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

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Pubblicato in:	Journal of Big Data vol. 12, no. 1 (Feb 2025), p. 52
Pubblicazione:	Springer Nature B.V.
Soggetti:	Encryption Accuracy Parallel processing Datasets Regression analysis Support vector machines Health care Privacy Polynomials Cardiovascular disease Machine learning Heart diseases Big Data Data processing Extraction Health services Disease Poisoning Cardiovascular diseases Data encryption Libraries Predictions Evasion Frame analysis Cryptography Tense Machinery Inference
Accesso online:	Citation/Abstract Full Text - PDF
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