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We introduce Concentrated Differential Privacy, a relaxation of Differential Privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations.
Propriétés locales des fonctions à séries de fourier aléatoires
J. Kahane · 1960
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Metric Characterization of Random Variables and Random Processes
V.V. Buldygin and I.U.V. Kozachenko · 2000
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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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
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Differential privacy
C. Dwork · 2006
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The price of privacy and the limits of lp decoding
C. Dwork, F. McSherry, and K. Talwar · 2007
Cited alongside, same era.
Differential privacy and robust statistics
C. Dwork and J. Lei · 2009
Cited alongside, same era.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
Cited alongside, same era.
Subgaussian random variables: An expository note
Omar Rivasplata · 2012
Later among the works it cites.
Concentrated differential privacy revisited
Mark Bun and Thomas Steinke · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
Later among the works it cites.
The complexity of computing the optimal composition of differential privacy
Jack Murtagh and Salil P. Vadhan · 2016
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