We study the relationship between adversarial robustness and differential privacy in high-
dimensional algorithmic statistics. We give the first black-box reduction from privacy to …

We introduce a universal framework for characterizing the statistical efficiency of a statistical
estimation problem with differential privacy guarantees. Our framework, which we call High …

We give the first polynomial time and sample (epsilon, delta)-differentially private (DP)
algorithm to estimate the mean, covariance and higher moments in the presence of a …

We study the problem of private distribution learning with access to public data. In this setup,
which we refer to as* public-private learning*, the learner is given public and private …

We present a fairly general framework for reducing $(\varepsilon,\delta) $-differentially
private (DP) statistical estimation to its non-private counterpart. As the main application of …

We introduce general tools for designing efficient private estimation algorithms, in the high-
dimensional settings, whose statistical guarantees almost match those of the best known …

In this work, we give efficient algorithms for privately estimating a Gaussian distribution in
both pure and approximate differential privacy (DP) models with optimal dependence on the …

We initiate the study of differentially private (DP) estimation with access to a small amount of
public data. For private estimation of $ d $-dimensional Gaussians, we assume that the …

We give the first polynomial-time, polynomial-sample, differentially private estimator for the
mean and covariance of an arbitrary Gaussian distribution $ N (\mu,\Sigma) $ in $\R^ d $. All …

We study the relationship between two desiderata of algorithms in statistical inference and
machine learning—differential privacy and robustness to adversarial data corruptions. Their …