Proximity tracing: new opportunities

15 Aprile 2020 • Articolo di Fausto Villani

Executive summary


The SARS-CoV-2 pandemic highlighted the relevance of digital technologies as tools to support prevention and contain the spreading of the virus.

The large availability of “digital breadcrumbs” by everyone left behind enables several people tracking systems but puts in danger their privacy at the same time. The approach that is gaining momentum at international level is built on the following principles: the need to protect the privacy of citizens; the importance to implement projects which ensure cross-border interoperability and great scalability; the use of ( Bluetooth Low Energy) BLE-based tracking technologies.

Both the Pan-European PEPP-PT project and the proposal by Apple e Google — the two monopolists in the smartphone operating systems — go in such direction. The aim of this paper is to depict how other uses of proximity tracing could emerge from this scenario that might support public Authorities, companies and citizens in order to better organize and manage the so-called “phase 2” of the present crisis.

Moreover, via this document, we would like to receive feedback on the high-level design, any privacy-linked criticalities and to collect contributions for the implementation of the proposed system.


Background


The BLE technology is the basis for all the solutions and frameworks for the proximity tracing currently used or to be shortly implemented (TraceTogether, Stopp Corona, PEPP-PT, DP3T, Google/Apple tracing tools). Coupled with cryptography and anonymization techniques, BLE technologie represents the best solution for the reconstruction of the contagion chain safeguarding privacy without tracking people’s movements.

At the same time, the BLE solution can be easily implemented because no action is required to the customers during the data acquisition. As a consequence, all the mobile devices (in accordance with each country’s policies and regulations), will broadcast advertisement signals to inform each other about their presence.


Our Proposal


The use of proximity tracing applications, de facto, makes visible all the mobile devices that could not be discoverable using past technologies. These applications imply the frequent exchange of advertising messages. Even if the content of these messages is not readable, because is considered the exchange messages per se, not their content. In fact, as foreseen in the BLE protocol, every message contains a piece of invariant information that allows us to attribute the message to the emitting device.

Using the signal strength it would be possible to have an idea about the distance between the emitting and the “listening” device. The reception of the advertising message allows us to infer about the presence of that specific device within the range of the listening device.

By positioning one or more BLE listening devices, it is possible to identify the devices within their BLE communication range. In order to estimate the number of devices within a specific area it will be used both the strength of the received signal and the time difference between the different acquisition points. In case of wide places or areas with a complex topology, differentiated information might be obtained by splitting the space in several sub-areas.


Application Scenarios


  1. Estimating the number of people within a specific perimeter
  2. Understanding the paths covered in a specific perimeter and the time spent between the different steps.

In order to ensure a limited conservation of data and the privacy, we foresee that the listening device (gateway) performs some pre-processing activities to aggregate or anonymize the data. For instance, in the 1 scenario, the information on the device could be missed and only the number of devices for any given perimeter could be saved. The information collected in such a way and anonymized could be shared with third parties or made available in order to support the development of other applications which cannot be foreseen at the present.


Use Cases


  1. Indoor (Supermarket/Mall)
  2. Given a maximum number of people allowed to be at the same time in a specific place, i.e. supermarket, the possibility to estimate with a good approximation the number of devices inside, enables the assessment of a density index, to alert the supermarket manager when the threshold is approaching limiting the number of devices (people). This would help the management of queues, the planning of spaces and enabling the user, via a WebApp, to know in advance how crowded is a place where he/she is willing to go.


    tboxchain-indoor-tracing

  3. Outdoor (Beach, Archeological Sites)
  4. In the case of outdoors, in areas that might be different in terms of size, with or without defined boundaries, by properly placing the listening devices, it would be possible to identify and diversify different zones or paths. In this way, we would obtain more density indexes, one for any selected area. Also in this case, the manager of the beach/site and the user as well could access several indicators. The former to keep the respect of the limits under control; the latter to decide the paths or the zone to go to depending on the level of crowd.

    tboxchain-outdoor-tracing

    Conclusions


    This scenario, where a lot of countries are starting to use proximity tracing apps, lays the basis for other apps to emerge and contribute to restart the economic activities and bring people again in places for work, leisure, culture or sport. All these items should be pursued in accordance with privacy principles, using open-code and in an open-innovation perspective. We would not exclude the possibility that such digital technologies might help this crisis to trigger new and better ways to use time and places.

    The foreseeable use of state proximity tracing apps (it is assumed that at least 60% of the population will use them) as well as the deployment of smart listening devices, easy-to-use and low-cost, to be installed in relevant/critical positions such as: shopping malls, public squares, museums, archeological sites, beaches, etc. or a smart use of collected data; all these actions might become a useful and additional tool to “rethink” the way we visit places in a “safety” perspective. Obviously, to obtain a reliable and useful estimation for our aims (social distancing) we would need precise field tests, in different operational conditions and with the use of AI.

    Thus we would like to invite researchers, research organizations, companies and startups interested in developing this idea, not for commercial purposes, to send us their remarks. Write us at recovery@tboxchain.com

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