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.

翻译：2020年和2021年的疫情带来了巨大的经济和社会后果，研究表明接触追踪算法在病毒早期遏制中可能发挥关键作用。尽管在更有效的接触追踪算法方面已取得重大进展，但我们认为隐私问题目前阻碍了其部署。接触追踪算法的本质在于风险得分的通信。然而，正是这种向用户通信和发布得分的行为，使得攻击者能够利用它来评估个体的私人健康状况。我们确定了一种现实的攻击场景，并提出了一种针对这种攻击具有差分隐私保证的接触追踪算法。该算法在两个最广泛使用的基于智能体的COVID19模拟器上进行了测试，并在多种设置下展现出优越的性能。特别是在现实测试场景中，当以epsilon=1的差分隐私发布每个风险得分时，我们实现了病毒感染率的两到十倍降低。据我们所知，这是首个在揭示COVID19风险得分时提供差分隐私保证的接触追踪算法。