4 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Exponential Separations in Local Differential Privacy
Matthew Joseph, Jieming Mao, Aaron Roth
We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connec…
The Role of Interactivity in Local Differential Privacy
Matthew Joseph, Jieming Mao, Seth Neel +1
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially…
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao +4
Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attri…
Locally Private Gaussian Estimation
Matthew Joseph, Janardhan Kulkarni, Jieming Mao +1
We study a basic private estimation problem: each of users draws a single i.i.d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaus…