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We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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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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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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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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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank Mcsherry · 2012
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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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Boosting: Foundations and algorithms
Robert E Schapire and Yoav Freund · 2013
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Answering n<sub>2+o(1)</sub> counting queries with differential privacy is hard
Jonathan Ullman · 2013
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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
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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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
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Private multiplicative weights beyond linear queries
Jonathan Ullman · 2015
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Concentrated differential privacy, 2016
Cynthia Dwork and Guy N. Rothblum · 2016
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Andre Martins and Ramon Astudillo · 2016
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Consistency with external knowledge: The topdown algorithm, 2019
Daniel Kifer · 2019
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Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, and Gerome Miklau · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2019
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Private query release assisted by public data
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, and Zhiwei Steven Wu · 2020
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The U.S. census bureau adopts differential privacy
John M. Abowd · 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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Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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The unusual effectiveness of averaging in gan training
Yasin Yazıcı, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, and Vijay Chandrasekhar · 2018
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Limits of private learning with access to public data
Noga Alon, Raef Bassily, and Shay Moran · 2019
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Learning from mixtures of private and public populations
Raef Bassily, Shay Moran, and Anupama Nandi · 2020
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Unifying privacy loss composition for data analytics
Mark Cesar and Ryan Rogers · 2020
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The power of factorization mechanisms in local and central differential privacy
Alexander Edmonds, Aleksandar Nikolov, and Jonathan Ullman · 2020
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Ipums usa: Version 10.0, doi: 10.18128/d010
S Ruggles et al · 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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Differentially private query release through adaptive projection
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, and Ankit Siva · 2021
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Leveraging public data for practical private query release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Zhiwei Steven Wu · 2021
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Private post-{gan} boosting
Marcel Neunhoeffer, Steven Wu, and Cynthia Dwork · 2021
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