论文标题
在分散的移动触点跟踪应用程序中为个人重新识别的个人重新识别建模
Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications
论文作者
论文摘要
2020年，冠状病毒爆发改变了全球人的生活。经过最初的时间段，目前尚不清楚如何与病毒作斗争，社会疏远在全球范围内被认为是减轻疾病传播的有效方法。这呼吁使用以数字方式跟踪人们的移动联系跟踪应用程序（MCTA）之类的技术工具，如果发现有积极的情况，将通知已安装申请的人。根据人类的记忆，偏心的MCTA可能会遭受一种新颖的隐私攻击，该攻击是人类的记忆，一旦通知应用程序，可以确定谁是负责通知的积极个人。我们的结果表明，确实有可能在人类的一系列接触中识别积极的人，当积极个人的社交性低时，这甚至更容易。在实践中，我们的仿真结果表明，根据场景，可以以超过90％的精度进行识别。我们还提供了三种缓解策略，可以在偏心化的MCTA中实施，并分析这三种策略在限制这种新颖的攻击方面更有效。
In 2020 the coronavirus outbreak changed the lives of people worldwide. After an initial time period in which it was unclear how to battle the virus, social distancing has been recognised globally as an effective method to mitigate the disease spread. This called for technological tools such as Mobile Contact Tracing Applications (MCTA), which are used to digitally trace contacts among people, and in case a positive case is found, people with the application installed which had been in contact will be notified. De-centralised MCTA may suffer from a novel kind of privacy attack, based on the memory of the human beings, which upon notification of the application can identify who is the positive individual responsible for the notification. Our results show that it is indeed possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario. We also provide three mitigation strategies which can be implemented in de-centralised MCTA and analyse which of the three are more effective in limiting this novel kind of attack.
