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Don't Generate Me: Training Differentially Private Generative Models
  with Sinkhorn Divergence

Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence

1 November 2021
Tianshi Cao
Alex Bie
Arash Vahdat
Sanja Fidler
Karsten Kreis
    SyDa
    DiffM
ArXivPDFHTML

Papers citing "Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence"

46 / 46 papers shown
Title
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Hadrien Reynaud
Alberto Gomez
Paul Leeson
Qingjie Meng
B. Kainz
MedIm
56
0
0
28 Mar 2025
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
Chia-Yi Hsu
Jia-You Chen
Yu-Lin Tsai
Chih-Hsun Lin
Pin-Yu Chen
Chia-Mu Yu
Chun-ying Huang
52
0
0
20 Mar 2025
Optimal Differentially Private Sampling of Unbounded Gaussians
Valentio Iverson
Gautam Kamath
Argyris Mouzakis
46
0
0
03 Mar 2025
Private Text Generation by Seeding Large Language Model Prompts
Private Text Generation by Seeding Large Language Model Prompts
Supriya Nagesh
Justin Y. Chen
Nina Mishra
Tal Wagner
SyDa
SILM
62
1
0
20 Feb 2025
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
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
Generate to Discriminate: Expert Routing for Continual Learning
Generate to Discriminate: Expert Routing for Continual Learning
Yewon Byun
Sanket Vaibhav Mehta
Saurabh Garg
Emma Strubell
Michael Oberst
Bryan Wilder
Zachary Chase Lipton
78
0
0
31 Dec 2024
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model
  Training
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
Kristjan Greenewald
Yuancheng Yu
Hao Wang
Kai Xu
36
0
0
25 Oct 2024
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
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
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 Differentially Private Diffusion Models via Stochastic
  Adversarial Distillation
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
Bochao Liu
Pengju Wang
Shiming Ge
46
1
0
27 Aug 2024
Efficient Differentially Private Fine-Tuning of Diffusion Models
Efficient Differentially Private Fine-Tuning of Diffusion Models
Jing Liu
Andrew Lowy
T. Koike-Akino
K. Parsons
Ye Wang
25
0
0
07 Jun 2024
PrE-Text: Training Language Models on Private Federated Data in the Age
  of LLMs
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou
Akshat Shrivastava
Hongyuan Zhan
Rylan Conway
Trang Le
Adithya Sagar
Giulia Fanti
Daniel Lazar
33
8
0
05 Jun 2024
Differentially Private Fine-Tuning of Diffusion Models
Differentially Private Fine-Tuning of Diffusion Models
Yu-Lin Tsai
Yizhe Li
Zekai Chen
Po-yu Chen
Chia-Mu Yu
Xuebin Ren
Francois Buet-Golfouse
52
3
0
03 Jun 2024
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
Hasan Hammoud
Hani Itani
Fabio Pizzati
Philip H. S. Torr
Adel Bibi
Guohao Li
CLIP
VLM
120
36
0
02 Feb 2024
Privacy-preserving data release leveraging optimal transport and
  particle gradient descent
Privacy-preserving data release leveraging optimal transport and particle gradient descent
Konstantin Donhauser
Javier Abad
Neha Hulkund
Fanny Yang
41
4
0
31 Jan 2024
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
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
PAC Privacy Preserving Diffusion Models
PAC Privacy Preserving Diffusion Models
Qipan Xu
Youlong Ding
Xinxi Zhang
Jie Gao
Hao Wang
DiffM
26
0
0
02 Dec 2023
A Unified View of Differentially Private Deep Generative Modeling
A Unified View of Differentially Private Deep Generative Modeling
Dingfan Chen
Raouf Kerkouche
Mario Fritz
SyDa
23
4
0
27 Sep 2023
Robust Representation Learning for Privacy-Preserving Machine Learning:
  A Multi-Objective Autoencoder Approach
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
Sofiane Ouaari
Ali Burak Ünal
Mete Akgun
Nícolas Pfeifer
32
0
0
08 Sep 2023
Large-Scale Public Data Improves Differentially Private Image Generation
  Quality
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu
Chuan Guo
Kamalika Chaudhuri
21
2
0
04 Aug 2023
SoK: Privacy-Preserving Data Synthesis
SoK: Privacy-Preserving Data Synthesis
Yuzheng Hu
Fan Wu
Yue Liu
Yunhui Long
Gonzalo Munilla Garrido
Chang Ge
Bolin Ding
David A. Forsyth
Bo-wen Li
D. Song
60
25
0
05 Jul 2023
Recent Advances in Optimal Transport for Machine Learning
Recent Advances in Optimal Transport for Machine Learning
Eduardo Fernandes Montesuma
Fred-Maurice Ngole-Mboula
Antoine Souloumiac
OOD
OT
17
32
0
28 Jun 2023
Training generative models from privatized data
Training generative models from privatized data
Daria Reshetova
Wei-Ning Chen
Ayfer Özgür
FedML
28
2
0
15 Jun 2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to
  CLIP Training
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
Alyssa Huang
Peihan Liu
Ryumei Nakada
Linjun Zhang
Wanrong Zhang
VLM
71
5
0
13 Jun 2023
Differentially Private Latent Diffusion Models
Differentially Private Latent Diffusion Models
Saiyue Lyu
Michael F. Liu
Margarita Vinaroz
Mijung Park
24
24
0
25 May 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin
Sivakanth Gopi
Janardhan Kulkarni
Harsha Nori
Sergey Yekhanin
41
36
0
24 May 2023
Model Conversion via Differentially Private Data-Free Distillation
Model Conversion via Differentially Private Data-Free Distillation
Bochao Liu
Pengju Wang
Shikun Li
Dan Zeng
Shiming Ge
FedML
18
3
0
25 Apr 2023
DPAF: Image Synthesis via Differentially Private Aggregation in Forward
  Phase
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Chih-Hsun Lin
Chia-Yi Hsu
Chia-Mu Yu
Yang Cao
Chun-ying Huang
29
1
0
20 Apr 2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving
  Data Generation
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Yilin Yang
Kamil Adamczewski
Danica J. Sutherland
Xiaoxiao Li
Mijung Park
33
14
0
03 Mar 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images
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
Membership Inference Attacks against Synthetic Data through Overfitting Detection
B. V. Breugel
Hao Sun
Zhaozhi Qian
M. Schaar
33
45
0
24 Feb 2023
Machine Learning for Synthetic Data Generation: A Review
Machine Learning for Synthetic Data Generation: A Review
Ying-Cheng Lu
Minjie Shen
Huazheng Wang
Xiao Wang
Capucine Van Rechem
Tianfan Fu
Wenqi Wei
SyDa
42
140
0
08 Feb 2023
Private GANs, Revisited
Private GANs, Revisited
Alex Bie
Gautam Kamath
Guojun Zhang
24
14
0
06 Feb 2023
Differentially Private Kernel Inducing Points using features from
  ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Margarita Vinaroz
M. Park
DD
25
0
0
31 Jan 2023
Data Distillation: A Survey
Data Distillation: A Survey
Noveen Sachdeva
Julian McAuley
DD
45
73
0
11 Jan 2023
IPProtect: protecting the intellectual property of visual datasets
  during data valuation
IPProtect: protecting the intellectual property of visual datasets during data valuation
Gursimran Singh
Chendi Wang
Ahnaf Tazwar
Lanjun Wang
Yong Zhang
19
0
0
22 Dec 2022
DPD-fVAE: Synthetic Data Generation Using Federated Variational
  Autoencoders With Differentially-Private Decoder
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder
Bjarne Pfitzner
B. Arnrich
FedML
30
19
0
21 Nov 2022
Private Set Generation with Discriminative Information
Private Set Generation with Discriminative Information
Dingfan Chen
Raouf Kerkouche
Mario Fritz
DD
27
34
0
07 Nov 2022
Differentially Private Diffusion Models
Differentially Private Diffusion Models
Tim Dockhorn
Tianshi Cao
Arash Vahdat
Karsten Kreis
DiffM
32
91
0
18 Oct 2022
DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders
DP2^22-VAE: Differentially Private Pre-trained Variational Autoencoders
Dihong Jiang
Guojun Zhang
Mahdi Karami
Xi Chen
Yunfeng Shao
Yaoliang Yu
43
15
0
05 Aug 2022
Privacy for Free: How does Dataset Condensation Help Privacy?
Privacy for Free: How does Dataset Condensation Help Privacy?
Tian Dong
Bo-Lu Zhao
Lingjuan Lyu
DD
24
113
0
01 Jun 2022
Pre-trained Perceptual Features Improve Differentially Private Image
  Generation
Pre-trained Perceptual Features Improve Differentially Private Image Generation
Fredrik Harder
Milad Jalali Asadabadi
Danica J. Sutherland
Mijung Park
27
28
0
25 May 2022
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Kaidi Wang
Bo-Lu Zhao
Xiangyu Peng
Zheng Hua Zhu
Jiankang Deng
Xinchao Wang
Hakan Bilen
Yang You
PICV
45
11
0
23 May 2022
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data
  Release
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
Donghao Li
Yang Cao
Yuan Yao
35
2
0
14 Feb 2022
Hermite Polynomial Features for Private Data Generation
Hermite Polynomial Features for Private Data Generation
Margarita Vinaroz
Mohammad-Amin Charusaie
Frederik Harder
Kamil Adamczewski
Mijung Park
18
26
0
09 Jun 2021
P3GM: Private High-Dimensional Data Release via Privacy Preserving
  Phased Generative Model
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
Shun Takagi
Tsubasa Takahashi
Yang Cao
Masatoshi Yoshikawa
SyDa
14
38
0
22 Jun 2020
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