Dharavath, Ramesh, Dharavath Vinod, Kumara and Leb, Chi Hieu ORCID: https://orcid.org/0000-0002-5168-2297 (2025) Enhancing healthcare privacy: a synergistic approach with Federated Learning and Blockchain Integration. In: Intelligent Strategies for ICT Proceedings of ICTCS 2024. Lecture Notes in Networks and Systems (LNNS) - International Conference on Information and Communication Technology for Competitive Strategies, 5 (1320). Springer, Singapore, pp. 289-304. ISBN 978-9819641482 ISSN 2367-3370 (Print), 2367-3389 (Online) (doi:10.1007/978-981-96-4148-2_24)

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Abstract

Preserving privacy in healthcare data is crucial for maintaining patient trust, ensuring data security, and upholding ethical standards. Protecting sensitive medical information from unauthorized access mitigates risks such as identity theft and cybercrime. This paper explores a synergistic approach to safeguarding healthcare data through the integration of Federated Learning, Homomorphic Encryption, and Blockchain technology. Federated Learning enables collaborative model training across multiple healthcare institutions without sharing sensitive data, thereby maintaining patient confidentiality. Homomorphic Encryption complements this by allowing computations to be performed on encrypted data, ensuring privacy throughout the learning process. Blockchain technology further enhances this framework by providing a transparent and immutable ledger of data transactions, reinforcing the integrity and accountability of model updates. Together, these technologies offer a comprehensive solution to privacy challenges in health care, enabling secure data sharing and robust machine learning model development without compromising patient confidentiality. The experimental analysis conducted on the proposed methodology demonstrates its effectiveness and significance in preserving and improving the security of healthcare records in a more reliable and efficient manner.

Item Type:	Conference Proceedings
Title of Proceedings:	Intelligent Strategies for ICT Proceedings of ICTCS 2024
Additional Information:	This work is sponsored by the ISEA (Information Security Education and Awareness) Project, Phase III (Project No. MeitY/2024-2025/1106/CSE) sponsored by the Ministry of Electronics and Information Technology (MeitY), Govt. of India..* Please note that the name of the author on the publication is Chi Hieu LEB and not Chi Hieu LE as registered with UoG (email sent to author to confirm writing of this paper. Chi Hieu LEB does not exist on register. - MP
Uncontrolled Keywords:	privacy preservation, Federated Learning, Blockchain technology, health care, homomorphic encryption
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Related URLs:	Publisher
Last Modified:	20 Oct 2025 14:15
URI:	https://gala.gre.ac.uk/id/eprint/51255

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