Federated Analysis for Health Data
Privacy-preserving distributed analytics enabling international health research without sharing sensitive patient data
Overview
Sharing health data across institutions and international jurisdictions faces major privacy barriers, particularly under regulations like GDPR. Federated analysis offers a solution by performing computations locally on distributed datasets while exchanging only aggregate statistical results—never raw patient data. This project implements and evaluates federated analysis infrastructure for enabling multi-country health research.
Key Contributions
- Cross-jurisdictional infrastructure: Established a two-node federated analysis system between Montreal (Canada) and Vienna (Austria) using DataSHIELD, enabling pooled analysis without data transfer
- Privacy-preserving pooling: Demonstrated that federated approaches yield identical results to centralized pooled analysis while keeping individual-level data on local servers
- Comparative evaluation: Benchmarked federated analysis against synthetic data generation for utility, privacy, and implementation effort
- Cardiovascular health application: Applied the infrastructure to assess sex and gender differences in cardiovascular health across Canadian and Austrian populations
Methodology
The federated analysis framework operates through distributed computation:
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Node Setup: Establish secure servers at each participating institution with requisite security protocols for cross-jurisdictional remote access
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Data Harmonization: Align variables across datasets using Maelström research guidelines, creating compatible data dictionaries without transferring data
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Distributed Regression: Execute analysis centrally while individual-level participant data remains on local servers—only intermediate regression results are exchanged between nodes
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Result Aggregation: Combine study-level estimates through meta-analysis or direct pooling of sufficient statistics
Results
The federated analysis system successfully enabled international comparison of cardiovascular health determinants across 79,000+ individuals from Canadian and Austrian health surveys. The approach produced results equivalent to centralized pooled analysis, confirming that privacy-preserving methods need not sacrifice analytical validity. Implementation required approximately 18 months for infrastructure setup, highlighting the importance of institutional coordination and security protocols.
Technologies
- R
- DataSHIELD / Opal
- Secure Server Infrastructure
- GDPR-compliant Protocols
- Statistical Modeling