GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

15 June 2020

Dingfan Chen

Tribhuvanesh Orekondy

Mario Fritz

SyDa

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Papers citing "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators"

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Title
A Comprehensive Survey of Synthetic Tabular Data Generation Ruxue Shi Yili Wang Mengnan Du Xu Shen Xin Wang 49 2 0 23 Apr 2025
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis Chen Gong Kecen Li Zinan Lin Tianhao Wang 61 3 0 18 Mar 2025
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models Kyeongkook Seo Dong-Jun Han Jaejun Yoo 45 0 0 11 Mar 2025
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models Tanqiu Jiang Changjiang Li Fenglong Ma Ting Wang 70 0 0 18 Feb 2025
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Zinan Lin Tadas Baltrusaitis Wenyu Wang Sergey Yekhanin SyDa 88 1 0 08 Feb 2025
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation Bochao Liu Jianghu Lu Pengju Wang Junjie Zhang Dan Zeng Zhenxing Qian Shiming Ge 25 1 0 19 Sep 2024
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation Shiming Ge Bochao Liu Pengju Wang Yong Li Dan Zeng FedML 42 9 0 04 Sep 2024
Privacy-Enhanced Database Synthesis for Benchmark Publishing (Technical Report) Yongrui Zhong Yunqing Ge Jianbin Qin Yongrui Zhong Bo Tang Yu-Xuan Qiu Rui Mao Ye Yuan Makoto Onizuka Chuan Xiao 34 0 0 02 May 2024
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy Zepeng Jiang Weiwei Ni Yifan Zhang PICV 21 1 0 19 Apr 2024
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication Xun Yuan Yang Yang P. Gope A. Pasikhani Biplab Sikdar 42 2 0 15 Apr 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 50 3 0 25 Feb 2024
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets Ossi Raisa Antti Honkela 78 0 0 06 Feb 2024
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance Ilana Sebag Muni Sreenivas Pydi Jean-Yves Franceschi Alain Rakotomamonjy Mike Gartrell Jamal Atif Alexandre Allauzen 24 2 0 13 Dec 2023
PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining Kecen Li Chen Gong Zhixiang Li Yuzhong Zhao Xinwen Hou Tianhao Wang 33 10 0 19 Oct 2023
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation Xinting Liao Weiming Liu Xiaolin Zheng Binhui Yao Chaochao Chen 36 13 0 11 May 2023
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification Dominique Mercier Andreas Dengel Sheraz Ahmed AI4TS 18 0 0 28 Mar 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images Sahra Ghalebikesabi Leonard Berrada Sven Gowal Ira Ktena Robert Stanforth Jamie Hayes Soham De Samuel L. Smith Olivia Wiles Borja Balle DiffM 31 69 0 27 Feb 2023
Membership Inference Attacks against Synthetic Data through Overfitting Detection B. V. Breugel Hao Sun Zhaozhi Qian M. Schaar 33 45 0 24 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 27 14 0 06 Feb 2023
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models Dongha Kim Jaesung Hwang Jongjin Lee Kunwoong Kim Yongdai Kim OODD 26 1 0 11 Jan 2023
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation Yucong Liu ChiHua Wang Guang Cheng 29 7 0 28 Nov 2022
Private Multi-Winner Voting for Machine Learning Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith M. Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 42 1 0 23 Nov 2022
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder Bjarne Pfitzner B. Arnrich FedML 30 19 0 21 Nov 2022
Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry Gonzalo Munilla Garrido Xiaoyuan Liu Florian Matthes D. Song 28 24 0 07 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 27 34 0 07 Nov 2022
Using Autoencoders on Differentially Private Federated Learning GANs Gregor Schram Rui Wang K. Liang FedML AI4CE 19 1 0 24 Jun 2022
On the Privacy Properties of GAN-generated Samples Zinan Lin Vyas Sekar Giulia Fanti PICV 21 26 0 03 Jun 2022
Privacy for Free: How does Dataset Condensation Help Privacy? Tian Dong Bo-Lu Zhao Lingjuan Lyu DD 24 113 0 01 Jun 2022
Noise-Aware Statistical Inference with Differentially Private Synthetic Data Ossi Raisa Joonas Jälkö Samuel Kaski Antti Honkela SyDa 40 10 0 28 May 2022
CTAB-GAN+: Enhancing Tabular Data Synthesis Zilong Zhao A. Kunar Robert Birke L. Chen 27 78 0 01 Apr 2022
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release Donghao Li Yang Cao Yuan Yao 35 2 0 14 Feb 2022
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss Jinbao Wang Guoyang Xie Yawen Huang Yefeng Zheng Yaochu Jin Feng Zheng MedIm 37 9 0 29 Jan 2022
Differentially Private Generative Adversarial Networks with Model Inversion Dongjie Chen S. Cheung Chen-Nee Chuah Sally Ozonoff SyDa 15 13 0 10 Jan 2022
Benchmarking Differentially Private Synthetic Data Generation Algorithms Yuchao Tao Ryan McKenna Michael Hay Ashwin Machanavajjhala G. Miklau SyDa 33 82 0 16 Dec 2021
Adversarial Attacks Against Deep Generative Models on Data: A Survey Hui Sun Tianqing Zhu Zhiqiu Zhang Dawei Jin Wanlei Zhou AAML 37 42 0 01 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 16 71 0 01 Nov 2021
DTGAN: Differential Private Training for Tabular GANs A. Kunar Robert Birke Zilong Zhao L. Chen 30 11 0 06 Jul 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Wei Ping Fan Wu Yunhui Long Luka Rimanic Ce Zhang Bo-wen Li FedML 43 63 0 20 Mar 2021
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary Jean-Francois Rajotte S. Mukherjee Caleb Robinson Anthony Ortiz Christopher West J. L. Ferres R. Ng FedML MedIm 130 40 0 18 Jan 2021
Private data sharing between decentralized users through the privGAN architecture Jean-Francois Rajotte R. Ng FedML 16 3 0 14 Sep 2020
DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation Frederik Harder Kamil Adamczewski Mijung Park SyDa 25 101 0 26 Feb 2020
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators Yunhui Long Wei Ping Zhuolin Yang B. Kailkhura Aston Zhang C.A. Gunter Bo-wen Li 14 72 0 21 Jun 2019