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Developing a prototype for federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data

Solmaz Eradat Oskoui, Matthew Retford, Eoghan Forde, Rodrigo Barnes, Karen J Hunter, Anne Wozencraft, Simon Ellwood-Thompson, Chris Orton Orcid Logo, David Ford Orcid Logo, Sharon Heys, Julie Kennedy, Cynthia McNerney, Jeffrey Peng, Hamed Ghanbarialadolat, Sarah Rees Orcid Logo, Rachel H Mulholland, Aziz Sheikh, David Burgner, Meredith Brockway, Meghan B Azad, Natalie Rodriguez, Helga Zoega, Sarah J Stock, Clara Calvert, Jessica E Miller, Nicole Fiorentino, Amy Racine, Jonas Haggstrom, Neil Postlethwaite

International Journal of Medical Informatics, Volume: 195, Start page: 105708

Swansea University Authors: Simon Ellwood-Thompson, Chris Orton Orcid Logo, David Ford Orcid Logo, Sharon Heys, Julie Kennedy, Cynthia McNerney, Jeffrey Peng, Hamed Ghanbarialadolat, Sarah Rees Orcid Logo

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DOI (Published version): 10.1016/j.ijmedinf.2024.105708

Abstract

The use of federated networks can reduce the risk of disclosure for sensitive datasets by removing the requirement to physically transfer data. Federated networks support federated analytics, a type of privacy-enhancing technology, enabling trustworthy data analysis without the movement of source da...

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Published in: International Journal of Medical Informatics
ISSN: 1386-5056 1872-8243
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68610
Abstract: The use of federated networks can reduce the risk of disclosure for sensitive datasets by removing the requirement to physically transfer data. Federated networks support federated analytics, a type of privacy-enhancing technology, enabling trustworthy data analysis without the movement of source data. To set out the methodology used by the International COVID-19 Data Alliance (ICODA) and its partners, the Secure Anonymised Information Linkage (SAIL) Databank and Aridhia Informatics in piloting a federated network infrastructure and consequently testing federated analytics using test data provided from an ICODA project, the International Perinatal Outcome in the Pandemic (iPOP) Study. To share the challenges and benefits of using a federated network infrastructure to enable trustworthy analysis of health-related data from multiple countries and sources. This project successfully developed a federated network between the SAIL Databank and the ICODA Workbench and piloted the use of federated analysis using aggregate-level model outputs as test data from the iPOP Study, a one-year, multi-country COVID-19 research project. This integration is a first step in implementing the necessary technical, governance and user experiences for future research studies to build upon, including those using individual-level datasets from multiple data nodes. Creating federated networks requires extensive investment from a data governance, technology, training, resources, timing and funding perspective. For future initiatives, the establishment of a federated network should be built into medium to long term plans to provide researchers with a secure and robust data analysis platform to perform joint multi-site collaboration. Federated networks can unlock the enormous potential of national and international health datasets through enabling collaborative research that addresses critical public health challenges, whilst maintaining privacy and trustworthiness by preventing direct access to the source data.
Keywords: Federated Networks; Federated Analytics; COVID-19; Health Data Research; Privacy-Preserving; Secondary Data; Data Re-use
College: Faculty of Medicine, Health and Life Sciences
Funders: This work was supported by International COVID-19 Data Alliance (ICODA), an initiative funded by the COVID-19 Therapeutics Accelerator and convened by Health Data Research UK (HDR UK). We acknowledge funding via the COVID-19 Therapeutics Accelerator from the Bill & Melinda Gates Foundation (INV-017293), and the Minderoo Foundation (INV-017293) and support from Microsoft’s AI for Good Research Lab. Aridhia Informatics Ltd was funded by the Bill & Melinda Gates Foundation (INV-021793). Cloud hosting support was provided by Microsoft AI for Health. SAIL Databank and the Secure eResearch Platform (SeRP) UK, based at Swansea University, were funded by an award from Health Data Research UK (2020.112), supported by funds from the ICODA initiative, to develop the underlying infrastructure and providing expertise in establishing the federated analytics platform and governance models. This study makes use of anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. We would like to acknowledge the iPOP data providers who made their anonymised data available for research [15]. This work used data collected on behalf of patients as part of their care and support. This project was approved by the SAIL Information Governance Review Panel, under project numbers 1292 and 1299. Helga Zoega was supported by a UNSW Scientia Program Award during the conduct of this study. Sarah J Stock was funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). Meghan B. Azad is supported by a Canada Research Chair in the Developmental Origins of Chronic Disease. All authors approved the version of the manuscript to be published. This publication is based on research funded in part by the Bill & Melinda Gates Foundation. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation.
Start Page: 105708

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