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Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis.
A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Charles Stein · 1972
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Tutorial on large deviations for the binomial distribution
Richard Arratia and Louis Gordon · 1989
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Use of exchangeable pairs in the analysis of simulations
Charles Stein, Persi Diaconis, Susan Holmes, and Gesine Reinert · 2004
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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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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Optimal probabilistic fingerprint codes
Gábor Tardos · 2008
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Iterative hard thresholding for compressed sensing
Thomas Blumensath and Mike E Davies · 2009
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
Sahand Negahban, Bin Yu, Martin J Wainwright, and Pradeep K Ravikumar · 2009
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Introduction to nonparametric estimation
Alexandre B Tsybakov · 2009
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Fast global convergence rates of gradient methods for high-dimensional statistical recovery
Alekh Agarwal, Sahand Negahban, and Martin J Wainwright · 2010
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Using the method of pairwise comparison to obtain reliable teacher assessments
Sandra Heldsinger and Stephen Humphry · 2010
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Differentially private m-estimators
Jing Lei · 2011
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Orbitopes
Raman Sanyal, Frank Sottile, and Bernd Sturmfels · 2011
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Two of a kind or the ratings game? adaptive pairwise preferences and latent factor models
Suhrid Balakrishnan and Sumit Chopra · 2012
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Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
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Asymptotic methods in statistical decision theory
Lucien Le Cam · 2012
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The ranking lasso and its application to sport tournaments
Guido Masarotto and Cristiano Varin · 2012
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New Statistical Applications for Differential Privacy
Rob Hall · 2013
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Differential privacy for functions and functional data
Rob Hall, Alessandro Rinaldo, and Larry Wasserman · 2013
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Privacy and statistical risk: Formalisms and minimax bounds
Rina Foygel Barber and John C Duchi · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 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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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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On iterative hard thresholding methods for high-dimensional m-estimation
Differentially private testing of identity and closeness of discrete distributions
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
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Differentially private false discovery rate control
Cynthia Dwork, Weijie J Su, and Li Zhang · 2018
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2018
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Yu-Xiang Wang · 2018
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Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Guha Thakurta · 2019
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Prateek Jain, Ambuj Tewari, and Purushottam Kar · 2014
Cited alongside, same era.
The application of differential privacy for rank aggregation: Privacy and accuracy
Shang Shang, Tiance Wang, Paul Cuff, and Sanjeev Kulkarni · 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.
Regularized m-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Po-Ling Loh and Martin J Wainwright · 2015
Cited alongside, same era.
Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence
Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin Wainwright · 2015
Cited alongside, same era.
An introduction to matrix concentration inequalities
Joel A Tropp · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
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Privacy-preserving causal inference via inverse probability weighting
Si Kai Lee, Luigi Gresele, Mijung Park, and Krikamol Muandet · 2019
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Private causal inference using propensity scores
Si Kai Lee, Luigi Gresele, Mijung Park, and Krikamol Muandet · 2019
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Ultimate power of inference attacks: Privacy risks of high-dimensional models
Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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Implementing differential privacy: Seven lessons from the 2020 united states census
Michael B. Hawes · 2020
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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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Privately learning markov random fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, and Steven Wu · 2020
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Differentially private assouad, fano, and le cam
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2021
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Privacy-preserving parametric inference: a case for robust statistics
Marco Avella-Medina · 2021
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Differentially private inference via noisy optimization
Marco Avella-Medina, Casey Bradshaw, and Po-Ling Loh · 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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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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Differentially private condorcet voting
Zhechen Li, Ao Liu, Lirong Xia, Yongzhi Cao, and Hanpin Wang · 2022
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Distributed differentially private ranking aggregation
Baobao Song, Qiujun Lan, Yang Li, and Gang Li · 2022
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Supplement to “score attack: a lower bound technique for optimal differentially private learning ”
T Tony Cai, Yichen Wang, and Linjun Zhang · 2023
Closest in time.
Shirong Xu, Will Wei Sun, and Guang Cheng · 2023
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