73 citations · 585 across the 58 of their papers we have counts for
5 papers · 2 filters
The Power of Factorization Mechanisms in Local and Central Differential Privacy
Alexander Edmonds, Aleksandar Nikolov, Jonathan Ullman
We give new characterizations of the sample complexity of answering linear queries (statistical queries) in the local and central models of differential privacy: *In the non-intera…
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal +1
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the par…
Manipulation Attacks in Local Differential Privacy
Albert Cheu, Adam Smith, Jonathan Ullman
Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems…
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Adam Sealfon, Jonathan Ullman
We give a simple, computationally efficient, and node-differentially-private algorithm for estimating the parameter of an Erdos-Renyi graph---that is, estimating p in a G(n,p)---wi…
Private Identity Testing for High-Dimensional Distributions
Clément L. Canonne, Gautam Kamath, Audra McMillan +2
In this work we present novel differentially private identity (goodness-of-fit) testers for natural and widely studied classes of multivariate product distributions: Gaussians in $…