Combining Riemann–Lebesgue-Based Key Generator and Machine-Learning-Based Intelligent Encryption Scheme for IoMT Images Infosecurity

Abstract

Imaging examinations, such as chest X-ray (CXR) images, can be used for early detection of diseases inside the human body, such as lung cancer, and cardiopulmonary-related diseases; however, they may contain patients’ private information, which raises concerns about the security levels of medical images in internet of medical things (IoMT) systems. The promising authorization protocol algorithm in 5th-generation networking technology can protect medical image confidentiality, recoverability, and availability. Hence, we propose a symmetric cryptography protocol based on the Riemann-Lebesgue (R-L) -based key generator and a machine learning (ML)-based cryptography scheme for CXR image infosecurity. The R-L-based key generator uses the composite function“, g(x)sin((1/2+N)x)”, to dynamically produce the oscillation function by controlling the terms of the nonlinear function“, g(x)”, and frequency“, N”, which are used to produce the randomnumber seed and to select non-ordered numbers and nonrepeated 256 secret keys in the key space. The ML-based scheme uses these random secret keys to train an image encryptor and decryptor, respectively. Based on the diffusion method, the ML-based cryptography scheme is used to change image pixel values. For evaluation experiments, CXR images are collected from the National Institutes of Health Chest X-ray database and used to evaluate image quality after encryption and decryption processes. For different cardiopulmonary-related disease images, the number of pixel change rates (NPCR) and unified averaged changed intensity (UACI) are used to evaluate the security level and decryption quality. The experimental results show an average NPCR of 99.6% and average UACI of 32.61% between plain and cipher images for evaluating the security level, enabling further online diagnosis applications in IoMT systems.