Privacy-Preserving Non-Participatory Surveillance System for COVID-19-Like Pandemics

Abstract

COVID-19 pandemic has revealed a pressing need for an effective surveillance system to control the spread of infection. However, the existing systems are run by the people's smartphones and without a strong participation from the people, the systems become ineffective. Moreover, these systems can be misused to spy on people and breach their privacy. Due to recent privacy breaches, people became anxious about their privacy, and without privacy reassurance, the people may not accept the systems. In this paper, we propose a non-participatory privacy-preserving surveillance system for COVID-19-like pandemics. The system aims to control the spread of COVID-19 infection without depending on the participation of the people and with privacy preservation. In the proposed system, surveillance cameras of public places take images of visitors and compute embedding vectors that encode the facial features of the images by using deep learning techniques. A searchable encryption scheme is used by public places to encrypt the embedding vectors and send them to a cloud server (service manager). Similarly, hospitals and test centers send the encryptions of the embedding vectors of the images of positively-tested people to the cloud server. Finally, the server does operations on encrypted data to learn whether an infected person visited a public place for contact tracing without being able to learn the images or identify the visitors of the places and the infected persons, and then it alerts these places to take precautionary measures. Our analysis indicates that our system is secure and can preserve privacy. Our experimental results demonstrate that our system has high success rate and low false alarm rate, and needs low computation and communication overhead and acceptable search time.