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We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing, with essentially no loss in power under certain conditions.
Probability inequalities for the sum of independent random variables
G. Bennett · 1962
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Order Statistics
H. David and H. Nagaraja · 1970
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Controlling the false discovery rate – A practical and powerful approach to multiple testing
Y. Benjamini and Y. Hochberg · 1995
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The control of the false discovery rate in multiple testing under dependency
Y. Benjamini and D. Yekutieli · 2001
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Adapting to unknown sparsity by controlling the false discovery rate
F. Abramovich, Y. Benjamini, D. Donoho, and I. Johnstone · 2006
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Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Cited alongside, same era.
Stepup procedures controlling generalized FWER and generalized FDR
S. K. Sarkar · 2007
Cited alongside, same era.
An adaptive step-down procedure with proven FDR control under independence
Y. Gavrilov, Y. Benjamini, and S. K. Sarkar · 2009
Cited alongside, same era.
On a generalized false discovery rate
S. K. Sarkar and W. Guo · 2009
Cited alongside, same era.
Discovering frequent patterns in sensitive data
R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta · 2010
Later among the works it cites.
Probability: Theory and Examples
R. Durrett · 2010
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Boosting and differential privacy
C. Dwork, G. Rothblum, and S. Vadhan · 2010
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The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2014
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The reusable holdout: Preserving validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
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