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We introduce a new differential privacy (DP) accountant called the saddle-point accountant (SPA).
Tail Probability Approximations
H. E. Daniels · 1987
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Saddlepoint Methods and Statistical Inference
N. Reid · 1988
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Small sample asymptotics
Christopher (Christopher A.) Field · 1990
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Methods of Mathematical Physics
Harold Jeffreys and Bertha Jeffreys · 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
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Series approximation methods in statistics
John E. Kolassa · 2006
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Robust Statistics, Second Edition
Peter J. Huber and Elvezio M. Ronchetti · 2009
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Cifar-10 dataset
Alex Krizhevsky · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Channel coding rate in the finite blocklength regime
Yury Polyanskiy, H Vincent Poor, and Sergio Verdú · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Beyond differential privacy: Composition theorems and relational logic for f f -divergences between probabilistic programs
G. Barthe and F. Olmedo · 2013
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The bootstrap and Edgeworth expansion
Peter Hall · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 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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Rényi differential privacy mechanisms for posterior sampling
Joseph Geumlek, Shuang Song, and Kamalika Chaudhuri · 2017
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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2017
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A better bound gives a hundred rounds: Enhanced privacy guarantees via f f -divergences
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, and Lalitha Sankar · 2020
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Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2020
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The discrete gaussian for differential privacy
Clément L Canonne, Gautam Kamath, and Thomas Steinke · 2020
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Computing tight differential privacy guarantees using fft
Antti Koskela, Joonas Jälkö, and Antti Honkela · 2020
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Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
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mpmath: a Python library for arbitrary-precision floating-point arithmetic (version 1.2.0)
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E γ {E_{{\gamma}}} -resolvability
J. Liu, P. Cuff, and S. Verdú · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
B. Balle, G. Barthe, and M. Gaboardi · 2018
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Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2019
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Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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Fredrik Johansson et al · 2021
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Computing differential privacy guarantees for heterogeneous compositions using fft
Antti Koskela and Antti Honkela · 2021
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Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using fft
Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela · 2021
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PRV accountant breaks for small delta values
Shubhankar Mohapatra · 2021
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Connect the dots: Tighter discrete approximations of privacy loss distributions
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
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Faster privacy accounting via evolving discretization
Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
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http://dlmf.nist.gov/, Release 1.1.6 of 2022-06-30
NIST Digital Library of Mathematical Functions · 2022
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Analytical composition of differential privacy via the edgeworth accountant
Hua Wang, Sheng Gao, Huanyu Zhang, Milan Shen, and Weijie J. Su · 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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