VALORIS: One-shot and lossless vertical logistic regression for privacy-protecting multi-site health analytics

VALORIS: One-shot and lossless vertical logistic regression for privacy-protecting multi-site health analytics | 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 Article VALORIS: One-shot and lossless vertical logistic regression for privacy-protecting multi-site health analytics Félix Camirand Lemyre, Marie-Pier Domingue, Jean-Philippe Morissette, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7973226/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Health analytics increasingly relies on variables held by different entities, such as clinical, laboratory, environmental, and genomic data. Due to legal, ethical, and social acceptability constraints, these vertically partitioned data often cannot be shared across organizations holding them. Conducting statistical analyses in such settings requires methods that protect privacy. We introduce VALORIS (Vertically partitioned Analytics under the LOgistic Regression model for Inference in Statistics), a novel method that enables lossless statistical inference (equivalent to the pooled analyses) under a logistic regression model without disclosing any individual-level data—including the outcome variable. VALORIS is a practical, one-shot algorithm that requires no third-party coordinator. The privacy-preserving properties of VALORIS were mathematically assessed, and a privacy-aware setting-dependent framework was provided to ensure individual-data privacy. We demonstrate the accuracy and feasibility of VALORIS through the investigation of potential factors associated with kidney failure among pediatric patients with chronic kidney disease using real health data from Necker–Enfants Malades Hospital. We further validate the proposed algorithm on a larger scale with a reproducible application using the MIMIC-IV database. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files VALORISSupplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 08 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor assigned by journal 02 Dec, 2025 Editor invited by journal 17 Nov, 2025 Submission checks completed at journal 12 Nov, 2025 First submitted to journal 12 Nov, 2025 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. 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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-7973226","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":554224003,"identity":"c715825f-a09e-4fa1-85bd-c2c2371a9b78","order_by":0,"name":"Félix Camirand Lemyre","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Félix","middleName":"Camirand","lastName":"Lemyre","suffix":""},{"id":554224004,"identity":"cb6bcb85-63ad-42dd-9cbf-ad29ed037d65","order_by":1,"name":"Marie-Pier Domingue","email":"","orcid":"","institution":"Université de 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