Efficient lightweight cryptographic solutions for enhancing data security in healthcare systems based on IoT

The growing integration of the Internet of Things (IoT) within healthcare systems has notably enhanced patient monitoring and data collection processes. Nonetheless, IoT devices exhibit significant susceptibility to cyber threats, primarily attributed to their constrained computational capabilities and their exposure to network-based attacks. Conventional encryption techniques, including advanced encryption standard (AES) and rivest–shamir–adleman (RSA), frequently fall short for IoT applications because of their significant processing demands. Consequently, creating lightweight cryptographic solutions is crucial for guaranteeing secure and efficient data transmission in healthcare environments that utilise IoT technology. This study presents three efficient cryptographic methods: (1) a hybrid encryption algorithm that incorporates a Fibonacci sequence and a 6D hyper-chaotic system to improve confusion and diffusion; (2) hybrid lightweight encryption that utilizes logistic parity-based chaotic maps for secure data transformation; and (3) combined transformation and expansion (CTE)-based lightweight cryptography that employs dynamic chaotic systems for strong encryption. The proposed models are assessed through various security metrics, including Unified Averaged Change Intensity (UACI), Number of Pixel Change Rate (NPCR), and Cross-Entropy. The findings from the experiments demonstrate that the suggested encryption techniques surpass traditional methods regarding both efficiency and resilience in encryption. The Fibonacci Q-matrix and logistic-parity-based chaotic maps exhibit significant resilience against differential and brute-force attacks. The UACI and NPCR values indicate that the encryption techniques produce ciphertexts that are highly random and difficult to anticipate, all while requiring minimal additional computational resources. This study introduces innovative lightweight cryptographic algorithms aimed at enhancing security in IoT-based healthcare systems. The proposed models demonstrate excellent encryption performance, minimal computational complexity, and robust resistance to attacks, rendering them well-suited for resource-limited IoT settings. Future efforts will concentrate on enhancing this technique for real-time application within extensive healthcare systems.