Secure Medical Image Cryptanalysis with Quantum Neural Networks for IoT-Enabled Cloud Storage

Secure Medical Image Cryptanalysis with Quantum Neural Networks for IoT-Enabled Cloud Storage | 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 Secure Medical Image Cryptanalysis with Quantum Neural Networks for IoT-Enabled Cloud Storage G. Soniya Priyatharsini, Jayalakshmi Sambandam, R. Kavitha, R. M Saritha, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9313076/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The rapid development of IoT-based medical image systems has significantly changed modern healthcare systems, enabling real-time data acquisition, remote diagnosis, and efficient patient monitoring. Nevertheless, medical image communication and storage in cloud computing systems have faced critical challenges, such as security threats, privacy issues, and new security threats from quantum computing. Current solutions have primarily focused on traditional data encryption techniques, which cannot withstand new security threats and have not considered cryptanalysis and attack detection. Moreover, they have not considered privacy-preserving distributed learning, making them less effective in practical applications. In this regard, this study proposes a unified framework for medical image security, including hybrid post-quantum encryption, quantum neural network-based cryptanalysis, federated deep learning, and secure cloud storage with access control. The proposed hybrid encryption technique includes post-quantum cryptography, chaos-based diffusion, and AES-GCM for efficient data security and protection against modern security threats. A Federated Quantum Neural Cryptanalysis Network (FQNC-Net) is proposed for analyzing encrypted medical images and identifying possible attacks while maintaining data privacy among various clients. The proposed framework is tested using KiTS19, KiTS21, and KiTS23 datasets. From the experiment, the proposed model attains an accuracy of 97.84%, precision of 97.21%, recall of 96.88%, and F1-score of 97.04%, along with an attack detection rate of 98.12%. Moreover, it attains high entropy of 7.998 and low correlation of 0.0012 for the proposed encryption model. In addition, it attains efficient cloud performance with low latency of 0.82 seconds and high integrity verification rate of 99.92%. The proposed framework is highly suitable for IoT-based healthcare systems. Quantum Neural Networks Federated Learning PQC Medical Image Security IoT-enabled Healthcare Secure Cloud Storage Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviews received at journal 09 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 08 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 03 Apr, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9313076","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628235822,"identity":"2b377506-28e1-4215-9573-529e984c6fc9","order_by":0,"name":"G. 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