Privacy-preserving data quality assessment for federated health data networks
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Article in Periodical |
| Magazine / Source | BMC MEDICAL INFORMATICS AND DECISION MAKING |
| MU Faculty or unit | |
| Citation | |
| web | URL |
| Doi | https://doi.org/10.1186/s12911-025-03328-6 |
| Keywords | Differential privacy; Data quality; Federated data; Medical informatics; BBMRI; CQL |
| Description | BackgroundAssessing data quality in federated health data systems presents unique challenges, particularly when data custodians cannot expose raw data due to privacy regulations. Traditional quality assessment approaches often require centralised access, which conflicts with the principles of data sovereignty and confidentiality.MethodsIn this study, we evaluate the utility of federated data quality assessment with differential privacy techniques to safeguard sensitive health data. The aim is to develop tooling and demonstrate a proof-of-concept implementation over a synthetic dataset of observational medical data.ResultsWe present a privacy-preserving framework for evaluating data quality in federated environments using differential privacy. Our approach enables individual data providers to compute local quality metrics and share only aggregated, privacy-protected results. We implement a proof-of-concept that supports predefined quality checks across different data models and demonstrate how meaningful insights into data quality can be obtained without compromising sensitive information.ConclusionThis work demonstrates that differential privacy can be effectively applied to enable federated quality assessment in health data networks without compromising individual privacy. By implementing a proof-of-concept system over synthetic health data, we show that it is possible to obtain meaningful quality metrics in a decentralised setting. |
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