Enhancing Security and Privacy in Cloud – Based Healthcare Data Through Machine Learning
Aasheesh Shukla, Hemant Singh Pokhariya, Jacob Michaelson, Arun Pratap Srivastava, Laxmi Narayanamma, Amit Srivastava · 2023
It is becoming more and more important for healthcare providers to protect the integrity and security of sensitive medical data as they use cloud computing for data processing and storage. This work explores the field of machine learning algorithms that are secure and privacy-preserving when applied to healthcare information in cloud environments. We investigate sophisticated cryptography, federated learning, and differentiating privacy techniques using an interpretive philosophy and a method based on deduction. Our results highlight the computational expense associated with cryptographic protocols, while also revealing their nuanced performance and potential for enabling secure calculations. Federated learning is shown to be effective in collaborative model training, providing a workable approach to privacy-preserving data analysis over-dispersed healthcare datasets. Differential privacy systems require careful parameter calibration because they demonstrate a delicate balance between data value and privacy preservation.