73 citations · 585 across the 58 of their papers we have counts for
9 papers · 1 filter
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
Naty Peter, Eliad Tsfadia, Jonathan Ullman
Fingerprinting arguments, first introduced by Bun, Ullman, and Vadhan (STOC 2014), are the most widely used method for establishing lower bounds on the sample complexity or error o…
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, Alina Oprea
We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis. We do so via novel data poisoning atta…
Tight Lower Bounds for Locally Differentially Private Selection
Jonathan Ullman
We prove a tight lower bound (up to constant factors) on the sample complexity of any non-interactive local differentially private protocol for optimizing a linear function over th…
PSI (Ψ): a Private data Sharing Interface
Marco Gaboardi, James Honaker, Gary King +4
We provide an overview of PSI ("a Private data Sharing Interface"), a system we are developing to enable researchers in the social sciences and other fields to share and explore pr…
Strong Hardness of Privacy from Weak Traitor Tracing
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman +1
Despite much study, the computational complexity of differential privacy remains poorly understood. In this paper we consider the computational complexity of accurately answering a…
Make Up Your Mind: The Price of Online Queries in Differential Privacy
Mark Bun, Thomas Steinke, Jonathan Ullman
We consider the problem of answering queries about a sensitive dataset subject to differential privacy. The queries may be chosen adversarially from a larger set Q of allowable que…