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The application of secure multiparty computation (MPC) in machine learning, especially privacy-preserving neural network training, has attracted tremendous attention from the research community in recent years.
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Ronald Cramer, Ivan Bjerre Damgård, and Jesper Buus Nielsen. 2015 · 2015
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Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 19–38
Payman Mohassel and Yupeng Zhang. 2017 · 2017
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SPDZ2k: efficient MPC mod 2 k 2^{k} for dishonest majority. In Annual international cryptology conference
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{ \{ GAZELLE } \} : A low latency framework for secure neural network inference. In 27th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 18) . 1651–1669
Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. 2018 · 2018
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CV Horsen. 2016 · 2016
Cited alongside, same era.
MASCOT: faster malicious arithmetic secure computation with oblivious transfer. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . 830–842
Marcel Keller, Emmanuela Orsini, and Peter Scholl. 2016 · 2016
Cited alongside, same era.
Carmit Hazay, Gert Læssøe Mikkelsen, Tal Rabin, Tomas Toft, and Angelo Agatino Nicolosi. 2019 · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Securenn: 3-party secure computation for neural network training
Sameer Wagh, Divya Gupta, and Nishanth Chandran. 2019 · 2019
Later among the works it cites.