Dynamic smart random preference for higher medical image confidentiality

Abstract

It is essential to secure the information to store or transfer medical digital files without destruction. Currently, all used e-health files requests to be utilized in well-controlled, protected, and dependable style avoiding breaches and hacking. This research focuses on medical confidentiality encrypting grayscale health images for comfortable safe utilization. The work depends on resilience randomization and XOR operations for its medical-image cryptography. It tests performance of some random generators conveying the best every time running that is dynamically changing depending on e-health image variations. The research tests several randomizations structures processed as two sequenced encryption methods adopting substitution and transposition. The work tested random variations to encrypt different medical grayscale images revealing attractive remarks. The paper investigation intends to recognize appropriate preference via secrecy testing typical notations. The work indicates this flexibility of best applicable PRNG and its change features interesting privacy intellectual medical gray-image security for open e-health research direction to benefit from.