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

TL;DR

This work targets secure, privacy-preserving image transmission for IoT and edge networks by introducing a lightweight, feature-aware chaotic encryption framework. It combines feature-aware pixel segmentation (FAPS) with chaotic chain permutation and confusion, using Sobel-edge-based pixel grouping, a block-wise logistic map with SHA-256-based dynamic keys, and seed-matrix XORing to break pixel correlations and resist statistical and differential attacks. Empirical results show near-zero inter-pixel correlations, entropy near 8, and strong avalanche behavior, demonstrating strong diffusion and robustness suitable for real-time, resource-constrained environments. The scheme offers practicality for secure image handling in decentralized AI/ML pipelines and can be extended with hardware acceleration and adaptive chaotic models.

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.

Figures (6)

• Figure 1: Overview of the Proposed Feature-Aware Encryption Scheme
• Figure 2: Complete block diagram of the proposed Feature-Aware Encryption Scheme
• Figure 3: Overview of the High Edge and Low Edge Pixel Classification in FAPS
• Figure 4: Different stages of the feature extraction process on Cameraman image.
• Figure 5: Encryption results with histogram and correlation analysis