Revisiting Differentially Private Hypothesis Tests for Categorical Data
Revisiting Differentially Private Hypothesis Tests for Categorical Data
复制标题
重新审视分类数据的差分隐私假设检验
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Daniel Kifer
中科院分区：
文献类型：
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作者：
Yue Wang;Jaewoo Lee;Daniel Kifer
在本文中，我们考虑了对受统计披露控制技术保护的数据进行假设检验的方法，称为差异隐私。对差异化假设测试的先前方法，可以使测试统计量具有较大的差异（并导致大量功率损失）或直接添加到数据中的较小噪声，但未能根据添加噪声来调整测试（导致偏见，不可靠的$ p $值）。在本文中，我们开发了各种解决这些问题的实用假设检验。使用不同的渐近方制度，更适合于隐私的假设检验，我们在卡方检验和似然比测试之间显示了修改的等效性。然后，我们为各种表格数据（即独立性，样本比例和拟合优点测试）开发了私人私有可能性比和卡方测试。使用各种隐私设置对小型和大型数据集进行的实验评估证明了我们方法的实用性和可靠性。
In this paper, we consider methods for performing hypothesis tests on data protected by a statistical disclosure control technology known as differential privacy. Previous approaches to differentially private hypothesis testing either perturbed the test statistic with random noise having large variance (and resulted in a significant loss of power) or added smaller amounts of noise directly to the data but failed to adjust the test in response to the added noise (resulting in biased, unreliable $p$-values). In this paper, we develop a variety of practical hypothesis tests that address these problems. Using a different asymptotic regime that is more suited to hypothesis testing with privacy, we show a modified equivalence between chi-squared tests and likelihood ratio tests. We then develop differentially private likelihood ratio and chi-squared tests for a variety of applications on tabular data (i.e., independence, sample proportions, and goodness-of-fit tests). Experimental evaluations on small and large datasets using a wide variety of privacy settings demonstrate the practicality and reliability of our methods.