Medical Image Encryption Using DNA Computing and a BiologicallyInspired PRNG for Healthcare Data Privacy

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The paper proposes a medical image encryption scheme that combines a Hardy–Weinberg-equilibrium-inspired pseudorandom number generator using nonlinear quadratic functions with DNA encoding of image data. In the encryption pipeline, the generated PRNG sequences are used to create diffusion keys, while an AES S-box is used for the confusion stage via permutation. The authors report that their PRNG-based diffusion keys pass all tests in the NIST statistical test suite across two sets of generated sequences (30 and 100 sequences) and that overall encryption performance shows improved statistical randomness, key sensitivity, and cryptographic-attack resistance versus recent methods, while noting the work is a preprint and not peer reviewed. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract With the rise of digital healthcare, ensuring the confidentiality of medical data has become increasingly critical. The widespread adoption of electronic medical records requires robust encryption techniques to protect sensitive patient information from unauthorized access and cyberattacks. We propose a novel pseudorandom number generator (PRNG) inspired by the Hardy–Weinberg equilibrium principle, integrating cryptographic techniques with nonlinear dynamics through quadratic functions to enhance randomness and security. The generated sequences are applied within a medical image encryption framework. Specifically, DNA encoding is used to transform medical image data, while the proposed PRNG provides highly random diffusion keys. For the confusion stage, the advanced encryption standard substitution box is used to introduce permutation. The effectiveness of the proposed PRNG is validated through a comprehensive experimental evaluation, including the National Institute of Standards and Technology (NIST) statistical test suite, in which it successfully passes all tests across two trial sets consisting of 30 and 100 generated sequences, respectively. Furthermore, the performance of the proposed encryption scheme is evaluated on medical images through statistical analyzes. Comparative results with recent state-of-the-art medical image encryption methods indicate enhanced security properties, particularly in terms of statistical randomness, key sensitivity, and resistance to cryptographic attacks.
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Medical Image Encryption Using DNA Computing and a BiologicallyInspired PRNG for Healthcare Data Privacy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Medical Image Encryption Using DNA Computing and a BiologicallyInspired PRNG for Healthcare Data Privacy Faiza Waheed, Takreem Haider This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8246046/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the rise of digital healthcare, ensuring the confidentiality of medical data has become increasingly critical. The widespread adoption of electronic medical records requires robust encryption techniques to protect sensitive patient information from unauthorized access and cyberattacks. We propose a novel pseudorandom number generator (PRNG) inspired by the Hardy–Weinberg equilibrium principle, integrating cryptographic techniques with nonlinear dynamics through quadratic functions to enhance randomness and security. The generated sequences are applied within a medical image encryption framework. Specifically, DNA encoding is used to transform medical image data, while the proposed PRNG provides highly random diffusion keys. For the confusion stage, the advanced encryption standard substitution box is used to introduce permutation. The effectiveness of the proposed PRNG is validated through a comprehensive experimental evaluation, including the National Institute of Standards and Technology (NIST) statistical test suite, in which it successfully passes all tests across two trial sets consisting of 30 and 100 generated sequences, respectively. Furthermore, the performance of the proposed encryption scheme is evaluated on medical images through statistical analyzes. Comparative results with recent state-of-the-art medical image encryption methods indicate enhanced security properties, particularly in terms of statistical randomness, key sensitivity, and resistance to cryptographic attacks. Medical image encryption DNA encoding Hardy-Weinberg principle Cryptographic security Privacy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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