Open Access

ARTICLE

Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

by Akashdeep Bhardwaj¹, Ahmed A. Mohamed^2,3,*, Manoj Kumar¹, Mohammed Alshehri⁴, Ahed Abugabah⁵

1 School of Computer Science, University of Petroleum & Energy Studies (UPES), Dehradun, 248007, India
2 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia
3 Department of Information Technology, Faculty of Computer and Information, Assiut University, Assiut, 71515, Egypt
4 Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia
5 Department of Information System, College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates

* Corresponding Author: Ahmed A. Mohamed. Email:

(This article belongs to the Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)

Computers, Materials & Continua 2022, 70(1), 1017-1032. https://doi.org/10.32604/cmc.2022.018736

Received 19 March 2021; Accepted 14 May 2021; Issue published 07 September 2021

Abstract

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual’s location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual’s PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

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Keywords

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