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Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution.
Privacy preserving data mining
Y. Lindell and B. Pinkas · 2000
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Privacy-preserving multivariate statistical analysis: Linear regression and classification
W. Du, Y. S. Han, and S. Chen · 2004
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Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Differential privacy: A survey of results
C. Dwork · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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De-anonymizing social networks
A. Narayanan and V. Shmatikov · 2009
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“You Might Also Like:” Privacy Risks of Collaborative Filtering
J. A. Calandrino, A. Kilzer, A. Narayanan, E. W. Felten, and V. Shmatikov · 2011
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Privacy aware learning
M. J. Wainwright, M. I. Jordan, and J. C. Duchi · 2012
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Generalized denoising auto-encoders as generative models
Y. Bengio, L. Yao, G. Alain, and P. Vincent · 2013
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Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2013
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici · 2015
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Generalization in adaptive data analysis and holdout reuse
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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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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On your social network de-anonymizablity: Quantification and large scale evaluation with seed knowledge
S. Ji, W. Li, N. Z. Gong, P. Mittal, and R. A. Beyah · 2015
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Diabetic Retinopathy Detection
Kaggle.com · 2015
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Differentially Private Bayesian Optimization
M. J. Kusner, J. R. Gardner, R. Garnett, and K. Q. Weinberger · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
Cited alongside, same era.
Revisiting differentially private regression: Lessons from learning theory and their consequences
X. Wu, M. Fredrikson, W. Wu, S. Jha, and J. F. Naughton · 2015
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Membership Privacy in MicroRNA-based Studies
M. Backes, P. Berrang, M. Humbert, and P. Manoharan · 2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
N. Dowlin, R. Gilad-Bachrach, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing · 2016
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther · 2016
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
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Generative Models
A. Karpathy, P. Abbeel, G. Brockman, P. Chen, V. Cheung, R. Duan, I. Goodfellow, D. Kingma, J. Ho, R. Houthooft, T. Salimans, J. Schulman, I. Sutskever, and W. Zaremba · 2017
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Are GANs Created Equal? A Large-Scale Study
M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet · 2017
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2017
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Medical Image Synthesis with Context-Aware Generative Adversarial Networks
D. Nie, R. Trullo, C. Petitjean, S. Ruan, and D. Shen · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar · 2017
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Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2016
Cited alongside, same era.
Statistical inference considered harmful
F. McSherry · 2016
Cited alongside, same era.
Generating Large Images from Latent Vectors
otoro.net · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
Cited alongside, same era.
De-anonymizing social networks and inferring private attributes using knowledge graphs
J. Qian, X.-Y. Li, C. Zhang, and L. Chen · 2016
Cited alongside, same era.
Improved Techniques for Training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen · 2016
Cited alongside, same era.
Closest in time.
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy
A. Pyrgelis, C. Troncoso, and E. De Cristofaro · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
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Lossy image compression with compressive autoencoders
L. Theis, W. Shi, A. Cunningham, and F. Huszár · 2017
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On the Quantitative Analysis of Decoder-Based Generative Models
Y. Wu, Y. Burda, R. Salakhutdinov, and R. Grosse · 2017
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The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
N. Carlini, C. Liu, J. Kos, Ú. Erlingsson, and D. Song · 2018
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Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2018
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Attriguard: A practical defense against attribute inference attacks via adversarial machine learning
J. Jia and N. Z. Gong · 2018
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Understanding Membership Inferences on Well-Generalized Learning Models
Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen · 2018
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Inference Attacks Against Collaborative Learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2018
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Machine Learning with Membership Privacy using Adversarial Regularization
M. Nasr, R. Shokri, and A. Houmansadr · 2018
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Scalable Private Learning with PATE
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson · 2018
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Knock Knock, Who’s There? Membership Inference on Aggregate Location Data
A. Pyrgelis, C. Troncoso, and E. De Cristofaro · 2018
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Membership Inference Attack against Differentially Private Deep Learning Model
M. A. Rahman, T. Rahman, R. Laganiere, N. Mohammed, and Y. Wang · 2018
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Generating differentially private datasets using gans
A. Triastcyn and B. Faltings · 2018
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Towards Demystifying Membership Inference Attacks
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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