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We present a fairly general framework for reducing $(\varepsilon, \delta)$ differentially private (DP) statistical estimation to its non-private counterpart.
Information Theory and Statistics
Solomon Kullback · 1968
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Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
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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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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Introduction to Nonparametric Estimation
Alexandre B Tsybakov · 2009
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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A near-optimal algorithm for differentially-private principal components
Kamalika Chaudhuri, Anand D Sarwate, and Kaushik Sinha · 2013
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Algorithms and hardness for robust subspace recovery
Moritz Hardt and Ankur Moitra · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
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Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
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Locating a small cluster privately
Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2016
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Analysis of a privacy-preserving pca algorithm using random matrix theory
Lu Wei, Anand D Sarwate, Jukka Corander, Alfred Hero, and Vahid Tarokh · 2016
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2017
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The complexity of differential privacy
Salil Vadhan · 2017
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
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Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Steven Z Wu · 2019
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Differentially private algorithms for learning mixtures of separated gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2020
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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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A primer on private statistics
Gautam Kamath and Jonathan Ullman · 2020
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On the sample complexity of privately learning unbounded high-dimensional gaussians
Ishaq Aden-Ali, Hassan Ashtiani, and Gautam Kamath · 2021
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Privately learning mixtures of axis-aligned gaussians
Ishaq Aden-Ali, Hassan Ashtiani, and Christopher Liaw · 2021
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Covariance-aware private mean estimation without private covariance estimation
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Simultaneous private learning of multiple concepts
Mark Bun, Kobbi Nissim, and Uri Stemmer · 2019
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Robust estimators in high-dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2019
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Quantum entropy scoring for fast robust mean estimation and improved outlier detection
Yihe Dong, Samuel Hopkins, and Jerry Li · 2019
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint
Martin J. Wainwright · 2019
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Near-optimal sample complexity bounds for robust learning of gaussian mixtures via compression schemes
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Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou · 2021
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Wei Dong and Ke Yi · 2021
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A private and computationally-efficient estimator for unbounded gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman · 2021
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Private robust estimation by stabilizing convex relaxations
Pravesh K Kothari, Pasin Manurangsi, and Ameya Velingker · 2021
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Robust and differentially private mean estimation
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Friendlycore: Practical differentially private aggregation
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Hassan Ashtiani and Christopher Liaw · 2022
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Differentially private covariance revisited
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