Nowadays, the demand for digital images from different intelligent devices and sensors has dramatically increased in smart healthcare. Due to advanced low-cost and easily available tools and software, manipulation of these images is an easy task. Thus, the security of digital images is a serious challenge for the content owners, healthcare communities, and researchers against illegal access and fraudulent usage. In this article, a secure medical image encryption algorithm, EiMOL, based on optimization and the Lorenz system, is proposed for smart healthcare applications. In the first stage, an optimized random sequence (ORS) is generated through directed weighted complex network particle swarm optimization using the genetic algorithm (GDWCN-PSO). This random number matrix and the Lorenz system are adopted to encrypt plain medical images, obtaining the cipher messages with a relationship to the plain images. According to our obtained results, the proposed EiMOL encryption algorithm is effective and resistant to the many attacks on benchmark Kaggle and Open-i datasets. Further, extensive experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.

EiMOL: A Secure Medical Image Encryption Algorithm based on Optimization and the Lorenz System

The Internet of Things (IoT) is integrated with the healthcare system to create an intelligent hospital ecosystem. Protecting the privacy and security of medical data is a central problem in such an ecosystem. This article proposes a novel yet simple security framework to ensure the confidentiality of medical data during the communication between IoT hops. The proposed framework utilizes the concept of encryption to ensure the security. It essentially involves inducing confusion among pixels by employing image pixel scrambling achieved by spiralizing the medical data matrix, followed by a diffusion process using matrix exponentials and pseudorandom number generation by sequential chaotic maps. A reliable decryption scheme is finally designed to obtain the original data from the encrypted data. Experimental results and analyzes demonstrate the efficiency and robustness of the proposed framework and, thus, making it more suitable for the IoT ecosystem.

Apoorva Lakshman

Papers

A Novel Security Framework for Medical Data in IoT Ecosystems

A Novel Logo-Inspired Chaotic Random Number Generator