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This work studies differential privacy in the context of the recently proposed shuffle model.
Lectures on the coupling method
Torgny Lindvall · 2002
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Probability theory: The logic of science
Edwin T Jaynes · 2003
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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Cryptography from anonymity
Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky, and Amit Sahai · 2006
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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 Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2008
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Probabilistic relational reasoning for differential privacy
Gilles Barthe, Boris Köpf, Federico Olmedo, and Santiago Zanella Béguelin · 2012
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Optimal lower bound for differentially private multi-party aggregation
T.-H. Hubert Chan, Elaine Shi, and Dawn Song · 2012
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Beyond differential privacy: Composition theorems and relational logic for f-divergences between probabilistic programs
Gilles Barthe and Federico Olmedo · 2013
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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RAPPOR: randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Proving differential privacy via probabilistic couplings
Gilles Barthe, Marco Gaboardi, Benjamin Grégoire, Justin Hsu, and Pierre-Yves Strub · 2016
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Discrete distribution estimation under local privacy
Peter Kairouz, Keith Bonawitz, and Daniel Ramage · 2016
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Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2016
Learning with privacy at scale
Apple’s Differential Privacy Team · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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Protection Against Reconstruction and Its Applications in Private Federated Learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
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Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
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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
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Distributed differential privacy via shuffling
Albert Cheu, Adam D. Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
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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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The role of interactivity in local differential privacy
Matthew Joseph, Jieming Mao, Seth Neel, and Aaron Roth · 2019
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