The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts.
In the near future, using electric vehicles will almost certainly be required for the sustainability of nature and our planet. The most significant challenge that users are concerned about is the availability of electric vehicle charging stations. Therefore, to maximize the availability of electric vehicle charging stations, we suggest taking benefit from individual sellers who produce renewable energy from their homes or electric vehicle owners who have charging piles installed in their homes. However, energy services that are rapidly being offered by these businesses do not have a trust connection developed with the consumers and stakeholders in these new systems. Exchange of data related to electric vehicles and energy aggregators can be used to identify users’ behavior and compromise their privacy. Consequently, it is necessary to set up a charging system that will guarantee privacy and security. Several electric vehicle charging systems have been proposed to provide security and privacy preservation. However, ensuring anonymity alone is not enough to guarantee protection from reconstructing the victim vehicle’s route by the tracking adversary, even if the exchanged messages are completely anonymous. Furthermore, anonymity should not be absolute in order to protect the system and function as necessary by all entities. In this research, we propose an effective, secure, and privacy-preserving authentication method based on the Elliptic Curve Qu–Vanstone for an electric vehicle charging system. The proposed scheme provides all the necessary requirements and a reauthentication protocol to minimize the overhead of subsequent authentication processes. To create credentials and validate electric vehicles and energy aggregators, the scheme makes use of the Elliptic Curve Qu–Vanstone implicit certificate mechanism. The new protocols give EVs security and privacy while cutting computational time by 95% thanks to reauthentication, as demonstrated by the performance comparison with earlier works.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.