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There has been much recent work in the shuffle model of differential privacy, particularly for approximate $d$-bin histograms.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
Earlier work this paper cites.
Blind signatures for untraceable payments
David Chaum · 1982
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.
Cryptographic randomized response techniques
Andris Ambainis, Markus Jakobsson, and Helger Lipmaa · 2004
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Polling with physical envelopes: A rigorous analysis of a human-centric protocol
Tal Moran and Moni Naor · 2006
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.
You are where you tweet: a content-based approach to geo-locating twitter users
Zhiyuan Cheng, James Caverlee, and Kyumin Lee · 2010
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
Local, private, efficient protocols for succinct histograms
Raef Bassily and Adam 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 Tinnes, and Bernhard Seefeld · 2017
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
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Later among the works it cites.
Manipulation attacks in local differential privacy
Albert Cheu, Adam D. Smith, and Jonathan R. Ullman · 2019
Later among the works it cites.
On the power of multiple anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker · 2019
Later among the works it cites.
Separating local and shuffled differential privacy via histograms
Victor Balcer and Albert Cheu · 2020
Later among the works it cites.
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2020
Later among the works it cites.
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Data poisoning attacks to local differential privacy protocols
Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2019
Cited alongside, same era.
Private counting from anonymous messages: Near-optimal accuracy with vanishing communication overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Rasmus Pagh · 2020
Later among the works it cites.