Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications

In 2020 the coronavirus outbreak changed lives of people worldwide. After an initial time period in which it was unclear how to battle virus, social distancing has been recognised globally as effective method mitigate disease spread. This called for technological tools such Mobile Contact Tracing Applications (MCTA), are used digitally trace contacts among people, and case a positive is found, with application installed had contact will be notified. De-centralised MCTA may suffer from novel kind privacy attack, based on memory human beings, upon notification can identify who individual responsible notification. Our results show that indeed possible group being, this even easier when sociability low. practice, our simulation identification made accuracy more than 90% depending scenario. We also provide three mitigation strategies implemented de-centralised analyse limiting attack.