Fetching the paper…
Reading the bibliography…
The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geographic units, which is known as the geographic spine.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Boosting the accuracy of differentially private histograms through consistency
Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu · 2010
Earlier work this paper cites.
Optimizing linear counting queries under differential privacy
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, and Andrew McGregor · 2010
Earlier work this paper cites.
A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Earlier work this paper cites.
No free lunch in data privacy
Daniel Kifer and Ashwin Machanavajjhala · 2011
Cited alongside, same era.
Universally utility-maximizing privacy mechanisms
Arpita Ghosh, Tim Roughgarden, and Mukund Sundararajan · 2012
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala · 2018
Cited alongside, same era.
2020 Census Data Products: Data Needs and Privacy Considerations: Proceedings of a Workshop
Randall Akee and Norman DeWeaver · 2020
Cited alongside, same era.
The discrete gaussian for differential privacy
Clément L Canonne, Gautam Kamath, and Thomas Steinke · 2020
Later among the works it cites.
The 2020 Census Disclosure Avoidance System TopDown Algorithm
John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, Brett Moran, William Sexton, Matthew Spence, and Pavel Zhuravlev · 2022
Closest in time.
Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2022
Closest in time.
Daniel Kifer, John M Abowd, Robert Ashmead, Ryan Cumings-Menon, Philip Leclerc, Ashwin Machanavajjhala, William Sexton, and Pavel Zhuravlev · 2022
Closest in time.
Disclosure avoidance for the 2020 census demographic and housing characteristics file
Ryan Cumings-Menon, Robert Ashmead, Daniel Kifer, Philip Leclerc, Matthew Spence, Pavel Zhuravlev, and John M Abowd · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Closest in time.