
CovarianceAware Private Mean Estimation Without Private Covariance Estimation
We present two sampleefficient differentially private mean estimators f...
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Leveraging Public Data for Practical Private Query Release
In many statistical problems, incorporating priors can significantly imp...
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Fair and Optimal Cohort Selection for Linear Utilities
The rise of algorithmic decisionmaking has created an explosion of rese...
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The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation
There has been a recent wave of interest in intermediate trust models fo...
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Auditing Differentially Private Machine Learning: How Private is Private SGD?
We investigate whether Differentially Private SGD offers better privacy ...
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CoinPress: Practical Private Mean and Covariance Estimation
We present simple differentially private estimators for the mean and cov...
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A Primer on Private Statistics
Differentially private statistical estimation has seen a flurry of devel...
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Private Query Release Assisted by Public Data
We study the problem of differentially private query release assisted by...
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Private Mean Estimation of HeavyTailed Distributions
We give new upper and lower bounds on the minimax sample complexity of d...
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The Power of Factorization Mechanisms in Local and Central Differential Privacy
We give new characterizations of the sample complexity of answering line...
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Manipulation Attacks in Local Differential Privacy
Local differential privacy is a widely studied restriction on distribute...
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Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Learning the parameters of a Gaussian mixtures models is a fundamental a...
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Private Identity Testing for HighDimensional Distributions
In this work we present novel differentially private identity (goodness...
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Efficiently Estimating ErdosRenyi Graphs with Node Differential Privacy
We give a simple, computationally efficient, and nodedifferentiallypri...
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Efficient Private Algorithms for Learning Halfspaces
We present new differentially private algorithms for learning a largema...
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Differentially Private Fair Learning
We design two learning algorithms that simultaneously promise differenti...
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The Structure of Optimal Private Tests for Simple Hypotheses
Hypothesis testing plays a central role in statistical inference, and is...
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Distributed Differential Privacy via Mixnets
We consider the problem of designing scalable, robust protocols for comp...
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The Limits of PostSelection Generalization
While statistics and machine learning offers numerous methods for ensuri...
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Privately Learning HighDimensional Distributions
We design nearly optimal differentially private algorithms for learning ...
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Local Differential Privacy for Evolving Data
There are now several large scale deployments of differential privacy us...
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Tight Lower Bounds for Locally Differentially Private Selection
We prove a tight lower bound (up to constant factors) on the sample comp...
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Jonathan Ullman
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