Communication Efficient, Sample Optimal, Linear Time Locally Private Discrete Distribution Estimation

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Federated Heavy Hitters Discovery with Differential Privacy (2019, background): “…and Xiong, 2013; Diakonikolas et al., 2015; Xu et al., 2016; Zhou and Lin, 2018; Kairouz et al., 2016; Wang et al., 2017; Bassily et al., 2017; Acharya et al., 2018; Ye and Barg, 2018; Avent et al., 2017; Bun et al., 2018; Cormode et al., 2018), and some recent works combine FL with central…”
Optimal Schemes for Discrete Distribution Estimation Under Locally Differential Privacy (2017, background): “[17] proposed a new privatization mechanism with improved communication complexity while maintaining the optimal order of sample complexity.”
INSPECTRE: Privately Estimating the Unseen (2018, background): “There has also been study on private distribution learning (Diakonikolas et al., 2015; Duchi et al., 2017; Karwa & Vadhan, 2018; Acharya et al., 2018; Kamath et al., 2018), in which we wish to estimate parameters of the distribution, rather than just a particular property of interest.”
Test without Trust: Optimal Locally Private Distribution Testing (2018, methodology): “In particular, we set W to the popular Rappor mechanism of [19] and a recently introduced mechanism called Hadamard Response (HR) [5].”
Differentially Private Testing of Identity and Closeness of Discrete Distributions (2017, background): “There has been a line of research for statistical testing and estimation problems under the notion of local differential privacy [57, 28, 34, 50, 40, 58, 63, 6, 53, 1].”