Certificateless aggregate signcryption scheme with multi-ciphertext equality test for the internet of vehicles.

Authors

Xiaodong Yang, Xilai Luo, Ruixia Liu, Songyu Li, Ke Yao

Published in

PloS one. Volume 20. Issue 5. Pages e0322185. Epub May 27, 2025.

Abstract

The Internet of Vehicles (IoV) facilitates connectivity among vehicles, roadside units, and smart terminals, enabling the evolution of traditional traffic networks into intelligent transport systems. The IoV has an open communication character, which enables various applications and services. However, this also exposes it to the risk of message tampering or the leaking of private data in the communication process. Such vulnerabilities may lead to security issues. At present, there are many solutions to solve the above problems, but most of them have great computational and communication overhead. Multi-ciphertext equality test can compare the equality between two ciphertexts without decryption, which avoids the user 's repeated decryption of the same ciphertext to a certain extent. However, it still has a large computational overhead. For the above problems, we propose a certificateless aggregate signcryption scheme for the IoV that supports multi-ciphertext equality testing. The proposed scheme addresses the key escrow and certificate management issues inherent in identity-based systems by employing a certificateless signcryption mechanism. To prevent redundant retrieval of ciphertexts that correspond to identical plaintexts, a multi-ciphertext equivalence test feature has been incorporated. Furthermore, the aggregation capability of this scheme significantly enhances the efficiency of signing multiple vehicle data entries. By leveraging the computational complexities associated with the Diffie-Hellman problem and the discrete logarithm problem, it is demonstrated that the scheme maintains confidentiality and unforgeability within the random oracle model. When compared to similar schemes, this approach exhibits reduced computational overhead while providing superior security features.

PMID:
40424323
Bibliographic data and abstract were imported from PubMed on 28 May 2025.

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