Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Haokun Fang and Quan Qian
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Haokun Fang: School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China
Quan Qian: School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China

Future Internet, 2021, vol. 13, issue 4, 1-20

Abstract: 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.

Keywords: multi-party machine learning; privacy preserving machine learning; homomorphic encryption (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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