Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges
Thien Duc Nguyen, Markus Miettinen, Alexandra Dmitrienko and, Ahmad-Reza Sadeghi, Ivan Visconti

TL;DR
This paper systematically analyzes digital contact tracing solutions for COVID-19, highlighting security and privacy issues in current systems like GAEN, and proposes TRACECORONA as a more secure alternative validated through implementation and testing.
Contribution
It provides a comprehensive categorization and evaluation of DCT solutions, identifies security flaws in GAEN, and introduces TRACECORONA, a more secure contact tracing approach based on Diffie-Hellman.
Findings
GAEN has significant security and privacy vulnerabilities.
TRACECORONA outperforms existing solutions in security requirements.
Beta version of TRACECORONA was successfully tested by over 2000 users.
Abstract
The COVID-19 pandemic has caused many countries to deploy novel digital contact tracing (DCT) systems to boost the efficiency of manual tracing of infection chains. In this paper, we systematically analyze DCT solutions and categorize them based on their design approaches and architectures. We analyze them with regard to effectiveness, security, privacy, and ethical aspects and compare prominent solutions with regard to these requirements. In particular, we discuss the shortcomings of the Google and Apple Exposure Notification API (GAEN) that is currently widely adopted all over the world. We find that the security and privacy of GAEN have considerable deficiencies as it can be compromised by severe, large-scale attacks. We also discuss other proposed approaches for contact tracing, including our proposal TRACECORONA, that are based on Diffie-Hellman (DH) key exchange and aim at…
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Taxonomy
TopicsCOVID-19 Digital Contact Tracing · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection
