Privacy Preservation of User Identity in Contact Tracing for COVID-19-Like Pandemics Using Edge Computing

Pandemic and infectious disease outbreaks put pressure on health authorities and require lockdowns. These outbreaks, which strain limited healthcare resources, must be swiftly controlled and monitored. A large number of healthcare authorities are currently investigating automated systems to support outbreak monitoring and control. However, current contact tracing systems face many privacy, participation, and power constraints. Furthermore, elderly or less financially able individuals often cannot participate in automated contact tracing due to not owning a smartphone. This paper proposes a new system that enables health authorities to track exposure among individuals participating in the automated system, aid health authorities in interviewing non-participating individuals, and minimize the processing required by offloading to nearby edge computing devices. The proposed system utilizes edge servers to assist health authorities in tracking users who withdraw from or are unable to use contact tracing. Edge computing devices have access to more contextual information, resulting in minimal data collection and thus enabling businesses, houses, and offices to participate in contact tracing as locations. Edge computing devices enable location-based data collection of contact tracing data using proximity-based sensors for offices, homes, and shops, thereby assisting health authorities to notify users of exposure without disclosing the identities of businesses or individuals. Moreover, the proposed system reduces the overall power for end users up to 97% by delegating contact tracing to nearby edge computing devices. In addition, the system mitigates data poisoning attacks that target individuals’ smartphones, edge devices, or cloud servers by utilizing blockchain to store contacts, delegations, and identities