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Differential privacy is a leading protection setting, focused by design on individual privacy.
Thoughts on hypothesis boosting, 1988
M. Kearns · 1988
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Improved boosting algorithms using confidence-rated predictions
R. E. Schapire and Y. Singer · 1999
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Methods of Information Geometry
S.-I. Amari and H. Nagaoka · 2000
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Elliptic Partial Differential Equations of Second Order
D. Gilbarg and N. Trudinger · 2001
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Privacy: Theory meets practice on the map
A. Machanavajjhala, D. Kifer, J.-M. Abowd, J. Gehrke, and L. Vilhuber · 2008
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Bregman voronoi diagrams
J.-D. Boissonnat, F. Nielsen, and R. Nock · 2010
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A statistical framework for differential privacy
L. Wasserman and S. Zhou · 2010
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Faster algorithms for privately releasing marginals
J. Thaler, J. Ullman, and S.-P. Vadhan · 2012
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Computational Statistics
G.-F. Givens and J.-A. Hoeting · 2013
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Differential privacy for functions and functional data
R. Hall, A. Rinaldo, and L.-A. Wasserman · 2013
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Differential privacy: an exploration of the privacy-utility landscape
D.-J. Mir · 2013
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Robust and private bayesian inference
C. Dimitratakis, B. Nelson, A. Mitrokotsa, and B. Rubinstein · 2014
Cited alongside, same era.
The algorithmic foudations of differential privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
Constrained forms of statistical minimax: computation, communication, and privacy
M. Wainwright · 2014
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient Monte Carlo
Y.-X. Wang, S. Fienberg, and A.-J. Smola · 2015
Cited alongside, same era.
A survey on measuring indirect discrimination in machine learning
I. Zliobaite · 2015
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Big data’s disparate impact
S. Barocas and A.-D. Selbst · 2016
Cited alongside, same era.
C. Culnane, B. Rubinstein, and V. Teague · 2017
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Learning with differential privacy at scale, 2017
Differential privacy team, Apple · 2017
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Group differential privacy-preserving disclosure of multi-level association graphs
B. Palanisamy, C. Li, and P. Krishnamurthy · 2017
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Pain-free random differential privacy with sensivity sampling
B. Rubinstein and F. Aldà · 2017
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Adagan: Boosting generative models
I.-O. Tolstikhin, S. Gelly, O. Bousquet, C. Simon-Gabriel, and B. Schölkopf · 2017
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Data Privacy Beyond Differential Privacy
Z.-S. Wu · 2017
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Topics in differential privacy
M. Gaboardi · 2016
Cited alongside, same era.
Generative adversarial networks, 2016
I. Goodfellow · 2016
Cited alongside, same era.
Private algorithms for the protected in social network search
M. Kearns, A. Roth, Z.-S. Wu, and G. Yaroslavtsev · 2016
Cited alongside, same era.
Understanding the maths is crucial for protecting privacy
B. Rubinstein, V. Teague, and C. Culnane · 2016
Cited alongside, same era.
The Bernstein mechanism: Function release under differential privacy
F. Aldà and B. Rubinstein · 2017
Cited alongside, same era.
Local privacy and minimax bounds: sharp rates for probability estimation
J.-C. Duchi, M.-I. Jordan, and M. Wainwright
Cited in the paper.
Top 10 biggest healthcare data breaches of all time
N. Lord · 2018
Closest in time.
Differentially private generative adversarial network
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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Differential privacy has disparate impact on model accuracy
E. Bagdasaryan and V. Shmatikov · 2019
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Boosted density estimation remastered
Z. Cranko and R. Nock · 2019
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Differentially private fair learning
M. Jagielski, M.-J. Kearns, J. Mao, A. Oprea, A. Roth, S. Sharifi-Malvajerdi, and J. Ullman · 2019
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