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Ensuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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On the efficiency of checking perfect privacy
Ashwin Machanavajjhala and Johannes Gehrke · 2006
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Personal privacy vs population privacy: Learning to attack anonymization
Graham Cormode · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Learning fair representations
Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Blowfish privacy: tuning privacy-utility trade-offs using policies
Xi He, Ashwin Machanavajjhala, and Bolin Ding · 2014
Earlier work this paper cites.
Pufferfish: A framework for mathematical privacy definitions
Daniel Kifer and Ashwin Machanavajjhala · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Giuseppe Ateniese, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Cited alongside, same era.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2016
Cited alongside, same era.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
On the rate of convergence of empirical measure in ∞ − \infty- Wasserstein distance for unbounded density function, 2018
Anning Liu, Jian-Guo Liu, and Yulong Lu · 2018
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Linear-complexity data-parallel earth mover’s distance approximations
Kubilay Atasu and Thomas Mittelholzer · 2019
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Local distribution obfuscation via probability coupling
Yusuke Kawamoto and Takao Murakami · 2019
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Local obfuscation mechanisms for hiding probability distributions
Yusuke Kawamoto and Takao Murakami · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Gaussian-smoothed optimal transport: Metric structure and statistical efficiency
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Pufferfish privacy mechanisms for correlated data
Shuang Song, Yizhen Wang, and Kamalika Chaudhuri · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Property inference attacks on fully connected neural networks using permutation invariant representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
Cited alongside, same era.
Ziv Goldfeld and Kristjan Greenewald · 2020
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Overlearning reveals sensitive attributes
Congzheng Song and Vitaly Shmatikov · 2020
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Dataset-level attribute leakage in collaborative learning, 2020
Wanrong Zhang, Shruti Tople, and Olga Ohrimenko · 2020
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