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Differentially private statistical estimation has seen a flurry of developments over the last several years.
Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator
Aryeh Dvoretzky, Jack Kiefer, and Jacob Wolfowitz · 1956
Earlier work this paper cites.
The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
P. Massart · 1990
Earlier work this paper cites.
Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
Earlier work this paper cites.
Privacy-preserving datamining on vertically partitioned databases
Cynthia Dwork and Kobbi Nissim · 2004
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Practical privacy: The SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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.
Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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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
Earlier work this paper cites.
Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
Earlier work this paper cites.
Differential privacy for clinical trial data: Preliminary evaluations
Duy Vu and Aleksandra Slavković · 2009
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum · 2010
Earlier work this paper cites.
Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables
Stephen E. Fienberg, Alessandro Rinaldo, and Xiaolin Yang · 2010
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Private and continual release of statistics
T-H Hubert Chan, Elaine Shi, and Dawn Song · 2011
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Private analysis of graph structure
Vishesh Karwa, Sofya Raskhodnikova, Adam Smith, and Grigory Yaroslavtsev · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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The Johnson-Lindenstrauss transform itself preserves differential privacy
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2012
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
Earlier work this paper cites.
Differentially private data analysis of social networks via restricted sensitivity
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2013
Earlier work this paper cites.
Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2013
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Recursive mechanism: Towards node differential privacy and unrestricted joins
Shixi Chen and Shuigeng Zhou · 2013
Earlier work this paper cites.
Analyzing graphs with node differential privacy
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2013
Earlier work this paper cites.
Random projections, graph sparsification, and differential privacy
Jalaj Upadhyay · 2013
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Privacy-preserving data sharing for genome-wide association studies
Caroline Uhler, Aleksandra Slavković, and Stephen E. Fienberg · 2013
Earlier work this paper cites.
Impossibility of differentially private universally optimal mechanisms
Hai Brenner and Kobbi Nissim · 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.
Scalable privacy-preserving data sharing methodology for genome-wide association studies
Fei Yu, Stephen E. Fienberg, Aleksandra B. Slavković, and Caroline Uhler · 2014
Cited alongside, same era.
Private graphon estimation for sparse graphs
Christian Borgs, Jennifer Chayes, and Adam Smith · 2015
Cited alongside, same era.
Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2015
Cited alongside, same era.
Differentially private learning of structured discrete distributions
Ilias Diakonikolas, Moritz Hardt, and Ludwig Schmidt · 2015
Cited alongside, same era.
Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
Differentially private testing of identity and closeness of discrete distributions
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
Later among the works it cites.
Revealing network structure, confidentially: Improved rates for node-private graphon estimation
Christian Borgs, Jennifer Chayes, Adam Smith, and Ilias Zadik · 2018
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Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N. Rothblum, and Thomas Steinke · 2018
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Differentially private ANOVA testing
Zachary Campbell, Andrew Bray, Anna Ritz, and Adam Groce · 2018
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Differentially private change-point detection
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, and Wanrong Zhang · 2018
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
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Cited alongside, same era.
Revisiting differentially private hypothesis tests for categorical data
Yue Wang, Jaewoo Lee, and Daniel Kifer · 2015
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
Cited alongside, same era.
Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing
Marco Gaboardi, Hyun-Woo Lim, Ryan M. Rogers, and Salil P. Vadhan · 2016
Cited alongside, same era.
Inference using noisy degrees: Differentially private β \beta -model and synthetic graphs
Vishesh Karwa and Aleksandra Slavković · 2016
Cited alongside, same era.
Lipschitz extensions for node-private graph statistics and the generalized exponential mechanism
Sofya Raskhodnikova and Adam D. Smith · 2016
Cited alongside, same era.
Later among the works it cites.
Statistical approximating distributions under differential privacy
Yue Wang, Daniel Kifer, Jaewoo Lee, and Vishesh Karwa · 2018
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Private testing of distributions via sample permutations
Maryam Aliakbarpour, Ilias Diakonikolas, Daniel M. Kane, and Ronitt Rubinfeld · 2019
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On differentially private graph sparsification and applications
Raman Arora and Jalaj Upadhyay · 2019
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Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
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Average-case averages: Private algorithms for smooth sensitivity and mean estimation
Mark Bun and Thomas Steinke · 2019
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Privately detecting changes in unknown distributions
Rachel Cummings, Sara Krehbiel, Yuliia Lut, and Wanrong Zhang · 2019
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The structure of optimal private tests for simple hypotheses
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman · 2019
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Private identity testing for high-dimensional distributions
Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, and Lydia Zakynthinou · 2019
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Differentially private nonparametric hypothesis testing
Simon Couch, Zeki Kazan, Kaiyan Shi, Andrew Bray, and Adam Groce · 2019
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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 · 2019
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Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2019
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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Improved differentially private analysis of variance
Marika Swanberg, Ira Globus-Harris, Iris Griffith, Anna Ritz, Adam Groce, and Andrew Bray · 2019
Later among the works it cites.
Efficiently estimating Erdos-Renyi graphs with node differential privacy
Adam Sealfon and Jonathan Ullman · 2019
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Differentially private Assouad, Fano, and Le Cam
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2020
Closest in time.
Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan Kulkarni, and Yin Tat Lee · 2020
Closest in time.
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 Zhiwei Steven Wu · 2020
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