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 …
A private and computationally-efficient estimator for unbounded gaussians
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 …
mean and covariance of an arbitrary Gaussian distribution $ N (\mu,\Sigma) $ in $\R^ d $. All …
From robustness to privacy and back
We study the relationship between two desiderata of algorithms in statistical inference and
machine learning—differential privacy and robustness to adversarial data corruptions. Their …
machine learning—differential privacy and robustness to adversarial data corruptions. Their …
Private mean estimation of heavy-tailed distributions
We give new upper and lower bounds on the minimax sample complexity of differentially
private mean estimation of distributions with bounded $ k $-th moments. Roughly speaking …
private mean estimation of distributions with bounded $ k $-th moments. Roughly speaking …
New lower bounds for private estimation and a generalized fingerprinting lemma
We prove new lower bounds for statistical estimation tasks under the constraint of
$(\varepsilon,\delta) $-differential privacy. First, we provide tight lower bounds for private …
$(\varepsilon,\delta) $-differential privacy. First, we provide tight lower bounds for private …
Average-case averages: Private algorithms for smooth sensitivity and mean estimation
The simplest and most widely applied method for guaranteeing differential privacy is to add
instance-independent noise to a statistic of interest that is scaled to its global sensitivity …
instance-independent noise to a statistic of interest that is scaled to its global sensitivity …
Privacy-preserving parametric inference: a case for robust statistics
M Avella-Medina - Journal of the American Statistical Association, 2021 - Taylor & Francis
Differential privacy is a cryptographically motivated approach to privacy that has become a
very active field of research over the last decade in theoretical computer science and …
very active field of research over the last decade in theoretical computer science and …
Private mean estimation with person-level differential privacy
We study person-level differentially private (DP) mean estimation in the case where each
person holds multiple samples. DP here requires the usual notion of distributional stability …
person holds multiple samples. DP here requires the usual notion of distributional stability …
A polynomial time, pure differentially private estimator for binary product distributions
V Singhal - International Conference on Algorithmic …, 2024 - proceedings.mlr.press
We present the first $\varepsilon $-differentially private, computationally efficient algorithm
that estimates the means of product distributions over $\{0, 1\}^ d $ accurately in total …
that estimates the means of product distributions over $\{0, 1\}^ d $ accurately in total …