Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Contents
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 homomor...
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Full title
Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
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TN_cdi_doaj_primary_oai_doaj_org_article_d3c463937dbe41f6adac75d1d5756dbf
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d3c463937dbe41f6adac75d1d5756dbf
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ISSN
1999-5903
E-ISSN
1999-5903
DOI
10.3390/fi13040094
