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Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems.
Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L. Warner · 1965
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Efficient noise-tolerant learning from statistical queries
Michael J. Kearns · 1993
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Limiting privacy breaches in privacy preserving data mining
Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant · 2003
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Cryptographic randomized response techniques
Andris Ambainis, Markus Jakobsson, and Helger Lipmaa · 2004
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Practical privacy: the SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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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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Polling with physical envelopes: A rigorous analysis of a human-centric protocol
Tal Moran and Moni Naor · 2006
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2008
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Distributed private data analysis: On simultaneously solving how and what
Amos Beimel, Kobbi Nissim, and Eran Omri · 2011
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Distributed private heavy hitters
Justin Hsu, Sanjeev Khanna, and Aaron Roth · 2012
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Topics in Random Matrix Theory
Terence Tao · 2012
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Local privacy and minimax bounds: Sharp rates for probability estimation
John Duchi, Michael Jordan, and Martin Wainwright · 2013
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Local privacy and statistical minimax rates
John Duchi, Michael Jordan, and Martin Wainwright · 2013
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Learning with privacy at scale, December 2017
Apple Differential Privacy Team · 2017
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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
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Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta · 2017
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Heavy hitters and the structure of local privacy
Mark Bun, Jelani Nelson, and Uri Stemmer · 2018
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The complexity of computing the optimal composition of differential privacy
Jack Murtagh and Salil P. Vadhan · 2018
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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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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Local, private, efficient protocols for succinct histograms
Raef Bassily and Adam D. Smith · 2015
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Optimal schemes for discrete distribution estimation under locally differential privacy
Min Ye and Alexander Barg · 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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Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
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