Privacy-Preserving Presence Tracing for Pandemics Via Machine-to-Machine Exposure Notifications

Laoudias, Christos, Raspopoulos, Marios orcid iconORCID: 0000-0003-1513-6018, Christoforou, Stefanos and Kamilaris, Andreas (2022) Privacy-Preserving Presence Tracing for Pandemics Via Machine-to-Machine Exposure Notifications. In: The 23rd IEEE International Conference on Mobile Data Management, 6-9 June 2022, Online.

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At the onset of Covid-19 several Mobile Contact Tracing Applications (MCTA) were deployed and in many cases contributed to curbing the pandemic by triggering Exposure Notifications (EN) to users who were in proximity to infected users. Recently, a number of MCTA were enhanced with Digital Presence Tracing (DPT) functionality in an effort of the public health authorities to break infection chains mostly in indoor crowded spaces and manage super-spreading events (e.g.,concerts, parties). That is, alerting individuals who visited the same place or attended the same event with infected users. This is typically implemented by scanning a QR code at the venue entrance. In this work, we present a DPT solution that relies on EN-Hubs, i.e., Bluetooth-enabled IoT devices, that propagate EN in a machine-to-machine fashion reaching all visitors/attendants seamlessly through their MCTA. The proposed solution removes the overhead of issuing, managing, and scanning QR codes every time people visit a place. In addition, it can be conveniently retrofitted to existing nation-wide MCTA offering DPT capabilities with limited implementation cost.

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