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Found 14 papers, 2 papers with code
Instance-Optimal Private Density Estimation in the Wasserstein Distance
For distributions $P$ over $\mathbb{R}$, we consider a strong notion of instance-optimality: an algorithm that uniformly achieves the instance-optimal estimation rate is competitive with an algorithm that is told that the distribution is either $P$ or $Q_P$ for some distribution $Q_P$ whose probability density function (pdf) is within a factor of 2 of the pdf of $P$.
Mean Estimation with User-level Privacy under Data Heterogeneity
In this work we propose a simple model of heterogeneous user data that allows user data to differ in both distribution and quantity of data, and provide a method for estimating the population-level mean while preserving user-level differential privacy.
Samplable Anonymous Aggregation for Private Federated Data Analysis
This gap has led to significant interest in the design and implementation of simple cryptographic primitives, that can allow central-like utility guarantees without having to trust a central server.
Differentially Private Heavy Hitter Detection using Federated Analytics
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection.
Instance-Optimal Differentially Private Estimation
In this work, we study local minimax convergence estimation rates subject to $\epsilon$-differential privacy.
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
Our second contribution is a new analysis of privacy amplification by shuffling.
Non-parametric Differentially Private Confidence Intervals for the Median
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data.
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
As a direct corollary of our analysis we derive a simple and nearly optimal algorithm for frequency estimation in the shuffle model of privacy.
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design.
Differentially Private Simple Linear Regression
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data.
Private Identity Testing for High-Dimensional Distributions
In this work we present novel differentially private identity (goodness-of-fit) testers for natural and widely studied classes of multivariate product distributions: Gaussians in $\mathbb{R}^d$ with known covariance and product distributions over $\{\pm 1\}^{d}$.
The Structure of Optimal Private Tests for Simple Hypotheses
Specifically, we characterize this sample complexity up to constant factors in terms of the structure of $P$ and $Q$ and the privacy level $\varepsilon$, and show that this sample complexity is achieved by a certain randomized and clamped variant of the log-likelihood ratio test.
Online Learning via the Differential Privacy Lens
In this paper, we use differential privacy as a lens to examine online learning in both full and partial information settings.
When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models?
We provide a lower bound on the accuracy of estimators for block graphons with a large number of blocks.
