论文标题
COVID-19暴露通知系统的目标隐私威胁建模
Target Privacy Threat Modeling for COVID-19 Exposure Notification Systems
论文作者
论文摘要
在COVID-19大多数期间，采用数字接触跟踪（DCT）技术已显示出多种好处，包括帮助减慢传染病的传播并改善准确信息的传播。但是，要支持道德技术部署和用户采用，隐私必须处于最前沿。由于失去隐私是一个关键的威胁，彻底的威胁建模将有助于我们在数字接触跟踪技术的前进时制定战略和保护隐私。当今存在各种威胁建模框架，例如Linddun，STRIDE，PASTA和NIST，分别集中在软件系统隐私，系统安全，应用程序安全和以数据为中心的风险上。当应用于曝光通知系统（ENS）上下文时，这些模型可以详尽地了解软件方面，但在解决此类系统中涉及的硬件，人类，法规和软件的综合性质方面缺乏。我们的方法解决了ENSSA的整体，并提供了一个模型，以解决多方面解决方案的隐私复杂性。我们定义隐私原则，隐私威胁，攻击者能力和全面威胁模型。最后，我们概述了应对模型中定义的各种威胁的缓解威胁策略
The adoption of digital contact tracing (DCT) technology during the COVID-19pandemic has shown multiple benefits, including helping to slow the spread of infectious disease and to improve the dissemination of accurate information. However, to support both ethical technology deployment and user adoption, privacy must be at the forefront. With the loss of privacy being a critical threat, thorough threat modeling will help us to strategize and protect privacy as digital contact tracing technologies advance. Various threat modeling frameworks exist today, such as LINDDUN, STRIDE, PASTA, and NIST, which focus on software system privacy, system security, application security, and data-centric risk, respectively. When applied to the exposure notification system (ENS) context, these models provide a thorough view of the software side but fall short in addressing the integrated nature of hardware, humans, regulations, and software involved in such systems. Our approach addresses ENSsas a whole and provides a model that addresses the privacy complexities of a multi-faceted solution. We define privacy principles, privacy threats, attacker capabilities, and a comprehensive threat model. Finally, we outline threat mitigation strategies that address the various threats defined in our model
