PRISC: Privacy-Preserved Pandemic Infection Risk Computation through Cellular Enabled IoT Devices
IEEE Internet of Things Journal
; : 1-1, 2023.
Article
in English
| Scopus | ID: covidwho-2294973
ABSTRACT
The pandemics such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this paper, we develop a novel privacy-preserved pandemic tracing system, PRISC, to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties a mobile network operator, a social network provider, and the health department. The physical contact records between users are obtained by the mobile network operator from the users’cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for datasets with 100,000 users. IEEE
Data privacy; Heating systems; Internet of Things; IoT; Pandemic risk estimation; Pandemics; Protocols; secure multiparty computation; Servers; Smart phones; Health risks; mHealth; Mobile telecommunication systems; Risk perception; Smartphones; Social networking (online); Wireless networks; Cellulars; Contact tracing; Heating system; Pandemic; Risk estimation; Risk information; Secure multi-party computation
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
IEEE Internet of Things Journal
Year:
2023
Document Type:
Article
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