Secure Health Data Management via Federated Learning and Privacy-Aware Large Language Models

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Author(s): R. Felista Lizy (Department of Computer Science, A.P.C. Mahalaxmi College for Women, Thoothukudi, India), Amit Kumar Tyagi (School of Law, Forensic Justice and Policy Studies, National Forensic Sciences University, Gandhinagar, India), K. R. Pundareeka Vittala (ICFAI Foundation for Higher Education (Deemed to be University), Bengaluru, India)and Smirty Prasad (Christ University, Bengaluru, India)
Copyright: 2027
Pages: 24
Source title: Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), George Pappas (Lawrence Technological University, USA)and Sandeep Thota (Oracle Inc., USA)
DOI: 10.4018/979-8-3373-7426-0.ch011

Keywords: Health Information Systems / Information Security & Privacy / Medical Information Science Reference / Security & Forensics

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Abstract

Healthcare has moved today with rapid digital transformation, leading to the generation of vast volumes of sensitive patient data that are stored and analyzed on heterogeneous platforms. Although improved machine learning and large language models (LLMs) can bring a radical change in clinical decision support, diagnostics, and operational efficiency, they also raise serious issues concerning data privacy, security, regulatory adherence, and ethical governance. Conventional centralized data analytics methods are becoming increasingly infeasible when it comes to serious health privacy regulations and the need to build trust among the population. This chapter presents an in-depth discussion of secure health data management systems that combine federated learning (FL) with privacy-conscious large language models. It explores architectural concepts, threat analysis, privacy preservation mechanisms and system design strategies that can facilitate collaborative intelligence without affecting patient privacy.

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Secure Health Data Management via Federated Learning and Privacy-Aware Large Language Models

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