13 citations · 50 across the 15 of their papers we have counts for
20 papers
Understanding Private Evolution as Learning-Augmented Clustering
Audra McMillan, Kunal Talwar, Felix Zhou
Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm, it performs much better in p…
Local Pan-Privacy for Federated Analytics
Vitaly Feldman, Audra McMillan, Guy N. Rothblum +1
Pan-privacy was proposed by Dwork et al. as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system…
Private Selection with Heterogeneous Sensitivities
Daniela Antonova, Allegra Laro, Audra McMillan +1
Differentially private (DP) selection involves choosing a high-scoring candidate from a finite candidate pool, where each score depends on a sensitive dataset. This problem arises…
Instance-Optimal Private Density Estimation in the Wasserstein Distance
Vitaly Feldman, Audra McMillan, Satchit Sivakumar +1
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric fo…
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan +1
A key challenge in many modern data analysis tasks is that user data are heterogeneous. Different users may possess vastly different numbers of data points. More importantly, it ca…
Differentially Private Heavy Hitter Detection using Federated Analytics
Karan Chadha, Junye Chen, John Duchi +5
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection. Our model assumes each use…
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar, Shan Wang, Audra McMillan +34
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private…
Private Federated Statistics in an Interactive Setting
Audra McMillan, Omid Javidbakht, Kunal Talwar +21
Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interacti…
Instance-Optimal Differentially Private Estimation
Audra McMillan, Adam Smith, Jon Ullman
In this work, we study local minimax convergence estimation rates subject to -differential privacy. Unlike worst-case rates, which may be conservative, algorithms that are local…
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
Vitaly Feldman, Audra McMillan, Kunal Talwar
The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key re…
Non-parametric Differentially Private Confidence Intervals for the Median
Joerg Drechsler, Ira Globus-Harris, Audra McMillan +2
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper…
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Vitaly Feldman, Audra McMillan, Kunal Talwar
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling amplifies differential privacy guarantees of locally ra…
Differentially Private Simple Linear Regression
Daniel Alabi, Audra McMillan, Jayshree Sarathy +2
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. Unfortun…
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Mark Bun, Jörg Drechsler, Marco Gaboardi +2
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism r…
Private Identity Testing for High-Dimensional Distributions
Clément L. Canonne, Gautam Kamath, Audra McMillan +2
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 $…
The Structure of Optimal Private Tests for Simple Hypotheses
Clément L. Canonne, Gautam Kamath, Audra McMillan +2
Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privat…
Property Testing for Differential Privacy
Anna Gilbert, Audra McMillan
We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular,…
Online Learning via the Differential Privacy Lens
Jacob Abernethy, Young Hun Jung, Chansoo Lee +2
In this paper, we use differential privacy as a lens to examine online learning in both full and partial information settings. The differential privacy framework is, at heart, less…
Local Differential Privacy for Physical Sensor Data and Sparse Recovery
Anna C. Gilbert, Audra McMillan
In this work we explore the utility of locally differentially private thermal sensor data. We design a locally differentially private recovery algorithm for the 1-dimensional, disc…
When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models?
Audra McMillan, Adam Smith
Block graphons (also called stochastic block models) are an important and widely-studied class of models for random networks. We provide a lower bound on the accuracy of estimators…