RAPID: Poirot: From Contact Tracing to Private Exposure Detection

众所周知，冠状病毒病2019(新冠肺炎)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，感染控制有赖于(1)对感染者的快速识别和隔离；(2)称为接触者追踪的过程；和(2)减少人与人之间接触的极端社会距离措施。目前使用的联系人跟踪流程是手动的、耗时的、容易出错的，而且不能扩展。通过使用智能手机扩大联系人追踪范围会对个人隐私和机密性构成风险。各国政府实施的另一种物理距离办法造成了社会和经济困境。这个项目构建了Poiro来自动检测隐私保护方式下的暴露。随着物理距离措施的放松，可以使用POIROT之类的工具来帮助基本工作人员跟踪接触感染的情况。POIROT是一个隐私保护系统，它使用智能手机来(A)检测与潜在感染者的接触，以及(B)提供感染控制(例如，隔离、测试)的建议。与使用智能手机的其他建议相比，Poirow有三个关键创新：(I)它在通知用户时考虑了感染的传播性，(Ii)它利用用户提供的信息，而不仅仅是联系人(例如，使用个人防护装备)来评估接触状态，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。这些功能在隐私、加密、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，任何暴露检测应用程序，尽管是紧急情况，都必须确保个人的基本隐私权。波罗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功能的新技术。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完成的。波罗使用新的方法来加快安全多方计算的速度，方法是揭示不同的私有统计数据并使用来自数据密集型计算的技术。最后，通过智能手机识别的联系人和用户贡献的信息向个人泄露暴露状态可能会导致侵犯隐私。因此，波罗选择的通知策略将通过对通知用户其暴露的不同方式的隐私影响的严格分析而确定。应对这些挑战有助于开发一种实用且可扩展的暴露检测解决方案，该解决方案可由公共卫生机构在当前和未来的疫情中广泛实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.