Privacy-Preserving Machine Learning Models for Medical Data Ensuring Security in Smart Healthcare Systems

Abstract

Machine Learning (ML) is being adopted in the healthcare industry across diagnostics, treatment options, and analysis of patient information. The management of data increases risks to the patient's private information. This chapter explores the theoretical frameworks and consequences of privacy-enhancing ML methods, namely, differential privacy, Federated Learning (FL), and homomorphic encryption, to apply medical data for meaningful analysis while maintaining the non-disclosure of patient information. Differential privacy adds noise control to minimize identifying data corresponding to an individual. FL allows the training of models while avoiding data centralization, and homomorphic encryption will enable computations on top of encrypted data to guarantee data safety during processing. We also explain how blockchain could improve these privacy-preserving approaches; it can provide safe data sharing, models, and audit and accountability of model management.

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