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We study a protocol for distributed computation called shuffled check-in, which achieves strong privacy guarantees without requiring any further trust assumptions beyond a trusted shuffler.
Untraceable electronic mail, return addresses, and digital pseudonyms
David L Chaum · 1981
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Limiting privacy breaches in privacy preserving data mining
Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant · 2003
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Integer partitions
George E Andrews and Kimmo Eriksson · 2004
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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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Generating all partitions: a comparison of two encodings
Jerome Kelleher and Barry O’Sullivan · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 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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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
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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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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
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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 secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Cs7880: Rigorous approaches to data privacy, spring 2017
Jonathan Ullman · 2017
Cited alongside, same era.
The complexity of differential privacy
Salil Vadhan · 2017
Cited alongside, same era.
The skellam mechanism for differentially private federated learning
Naman Agarwal, Peter Kairouz, and Ziyu Liu · 2021
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Three variants of differential privacy: Lossless conversion and applications
Shahab Asoodeh, Jiachun Liao, Flavio P Calmon, Oliver Kosut, and Lalitha Sankar · 2021
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Shuffled model of differential privacy in federated learning
Antonious Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh · 2021
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Differentially private federated learning with shuffling and client self-sampling
Antonious M Girgis, Deepesh Data, and Suhas Diggavi · 2021
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On the renyi differential privacy of the shuffle model
Antonious M Girgis, Deepesh Data, Suhas Diggavi, Ananda Theertha Suresh, and Peter Kairouz · 2021
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Numerical composition of differential privacy
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
Cited alongside, same era.
Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
Cited alongside, same era.
Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
Cited alongside, same era.
Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
Cited alongside, same era.
Poission subsampled rényi differential privacy
Yuqing Zhu and Yu-Xiang Wang · 2019
Cited alongside, same era.
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
Peter Kairouz, Ziyu Liu, and Thomas Steinke · 2021
Later among the works it cites.
Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Later among the works it cites.
Skellam mixture mechanism: a novel approach to federated learning with differential privacy
Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Tan, and Khin Mi Mi Aung · 2022
Closest in time.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2022
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Network shuffling: Privacy amplification via random walks
Seng Pei Liew, Tsubasa Takahashi, Shun Takagi, Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa · 2022
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba · 2022
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Composition of differential privacy & privacy amplification by subsampling
Thomas Steinke · 2022
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Walking to hide: Privacy amplification via random message exchanges in network
Hao Wu, Olga Ohrimenko, and Anthony Wirth · 2022
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Is federated learning a practical pet yet?
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot · 2023
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Numerical accounting in the shuffle model of differential privacy
Antti Koskela, Mikko A. Heikkilä, and Antti Honkela · 2023
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