论文标题
量化COVID-19中的隐私 - 实用性权衡取舍
Quantifying the Privacy-Utility Trade-offs in COVID-19 Contact Tracing Apps
论文作者
论文摘要
对于大多数国家来说，如何控制Covid-19病毒的传播是一个主要问题。随着局势的不断变化，各个国家正在努力通过取消一些限制并实施新措施来防止蔓延，以重新开放经济。在这项工作中，我们审查了一些已采用的方法，这些方法已包含COVID-19病毒，例如接触跟踪，集群识别，运动限制和状态验证。具体而言，我们根据某些特征（例如技术，建筑，权衡（隐私与实用程序）和采用阶段）对可用技术进行了分类。我们提出了一种新的方法，可以使用对接触跟踪应用程序的隐私效果评估的定性和定量度量来评估隐私。在这种新方法中，我们将实用程序分为三（3）个不同的级别：没有隐私，100％的隐私，以及在k设置k设置的k处，提供实用程序或隐私。
How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their economies by lifting some restrictions and enforcing new measures to prevent the spread. In this work, we review some approaches that have been adopted to contain the COVID-19 virus such as contact tracing, clusters identification, movement restrictions, and status validation. Specifically, we classify available techniques based on some characteristics such as technology, architecture, trade-offs (privacy vs utility), and the phase of adoption. We present a novel approach for evaluating privacy using both qualitative and quantitative measures of privacy-utility assessment of contact tracing applications. In this new method, we classify utility at three (3) distinct levels: no privacy, 100% privacy, and at k where k is set by the system providing the utility or privacy.
