632 citations · 883 across the 13 of their papers we have counts for
3 papers · 1 filter
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…