The prevalence of mobile devices and Location-Based Services (LBS) necessitates the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBSes due to the noise they add to users’ locations. Here, we consider the remapping technique, which presumes the adversary has a perfect statistical model for the user location. We consider this assumption and show that under practical assumptions on the adversary’s knowledge, the remapping technique leaks privacy not only about the true location data, but also about the statistical model. Finally, we introduce a novel method termed Randomized Remapping to provide a trade-off between leakage of the users’ location and leakage of the users’ model for a given utility.

中文翻译：

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在位置隐私保护机制设计中利用先验知识不对称

移动设备和基于位置的服务 (LBS) 的流行需要研究位置隐私保护机制 (LPPM)。然而，LPPM 会降低 LBS 的效用，因为它们会增加用户位置的噪音。在这里，我们考虑重新映射技术，该技术假设对手具有完美的用户位置统计模型。我们考虑了这个假设，并表明在对手知识的实际假设下，重新映射技术不仅会泄露关于真实位置数据的隐私，还会泄露关于统计模型的隐私。最后，我们引入了一种称为随机重映射的新方法，以在给定效用的用户位置泄漏和用户模型泄漏之间进行权衡。