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Characterizing the privacy degradation over compositions, i.e., privacy accounting, is a fundamental topic in differential privacy (DP) with many applications to differentially private machine learning and federated learning.
Interpolation
Josef Stoer and Roland Bulirsch · 2002
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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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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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The bad truth about laplace’s transform
Charles L Epstein and John Schotland · 2008
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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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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
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Beyond differential privacy: Composition theorems and relational logic for f-divergences between probabilistic programs
Gilles Barthe and Federico Olmedo · 2013
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Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Guha Thakurta and Adam Smith · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Rényi divergence and kullback-leibler divergence
Tim Van Erven and Peter Harremos · 2014
Cited alongside, same era.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 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.
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.
Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
Later among the works it cites.
Poisson subsampled rényi differential privacy
Yuqing Zhu and Yu-Xiang Wang · 2019
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Hypothesis testing interpretations and rényi 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
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Learning with privacy at scale
Apple, Differential Privacy Team · 2017
Cited alongside, same era.
The modernization of statistical disclosure limitation at the u.s. census bureau
Aref Dajani, Amy Lauger, Phyllis Singer, Daniel Kifer, Jerome Reiter, Ashwin Machanavajjhala, Simon Garfinkel, Scot Dahl, Matthew Graham, Vishesh Karwa, Hang Kim, Philip Leclerc, Ian Schmutte, William Sexton, Lars Vilhuber, and John Abowd · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Improving gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
Cited alongside, same era.
Tight on budget? tight bounds for r-fold approximate differential privacy
Sebastian Meiser and Esfandiar Mohammadi · 2018
Cited alongside, same era.
Antti Koskela, Joonas Jälkö, and Antti Honkela · 2020
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Private-knn: Practical differential privacy for computer vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang · 2020
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Three variants of differential privacy: Lossless conversion and applications
Shahab Asoodeh, Jiachun Liao, Flavio P Calmon, Oliver Kosut, and Lalitha Sankar · 2021
Closest in time.
Gaussian differential privacy
Dong, Aaron Roth, and Weijie J Su · 2021
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Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
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Computing differential privacy guarantees for heterogeneous compositions using fft
Antti Koskela and Antti Honkela · 2021
Closest in time.
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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