The shuffle model of differential privacy (Erlingsson et al.
Reading the bibliography…
Differentially private summation with multi-message shuffling
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 1906
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.
Improved summation from shuffling
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 1909
Earlier work this paper cites.
Manipulation attacks in local differential privacy
Albert Cheu, Adam D. Smith, and Jonathan Ullman · 1909
Earlier work this paper cites.
How to recycle random bits
Russell Impagliazzo and David Zuckerman · 1989
Earlier work this paper cites.
Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Ron Kohavi · 1996
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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
Cited alongside, same era.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2008
Cited alongside, same era.
Privacy-preserving aggregation of time-series data
Elaine Shi, Richard Chow, T h. Hubert Chan, Dawn Song, and Eleanor Rieffel · 2011
Cited alongside, same era.
Optimal lower bound for differentially private multi-party aggregation
T.-H. Hubert Chan, Elaine Shi, and Dawn Song · 2012
Cited alongside, same era.
A comprehensive comparison of multiparty secure additions with differential privacy
S. Goryczka and L. Xiong · 2015
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen E. Fienberg, and Alexander J. Smola · 2015
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 Tinnés, and Bernhard Seefeld · 2017
Cited alongside, same era.
Scalable and differentially private distributed aggregation in the shuffled model
Badih Ghazi, Rasmus Pagh, and Ameya Velingker
Cited in the paper.
Pure differentially private summation from anonymous messages, 2020a
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker
Cited in the paper.
Separating local & shuffled differential privacy via histograms, 2019
Victor Balcer and Albert Cheu · 2019
Later among the works it cites.
The privacy blanket of the shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
Later among the works it cites.
Distributed differential privacy via shuffling
Albert Cheu, Adam D. 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.
Murs: Practical and robust privacy amplification with multi-party differential privacy, 2019
Tianhao Wang, Min Xu, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang, Ninghui Li, and Somesh Jha · 2019
Later among the works it cites.
Private aggregation from fewer anonymous messages
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2020
Closest in time.
Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation, 2020
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta · 2020
Closest in time.
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