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In the \emph{shuffle model} of differential privacy, data-holding users send randomized messages to a secure shuffler, the shuffler permutes the messages, and the resulting collection of messages must be differentially private with regard to user data.
Differentially private summation with multi-message shuffling
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 1906
Earlier work this paper cites.
Scalable and differentially private distributed aggregation in the shuffled model
Badih Ghazi, Rasmus Pagh, and Ameya Velingker · 1906
Earlier work this paper cites.
Domain compression and its application to randomness-optimal distributed goodness-of-fit
Jayadev Acharya, Clément L. Canonne, Yanjun Han, Ziteng Sun, and Himanshu Tyagi · 1907
Earlier work this paper cites.
On the power of multiple anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker · 1908
Earlier work this paper cites.
Cryptography from anonymity
Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky, and Amit Sahai · 2006
Earlier work this paper cites.
Distributed private data analysis: Simultaneously solving how and what
Amos Beimel, Kobbi Nissim, and Eran Omri · 2008
Earlier work this paper cites.
Bounds on the sample complexity for private learning and private data release
Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2010
Earlier work this paper cites.
Pan-private streaming algorithms
Cynthia Dwork, Moni Naor, Toniann Pitassi, Guy N Rothblum, and Sergey Yekhanin · 2010
Earlier work this paper cites.
On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Cited alongside, same era.
Pan-private algorithms via statistics on sketches
Darakhshan Mir, Shan Muthukrishnan, Aleksandar Nikolov, and Rebecca N Wright · 2011
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
Optimal testing for properties of distributions
Jayadev Acharya, Constantinos Daskalakis, and Gautam Kamath · 2015
Cited alongside, same era.
Simultaneous private learning of multiple concepts
Mark Bun, Kobbi Nissim, and Uri Stemmer · 2016
Cited alongside, same era.
Blender: Enabling local search with a hybrid differential privacy model
Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, and Benjamin Livshits · 2017
Cited alongside, same era.
Differentially private testing of identity and closeness of discrete distributions
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
Later among the works it cites.
Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Later among the works it cites.
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
Later among the works it cites.
Pan-private uniformity testing
Kareem Amin, Matthew Joseph, and Jieming Mao · 2020
Closest in time.
Separating local and shuffled differential privacy via histograms
Victor Balcer and Albert Cheu · 2020
Closest in time.
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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
Cited alongside, same era.
Priv’it: private and sample efficient identity testing
Bryan Cai, Constantinos Daskalakis, and Gautam Kamath · 2017
Cited alongside, same era.
A short note on poisson tail bounds, 2017
Clément L. Canonne · 2017
Cited alongside, same era.
Test without trust: Optimal locally private distribution testing
Jayadev Acharya, Clément Canonne, Cody Freitag, and Himanshu Tyagi
Cited in the paper.
The privacy blanket of the shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim
Cited in the paper.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor
Cited in the paper.
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2020
Closest in time.
Pure differentially private summation from anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2020
Closest in time.
Exponential separations in local differential privacy
Matthew Joseph, Jieming Mao, and Aaron Roth · 2020
Closest in time.