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Traditional differential privacy is independent of the data distribution.
Exchangeability and related topics
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Extending and benchmarking Cascade-Correlation: extensions to the Cascade-Correlation architecture and benchmarking of feed-forward supervised artificial neural networks
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Gradient-based learning applied to document recognition
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Differential privacy
Dwork, C · 2006
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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A bayesian perspective on estimating mean, variance, and standard-deviation from data
Oliphant, T. E · 2006
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Privacy without noise
Duan, Y · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Noiseless database privacy
Bhaskar, R., Bhowmick, A., Goyal, V., Laxman, S., and Thakurta, A · 2011
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Hall, R., Rinaldo, A., and Wasserman, L · 2011
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Bayesian mechanism design with efficiency, privacy, and approximate truthfulness
Leung, S. and Lui, E · 2012
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Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy
Bassily, R., Groce, A., Katz, J., and Smith, A · 2013
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A learning theory approach to noninteractive database privacy
Blum, A., Ligett, K., and Roth, A · 2013
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Rényi divergence measures for commonly used univariate continuous distributions
Gil, M., Alajaji, F., and Linder, T · 2013
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Blowfish privacy: Tuning privacy-utility trade-offs using policies
He, X., Machanavajjhala, A., and Ding, B · 2014
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Pufferfish: A framework for mathematical privacy definitions
Kifer, D. and Machanavajjhala, A · 2014
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Bassily, R. and Freund, Y · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Bun, M. and Steinke, T · 2016
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Concentrated differential privacy
Dwork, C. and Rothblum, G. N · 2016
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Differentially private variational inference for non-conjugate models
Jälkö, J., Dikmen, O., and Honkela, A · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., and Talwar, K · 2016
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Model inversion attacks that exploit confidence information and basic countermeasures
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Privacy-preserving deep learning
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Yang, B., Sato, I., and Nakagawa, H · 2015
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A teaser for differential privacy
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Renyi differential privacy
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Membership inference attacks against machine learning models
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Learning differentially private recurrent language models
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Scalable private learning with pate
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