Privacy without Noisy Gradients: Slicing Mechanism for Generative Model
Training

Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training

25 October 2024

Kristjan Greenewald

Papers citing "Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training"

19 / 19 papers shown

Title
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 65 0 0 03 Feb 2025
Privacy-preserving data release leveraging optimal transport and particle gradient descent Konstantin Donhauser Javier Abad Neha Hulkund Fanny Yang 66 4 0 31 Jan 2024
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 67 14 0 06 Feb 2023
Private Synthetic Data for Multitask Learning and Marginal Queries G. Vietri Cédric Archambeau Sergul Aydore William Brown Michael Kearns Aaron Roth Ankit Siva Shuai Tang Zhiwei Steven Wu SyDa 53 29 0 15 Sep 2022
Benchmarking Differentially Private Synthetic Data Generation Algorithms Yuchao Tao Ryan McKenna Michael Hay Ashwin Machanavajjhala G. Miklau SyDa 47 83 0 16 Dec 2021
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Tianshi Cao Alex Bie Arash Vahdat Sanja Fidler Karsten Kreis SyDa DiffM 46 71 0 01 Nov 2021
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data Ryan McKenna G. Miklau Daniel Sheldon SyDa 24 119 0 11 Aug 2021
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods Terrance Liu G. Vietri Zhiwei Steven Wu SyDa 40 61 0 14 Jun 2021
Polynomial methods in statistical inference: theory and practice Yihong Wu Pengkun Yang 11 15 0 15 Apr 2021
Differentially Private Query Release Through Adaptive Projection Sergul Aydore William Brown Michael Kearns K. Kenthapadi Luca Melis Aaron Roth Ankit Siva 62 64 0 11 Mar 2021
New Oracle-Efficient Algorithms for Private Synthetic Data Release G. Vietri Grace Tian Mark Bun Thomas Steinke Zhiwei Steven Wu SyDa 138 76 0 10 Jul 2020
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators Dingfan Chen Tribhuvanesh Orekondy Mario Fritz SyDa 32 183 0 15 Jun 2020
DP-CGAN: Differentially Private Synthetic Data and Label Generation Reihaneh Torkzadehmahani Peter Kairouz B. Paten SyDa 32 236 0 27 Jan 2020
Gaussian-Smooth Optimal Transport: Metric Structure and Statistical Efficiency Ziv Goldfeld Kristjan Greenewald OT 40 41 0 24 Jan 2020
Sliced Gromov-Wasserstein Titouan Vayer Rémi Flamary Romain Tavenard Laetitia Chapel Nicolas Courty OT 25 100 0 24 May 2019
Graphical-model based estimation and inference for differential privacy Ryan McKenna Daniel Sheldon G. Miklau 42 140 0 26 Jan 2019
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 59 382 0 04 Jul 2018
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin Botond Cseke Ryota Tomioka GAN 84 1,648 0 02 Jun 2016
On Sampling, Anonymization, and Differential Privacy: Or, k-Anonymization Meets Differential Privacy Ninghui Li Wahbeh H. Qardaji D. Su 60 277 0 13 Jan 2011