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Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data.
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 D. Smith · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2007
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
Earlier work this paper cites.
Differential privacy for clinical trial data: Preliminary evaluations
D. Vu and A. Slavkovic · 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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Optimizing linear counting queries under differential privacy
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, and Andrew McGregor · 2010
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Private and continual release of statistics
T.-H. Hubert Chan, Elaine Shi, and Dawn Song · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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Privacy-preserving data exploration in genome-wide association studies
Aaron Johnson and Vitaly Shmatikov · 2013
Earlier work this paper cites.
Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Guha Thakurta and Adam Smith · 2013
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Private empirical risk minimization, revisited
R. Bassily, A. D. Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Robust and private bayesian inference
Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, and Benjamin I. P. Rubinstein · 2014
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Differentially private learning of structured discrete distributions
Ilias Diakonikolas, Moritz Hardt, and Ludwig Schmidt · 2015
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Differential privacy for social science inference
Vito D’Orazio, J. Honaker, and G. King · 2015
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Efficient use of differentially private binary trees, 2015
James Honaker · 2015
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Perturbation techniques in online learning and optimization
Jacob Abernethy, Chansoo Lee, and Ambuj Tewari · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Concentrated differential privacy
C. Dwork and G. N. Rothblum · 2016
Cited alongside, same era.
On the theory and practice of privacy-preserving bayesian data analysis
James Foulds, Joseph Geumlek, Max Welling, and Kamalika Chaudhuri · 2016
Cited alongside, same era.
Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing
Marco Gaboardi, Hyun Lim, Ryan Rogers, and Salil Vadhan · 2016
Cited alongside, same era.
Differentially private significance tests for regression coefficients
Andr’es F. Barrientos, J. Reiter, Ashwin Machanavajjhala, and Yan Chen · 2017
Cited alongside, same era.
Exact inference with approximate computation for differentially private data via perturbations, 2019
Ruobin Gong · 2019
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Differentially private simple linear regression, 2020
Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam Smith, and Salil Vadhan · 2020
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Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Hilal Asi and John C Duchi · 2020
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Coinpress: Practical private mean and covariance estimation
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman · 2020
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Propose, test, release: Differentially private estimation with high probability
Victor-Emmanuel Brunel and Marco Avella-Medina · 2020
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Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, and Antti Honkela · 2017
Cited alongside, same era.
Staring-down the database reconstruction theorem
John M Abowd · 2018
Cited alongside, same era.
Differentially private bayesian inference for exponential families
Garrett Bernstein and Daniel R Sheldon · 2018
Cited alongside, same era.
Bootstrap inference and differential privacy: Standard errors for free
Thomas W. Brawner and J. Honaker · 2018
Cited alongside, same era.
On differentially private gaussian hypothesis testing
K. H. Degue and J. L. Ny · 2018
Cited alongside, same era.
Finite sample differentially private confidence intervals
V. Karwa and S. Vadhan · 2018
Cited alongside, same era.
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
Yu-Xiang Wang · 2018
Cited alongside, same era.
Differentially private confidence intervals
Wenxin Du, Canyon Foot, Monica Moniot, Andrew Bray, and Adam Groce · 2020
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General-purpose differentially-private confidence intervals
C. Ferrando, Shu-Fan Wang, and D. Sheldon · 2020
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Current population reports, p60-270, income and poverty in the united states: 2019
Jessica Semega, M. Kollar, E.A. Shrider, and John F. Creamer · 2020
Later among the works it cites.
Optimal private median estimation under minimal distributional assumptions
Christos Tzamos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, and Ilias Zadik · 2020
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Income data tables
U.S. Census Bureau · 2020
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Income, poverty, and health insurance: 2019
U.S. Census Bureau · 2020
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Differential privacy for government agencies–are we there yet?
Joerg Drechsler · 2021
Closest in time.
Statistically valid inferences from differentially private data releases, with application to the facebook urls dataset
Georgina Evans and Gary King · 2021
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Statistically valid inferences from privacy protected data, 2021
Georgina Evans, Gary King, Margaret Schwenzfeier, and Abhradeep Thakurta · 2021
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Differentially private quantiles, 2021
Jennifer Gillenwater, Matthew Joseph, and Alex Kulesza · 2021
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Improving estimates of neighborhood change with constant tract boundaries
John R. Logan, Charles Zhang, Brian Stults, and Todd Gardner · 2021
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Ipums usa: Version 11.0 1940 decennial census
Steven Ruggles, Sarah Flood, Sophia Foster, Ronald Goeken, Jose Pacas, Megan Schouweiler, and Matthew Sobek · 2021
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