3 citations · 5 across the 3 of their papers we have counts for
8 papers
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…
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…
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…