1 citations · 1 across the 2 of their papers we have counts for
7 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…
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…
Combining Public and Private Data
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza
Differential privacy is widely adopted to provide provable privacy guarantees in data analysis. We consider the problem of combining public and private data (and, more generally, d…
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…
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing +1
Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that p…
Duff: A Dataset-Distance-Based Utility Function Family for the Exponential Mechanism
Andrés Muñoz Medina, Jenny Gillenwater
We propose and analyze a general-purpose dataset-distance-based utility function family, Duff, for differential privacy's exponential mechanism. Given a particular dataset and a st…