论文标题
包含：流行病的面向隐私的联系跟踪协议
CONTAIN: Privacy-oriented Contact Tracing Protocols for Epidemics
论文作者
论文摘要
Covid-19，Sars-Cov2和埃博拉病毒等大流行和流行病已经传播到多个国家，并感染了数千人。这些疾病主要通过人与人之间的联系传播。卫生保健当局建议联系追踪程序，以防止扩散到广泛的人口。尽管已经开发了几个移动应用程序来追踪联系人，但它们通常需要收集隐私侵入性信息，例如GPS位置，以及在第三方服务器上对隐私敏感数据的记录，或者需要其他基础架构，例如具有已知位置的WiFi AP。在本文中，我们介绍了一个面向隐私的移动联系跟踪应用程序，该应用程序不依赖GPS或任何其他形式的基于基础架构的位置传感，也不依赖于服务器上任何其他个人身份信息的连续记录。包含的目的是允许用户完全隐私确定是否在很短的距离内，尤其是蓝牙无线范围，即受感染的人，并且有可能何时。我们确定并证明我们方法提供的隐私保证。我们利用涉及100个移动设备和大约60000个记录的经验跟踪数据集（asturies）的模拟研究表明，用户可以通过在活动时间内打开该应用程序来识别他们是否靠近受感染用户的可能性。
Pandemic and epidemic diseases such as CoVID-19, SARS-CoV2, and Ebola have spread to multiple countries and infected thousands of people. Such diseases spread mainly through person-to-person contacts. Health care authorities recommend contact tracing procedures to prevent the spread to a vast population. Although several mobile applications have been developed to trace contacts, they typically require collection of privacy-intrusive information such as GPS locations, and the logging of privacy-sensitive data on a third party server, or require additional infrastructure such as WiFi APs with known locations. In this paper, we introduce CONTAIN, a privacy-oriented mobile contact tracing application that does not rely on GPS or any other form of infrastructure-based location sensing, nor the continuous logging of any other personally identifiable information on a server. The goal of CONTAIN is to allow users to determine with complete privacy if they have been within a short distance, specifically, Bluetooth wireless range, of someone that is infected, and potentially also when. We identify and prove the privacy guarantees provided by our approach. Our simulation study utilizing an empirical trace dataset (Asturies) involving 100 mobile devices and around 60000 records shows that users can maximize their possibility of identifying if they were near an infected user by turning on the app during active times.
