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Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

Mantey, Eric Appiah; Zhou, Conghua; Mani, Vinodhini; Arthur, John Kingsley; Ibeke, Ebuka

Authors

Eric Appiah Mantey

Conghua Zhou

Vinodhini Mani

John Kingsley Arthur



Abstract

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing.

Citation

MANTEY, E.A., ZHOU, C., MANI, V., ARTHUR, J.K. and IBEKE, E. 2022. Maintaining privacy for a recommender system diagnosis using blockchain and deep learning. Human-centric computing and information science [online], (accepted).

Journal Article Type Article
Acceptance Date Apr 17, 2022
Deposit Date Apr 19, 2022
Publicly Available Date Apr 19, 2022
Journal Human-centric computing and information sciences
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Keywords Blockchain; AI; Recommender systems; Patients; Health records; Secured data; COVID-19
Public URL https://rgu-repository.worktribe.com/output/1645830

Files

MANTEY 2022 Maintaining privacy (AAM) (1.1 Mb)
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc/3.0/

Copyright Statement
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.





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