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cs.LG2025
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…
cs.LG2025
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 (…