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The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and the additive Gaussian noise---has been successfully used in a number of machine learning applications.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Differential privacy and the secrecy of the sample
Adam D. Smith · 2009
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Cited alongside, same era.
Rényi divergence and Kullback-Leibler divergence
Tim van Erven and Peter Harremoës · 2014
Cited alongside, same era.
Deep learning with differential privacy
Martín 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.
Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
Later among the works it cites.
Rényi differential privacy
Ilya Mironov · 2017
Later among the works it cites.
Composable and versatile privacy via Truncated CDP
Mark Bun, Cynthia Dwork, Guy N. Rothblum, and Thomas Steinke · 2018
Later among the works it cites.
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