Robustness implies privacy in statistical estimation
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 …
dimensional algorithmic statistics. We give the first black-box reduction from privacy to …
Differential privacy and robust statistics in high dimensions
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 …
estimation problem with differential privacy guarantees. Our framework, which we call High …
Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism
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 …
distribution with bounded covariance from Õ (d) independent samples subject to pure …
Private robust estimation by stabilizing convex relaxations
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 …
algorithm to estimate the mean, covariance and higher moments in the presence of a …
Private distribution learning with public data: The view from sample compression
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 …
which we refer to as* public-private learning*, the learner is given public and private …
Robust and differentially private mean estimation
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 …
platforms such as federated learning and meta-learning, there are two major concerns …
Private and polynomial time algorithms for learning Gaussians and beyond
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 …
private (DP) statistical estimation to its non-private counterpart. As the main application of …
Privately estimating a Gaussian: Efficient, robust, and optimal
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 …
both pure and approximate differential privacy (DP) models with optimal dependence on the …
Private estimation with public data
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 …
public data. For private estimation of $ d $-dimensional Gaussians, we assume that the …
User-level differential privacy with few examples per user
Previous work on user-level differential privacy (DP)[Ghazi et al. NeurIPS 2021, Bun et al.
STOC 2023] obtained generic algorithms that work for various learning tasks. However, their …
STOC 2023] obtained generic algorithms that work for various learning tasks. However, their …