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With the increasing awareness of privacy protection and data fragmentation problem, federated learning has been emerging as a new paradigm of machine learning.
Modular multiplication without trial division
Peter L Montgomery · 1985
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Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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Privacy-preserving bayesian network structure computation on distributed heterogeneous data
Rebecca N Wright and Zhiqiang Yang · 2004
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Secure two-party k-means clustering
Paul Bunn and Rafail Ostrovsky · 2007
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An RSA encryption hardware algorithm using a single DSP block and a single block RAM on the FPGA
Bo Song, Kensuke Kawakami, Koji Nakano, and Yasuaki Ito · 2010
Earlier work this paper cites.
New hardware architectures for montgomery modular multiplication algorithm
Miaoqing Huang, Kris Gaj, and Tarek A. El-Ghazawi · 2011
Earlier work this paper cites.
Large-scale reconfigurable computing in a microsoft datacenter
Andrew Putnam · 2014
Earlier work this paper cites.
Improving the computational efficiency of modular operations for embedded systems
Ismail San and Nuray At · 2014
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
Cited alongside, same era.
Privacy-preserving deep learning via additively homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al · 2017
Cited alongside, same era.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
Cited alongside, same era.
A secure face-verification scheme based on homomorphic encryption and deep neural networks
Yukun Ma, Lifang Wu, Xiaofeng Gu, Jiaoyu He, and Zhou Yang · 2017
Cited alongside, same era.
Secure federated transfer learning
Yang Liu, Tianjian Chen, and Qiang Yang · 2018
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Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang · 2019
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Federated ai ecosystem. https://fate.fedai.org/, 2019
2019
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Privacy accounting and quality control in the sage differentially private ml platform
Mathias L‘ecuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, and Daniel Hsu · 2019
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Privacy-preserving reinforcement learning design for patient-centric dynamic treatment regimes
Ximeng Liu, Robert Deng, Kim-Kwang Raymond Choo, and Yang Yang · 2019
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Later among the works it cites.