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

Whaiduzzaman, Md. Razon Hossain+5 more|arXiv (Cornell University)|Jun 23, 2020

COVID-19 Digital Contact Tracing26 references4 citations

TL;DR

This paper proposes PPMF, a privacy-preserving mobile and fog computing framework for tracing and preventing COVID-19 community transmission. It uses encrypted temporary identifiers via Bluetooth Low Energy, decentralized fog nodes (ARC and SUDUN), and user-controlled data handling to enable real-time risk detection and contact tracing without exposing user identities or locations, achieving full privacy compliance and voluntary participation with data deletion in 14 days.

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.

Motivation & Objective

• To address privacy violations in existing contact tracing apps that collect excessive personal data and lack transparency.
• To design an integrated mobile and fog computing framework that enables real-time detection of infected individuals in public spaces while preserving user anonymity.
• To ensure user control over data, including voluntary participation, minimal data collection, and the ability to delete data within 14 days.
• To develop a scalable, e-government-ready system that supports economic continuity by enabling targeted isolation instead of mass lockdowns.
• To provide a transparent, open-source, and compliant framework that prevents misuse of sensitive health and location data.

Proposed method

• Users register with minimal data (mobile number, postal code, age group) and receive a Unique Encrypted Reference Code (UERC) stored locally.
• Mobile devices broadcast Rotational Unique Encrypted Reference Codes (RUERC) via Bluetooth Low Energy (BLE) for anonymous proximity detection.
• Automatic Risk Checkers (ARCs) are deployed at building entrances to detect nearby confirmed or suspected cases and issue anonymous alerts without revealing identities.
• Suspected or infected users upload contact logs via Suspected User Data Uploader Nodes (SUDUNs) at health centers to a central cloud application.
• The central application maps infection clusters using postal codes and age groups, enabling super-spreader detection and visualization without storing raw location data.
• The system enforces privacy through voluntary use, minimal data collection, data destruction on request within 14 days, and open-source transparency.

Experimental results

Research questions

• RQ1 What are the key privacy and technical issues in existing mobile contact tracing applications?
• RQ2 How can mobile and fog computing be integrated to enable real-time, privacy-preserving contact tracing in public spaces?
• RQ3 How can a scalable, e-government framework be designed to ensure user consent, data minimization, and transparency while enabling effective outbreak containment?
• RQ4 What role do fog nodes (ARC and SUDUN) play in enhancing privacy and system responsiveness compared to cloud-only or mobile-only solutions?
• RQ5 How can data be collected, processed, and deleted in a way that ensures compliance with privacy regulations and user trust?

Key findings

• The PPMF framework supports full user data privacy by eliminating the collection of individual location data and using temporary, rotating encrypted identifiers.
• Users can delete their data from the system within 14 days, a significant improvement over systems like COVIDSafe that require waiting until the pandemic ends.
• The ARC fog nodes enable immediate, anonymous risk alerts in public buildings without revealing the identity of infected individuals.
• The SUDUN infrastructure allows health centers to securely upload test results and contact logs, enabling cluster mapping by postal code and age group.
• The framework achieves full compliance with privacy principles: voluntary participation, minimal data collection, data usage limitation, transparency, and user control.
• Compared to existing solutions, PPMF uniquely supports all key privacy-preserving features—voluntary use, data minimization, destruction, transparency, and fog-based risk detection—without compromising functionality.