Work

SGDID: A Privacy-Enhanced Supervised Distributed Identity Model for Smart Grid and Electric Vehicle Integration

Year: 2025

Type: article

Abstract: The rapidly growing number of electric vehicles and the large-scale user privacy management in smart grids have led to a symmetrical phenomenon. While decentralized identifiers (DIDs) offer a promising solution for users to better control their private data, the frequent interactions between vehicles and the grid require a vast number of identities. Existing methods, while focusing on efficiency, often neglect privacy protection, especially in Vehicle-to-Grid (V2G) scenarios. They also overlook fundamental features such as resistance to Sybil attacks and the ability to supervise malicious identities, which may seem contradictory to privacy protection. In this paper, we propose an identity authentication scheme based on decentralized identifiers (DIDs) that allow massive numbers of electric vehicle users to autonomously control the disclosure of their information. We also introduce a mechanism that simultaneously protects privacy while resisting Sybil attacks and strengthening privacy in V2G scenarios. Furthermore, our scheme enables anonymity while maintaining supervisory capabilities. Experimental results and formal proofs demonstrate that the proposed scheme performs well in terms of authentication efficiency and security, making it suitable for large-scale V2G deployments.

Source: Symmetry

DOI: 10.3390/sym17020253

Authors: Bo Tang, Shixiong Yao, Ling Su, Fuxiang Xu

Institutions: University of Wollongong, Central China Normal University

Language: English

Topic: Advanced Authentication Protocols Security

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896