  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.
