About: A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission

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About: A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission
Creator	Barros, Buyya, Alistair Hossain, Rahman Laszka, Rajkumar Roy, Aron Shovon, Shanto Whaiduzzaman, Md
source	ArXiv
abstract	To slow down the spread of COVID-19, governments around the world are trying to identify infected people and to contain the virus by enforcing isolation and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission and mass infection. To address this problem, we develop an e-government Privacy Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named Automatic Risk Checkers (ARC) and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. If any confirmed case is found, the ARCs broadcast pre-cautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.
has issue date	2020-06-23(xsd:dateTime)
has license	arxiv
sha1sum (hex)	348b9019a84020c034ca2060fd9b761edfdd1d2b
resource representing a document's title	A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission
resource representing a document's body	covid:348b9019a84020c034ca2060fd9b761edfdd1d2b#body_text
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