Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption
Homomorphic encryption (HE) is one of promising cryptographic candidates resolving privacy issues in machine learning on sensitive data such as biomedical data and financial data. However, HE-based solutions commonly suffer from relatively high computational costs due to a large number of iterations...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8444365/ |