RAPID: Poirot: From Contact Tracing to Private Exposure Detection

众所周知，2019冠状病毒病（COVID-19）等传染病会在人与人之间迅速传播。在缺乏有效疫苗或药物治疗的情况下，感染控制依赖于：(i)迅速识别和隔离感染者；一种称为接触者追踪的过程，和/或（ii）采取极端的社会距离措施，以减少人与人之间的接触。目前使用的接触者追踪流程是手动的、耗时的、容易出错的，而且无法扩展。通过使用智能手机扩大接触者追踪对个人隐私和保密构成风险。政府实施的另一种保持身体距离的方法造成了社会和经济困境。这个项目构建了波洛以一种保护隐私的方式自动检测暴露。随着物理距离措施的放松，可以使用波洛（Poirot）等工具来帮助基本工作人员追踪感染情况。Poirot是一种隐私保护系统，它使用智能手机(a)检测与潜在感染个体的接触，(b)提供感染控制建议（例如隔离、检测）。与其他利用智能手机的建议相比，波洛提出了三个关键创新：(i)在通知用户时考虑到感染的传递性，（ii）利用用户提供的信息，而不仅仅是接触者（例如，使用个人防护装备）来评估暴露状态，以及（iii）它可以追溯地纳入信息，例如最初不在系统中的受感染用户的追溯活动。这些特征在隐私、密码学、安全多方计算、传染病流行病学和数据密集型系统方面提出了独特的挑战。首先，任何暴露检测应用，无论在紧急情况下，都必须确保个人的基本隐私权。波洛提出了使用安全多方计算算法来解决这一挑战并实现上述特性的新技术。其次，任何暴露检测解决方案都必须扩展到数百万用户，这是目前通用的安全多方计算工具无法做到的。波洛采用新颖的方法，通过揭示不同的私有统计数据和使用数据密集型计算技术来加速安全的多方计算。最后，通过智能手机识别联系人和用户提供的信息向个人透露暴露状态可能会导致侵犯隐私。因此，波洛选择的通知策略将通过严格分析不同方式的隐私影响通知用户他们的暴露。应对这些挑战有助于制定一种务实和可扩展的暴露检测解决方案，可由公共卫生机构在面对当前和未来的大流行病时广泛实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Infectious diseases such as COronaVIrus Disease 2019 (COVID-19) are known to spread rapidly from person to person. In the absence of an effective vaccine or drug treatment, infection control relies on (i) rapid identification and isolation of persons with infection; a process called contact tracing, and/or (ii) extreme social distancing measures to reduce contact between people. Contact tracing processes used today are manual, time-consuming, error-prone, and do not scale. Scaling contact tracing through the use of smartphones pose risks to individuals' privacy and confidentiality. The other approach of physical distancing that has been implemented by governments has resulted in societal and economic distress. This project builds Poirot to detect exposure in a privacy-preserving manner automatically. As physical distancing measures are eased, a tool such as Poirot can be used to help the essential workers track exposure to the infection.Poirot is a privacy-preserving system that uses smartphones to (a) detect contact with potentially infectious individuals, and (b) provide recommendations for infection control (e.g., isolation, testing). Compared to other suggestions that utilize smartphones, Poirot makes three key innovations: (i) it takes into account the transitive nature of infection when notifying users, (ii) it utilizes user-contributed information beyond just contacts (e.g., use of personal protective equipment) to assess exposure status, and (iii) it can incorporate information retroactively, such as back-dated activities of infected users who were initially not in the system. These features present unique challenges in privacy, cryptography, secure multiparty computation, infectious disease epidemiology, and data-intensive systems. First, any exposure detection application, despite the emergency, must ensure the fundamental privacy rights of an individual. Poirot presents novel techniques in using secure multiparty computation algorithms to address this challenge and implement the features mentioned above. Second, any exposure detection solution must scale to millions of users, which cannot be done with current general-purpose secure multiparty computation tools. Poirot employs novel methods to speed up secure multiparty computation by revealing differentially private statistics and using techniques from data-intensive computing. Finally, revealing exposure status to individuals from smartphone identified contacts and user-contributed information can result in privacy violations. Thus, Poirot's selection of notification strategies will be informed by rigorous analysis of the privacy implications of different ways of notifying users of their exposure. Addressing these challenges helps develop a pragmatic and scalable solution for exposure detection that can be widely implemented by public health agencies in the face of the current and future pandemics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.