Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 11, Issue 3


Published
on


Pages

2119-2128


DOI

Article

A Privacy -Preserving Medical Data Sharing Framework: Techniques, Applications, and Challenges

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Authors

Mahesh Manchanda Affiliation:
Professor, Department of CSE (Computer sc), GEHU-Dehradun Campus


Abstract

To improve patient outcomes and advance medical research, it is crucial that healthcare data are shared securely and effectively. It is difficult to communicate data while retaining confidentiality, though, due to the delicate nature of healthcare data and worries about patient privacy. Sensitive patient data must be safeguarded while facilitating secure data sharing, which calls for a framework that protects privacy. For implementing such a system, various methods like differential privacy, secure multi-party computation, and homomorphic encryption have been suggested. It is necessary to handle issues including interoperability problems, legal and ethical dilemmas, and technical difficulties. In order to create a framework for sharing medical data while protecting privacy, this study suggests a method that combines homomorphic encryption and blockchain technology. The suggested method offers a safe and effective means of exchanging and storing encrypted healthcare data while preserving data integrity and privacy. A multidisciplinary strategy combining cooperation between healthcare providers, data scientists, privacy experts, and regulatory agencies is necessary for the development and implementation of a medical data sharing framework that protects patient privacy. A privacy-preserving framework for medical data sharing can promote effective and secure data exchange for better healthcare outcomes by addressing the issues and utilizing the tools at hand.


Keywords

healthcare, patient privacy, data security, blockchain, homomorphic encryption, safe multi-party computation, differential privacy, privacy-preserving medical data sharing


Citation

Manchanda, M. (2020). A privacy -preserving medical data sharing framework: Techniques, applications, and challenges. Turkish Journal of Computer and Mathematics Education, 11(3), 2119–2128. https://doi.org/10.17762/turcomat.v11i3.13609

Published by: Engineering Journals

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