Securing IoMT-Based Healthcare Systems with Meta-Heuristic Quantum Cryptography and Honeybee Optimization

Securing IoMT-Based Healthcare Systems with Meta-Heuristic Quantum Cryptography and Honeybee Optimization | 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 Securing IoMT-Based Healthcare Systems with Meta-Heuristic Quantum Cryptography and Honeybee Optimization Mohamed A.G. Hazber, Abdulrahman Alreshidi, Mohammed Altamimi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5447617/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 The proliferation of sensing technology, coupled with the integration of cloud infrastructure in Internet of Medical Things (IoMT)-based healthcare frameworks, revolutionizes patient healthcare by enabling remote monitoring and leveraging the scalability, flexibility, and computational power of the cloud for efficient data processing, analysis, and decision-making. This transformative approach enhances patient care quality, mitigates risks, reduces waiting times and costs, and avoids the need for frequent visits to doctors. Albeit this development empowers seamless data processing across a sophisticated network of interconnected devices and servers, while concurrently posing noteworthy security risks that magnify the potential for vulnerabilities and breaches throughout the entire data processing pipeline. Conventional cryptographic algorithms, based on computational complexity, struggle to sufficiently secure sensitive healthcare data against the rapid advancements in computing power and the potential breakthroughs in algorithmic attacks. This underscores the necessity for powerful cryptographic algorithms that can effectively defend against advanced computational attacks, ensuring enhanced security and maintaining the confidentiality and integrity of information. To address these challenges, this paper presents a novel hybrid security model that combines the honeybee optimization algorithm with quantum cryptography. Quantum cryptography ensures the secure generation and distribution of encryption keys within healthcare systems, mitigating vulnerabilities. The integration of Honeybee optimization in the cloud server enhances data integrity and optimizes system performance. The honeybee fitness function accurately estimates the integrity of encrypted data by comparing it with the original hash value. Additionally, a user verification module is developed to authenticate authorized users and prevent unauthorized data access. The proposed framework is applied to a publicly available smart healthcare dataset, revealing a confidentiality rate of 98.00 %, a data integrity rate of 99.00 %, and a low error rate of 0.12 %. These results showcase the superior performance of the model compared to existing techniques. This research provides a robust solution to the security and privacy concerns in IoMT-based healthcare systems, fostering trust and ensuring the integrity of sensitive healthcare information. Healthcare System Honeybee Optimization Internet-of-Medical-Things Quantum Cryptography Twofish Encryption 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. 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-5447617","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":388337068,"identity":"c06f094c-3696-4e15-9868-e8b098f0e0d4","order_by":0,"name":"Mohamed A.G. 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This transformative approach enhances patient care quality, mitigates risks, reduces waiting times and costs, and avoids the need for frequent visits to doctors. Albeit this development empowers seamless data processing across a sophisticated network of interconnected devices and servers, while concurrently posing noteworthy security risks that magnify the potential for vulnerabilities and breaches throughout the entire data processing pipeline. Conventional cryptographic algorithms, based on computational complexity, struggle to sufficiently secure sensitive healthcare data against the rapid advancements in computing power and the potential breakthroughs in algorithmic attacks. This underscores the necessity for powerful cryptographic algorithms that can effectively defend against advanced computational attacks, ensuring enhanced security and maintaining the confidentiality and integrity of information. To address these challenges, this paper presents a novel hybrid security model that combines the honeybee optimization algorithm with quantum cryptography. Quantum cryptography ensures the secure generation and distribution of encryption keys within healthcare systems, mitigating vulnerabilities. The integration of Honeybee optimization in the cloud server enhances data integrity and optimizes system performance. The honeybee fitness function accurately estimates the integrity of encrypted data by comparing it with the original hash value. Additionally, a user verification module is developed to authenticate authorized users and prevent unauthorized data access. The proposed framework is applied to a publicly available smart healthcare dataset, revealing a confidentiality rate of 98.00 %, a data integrity rate of 99.00 %, and a low error rate of 0.12 %. These results showcase the superior performance of the model compared to existing techniques. This research provides a robust solution to the security and privacy concerns in IoMT-based healthcare systems, fostering trust and ensuring the integrity of sensitive healthcare information. 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