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Differential privacy is a mathematical concept that provides an information-theoretic security guarantee.
Universal declaration of human rights
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On the shortest spanning subtree of a graph and the traveling salesman problem
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Shortest connection networks and some generalizations
Robert Clay Prim · 1957
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Schauder bases in Banach spaces of continuous functions
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Cook. the traveling salesman problem: A computational study
David L Applegate, Robert E Bixby, and William J Vašek Chvátal · 2007
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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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Rabindra Nath Bhattacharya and Edward C Waymire · 2007
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Optimal transport: old and new
Cédric Villani · 2009
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Boosting the accuracy of differentially-private histograms through consistency
Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu · 2010
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Differential privacy via wavelet transforms
Xiaokui Xiao, Guozhang Wang, and Johannes Gehrke · 2010
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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
Cited alongside, same era.
Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil Vadhan · 2012
Cited alongside, same era.
Geo-indistinguishability: Differential privacy for location-based systems
Miguel E Andrés, Nicolás E Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi · 2013
Cited alongside, same era.
A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
Cited alongside, same era.
Understanding hierarchical methods for differentially private histograms
Wahbeh Qardaji, Weining Yang, and Ninghui Li · 2013
Cited alongside, same era.
Differentially private histogram publication
Jia Xu, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, Ge Yu, and Marianne Winslett · 2013
Census topdown: Differentially private data, incremental schemas, and consistency with public knowledge
John Abowd, Robert Ashmead, Garfinkel Simson, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, and William Sexton · 2019
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Privacy and synthetic datasets
Steven M Bellovin, Preetam K Dutta, and Nathan Reitinger · 2019
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Synsys: A synthetic data generation system for healthcare applications
Jessamyn Dahmen and Diane Cook · 2019
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Differential privacy in practice: Expose your epsilons!
Cynthia Dwork, Nitin Kohli, and Deirdre Mulligan · 2019
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 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.
New inapproximability bounds for TSP
Marek Karpinski, Michael Lampis, and Richard Schmied · 2015
Cited alongside, same era.
Differential privacy: From theory to practice
Ninghui Li, Min Lyu, Dong Su, and Weining Yang · 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.
Privtree: A differentially private algorithm for hierarchical decompositions
Jun Zhang, Xiaokui Xiao, and Xing Xie · 2016
Cited alongside, same era.
Boel Nelson and Jenni Reuben · 2019
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How much still needs to be done to make algorithms more ethical
Michael Kearns and Aaron Roth · 2020
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Changes to the Census could make small towns disappear
Gus Wezerek and David Van Riper · 2020
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Clustering analysis via deep generative models with mixture models
Lin Yang, Wentao Fan, and Nizar Bouguila · 2020
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The many faces of 1-Lipschitz neural networks
Louis Béthune, Alberto González-Sanz, Franck Mamalet, and Mathieu Serrurier · 2021
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Covariance’s Loss is Privacy’s Gain: Computationally Efficient, Private and Accurate Synthetic Data
March Boedihardjo, Thomas Strohmer, and Roman Vershyin · 2021
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Private sampling: a noiseless approach for generating differentially private synthetic data
March Boedihardjo, Thomas Strohmer, and Roman Vershyin · 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
Mathew E Hauer and Alexis R Santos-Lozada · 2021
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A (slightly) improved approximation algorithm for metric TSP
Anna R Karlin, Nathan Klein, and Shayan Oveis Gharan · 2021
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Lipschitz clustering in metric spaces
Leonid V Kovalev · 2021
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Leveraging public data for practical private query release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Steven Wu · 2021
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Fidelity and privacy of synthetic medical data
Ofer Mendelevitch and Michael D Lesh · 2021
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Differentially private sampling from distributions
Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, and Marika Swanberg · 2021
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