632 citations · 883 across the 13 of their papers we have counts for
15 papers
Subset-Based Instance Optimality in Private Estimation
Travis Dick, Alex Kulesza, Ziteng Sun +1
We propose a new definition of instance optimality for differentially private estimation algorithms. Our definition requires an optimal algorithm to compete, simultaneously for eve…
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…
Learning with User-Level Privacy
Daniel Levy, Ziteng Sun, Kareem Amin +4
We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samp…
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…
Information Extraction from Larger Multi-layer Social Networks
Brandon Oselio, Alex Kulesza, Alfred Hero
Social networks often encode community structure using multiple distinct types of links between nodes. In this paper we introduce a novel method to extract information from such mu…