15 citations · 20 across the 11 of their papers we have counts for
11 papers
Correlated Noise Mechanisms for Differentially Private Learning
Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo +9
This monograph explores the design and analysis of correlated noise mechanisms for differential privacy (DP), focusing on their application to private training of AI and machine le…
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…
Binned Group Algebra Factorization for Differentially Private Continual Counting
Monika Henzinger, Nikita P. Kalinin, Jalaj Upadhyay
We study memory-efficient matrix factorization for differentially private counting under continual observation. While recent work by Henzinger and Upadhyay 2024 introduced a factor…
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…
Improved Differentially Private Continual Observation Using Group Algebra
Monika Henzinger, Jalaj Upadhyay
Differentially private weighted prefix sum under continual observation is a crucial component in the production-level deployment of private next-word prediction for Gboard, which,…
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…