Title
Quantifying Location Privacy for Navigation Services in Sustainable Vehicular Networks
Abstract
Current connected and autonomous vehicles will contribute to various and green vehicular services. However, sharing personal data with untrustworthy Navigation Service Providers (NSPs) raises serious location concerns. To address this issue, many Location Privacy-Preserving Mechanisms (LPPMs) have been proposed. In addition, several quantification methods have been designed to help understand location privacy and illustrate how location privacy is leaked. However, their assessment is insufficient due to the incomplete assumptions about the adversary’s model. In particular, users tend to request the same navigation routes from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">home</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workplace</i> and acquire traffic information along the route. An adversary can collect the coordinates of adjacent locations and infer the two true locations. In this paper, we provide a formal framework for the analysis of LPPMs in navigation services. Our framework captures extra information that is available to an adversary performing localization attacks. By formalizing the adversary’s performance, we also propose and justify two new metrics to quantify location privacy in navigation services, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility</i> . We assess the efficacy of two popular LPPMs for location privacy, i.e., differential privacy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity. Experimental results demonstrate that the adversary can recover users’ locations with a high probability.
Year
DOI
Venue
2022
10.1109/TGCN.2022.3144641
IEEE Transactions on Green Communications and Networking
Keywords
DocType
Volume
Vehicular networks,navigation services,location privacy,privacy quantification
Journal
6
Issue
ISSN
Citations 
3
2473-2400
0
PageRank 
References 
Authors
0.34
26
5
Name
Order
Citations
PageRank
Ming-lu Li12584235.94
Yen-Wen Chen214424.44
Neeraj Kumar32889236.13
c lal400.68
Mauro Conti52430203.80