Background The use of wearables facilitates data collection at a previously unobtainable
scale, enabling the construction of complex predictive models with the potential to improve …

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 algorithm to estimate the mean of ad-variate probability
distribution with bounded covariance from Õ (d) independent samples subject to pure …

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 propose and analyze algorithms to solve a range of learning tasks under user-level
differential privacy constraints. Rather than guaranteeing only the privacy of individual …

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 …

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 present simple differentially private estimators for the parameters of multivariate sub-
Gaussian data that are accurate at small sample sizes. We demonstrate the effectiveness of …