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The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19].
“Scalable and Differentially Private Distributed Aggregation in the Shuffled Model”, 2019
Badih Ghazi, Rasmus Pagh and Ameya Velingker · 1906
Earlier work this paper cites.
“Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation”, 2020
\’Ulfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar and Abhradeep Thakurta · 2001
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.
“Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs”, 2020
Antonious. Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz and Ananda Suresh · 2008
Earlier work this paper cites.
“What Can We Learn Privately?”
S.. Kasiviswanathan, H.. Lee, K. Nissim, S. Raskhodnikova and A. Smith · 2008
Earlier work this paper cites.
“The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation”
Albert Cheu and Jonathan. Ullman · 2009
Earlier work this paper cites.
Vitaly Feldman, Audra McMillan and Kunal Talwar · 2012
Earlier work this paper cites.
“RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response”
\’Ulfar Erlingsson, Vasyl Pihur and Aleksandra Korolova · 2014
Earlier work this paper cites.
“Extremal Mechanisms for Local Differential Privacy”
Peter Kairouz, Sewoong Oh and Pramod Viswanath · 2014
Earlier work this paper cites.
“Lecture Notes: High Dimensional Statistics”, https://ocw.mit.edu/courses/18-s997-high-dimensional-statistics-spring-2015/pages/lecture-notes/ , 2015
Philippe Rigollet · 2015
Earlier work this paper cites.
“Deep Learning with Differential Privacy”
Mart\’n Abadi, Andy Chu, Ian. Goodfellow, H. McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
Earlier work this paper cites.
“Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds”
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
“Concentrated Differential Privacy”
Cynthia Dwork and Guy. Rothblum · 2016
Cited alongside, same era.
“Learning with privacy at scale”
Apple’s Differential Privacy Team · 2017
Cited alongside, same era.
“Prochlo: Strong Privacy for Analytics in the Crowd”
Andrea Bittau, \’Ulfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes and Bernhard Seefeld · 2017
Cited alongside, same era.
“Prio: Private, Robust, and Scalable Computation of Aggregate Statistics”
Henry Corrigan-Gibbs and Dan Boneh · 2017
Cited alongside, same era.
“Collecting Telemetry Data Privately”
Bolin Ding, Janardhan Kulkarni and Sergey Yekhanin · 2017
“On the Power of Multiple Anonymous Messages”
Badih Ghazi, Noah Golowich, R. Kumar, R. Pagh and A. Velingker · 2019
Later among the works it cites.
“Private Summation in the Multi-Message Shuffle Model”
B. Balle, J. Bell, A. Gasc\’on and Kobbi Nissim · 2020
Later among the works it cites.
“Separating Local & Shuffled Differential Privacy via Histograms”
Victor Balcer and Albert Cheu · 2020
Later among the works it cites.
“Privacy Amplification via Random Check-Ins”
B. Balle, Peter Kairouz, H. McMahan, Om Thakkar and Abhradeep Thakurta · 2020
Later among the works it cites.
“The Discrete Gaussian for Differential Privacy”
Clément. Canonne, Gautam Kamath and Thomas Steinke · 2020
Later among the works it cites.
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Cited alongside, same era.
“Rényi differential privacy”
Ilya Mironov · 2017
Cited alongside, same era.
“Optimal schemes for discrete distribution estimation under locally differential privacy”
Min Ye and Alexander Barg · 2018
Cited alongside, same era.
“The Privacy Blanket of the Shuffle Model”
Borja Balle, James Bell, Adri\‘a Gasc\’on 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
Cited alongside, same era.
“Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity”
\’Ulfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar and Abhradeep Thakurta · 2019
Cited alongside, same era.
“Private Aggregation from Fewer Anonymous Messages”
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh and Ameya Velingker · 2020
Later among the works it cites.
“Computing tight differential privacy guarantees using fft”
Antti Koskela, Joonas J\"alk\"o and Antti Honkela · 2020
Later among the works it cites.
“Improving Utility and Security of the Shuffler-based Differential Privacy”
Tianhao Wang, Min Xu, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang, Ninghui Li and Somesh Jha · 2020
Later among the works it cites.
“Exposure Notification Privacy-preserving Analytics (ENPA) White Paper”, https://covid19-static.cdn-apple.com/applications/covid19/current/static/contact-tracing/pdf/ENPA_White_Paper.pdf , 2021
Apple and Google · 2021
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 · 2021
Later among the works it cites.
“Tight Accounting in the Shuffle Model of Differential Privacy”, 2021
Antti Koskela, Mikko. Heikkilä and Antti Honkela · 2021
Later among the works it cites.