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Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
Earlier work this paper cites.
Limiting privacy breaches in privacy preserving data mining
Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant · 2003
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.
Distributed private data analysis: Simultaneously solving how and what
Amos Beimel, Kobbi Nissim, and Eran Omri · 2008
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 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.
On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
Earlier work this paper cites.
Local, private, efficient protocols for succinct histograms
Raef Bassily and Adam D. Smith · 2015
Cited alongside, same era.
Simultaneous private learning of multiple concepts
Mark Bun, Kobbi Nissim, and Uri Stemmer · 2016
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.
Differentially private summation with multi-message shuffling
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 2019
Cited alongside, same era.
The privacy blanket of the shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
Cited alongside, same era.
Distributed differential privacy via shuffling
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
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On the power of multiple anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker · 2019
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The role of interactivity in local differential privacy
Matthew Joseph, Jieming Mao, Seth Neel, and Aaron Roth · 2019
Closest in time.
Practical and robust privacy amplification with multi-party differential privacy
Tianhao Wang, Min Xu, Bolin Ding, Jingren Zhou, Ninghui Li, and Somesh Jha · 2019
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Pure differentially private summation from anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2020
Closest in time.
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Albert Cheu, Adam D. Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Cited alongside, same era.
Exponential separations in local differential privacy
Matthew Joseph, Jieming Mao, and Aaron Roth · 2020
Closest in time.