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Diffusion models (DMs) are one of the most widely used generative models for producing high quality images.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar · 2017
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Dp-em: Differentially private expectation maximization
Mijung Park, James Foulds, Kamalika Choudhary, and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Wide residual networks, 2017
Sergey Zagoruyko and Nikos Komodakis · 2017
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Differentially private mixture of generative neural networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Differentially private generative adversarial networks for time series, continuous, and discrete open data
Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
Cited alongside, same era.
PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2019
Cited alongside, same era.
Gs-wgan: A gradient-sanitized approach for learning differentially private generators
Dingfan Chen, Tribhuvanesh Orekondy, and Mario Fritz · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Don’t generate me: Training differentially private generative models with sinkhorn divergence
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Considerations for differentially private learning with large-scale public pretraining
Florian Tramèr, Gautam Kamath, and Nicholas Carlini · 2022
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Hermite polynomial features for private data generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, and Mi Jung Park · 2022
Later among the works it cites.
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2022
Later among the works it cites.
Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, and Karsten Kreis · 2021
Cited alongside, same era.
DP-MERF: Differentially private mean embeddings with random features for practical privacy-preserving data generation
Frederik Harder, Kamil Adamczewski, and Mijung Park · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Wilds: A benchmark of in-the-wild distribution shifts, 2021
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
Cited alongside, same era.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
Tedigan: Text-guided diverse face image generation and manipulation
Weihao Xia, Yujiu Yang, Jing-Hao Xue, and Baoyuan Wu · 2021
Cited alongside, same era.
Differentially private diffusion models, 2023
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis · 2023
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Are diffusion models vulnerable to membership inference attacks?
Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, and Kaidi Xu · 2023
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Differentially private diffusion models generate useful synthetic images, 2023
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, and Borja Balle · 2023
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Pre-trained perceptual features improve differentially private image generation
Frederik Harder, Milad Jalali, Danica J. Sutherland, and Mijung Park · 2023
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Membership inference of diffusion models, 2023
Hailong Hu and Jun Pang · 2023
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Sok: Privacy-preserving data synthesis
Yuzheng Hu, Fan Wu, Qinbin Li, Yunhui Long, Gonzalo Garrido, Chang Ge, Bolin Ding, David Forsyth, Bo Li, and Dawn Song · 2023
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Differentially private synthetic data via foundation model apis 1: Images, 2023
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, and Sergey Yekhanin · 2023
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Membership inference attacks against diffusion models
Tomoya Matsumoto, Takayuki Miura, and Naoto Yanai · 2023
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Shape-guided diffusion with inside-outside attention, 2023
Dong Huk Park, Grace Luo, Clayton Toste, Samaneh Azadi, Xihui Liu, Maka Karalashvili, Anna Rohrbach, and Trevor Darrell · 2023
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How to dp-fy ml: A practical tutorial to machine learning with differential privacy
Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, and Chiyaun Zhang · 2023
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Toast: Transfer learning via attention steering, 2023
Baifeng Shi, Siyu Gai, Trevor Darrell, and Xin Wang · 2023
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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Membership inference attacks on diffusion models via quantile regression, 2023
Shuai Tang, Zhiwei Steven Wu, Sergul Aydore, Michael Kearns, and Aaron Roth · 2023
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Membership inference attacks against text-to-image generation models, 2023
Yixin Wu, Ning Yu, Zheng Li, Michael Backes, and Yang Zhang · 2023
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Transferring pretrained diffusion probabilistic models, 2023
Fuming You and Zhou Zhao · 2023
Closest in time.
dp-promise: Differentially private diffusion probabilistic models for image synthesis
Haichen Wang, Shuchao Pang, Lu Zhigang, Yihang Rao, Yongbin Zhou, and Xue Minhui · 2024
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