TY - GEN
T1 - GeauxTrace
T2 - 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
AU - Lu, Tao
AU - Qi, Fang
AU - Ner, John
AU - Feng, Tianqing
AU - Cunningham, Brian
AU - Peng, Lu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second.
AB - Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second.
KW - Big data infrastructur
KW - Blockchain applicatio
KW - Contact tracin
KW - Privacy protectio
UR - http://www.scopus.com/inward/record.url?scp=85150678198&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150678198&partnerID=8YFLogxK
U2 - 10.1109/BDCAT56447.2022.00020
DO - 10.1109/BDCAT56447.2022.00020
M3 - Conference contribution
AN - SCOPUS:85150678198
T3 - Proceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
SP - 100
EP - 109
BT - Proceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2022 through 9 December 2022
ER -