A quantum resistant chaos driven image encryption framework for secure visual data transmission in intelligent...

Area of Science:

• Cybersecurity
• Intelligent Transportation Systems (ITS)
• Quantum Computing

Background:

• Intelligent Transportation Systems (ITS) generate vast amounts of high-resolution visual data.
• This data is vulnerable to interception, tampering, and privacy breaches, especially from quantum computing attacks.
• Classical encryption methods are insufficient against future quantum threats.

Purpose of the Study:

• To present a novel Hybrid Quantum-Classical Image Encryption Framework for ITS.
• To ensure high-security protection of visual data in ITS infrastructures against quantum attacks.
• To enable secure, intelligent, and privacy-preserving ITS operations in the post-quantum era.

Main Methods:

• Integration of chaos-based bit-level image encryption with quantum-resistant measures.
• Utilizing a Rearranged Arnold Cat Map (R-ACM) and 2D Logistic-Sine Chaotic Maps for confusion and diffusion.
• Incorporating Quantum Key Distribution (QKD) or Kyber Key Encapsulation Mechanism (KEM) for secure key negotiation.

Main Results:

• Superior performance demonstrated on ITS-relevant image datasets.
• High sensitivity to key and plaintext variations with immunity to various attacks.
• Achieved mean NPCR > 99.60%, UACI ≈ 33.5%, entropy close to 8.0, and suppressed pixel correlation.
• Key space complexity exceeding 2^256, rendering brute-force and quantum attacks infeasible.

Conclusions:

• The proposed framework offers robust security for image data in ITS.
• It is suitable for real-time implementation in autonomous vehicles and traffic monitoring systems.
• Enables secure and privacy-preserving ITS infrastructure in the post-quantum era.