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We present a highly effective algorithmic approach for generating $\varepsilon$-differentially private synthetic data in a bounded metric space with near-optimal utility guarantees under the 1-Wasserstein distance.
Matching random samples in many dimensions
Michel Talagrand · 1992
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ε \varepsilon -entropy and ε \varepsilon -capacity of sets in functional spaces
VM Tikhomirov · 1993
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Approximation algorithms
Vijay V Vazirani · 2001
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Rademacher and Gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Distance-based classification with Lipschitz functions
Ulrike von Luxburg and Olivier Bousquet · 2004
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The generic chaining: upper and lower bounds of stochastic processes
Michel Talagrand · 2005
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A discrete analogue of the Laplace distribution
Seidu Inusah and Tomasz Kozubowski · 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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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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Optimal transport: old and new
Cédric Villani · 2009
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N Rothblum · 2010
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Empirical processes with a bounded ψ \psi 1 diameter
Shahar Mendelson · 2010
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Sublinear time algorithms for earth mover’s distance
Khanh Do Ba, Huy L Nguyen, Huy N Nguyen, and Ronitt Rubinfeld · 2011
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Private and continual release of statistics
T-H Hubert Chan, Elaine Shi, and Dawn Song · 2011
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PCPs and the hardness of generating private synthetic data
Jonathan Ullman and Salil Vadhan · 2011
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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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Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil Vadhan · 2012
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
Cited alongside, same era.
Constructive quantization: Approximation by empirical measures
Steffen Dereich, Michael Scheutzow, and Reik Schottstedt · 2013
Cited alongside, same era.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Tail bounds via generic chaining
Sjoerd Dirksen · 2015
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.
Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance
Jonathan Weed and Francis Bach · 2019
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Implementing differential privacy: Seven lessons from the 2020 United States Census
Michael B Hawes · 2020
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A simple fourier analytic proof of the AKT optimal matching theorem
Sergey G Bobkov and Michel Ledoux · 2021
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A universal law of robustness via isoperimetry
Sébastien Bubeck and Mark Sellke · 2021
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The limits of differential privacy (and its misuse in data release and machine learning)
Josep Domingo-Ferrer, David Sánchez, and Alberto Blanco-Justicia · 2021
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Differential privacy in the 2020 census will distort covid-19 rates
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Adaptive metric dimensionality reduction
Lee-Ad Gottlieb, Aryeh Kontorovich, and Robert Krauthgamer · 2016
Cited alongside, same era.
Differentially private k k -means clustering
Dong Su, Jianneng Cao, Ninghui Li, Elisa Bertino, and Hongxia Jin · 2016
Cited alongside, same era.
Differentially private data releasing for smooth queries
Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, and Liwei Wang · 2016
Cited alongside, same era.
The complexity of differential privacy
Salil Vadhan · 2017
Cited alongside, same era.
Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
Cited alongside, same era.
Mathew E Hauer and Alexis R Santos-Lozada · 2021
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Iterative methods for private synthetic data: Unifying framework and new methods
Terrance Liu, Giuseppe Vietri, and Steven Z Wu · 2021
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The 2020 census disclosure avoidance system topdown algorithm
John M Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, et al · 2022
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Covariance’s loss is privacy’s gain: Computationally efficient, private and accurate synthetic data
March Boedihardjo, Thomas Strohmer, and Roman Vershynin · 2022
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Privacy of synthetic data: A statistical framework
March Boedihardjo, Thomas Strohmer, and Roman Vershynin · 2022
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Private measures, random walks, and synthetic data
March Boedihardjo, Thomas Strohmer, and Roman Vershynin · 2022
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Private sampling: a noiseless approach for generating differentially private synthetic data
March Boedihardjo, Thomas Strohmer, and Roman Vershynin · 2022
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Lipschitz clustering in metric spaces
Leonid V Kovalev · 2022
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A dynamical system perspective for Lipschitz neural networks
Laurent Meunier, Blaise J Delattre, Alexandre Araujo, and Alexandre Allauzen · 2022
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Private synthetic data for multitask learning and marginal queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael Kearns, Aaron Roth, Ankit Siva, Shuai Tang, and Steven Wu · 2022
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Covariance loss, Szemeredi regularity, and differential privacy
March Boedihardjo, Thomas Strohmer, and Roman Vershynin · 2023
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