  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.
