7 citations · 7 across the 4 of their papers we have counts for
4 papers
High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
Shurong Lin, Eric D. Kolaczyk, Adam Smith +1
The interplay between optimization and privacy has become a central theme in privacy-preserving machine learning. Noisy stochastic gradient descent (SGD) has emerged as a cornersto…
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
Shurong Lin, Aleksandra Slavković, Deekshith Reddy Bhoomireddy
In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (…
Differentially Private Linear Regression with Linked Data
Shurong Lin, Elliot Paquette, Eric D. Kolaczyk
There has been increasing demand for establishing privacy-preserving methodologies for modern statistics and machine learning. Differential privacy, a mathematical notion from comp…
Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling
Shurong Lin, Mark Bun, Marco Gaboardi +2
Confidence intervals are a fundamental tool for quantifying the uncertainty of parameters of interest. With the increase of data privacy awareness, developing a private version of…