A privacy preserving querying mechanism with high utility for electric vehicles

Abstract

Recently, with the rising awareness about advancing towards a sustainable future, electric vehicles (EVs) are becoming more and more popular. As there are disproportionately fewer charging stations than EVs, range anxiety plays a major role in the rise in the number of queries made by EVs along their journeys to find or schedule a slot in the nearest available charging station, and a significant portion of such online querying is handled by third-party service providers and communicated via the Edge cloud. On the other hand, with the recent drive towards an information-based society, the use of personal data in various kinds of analytics is surging like never before. Hence, privacy threats to personal data and the risk of privacy violation are also increasing manifold. One of the recently popularised methods formalising utility-preserving location privacy is geo-indistinguishability (GeoI), a generalisation of the local variant of differential privacy (DP), which the state-of-the-art standard for privacy protection. However, the noise should be calibrated carefully, taking into account the implications for potential utility-loss for the users affecting their quality of service (QoS). In this paper, we focus on the environment where EVs dynamically query about charging stations along their journeys, and we introduce the idea of approximate geo-indistinguishability (AGeoI) on road networks which allows the EVs to obfuscate the individual query locations while ensuring that they remain within their preferred area of interest, as journeys are very sensitive to a sharp loss in QoS which may incur a high cost for the extra distance to be covered due to privacy. We propose a novel method to protect the privacy of EVs, both for the individual query locations and against the threat of tracing the trajectory of their journeys from adversarial third-parties, by applying AGeoI that enables the users to substantially preserve their QoS. We lay out an analytical insight on the working of our method and experimentally illustrate that under our method a very high fraction of the EVs attain privacy for free, and, in general, the utility-loss suffered due to gain in privacy is significantly low.