Revisiting utility metrics for location privacy-preserving mechanisms
Revisiting utility metrics for location privacy-preserving mechanisms
复制标题
DOI：
10.1145/3359789.3359829
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
Virat Shejwalkar;Amir Houmansadr;H. Pishro-Nik;D. Goeckel
中科院分区：
文献类型：
--
作者：
Virat Shejwalkar;Amir Houmansadr;H. Pishro-Nik;D. Goeckel
为了提高基于位置服务(LBS)用户的位置隐私，文献广泛研究了各种位置隐私保护机制(LPPM)。然而，这种隐私是以降低底层LBS的效用为代价的。以前的主要工作使用了一种基于距离的通用度量来量化使用LPPM时发生的质量损失。在本文中，我们认为使用这样的通用效用度量误导了LPPM的设计和评估，因为通用效用度量没有捕捉到用户所感知的实际效用。我们在叫车服务中展示了这一点，这是一类很受欢迎的LBS，具有复杂的公用事业行为。具体地说，我们设计了一个保护隐私的叫车服务，名为Pride，并展示了它的通用指标和定制指标之间的显著区别。通过不同的实验，我们展示了在LPPM的设计和评估中使用通用效用度量的重要意义。我们的工作结论是，LPPM设计和评估应该使用针对各个LBS量身定做的效用度量。
The literature has extensively studied various location privacy-preserving mechanisms (LPPMs) in order to improve the location privacy of the users of location-based services (LBSes). Such privacy, however, comes at the cost of degrading the utility of the underlying LBSes. The main body of previous work has used a generic distance-only based metric to quantify the quality loss incurred while employing LPPMs. In this paper, we argue that using such generic utility metrics misleads the design and evaluation of LPPMs, since generic utility metrics do not capture the actual utility perceived by the users. We demonstrate this for ride-hailing services, a popular class of LBS with complex utility behavior. Specifically, we design a privacy-preserving ride-hailing service, called PRide, and demonstrate the significant distinction between its generic and tailored metrics. Through various experiments we show the significant implications of using generic utility metrics in the design and evaluation of LPPMs. Our work concludes that LPPM design and evaluation should use utility metrics that are tailored to the individual LBSes.