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.

5G网络作为最新的通信平台，正在迅速发展。同时，出于各种目的，使用5G网络不断生成和共享越来越多的敏感数据，尤其是位置信息。发布数据中的位置和轨迹信息一直以来都是，并将不断吸引恶意对手的风险和攻击。因此，由于5G信号塔的覆盖范围较短，仅通过共享原始数据（尤其是带有位置信息的数据）仍然存在隐私泄漏威胁。为了更好地解决这些问题，我们提出了一种生成对抗网络（GAN）增强的位置隐私保护模型，以掩盖位置甚至轨迹信息。我们使用后验采样来生成数据的子集，事实证明，这符合最终设备方面不同的隐私要求。之后，设计了从经典GAN修改而来的数据增强算法，以从中央服务器端生成一系列保留隐私的完整合成数据。利用从真实数据集生成的综合数据，我们证明了所提出模型在位置隐私保护，数据实用性和预测准确性方面的优越性。