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Privacy Preserving Machine Learning for Electric Vehicles: A Survey

Abdul Rahman Sani, Muneeb Ul Hassan, Longxiang Gao, Jinjun Chen

TL;DR

Privacy-preserving machine learning is essential in EVs due to pervasive V2X data sharing that can reveal sensitive user, vehicle, and pedestrian information. The paper surveys PPML methods (DP, HE, FL, etc.) and maps integration scenarios from resource management to autonomous driving, highlighting current works and gaps. It introduces a taxonomy of privacy surfaces and discusses challenges in energy trading, location privacy, IDS, and adversarial resiliency. The findings suggest that combining decentralized learning with cryptographic protections and incentive-alignment mechanisms is key to enabling privacy while preserving utility in EV ecosystems.

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.

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

TL;DR

Privacy-preserving machine learning is essential in EVs due to pervasive V2X data sharing that can reveal sensitive user, vehicle, and pedestrian information. The paper surveys PPML methods (DP, HE, FL, etc.) and maps integration scenarios from resource management to autonomous driving, highlighting current works and gaps. It introduces a taxonomy of privacy surfaces and discusses challenges in energy trading, location privacy, IDS, and adversarial resiliency. The findings suggest that combining decentralized learning with cryptographic protections and incentive-alignment mechanisms is key to enabling privacy while preserving utility in EV ecosystems.

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.
Paper Structure (48 sections, 3 figures, 4 tables)

This paper contains 48 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Comparison with Related Survey Articles
  3. Contributions of Our Survey Article on PPML in EVs
  4. Article Structure
  5. Preliminaries of Privacy and Machine Learning in EVs
  6. Electric Vehicles
  7. Vehicular Ad Hoc Networks (VANETs)
  8. On-Board Unit (OBU)
  9. Road Side Unit (RSU)
  10. Trusted Authority (TA)
  11. Machine Learning
  12. Supervised Machine Learning
  13. Unsupervised Machine Learning
  14. Reinforcement Learning
  15. Privacy Preservation Techniques
  16. ...and 33 more sections

Figures (3)

  • Figure 1: Graphical Illustration of Vehicle to Everything (V2X) communication including the communication of Electric Vehicles with Pedestrians, Vehicles, Grid, and Infrastructure (adapted from intref04).
  • Figure 2: Classification of Privacy Attack Surfaces in Electric Vehicles from perspective of vehicles, EV users, and pedestrians.
  • Figure 3: Overview of Privacy Preserving Machine Learning Integration Scenarios in Electric Vehicles