35th Annual Computer Security Applications Conference (ACSAC 2019)

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Revisiting Utility Metrics for Location Privacy-Preserving Mechanisms

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 \textsf{PRide}, and demonstrate the big 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.

Virat Shejwalkar
University of Massachusetts Amherst

Amir Houmansadr
University of Massachusetts Amherst

Hossein Pishro-Nik
University of Massachusetts Amherst

Dennis Goeckel
University of Massachusetts Amherst


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