In the recent years, the interest of individual users in modern electric vehicles (EVs) has grown exponentially. An EV has two major components, which make it different from traditional vehicles, first is its environment friendly nature because of being electric, and second is the interconnection ability of these vehicles because of modern information and communication technologies (ICTs). Both of these features are playing a key role in the development of EVs, and both academia and industry personals are working towards development of modern protocols for EV networks. All these interactions, whether from energy perspective or from communication perspective, both are generating a tremendous amount of data every day. In order to get most out of this data collected from EVs, research works have highlighted the use of machine/deep learning techniques for various EV applications. This interaction is quite fruitful, but it also comes with a critical concern of privacy leakage during collection, storage, and training of vehicular data. Therefore, alongside developing machine/deep learning techniques for EVs, it is also critical to ensure that they are resilient to private information leakage and attacks. In this paper, we begin with the discussion about essential background on EVs and privacy preservation techniques, followed by a brief overview of privacy preservation in EVs using machine learning techniques. Particularly, we also focus on an in-depth review of the integration of privacy techniques in EVs and highlighted different application scenarios in EVs. Alongside this, we provide a a very detailed survey of current works on privacy preserving machine/deep learning techniques used for modern EVs. Finally, we present the certain research issues, critical challenges, and future directions of research for researchers working in privacy preservation in EVs.

翻译：近年来，个人用户对现代电动汽车（EV）的关注度呈指数级增长。电动汽车与传统车辆相比具有两大核心特征：其一因其电力驱动特性而具备环境友好性，其二借助现代信息通信技术（ICT）实现车辆互联能力。这两大特征在电动汽车的发展中发挥着关键作用，学界与工业界正致力于开发面向电动汽车网络的现代协议。无论是从能源视角还是通信视角，这些交互行为每日都在产生海量数据。为充分挖掘电动汽车采集数据的价值，已有研究强调将机器学习/深度学习技术应用于各类电动汽车场景。这种结合虽成果显著，却同时引发了车辆数据在收集、存储与训练过程中隐私泄露的重大隐患。因此，在开发面向电动汽车的机器学习/深度学习技术时，确保其具备抵御隐私信息泄露与攻击的能力至关重要。本文首先探讨电动汽车与隐私保护技术的基础背景，继而概述基于机器学习技术的电动汽车隐私保护研究。特别地，我们深入评述了隐私保护技术与电动汽车的融合路径，并重点剖析了电动汽车中的不同应用场景。在此基础上，本文系统梳理了当前面向现代电动汽车的隐私保护机器学习/深度学习技术研究进展。最后，针对从事电动汽车隐私保护领域的研究人员，本文提出了若干待解决的研究问题、关键挑战与未来研究方向。