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

来源：

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) | 2021年

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

页码：288 / 295

页数：8

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