匿名数据仓库:从数字健康应用程序中匿名数据的实践框架
André Naef1, Enzo Coduti1, Paul Y Windisch1
1Innovation Team, dacadoo AG, Zürich, CHE.
Cureus
|May 6, 2024
概括
本研究提出了一个匿名数字健康数据的实际框架,解决了对安全数据共享的日益增长的需求. 提出的方法平衡了数据的实用性,尽量减少重新识别风险.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 数据安全 数据安全
背景情况:
- 数字健康行业正在扩大,增加了对分享匿名健康数据的需求.
- 目前的医学文献缺乏对数据匿名化技术的全面覆盖.
- 数字健康数据为匿名化带来了独特的特性和挑战.
研究的目的:
- 为数字健康应用量身定制的数据匿名化提供一个实用的框架.
- 将文学趋势和常见的匿名化方法合成一个可用的结构.
- 解决数字健康数据固有的具体机遇和挑战.
主要方法:
- 综合有关匿名化技术和数字健康数据趋势的文献.
- 开发一个重点关注单独数据存储和局部匿名化的框架.
- 实施策略,例如最小化静态属性和确保k-匿名性.
- 使用聚合,圆和封顶来防止准标识符.
主要成果:
- 详细介绍了一项拟议的数字健康数据匿名化框架.
- 该框架强调分离数据存储,本地化匿名化和选择性数据出口.
- 概述了确保用户和属性的k-匿名性和防止准识别器风险的方法.
结论:
- 对数据匿名化的务实方法对于保持数据实用性,同时最大限度地降低重新识别风险至关重要.
- 拟议的框架为数字健康数据匿名化提供了一个可适应的结构.
- 该框架需要根据特定数据集特征进行修改.
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