Revisiting Differentially Private Hypothesis Tests for Categorical Data

By Yue Wang, Jaewoo Lee, Daniel Kifer. 2015

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Citations in the corpus

The structure of optimal private tests for simple hypotheses (2018, background): “One line of work [17, 21, 40, 44, 49, 71, 76] designs differentially private versions of popular test statistics for testing goodness-of-fit, closeness, and independence, as well as private ANOVA, focusing on the performance at small sample sizes.”
Test without Trust: Optimal Locally Private Distribution Testing (2018, methodology): “Here the algorithm itself is run by a trusted curator who has access to all the user data, but needs to ensure that the output of the test maintains differential privacy; see [21, 28, 33] for a sampling of results on identity and independence testing.”
Differentially Private Uniformly Most Powerful Tests for Binomial Data (2018, background): “Gaboardi et al. (2016) and Wang et al. (2015) build on Vu and Slavkovic (2009), developing additional tests for multinomial data.”
Improved Differentially Private Analysis of Variance (2019, methodology): “Other work has used Monte Carlo simulations (as we do in this work) to give more accurate reference distributions for small N [10, 22].”
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms (2020, background): “Besides goodness-of-ﬁt, there is also work in this setting on testing independence between discrete variables [Wang et al., 2015, Gaboardi et al., 2016], and in testing independence between a discrete and a continuous variable by constructing diﬀerentially private versions of classical rank-based…”
Local Private Hypothesis Testing: Chi-Square Tests (2017, methodology): “…there has also been general work in private chi-square hypothesis tests, where the main tests are for goodness of fit and independence testing, although some do extend to more general tests (Wang et al., 2015; Gaboardi et al., 2016; Kifer and Rogers, 2017; Cai et al., 2017; Kakizaki et al., 2017).”
Inference Under Information Constraints III: Local Privacy Constraints (2021, background): “Independence testing under differentially privacy has been studied in [31], [40], [47] and the first algorithm with finite sample guarantee was given in [10].”
Differentially Private Nonparametric Hypothesis Testing (2019, methodology): “For these same tests, Monte Carlo simulation has been shown to offer more precise analysis in some cases [12, 33].”
Finite Sample Differentially Private Confidence Intervals (2017, methodology): “…and McSherry (2010); Karwa and Slavković (2012); Karwa et al. (2015, 2014); Karwa and Slavković (2016); Gaboardi et al. (2016b); Solea (2014); Wang et al. (2015); Kifer and Rogers (2016), but generally the utility of these methods (e.g. the expected length of a confidence interval or power…”