Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

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 ju...

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Veröffentlicht in:	Future internet Jg. 13; H. 4; S. 94
Hauptverfasser:	Fang, Haokun, Qian, Quan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.04.2021
Schlagworte:	Accuracy Algorithms Data integrity Deep learning Efficiency Encryption Federated learning homomorphic encryption Internet Machine learning Metal fatigue multi-party machine learning Noise Privacy privacy preserving machine learning
ISSN:	1999-5903, 1999-5903
Online-Zugang:	Volltext
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