1.1k citations · 2.4k across the 49 of their papers we have counts for
52 papers
Metric geometry of the privacy-utility tradeoff
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Synthetic data are an attractive concept to enable privacy in data sharing. A fundamental question is how similar the privacy-preserving synthetic data are compared to the true dat…
An Algorithm for Streaming Differentially Private Data
Girish Kumar, Thomas Strohmer, Roman Vershynin
Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practi…
Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets
Yiyun He, Thomas Strohmer, Roman Vershynin +1
Differentially private synthetic data provide a powerful mechanism to enable data analysis while protecting sensitive information about individuals. However, when the data lie in a…
Covariance loss, Szemeredi regularity, and differential privacy
March Boedihardjo, Thomas Strohmer, Roman Vershynin
We show how randomized rounding based on Grothendieck's identity can be used to prove a nearly tight bound on the covariance loss--the amount of covariance that is lost by taking c…
Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold
Junda Sheng, Thomas Strohmer
The stochastic block model is a canonical random graph model for clustering and community detection on network-structured data. Decades of extensive study on the problem have estab…
Fair Data Representation for Machine Learning at the Pareto Frontier
Shizhou Xu, Thomas Strohmer
As machine learning powered decision-making becomes increasingly important in our daily lives, it is imperative to strive for fairness in the underlying data processing. We propose…