Privacy-Preserving Multi-Operator Contact Tracing for Early Detection of Covid19 Contagions

TL;DR

This paper introduces a privacy-preserving protocol for multi-operator contact tracing that enables the detection of Covid-19 contagions while safeguarding user privacy, leveraging geo-location data and infection status sharing among mobile operators and a government authority.

Contribution

It presents a novel privacy-preserving protocol for multi-operator contact tracing that balances data utility and user privacy in Covid-19 detection.

Findings

The protocol effectively preserves user privacy with acceptable data overhead.

Simulation results show the protocol's efficiency and potential for privacy trade-offs.

Cost can be reduced with minimal privacy compromise.

Abstract

The outbreak of coronavirus disease 2019 (covid-19) is imposing a severe worldwide lock-down. Contact tracing based on smartphones' applications (apps) has emerged as a possible solution to trace contagions and enforce a more sustainable selective quarantine. However, a massive adoption of these apps is required to reach the critical mass needed for effective contact tracing. As an alternative, geo-location technologies in next generation networks (e.g., 5G) can enable Mobile Operators (MOs) to perform passive tracing of users' mobility and contacts with a promised accuracy of down to one meter. To effectively detect contagions, the identities of positive individuals, which are known only by a Governmental Authority (GA), are also required. Note that, besides being extremely sensitive, these data might also be critical from a business perspective. Hence, MOs and the GA need to exchange and process users' geo-locations and infection status data in a privacy-preserving manner. In this work, we propose a privacy-preserving protocol that enables multiple MOs and the GA to share and process users' data to make only the final users discover the number of their contacts with positive individuals. The protocol is based on existing privacy-enhancing strategies that guarantee that users' mobility and infection status are only known to their MOs and to the GA, respectively. From extensive simulations, we observe that the cost to guarantee total privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable, and can also be significantly reduced if we accept a negligible compromise in users' privacy.