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Locally Differentially Private (LDP) Reports are commonly used for collection of statistics and machine learning in the federated setting.
“Advances and Open Problems in Federated Learning”, 2019
Peter Kairouz et al · 1912
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“Randomized response: A survey technique for eliminating evasive answer bias”
Stanley Warner · 1965
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“Pseudorandom generators for space-bounded computation”
N. Nisan · 1992
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“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
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“Limiting privacy breaches in privacy preserving data mining”
Alexandre. Evfimievski, Johannes Gehrke and Ramakrishnan Srikant · 2003
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“Calibrating noise to sensitivity in private data analysis”
C. Dwork, F. McSherry, K. Nissim and A. Smith · 2006
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“Pairwise independence and derandomization”
Michael Luby, Michael Luby and Avi Wigderson · 2006
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“Privacy via Pseudorandom Sketches”
Nina Mishra and Mark Sandler · 2006
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“Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs”, 2020
Antonious. Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz and Ananda Suresh · 2008
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“Computational Differential Privacy”
Ilya Mironov, Omkant Pandey, Omer Reingold and Salil” Vadhan · 2009
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“Uncertainty principles and vector quantization”
Y. Lyubarskii and R. Vershynin · 2010
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“The Limits of Two-Party Differential Privacy”
A. McGregor, I. Mironov, T. Pitassi, O. Reingold, K. Talwar and S. Vadhan · 2010
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Vitaly Feldman, Audra McMillan and Kunal Talwar · 2012
Earlier work this paper cites.
“Distributed private heavy hitters”
Justin Hsu, Sanjeev Khanna and Aaron Roth · 2012
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“Private Empirical Risk Minimization, Revisited”
Raef Bassily, Adam Smith and Abhradeep Thakurta · 2014
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“The Algorithmic Foundations of Differential Privacy”
Cynthia Dwork and Aaron Roth · 2014
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“Rappor: Randomized aggregatable privacy-preserving ordinal response”
“’Ulfar Erlingsson, Vasyl Pihur and Aleksandra Korolova · 2014
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“Local, private, efficient protocols for succinct histograms”
Raef Bassily and Adam Smith · 2015
Cited alongside, same era.
Vitaly Feldman, Cristobal Guzman and Santosh Vempala · 2015
Cited alongside, same era.
“Deep Learning with Differential Privacy”
Mart“’n Abadi, Andy Chu, Ian. Goodfellow, H. McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
Cited alongside, same era.
“Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds”
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
“Concentrated Differential Privacy”
C. Dwork and G.. Rothblum · 2016
Cited alongside, same era.
“Handbook of applied cryptography”
Alfred Menezes, Paul Van and Scott Vanstone · 2018
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“Optimal schemes for discrete distribution estimation under locally differential privacy”
Min Ye and Alexander Barg · 2018
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“Communication complexity in locally private distribution estimation and heavy hitters”
Jayadev Acharya and Ziteng Sun · 2019
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“Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication” 89
Jayadev Acharya, Ziteng Sun and Huanyu Zhang · 2019
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“The Privacy Blanket of the Shuffle Model”
Borja Balle, James Bell, Adri“‘a Gasc“’on and Kobbi Nissim · 2019
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“Protection Against Reconstruction and Its Applications in Private Federated Learning”, 2019
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Peter Kairouz, Keith Bonawitz and Daniel Ramage · 2016
Cited alongside, same era.
“Mutual information optimally local private discrete distribution estimation”
Shaowei Wang, Liusheng Huang, Pengzhan Wang, Yiwen Nie, Hongli Xu, Wei Yang, Xiang-Yang Li and Chunming Qiao · 2016
Cited alongside, same era.
“QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding”
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka and Milan Vojnovic · 2017
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.
“Collecting Telemetry Data Privately”
Bolin Ding, Janardhan Kulkarni and Sergey Yekhanin · 2017
Cited alongside, same era.
“Rényi Differential Privacy”
I. Mironov · 2017
Cited alongside, same era.
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor and Ryan Rogers · 2019
Later among the works it cites.
“Heavy hitters and the structure of local privacy”
Mark Bun, Jelani Nelson and Uri Stemmer · 2019
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.
“Lower Bounds for Locally Private Estimation via Communication Complexity”
John Duchi and Ryan Rogers · 2019
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“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
Later among the works it cites.
“vqsgd: Vector quantized stochastic gradient descent”
Venkata Gandikota, Daniel Kane, Raj Maity and Arya Mazumdar · 2019
Later among the works it cites.
“Practical Locally Private Heavy Hitters.”
Raef Bassily, Kobbi Nissim, Uri Stemmer and Abhradeep Thakurta · 2020
Later among the works it cites.
“Breaking the Communication-Privacy-Accuracy Trilemma”
Wei-Ning Chen, Peter Kairouz and Ayfer “”Ozg“”ur · 2020
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“Adaptive Gradient Quantization for Data-Parallel SGD”
Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel. Roy and Ali Ramezani-Kebrya · 2020
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“Limits on Gradient Compression for Stochastic Optimization”
P. Mayekar and H. Tyagi · 2020
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“An implementation of Kashine based mean estimation scheme” Accessed: 2021-02-17, https://github.com/WeiNingChen/Kashin-mean-estimation
2021
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