Electric vehicles (EVs) represent the long-term green substitute for traditional fuel-based vehicles. To encourage EV adoption, the trust of the end-users must be assured. In this work, we focus on a recently emerging privacy threat of profiling and identifying EVs via the analog electrical data exchanged during the EV charging process. The core focus of our work is to investigate the feasibility of such a threat at scale. To this end, we first propose an improved EV profiling approach that outperforms the state-of-the-art EV profiling techniques. Next, we exhaustively evaluate the performance of our improved approach to profile EVs in real-world settings. In our evaluations, we conduct a series of experiments including 25032 charging sessions from 530 real EVs, sub-sampled datasets with different data distributions, etc. Our results show that even with our improved approach, profiling and individually identifying the growing number of EVs is not viable in practice; at least with the analog charging data utilized throughout the literature. We believe that our findings from this work will further foster the trust of potential users in the EV ecosystem, and consequently, encourage EV adoption.

翻译：电动汽车（EV）是传统燃油汽车的长期绿色替代方案。为促进电动汽车的普及，必须确保终端用户的信任。本研究聚焦于近期出现的隐私威胁，即通过电动汽车充电过程中交换的模拟电气数据对其实施画像与身份识别。我们的核心工作是探究此类威胁在大规模场景下的可行性。为此，我们首先提出一种改进型电动汽车画像方法，其性能优于现有最先进技术。随后，我们在真实环境中对改进方法的画像能力进行穷举式评估。实验涵盖530辆真实电动汽车的25032次充电会话，并构建了不同数据分布下的子采样数据集。结果表明：即使采用改进方法，对日益增长的电动汽车群体进行画像和个体识别在现实中仍不可行——至少基于文献中普遍采用的模拟充电数据无法实现。我们相信，本研究成果将进一步提升潜在用户对电动汽车生态系统的信任度，从而推动电动汽车的普及。