论文标题
可解释保存隐私联系跟踪的链接预测
Explainable Link Prediction for Privacy-Preserving Contact Tracing
论文作者
论文摘要
接触跟踪已被用来识别与感染SARS-COV2冠状病毒的人非常接近的人。引入了许多数字合同追踪应用程序，以促进或补充物理接触跟踪。但是，实施合同追踪应用程序存在许多隐私问题，这使得人们不愿意在这些应用程序上安装或更新其感染状态。在此概念论文中，我们介绍了图形神经网络和解释性的想法，可以改善对这些应用的信任，并鼓励人们采用人们。
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.
