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While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) limit its full effectiveness.
Pattern Recognition and Machine Learning (Information Science and Statistics)
C. M. Bishop · 2006
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Differential privacy: A survey of results
C. Dwork · 2008
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Robust de-anonymization of large sparse datasets
A. Narayanan and V. Shmatikov · 2008
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The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
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Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 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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Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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The cramer distance as a solution to biased wasserstein gradients
M. G. Bellemare, I. Danihelka, W. Dabney, S. Mohamed, B. Lakshminarayanan, S. Hoyer, and R. Munos · 2017
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Generating multi-label discrete patient records using generative adversarial networks
E. Choi, S. Biswal, B. Malin, J. Duke, W. F. Stewart, and J. Sun · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Rényi differential privacy
I. Mironov · 2017
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Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 2017
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Deep & cross network for ad click predictions
R. Wang, B. Fu, G. Fu, and M. Wang · 2017
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Generative adversarial networks for electronic health records: A framework for exploring and evaluating methods for predicting drug-induced laboratory test trajectories
A. Yahi, R. Vanguri, and N. Elhadad · 2017
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Pate-gan: Generating synthetic data with differential privacy guarantees
J. Jordon, J. Yoon, and M. van der Schaar · 2018
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Airline Passenger Name Record Generation using Generative Adversarial Networks
A. Mottini, A. Lheritier, and R. Acuna-Agost · 2018
Modeling tabular data using conditional gan
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni · 2019
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Robust anomaly detection on unreliable data
Z. Zhao, S. Cerf, R. Birke, B. Robu, S. Bouchenak, S. B. Mokhtar, and L. Y. Chen · 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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Gan-leaks: A taxonomy of membership inference attacks against generative models
D. Chen, N. Yu, Y. Zhang, and M. Fritz · 2020
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Conditional wasserstein gan-based oversampling of tabular data for imbalanced learning
J. Engelmann and S. Lessmann · 2020
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Data synthesis based on generative adversarial networks
N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park, and Y. Kim · 2018
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Differentially private generative adversarial network
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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Differentially private releasing via deep generative model (technical report)
X. Zhang, S. Ji, and T. Wang · 2018
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
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Scalable differentially private generative student model via pate
Y. Long, S. Lin, Z. Yang, C. A. Gunter, and B. Li · 2019
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Dp-cgan: Differentially private synthetic data and label generation
R. Torkzadehmahani, P. Kairouz, and B. Paten · 2019
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Implementing differential privacy: Seven lessons from the 2020 united states census
M. B. Hawes · 2020
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Pacgan: The power of two samples in generative adversarial networks
Z. Lin, A. Khetan, G. Fanti, and S. Oh · 2020
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Synthetic data–a privacy mirage
T. Stadler, B. Oprisanu, and C. Troncoso · 2020
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Differentially private synthetic medical data generation using convolutional gans
A. Torfi, E. A. Fox, and C. K. Reddy · 2020
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Qactor: On-line active learning for noisy labeled stream data
T. Younesian, Z. Zhao, A. Ghiassi, R. Birke, and L. Y. Chen · 2020
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Comicgan: Text-to-comic generative adversarial network
B. Proven-Bessel, Z. Zhao, and L. Chen · 2021
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Enhancing robustness of on-line learning models on highly noisy data
Z. Zhao, R. Birke, R. Han, B. Robu, S. Bouchenak, S. B. Mokhtar, and L. Y. Chen · 2021
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Ctab-gan: Effective table data synthesizing
Z. Zhao, A. Kunar, R. Birke, and L. Y. Chen · 2021
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