通过基于差异性隐私的合成算法加强体检数据的隐私保护
Weili Zhang1, Ran Liu2, Xinyi Zhu3
1Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, 210009, China.
BMC medical informatics and decision making
|September 1, 2025
概括
不同的隐私合成数据生成增强了物理检查数据的研究实用性. 与现有方法相比,DP-Gibbs提供了更高的隐私保护和数据实用性,使得医疗数据的安全分析成为可能.
科学领域:
- 计算隐私
- 卫生信息学
- 数据科学
背景情况:
- 身体检查对于早期发现疾病至关重要,
- 使用差异隐私的合成数据生成为数据实用性和隐私提供了解决方案.
- 这项研究探讨了物理检查数据的差异隐私算法.
研究的目的:
- 评估用于保护隐私的物理检查数据的合成数据算法的有效性.
- 在隐私和数据实用性方面比较DP-Gibbs的性能.
- 探索不同级别的隐私保护与数据实用性之间的平衡.
主要方法:
- 使用了三种差异性隐私合成算法 (PrivBayes,PeGS,DP-Gibbs).
- 用于生成合成数据集的物理检查数据包括分类变量.
- 将性能与现有的Private-PGM算法进行比较.
主要成果:
- 与现有算法 (2.012) 相比,DP-Gibbs 的隐私保护能力显著提高 (4.686).
- DP-Gibbs 实现了优异的数据效用指标:精度为 0. 620,F1 评分为 0. 539,卡帕系数为 0. 342,AUC 评分为 0. 765.
- 现有算法的效用得分较低:精度为0.520,F1得分为0.321,卡帕系数为0.188,AUC得分为0.695.
结论:
- DP-Gibbs提供了强大的隐私保护和增强的物理检查数据实用性.
- 这种方法可以安全地挖掘和应用医疗数据,同时保护患者的隐私.
- 临床研究人员可以从未识别的体检数据中获得宝贵的见解,而不会造成隐私风险.
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