Fetching the paper…
Reading the bibliography…
Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy.
Statistical estimation and optimal recovery
David L Donoho · 1994
Earlier work this paper cites.
On minimax estimation of a sparse normal mean vector
Iain M Johnstone · 1994
Earlier work this paper cites.
Sparse spatial autoregressions
R Kelley Pace and Ronald Barry · 1997
Earlier work this paper cites.
Balls into bins – a simple and tight analysis
Martin Raab and Angelika Steger · 1998
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
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
Earlier work this paper cites.
Iterative hard thresholding for compressed sensing
Thomas Blumensath and Mike E Davies · 2009
Earlier work this paper cites.
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
Earlier work this paper cites.
Fast global convergence rates of gradient methods for high-dimensional statistical recovery
Alekh Agarwal, Sahand Negahban, and Martin J Wainwright · 2010
Earlier work this paper cites.
A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
Differentially private m-estimators
Jing Lei · 2011
Earlier work this paper cites.
Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
Earlier work this paper cites.
Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
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 iterative hard thresholding methods for high-dimensional m-estimation
Prateek Jain, Ambuj Tewari, and Purushottam Kar · 2014
Cited alongside, same era.
Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
Impact of IQ on the diagnostic yield of chromosomal microarray in a community sample of adults with schizophrenia
Chelsea Lowther, Daniele Merico, Gregory Costain, Jack Waserman, Kerry Boyd, Abdul Noor, Marsha Speevak, Dimitri J Stavropoulos, John Wei, Anath C Lionel, et al · 2017
Later among the works it cites.
Renyi differential privacy
Ilya Mironov · 2017
Later among the works it cites.
Differentially private ordinary least squares
Or Sheffet · 2017
Later among the works it cites.
Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2017
Later among the works it cites.
Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
Later among the works it cites.
Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Nearly optimal private lasso
Kunal Talwar, Abhradeep Guha Thakurta, and Li Zhang · 2015
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 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.
The challenge of scientific reproducibility and privacy protection for statistical agencies
John M. Abowd · 2016
Cited alongside, same era.
Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
Cited alongside, same era.
Cynthia Dwork and Vitaly Feldman · 2018
Later among the works it cites.
Differentially private false discovery rate control
Cynthia Dwork, Weijie J Su, and Li Zhang · 2018
Later among the works it cites.
Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2018
Later among the works it cites.
Geometrizing rates of convergence under differential privacy constraints
Angelika Rohde and Lukas Steinberger · 2018
Later among the works it cites.
Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Guha Thakurta · 2019
Closest in time.
Deep learning with gaussian differential privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J Su · 2019
Closest in time.
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
Closest in time.
High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
Closest in time.
Coinpress: Practical private mean and covariance estimation
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman · 2020
Closest in time.
Supplement to “the cost of privacy: optimal rates of convergence for parameter estimation with differential privacy”
T. Tony Cai, Yichen Wang, and Linjun Zhang · 2020
Closest in time.