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We study the problem of differentially private query release assisted by access to public data.
On the density of families of sets
Norbert Sauer · 1972
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Densité et dimension
Patrick Assouad · 1983
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Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Nick Littlestone · 1988
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Agnostic online learning
Shai Ben-David, Dávid Pál, and Shai Shalev-Shwartz · 2009
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N Rothblum · 2010
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Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
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Optimal private halfspace counting via discrepancy
Shanmugavelayutham Muthukrishnan and Aleksandar Nikolov · 2012
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
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Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil P. Vadhan · 2015
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
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The modernization of statistical disclosure limitation at the U.S. census bureau, 2017
Aref N. Dajani, Amy D. Lauger, Phyllis E. Singer, Daniel Kifer, Jerome P. Reiter, Ashwin Machanavajjhala, Simson L. Garfinkel, Scot A. Dahl, Matthew Graham, Vishesh Karwa, Hang Kim, Philip Lelerc, Ian M. Schmutte, William N. Sexton, Lars Vilhuber, and John M. Abowd · 2017
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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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Model-agnostic private learning
Raef Bassily, Abhradeep Guha Thakurta, and Om Dipakbhai Thakkar · 2018
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2018
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Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2015
Cited alongside, same era.
Learning privately from multiparty data
Jihun Hamm, Yingjun Cao, and Mikhail Belkin · 2016
Cited alongside, same era.
Limits of private learning with access to public data
Noga Alon, Raef Bassily, and Shay Moran · 2019
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Private pac learning implies finite littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris, and Shay Moran · 2019
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Privately answering classification queries in the agnostic pac model
Anupama Nandi and Raef Bassily · 2019
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