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In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid $p$-value corrections.
Introduction to Coding Theory
Jacobus Hendricus van Lint · 1999
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank Mcsherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Introduction to Algorithms, Third Edition
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein · 2009
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
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The limits of two-party differential privacy
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, and Salil P. Vadhan · 2011
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False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant
J. P. Simmons, L. D. Nelson, and U. Simonsohn · 2011
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Lower bounds in differential privacy
Anindya De · 2012
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Valid post-selection inference
Richard Berk, Lawrence Brown, Andreas Buja, Kai Zhang, and Linda Zhao · 2013
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Privacy-preserving data exploration in genome-wide association studies
Aaron Johnson and Vitaly Shmatikov · 2013
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Exact post-selection inference, with application to the lasso
Jason D Lee, Dennis L Sun, Yuekai Sun, and Jonathan E Taylor · 2013
Earlier work this paper cites.
Differentially private feature selection via stability arguments, and the robustness of the lasso
Adam Smith and Abhradeep Thakurta · 2013
Cited alongside, same era.
Privacy-preserving data sharing for genome-wide association studies
Caroline Uhler, Aleksandra Slavkovic, and Stephen E. Fienberg · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Optimal inference after model selection
William Fithian, Dennis Sun, and Jonathan Taylor · 2014
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The statistical crisis in science
Andrew Gelman and Eric Loken · 2014
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Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan Ullman · 2014
Differentially private least squares: Estimation, confidence and rejecting the null hypothesis
Or Sheffet · 2015
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Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan Ullman · 2015
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Differentially private hypothesis testing, revisited
Yue Wang, Jaewoo Lee, and Daniel Kifer · 2015
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Adaptive learning with robust generalization guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu · 2016
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Cited alongside, same era.
On the ‘Semantics’ of Differential Privacy: A Bayesian Formulation
S.P. Kasiviswanathan and A. Smith · 2014
Cited alongside, same era.
Scalable privacy-preserving data sharing methodology for genome-wide association studies
Fei Yu, Stephen E. Fienberg, Aleksandra B. Slavkovic, and Caroline Uhler · 2014
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Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, and Aaron Roth · 2015
Cited alongside, same era.
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
Cited alongside, same era.
Private false discovery rate control
Cynthia Dwork, Weijie Su, and Li Zhang · 2015
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
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Inference using noisy degrees: Differentially private beta-model and synthetic graphs
Vishesh Karwa and Aleksandra Slavković · 2016
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Max-information, differential privacy, and post-selection hypothesis testing
Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar · 2016
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Controlling bias in adaptive data analysis using information theory
Daniel Russo and James Zou · 2016
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The asa’s statement on p-values: context, process, and purpose
Ronald L. Wasserstein and Nicole A. Lazar · 2016
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A minimax theory for adaptive data analysis
Yu-Xiang Wang, Jing Lei, and Stephen E. Fienberg · 2016
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