Y Y, Zhang J W, Li R D, et al. Generative adversarial networks enhanced location privacy in 5G networks. Sci China Inf Sci, 2020, 63(12): 220303, https://doi.org/10.1007/s11432-019-2834-x
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Introduction
Fast popularization of 5G networks and intelligent devices reshape this big data era. Rapidly increasing volume of data, including the sensitive data, is generated and shared in real-time among 5G networks [1]. According to [2], there is over 2.5 quintillion bytes of generated data in every single day, and it is only going to grow from there. With the help of the released data, significant convenience is provided from various perspectives of daily life [3]. However, the sharing of data also raises great concerns on location privacy l
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