幻象匿名化:在匿名健康数据中对会员推断风险的对抗性测试
Thierry Meurers1, Mehmed Halilovic1, Karen Otte1
1Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Computers in biology and medicine
|July 13, 2025
概括
在匿名健康数据中量化会员推断风险对隐私至关重要. 我们的框架经验性地评估剩余风险,使匿名和合成数据集之间的比较.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 网络安全 网络安全
背景情况:
- 包含敏感个人信息的医学研究数据集存在重大隐私风险.
- 匿名化技术对于保护健康数据至关重要,但量化残留风险仍然具有挑战性.
研究的目的:
- 在匿名表格数据中引入一个新的框架来量化剩余会员推断风险.
- 适应和应用合成数据评估技术来评估匿名化有效性.
主要方法:
- 开发了一个框架,使用训练有素的分类器来检测匿名数据集中的目标记录.
- 使用的数据匿名化,使用与用于分类器培训的目标数据集相同的方法.
- 在各种匿名化策略和敌对条件下进行了实验.
主要成果:
- 拟议的框架有效地识别了匿名数据集中的剩余隐私风险.
- 匿名化方法的有效性取决于所选择的隐私模式和数据修改策略.
- 根据数据如何达到预定义的风险值,观察到风险的显著变化.
结论:
- 该框架提供了一种经验方法,用于评估各种匿名化技术中的会员推断风险.
- 由于共享的方法,可以直接比较匿名和合成数据集之间的剩余风险.
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