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We initiate the study of hypothesis selection under local differential privacy.
Ix. on the problem of the most efficient tests of statistical hypotheses
Jerzy Neyman and Egon Sharpe Pearson · 1933
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L. Warner · 1965
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Parallelism in comparison problems
Leslie G. Valiant · 1975
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Parallel sorting with constant time for comparisons
Roland Häggkvist and Pavol Hell · 1981
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An o ( n log n ) o(n\log n) sorting network
Miklós Ajtai, János Komlós, and Endre Szemerédi · 1983
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Parallel sorting
Béla Bollobás and Andrew Thomason · 1983
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Searching, merging, and sorting in parallel computation
Clyde P. Kruskal · 1983
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Tight bounds on the complexity of parallel sorting
Tom Leighton · 1984
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Expanders, sorting in rounds and superconcentrators of limited depth
Noga Alon · 1985
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Sorting and graphs
Béla Bollobás and Pavol Hell · 1985
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Rates of convergence of minimum distance estimators and Kolmogorov’s entropy
Yannis G. Yatracos · 1985
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Tight complexity bounds for parallel comparison sorting
Noga Alon, Yossi Azar, and Uzi Vishkin · 1986
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Tight comparison bounds on the complexity of parallel sorting
Yossi Azar and Uzi Vishkin · 1987
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Sorting and selecting in rounds
Nicholas Pippenger · 1987
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The average complexity of deterministic and randomized parallel comparison-sorting algorithms
Noga Alon and Yossi Azar · 1988
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Sorting, approximate sorting, and searching in rounds
Noga Alon and Yossi Azar · 1988
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Parallel selection
Yossi Azar and Nicholas Pippenger · 1990
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Parallel selection with high probability
Béla Bollobás and Graham Brightwell · 1990
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Computing with noisy information
Uriel Feige, Prabhakar Raghavan, David Peleg, and Eli Upfal · 1994
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Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1994
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A universally acceptable smoothing factor for kernel density estimation
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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Efficient noise-tolerant learning from statistical queries
Michael J. Kearns · 1998
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Combinatorial methods in density estimation
Luc Devroye and Gábor Lugosi · 2001
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Limiting privacy breaches in privacy preserving data mining
Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant · 2003
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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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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Density estimation in linear time
Satyaki Mahalanabis and Daniel Stefankovic · 2008
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Sorting and selection with imprecise comparisons
Miklós Ajtai, Vitaly Feldman, Avinatan Hassidim, and Jelani Nelson · 2009
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Differential privacy for clinical trial data: Preliminary evaluations
Duy Vu and Aleksandra Slavković · 2009
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Private and continual release of statistics
T-H Hubert Chan, Elaine Shi, and Dawn Song · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Learning Poisson binomial distributions
Constantinos Daskalakis, Ilias Diakonikolas, and Rocco A. Servedio · 2012
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Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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Inspectre: Privately estimating the unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun, and Huanyu Zhang · 2018
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Differentially private uniformly most powerful tests for binomial data
Jordan Awan and Aleksandra Slavković · 2018
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Differentially private testing of identity and closeness of discrete distributions
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
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Differentially private ANOVA testing
Zachary Campbell, Andrew Bray, Anna Ritz, and Adam Groce · 2018
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The total variation distance between high-dimensional Gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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Local private hypothesis testing: Chi-square tests
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Privacy-preserving data sharing for genome-wide association studies
Caroline Uhler, Aleksandra Slavković, and Stephen E. Fienberg · 2013
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Sorting with adversarial comparators and application to density estimation
Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, and Ananda Theertha Suresh · 2014
Cited alongside, same era.
Impossibility of differentially private universally optimal mechanisms
Hai Brenner and Kobbi Nissim · 2014
Cited alongside, same era.
Faster and sample near-optimal algorithms for proper learning mixtures of Gaussians
Constantinos Daskalakis and Gautam Kamath · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Cited alongside, same era.
Marco Gaboardi and Ryan Rogers · 2018
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Locally private hypothesis testing
Or Sheffet · 2018
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Tight lower bounds for locally differentially private selection
Jonathan Ullman · 2018
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Test without trust: Optimal locally private distribution testing
Jayadev Acharya, Clément L. Canonne, Cody Freitag, and Himanshu Tyagi · 2019
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Inference under information constraints: Lower bounds from chi-square contraction
Jayadev Acharya, Clément L. Canonne, and Himanshu Tyagi · 2019
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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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Pan-private uniformity testing
Kareem Amin, Matthew Joseph, and Jieming Mao · 2019
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The optimal approximation factor in density estimation
Olivier Bousquet, Daniel M. Kane, and Shay Moran · 2019
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Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
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Sorted top-k in rounds
Mark Braverman, Jieming Mao, and Yuval Peres · 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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Locally private learning without interaction requires separation
Amit Daniely and Vitaly Feldman · 2019
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Lower bounds for locally private estimation via communication complexity
John Duchi and Ryan Rogers · 2019
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The role of interactivity in local differential privacy
Matthew Joseph, Jieming Mao, Seth Neel, and Aaron Roth · 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
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Collaborative learning with limited interaction: Tight bounds for distributed exploration in multi-armed bandits
Chao Tao, Qin Zhang, and Yuan Zhou · 2019
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Instance-optimality in the noisy value-and comparison-model* accept, accept, strong accept: Which papers get in?
Vincent Cohen-Addad, Frederik Mallmann-Trenn, and Claire Mathieu · 2020
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Exponential separations in local differential privacy through communication complexity
Matthew Joseph, Jieming Mao, and Aaron Roth · 2020
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A primer on private statistics
Gautam Kamath and Jonathan Ullman · 2020
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