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<p>The demand for demographically and geographically diverse, high-quality, fit-for-purpose real-world data has been increasing to support regulatory and other healthcare decision making. Accessing and sharing healthcare data across sites, regions, and countries while ensuring data privacy has been a long-standing challenge. We discuss synthetic data and federated data networks as examples of emerging privacy-preserving technologies and provide real-life use cases from government, industry, and academia with their opportunities and challenges.</p>

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Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

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  • Echo H. Wang,
  • Puja Myles,
  • Randi Foraker,
  • Sengwee Toh,
  • Lucy Mosquera,
  • Khaled El Emam,
  • Mehmet Burcu

摘要

The demand for demographically and geographically diverse, high-quality, fit-for-purpose real-world data has been increasing to support regulatory and other healthcare decision making. Accessing and sharing healthcare data across sites, regions, and countries while ensuring data privacy has been a long-standing challenge. We discuss synthetic data and federated data networks as examples of emerging privacy-preserving technologies and provide real-life use cases from government, industry, and academia with their opportunities and challenges.