A Semantic Framework for Secure and Efficient Contact Tracing of Infectious Diseases

Abstract

Contact tracing is the process of identifying people who came into contact with an infected person (“case”) and collecting information about these contacts. Contact tracing is an essential part of public health infrastructure and slows down the spread of infectious diseases. Existing contact tracing methods are extremely time and labor intensive due to their reliance on manually interviewing cases, contacts, and locations visited by cases. Additionally, complex privacy regulations mean that contact tracers must be extensively trained to avoid improper data sharing. App-based contact tracing, a proposed solution to these problems, has not been widely adopted by the general public due to privacy and security concerns. We develop a secure, semantically rich framework for automating the contact tracing process. This framework includes a novel, flexible ontology for contact tracing and is based on a semi-federated data-as-a-service architecture that automates contact tracing operations. Our framework supports security and privacy through situation-aware access control, where distributed query rewriting and semantic reasoning are used to automatically add situation based constraints to protect data. In this paper, we present our framework along with the validation of our system via common use cases extracted from CDC guidelines on COVID-19 contact tracing.