16 citations · 24 across the 7 of their papers we have counts for
5 papers · 1 filter
Private Means and the Curious Incident of the Free Lunch
Jack Fitzsimons, James Honaker, Michael Shoemate +1
We show that the most well-known and fundamental building blocks of DP implementations -- sum, mean, count (and many other linear queries) -- can be released with substantially red…
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees
Michael Shoemate, Kevin Jett, Ethan Cowan +3
Speech data is expensive to collect, and incredibly sensitive to its sources. It is often the case that organizations independently collect small datasets for their own use, but of…
The Privacy-preserving Padding Problem: Non-negative Mechanisms for Conservative Answers with Differential Privacy
Benjamin M. Case, James Honaker, Mahnush Movahedi
Differentially private noise mechanisms commonly use symmetric noise distributions. This is attractive both for achieving the differential privacy definition, and for unbiased expe…
Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment
Mahnush Movahedi, Benjamin M. Case, Andrew Knox +6
In this paper, we outline a way to deploy a privacy-preserving protocol for multiparty Randomized Controlled Trials on the scale of 500 million rows of data and more than a billion…
PSI (Ψ): a Private data Sharing Interface
Marco Gaboardi, James Honaker, Gary King +4
We provide an overview of PSI ("a Private data Sharing Interface"), a system we are developing to enable researchers in the social sciences and other fields to share and explore pr…