Abstract “Concentrated differential privacy” was recently introduced by Dwork and Rothblum
as a relaxation of differential privacy, which permits sharper analyses of many privacy …

Differential privacy is a theoretical framework for ensuring the privacy of individual-level data
when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an …

Many commonly used learning algorithms work by iteratively updating an intermediate
solution using one or a few data points in each iteration. Analysis of differential privacy for …

When two different parties use the same learning rule on their own data, how can we test
whether the distributions of the two outcomes are similar? In this paper, we study the …

We study the problem of estimating finite sample confidence intervals of the mean of a
normal population under the constraint of differential privacy. We consider both the known …

In statistical learning and analysis from shared data, which is increasingly widely adopted in
platforms such as federated learning and meta-learning, there are two major concerns …

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 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 present two sample-efficient differentially private mean estimators for $ d $-dimensional
(sub) Gaussian distributions with unknown covariance. Informally, given $ n\gtrsim d/\alpha …

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 …