不同的私有合成数据是否导致合成发现?
Ileana Montoya Perez1, Parisa Movahedi1, Valtteri Nieminen1
1Department of Computing, University of Turku, Turku, Finland.
Methods of information in medicine
|August 13, 2024
概括
不同隐私 (DP) 合成数据可以在隐私预算 (epsilon) 较低时膨胀统计测试错误,特别是I型错误. 一个DP光滑图谱方法显示了有效的I型错误,但需要大数据集和更高的隐私预算才能获得良好的电力.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据 隐私 数据 隐私 数据
- 统计分析 统计分析
背景情况:
- 合成数据生成对于共享敏感的生物医学信息,同时保持隐私至关重要.
- 不同隐私 (DP) 是平衡数据实用性和个人隐私的标准.
- 从DP合成数据评估统计结果的可靠性至关重要.
研究的目的:
- 通过使用独立的样本测试,评估从DP合成数据中发现的组差异的可靠性.
- 在DP合成数据的统计测试中量化I型 (错误发现) 和II型 (错误发现) 错误.
- 了解不同DP合成数据生成方法对统计有效性的影响.
主要方法:
- 曼-惠特尼大学的评估,学生的t测试,奇平方和中位数测试.
- 从现实世界 (前列腺癌,心血管) 和模拟数据集生成DP合成数据.
- 五种DP合成数据生成方法的比较,包括基于直方图,MWEM,Private-PGM和DP GAN.
主要成果:
- 许多DP合成数据生成方法在低隐私级别 (epsilon <= 1) 时表现出明显膨胀的I型错误.
- 在统计测试中,低的p值可能是DP噪声造成的,而不是真正的影响.
- 一个DP光滑图谱方法在隐私级别中保持了有效的I型错误,但需要大数据集和epsilon>=5可接受的II型错误.
结论:
- 在解释DP合成数据的统计结果时建议谨慎,因为可能会增加I型错误.
- 选择DP合成数据生成方法对统计分析的有效性和可靠性产生了重大影响.
- 实现隐私和统计准确性需要仔细考虑数据集大小,隐私预算和生成方法.
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