FAIR and Square: Privacy Compliance Framework for Healthcare Databases

FAIR and Square: Privacy Compliance Framework for Healthcare Databases | 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 FAIR and Square: Privacy Compliance Framework for Healthcare Databases Zefan Zhu, Matthias N. Louws, Roland V. Bumbuc, Jelle Sakkers, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8205252/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 High-throughput technologies generate vast amounts of data and may include sensitive data. This raises significant privacy concerns particularly for individuals whose genomic DNA (data) are collected, along with their personal data (metadata). Existing privacy-preserving techniques fail to adequately safeguard against the risks of singling out individuals, linkability, and inference attacks, such as those articulated in Recital 26 and WP216 of General Data Protection Regulation (GDPR). To reduce the possibility of such attacks, we have developed a privacy framework for patient health related datasets. By using a novel P 29 Score, we quantify GDPR-compliance. In addition, a web interface was designed and implemented with inputs from various stakeholders, such as researchers, data stewards, and the general public. Initial evaluations using various machine-learning algorithms on pre- and post- generalized datasets demonstrate the framework’s effectiveness in mitigating privacy risks while preserving data usability. Privacy-preserving Data Privacy GDPR Compliance UI and UX Full Text Additional Declarations No competing interests reported. Supplementary Files cleansupmatFAIRandSquare.docx FigSI.pdf 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-8205252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554261783,"identity":"d12bef0c-794a-4b28-aa4a-89becbcb5660","order_by":0,"name":"Zefan Zhu","email":"","orcid":"","institution":"University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Zefan","middleName":"","lastName":"Zhu","suffix":""},{"id":554261784,"identity":"02dd9485-ce1c-4fed-8a4a-92aef7da36d9","order_by":1,"name":"Matthias N. 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