A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Abstract

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. However, the limited availability of charging stations (CSs) compared to the number of EVs on the road has increased range anxiety and CS queries during trips. Simultaneously, personal data use for various forms of analytics is growing at an unprecedented rate, raising concerns about privacy violations. One standard for formalising location privacy is geo-indistinguishability as a generalisation of local differential privacy. However, the noise has to be carefully calibrated considering the implications of potential utility loss. In this paper, we introduce the notion of approximate geo-indistinguishability (AGeoI) which allows the EVs to obfuscate the individual query locations while ensuring that they remain within their preferred area of interest. It is vital because journeys are often sensitive to a sharp drop in quality of service (QoS), which has a high cost for the extra distance to be covered. Our proposed method combines the application of AGeoI and the generation of dummy data to provide two-fold privacy protection (individual query locations and the trajectory of the entire journeys) for EVs while preserving a high level of QoS. Analytical insights and experiments are used to demonstrate that a very high percentage of EVs get "privacy for free" and that the utility loss caused by the gain in privacy guarantees is minuscule. Aside from harbouring a high QoS for the EVs, using the iterative Bayesian update, our method allows for a private and precise prediction occupancies of CSs which is vital in unprecedented traffic congestion scenarios and efficient route-planning.