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Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their privacy.
“On the Power of Multiple Anonymous Messages”, 2019
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh and Ameya Velingker · 1908
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“Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias” PMID: 12261830
Stanley. Warner · 1965
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“Safeguarding cryptographic keys”
George Blakley · 1979
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“How to Share a Secret”
Adi Shamir · 1979
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“Protocols for secure computations”
A.. Yao · 1982
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“Verifiable secret sharing and achieving simultaneity in the presence of faults”
Benny Chor, Shafi Goldwasser, Silvio Micali and Baruch Awerbuch · 1985
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“How to play any mental game”
Oded Goldreich, Silvio Micali and Avi Wigderson · 1987
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“Private information retrieval”
Benny Chor, Oded Goldreich, Eyal Kushilevitz and Madhu Sudan · 1995
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“Our Data, Ourselves: Privacy Via Distributed Noise Generation”
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov and Moni Naor · 2006
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“Calibrating Noise to Sensitivity in Private Data Analysis” http://repository.cmu.edu/jpc/vol7/iss3/2
Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith · 2006
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“Privacy via pseudorandom sketches”
Nina Mishra and Mark Sandler · 2006
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“Mechanism Design via Differential Privacy”
F. McSherry and K. Talwar · 2007
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“Distributed Private Data Analysis: Simultaneously Solving How and What”
Amos Beimel, Kobbi Nissim and Eran Omri · 2008
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“What Can We Learn Privately?”
Shiva Kasiviswanathan, Homin. Lee, Kobbi Nissim, Sofya Raskhodnikova and Adam. Smith · 2008
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“Secure Multiparty Computation for Privacy-Preserving Data Mining”, 2008
Yehuda Lindell and Benny Pinkas · 2008
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“Numerical linear algebra in the streaming model”
Kenneth Clarkson and David Woodruff · 2009
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“Computational Differential Privacy”
Ilya Mironov, Omkant Pandey, Omer Reingold and Salil Vadhan · 2009
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“Secure Two-Party Computation Is Practical”
Benny Pinkas, Thomas Schneider, Nigel. Smart and Stephen. Williams · 2009
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“How to generate and exchange secrets”
Andrew Chi-Chih Yao · 2009
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“Differential privacy under continual observation”
Cynthia Dwork, Moni Naor, Toniann Pitassi and Guy Rothblum · 2010
Cited alongside, same era.
“A multiplicative weights mechanism for privacy-preserving data analysis”
Moritz Hardt and Guy Rothblum · 2010
Cited alongside, same era.
“The Limits of Two-Party Differential Privacy”
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar and Salil. Vadhan · 2010
Cited alongside, same era.
“Privacy-preserving aggregation of time-series data”
Elaine Shi, HTH Chan, Eleanor Rieffel, Richard Chow and Dawn Song · 2011
Cited alongside, same era.
“Privacy-preserving stream aggregation with fault tolerance”
T-H Chan, Elaine Shi and Dawn Song · 2012
Cited alongside, same era.
“Optimal lower bound for differentially private multi-party aggregation”
TH Chan, Elaine Shi and Dawn Song · 2012
Cited alongside, same era.
“Heavy hitter estimation over set-valued data with local differential privacy”
Zhan Qin, Yin Yang, Ting Yu, Issa Khalil, Xiaokui Xiao and Kui Ren · 2016
Later among the works it cites.
“Practical secure aggregation for privacy-preserving machine learning”
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal and Karn Seth · 2017
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“Practical locally private heavy hitters”
Raef Bassily, Uri Stemmer and Abhradeep Thakurta · 2017
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“Prio: Private, Robust, and Scalable Computation of Aggregate Statistics.”
Henry Corrigan-Gibbs and Dan Boneh · 2017
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“Minimax Optimal Procedures for Locally Private Estimation”
John. Duchi, Michael. Jordan and Martin. Wainwright · 2017
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“Collecting Telemetry Data Privately”
Bolin Ding, Janardhan Kulkarni and Sergey Yekhanin · 2017
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“Distributed private heavy hitters”
Justin Hsu, Sanjeev Khanna and Aaron Roth · 2012
Cited alongside, same era.
“DJoin: Differentially Private Join Queries over Distributed Databases.”
Arjun Narayan and Andreas Haeberlen · 2012
Cited alongside, same era.
“Efficient Secure Two-Party Computation Using Symmetric Cut-and-Choose”
Yan Huang, Jonathan Katz and David Evans · 2013
Cited alongside, same era.
“The Algorithmic Foundations of Differential Privacy”
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
“RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response”
“’Ulfar Erlingsson, Vasyl Pihur and Aleksandra Korolova · 2014
Cited alongside, same era.
“Function secret sharing”
Elette Boyle, Niv Gilboa and Yuval Ishai · 2015
Cited alongside, same era.
Later among the works it cites.
“A comprehensive comparison of multiparty secure additions with differential privacy”
Slawomir Goryczka and Li Xiong · 2017
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“Splinter: Practical Private Queries on Public Data.”
Frank Wang, Catherine Yun, Shafi Goldwasser, Vinod Vaikuntanathan and Matei Zaharia · 2017
Later among the works it cites.
“Heavy hitters and the structure of local privacy”
Mark Bun, Jelani Nelson and Uri Stemmer · 2018
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“The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information”
John Duchi and Feng Ruan · 2018
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“Tight Lower Bounds for Locally Differentially Private Selection”
Jonathan Ullman · 2018
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“Function Secret Sharing (FSS) Library”, https://github.com/frankw2/libfss , 2018
Frank Wang · 2018
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“Distributed differential privacy via shuffling”
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber and Maxim Zhilyaev · 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
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“The role of interactivity in local differential privacy”
Matthew Joseph, Jieming Mao, Seth Neel and Aaron Roth · 2019
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“Advances and open problems in federated learning”
Peter Kairouz, H McMahan, Brendan Avent, Aur“’elien Bellet, Mehdi Bennis, Arjun Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode and Rachel Cummings · 2019
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“The Discrete Gaussian for Differential Privacy”
Cl“’ement Canonne, Gautam Kamath and Thomas Steinke · 2020
Closest in time.
“Exponential separations in local differential privacy”
Matthew Joseph, Jieming Mao and Aaron Roth · 2020
Closest in time.