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arXiv · 2505.00593

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

Muhammad Shahbaz Khan·Ahmed Al-Dubai·Jawad Ahmad·Nikolaos Pitropakis·Baraq Ghaleb
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Abstract

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.

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Muhammad Shahbaz Khan, Ahmed Al-Dubai, Jawad Ahmad, Nikolaos Pitropakis, Baraq Ghaleb. 2025-05-01. A Novel Feature-Aware Chaotic Image Encryption Scheme For Data Security and Privacy in IoT and Edge Networks. https://arxiv.org/abs/2505.00593

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