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Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy guarantees, which is essential for scientific progress in sensitive domains.
Rényi differential privacy of the sampled gaussian mechanism
I. Mironov, K. Talwar, and L. Zhang · 1908
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Rényi differential privacy of the sampled gaussian mechanism
I. Mironov, K. Talwar, and L. Zhang · 1908
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Differential privacy: A survey of results
C. Dwork · 2008
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On the complexity of differentially private data release: efficient algorithms and hardness results
C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. Vadhan · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Herding dynamical weights to learn
M. Welling · 2009
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Privacy-preserving data publishing: A survey of recent developments
B. C. Fung, K. Wang, R. Chen, and P. S. Yu · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
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Interactive privacy via the median mechanism
A. Roth and T. Roughgarden · 2010
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A simple and practical algorithm for differentially private data release
M. Hardt, K. Ligett, and F. McSherry · 2012
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A learning theory approach to noninteractive database privacy
A. Blum, K. Ligett, and A. Roth · 2013
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The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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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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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
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Privacy-preserving generative deep neural networks support clinical data sharing. biorxiv
B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, and C. S. Greene · 2017
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Learning without forgetting
Z. Li and D. Hoiem · 2017
Cited alongside, same era.
Rényi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert · 2017
Subsampled rényi differential privacy and analytical moments accountant
Y.-X. Wang, B. Balle, and S. P. Kasiviswanathan · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
J. Yoon, J. Jordon, and M. van der Schaar · 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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Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila · 2020
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Mnemonics training: Multi-class incremental learning without forgetting
Y. Liu, Y. Su, A.-A. Liu, B. Schiele, and Q. Sun · 2020
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Coresets for data-efficient training of machine learning models
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Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
F. Zenke, B. Poole, and S. Ganguli · 2017
Cited alongside, same era.
Scalable private learning with pate
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and U. Erlingsson · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
J. Schwarz, W. Czarnecki, J. Luketina, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, and R. Hadsell · 2018
Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
M. Toneva, A. Sordoni, R. T. d. Combes, A. Trischler, Y. Bengio, and G. J. Gordon · 2018
Cited alongside, same era.
Deep image prior
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2018
Cited alongside, same era.
B. Mirzasoleiman, J. Bilmes, and J. Leskovec · 2020
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Gdumb: A simple approach that questions our progress in continual learning
A. Prabhu, P. H. Torr, and P. K. Dokania · 2020
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Differentially private learning needs better features (or much more data)
F. Tramer and D. Boneh · 2020
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New oracle-efficient algorithms for private synthetic data release
G. Vietri, G. Tian, M. Bun, T. Steinke, and S. Wu · 2020
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Dataset condensation with gradient matching
B. Zhao, K. R. Mopuri, and H. Bilen · 2020
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Don’t generate me: Training differentially private generative models with sinkhorn divergence
T. Cao, A. Bie, A. Vahdat, S. Fidler, and K. Kreis · 2021
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Dp-merf: Differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation
F. Harder, K. Adamczewski, and M. Park · 2021
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G-PATE: Scalable differentially private data generator via private aggregation of teacher discriminators
Y. Long, B. Wang, Z. Yang, B. Kailkhura, A. Zhang, C. A. Gunter, and B. Li · 2021
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Datalens: Scalable privacy preserving training via gradient compression and aggregation
B. Wang, F. Wu, Y. Long, L. Rimanic, C. Zhang, and B. Li · 2021
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Opacus: User-friendly differential privacy library in PyTorch
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov · 2021
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Privacy for free: How does dataset condensation help privacy?
T. Dong, B. Zhao, and L. Lyu · 2022
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