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We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information.
A source coding problem for sources with additional outputs to keep secret from the receiver or wiretappers
H. Yamamoto · 1983
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The information bottleneck method
N. Tishby, F. C. Pereira, and W. Bialek · 1999
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Simple demographics often identify people uniquely
L. Sweeney · 2000
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k-anonymity: A model for protecting privacy
L. Sweeney · 2002
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The IM algorithm: A variational approach to information maximization
D. Barber and F. Agakov · 2003
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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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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L-diversity: Privacy beyond k-anonymity
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam · 2007
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Robust de-anonymization of large sparse datasets
A. Narayanan and V. Shmatikov · 2008
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From t-closeness-like privacy to postrandomization via information theory
D. Rebollo-Monedero, J. Forné, and J. Domingo-Ferrer · 2010
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No free lunch in data privacy
D. Kifer and A. Machanavajjhala · 2011
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Privacy against statistical inference
F. P. Calmon and N. Fawaz · 2012
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Nonparametric estimation of conditional information and divergences
B. Poczos and J. Schneider · 2012
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Elements of information theory
T. M. Cover and J. A. Thomas · 2012
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Privacy-utility tradeoff under statistical uncertainty
A. Makhdoumi and N. Fawaz · 2013
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Utility-privacy tradeoffs in databases: An information-theoretic approach
L. Sankar, S. R. Rajagopalan, and H. V. Poor · 2013
Cited alongside, same era.
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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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Dependence makes you vulnberable: Differential privacy under dependent tuples
C. Liu, S. Chakraborty, and P. Mittal · 2016
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Enhancing utility and privacy with noisy minimax filters
J. Hamm · 2017
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Minimax filter: Learning to preserve privacy from inference attacks
J. Hamm · 2017
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The GAN zoo
A. Hindupur · 2017
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Context-aware generative adversarial privacy
C. Huang, P. Kairouz, X. Chen, L. Sankar, and R. Rajagopal · 2017
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From the information bottleneck to the privacy funnel
A. Makhdoumi, S. Salamatian, N. Fawaz, and M. Médard · 2014
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Censoring representations with an adversary
H. Edwards and A. J. Storkey · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey · 2015
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Chainer: a next-generation open source framework for deep learning
S. Tokui, K. Oono, S. Hido, and J. Clayton · 2015
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On privacy-utility tradeoffs for constrained data release mechanisms
Y. O. Basciftci, Y. Wang, and P. Ishwar · 2016
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Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh · 2017
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Privacy-utility tradeoffs under constrained data release mechanisms
Y. Wang, Y. O. Basciftci, and P. Ishwar · 2017
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Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images
V. Mirjalili, S. Raschka, A. Namboodiri, and A. Ross · 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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