Differentially Private Significance Testing on Paired-Sample Data
Differentially Private Significance Testing on Paired-Sample Data
复制标题
配对样本数据的差异私有显着性测试
DOI：
10.1137/1.9781611974348.18
复制
发表时间：
2016
影响因子：
3.6
通讯作者：
Chris Clifton
中科院分区：
文献类型：
--
作者：
Christine Task;Chris Clifton
严格的数据挖掘结果需要对结果的统计重要性进行度量。数据和模型的复杂性使这成为一个挑战；保护隐私的方法使问题进一步复杂化。我们演示了如何在社交网络分析问题的背景下估计结果的统计重要性；提供差异隐私所需的噪声的影响被包括在重要性度量中。因此，提供隐私并不会使分析的使用复杂化。虽然演示了社交网络分析，但该方法是通用的。使用的Wilcoxon符号等级检验适用于具有“之前”和“之后”测量的各种数据，并且很好地适应了不同的隐私。我们在已知隐私问题的公开数据上进行了演示，表明一些明显的大差异并不显著，一些小的差异是显著的，并且当使用差异隐私进行分析时，可以在保护个人隐私的同时获得相同的结果。
Rigorous data mining results require measures of the statistical significance of the outcomes. The complexity of the data and models makes this a challenge; methods to protect privacy further complicate the issue. We demonstrate how to estimate statistical significance of results in the context of a social network analysis problem; the impact of the noise required to provide differential privacy is included in the significance measure. As a result, providing privacy does not complicate the use of the analysis. While demonstrated for social network analysis, the approach is general. The Wilcoxon signed-rank test used is appropriate for a wide variety of data with “before” and “after” measurements, and adapts well to differential privacy. We demonstrate on publicly available data with known privacy issues, showing that some apparently large differences are not significant, some small differences are, and that when the analysis is done using differential privacy, the same results can been achieved while protecting individual privacy.