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Enhancing Medical Data Security on Public Cloud

Santos, Nelson, Younis, Waleed, Ghita, Bogdan, Masala, Giovanni Luca (2021) Enhancing Medical Data Security on Public Cloud. In: Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE ISBN 978-1-6654-0285-9. (doi:10.1109/CSR51186.2021.9527987) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:114274)

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https://doi.org/10.1109/CSR51186.2021.9527987

Abstract

Cloud computing, supported by advancements in virtualisation and distributed computing, became the default options for implementing the IT infrastructure of organisations. Medical data and in particular medical images have increasing storage space and remote access requirements. Cloud computing satisfies these requirements but unclear safeguards on data security can expose sensitive data to possible attacks. Furthermore, recent changes in legislation imposed additional security constraints in technology to ensure the privacy of individuals and the integrity of data when stored in the cloud. In contrast with this trend, current data security methods, based on encryption, create an additional overhead to the performance, and often they are not allowed in public cloud servers. Hence, this paper proposes a mechanism that combines data fragmentation to protect medical images on the public cloud servers, and a NoSQL database to secure an efficient organisation of such data. Results of this paper indicate that the latency of the proposed method is significantly lower if compared with AES, one of the most adopted data encryption mechanisms. Therefore, the proposed method is an optimal trade-off in environments with low latency requirements or limited resources.

Item Type: Conference proceeding
DOI/Identification number: 10.1109/CSR51186.2021.9527987
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Institutional Unit: Schools > School of Computing
Former Institutional Unit:
There are no former institutional units.
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Giovanni Masala
Date Deposited: 01 May 2026 10:07 UTC
Last Modified: 05 May 2026 15:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/114274 (The current URI for this page, for reference purposes)

University of Kent Author Information

Masala, Giovanni Luca.

Creator's ORCID: https://orcid.org/0000-0001-6734-9424
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