Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Improved summation from shuffling, http://arxiv.org/abs/1909.11225

Original

B. Balle, J. Bell, A. Gascón, and K. Nissim · 1909

Earlier work this paper cites.

Efficient noise-tolerant learning from statistical queries

M. Kearns · 1998

Earlier work this paper cites.

Cryptography from anonymity

Y. Ishai, E. Kushilevitz, R. Ostrovsky, and A. Sahai · 2006

Earlier work this paper cites.

Synopses for massive data: Samples, histograms, wavelets, sketches

G. Cormode, M. Garofalakis, P. J. Haas, C. Jermaine, et al · 2011

Earlier work this paper cites.

Secure multiparty aggregation with differential privacy: A comparative study

S. Goryczka, L. Xiong, and V. Sunderam · 2013

Earlier work this paper cites.

Sketching as a tool for numerical linear algebra

D. P. Woodruff et al · 2014

Earlier work this paper cites.

Deep learning with differential privacy

M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016

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Communication-efficient learning of deep networks from decentralized data

Original

H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2016

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Prochlo: Strong privacy for analytics in the crowd

A. Bittau, Ú. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnés, and B. Seefeld · 2017

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Practical secure aggregation for privacy-preserving machine learning

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017

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Federated learning: Collaborative machine learning without centralized training data

H. B. McMahan and D. Ramage · 2017

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Differentially Private Summation with Multi-Message Shuffling

Original

B. Balle, J. Bell, A. Gascon, and K. Nissim · 2019

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The privacy blanket of the shuffle model

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B. Balle, J. Bell, A. Gascón, and K. Nissim · 2019

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Distributed differential privacy via shuffling

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Private aggregation from fewer anonymous messages

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B. Ghazi, P. Manurangsi, R. Pagh, and A. Velingker · 2019

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