COVID-LPS: Location-Protected Services for COVID-19 Prevention
2022 IEEE Global Communications Conference, GLOBECOM 2022
; : 3035-3040, 2022.
Article
in English
| Scopus | ID: covidwho-2236420
ABSTRACT
The COVID-19 pandemic has caused not only worldwide health problems but also economic damage. Numerous researchers and intuitions have attempted to visualize confirmed COVID-19 cases with maps to provide timely information to users (e.g., warnings upon entry of crowded areas) and prevent the spread of COVID-19. However, such systems are limited by their poor protection of private information because they must collect sensitive information, such as the locations of individuals. We propose a practical method of obtaining a distribution of users while anonymizing their location data that can be used in location-based services for the prevention of the spread of COVID-19. Generalization and local differential privacy are used to guarantee user and data anonymity while maintaining high data utility and accuracy. To our knowledge, COVID-LPS is not only the first COVID-19 tracing system in Taiwan but also the first system to visualize user distributions for location-based services while protecting user privacy through generalization and local differential privacy. © 2022 IEEE.
COVID-19; differential privacy; location services; privacy protection; randomized location response; Location; Location based services; Sensitive data; Telecommunication services; Differential privacies; Economic damages; Generalisation; Location-based services; Practical method; Private information; Sensitive informations
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 IEEE Global Communications Conference, GLOBECOM 2022
Year:
2022
Document Type:
Article
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