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Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves.
How to share a secret
Adi Shamir · 1979
Earlier work this paper cites.
The dining cryptographers problem: unconditional sender and recipient untraceability
David Chaum · 1988
Earlier work this paper cites.
Language modeling for soft keyboards
Joshua Goodman, Gina Venolia, Keith Steury, and Chauncey Parker · 2002
Earlier work this paper cites.
Aegis: architecture for tamper-evident and tamper-resistant processing
G Edward Suh, Dwaine Clarke, Blaise Gassend, Marten Van Dijk, and Srinivas Devadas · 2003
Earlier work this paper cites.
Dining cryptographers revisited
Philippe Golle and Ari Juels · 2004
Earlier work this paper cites.
Differentially private aggregation of distributed time-series with transformation and encryption
Vibhor Rastogi and Suman Nath · 2010
Earlier work this paper cites.
I have a DREAM! (DiffeRentially privatE smArt Metering)
Gergely Ács and Claude Castelluccia · 2011
Cited alongside, same era.
Proactively accountable anonymous messaging in verdict
Henry Corrigan-Gibbs, David Isaac Wolinsky, and Bryan Ford · 2013
Cited alongside, same era.
Sanctum: Minimal hardware extensions for strong software isolation
Victor Costan, Ilia Lebedev, and Srinivas Devadas · 2015
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Cited alongside, same era.
A comprehensive comparison of multiparty secure additions with differential privacy
Slawomir Goryczka and Li Xiong · 2015
Cited alongside, same era.
Riffle: An efficient communication system with strong anonymity
Young Hyun Kwon · 2015
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Later among the works it cites.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Closest in time.
Revisiting distributed synchronous sgd
Jianmin Chen, Rajat Monga, Samy Bengio, and Rafal Jozefowicz · 2016
Closest in time.
Intel SGX explained
Victor Costan and Srinivas Devadas · 2016
Closest in time.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Closest in time.
Communication-efficient learning of deep networks from decentralized data
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Cited alongside, same era.
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
Closest in time.