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The permute-and-flip mechanism is a recently proposed differentially private selection algorithm that was shown to outperform the exponential mechanism.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Privacy: From theory to practice on the map
Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, and Lars Vilhuber · 2008
Earlier work this paper cites.
Discovering frequent patterns in sensitive data
Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta · 2010
Earlier work this paper cites.
Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
Earlier work this paper cites.
A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
Earlier work this paper cites.
Privgene: differentially private model fitting using genetic algorithms
Jun Zhang, Xiaokui Xiao, Yin Yang, Zhenjie Zhang, and Marianne Winslett · 2013
Earlier work this paper cites.
The large margin mechanism for differentially private maximization
Kamalika Chaudhuri, Daniel Hsu, and Shuang Song · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Cited alongside, same era.
Differentially private password frequency lists
Jeremiah Blocki, Anupam Datta, and Joseph Bonneau · 2016
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
Cited alongside, same era.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
Cited alongside, same era.
Utility cost of formal privacy for releasing national employer-employee statistics
Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, Matthew Graham, Mark Kutzbach, and Lars Vilhuber · 2017
Cited alongside, same era.
Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
Detecting violations of differential privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
Later among the works it cites.
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
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Free gap information from the differentially private sparse vector and noisy max mechanisms
Zeyu Ding, Yuxin Wang, Danfeng Zhang, and Daniel Kifer · 2019
Later among the works it cites.
Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 2019
Later among the works it cites.
Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 2019
Later among the works it cites.
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Cited alongside, same era.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
The US Census Bureau adopts differential privacy
John M Abowd · 2018
Cited alongside, same era.
Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N Rothblum, and Thomas Steinke · 2018
Cited alongside, same era.
Clément Canonne, Gautam Kamath, and Thomas Steinke · 2020
Later among the works it cites.
Optimal differential privacy composition for exponential mechanisms
Jinshuo Dong, David Durfee, and Ryan Rogers · 2020
Later among the works it cites.
Implementing the exponential mechanism with base-2 differential privacy
Christina Ilvento · 2020
Later among the works it cites.
Permute-and-flip: A new mechanism for differentially private selection
Ryan McKenna and Daniel R Sheldon · 2020
Later among the works it cites.