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Although robust learning and local differential privacy are both widely studied fields of research, combining the two settings is just starting to be explored.
Manipulation attacks in local differential privacy, 2019
Albert Cheu, Adam Smith, and Jonathan Ullman · 1909
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Robust confidence limits
Peter J Huber · 1968
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Robust estimation of a location parameter
Peter J Huber · 1992
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Limiting privacy breaches in privacy preserving data mining
Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant · 2003
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Robust statistics , volume 523
Peter J Huber · 2004
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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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Interactive inference under information constraints, 2020a
Jayadev Acharya, Clément L. Canonne, Yuhan Liu, Ziteng Sun, and Himanshu Tyagi · 2007
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Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2008
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Introduction to nonparametric estimation
Alexandre B Tsybakov · 2008
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Robust statistics. 2nd john wiley & sons
Peter J Huber and EM Ronchetti · 2009
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Jayadev Acharya, Clément L. Canonne, Ziteng Sun, and Himanshu Tyagi · 2010
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Estimating sparse discrete distributions under local privacy and communication constraints, 2020c
Jayadev Acharya, Peter Kairouz, Yuhan Liu, and Ziteng Sun · 2011
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Robust statistics for outlier detection
Peter J Rousseeuw and Mia Hubert · 2011
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Challenging the empirical mean and empirical variance: a deviation study
Olivier Catoni · 2012
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Local privacy, data processing inequalities, and statistical minimax rates, 2014
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2014
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.
Discrete distribution estimation under local privacy, 2016
Peter Kairouz, Keith Bonawitz, and Daniel Ramage · 2016
Cited alongside, same era.
Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2017
Cited alongside, same era.
Local differential privacy: Elbow effect in optimal density estimation and adaptation over besov ellipsoids
Cristina Butucea, Amandine Dubois, Martin Kroll, and Adrien Saumard · 2020
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Efficiently learning structured distributions from untrusted batches
Sitan Chen, Jerry Li, and Ankur Moitra · 2020
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All-in-one robust estimator of the gaussian mean
Arnak S Dalalyan and Arshak Minasyan · 2020
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Optimal robust learning of discrete distributions from batches, 2020
Ayush Jain and Alon Orlitsky · 2020
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Minimax optimal goodness-of-fit testing for densities under a local differential privacy constraint
Joseph Lam-Weil, Béatrice Laurent, and Jean-Michel Loubes · 2020
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Mingda Qiao and Gregory Valiant · 2017
Cited alongside, same era.
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T Tony Cai, Yichen Wang, and Linjun Zhang · 2019
Cited alongside, same era.
Robust estimators in high dimensions without the computational intractability, 2019
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2019
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy, 2019
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Mean estimation and regression under heavy-tailed distributions: A survey
Gábor Lugosi and Shahar Mendelson · 2019
Cited alongside, same era.
A unified view on differential privacy and robustness to adversarial examples, 2019
Rafael Pinot, Florian Yger, Cédric Gouy-Pailler, and Jamal Atif · 2019
Cited alongside, same era.
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Thomas Berrett and Cristina Butucea · 2020
Cited alongside, same era.
Robust classification via mom minimization
Guillaume Lecué, Matthieu Lerasle, and Timlothée Mathieu · 2020
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Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy
Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro · 2020
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Robust testing and estimation under manipulation attacks
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2021
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Private and polynomial time algorithms for learning gaussians and beyond, 2021
Hassan Ashtiani and Christopher Liaw · 2021
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Samuel B. Hopkins, Gautam Kamath, and Mahbod Majid · 2021
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Robust density estimation from batches: The best things in life are (nearly) free
Ayush Jain and Alon Orlitsky · 2021
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On robustness and local differential privacy
Mengchu Li, Thomas B Berrett, and Yi Yu · 2022
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