1 citations · 2 across the 6 of their papers we have counts for
8 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,…
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…
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…
Almost linear time differentially private release of synthetic graphs
Jingcheng Liu, Jalaj Upadhyay, Zongrui Zou
In this paper, we give an almost linear time and space algorithms to sample from an exponential mechanism with an -score function defined over an exponentially large non-co…
Optimality of Matrix Mechanism on -metric
Jingcheng Liu, Jalaj Upadhyay, Zongrui Zou
In this paper, we introduce the -error metric (for ) when answering linear queries under the constraint of differential privacy. We characterize such an error u…
The Discrepancy of Shortest Paths
Greg Bodwin, Chengyuan Deng, Jie Gao +3
The hereditary discrepancy of a set system is a certain quantitative measure of the pseudorandom properties of the system. Roughly, hereditary discrepancy measures how well one can…
Optimal Bounds on Private Graph Approximation
Jingcheng Liu, Jalaj Upadhyay, Zongrui Zou
We propose an efficient -differentially private algorithm, that given a simple {\em weighted} -vertex, -edge graph with a \emph{maximum unweighted} degree $Δ(G) \leq n…