Piloting the use of federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data.

Background: 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 (PET) enabling trustworthy data analysis without the movement of data.

Objectives: To outline 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 implementing 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.

Results: This project successfully established a federated network and piloted the use of federated analysis using aggregate-level test data from the iPOP Study, a multi-country COVID-19 research project. This integration is a first step in implementing the necessary technical, governance and user experiences for future project studies to build and expand upon, including those using individual-level datasets from multiple data nodes. While the federated network was established, federated analytics was not used in the final analysis supporting the iPOP Study due to challenges around data availability, access to technology, training and project duration.

Conclusions: Creating federated networks requires an extensive investment from a funding, data governance, technology, training and people perspective. For future initiatives, providing researchers with a secure and robust data analysis platform to perform joint multi-site collaboration, establishing a federated network should be built into the medium to long term plans. Federated networks unlock the enormous potential of national and international health datasets through enabling collaborative research that addresses key public health questions, whilst maintaining privacy and engendering trustworthiness by preventing direct access to the underlying data.

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