Exploring the future of privacy-preserving heart disease prediction: a fully homomorphic encryption-driven logistic regression approach

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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Bibliographic Details
Main Authors: Vankamamidi S. Naresh, Sivaranjani Reddi
Format: Article
Language:English
Published: SpringerOpen 2025-02-01
Series:Journal of Big Data
Subjects:
CKKS
Gradient Descent
Healthcare
Heart disease prediction
Homomorphic Encryption
Logistic Regression
Online Access:https://doi.org/10.1186/s40537-025-01098-6

Internet

https://doi.org/10.1186/s40537-025-01098-6

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