Differentially Private Chi-squared Test by Unit Circle Mechanism

By Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma. 2017

23 citations recorded by the source corpus.

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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.”
INSPECTRE: Privately Estimating the Unseen (2018, background): “Other works investigating private hypothesis testing include (Wang et al., 2015a; Gaboardi et al., 2016; Kifer & Rogers, 2017; Kakizaki et al., 2017; Rogers, 2017; Gaboardi & Rogers, 2017), which focus less on characterizing the finite-sample guarantees of such tests, and more on understanding their”
Priv'IT: Private and Sample Efficient Identity Testing (2017, background): “, 2016; Kifer & Rogers, 2017) (and a simultaneous work, focused on independence testing (Kakizaki et al., 2017)) investigate differential privacy with the former set of goals.”
Local Private Hypothesis Testing: Chi-Square Tests (2017, unknown): “Following these works, 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; Ka”
Privately Learning High-Dimensional Distributions (2018, background): “For example, Wang et al. (2015); Gaboardi et al. (2016b); Kifer and Rogers (2017); Cai et al. (2017); Kakizaki et al. (2017) gave private algorithms for goodness-of-fit testing, closeness, and independence testing.”