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年至2021年的大流行造成了巨大的经济和社会后果，研究表明接触者追踪算法在病毒早期控制中至关重要。尽管在开发更有效的接触者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。接触者追踪算法的实质在于风险评分的通信。然而，正是这种向用户发送和发布评分的行为，使攻击者能够借此评估个人的私人健康状况。我们确定了一种现实的攻击场景，并提出了一种针对该攻击具备差分隐私保证的接触者追踪算法。该算法在两种最广泛使用的基于智能体的COVID-19模拟器上进行测试，并在多种设置下展现出优越性能。特别是在现实测试场景中，每次发布风险评分时采用ε=1的差分隐私，我们实现了病毒传播率两倍至十倍的降低。据我们所知，这是首个在披露COVID-19风险评分时提供差分隐私保证的接触者追踪算法。