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.

Multilayer privacy‐preserving model for security and privacy access control in edge computing

“…The architecture is designed with both available resources and the specific characteristics of a group of people with a different perspective 25–28 . Most of the consumer utilizes IoT application with intellectual ability to form their life more comfortable, and they typically intercommunicate with IoT devices via experiential interfaces offered by the edge or cloud instead of interacting with IoT devices.…”

“…The architecture is designed with both available resources and the specific characteristics of a group of people with a different perspective. [25][26][27][28] Most of the consumer utilizes IoT application with intellectual ability to form their life more comfortable, and they typically…”

Arabic text watermarking tuned for medical e-record semi-authentication

“…The work enjoyed high capacity on the price of speed, needing to check all the watermarked text. These watermarking analyses need to be tested further in future studies, as their performance tuned to utilizing both strategies of image watermarking semi-authentication (Gutub, 2022a) and dynamic smart preference for medical image confidentiality (Gutub, 2022b) which can innovate enhancement attractive opportunities.…”

“…Among the primary responsibilities of the healthcare sector collection of patient data is keeping the collected data confidential, used lawfully and sensitively secure. After data collection, the next step is the simultaneous sharing of sensitive data with the patients and the intended staff of the hospital caring (Gutub, 2022b). As the information is confidential and private needing information sharing, the security of private materials is considered basic requirement in the healthcare systems, which implies that medical systems must use up-to-date confidentiality technologies (Gutub, 2022c).…”

Latest Trends in Deep Learning Techniques for Image Steganography

“…Steganography has become one the most popular methods for information hiding because of its simplicity and easy communication through already existing technology and communication channels like IP cameras, smartphones, and social media apps like WeChat, WhatsApp, Telegram, Signal, etc. without any extra cost of additional infrastructures like private key sharing or private communication channels, etc. There have been various works which have not included deep learning in their work, however, have contributed in the field of watermarking and steganography (Hassaballah et al, 2020;Gutub and Al-Ghamdi, 2020;Gutub and Al-Roithy, 2021;Hassan and Gutub, 2021;Al-Roithy and Gutub, 2021;AlKhodaidi and Gutub, 2021;Hameed, Abdel-Aleem and Hassaballah, 2022;Gutub, 2022aGutub, , 2022b.…”