Generative adversarial networks enhanced location privacy in 5G networks
Generative adversarial networks enhanced location privacy in 5G networks
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DOI：
10.1007/s11432-019-2834-x
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发表时间：
2020-11-04
影响因子：
8.8
通讯作者：
Yu, Shui
中科院分区：
文献类型：
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作者：
Qu, Youyang;Zhang, Jingwen;Yu, Shui
5G网络作为最新的通信平台，正在经历着快速的发展。与此同时，出于各种目的，不断使用5G网络生成和共享越来越多的敏感数据，特别是位置信息。发布的数据中的位置和轨迹信息一直是并将继续招致恶意对手的风险和攻击。因此，由于5G信号塔覆盖范围较短，单纯共享原始数据，特别是与位置信息共享的数据，仍然存在隐私泄露威胁。为了更好地解决这些问题，我们提出了一种生成性对抗网络(GAN)增强的位置隐私保护模型来隐藏位置甚至轨迹信息。我们使用后验抽样来生成数据子集，这被证明从终端设备端符合不同的隐私要求。在此基础上，设计了一种改进的数据增强算法，从中心服务器端生成一系列隐私保护的全尺寸合成数据。使用真实数据集生成的合成数据，我们证明了该模型在位置隐私保护、数据效用和预测精度方面的优越性。
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.