Exploring the future of privacy-preserving heart disease prediction: a fully homomorphic encryption-driven logistic regression approach
Abstract Homomorphic Encryption (HE) offers a revolutionary cryptographic approach to safeguarding privacy in machine learning (ML), especially in processing sensitive healthcare data. This study aims to address the critical issue of privacy-preserving heart disease prediction by developing a novel...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-02-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01098-6 |
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