Securing healthcare insights: A deep dive into privacy preserving federated learning
Abstract
Healthcare is being transformed by a pattern shift toward data-driven approach to improve patient care, prediction of diseases, discovery of drugs, and management of healthcare. In light of these dual imperatives, privacy-preserving federated learning is a recent disruptive solution for the training of ML models on sensitive data. This paper will offer some necessary contextual background information: primer on core methodologies in privacy protection, such as differential privacy, homomorphic cryptography, secure multi-party computing, and data anonymization. Following this, the next concept, federated learning, is presented as a decentralized approach to machine learning with specific ideal characteristics in healthcare environments [1] . The paper also deals with some assessment metrics for estimating the effectiveness and privacy guarantees of the privacy-preserving federated learning models. It recognizes the challenges and impossibilities of implementing these techniques but underlines the need for care in ethical considerations and compliance with healthcare data regulation such as HIPAA and GDPR.
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