4 papers
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
Nikita P. Kalinin, Ryan McKenna, Jalaj Upadhyay +1
Matrix factorization mechanisms for differentially private training have emerged as a promising approach to improve model utility under privacy constraints. In practical settings,…
Continual Release Moment Estimation with Differential Privacy
Nikita P. Kalinin, Jalaj Upadhyay, Christoph H. Lampert
We propose Joint Moment Estimation (JME), a method for continually and privately estimating both the first and second moments of data with reduced noise compared to naive approache…
On the Price of Differential Privacy for Hierarchical Clustering
Chengyuan Deng, Jie Gao, Jalaj Upadhyay +2
Hierarchical clustering is a fundamental unsupervised machine learning task with the aim of organizing data into a hierarchy of clusters. Many applications of hierarchical clusteri…
A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances
Michael Dinitz, Chenglin Fan, Jingcheng Liu +2
We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected…