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. 2023 Feb 1:199:87-97.
doi: 10.1016/j.comcom.2022.12.004. Epub 2022 Dec 14.

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Chandramohan Dhasarathan  1 , Mohammad Kamrul Hasan  2 , Shayla Islam  3 , Salwani Abdullah  2 , Umi Asma Mokhtar  2 , Abdul Rehman Javed  4   5 , Sam Goundar  6   7
Affiliations

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Chandramohan Dhasarathan et al. Comput Commun. .

Abstract

COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.

Keywords: Deep learning system; Healthcare; Homomorphic; Privacy metrics; Privacy preserving; Security.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Private data metrics using homomorphic enforcement for COVID-19 big data.
Fig. 2
Fig. 2
Deep learning-based privacy preserving system for a Health Care Management Model.
Fig. 3
Fig. 3
DPLS — A coordinator-specific Intra-cluster Information Updating system.
Fig. 4
Fig. 4
COVID patient analysis eligible to prevent personal information.
Fig. 5
Fig. 5
Homomorphic enforcement in critical scenario.
Fig. 6
Fig. 6
Opportunistic health data analysis and homomorphic metric for COVID patients.
Fig. 7
Fig. 7
Homomorphic opportunistic computing with eligible privacy metrics.
Fig. 8a
Fig. 8a
COVID-19 patient data analysis.
Fig. 8b
Fig. 8b
COVID-19 prediction.
Fig. 8c
Fig. 8c
COVID-19 health data metric privacy performance compared with other approaches.
See this image and copyright information in PMC

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