论文标题
关于通过充电数据分析电动汽车的可行性
On the Feasibility of Profiling Electric Vehicles through Charging Data
论文作者
论文摘要
电动汽车（EV）代表了传统燃油汽车的长期绿色替代品。为了鼓励采用电动汽车，必须确保最终用户的信任。在这项工作中，我们专注于最近通过在EV充电过程中交换的模拟电数据进行分析和识别电动汽车的新兴隐私威胁。我们工作的核心重点是调查大规模威胁的可行性。为此，我们首先提出了一种改进的EV分析方法，以优于最先进的EV分析技术。接下来，我们详尽地评估了在现实世界中改进的对电动汽车的改进方法的性能。在我们的评估中，我们进行了一系列实验，包括来自530个真实电动汽车的25032次充电会话，具有不同数据分布的子采样数据集等。我们的结果表明，即使我们进行了改进的方法，分析和单独识别不断增长的电动汽车的数量在实践中也不可行；至少在整个文献中使用模拟充电数据。我们认为，这项工作的发现将进一步促进EV生态系统中潜在用户的信任，从而鼓励采用电动汽车。
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.
