Development of Lightweight Image Encryption Algorithm for Ensuring Confidentiality and Privacy in Internet of Things Devices

ABSTRACT Lightweight image encryption for the Internet of Things (IoT) enables secure image data transmission between connected devices while accommodating the limited processing memory and power of IoT systems. A general drawback of lightweight image encryption is that it often sacrifices security strength for efficiency, making it more vulnerable to attacks compared to more robust encryption methods. In this manuscript, the development of a lightweight image encryption algorithm for ensuring confidentiality and privacy in Internet of Things devices (DLWIE‐ECP‐IoTD) is proposed. Firstly, the input image is gathered from the Caltech‐101 dataset. Then, a chaotic pattern generation using a one‐dimensional discrete chaos mapping system is applied to generate unpredictable patterns to secure image encryption, followed by a chaotic model of information encryption used for securing the image through unpredictable transformations. Then, a Uniform Physics Informed Neural Network (UPINN) is adapted to produce pseudo‐random encryption keys from chaotic parameters, providing an efficient and secure key‐generation mechanism. Finally, Fully Dynamic Advanced Encryption Standard (FDAES) is utilized for lightweight encryption of image data. The proposed DLWIE‐ECP‐IoTD approach is implemented in Python. The proposed DLWIE‐ECP‐IoTD approach achieves 1.120 s of computational time and 0.03% mean squared error (MSE) with existing methods, such as a lightweight multichaos‐based image encryption scheme for IoT networks (LWMC‐IES‐IoTN), a lightweight image encryption scheme for IoT environments and machine learning‐driven robust S‐box selection (LWIE‐IoTE‐ML‐DRSBS), and a lightweight image encryption scheme for the safe Internet of Things using a novel chaotic technique (LWIE‐NCT‐SIoT).