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The proliferation of big data has brought an urgent demand for privacy-preserving data publishing.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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On the design and quantification of privacy preserving data mining algorithms
D. Agrawal and C. C. Aggarwal · 2001
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Database management systems (3. ed.)
R. Ramakrishnan and J. Gehrke · 2003
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Outlier protection in continuous microdata masking
J. M. Mateo-Sanz, F. Sebé, and J. Domingo-Ferrer · 2004
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar · 2007
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Conditional functional dependencies for data cleaning
P. Bohannon, W. Fan, F. Geerts, X. Jia, and A. Kementsietsidis · 2007
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t-closeness: Privacy beyond k-anonymity and l-diversity
N. Li, T. Li, and S. Venkatasubramanian · 2007
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The cost of privacy: destruction of data-mining utility in anonymized data publishing
J. Brickell and V. Shmatikov · 2008
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A survey of inference control methods for privacy-preserving data mining
J. Domingo-Ferrer · 2008
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Differential privacy via wavelet transforms
X. Xiao, G. Wang, and J. Gehrke · 2011
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Synopses for massive data: Samples, histograms, wavelets, sketches
G. Cormode, M. N. Garofalakis, P. J. Haas, and C. Jermaine · 2012
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Speech recognition with deep recurrent neural networks
A. Graves, A. Mohamed, and G. E. Hinton · 2013
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Dpsynthesizer: Differentially private data synthesizer for privacy preserving data sharing
H. Li, L. Xiong, L. Zhang, and X. Jiang · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Pegs: Perturbed gibbs samplers that generate privacy-compliant synthetic data
Y. Park and J. Ghosh · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Privbayes: private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Seqgan: Sequence generative adversarial nets with policy gradient
L. Yu, W. Zhang, J. Wang, and Y. Yu · 2017
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Privbayes: Private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2017
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MIDA: multiple imputation using denoising autoencoders
L. Gondara and K. Wang · 2018
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Are gans created equal? A large-scale study
M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet · 2018
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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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Data privacy protection mechanisms in cloud
N. Singh and A. K. Singh · 2018
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Cited alongside, same era.
Tutorial on variational autoencoders
C. Doersch · 2016
Cited alongside, same era.
Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2016
Cited alongside, same era.
The synthetic data vault
N. Patki, R. Wedge, and K. Veeramachaneni · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
Cited alongside, same era.
Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
Cited alongside, same era.
Differentially private generative adversarial network
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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Synthesizing tabular data using generative adversarial networks
L. Xu and K. Veeramachaneni · 2018
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Cost-effective data annotation using game-based crowdsourcing
J. Yang, J. Fan, Z. Wei, G. Li, T. Liu, and X. Du · 2018
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Synthesizing electronic health records using improved generative adversarial networks
M. K. Baowaly, C. Lin, C. Liu, and K. Chen · 2019
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Faketables: Using gans to generate functional dependency preserving tables with bounded real data
H. Chen, S. Jajodia, J. Liu, N. Park, V. Sokolov, and V. S. Subrahmanian · 2019
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PATE-GAN: generating synthetic data with differential privacy guarantees
J. Jordon, J. Yoon, and M. van der Schaar · 2019
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Bounded approximate query processing
K. Li, Y. Zhang, G. Li, W. Tao, and Y. Yan · 2019
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Crowdgame: A game-based crowdsourcing system for cost-effective data labeling
T. Liu, J. Yang, J. Fan, Z. Wei, G. Li, and X. Du · 2019
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Empirical evaluation on synthetic data generation with generative adversarial network
P. Lu, P. Wang, and C. Yu · 2019
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Approximate query processing using deep generative models
S. Thirumuruganathan, S. Hasan, N. Koudas, and G. Das · 2019
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Modeling tabular data using conditional GAN
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni · 2019
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Relation data synthesis using generative adversarial network: A design space exploration
J. Fan, T. Liu, G. Li, J. Chen, Y. Shen, and X. Du · 2020
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