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<p>This paper presents a novel Quantum Chaotic Image Encryption (QCIE) framework designed to enhance the security of medical images in Internet of Medical Things (IoMT) applications. The proposed method integrates quantum key generation and chaotic transformations to achieve high encryption strength while maintaining computational efficiency. A two-step process is employed: first, a quantum-generated key ensures unpredictability and resistance to quantum attacks; second, pixel scrambling using the 2D Logistic-Sine Coupled Map (2D-LSCM) enhances confusion and diffusion. Experimental results demonstrate the framework’s robustness, with an entropy of 7.9992, NPCR of 99.63%, and UACI of 33.53%, confirming strong resistance to statistical and differential attacks. Additionally, the method achieves fast encryption/decryption times (e.g., 1.18&#xa0;s for 425 × 425 images), making it suitable for real-time IoMT applications. Comparative analysis with state-of-the-art techniques shows superior performance in correlation reduction and computational efficiency. The framework ensures lossless decryption, preserving diagnostic quality, and addresses the limitations of existing methods by balancing security, speed, and adaptability for resource-constrained IoMT devices.</p>

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Quantum chaotic techniques for medical image encryption in next-generation IoMT applications

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Zied Guitouni,
  • Hafedh Mahmoud Zayani

摘要

This paper presents a novel Quantum Chaotic Image Encryption (QCIE) framework designed to enhance the security of medical images in Internet of Medical Things (IoMT) applications. The proposed method integrates quantum key generation and chaotic transformations to achieve high encryption strength while maintaining computational efficiency. A two-step process is employed: first, a quantum-generated key ensures unpredictability and resistance to quantum attacks; second, pixel scrambling using the 2D Logistic-Sine Coupled Map (2D-LSCM) enhances confusion and diffusion. Experimental results demonstrate the framework’s robustness, with an entropy of 7.9992, NPCR of 99.63%, and UACI of 33.53%, confirming strong resistance to statistical and differential attacks. Additionally, the method achieves fast encryption/decryption times (e.g., 1.18 s for 425 × 425 images), making it suitable for real-time IoMT applications. Comparative analysis with state-of-the-art techniques shows superior performance in correlation reduction and computational efficiency. The framework ensures lossless decryption, preserving diagnostic quality, and addresses the limitations of existing methods by balancing security, speed, and adaptability for resource-constrained IoMT devices.