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The ability to generate privacy-preserving synthetic versions of sensitive image datasets could unlock numerous ML applications currently constrained by data availability.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Statistical significance of the netflix challenge
A. Feuerverger, Y. He, and S. Khatri · 2012
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D4d-senegal: the second mobile phone data for development challenge
Y.-A. de Montjoye, Z. Smoreda, R. Trinquart, C. Ziemlicki, and V. D. Blondel · 2014
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
P. Chrabaszcz, I. Loshchilov, and F. Hutter · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
P. Bandi, O. Geessink, Q. Manson, M. Van Dijk, M. Balkenhol, M. Hermsen, B. E. Bejnordi, B. Lee, K. Paeng, A. Zhong, et al · 2018
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M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
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Generative models for effective ml on private, decentralized datasets
S. Augenstein, H. B. McMahan, D. Ramage, S. Ramaswamy, P. Kairouz, M. Chen, R. Mathews, et al · 2019
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Gs-wgan: A gradient-sanitized approach for learning differentially private generators
D. Chen, T. Orekondy, and M. Fritz · 2020
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A survey of differentially private generative adversarial networks
L. Fan · 2020
Cited alongside, same era.
Don’t generate me: Training differentially private generative models with sinkhorn divergence
T. Cao, A. Bie, A. Vahdat, S. Fidler, and K. Kreis · 2021
Cited alongside, same era.
Differentially private generative adversarial networks with model inversion
D. Chen, S.-c. S. Cheung, C.-N. Chuah, and S. Ozonoff · 2021
Cited alongside, same era.
Fake it till you make it: Guidelines for effective synthetic data generation
F. K. Dankar and M. Ibrahim · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
Dpnet: Differentially private network traffic synthesis with generative adversarial networks
L. Fan and A. Pokkunuru · 2021
Private gans, revisited
A. Bie, G. Kamath, and G. Zhang · 2022
Later among the works it cites.
Fine-tuning with differential privacy necessitates an additional hyperparameter search
Y. Cattan, C. A. Choquette-Choo, N. Papernot, and A. Thakurta · 2022
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Differentially private facial obfuscation via generative adversarial networks
W. L. Croft, J.-R. Sack, and W. Shi · 2022
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
S. De, L. Berrada, J. Hayes, S. L. Smith, and B. Balle · 2022
Later among the works it cites.
Differentially private diffusion models
T. Dockhorn, T. Cao, A. Vahdat, and K. Kreis · 2022
Later among the works it cites.
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Cited alongside, same era.
Drawing multiple augmentation samples per image during training efficiently decreases test error
S. Fort, A. Brock, R. Pascanu, S. De, and S. L. Smith · 2021
Cited alongside, same era.
Geolocated data generation and protection using generative adversarial networks
H. Alatrista-Salas, P. Montalvo-Garcia, M. Nunez-del Prado, and J. Salas · 2022
Cited alongside, same era.
Spot the fake lungs: Generating synthetic medical images using neural diffusion models
H. Ali, S. Murad, and Z. Shah · 2022
Cited alongside, same era.
Scalable and efficient training of large convolutional neural networks with differential privacy
Z. Bu, J. Mao, and S. Xu
Cited in the paper.
Automatic clipping: Differentially private deep learning made easier and stronger
Z. Bu, Y.-X. Wang, S. Zha, and G. Karypis
Cited in the paper.
Roentgen: Vision-language foundation model for chest x-ray generation
P. Chambon, C. Bluethgen, J.-B. Delbrouck, R. Van der Sluijs, M. Połacin, J. M. Z. Chaves, T. M. Abraham, S. Purohit, C. P. Langlotz, and A. Chaudhari
Cited in the paper.
Dp-ctgan: Differentially private medical data generation using ctgans
M. L. Fang, D. S. Dhami, and K. Kersting · 2022
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Out-of-distribution robustness via targeted augmentations
I. Gao, S. Sagawa, P. W. Koh, T. Hashimoto, and P. Liang · 2022
Later among the works it cites.
Extracting training data from diffusion models, 2023
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace · 2023
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Are diffusion models vulnerable to membership inference attacks?, 2023
J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu · 2023
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