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:近些年来,个人用户对现代电动车辆(EV)的兴趣成倍增长。EV有两个主要组成部分,它与传统车辆不同,首先是电动带来的环境友好性质,其次是现代信息和通信技术(ICT)的这些车辆的互联能力。这两个特征在EV的开发中发挥着关键作用,学术界和行业个人都在努力为EV网络开发现代协议。所有这些从能源角度或通信角度出发的深入互动,每天都产生大量数据。为了从从EV中收集的数据中获取大部分数据,首先是其环境友好性质,首先是由于是电动的,其次是这些车辆的环保性质。这些特征都与现代信息和通信技术(ICT)开发过程中的隐私渗漏问题密切相关,因此,除了为EV网络开发机器/深层学习技术外,还必须确保它们能够适应私人信息渗漏和攻击。为了从EVD收集的数据中获取大部分数据,我们首先从关于各种EVEV技术应用的关键性背景的讨论开始,我们利用了在目前对各种EVEV技术的精确性研究中学习了一种关键的背景,最后,我们利用了对机器保存技术的精细化技术的研究,我们学习了对目前和不断研究的精细研究的精细研究,我们学习了在不断变变变学研究中的精细研究中的精细研究中,我们学习了在最后的精细研究中学习了在研究中学习了一种研究中学习了某种研究中学习了对不断的精。