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Differential privacy has become a widely accepted notion of privacy, leading to the introduction and deployment of numerous privatization mechanisms.
Sur la meilleure approximation de— x— par des polynomes de degrés donnés
Serge Bernstein · 1914
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Totik, v.: Moduli of smoothness
Z Ditzian · 1987
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Bias reduction by taylor series
Christopher Stroude Withers · 1987
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Constructive approximation
Ronald A DeVore and George G Lorentz · 1993
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Probability and computing: Randomized algorithms and probabilistic analysis
Michael Mitzenmacher and Eli Upfal · 2005
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Differential privacy
Cynthia Dwork · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Introduction to Nonparametric Estimation
A.B. Tsybakov · 2008
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank D McSherry · 2009
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Distance makes the types grow stronger: a calculus for differential privacy
Jason Reed and Benjamin C Pierce · 2010
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Proving programs robust
Swarat Chaudhuri, Sumit Gulwani, Roberto Lublinerman, and Sara Navidpour · 2011
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Testing composite hypotheses, hermite polynomials and optimal estimation of a nonsmooth functional
T Tony Cai, Mark G Low, et al · 2011
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Differential privacy
Cynthia Dwork · 2011
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The algorithmic foundations of data privacy, course notes, 2011
Aaron Roth · 2011
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Probabilistic relational reasoning for differential privacy
Gilles Barthe, Boris Köpf, Federico Olmedo, and Santiago Zanella Beguelin · 2012
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Universally utility-maximizing privacy mechanisms
Arpita Ghosh, Tim Roughgarden, and Mukund Sundararajan · 2012
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Testing the lipschitz property over product distributions with applications to data privacy
Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova, and Abhradeep Thakurta · 2013
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Applications of classical approximation theory to periodic basis function networks and computational harmonic analysis
Hrushikesh N Mhaskar, Paul Nevai, and Eugene Shvarts · 2013
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Estimating the unseen: improved estimators for entropy and other properties
Paul Valiant and Gregory Valiant · 2013
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Chebfun Guide
T. A Driscoll, N. Hale, and L. N. Trefethen · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Pufferfish: A framework for mathematical privacy definitions
Daniel Kifer and Ashwin Machanavajjhala · 2014
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Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2014
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Top-k frequent itemsets via differentially private fp-trees
Jaewoo Lee and Christopher W Clifton · 2014
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Estimating mutual information for discrete-continuous mixtures
Weihao Gao, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath · 2017
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
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Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2017
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Pufferfish privacy mechanisms for correlated data
Shuang Song, Yizhen Wang, and Kamalika Chaudhuri · 2017
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
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Ben Stoddard, Yan Chen, and Ashwin Machanavajjhala · 2014
Cited alongside, same era.
On the privacy properties of variants on the sparse vector technique
Yan Chen and Ashwin Machanavajjhala · 2015
Cited alongside, same era.
Differentially private high-dimensional data publication via sampling-based inference
Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu · 2015
Cited alongside, same era.
The staircase mechanism in differential privacy
Quan Geng, Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
Cited alongside, same era.
Minimax estimation of discrete distributions under ℓ 1 \ell_{1} loss
Yanjun Han, Jiantao Jiao, and Tsachy Weissman · 2015
Cited alongside, same era.
Minimax estimation of functionals of discrete distributions
Jiantao Jiao, Kartik Venkat, Yanjun Han, and Tsachy Weissman · 2015
Cited alongside, same era.
The u.s. census bureau adopts differential privacy
John Abowd · 2018
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Which distribution distances are sublinearly testable?
Constantinos Daskalakis, Gautam Kamath, and John Wright · 2018
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Detecting violations of differential privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
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Property testing for differential privacy
Anna C Gilbert and Audra McMillan · 2018
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Demystifying fixed k k -nearest neighbor information estimators
Weihao Gao, Sewoong Oh, and Pramod Viswanath · 2018
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Generative adversarial privacy
Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, and Ram Rajagopal · 2018
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The nearest neighbor information estimator is adaptively near minimax rate-optimal
Jiantao Jiao, Weihao Gao, and Yanjun Han · 2018
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Minimax estimation of the l1 distance
Jiantao Jiao, Yanjun Han, and Tsachy Weissman · 2018
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Pacgan: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti, and Sewoong Oh · 2018
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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Efficient multivariate entropy estimation via k k -nearest neighbour distances
Thomas B Berrett, Richard J Samworth, and Ming Yuan · 2019
Closest in time.
Proving differential privacy with shadow execution
Yuxin Wang, Zeyu Ding, Guanhong Wang, Daniel Kifer, and Danfeng Zhang · 2019
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Chebyshev polynomials, moment matching, and optimal estimation of the unseen
Yihong Wu and Pengkun Yang · 2019
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