Application of privacy protection technology to healthcare big data

Abstract

Abstract With the emergence of the 4th industrial revolution, demand for technologies that process and analyze big data in the healthcare has increased. As research is actively conducted, problems related to the protection of personal information included in healthcare data are being raised. We investigated privacy protection technology and their limitations applied to healthcare big data over the last decade to find solutions to these problems. For 4 technologies, blockchain, federated learning, differential privacy, and homomorphic encryption, we reviewed 10 studies each and summarized the used data, key findings, and limitations. It is necessary to establish a research environment that can utilize healthcare data, including sensitive personal and medical information of patients, more safely and widely by grasping the cases and current status of privacy protection technology. We aim to provide opinions on future research directions and insights of privacy protection technology to relevant researchers through this study.

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