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One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure. In this paper, we propose a vehicle traffic flow aware attack that leverages public traffic flow information to recover a vehicle's real location from obfuscated location. As a countermeasure, we then develop an adaptive strategy to obfuscate a vehicle's location by a "fake" trajectory that follows a realistic traffic flow. The fake trajectory is designed to not only hide the vehicle's real location but also guarantee the quality of service (QoS) of LBS. Our experimental results demonstrate that 1) the new threat model can accurately track vehicles' real locations, which have been obfuscated by two state-of-the-art algorithms, and 2) the proposed obfuscation method can effectively protect vehicles' location privacy under the new threat model without compromising QoS. 

In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless Power Transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We propose CatCharger to tackle this challenge. By analyzing a metropolitan-scale dataset, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks) and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the Kernel Density Estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multi-objective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate that CatCharger increases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost.