Efficient Lightweight Cryptographic Solutions for Enhancing Data Security in Healthcare Systems based on IoT

Abdul Muhammed Rasheed ^*

• Noorul Islam University, Kanyakumari, India

The swift advancement of the Internet of Things (IoT) across several industries underscores its revolutionary capacity to gather, process, and transmit real-world data over interconnected networks. This extraordinary development presents significant security issues, especially with the safeguarding of sensitive information produced by IoT devices. The extensive use of compact computer devices, including wireless sensors, RFID tags, and embedded systems, has markedly heightened data susceptibility. With the proliferation and capabilities of IoT devices, they are expected to manage substantial quantities of sensitive data, requiring comprehensive security frameworks to guarantee secure data transfer and storage. Traditional encryption methods such as AES and RSA, although successful in conventional computer contexts, sometimes prove inadequate for IoT applications due to their substantial processing demands and susceptibility to resource-based assaults. These constraints highlight the necessity for light-weight encryption methods that can deliver strong security without burdening the resource-limited characteristics of IoT devices. Contemporary lightweight encryption techniques, especially those focused on picture data, frequently entail pixel-level modifications and positioning adjustments. Nevertheless, these approaches sometimes demonstrate flaws, constraining their dependability and efficacy in real applications. This work aims to enhance IoT security by introducing a collection of novel lightweight cryptographic algorithms tailored for IoT healthcare equipment. The suggested approaches integrate a six-dimensional (6D) hyper-chaotic system, augmented by a Fibonacci Q-matrix, a Logistic-Parity-based chaotic map, and a Combined Transformation and Expansion (CTE) dynamic chaotic system. These methodologies seek to fulfil the dual imperatives of robust security and computing efficiency, rendering them particularly appropriate for the limited surroundings of IoT devices. The suggested models are evaluated utilising essential security measures, including the Unified Average Change Intensity (UACI), Number of Pixels Change Rate (NPCR), and Cross Entropy. The experimental findings indicate that the suggested algorithms attain enhanced encryption and decryption efficiency relative to current approaches, showcasing their capacity to provide strong data security with minimum computational burden. This research addresses the urgent requirement for effective and dependable encryption in IoT systems, especially within the healthcare sector, hence advancing the creation of secure, scalable, and resource-efficient IoT frameworks, which fosters increased confidence and acceptance of IoT technologies.