IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-Enabled Architecture for Contact Tracing

Contact tracing promises to help fight the spread of COVID–19 via an early detection of possible contagion events. To this end, most existing solutions share the following architecture: smartphones continuously broadcast random beacons that are intercepted by nearby devices and stored into their local contact logs. In this article, we propose an IoT-enabled architecture for contact tracing that relaxes the smartphone-centric assumption, and provides a solution that enjoys the following features: it reduces the overhead on the end user to the bare minimum - the mobile device only broadcasts its beacons; it provides the user with a degree of privacy not achieved by competing solutions - even in the most privacy adverse scenario, the solution provides $k$-anonymity; and it is flexible: the same architecture can be configured to support several models - ranging from fully decentralized to fully centralized ones - and the system parameters can be tuned to support the tracing of several social interaction models. What is more, our proposal can also be adopted to tackle future human-proximity transmissible diseases. Finally, we also highlight open issues and discuss a number of future research directions at the intersection of IoT and contact tracing.

[1]  Yang Lu,et al.  Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics , 2019, IEEE Internet of Things Journal.

[2]  Jason Bay,et al.  BlueTrace: A privacy-preserving protocol for community-driven contact tracing across borders , 2020 .

[3]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[4]  Roberto Di Pietro,et al.  Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges , 2020, ArXiv.

[5]  Qiang Tang,et al.  Privacy-Preserving Contact Tracing: current solutions and open questions , 2020, IACR Cryptol. ePrint Arch..

[6]  Ernest Foo,et al.  A Survey and Analysis of the GNSS Spoofing Threat and Countermeasures , 2016, ACM Comput. Surv..

[7]  Yehuda Lindell,et al.  Introduction to Modern Cryptography (Chapman & Hall/Crc Cryptography and Network Security Series) , 2007 .

[8]  Jiguo Yu,et al.  Achieving Personalized $k$-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS , 2020, IEEE Transactions on Industrial Informatics.

[9]  C. E. WHO Coronavirus Disease (COVID-19) Dashboard , 2020 .

[10]  Dermot Frederik Pustelnik,et al.  Mind the GAP: Security & Privacy Risks of Contact Tracing Apps , 2020, 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom).

[11]  Lawrence Carin,et al.  Digital technology and COVID-19 , 2020, Nature Medicine.

[12]  Chinmay Chakraborty,et al.  Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model , 2020, IEEE Access.

[13]  Pietro Manzoni,et al.  Mobile crowdsensing approaches to address the COVID‐19 pandemic in Spain , 2020, IET Smart Cities.