AUTHOR=Asiri Fatima , Al Malwi Wajdan , Zhukabayeva Tamara , Nafea Ibtehal , Aziz Abdullah , Gazem Nadhmi A. , Qayyum Abdullah 

TITLE=Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2025.1591972

DOI=10.3389/fncom.2025.1591972

ISSN=1662-5188

ABSTRACT=IntroductionPreserving 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.MethodsThe 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.ResultsTo evaluate the effectiveness of our algorithm, various tests are conducted, including correlation coefficient analysis (C.C < or negative), histogram analysis, key space [(1090)8] and sensitivity assessments, entropy evaluation [E(S) > 7.98], and occlusion analysis.ConclusionExtensive 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.