Improved Lightweight Image Encryption and Decryption for Medical IoT Devices Using 6D Chaotic Maps with XOR Diffusion, Permutation and Substitution

This research introduces a lightweight image encryption framework specifically designed for medical IoT devices, utilising a 6D chaotic map in conjunction with XOR diffusion, pixel permutation, and an optional substitution layer. The methodology utilises the intrinsic randomness, ergodicity, and sensitivity of high-dimensional chaotic systems to achieve robust encryption and secure transmission of sensitive medical images, including X-rays, MRIs, and ECGs. Comprehensive evaluations indicate that the framework effectively disrupts spatial coherence, attaining nearly zero pixel correlation and high entropy (~8), while maintaining computational efficiency suitable for resource-constrained IoT environments. The encryption scheme demonstrates significant sensitivity to input variations, with an average NPCR of 99.6% and a UACI surpassing 33%, highlighting its robustness against differential and statistical attacks. The comparative analysis of traditional and lower-dimensional chaotic encryption methods reveals that the proposed algorithm offers a superior balance between cryptographic security and performance. The findings demonstrate that the proposed system is a feasible solution for real-time, secure image processing in medical IoT applications. Future research will investigate adaptive parameter tuning and the integration of machine learning to improve encryption efficiency and robustness.