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Differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy in the past decade.
Über den zentralen grenzwertsatz der wahrscheinlichkeitsrechnung und das momentenproblem
Georg Pólya · 1920
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Über den zentralen grenzwertsatz der wahrscheinlichkeitsrechnung und das momentenproblem
Georg Pólya · 1920
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Introduction to mathematical probability
James Victor Uspensky · 1937
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Introduction to mathematical probability
James Victor Uspensky · 1937
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Comparison of experiments
David Blackwell · 1950
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Comparison of experiments
David Blackwell · 1950
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A differential geometric approach to statistical inference on the basis of contrast functionals
Shinto Eguchi et al · 1985
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A differential geometric approach to statistical inference on the basis of contrast functionals
Shinto Eguchi et al · 1985
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Elements of large-sample theory
Erich Leo Lehmann · 2004
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Elements of large-sample theory
Erich Leo Lehmann · 2004
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A face is exposed for AOL searcher no. 4417749
Michael Barbaro and Tom Zeller · 2006
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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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Testing statistical hypotheses
Erich L Lehmann and Joseph P Romano · 2006
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On divergences and informations in statistics and information theory
Friedrich Liese and Igor Vajda · 2006
Earlier work this paper cites.
A face is exposed for AOL searcher no. 4417749
Michael Barbaro and Tom Zeller · 2006
Earlier work this paper cites.
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.
Testing statistical hypotheses
Erich L Lehmann and Joseph P Romano · 2006
Earlier work this paper cites.
On divergences and informations in statistics and information theory
Friedrich Liese and Igor Vajda · 2006
Earlier work this paper cites.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J.V. Pearson, D.A. Stephan, S.F. Nelson, and D.W. Craig · 2008
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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J.V. Pearson, D.A. Stephan, S.F. Nelson, and D.W. Craig · 2008
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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
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Computational complexity: a modern approach
Sanjeev Arora and Boaz Barak · 2009
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Computational complexity: a modern approach
Sanjeev Arora and Boaz Barak · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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An improvement of convergence rate estimates in the lyapunov theorem
IG Shevtsova · 2010
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
An improvement of convergence rate estimates in the lyapunov theorem
IG Shevtsova · 2010
Earlier work this paper cites.
A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Cited alongside, same era.
On pairs of f f -divergences and their joint range
Peter Harremoës and Igor Vajda · 2011
Cited alongside, same era.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Cited alongside, same era.
Shannon meets blackwell and le cam: Channels, codes, and statistical experiments
Maxim Raginsky · 2011
Cited alongside, same era.
On pairs of
Peter Harremoës and Igor Vajda · 2011
Cited alongside, same era.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Cited alongside, same era.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Cs7880: Rigorous approaches to data privacy, spring 2017
Jonathan Ullman · 2017
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The US Census Bureau adopts differential privacy
John M Abowd · 2018
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Shannon meets blackwell and le cam: Channels, codes, and statistical experiments
Maxim Raginsky · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Approximation algorithms
Vijay V Vazirani · 2013
Cited alongside, same era.
Approximation algorithms
Vijay V Vazirani · 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.
Mark Bun, Cynthia Dwork, Guy N Rothblum, and Thomas Steinke · 2018
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2018
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Borja Balle and Yu-Xiang Wang · 2018
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Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
Later among the works it cites.
The right complexity measure in locally private estimation: It is not the fisher information
John C Duchi and Feng Ruan · 2018
Later among the works it cites.
Bag of tricks for image classification with convolutional neural networks
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2018
Later among the works it cites.
Privacy loss classes: The central limit theorem in differential privacy
David Sommer, Sebastian Meiser, and Esfandiar Mohammadi · 2018
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Subsampled r \ \backslash ’enyi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2018
Later among the works it cites.
The US Census Bureau adopts differential privacy
John M Abowd · 2018
Later among the works it cites.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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.
Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2018
Later among the works it cites.
Borja Balle and Yu-Xiang Wang · 2018
Later among the works it cites.
Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
Later among the works it cites.
The right complexity measure in locally private estimation: It is not the fisher information
John C Duchi and Feng Ruan · 2018
Later among the works it cites.
Bag of tricks for image classification with convolutional neural networks
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2018
Later among the works it cites.
Privacy loss classes: The central limit theorem in differential privacy
David Sommer, Sebastian Meiser, and Esfandiar Mohammadi · 2018
Later among the works it cites.
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2018
Later among the works it cites.
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T Tony Cai, Yichen Wang, and Linjun Zhang · 2019
Closest in time.
Probability: theory and examples
Rick Durrett · 2019
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Challenges in the analyses of multidomain longitudinal data: Layered solutions, 2019
Susan Holmes · 2019
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Bag of freebies for training object detection neural networks
Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2019
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The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T Tony Cai, Yichen Wang, and Linjun Zhang · 2019
Closest in time.
Probability: theory and examples
Rick Durrett · 2019
Closest in time.
Challenges in the analyses of multidomain longitudinal data: Layered solutions, 2019
Susan Holmes · 2019
Closest in time.
Bag of freebies for training object detection neural networks
Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2019
Closest in time.