Enhancing Security and Privacy in Cloud – Based Healthcare Data Through Machine Learning DOI
Aasheesh Shukla, Hemant Singh Pokhariya, Jacob J. Michaelson

et al.

Published: Dec. 29, 2023

It is becoming more and important for healthcare providers to protect the integrity security of sensitive medical data as they use cloud computing processing storage. This work explores field machine learning algorithms that are secure privacy-preserving when applied information in environments. We investigate sophisticated cryptography, federated learning, differentiating privacy techniques using an interpretive philosophy a method based on deduction. Our results highlight computational expense associated with cryptographic protocols, while also revealing their nuanced performance potential enabling calculations. Federated shown be effective collaborative model training, providing workable approach analysis over-dispersed datasets. Differential systems require careful parameter calibration because demonstrate delicate balance between value preservation.

Language: Английский

Enhancing Security and Privacy in Cloud – Based Healthcare Data Through Machine Learning DOI
Aasheesh Shukla, Hemant Singh Pokhariya, Jacob J. Michaelson

et al.

Published: Dec. 29, 2023

It is becoming more and important for healthcare providers to protect the integrity security of sensitive medical data as they use cloud computing processing storage. This work explores field machine learning algorithms that are secure privacy-preserving when applied information in environments. We investigate sophisticated cryptography, federated learning, differentiating privacy techniques using an interpretive philosophy a method based on deduction. Our results highlight computational expense associated with cryptographic protocols, while also revealing their nuanced performance potential enabling calculations. Federated shown be effective collaborative model training, providing workable approach analysis over-dispersed datasets. Differential systems require careful parameter calibration because demonstrate delicate balance between value preservation.

Language: Английский

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