Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Future Internet

Haokun Fang¹  Quan Qian¹ 

[1] School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China;

关键词: multi-party machine learning;    privacy preserving machine learning;    homomorphic encryption;   

DOI  :  10.3390/fi13040094

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown