Secure and Efficient Personalized Multi-Receiver Data Sharing With Cross-Domain Authentication for Internet of Vehicles

The Internet of Vehicles (IoV) has the potential to greatly improve traffic efficiency and road safety through the real-time exchange of data between vehicles and transport infrastructures. While many schemes employ Certificateless Signcryption (CLSC) to ensure data confidentiality and provide authentication in IoV, existing methods are predominantly designed for single-sender/single-receiver or multi-sender/single-receiver configurations. There is a notable gap in the research addressing the personalized sharing of data with multiple specific users. This paper addresses the practical and challenging problem of personalized multi-receiver data sharing, where customized data is sent to each recipient while remaining confidential from others. Furthermore, the dynamic and heterogeneous nature of vehicles introduces significant challenges for authentication across diverse geographic domains. To address these issues, we propose a novel, lightweight personalized multi-receiver CLSC algorithm featuring cross-domain authentication, thereby enhancing its applicability across various IoV scenarios. Our scheme also reduces computational complexity by enabling aggregated unsigncryption. Additionally, we introduce a pseudonym generation mechanism to ensure the anonymity of participants' identities while maintaining traceability. Rigorous formal security analyses demonstrate that our scheme satisfies all specified security requirements. Furthermore, the experimental evaluations confirm the efficiency and practicality of our scheme.

Institutions

Publication Details

Journal
IEEE Transactions on Dependable and Secure Computing
Published
2025-10-09
DOI
https://doi.org/10.1109/tdsc.2025.3619655
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Secure and Efficient Personalized Multi-Receiver Data Sharing With Cross-Domain Authentication for Internet of Vehicles

IEEE Transactions on Dependable and Secure Computing
Cryptography and Data Security
article

Secure and Efficient Personalized Multi-Receiver Data Sharing With Cross-Domain Authentication for Internet of Vehicles

article en

Abstract

The Internet of Vehicles (IoV) has the potential to greatly improve traffic efficiency and road safety through the real-time exchange of data between vehicles and transport infrastructures. While many schemes employ Certificateless Signcryption (CLSC) to ensure data confidentiality and provide authentication in IoV, existing methods are predominantly designed for single-sender/single-receiver or multi-sender/single-receiver configurations. There is a notable gap in the research addressing the personalized sharing of data with multiple specific users. This paper addresses the practical and challenging problem of personalized multi-receiver data sharing, where customized data is sent to each recipient while remaining confidential from others. Furthermore, the dynamic and heterogeneous nature of vehicles introduces significant challenges for authentication across diverse geographic domains. To address these issues, we propose a novel, lightweight personalized multi-receiver CLSC algorithm featuring cross-domain authentication, thereby enhancing its applicability across various IoV scenarios. Our scheme also reduces computational complexity by enabling aggregated unsigncryption. Additionally, we introduce a pseudonym generation mechanism to ensure the anonymity of participants' identities while maintaining traceability. Rigorous formal security analyses demonstrate that our scheme satisfies all specified security requirements. Furthermore, the experimental evaluations confirm the efficiency and practicality of our scheme.

IEEE Transactions on Dependable and Secure ComputingVol. 23(1)
Zhejiang University of Science and Technology (CN), Shanghai Jiao Tong University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 24%
Cryptography and Data Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.