Differentially Private ANOVA Testing
Differentially Private ANOVA Testing
复制标题
差异私人方差分析测试
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Adam Groce
中科院分区：
文献类型：
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作者：
Zachary Campbell;Andrew Bray;Anna M. Ritz;Adam Groce
现代社会产生了有关个人的不可思议的数据，并以证明保护个人隐私的方式发布了有关该数据的摘要统计数据，将为许多领域的研究人员提供宝贵的资源。我们提出了第一种用于分析差异隐私的方差分析（ANOVA）的算法，从而可以在敏感信息的数据库上进行此重要的统计检验（以及发布的结果）。除了我们用于F测试统计量的私人算法外，我们还展示了一种严格的方法来计算P值，以说明保留隐私所需的噪声。最后，我们提出了实验结果，以量化该测试的差异私有版本的统计能力，发现数千个观测值的样本足以检测组之间的变化。差异化的ANOVA算法是释放在科学和社会科学领域很有价值的常见测试统计数据的有前途的方法。
Modern society generates an incredible amount of data about individuals, and releasing summary statistics about this data in a manner that provably protects individual privacy would offer a valuable resource for researchers in many fields. We present the first algorithm for analysis of variance (ANOVA) that preserves differential privacy, allowing this important statistical test to be conducted (and the results released) on databases of sensitive information. In addition to our private algorithm for the F test statistic, we show a rigorous way to compute p-values that accounts for the added noise needed to preserve privacy. Finally, we present experimental results quantifying the statistical power of this differentially private version of the test, finding that a sample of several thousand observations is sufficient to detect variation between groups. The differentially private ANOVA algorithm is a promising approach for releasing a common test statistic that is valuable in fields in the sciences and social sciences.