ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare

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Title: ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare
Authors: Braeken, AnPorambage, PawaniPuvaneswaran, AmirthanLiyanage, Madhusanka
Permanent link: http://hdl.handle.net/10197/12098
Date: 26-Nov-2020
Online since: 2021-04-15T15:40:44Z
Abstract: The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.
Funding Details: Enterprise Ireland
European Commission
Irish Research Council
Science Foundation Ireland
University College Dublin
Funding Details: Business Finland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2020 IEEE
Keywords: Wireless communicationCloud computingAuthenticationComputer architectureMedical servicesSecurityAugmented reality
DOI: 10.1109/cloudtech49835.2020.9365909
Other versions: http://macc.ma/cloudtech20/
Language: en
Status of Item: Peer reviewed
Is part of: Essaaidi, M., Zbakh, M. and Ouacha, A. (eds.) Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech’20)
Conference Details: The 5th International Conference on Cloud Computing and Artificial Intelligence (CloudTech 2020), Marrakesh, Morocco (held online due to Coronavirus outbreak), 24-26 November 2020
ISBN: 978-1-7281-6175-4
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection

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