194 citations · 467 across the 14 of their papers we have counts for
16 papers
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…
Easy Differentially Private Linear Regression
Kareem Amin, Matthew Joseph, Mónica Ribero +1
Linear regression is a fundamental tool for statistical analysis. This has motivated the development of linear regression methods that also satisfy differential privacy and thus gu…
Plume: Differential Privacy at Scale
Kareem Amin, Jennifer Gillenwater, Matthew Joseph +2
Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems. Howev…
A Joint Exponential Mechanism For Differentially Private Top-
Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina +1
We present a differentially private algorithm for releasing the sequence of elements with the highest counts from a data domain of elements. The algorithm is a "joint" inst…
Shuffle Private Stochastic Convex Optimization
Albert Cheu, Matthew Joseph, Jieming Mao +1
In shuffle privacy, each user sends a collection of randomized messages to a trusted shuffler, the shuffler randomly permutes these messages, and the resulting shuffled collection…
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…