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A traceable and attribute–updatable data sharing scheme with privacy preserving based on attribute tree for cloud–assisted VANETs

Han Luo · Chang Liu · Menglong Qi · Chengzhi Yu · Jintian Lu
10.1007/s44443-026-00769-5 392 Views 0 Citations
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Abstract

Abstract
Cloud-assisted Vehicular Ad Hoc Networks (VANETs) facilitate efficient data sharing by leveraging cloud for storing critical traffic information. However, ensuring secure, privacy preserving, and accountable data access with dynamic attribute updates in such environments remains challenging. Therefore, this paper proposes a Traceable and Attribute-Updatable Data Sharing Scheme (TAUDS) with privacy preserving based on attribute tree for cloud-assisted VANETs. Firstly, we construct an attribute tree that enables a two stage black-box tracing mechanism. It first performs an attribute-based binary search to narrow the suspect user set, then conducts identity-based binary localization to precisely identify malicious users, which reduces tracing overhead. We design an attribute update mechanism via dynamic leaf node adjustments on the attribute tree. It supports efficient revocation and modification without reissuing the whole system. For privacy protection, TAUDS employs symmetric encryption to keep user identities confidential, and uses a linear secret sharing scheme to achieve policy hiding. Secondly, under the Decisional Bilinear Diffie-Hellman assumption, formal security analysis proves that TAUDS achieves indistinguishability under chosen-plaintext attack, while its collusion resistance is verified informally. Finally, performance evaluations show clear improvements in computational cost and energy consumption, which confirms that TAUDS is suitable for resource-constrained vehicular environments.

Cite this Article (APA)
Han, L., Chang, L., Menglong, Q., Chengzhi, Y., Jintian, L. (2026). A traceable and attribute–updatable data sharing scheme with privacy preserving based on attribute tree for cloud–assisted VANETs. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1007/s44443-026-00769-5
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Published in
ISSN 1319-1578
Quartile Q1
AMS Score 100
Field Computer Science & AI
Publisher Elsevier / King Saud University
Country 🇸🇦 Saudi Arabia
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Publication Details
Year 2026
Language English
Added 06 Jul 2026