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We present two sample-efficient differentially private mean estimators for $d$-dimensional (sub)Gaussian distributions with unknown covariance.
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 1905
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Does learning require memorization? A short tale about a long tail
Vitaly Feldman · 1906
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Differentially private sql with bounded user contribution
Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson · 1909
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A survey of sampling from contaminated distributions
John D. Tukey · 1960
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On uniform convergence of the frequencies of events to their probabilities
Vladimir Naumovich Vapnik and Aleksei Yakovlevich Chervonenkis · 1971
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Rates of Convergence of Minimum Distance Estimators and Kolmogorov’s Entropy
Yannis G. Yatracos · 1985
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Breakdown properties of location estimates based on halfspace depth and projected outlyingness
David L Donoho and Miriam Gasko · 1992
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A universally acceptable smoothing factor for kernel density estimates
Luc Devroye and Gábor Lugosi · 1996
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Nonasymptotic universal smoothing factors, kernel complexity and Yatracos classes
Luc Devroye and Gábor Lugosi · 1997
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Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
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Combinatorial methods in density estimation
Luc Devroye and Gábor Lugosi · 2001
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Robust statistics , volume 523
Peter J Huber · 2004
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Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman · 2006
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Simulated annealing in convex bodies and an o*(n4) volume algorithm
László Lovász and Santosh Vempala · 2006
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The price of privacy and the limits of LP decoding
Cynthia Dwork, Frank McSherry, and Kunal Talwar · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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New efficient attacks on statistical disclosure control mechanisms
Cynthia Dwork and Sergey Yekhanin · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Genomic privacy and limits of individual detection in a pool
Sriram Sankararaman, Guillaume Obozinski, Michael I Jordan, and Eran Halperin · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam Smith, and Jonathan Ullman · 2010
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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Matrix analysis
Roger A Horn and Charles R Johnson · 2012
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Privacy and statistical risk: Formalisms and minimax bounds
Rina Barber and John Duchi · 2014
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
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Analyze gauss: optimal bounds for privacy-preserving principal component analysis
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
Cited alongside, same era.
RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Cited alongside, same era.
On the ‘semantics’ of differential privacy: A bayesian formulation
Shiva Prasad Kasiviswanathan and Adam D. Smith · 2014
Cited alongside, same era.
Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
Cited alongside, same era.
Interactive fingerprinting codes and the hardness of preventing false discovery
Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers · 2019
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Locally private mean estimation:
Marco Gaboardi, Ryan Rogers, and Or Sheffet · 2019
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Locally private gaussian estimation
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, and Steven Z. Wu · 2019
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Privately learning high dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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Fast computation of tukey trimmed regions and median in dimension p> 2
Xiaohui Liu, Karl Mosler, and Pavlo Mozharovskyi · 2019
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Sub-Gaussian estimators of the mean of a random vector
Gábor Lugosi and Shahar Mendelson · 2019
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Thomas Steinke and Jonathan Ullman · 2015
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Simultaneous private learning of multiple concepts
Mark Bun, Kobbi Nissim, and Uri Stemmer · 2016
Cited alongside, same era.
Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
Cited alongside, same era.
Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
Cited alongside, same era.
The complexity of differential privacy
Salil Vadhan · 2016
Cited alongside, same era.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Robust statistics: theory and methods (with R)
Ricardo A Maronna, R Douglas Martin, Victor J Yohai, and Matías Salibián-Barrera · 2019
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint
Martin J. Wainwright · 2019
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Private identity testing for high-dimensional distributions
Clément L Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, and Lydia Zakynthinou · 2020
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Differentially private confidence intervals
Wenxin Du, Canyon Foot, Monica Moniot, Andrew Bray, and Adam Groce · 2020
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Robust and heavy-tailed mean estimation made simple, via regret minimization
Sam Hopkins, Jerry Li, and Fred Zhang · 2020
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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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How to find a point in the convex hull privately
Haim Kaplan, Micha Sharir, and Uri Stemmer · 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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When is memorization of irrelevant training data necessary for high-accuracy learning?
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar · 2021
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The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T Tony Cai, Yichen Wang, and Linjun Zhang · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Robust and private learning of halfspaces
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Thao Nguyen · 2021
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Instance-optimal mean estimation under differential privacy
Ziyue Huang, Yuting Liang, and Ke Yi · 2021
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Robust and differentially private mean estimation
Xiyang Liu, Weihao Kong, Sham Kakade, and Sewoong Oh · 2021
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Differentially private depth functions and their associated medians, 2021
Kelly Ramsay and Shoja’eddin Chenouri · 2021
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Linkedin’s audience engagements api: A privacy preserving data analytics system at scale
Ryan Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, and Parvez Ahammad · 2021
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Opacus: User-friendly differential privacy library in pytorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, et al · 2021
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Easy differentially private linear regression
Kareem Amin, Matthew Joseph, Mónica Ribero, and Sergei Vassilvitskii · 2022
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New lower bounds for private estimation and a generalized fingerprinting lemma
Gautam Kamath, Argyris Mouzakis, and Vikrant Singhal · 2022
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Better private linear regression through better private feature selection
Travis Dick, Jennifer Gillenwater, and Matthew Joseph · 2024
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