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The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if not done with care.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé M Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 1902
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
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On the lambertw function
Robert M Corless, Gaston H Gonnet, David EG Hare, David J Jeffrey, and Donald E Knuth · 1996
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Finding frequent items in data streams
Moses Charikar, Kevin Chen, and Martin Farach-Colton · 2002
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Finding hierarchical heavy hitters in data streams
Graham Cormode, Flip Korn, S. Muthukrishnan, and Divesh Srivastava · 2003
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Privacy preserving mining of association rules
Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, and Johannes Gehrke · 2004
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When random sampling preserves privacy
Kamalika Chaudhuri and Nina Mishra · 2006
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Finding frequent items in data streams
Graham Cormode and Marios Hadjieleftheriou · 2008
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang · 2009
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Discovering frequent patterns in sensitive data
Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta · 2010
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N Rothblum · 2010
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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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Crowd-blending privacy
Johannes Gehrke, Michael Hay, Edward Lui, and Rafael Pass · 2012
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On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
Ninghui Li, Wahbeh Qardaji, and Dong Su · 2012
Cited alongside, same era.
Mining frequent patterns with differential privacy
Luca Bonomi and Li Xiong · 2013
Cited alongside, same era.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
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.
Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2014
Cited alongside, same era.
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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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Federated learning: Collaborative machine learning without centralized training data, April 2017
H Brendan McMahan and Daniel Ramage · 2017
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Differentially private learning of structured discrete distributions
Ilias Diakonikolas, Moritz Hardt, and Ludwig Schmidt · 2015
Cited alongside, same era.
Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
Cited alongside, same era.
Discrete distribution estimation under local privacy
Peter Kairouz, Keith Bonawitz, and Daniel Ramage · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Differentially private frequent sequence mining
Shengzhi Xu, Xiang Cheng, Sen Su, Ke Xiao, and Li Xiong · 2016
Cited alongside, same era.
Learning with privacy at scale
Apple · 2017
Cited alongside, same era.
Locally differentially private protocols for frequency estimation
Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha · 2017
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Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
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Heavy hitters and the structure of local privacy
Mark Bun, Jelani Nelson, and Uri Stemmer · 2018
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Marginal release under local differential privacy
Graham Cormode, Tejas Kulkarni, and Divesh Srivastava · 2018
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Pripearl: A framework for privacy-preserving analytics and reporting at linkedin
Krishnaram Kenthapadi and Thanh TL Tran · 2018
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Optimal schemes for discrete distribution estimation under locally differential privacy
Min Ye and Alexander Barg · 2018
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Frequent sequence pattern mining with differential privacy
Fengli Zhou and Xiaoli Lin · 2018
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The privacy blanket of the shuffle model
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 2019
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