Differentially Private Identity and Equivalence Testing of Discrete Distributions

By Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld. 2018

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Citations in the corpus

Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms (2020, background): “Valiant and Valiant [2017] found near optimal separation rates that show that the difficulty of the problem depends intricately on the specific null hypothesis; see also Diakonikolas and Kane [2016] and the survey article Balakrishnan and Wasserman [2018].”
Locally Private Mean Estimation: Z-test and Tight Confidence Intervals (2018, methodology): “Cai et al. (2017) give a private identity tester based on noisy χ2test over large bins, Sheffet (2017) studies private Ordinary Least Squares using the JL transform, and Aliakbarpour et al. (2018) study identity and equivalence testing.”
Pan-Private Uniformity Testing (2019, methodology): “Speciﬁc methods include χ 2 -style statistics [12], collision-counting [5], and empirical total variation distance from U k [4], but all of these methods depend on accurate counts for each i ∈ [ k ]. the Cai et al. [12] observed that adding Laplace noise to each such count before analyzing…”
Inference Under Information Constraints III: Local Privacy Constraints (2021, background): “Under the notion of (global) differential privacy (DP) [28], identity testing has been considered in [11], [20], with a complete characterization of the sample complexity derived in [8].”
Differentially Private Assouad, Fano, and Le Cam (2020, methodology): “(2018b) developed a result similar to Theorem 1, which is used to establish sample complexity lower bounds for differentially private uniformity testing under total variation distance (Acharya et al., 2018b; Aliakbarpour et al., 2018), and for differentially private entropy and support size estimati”