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通过修改的ROC-GLM进行预测模型的分布式非披露性验证
Daniel Schalk1,2,3, Raphael Rehms4, Verena S Hoffmann4
1Department of Statistics, LMU Munich, Munich, Germany.
BMC medical research methodology
|August 29, 2024
概括
分布式统计分析允许使用接收器操作特征 (ROC) 和曲线下的面积 (AUC) 来评估保护隐私的模型. 不同的隐私参数会影响准确性,需要在分布式设置中仔细选择可靠的结果.
科学领域:
- 生物统计学 生物统计学
- 数据 隐私 数据 隐私 数据
- 机器学习评估 机器学习评估
背景情况:
- 分布式统计分析提供了一种保护隐私的方法,通过使用总结统计来分析跨多个数据库的数据.
- 评估歧视模型需要对独立数据进行预测或预测性能的评估.
- 接收器操作特征 (ROC) 和曲线下的面积 (AUC) 是对二进制分类模型性能的关键指标.
研究的目的:
- 以分布式,保护隐私的方式计算二进制分类的ROC和AUC.
- 在分布式框架内评估大尺度校准指标.
- 调查差异隐私对分布式模型验证的影响.
主要方法:
- 使用DataSHIELD进行分布式分析,并使用一种新的算法进行保护隐私的ROC分析.
- 为了确定ROC和AUC,采用了通用线性模型 (GLM) 近似方法 (ROC-GLM).
- 通过添加噪声来内置差异隐私 (DP),通过模拟研究DP参数影响.
主要成果:
- 分布式AUC测量显示与真实AUC的差异,严重受差异性隐私参数的影响.
- 分布式AUC估计器的准确性可能会受到DP的过度噪音的负面影响.
- 建议包括通过适当的DP参数选择模拟和检查分布式AUC估计器的准确性.
结论:
- 算法的适用性取决于统计模型的灵敏度.
- 在大多数模拟中,近似误差是可以接受的,但更高灵敏度的模型需要调整隐私参数.
- 像AUC这样的复杂措施可以有效地用于分布式设置中的验证,同时保持个人隐私.
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