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Composition is a key feature of differential privacy.
Etude critique de la notion de collectif
Jean Ville · 1939
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Equivalent comparisons of experiments
David Blackwell · 1953
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Statistical methods related to the law of the iterated logarithm
Herbert Robbins · 1970
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Probability: theory and examples
Richard Durrett · 1996
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Self-normalized processes: exponential inequalities, moment bounds, and iterated logarithm laws
Victor H de la Pena, Michael J Klass, and Tze Leung Lai · 2004
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Pseudo-maximization and self-normalized processes
Victor H. de la Peña, Michael J. Klass, and Tze Leung Lai · 2007
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Exponential inequalities for self-normalized martingales
Shanshan Chen, Zhenping Wang, Wenfei Xu, and Yu Miao · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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On the semantics of differential privacy: A Bayesian formulation
Shiva P Kasiviswanathan and Adam Smith · 2014
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Concentrated differential privacy: simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
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The complexity of computing the optimal composition of differential privacy
Jack Murtagh and Salil Vadhan · 2016
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Privacy odometers and filters: pay-as-you-go composition
Ryan M Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan · 2016
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Accuracy first: Selecting a differential privacy level for accuracy constrained erm
Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2021
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Individual privacy accounting via a Rényi filter
Vitaly Feldman and Tijana Zrnic · 2021
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Time-uniform, nonparametric, nonasymptotic confidence sequences
Steven R. Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon · 2021
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Mixture martingales revisited with applications to sequential tests and confidence intervals
Emilie Kaufmann and Wouter M Koolen · 2021
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Mathias Lécuyer · 2021
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Individual privacy accounting with gaussian differential privacy
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Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, and Steven Z Wu · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Probability: theory and examples , volume 49
Rick Durrett · 2019
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Time-uniform Chernoff bounds via nonnegative supermartingales
Steven R. Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon · 2020
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Bounding, concentrating, and truncating: Unifying privacy loss composition for data analytics
Mark Cesar and Ryan Rogers · 2021
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith
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Antti Koskela, Marlon Tobaben, and Antti Honkela · 2022
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Hyperparameter tuning with renyi differential privacy
Nicolas Papernot and Thomas Steinke · 2022
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Fully adaptive composition for gaussian differential privacy
Adam D. Smith and Abhradeep Thakurta · 2022
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Optimal accounting of differential privacy via characteristic function
Yuqing Zhu, Jinshuo Dong, and Yu-Xiang Wang · 2022
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Comparing approximate and probabilistic differential privacy parameters
Vincent Guingona, Alexei Kolesnikov, Julianne Nierwinski, and Avery Schweitzer · 2023
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