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We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables.
Inequalities concerning bessel functions and orthogonal polynomials
VR Thiruvenkatachar and TS Nanjundiah · 1951
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Computation of modified bessel functions and their ratios
Donald E Amos · 1974
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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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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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On significance of the least significant bits for differential privacy
Ilya Mironov · 2012
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High precision discrete gaussian sampling on fpgas
Sujoy Sinha Roy, Frederik Vercauteren, and Ingrid Verbauwhede · 2013
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 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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Sampling from discrete gaussians for lattice-based cryptography on a constrained device
Nagarjun C Dwarakanath and Steven D Galbraith · 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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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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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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A new type of sharp bounds for ratios of modified bessel functions
Diego Ruiz-Antolín and Javier Segura · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Computational differential privacy from lattice-based cryptography
Filipp Valovich and Francesco Alda · 2017
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cpSGD: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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dgs, Discrete Gaussians over the Integers
Martin R. Albrecht and Michael Walter · 2018
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Simple , fast and constant-time gaussian sampling over the integers for falcon
Thomas Prest, Thomas Ricosset, and Mélissa Rossi · 2019
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Locally private bayesian inference for count models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, and Hanna Wallach · 2019
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Poission subsampled rényi differential privacy
Yuqing Zhu and Yu-Xiang Wang · 2019
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A better bound gives a hundred rounds: Enhanced privacy guarantees via f-divergences
S. Asoodeh, J. Liao, F. P. Calmon, O. Kosut, and L. Sankar · 2020
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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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Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 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.
James Bell, K. A. Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
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The discrete gaussian for differential privacy
Clément Canonne, Gautam Kamath, and Thomas Steinke · 2020
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Privacy loss distributions
Google Differential Privacy Team · 2020
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Computing tight differential privacy guarantees using fft
Antti Koskela, Joonas Jälkö, and Antti Honkela · 2020
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Training production language models without memorizing user data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Federated analytics: Collaborative data science without data collection, May 2020
Google Research · 2020
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Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2020
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Federated learning via posterior averaging: A new perspective and practical algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, and Afshin Rostamizadeh · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
Peter Kairouz, Ziyu Liu, and Thomas Steinke · 2021
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Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
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