Hide Me: Enabling Location Privacy in Heterogeneous Vehicular Networks

Abstract

In order to support location-based services, vehicles share their location with a server to receive relevant data. Revealing a vehicle’s location compromises its privacy. One way to reduce this problem is obfuscating the vehicle’s location by adding artificial noise. However, this increases the area where the true location of the vehicle may be.Hence, under limited available bandwidth, the server will provide fewer data relevant to the vehicle’s true location, reducing the effectiveness of the location-based service. To compensate for this reduction, we allow that the data relevant to a vehicle is also shared through direct, ad hoc communication between neighboring vehicles. Through such Vehicle-to-Vehicle (V2V) cooperation, the impact of location obfuscation is mitigated. In this set up, and assuming that the data served may have different impact levels, we propose and study a game that determines the data subscription a vehicle should use, without explicit coordination among them. The aim is maximizing the expected impact of the data received, either directly from the server or via V2V. Our analysis and results show that the proposed V2V cooperation and derived strategy lead to significant performance increase compared to other uncoordinated approaches, and largely alleviates the impact of location obfuscation.