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We consider learning problems where the training set consists of two types of examples: private and public.
On the density of families of sets
Norbert Sauer · 1972
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Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Nick Littlestone · 1987
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2008
Earlier work this paper cites.
Agnostic online learning
Shai Ben-David, Dávid Pál, and Shai Shalev-Shwartz · 2009
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
Cited alongside, same era.
Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
Cited alongside, same era.
Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2013
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Cited alongside, same era.
Learning privately with labeled and unlabeled examples
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2015
Cited alongside, same era.
Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2015
Cited alongside, same era.
On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis · 2015
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Learning privately from multiparty data
Jihun Hamm, Yingjun Cao, and Mikhail Belkin · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martın Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Private pac learning implies finite littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris, and Shay Moran · 2018
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Model-agnostic private learning
Raef Bassily, Abhradeep Guha Thakurta, and Om Dipakbhai Thakkar · 2018
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Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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