Emerging Chaos Generation Methods and Memory Elements in Cryptographic Systems: An Entropy and Information Theory Perspective
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 50
Special Issue Editor
Interests: memristor; cryptography; chaos; image encryption; Boolean network
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Entropy, as a fundamental concept in information theory, plays a critical role in secure communications, cryptography, and nonlinear dynamical systems. Chaos-based cryptographic systems leverage high entropy and unpredictability to enhance security, making them ideal for encryption, secure key generation, and random number generation. Meanwhile, the advent of memory elements such as memristors, memcapacitors, and meminductors has introduced new dynamical behaviors that further enhance entropy generation and security in cryptographic applications.
This Special Issue aims to explore the intricate relationship between chaos theory, entropy, and cryptography, particularly in the context of emerging memory elements. By emphasizing entropy quantifications, randomness evaluations, and information-theoretic security, we seek novel contributions that link chaos generation, memory-based cryptographic architectures, and information security. We encourage submissions that provide theoretical insights, hardware implementations, and practical applications that leverage high-entropy systems for robust and efficient cryptographic solutions.
Dr. Suo Gao
Guest Editor
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Keywords
- entropy in chaos-based systems
- information-theoretic security
- memristors, meminductors, and memcapacitors in cryptography
- high-entropy pseudorandom number generators (PRNGs)
- chaos-based secure communication
- entropy optimization in nonlinear dynamical systems
- machine learning for chaos and entropy evaluation
- post-quantum cryptography using entropy-driven methods
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