A hybrid encryption model for medical image security in IoT healthcare

Modern healthcare systems depend upon the safe transmission of medical images, where maintaining patient confidentiality and the integrity of their data remain key factors. However, contemporary chaotic and DNA-based encryption schemes generally have low key sensitivity, fixed encoding rules, and high computational time and efficiency overheads, making them impractical options for IoT healthcare systems that demand security, efficiency, and real-time performance. Herein, the model presents a lightweight hybrid image encryption model with dynamic DNA encoding, logistic chaotic permutation, SHA-256-based pseudorandom stream generation, and an innovative Fibonacci-based circular shift diffusion transformation. The logistic map offers high sensitivity and efficiency for pixel permutation while being computationally inexpensive, and SHA-256 enhances key sensitivity while introducing randomization at the byte level. By using an adaptive rule set, each XORed byte can be mapped dynamically to a DNA sequence and diffused to its target using a reversible, key-dependent Fibonacci rotation that can also be adaptive. These processes provide the highest levels of confusion and diffusion via secure dynamic encoding, which allows our model to adequately address the shortcomings of low-dimensional chaotic systems and fixed operational DNA mappings by yielding roughly the same entropy at each snapshot during the encoding process. Rigorous experimental evaluations on medical images using entropy, NPCR, UACI, histogram uniformity, and correlation metrics have demonstrated stronger performance overall by our proposed model, ultimately yielding entropy ≈ 7.9976, NPCR ≈ 99.7268, and UACI ≈ 33.72077, with some histogram uniformity and less than average UACI or correlation rates across the pairs. Overall, our findings support the claim that our newly proposed hybrid encoding framework provides a means of delivering secure and efficient encoded image broadcasts, ideal for modern healthcare.