Exploring New Paradigm for COVID-19 Contact Tracing Through the Eye of IoT: A Blockchain Approach

Abstract

— Existing contact tracing technologies for the novel Coronavirus were built to take advantage of the user’s cellphone embedded features. Specifically, exposure notification system and Bluetooth proximity were widely adopted by many of today’s Covid-19 contact tracing technologies. This paper proposes a new technological paradigm that supports contact tracing capability for the Covid-19 virus. The proposed system’s models can be used to determine potential virus outbreaks within a controlled community. In this paradigm, we primarily focus on the enrichment of contact tracing capability for communities that have limited access to smartphones (e.g. children's schools, senior homes, and factories/warehouses). Serval technological and crypto innovations were incorporated into the proposed system’s model design: (i) Internet-of-Things contact tracing data logger (ii) Two-factor Authentication (iii) User’s anonymity and data integrity of the contact tracing data (iv) detection and localization of outbreaks in a community. Contact tracing data for Covid-19 are collected using usersauthorized IoT devices. Accesses to the Covid-19 contact tracing system were established by a new two-factor authentication. The proposed authentication scheme combined public key crypto, a blockchain validation approach, and a random token generator. Meanwhile, user anonymity was achieved via the exchange of randomly generated IDs, computed by light-weight crypto hash for supporting data logging of Covid-19 traces. To preserve the integrity of Covid-19 contacts tracing data, we have adapted a blockchain methodology. The proposed approach protects user data against data injection and data modification attacks. Finally, we have analyzed the resiliency of the proposed system’s models against two well-known IoT-based attack models: (i) Sybil attack (ii) Replay attacks. Two probabilistic models were introduced in this research that present system resiliency under different scales of threats