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Checking Contact Tracing App Implementations with Bespoke Static Analysis.

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Contact tracing apps, developed rapidly during the COVID-19 pandemic, show functional issues impacting user safety. Our analysis of Android apps using the Google/Apple Exposure Notification API highlights these critical security and privacy concerns.

Keywords:
AndroidCOVID-19Contact tracingMonSTERStatic analysis

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Area of Science:

  • Digital health technologies
  • Mobile application security
  • Epidemiological surveillance tools

Background:

  • The COVID-19 pandemic spurred rapid development of digital contact tracing apps.
  • These apps utilize frameworks like the Google/Apple Exposure Notification API for proximity detection.
  • Accelerated development cycles may compromise app functionality, privacy, and security.

Purpose of the Study:

  • To develop and apply a methodology for evaluating Android contact tracing apps.
  • Assess the functionality, privacy, and security of apps built on the Google/Apple Exposure Notification API.
  • Identify potential risks to user safety stemming from app implementation flaws.

Main Methods:

  • A three-pronged evaluation approach was employed.
  • Included manual analysis, general static analysis, and bespoke static analysis using the MonSTER tool.
  • Focused on Android applications leveraging the Google/Apple Exposure Notification API.

Main Results:

  • Most analyzed apps met baseline Google/Apple standards.
  • Identified specific functional issues in several applications.
  • These issues pose potential risks to user safety and data integrity.

Conclusions:

  • While foundational standards are often met, functional vulnerabilities persist in some contact tracing apps.
  • The developed methodology effectively identifies critical issues in app implementation.
  • Further rigorous testing is essential to ensure the safety and reliability of digital contact tracing tools.