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This paper studies how to learn variational autoencoders with a variety of divergences under differential privacy constraints.
Differential privacy
C. Dwork · 2006
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Comparing measures of sparsity
N. Hurley and S. Rickard · 2009
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Differential privacy
C. Dwork · 2011
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A firm foundation for private data analysis
C. Dwork · 2011
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Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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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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Differentially private high-dimensional data publication via sampling-based inference
R. Chen, Q. Xiao, Y. Zhang, and J. Xu · 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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Deep variational information bottleneck
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy · 2016
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Synthesizing plausible privacy-preserving location traces
V. Bindschaedler and R. Shokri · 2016
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Composing graphical models with neural networks for structured representations and fast inference
M. J. Johnson, D. K. Duvenaud, A. Wiltschko, R. P. Adams, and S. R. Datta · 2016
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Improved variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 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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Privtree: A differentially private algorithm for hierarchical decompositions
J. Zhang, X. Xiao, and X. Xie · 2016
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Plausible deniability for privacy-preserving data synthesis
V. Bindschaedler, R. Shokri, and C. A. Gunter · 2017
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Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2017
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A general approach to adding differential privacy to iterative training procedures
H. B. McMahan, G. Andrew, U. Erlingsson, S. Chien, I. Mironov, N. Papernot, and P. Kairouz · 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 covariance estimation
K. Amin, T. Dick, A. Kulesza, A. Munoz, and S. Vassilvitskii · 2019
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Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
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Capacity bounded differential privacy
K. Chaudhuri, J. Imola, and A. Machanavajjhala · 2019
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H. Xiao, K. Rasul, and R. Vollgraf · 2017
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The us census bureau adopts differential privacy
J. M. Abowd · 2018
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Differentially private mixture of generative neural networks
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A framework for the quantitative evaluation of disentangled representations
C. Eastwood and C. K. Williams · 2018
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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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Concentrated differentially private gradient descent with adaptive per-iteration privacy budget
J. Lee and D. Kifer · 2018
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B. Esmaeili, H. Wu, S. Jain, A. Bozkurt, N. Siddharth, B. Paige, D. H. Brooks, J. Dy, and J.-W. Meent · 2019
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Disentangling disentanglement in variational autoencoders
E. Mathieu, T. Rainforth, N. Siddharth, and Y. W. Teh · 2019
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Locally private bayesian inference for count models
A. Schein, Z. S. Wu, A. Schofield, M. Zhou, and H. Wallach · 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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On sparse linear regression in the local differential privacy model
D. Wang and J. Xu · 2019
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Differentially private model publishing for deep learning
L. Yu, L. Liu, C. Pu, M. E. Gursoy, and S. Truex · 2019
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