标题： 一种新颖的特征感知混沌图像加密方案用于物联网和边缘网络中的数据安全与隐私 显示英文标题

标题： A Novel Feature-Aware Chaotic Image Encryption Scheme For Data Security and Privacy in IoT and Edge Networks

摘要： 物联网（IoT）和边缘网络中图像数据的安全至关重要，因为实时决策的智能系统部署日益增多。传统的加密算法如AES和RSA对于资源受限的IoT设备来说计算成本高昂，并且对于大量图像数据效果不佳，导致隐私保护分布式学习应用效率低下。为了解决这些问题，本文提出了一种新颖的基于特征的混沌图像加密方案，该方案结合了基于特征的像素分割（FAPS）与混沌链置换和混淆机制，以增强安全性同时保持效率。所提出的方案包括三个阶段：（1）FAPS，提取并重新组织像素，基于高低边缘强度特征进行相关性破坏；（2）混沌链置换，使用基于SHA-256动态更新密钥的逻辑混沌映射进行分块置换；（3）混沌链混淆，利用动态生成的混沌种子矩阵进行位异或操作。广泛的安全性和性能评估表明，所提出的方案显著降低了像素相关性——接近零，实现了接近8的高熵值，并能抵抗差分密码攻击。所提方案的最佳设计使其适用于资源受限环境中的实时部署。 显示英文摘要

摘要： The security of image data in the Internet of Things (IoT) and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and ineffective for large-volume image data, leading to inefficiencies in privacy-preserving distributed learning applications. To address these concerns, this paper proposes a novel Feature-Aware Chaotic Image Encryption scheme that integrates Feature-Aware Pixel Segmentation (FAPS) with Chaotic Chain Permutation and Confusion mechanisms to enhance security while maintaining efficiency. The proposed scheme consists of three stages: (1) FAPS, which extracts and reorganizes pixels based on high and low edge intensity features for correlation disruption; (2) Chaotic Chain Permutation, which employs a logistic chaotic map with SHA-256-based dynamically updated keys for block-wise permutation; and (3) Chaotic chain Confusion, which utilises dynamically generated chaotic seed matrices for bitwise XOR operations. Extensive security and performance evaluations demonstrate that the proposed scheme significantly reduces pixel correlation -- almost zero, achieves high entropy values close to 8, and resists differential cryptographic attacks. The optimum design of the proposed scheme makes it suitable for real-time deployment in resource-constrained environments.