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The central question studied in this paper is Renyi Differential Privacy (RDP) guarantees for general discrete local mechanisms in the shuffle privacy model.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
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Calibrating noise to sensitivity in private data analysis
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
Ninghui Li, Wahbeh Qardaji, and Dong Su · 2012
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Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
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
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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High dimensional statistics
Phillippe Rigollet and Jan-Christian Hütter · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2020
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Private summation in the multi-message shuffle model
Borja Balle, James Bell, Adria Gascón, and Kobbi Nissim · 2020
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Differentially private summation with multi-message shuffling
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 2019
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Improved summation from shuffling
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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
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On the power of multiple anonymous messages
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The discrete gaussian for differential privacy
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Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta · 2020
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2020
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Three variants of differential privacy: Lossless conversion and applications
Shahab Asoodeh, Jiachun Liao, Flavio P Calmon, Oliver Kosut, and Lalitha Sankar · 2021
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Shuffled model of federated learning: Privacy, accuracy and communication trade-offs
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
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