The highly transmissible COVID-19 disease is a serious threat to people's health and life. To automate tracing those who have been in close physical contact with newly infected people and/or to analyse tracing-related data, researchers have proposed various ad-hoc programs that require being executed on users' smartphones. Nevertheless, the existing solutions have two primary limitations: (1) lack of generality: for each type of analytic task, a certain kind of data needs to be sent to an analyst; (2) lack of transparency: parties who provide data to an analyst are not necessarily infected individuals; therefore, infected individuals' data can be shared with others (e.g., the analyst) without their fine-grained and direct consent. In this work, we present Glass-Vault, a protocol that addresses both limitations simultaneously. It allows an analyst to run authorised programs over the collected data of infectious users, without learning the input data. Glass-Vault relies on a new variant of generic Functional Encryption that we propose in this work. This new variant, called DD-Steel, offers these two additional properties: dynamic and decentralised. We illustrate the security of both Glass-Vault and DD-Steel in the Universal Composability setting. Glass-Vault is the first UC-secure protocol that allows analysing the data of Exposure Notification users in a privacy-preserving manner. As a sample application, we indicate how it can be used to generate "infection heatmaps".
翻译:高度传染的COVID-19疾病严重威胁人们的健康和生命。为了自动追踪那些与新感染者有密切身体接触的人,并(或)分析与追踪有关的数据,研究人员提出了各种需要用用户智能手机执行的特设程序。然而,现有解决方案有两个主要限制:(1) 缺乏普遍性:对于每一种分析任务,需要向分析员发送某种类型的数据;(2) 缺乏透明度:向分析员提供数据的各方不一定是受感染的个人;因此,受感染的个人的数据可以与其他人(例如,分析员)分享,而没有他们精细的和直接的同意。在这项工作中,我们提出了同时解决两种限制的玻璃-Vault协议。它允许分析者对传染病使用者所收集的数据进行授权程序运行,而没有了解输入数据。玻璃-天线依赖我们在此工作中提出的一种通用功能加密新变式。这个称为DD-Stiel的新变式,提供了另外两种特性: " 动态和分散的对等用户进行安全性分析。我们把玻璃-VD-D-V-Viel 安全性地用于全球安全。我们把玻璃-DD-D-D-D-D-AV-S-S-S-S-S-S-S-S-S-S-S-tovivivivivivivicl-vivivial 用于一种安全性地分析。我们使用。我们使用。我们使用。我们使用玻璃-to