CM-CPPA: Chaotic Map-Based Conditional Privacy-Preserving Authentication Scheme in 5G-Enabled Vehicular Networks

Area of Science:

• Computer Science
• Cybersecurity
• Telecommunications

Background:

• Vehicular communication faces security and privacy challenges, often relying on complex Elliptic Curve (EC) or Bilinear Pair (BP) cryptography.
• Existing schemes struggle with efficient message signing and signature verification performance.
• 5G-enabled vehicular networks require robust and efficient authentication mechanisms.

Purpose of the Study:

• To propose an efficient and privacy-preserving authentication scheme for 5G vehicular networks.
• To address the performance limitations of EC- and BP-based cryptographic methods in vehicular communication.
• To enhance message signing and verification efficiency while maintaining security.

Main Methods:

• Developed a Chaotic Map-based Conditional Privacy-Preserving Authentication (CM-CPPA) scheme.
• Utilized Chebyshev polynomial mapping and a chaotic map-based hash function for signing and verification.
• Employed the AVISPA simulator for rigorous security analysis.

Main Results:

• The CM-CPPA scheme demonstrated good security and safety against common attacks via AVISPA simulation.
• Achieved significant performance improvements over EC and BP methods, reducing computation overhead for signature verification by 24.2% and message signing by 62.52%.
• Reduced communication overhead for the format tuple by 57.69%.

Conclusions:

• The proposed CM-CPPA scheme offers a secure and efficient solution for authentication in 5G vehicular networks.
• It effectively overcomes the performance bottlenecks associated with traditional EC and BP cryptographic operations.
• The scheme provides substantial reductions in both computational and communication overhead, enhancing overall network efficiency.