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We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy.
Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, Kobbi Nissim, and A. D. Smith · 2006
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
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On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan · 2009
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Python 3 Reference Manual
Guido Van Rossum and Fred L. Drake · 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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Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
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PCPs and the hardness of generating private synthetic data
Jonathan Ullman and Salil Vadhan · 2011
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
Earlier work this paper cites.
A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
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Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil Vadhan · 2012
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Privately releasing conjunctions and the statistical query barrier
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman · 2013
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The geometry of differential privacy: the sparse and approximate cases
Aleksandar Nikolov, Kunal Talwar, and Li Zhang · 2013
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Faster private release of marginals on small databases
Karthekeyan Chandrasekaran, Justin Thaler, Jonathan Ullman, and Andrew Wan · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Dual query: Practical private query release for high dimensional data
Marco Gaboardi, Emilio Jesús Gallego-Arias, Justin Hsu, Aaron Roth, and Zhiwei Steven Wu · 2014
Cited alongside, same era.
Efficient algorithms for privately releasing marginals via convex relaxations
Cynthia Dwork, Aleksandar Nikolov, and Kunal Talwar · 2015
Cited alongside, same era.
Adam: A method for stochastic gradient descent
Diederik P Kingma and Jimmy Lei Ba · 2015
Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2018
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Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala · 2018
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P Bhavnani, James Brian Byrd, and Casey S Greene · 2019
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Towards instance-optimal private query release
Jaroslaw Błasiok, Mark Bun, Aleksandar Nikolov, and Thomas Steinke · 2019
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Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers · 2019
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The matrix mechanism: optimizing linear counting queries under differential privacy
Chao Li, Gerome Miklau, Michael Hay, Andrew McGregor, and Vibhor Rastogi · 2015
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
From softmax to sparsemax: A sparse model of attention and multi-label classification
Andre Martins and Ramon Astudillo · 2016
Cited alongside, same era.
Answering nˆ2+o(1) counting queries with differential privacy is hard
Jonathan Ullman · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Ryan McKenna, Daniel Sheldon, and Gerome Miklau · 2019
Later among the works it cites.
How to use heuristics for differential privacy
Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2019
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DP-CGAN: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Oracle efficient private non-convex optimization
Seth Neel, Aaron Roth, Giuseppe Vietri, and Steven Wu · 2020
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Marcel Neunhoeffer, Zhiwei Steven Wu, and Cynthia Dwork · 2020
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P3GM: Private high-dimensional data release via privacy preserving phased generative model
Shun Takagi, Tsubasa Takahashi, Yang Cao, and Masatoshi Yoshikawa · 2020
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New oracle-efficient algorithms for private synthetic data release
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Steven Wu · 2020
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Bounding, concentrating, and truncating: Unifying privacy loss composition for data analytics
Mark Cesar and Ryan Rogers · 2021
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