Work

A Symmetrically Verifiable Outsourced Decryption Data Sharing Scheme with Privacy-Preserving for VANETs

Year: 2025

Type: article

Abstract: Frequent data sharing in Vehicular Ad Hoc Networks (VANETs) necessitates a robust foundation of secure access control to ensure data security. Existing ciphertext-policy attribute-based encryption schemes are constrained by the performance bottleneck of a single attribute authority. Furthermore, although many schemes adopt outsourced decryption, the verifiability of the decryption results is not guaranteed. Therefore, this paper proposes a Symmetrically Verifiable Outsourced Decryption Data Sharing Scheme with Privacy-Preserving for VANETs (VODDS). To balance the computational overhead across multiple authorities, VODDS introduces a distributed key distribution mechanism that organizes them into groups. Within each group, the key distribution credential is generated through a Group Key Agreement, with each round secured by a Byzantine consensus mechanism to achieve a balance between security and efficiency. User identities are converted into anonymous representations via hashing for embedding into the attribute keys. Furthermore, blockchain technology is used to record a hash commitment for the verification ciphertext. This enables the user to verify the outsourced result through a smart contract, which performs a symmetrical verification by matching the user’s locally computed hash against the on-chain record. Moreover, VODDS employs a linear secret sharing scheme to achieve policy hiding. We provide security analysis under the q-parallel Bilinear Diffie–Hellman Exponent and Decisional Diffie–Hellman assumptions, which proves the security of VODDS. In addition, VODDS exhibits higher efficiency compared to related schemes in the performance evaluation.

Source: Symmetry

DOI: 10.3390/sym17122032

Authors: Han Luo, Menglong Qi, Chengzhi Yu, Qianxi Liu, Jintian Lu

Institution: Jishou University

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Natural Science Foundation of Hunan Province