The rapid development of the Global Positioning System (GPS) devices and location-based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers. This phenomenon raises serious privacy concerns. However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users. In this way, the solutions are vulnerable to various inference attacks. Traditional privacy protection methods rely on trusted third-party service providers, but in reality, we are not sure whether the third party is trustable. In this paper, we propose a systematic solution to preserve location information. The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties. The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy. To formalize the privacy-protecting guarantee, we propose a new definition, L&A-location region, based on -anonymity and differential privacy. Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory. We simulate the proposed mechanism based on a dataset collected in real practice. The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard.

中文翻译：

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通过在无线网络中的时间相关性下组合差分隐私和匿名来保护移动用户的位置

全球定位系统（GPS）设备和基于位置的服务（LBS）的快速发展，为不可信/未知的LBS提供者提供了收集大量个人信息的便利。这种现象引起了严重的隐私问题。但是，大多数现有解决方案旨在在静态场景或单个时间戳中定位干扰，而无需考虑位置转移和移动用户时间之间的相关性。这样，解决方案容易受到各种推理攻击。传统的隐私保护方法依赖于受信任的第三方服务提供商，但实际上，我们不确定第三方是否可信任。在本文中，我们提出了一种系统的解决方案来保存位置信息。该保护提供了严格的隐私保证，而无需承担第三方的信誉。用户的历史轨迹信息被用作隐马尔可夫模型预测的基础，用户的可能预期位置被用作模型输出结果，以保护用户的轨迹隐私。为了使隐私保护保证正式化，我们提出了一个新的定义，即L＆A位置区域，基于-匿名和差异性隐私。基于提出的隐私定义，我们设计了一种新颖的机制来为用户的身份轨迹提供隐私保护保证。我们基于实际实践中收集的数据集来模拟所提出的机制。仿真结果表明，该算法可以为高标准的隐私提供保护。