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To slow down the spread of COVID-19, governments around the world are tryingto identify infected people and to contain the virus by enforcing isolation andquarantine. However, it is difficult to trace people who came into contact withan infected person, which causes widespread community transmission and massinfection. To address this problem, we develop an e-government PrivacyPreserving Mobile and Fog computing framework entitled PPMF that can traceinfected and suspected cases nationwide. We use personal mobile devices withcontact tracing app and two types of stationary fog nodes, named Automatic RiskCheckers (ARC) and Suspected User Data Uploader Node (SUDUN), to tracecommunity transmission alongside maintaining user data privacy. Each user'smobile device receives a Unique Encrypted Reference Code (UERC) whenregistering on the central application. The mobile device and the centralapplication both generate Rotational Unique Encrypted Reference Code (RUERC),which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs areplaced at the entry points of buildings, which can immediately detect if thereare positive or suspected cases nearby. If any confirmed case is found, theARCs broadcast pre-cautionary messages to nearby people without revealing theidentity of the infected person. The SUDUNs are placed at the health centersthat report test results to the central cloud application. The reported data islater used to map between infected and suspected cases. Therefore, using ourproposed PPMF framework, governments can let organizations continue theireconomic activities without complete lockdown.

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