The widespread use of mobile devices and location-based services has generated a large number of mobility databases. While processing these data is highly valuable, privacy issues can occur if personal information is revealed. The prior art has investigated ways to protect mobility data by providing a wide range of Location Privacy Protection Mechanisms (LPPMs). However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful processing. To tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and automatically configures it for each user in order to achieve user-defined objectives in terms of both privacy and utility. PULP uses nonlinear models to capture the impact of each LPPM on data privacy and utility levels. Evaluation of our framework is carried out with two protection mechanisms from the literature and four real-world mobility datasets. Results show the efficiency of PULP, its robustness and adaptability. Comparisons between LPPMs’ configurators and the state of the art further illustrate that PULP better realizes users’ objectives, and its computation time is in orders of magnitude faster.

中文翻译：

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移动数据的自动隐私和效用保护：一种基于非线性模型的方法

移动设备和基于位置的服务的广泛使用产生了大量的移动数据库。虽然处理这些数据非常有价值，但如果泄露个人信息，可能会出现隐私问题。现有技术已经研究了通过提供广泛的位置隐私保护机制（LPPM）来保护移动数据的方法。然而，受保护数据的隐私级别因所使用的保护机制、其配置和移动数据的特性而异。同时，受保护的数据仍然需要进行一些有用的处理。为了解决这些问题，我们提出了 PULP，这是一个框架，它可以找到合适的保护机制并为每个用户自动配置它，以便在隐私和实用性方面实现用户定义的目标。PULP 使用非线性模型来捕捉每个 LPPM 对数据隐私和效用级别的影响。我们的框架的评估是通过文献中的两种保护机制和四个现实世界的移动数据集进行的。结果显示了 PULP 的效率、鲁棒性和适应性。LPPM 的配置器与现有技术的比较进一步说明 PULP 更好地实现了用户的目标，并且其计算时间快了几个数量级。