Implementing Privacy-Preserving Machine Learning Models for Secure Healthcare Data Sharing

Daniel Ke Siegfried

Implementing Privacy-Preserving Machine Learning Models for Secure Healthcare Data Sharing

Authors

Daniel Ke Siegfried

Privacy-Preserving Machine Learning Engineer, USA.

Keywords:

privacy-preserving machine learning, federated learning, healthcare data, differential privacy, data security, homomorphic encryption

Synopsis

The digitization of healthcare has resulted in the exponential growth of sensitive patient data, presenting significant privacy and security challenges. Machine Learning (ML) has demonstrated immense potential in healthcare for disease prediction, personalized treatment, and resource optimization. However, conventional ML techniques often require centralized data access, posing risks of data leakage and regulatory non-compliance. In response, privacy-preserving machine learning (PPML) models have emerged as promising tools for secure data sharing. This paper evaluates current PPML techniques—such as Federated Learning, Homomorphic Encryption, and Differential Privacy—and explores their implementation in real-world healthcare settings. We identify technological bottlenecks, examine integration strategies, and highlight future research directions for secure, ethical AI deployment in healthcare.

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