Privacy Preserving Machine Learning for Electric Vehicles: A Survey

• URL: http://arxiv.org/abs/2205.08462v2
• Date: Fri, 20 Dec 2024 10:44:47 GMT
• Title: Privacy Preserving Machine Learning for Electric Vehicles: A Survey
• Authors: Abdul Rahman Sani, Muneeb Ul Hassan, Longxiang Gao, Jinjun Chen,
• Abstract summary: Electric vehicles (EVs) generate a tremendous amount of data every day. Machine/deep learning techniques are being used for various EV applications. Privacy leakage during collection, storage, and training of vehicular data is a critical concern.
• Score: 5.274179687547811
• License:
• Abstract: 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.

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