194 citations · 195 across the 2 of their papers we have counts for
7 papers · 1 filter
Better Private Linear Regression Through Better Private Feature Selection
Travis Dick, Jennifer Gillenwater, Matthew Joseph
Existing work on differentially private linear regression typically assumes that end users can precisely set data bounds or algorithmic hyperparameters. End users often struggle to…
Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Quantiles are often used for summarizing and understanding data. If that data is sensitive, it may be necessary to compute quantiles in a way that is differentially private, provid…
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…
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…
Local Differential Privacy for Evolving Data
Matthew Joseph, Aaron Roth, Jonathan Ullman +1
There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the dat…