Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3660
Title: A System For Storing Anonymous Patient Healthcare Data Using Blockchain And Its Applications
Authors: Öksüz, Özgür
Keywords: blockchain
machine learning
anonymity
unlinkability
COVID-19
privacy
medical Records
security
Issue Date: 2022
Publisher: Oxford Univ Press
Abstract: In this paper, a system is proposed which uses blockchain technology in healthcare. In this system, patients can access their health records anytime from anywhere. Moreover, the patients' health records are put into the blockchain anonymously. Whenever a patient visits a healthcare professional, the authorized entity filters patients' medical report out by eliminating the patients' sensitive information. Then, the filtered medical data are put into an off-chain database, while the address of the data is put into the blockchain with an assigned pseudo random identity of the patient. Thus, there are multi pseudo random identities for each patient. Unlike previous studies where the patients' identities/reports were linkable, in the proposed protocol the patients' identities are not linkable. The proposed system can also be used to show patients' health status to some entities when a pandemic happens (e.g. COVID-19). During the COVID-19 pandemic, the patients are required to show their series of vaccinations before they travel internationally/nationally or participate in some social events. To travel or join some events, the patient needs to show only a partial medical history to the security guard without leaking any private information. Furthermore, once the anonymous medical data are put into the off-chain database, the data can be used for data mining and machine learning.
Description: Article; Early Access
URI: https://doi.org/10.1093/comjnl/bxac155
https://hdl.handle.net/20.500.13091/3660
ISSN: 0010-4620
1460-2067
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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