LRCPA: Lattice-Based Robust and Conditional Privacy-Preserving Authentication for VANETs

Quantum-resistant authentication becomes crucial for securing vehicular ad-hoc networks (VANETs). However, existing solutions fail to support pseudonym unlinkability, and lacks a mechanism to ensure public verification of tracing and avoid malicious accusations against innocent vehicles for ensuring tracing robustness. In this paper, we propose a lattice-based robust and conditional privacy-preserving authentication (LRCPA) scheme for VANETs, which enables anonymous authentication on vehicles and adopts the small integer solution problem to withstand quantum attacks. Specially, obfuscated expiration dates are set for vehicles' credentials and pseudonyms to guarantee the unlinkability of new and old pseudonyms. The public verification of tracing is ensured by adopting vehicles' commitments or signatures on their real identities and pseudonyms as the proof of tracing, and tracing robustness is provided by employing vehicles' credentials issued by a central authority as the proof of malicious accusations or framing behaviors by the corrupted authority during tracing. LRCPA not only achieves message authentication and integrity, non-repudiation, anonymity, conditional privacy, unlinkability, key escrow freedom, public verification of tracing, and tracing robustness, but also effectively withstands most known attacks. Comprehensive performance analysis and evaluation show the potential usability of LRCPA.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-10-24
DOI
https://doi.org/10.1109/tvt.2024.3485671
Citations
5
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
1.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LRCPA: Lattice-Based Robust and Conditional Privacy-Preserving Authentication for VANETs

Gao Liu, Junqing Le, Tao Xiang, Ning Wang et al.
5 citations
IEEE Transactions on Vehicular Technology
Vehicular Ad Hoc Networks (VANETs)
1.04
article

LRCPA: Lattice-Based Robust and Conditional Privacy-Preserving Authentication for VANETs

Gao Liu, Junqing Le, Tao Xiang, Ning Wang, Yining Liu, Hao Li
article en
5 citations

Abstract

Quantum-resistant authentication becomes crucial for securing vehicular ad-hoc networks (VANETs). However, existing solutions fail to support pseudonym unlinkability, and lacks a mechanism to ensure public verification of tracing and avoid malicious accusations against innocent vehicles for ensuring tracing robustness. In this paper, we propose a lattice-based robust and conditional privacy-preserving authentication (LRCPA) scheme for VANETs, which enables anonymous authentication on vehicles and adopts the small integer solution problem to withstand quantum attacks. Specially, obfuscated expiration dates are set for vehicles' credentials and pseudonyms to guarantee the unlinkability of new and old pseudonyms. The public verification of tracing is ensured by adopting vehicles' commitments or signatures on their real identities and pseudonyms as the proof of tracing, and tracing robustness is provided by employing vehicles' credentials issued by a central authority as the proof of malicious accusations or framing behaviors by the corrupted authority during tracing. LRCPA not only achieves message authentication and integrity, non-repudiation, anonymity, conditional privacy, unlinkability, key escrow freedom, public verification of tracing, and tracing robustness, but also effectively withstands most known attacks. Comprehensive performance analysis and evaluation show the potential usability of LRCPA.

IEEE Transactions on Vehicular TechnologyVol. 74(3)
Wenzhou University (CN), Chongqing University (CN), Wenzhou University of Technology
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Venture and Innovation Support Program for Chongqing Overseas Returnees, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 23%
Vehicular Ad Hoc Networks (VANETs)
1.04
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.