PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 19, 2016IEEE Transactions on Information Forensics and Security92 citationsOpen Access

Authentication Scheme for Flexible Charging and Discharging of Mobile Vehicles in the V2G Networks

View Full Paper
NSNeetesh SaxenaBCBong Jun Choi

Key Points

Key points are not available for this paper at this time.

Abstract

Navigating security and privacy challenges is one of the crucial requirements in the vehicle-to-grid (V2G) network. Since electric vehicles (EVs) need to provide their private information to aggregators/servers when charging/discharging at different charging stations, privacy of the vehicle owners can be compromised if the information is misused, traced, or revealed. In a wide V2G network, where vehicles can move outside of their home network to visiting networks, security and privacy become even more challenging due to untrusted entities in the visiting networks. Although some privacy-preserving solutions were proposed in the literature to tackle this problem, they do not protect against well-known security attacks and generate a huge overhead. Therefore, we propose a mutual authentication scheme to preserve privacy of the EV's information from aggregators/servers in the home as well as distributed visiting V2G networks. Our scheme, based on a bilinear pairing technique with an accumulator performing batch verification, yields higher system efficiency, defeats various security attacks, and maintains untraceability, forward privacy, and identity anonymity. A performance analysis shows that our scheme, in comparison with the existing solutions, significantly generates lower communication and computation overheads in the home and centralized V2G networks, and comparable overheads in the distributed visiting V2G networks.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Saxena et al. (2016) studied this question.

synapsesocial.com/papers/6a209216bb4ba07152cc4869https://doi.org/10.1109/tifs.2016.2532840
Ask AI
Helpful
Bookmark
Share
View Full Paper