Scholarlink

Background <p>Assessing 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.</p> Methods <p>In 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.</p> Results <p>We 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.</p> Conclusion <p>This 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Privacy-preserving data quality assessment for federated health data networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

查看全文
  • Radovan Tomášik,
  • Tobias Kussel,
  • Zdenka Dudová,
  • Radoslava Kacová,
  • Roman Hrstka,
  • Martin Lablans,
  • Petr Holub

摘要

Background

Assessing 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.

Methods

In 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.

Results

We 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.

Conclusion

This 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.