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Differential privacy provides a rigorous framework for privacy-preserving data analysis.
Some concepts of dependence
E. L. Lehmann · 1966
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On the probability in the tail of a binomial distribution
J. Littlewood · 1969
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Some lower bounds of reliability
T. K. Sarkar · 1969
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Discrete-parameter martingales
J. Neveu · 1975
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On littlewood’s estimate for the binomial distribution
B. D. McKay · 1989
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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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A generalized step-up-down multiple test procedure
A. C. Tamhane, W. Liu, and C. W. Dunnett · 1998
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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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Some results on false discovery rate in stepwise multiple testing procedures
S. K. Sarkar · 2002
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The positive false discovery rate: a Bayesian interpretation and the
J. D. Storey · 2003
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Statistical significance for genomewide studies
J. D. Storey and R. Tibshirani · 2003
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Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach
J. D. Storey, J. E. Taylor, and D. Siegmund · 2004
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Adaptive linear step-up procedures that control the false discovery rate
Y. Benjamini, A. M. Krieger, and D. Yekutieli · 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
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar · 2007
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Extreme value theory: an introduction
L. De Haan and A. Ferreira · 2007
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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Stepup procedures controlling generalized FWER and generalized FDR
S. K. Sarkar · 2007
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Two simple sufficient conditions for FDR control
G. Blanchard and E. Roquain · 2008
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Multiple testing procedures with applications to genomics
S. Dudoit, M. J. Van Der Laan, and M. J. van der Laan · 2008
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Some step-down procedures controlling the false discovery rate under dependence
Y. Ge, S. C. Sealfon, and T. P. Speed · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
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On methods controlling the false discovery rate
S. K. Sarkar · 2008
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Adaptive false discovery rate control under independence and dependence
G. Blanchard and E. Roquain · 2009
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Robustness of multiple testing procedures against dependence
S. Clarke and P. Hall · 2009
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Differential privacy and robust statistics
C. Dwork and J. Lei · 2009
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On the false discovery rate and an asymptotically optimal rejection curve
H. Finner, T. Dickhaus, and M. Roters · 2009
Scalable privacy-preserving data sharing methodology for genome-wide association studies
F. Yu, S. E. Fienberg, A. Slavković, and C. Uhler · 2014
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SLOPE – adaptive variable selection via convex optimization
M. Bogdan, E. van den Berg, C. Sabatti, W. J. Su, and E. J. Candès · 2015
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Generalization in adaptive data analysis and holdout reuse
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
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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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Robust traceability from trace amounts
C. Dwork, A. Smith, T. Steinke, J. Ullman, and S. Vadhan · 2015
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Inequalities for the false discovery rate (FDR) under dependence
P. Heesen and A. Janssen · 2015
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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.
Bounds on the sample complexity for private learning and private data release
A. Beimel, S. P. Kasiviswanathan, and K. Nissim · 2010
Cited alongside, same era.
Probability: Theory and Examples
R. Durrett · 2010
Cited alongside, same era.
Boosting and differential privacy
C. Dwork, G. Rothblum, and S. Vadhan · 2010
Cited alongside, same era.
A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
Cited alongside, same era.
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
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Concentrated differential privacy
C. Dwork and G. N. Rothblum · 2016
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Differentially private Chi-squared hypothesis testing: Goodness of fit and independence testing
M. Gaboardi, H. Lim, R. Rogers, and S. Vadhan · 2016
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Inference using noisy degrees: Differentially private
V. Karwa and A. Slavković · 2016
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Differentially private model selection with penalized and constrained likelihood
J. Lei, A.-S. Charest, A. Slavković, A. Smith, and S. Fienberg · 2016
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Fingerprinting codes and the price of approximate differential privacy
M. Bun, J. Ullman, and S. P. Vadhan · 2018
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Finite sample differentially private confidence intervals
V. Karwa and S. Vadhan · 2018
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W. J. Su · 2018
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Y.-X. Wang · 2018
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Facebook–Cambridge Analytica data scandal
Wikipedia · 2018
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J. Dong, A. Roth, and W. J. Su · 2019
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Deep learning with Gaussian differential privacy
Z. Bu, J. Dong, Q. Long, and W. J. Su · 2020
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Oneshot differentially private top-k selection
G. Qiao, W. J. Su, and L. Zhang · 2021
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