GeauxTrace: A Scalable Privacy-Protecting Contact Tracing App Design Using Blockchain

【Author】 Lu, Tao; Qi, Fang; Ner, John; Feng, Tianqing; Cunningham, Brian; Peng, Lu

【Source】2022 IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES, BDCAT

【影响因子】

【Abstract】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.

【Keywords】Contact tracing; Big data infrastructure; Blockchain application; Privacy protection

【发表时间】2022

【收录时间】2023-07-09

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-隐私保护