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Electric vehicles (EVs) represent the long-term green substitute fortraditional fuel-based vehicles. To encourage EV adoption, the trust of theend-users must be assured. In this work, we focus on a recently emergingprivacy threat of profiling and identifying EVs via the analog electrical dataexchanged during the EV charging process. The core focus of our work is toinvestigate the feasibility of such a threat at scale. To this end, we firstpropose an improved EV profiling approach that outperforms the state-of-the-artEV profiling techniques. Next, we exhaustively evaluate the performance of ourimproved approach to profile EVs in real-world settings. In our evaluations, weconduct a series of experiments including 25032 charging sessions from 530 realEVs, sub-sampled datasets with different data distributions, etc. Our resultsshow that even with our improved approach, profiling and individuallyidentifying the growing number of EVs is not viable in practice; at least withthe analog charging data utilized throughout the literature. We believe thatour findings from this work will further foster the trust of potential users inthe EV ecosystem, and consequently, encourage EV adoption.

On the Feasibility of Profiling Electric Vehicles through Charging Data