1 citations · 1 across the 15 of their papers we have counts for
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How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
PREM: Privately Answering Statistical Queries with Relative Error
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +4
We introduce (Private Relative Error Multiplicative weight update), a new framework for generating synthetic data that achieves a relative error guarantee for stati…
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
Mario Bravo, Juan P. Flores-Mella, Cristóbal Guzmán
We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we…