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We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy.
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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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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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
Earlier work this paper cites.
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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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Earlier work this paper cites.
Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
Cited alongside, same era.
The complexity of computing the optimal composition of differential privacy
Jack Murtagh and Salil Vadhan · 2016
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N Rothblum, and Thomas Steinke · 2018
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
Cited alongside, same era.
Tight on budget? tight bounds for r-fold approximate differential privacy
Sebastian Meiser and Esfandiar Mohammadi · 2018
Privacy loss classes: The central limit theorem in differential privacy
David M Sommer, Sebastian Meiser, and Esfandiar Mohammadi · 2019
Later among the works it cites.
Computing tight differential privacy guarantees using fft
Antti Koskela, Joonas Jälkö, Antti Honkela, et al · 2020
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Fast and memory efficient differentially private-sgd via jl projections
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, and Uthaipon Tantipongpipat · 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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Cited alongside, same era.
Deep learning with gaussian differential privacy, 2019
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J. Su · 2019
Cited alongside, same era.
Gaussian differential privacy, 2019
Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2019
Cited alongside, same era.
Github repository for fourier accountant
Antti Koskela and Lukas Prediger · 2021
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Optimal accounting of differential privacy via characteristic function
Yuqing Zhu, Jinshuo Dong, and Yu-Xiang Wang · 2021
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