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Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data.
Discussion: statistical disclosure limitation
D. B. Rubin · 1993
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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How protective are synthetic data?
J. M. Abowd and L. Vilhuber · 2008
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A learning theory approach to non-interactive database privacy
A. Blum, K. Ligett, and A. Roth · 2008
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On the complexity of differentially private data release: Efficient algorithms and hardness results
C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. Vadhan · 2009
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Bounds on the sample complexity for private learning and private data release
A. Beimel, S. P. Kasiviswanathan, and K. Nissim · 2010
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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Differentially private data release through multidimensional partitioning
Y. Xiao, L. Xiong, and C. Yuan · 2010
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Publishing set-valued data via differential privacy
R. Chen, N. Mohammed, B. C. Fung, B. C. Desai, and L. Xiong · 2011
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Differentially private data release for data mining
N. Mohammed, R. Chen, B. C. Fung, and P. S. Yu · 2011
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Differentially private sequential data publication via variable-length n-grams
R. Chen, G. Acs, and C. Castelluccia · 2012
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Iterative constructions and private data release
A. Gupta, A. Roth, and J. Ullman · 2012
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A simple and practical algorithm for differentially private data release
M. Hardt, K. Ligett, and F. McSherry · 2012
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Non-interactive differential privacy: a survey
D. Leoni · 2012
Cited alongside, same era.
On significance of the least significant bits for differential privacy
I. Mironov · 2012
Cited alongside, same era.
Dpcube: Releasing differentially private data cubes for health information
Y. Xiao, J. Gardner, and L. Xiong · 2012
Cited alongside, same era.
Cancer risk among insulin users: comparing analogues with human insulin in the CARING five-country cohort study
A. But, M. L. De Bruin, M. T. Bazelier, V. Hjellvik, M. Andersen, A. Auvinen, J. Starup-Linde, M. K. Schmidt, K. Furu, F. de Vries, et al · 2017
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Differentially private bayesian learning on distributed data
M. Heikkilä, E. Lagerspetz, S. Kaski, K. Shimizu, S. Tarkoma, and A. Honkela · 2017
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Differentially private variational inference for non-conjugate models
J. Jälkö, O. Dikmen, and A. Honkela · 2017
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Privacy preserving synthetic data release using deep learning
N. C. Abay, Y. Zhou, M. Kantarcioglu, B. Thuraisingham, and L. Sweeney · 2018
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Finite sample differentially private confidence intervals
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Carat: Collaborative energy diagnosis for mobile devices
A. J. Oliner, A. P. Iyer, I. Stoica, E. Lagerspetz, and S. Tarkoma · 2013
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
PrivBayes: Private data release via Bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2014
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Differentially private mixture of generative neural networks
G. Acs, L. Melis, C. Castelluccia, and E. De Cristofaro · 2017
Cited alongside, same era.
Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor
Cited in the paper.
V. Karwa and S. Vadhan · 2018
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Excess mortality in Finnish diabetic subjects due to alcohol, accidents and suicide: a nationwide study
L. Niskanen, T. Partonen, A. Auvinen, and J. Haukka · 2018
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Ron-gauss: Enhancing utility in non-interactive private data release
T. Chanyaswad, C. Liu, and P. Mittal · 2019
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Private selection from private candidates
J. Liu and K. Talwar · 2019
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Randomness concerns when deploying differential privacy
S. L. Garfinkel and P. Leclerc · 2020
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