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A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset.
“Gaussian Differential Privacy”, 2019
Jinshuo Dong, Aaron Roth and Weijie. Su · 1905
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“13. various techniques used in connection with random digits”
John Von · 1951
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“Von Neumann’s comparison method for random sampling from the normal and other distributions”
George Forsythe · 1972
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“Probability with martingales”, Cambridge Mathematical Textbooks
David Williams · 1991
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“Does the Moment-Generating Function Characterize a Distribution?”
Peter McCullagh · 1994
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“Revealing information while preserving privacy”
Irit Dinur and Kobbi Nissim · 2003
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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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“Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT”, 2020
Antti Koskela, Joonas Jälkö, Lukas Prediger and Antti Honkela · 2006
Earlier work this paper cites.
“Smooth sensitivity and sampling in private data analysis”
Kobbi Nissim, Sofya Raskhodnikova and Adam Smith · 2007
Earlier work this paper cites.
“Trapdoors for hard lattices and new cryptographic constructions”
Craig Gentry, Chris Peikert and Vinod Vaikuntanathan · 2008
Earlier work this paper cites.
“Randomness Concerns When Deploying Differential Privacy”, 2020
Simson. Garfinkel and Philip Leclerc · 2009
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“On lattices, learning with errors, random linear codes, and cryptography”
Oded Regev · 2009
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“Boosting and differential privacy”
Cynthia Dwork, Guy Rothblum and Salil Vadhan · 2010
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“Permute-and-Flip: A new mechanism for differentially private selection”, 2020
Ryan McKenna and Daniel Sheldon · 2010
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“An efficient and parallel Gaussian sampler for lattices”
Chris Peikert · 2010
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“Universally utility-maximizing privacy mechanisms”
Arpita Ghosh, Tim Roughgarden and Mukund Sundararajan · 2012
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“On Significance of the Least Significant Bits for Differential Privacy”
Ilya Mironov · 2012
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“Subgaussian random variables: An expository note” http://www.stat.cmu.edu/~arinaldo/36788/subgaussians.pdf , 2012
Omar Rivasplata · 2012
Cited alongside, same era.
“The Gumbel-Max Trick for Discrete Distributions”, https://lips.cs.princeton.edu/the-gumbel-max-trick-for-discrete-distributions/ , 2013
Ryan Adams · 2013
Cited alongside, same era.
“Private empirical risk minimization: Efficient algorithms and tight error bounds”
Raef Bassily, Adam Smith and Abhradeep Thakurta · 2014
Cited alongside, same era.
“The algorithmic foundations of differential privacy.”
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
“Deep learning with differential privacy”
Martin Abadi, Andy Chu, Ian Goodfellow, H McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
Cited alongside, same era.
“A note on discrete Gaussian combinations of lattice vectors”
“cpSGD: Communication-efficient and differentially-private distributed SGD”
Naman Agarwal, Ananda Suresh, Felix Xinnan Yu, Sanjiv Kumar and Brendan McMahan · 2018
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“Composable and versatile privacy via truncated CDP”
Mark Bun, Cynthia Dwork, Guy Rothblum and Thomas Steinke · 2018
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Borja Balle and Yu-Xiang Wang · 2018
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Census https://github.com/uscensusbureau/census2020-das-e2e/blob/master/programs/engine/primitives.py , 2018
2018
Later among the works it cites.
“Differentially private hierarchical count-of-counts histograms”
Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay and Ashwin Machanavajjhala · 2018
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Divesh Aggarwal and Oded Regev · 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 Rothblum · 2016
Cited alongside, same era.
“Preserving differential privacy under finite-precision semantics”
Ivan Gazeau, Dale Miller and Catuscia Palamidessi · 2016
Cited alongside, same era.
“Sampling exactly from the normal distribution”
Charles Karney · 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.
“ f f -Divergence Inequalities”
Igal Sason and Sergio Verdu · 2016
Cited alongside, same era.
“Information theory from a functional viewpoint”, 2018
Jingbo Liu · 2018
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“Tight on budget? tight bounds for r-fold approximate differential privacy”
Sebastian Meiser and Esfandiar Mohammadi · 2018
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“Average-case averages: Private algorithms for smooth sensitivity and mean estimation”
Mark Bun and Thomas Steinke · 2019
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“Snapping Mechanism Notes” https://github.com/ctcovington/floating_point/blob/master/snapping_mechanism/notes/snapping_implementation_notes.pdf , 2019
Christian Covington · 2019
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“Implementing the Exponential Mechanism with Base-2 Differential Privacy”
Christina Ilvento · 2019
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“Locally Private Bayesian Inference for Count Models”
Aaron Schein, Zhiwei Wu, Alexandra Schofield, Mingyuan Zhou and Hanna Wallach · 2019
Later among the works it cites.
“COSAC: COmpact and Scalable Arbitrary-Centered Discrete Gaussian Sampling over Integers” https://eprint.iacr.org/2019/1011 , Cryptology ePrint Archive, Report 2019/1011, 2019
Raymond. Zhao, Ron Steinfeld and Amin Sakzad · 2019
Later among the works it cites.
“A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f f -Divergences”
Shahab Asoodeh, Jiachun Liao, Flavio Calmon, Oliver Kosut and Lalitha Sankar · 2020
Closest in time.
“An Improved Exact Sampling Algorithm for the Standard Normal Distribution”
Yusong Du, Baoying Fan and Baodian Wei · 2020
Closest in time.
https://github.com/IBM/discrete-gaussian-differential-privacy , 2020
2020
Closest in time.
“Privacy Loss Distributions” https://github.com/google/differential-privacy/blob/master/accounting/docs/Privacy_Loss_Distributions.pdf , 2020
Google Differential Privacy Team · 2020
Closest in time.
“Secure Noise Generation” https://github.com/google/differential-privacy/blob/master/common_docs/Secure_Noise_Generation.pdf , 2020
Google Differential Privacy Team · 2020
Closest in time.