Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation

25 March 2021

Papers citing "Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation"

9 / 9 papers shown

Title
Quantifying and Mitigating Privacy Risks for Tabular Generative Models Chaoyi Zhu Jiayi Tang Hans Brouwer Juan F. Pérez Marten van Dijk Lydia Y. Chen 60 5 0 12 Mar 2024
A Unified View of Differentially Private Deep Generative Modeling Dingfan Chen Raouf Kerkouche Mario Fritz SyDa 21 4 0 27 Sep 2023
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis Conor Hassan Roberto Salomone Kerrie Mengersen 18 6 0 28 Jul 2023
Improving Heterogeneous Model Reuse by Density Estimation A. Tang Yong Luo Han Hu Fengxiang He Kehua Su Bo Du Yixin Chen Dacheng Tao 13 5 0 23 May 2023
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records Bingyue Su Yu Wang Daniele E. Schiavazzi Fang Liu 16 2 0 11 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 8 14 0 06 Feb 2023
DP $^2$ -VAE: Differentially Private Pre-trained Variational Autoencoders Dihong Jiang Guojun Zhang Mahdi Karami Xi Chen Yunfeng Shao Yaoliang Yu 38 15 0 05 Aug 2022
Content-Aware Differential Privacy with Conditional Invertible Neural Networks Malte Tolle Ullrich Kothe F. André B. Meder Sandy Engelhardt 25 5 0 29 Jul 2022
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization P. Subramani Nicholas Vadivelu Gautam Kamath 11 83 0 18 Oct 2020