Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

A very high degree of security is required for the transmission of medical pictures via open access as these images are more important than other types of images in most applications, especially real-time applications like telemedicine. The rapid advancement of artificial intelligence (AI) technology has made the privacy and security of patient medical picture data an urgent issue in the field of image privacy protection. By combining quantum deep learning with cyber security research of cloud IoT networks, this study proposes a novel approach to encrypting medical photos. Here, a stream crypto cypher and deep, extreme convolutional networks encrypt the medical image. Afterwards, this encrypted picture was stored using a secure cloud IoT infrastructure. Encryption speed, structural similarity index measure (SSIM), root mean square error (RMSE), mean average precision (MAP), and peak signal-to-noise ratio (PSNR) are all used in the experimental investigation. To determine the efficacy of the proposed approach, experimental analyses and simulations were carried out. PSNR was 92%, RMSE was 85%, SSIM was 68%, MAP was 52%, and encryption speed was 88% using the suggested method.