Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map
Fatima Asiri, Wajdan Al Malwi, Tamara Zhukabayeva, Ibtehal Talal Nafea, Abdullah Nur Aziz, Nadhmi A. Gazem, Abdullah Qayyum · Frontiers in Computational Neuroscience · 2025
Introduction: Preserving privacy is a critical concern in medical imaging, especially in resource limited settings like smart devices connected to the IoT. To address this, a novel encryption method for medical images that operates at the bit plane level, tailored for IoT environments, is developed. Methods: The approach initializes by processing the original image through the Secure Hash Algorithm (SHA) to derive the initial conditions for the Chen chaotic map. Using the Chen chaotic system, three random number vectors are generated. The first two vectors are employed to shuffle each bit plane of the plaintext image, rearranging rows and columns. The third vector is used to create a random matrix, which further diffuses the permuted bit planes. Finally, the bit planes are combined to produce the ciphertext image. For further security enhancement, this ciphertext is embedded into a carrier image, resulting in a visually secured output. Results: ) > 7.98], and occlusion analysis. Conclusion: Extensive evaluations have proven that the designed scheme exhibits a high degree of resilience to attacks, making it particularly suitable for small IoT devices with limited processing power and memory.