13 citations · 50 across the 15 of their papers we have counts for
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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…