Privacy Preservation of COVID-19 Contact Tracing Data

被引:0
|
作者
Olawoyin, Anifat M. [1 ]
Leung, Carson K. [1 ]
Wen, Qi [1 ]
机构
[1] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
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; BIG DATA; ANALYTICS; PATTERNS;
D O I
10.1109/IUCC-CIT-DSCI-SMARTCNS55181.2021.00055
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:288 / 295
页数:8
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