The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks

Li, Yuancheng; Zhang, Pan; Wang, Yimeng

doi:10.3390/en11102625

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…In response to this pressing concern, ref. [51] presents an innovative spatial decomposition algorithm based on quadtrees. This algorithm is strategically devised to fortify the location privacy of electric vehicles, shielding them from inadvertent disclosure.…”

Section: Differential Privacymentioning

confidence: 99%

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Li,

Bi,

Jiang

et al. 2024

Electronics

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With the rapid development of wireless communication technology, positioning technology, and modern smart devices, Internet of Vehicles (IoVs) smart vehicles have brought great convenience to human production and life. Meanwhile, privacy and security issues are becoming extremely serious, with serious consequences if sensitive data such as vehicle location and trip patterns are leaked. This paper focuses on the demands for vehicular network security, especially privacy protection and existing privacy-protection techniques, including common cryptography methods and cryptography-based advanced technologies. At the same time, this paper also analyzes the advantages and challenges of these technologies in protecting privacy and network security in the Internet of Vehicles, such as the challenges of computational resource requirements and security efficiency in the implementation process, as well as the complexity of realizing effective privacy protection in the interactions among different entities. Finally, this paper envisions the development of privacy-preserving application scenarios and the prospects for crypotography-based privacy-preserving technologies.

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Section: Differential Privacymentioning

confidence: 99%

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Li,

Bi,

Jiang

et al. 2024

Electronics

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“…The research works covering the notion of PPML for LBS in EVs are not much abundant, however, in order to provide our readers a brief overview, we discuss two initial works that can pave the pathway for future works in their prospective direction. One such work using the notion of differential privacy with quadtree, Bernoulli random sampling, and sparse vector technique has been proposed by authors in [80]. The work targets the domain of V2G network, where the vehicles are interacting with grid to use grid-based services, such as charging stations, etc.…”

Section: B Location-based Services (Lbs) Privacy For Evsmentioning

confidence: 99%

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

Sani¹,

Hassan²,

Chen³

2022

Preprint

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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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“…Yuancheng Li et al show in [21] how differential privacy is an important matter to consider in V2G networks. The overall structure of a V2G is introduced, and the roles of each of its entities (control center, aggregator, distribution network, and charge station) are described as well.…”

Section: Related Workmentioning

confidence: 99%

A Model for Cost–Benefit Analysis of Privately Owned Vehicle-to-Grid Solutions

et al. 2020

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Although the increasing adoption of electric vehicles (EVs) is overall positive for the environment and for the sustainable use of resources, the extra effort that requires purchasing an EV when compared to an equivalent internal combustion engine (ICE) competitor make them less appealing from an economical point of view. In addition to that, there are other challenges in EVs (autonomy, battery, recharge time, etc.) that are non-existent in ICE vehicles. Nevertheless, the possibility of providing electricity to the power grid via vehicle-to-grid technology (V2G), along with lower maintenance costs, could prove that EVs are the most economically efficient option in the long run. Indeed, enabling V2G would make EVs capable of saving some costs for their vehicle owners, thus making them a better long-term mobility choice that could trigger deep changes in habits of vehicle owners. This paper describes a cost–benefit analysis of how consumers can make use of V2G solutions, in a way that they can use their vehicle for transport purposes and obtain revenues when injecting energy into the power grid.

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The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks

Cited by 16 publications

References 34 publications

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

A Model for Cost–Benefit Analysis of Privately Owned Vehicle-to-Grid Solutions

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