Privacy Preservation of COVID-19 Contact Tracing Data
20TH INT CONF ON UBIQUITOUS COMP AND COMMUNICAT (IUCC) / 20TH INT CONF ON COMP AND INFORMATION TECHNOLOGY (CIT) / 4TH INT CONF ON DATA SCIENCE AND COMPUTATIONAL INTELLIGENCE (DSCI) / 11TH INT CONF ON SMART COMPUTING, NETWORKING, AND SERV (SMARTCNS)
; : 288-295, 2021.
Article
in English
| Web of Science | ID: covidwho-1909241
ABSTRACT
Big data are everywhere. Examples of big data include contact tracing data of patients who contracted coronavirus disease 2019 (COVID-19). On the one hand, mining these contact tracing data can be for social good. For instance, it helps slow down the spread of COVID-19. It also helps people diagnosed with COVID-19 get referrals for services and resources they may need to isolate safely. On the other hand, it is also important to protect the privacy of these COVID-19 patients. Hence, we present in this paper a solution for privacy preservation of COVID-19 contact tracing data. Specifically, our solution preserves the privacy of individuals by publishing only their spatio-temporal representative locations. Evaluation results on real-life COVID-19 contact tracing data from South Korea demonstrate the effectiveness and practicality of our solution in preserving the privacy of COVID-19 contact tracing data.
computer science; information technology; database and data management; big data; data science and systems; data and informatics; information security; privacy; data mining; privacy preserving data mining; spatio-temporal data; spatial data; temporal data; spatio-temporal hierarchy; visualization; ANALYTICS; PATTERNS
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
20TH INT CONF ON COMP AND INFORMATION TECHNOLOGY (CIT)
Year:
2021
Document Type:
Article
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