在EHDS中,用于符合隐私的mHealth的合成数据和PET:观点分析分析
Francesco Capparelli1, Maria Rosaria de Ligio1, Giulia Finocchiaro1
1Italian Institute for Privacy and Data Protection, Italy.
Studies in health technology and informatics
|May 17, 2025
概括
合成数据和隐私增强技术在欧洲健康数据空间框架内为安全和互操作的移动医疗系统提供解决方案,确保GDPR的遵守.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 人工智能的人工智能
背景情况:
- 欧洲健康数据空间 (EHDS) 旨在在欧盟成员国之间标准化健康数据共享.
- 确保遵守通用数据保护条例 (GDPR) 是EHDS的主要目标.
- 移动健康 (mHealth) 系统在数据共享和监管遵守方面面临挑战.
研究的目的:
- 通过使用变量自编码器 (VAE) 调查合成数据生成.
- 探索隐私增强技术 (PETs),包括联合学习,用于mHealth.
- 评估这些方法是否适合在EHDS中维护隐私和可互操作的mHealth系统.
主要方法:
- 使用变量自编码器 (VAE) 生成合成健康数据.
- 隐私增强技术 (PETs) 的应用,如联合学习.
- 这些技术的评估与GDPR的隐私设计原则相比.
主要成果:
- 合成数据和PET显示了增强移动健康数据隐私的潜力.
- 这些方法符合GDPR对数据保护的要求.
- 通过这些保护隐私的解决方案,可以提高mHealth系统的互操作性.
结论:
- 合成数据和PET是EHDS下安全的移动健康数据共享的可行解决方案.
- 采用VAE和联合学习支持GDPR合规性和设计时的隐私.
- 这些技术有助于开发可互操作和可信的移动健康生态系统.
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