AI Insight
This paper presents a contact tracing algorithm for COVID-19 that incorporates differential privacy protections when communicating risk scores to users. The researchers identified a vulnerability where adversaries could infer individuals' health status from risk score communications and developed a solution that provides privacy guarantees while maintaining effectiveness. Testing on two major COVID-19 agent-based simulators showed the algorithm could reduce infection rates by 2-10 times compared to baseline scenarios while providing epsilon=1 differential privacy protection for risk score releases.
Why it matters
Contact tracing was crucial during the pandemic but faced significant privacy concerns that hindered adoption. This work demonstrates that effective disease containment and strong privacy protections are not mutually exclusive, potentially enabling broader public acceptance and deployment of contact tracing systems in future health emergencies.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. While large strides have been made towards more effective contact tracing algorithms, we argue that privacy concerns currently hold deployment back. The essence of a contact tracing algorithm constitutes the communication of a risk score. Yet, it is precisely the communication and release of this score to a user that an adversary can leverage to gauge the private health status of an individual. We pinpoint a realistic attack scenario and propose a contact tracing algorithm with differential privacy guarantees against this attack. The algorithm is tested on the two most widely used agent-based COVID19 simulators and demonstrates superior performance in a wide range of settings. Especially for realistic test scenarios and while releasing each risk score with epsilon=1 differential privacy, we achieve a two to ten-fold reduction in the infection rate of the virus. To the best of our knowledge, this presents the first contact tracing algorithm with differential privacy guarantees when revealing risk scores for COVID19.
Source: Protect Your Score: Contact Tracing With Differential Privacy Guarantees