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Article

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

by 1,† and 1,2,*
1
School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China
2
Materials Genome Institute, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Current address: NO.99 Shangda Road, BaoShan District, Shanghai 200444, China.
Academic Editor: Francesco Buccafurri
Future Internet 2021, 13(4), 94; https://doi.org/10.3390/fi13040094
Received: 2 March 2021 / Revised: 24 March 2021 / Accepted: 28 March 2021 / Published: 8 April 2021
(This article belongs to the Section Cybersecurity)
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper. View Full-Text
Keywords: multi-party machine learning; privacy preserving machine learning; homomorphic encryption multi-party machine learning; privacy preserving machine learning; homomorphic encryption
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MDPI and ACS Style

Fang, H.; Qian, Q. Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning. Future Internet 2021, 13, 94. https://doi.org/10.3390/fi13040094

AMA Style

Fang H, Qian Q. Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning. Future Internet. 2021; 13(4):94. https://doi.org/10.3390/fi13040094

Chicago/Turabian Style

Fang, Haokun, and Quan Qian. 2021. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning" Future Internet 13, no. 4: 94. https://doi.org/10.3390/fi13040094

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