@inproceedings{58d1648292fd43f5823ffc80d0996031,
title = "P2B-Trace: Privacy-Preserving Blockchain-based Contact Tracing to Combat Pandemics",
abstract = "The eruption of a pandemic, such as COVID-19, can cause an unprecedented global crisis. Contact tracing, as a pillar of communicable disease control in public health for decades, has shown its effectiveness on pandemic control. Despite intensive research on contact tracing, existing schemes are vulnerable to attacks and can hardly simultaneously meet the requirements of data integrity and user privacy. The design of a privacy-preserving contact tracing framework to ensure the integrity of the tracing procedure has not been sufficiently studied and remains a challenge. In this paper, we propose P2B-Trace, a privacy-preserving contact tracing initiative based on blockchain. First, we design a decentralized architecture with blockchain to record an authenticated data structure of the user's contact records, which prevents the user from intentionally modifying his local records afterward. Second, we develop a zero-knowledge proximity verification scheme to further verify the user's proximity claim while protecting user privacy. We implement P2B-Trace and conduct experiments to evaluate the cost of privacy-preserving tracing integrity verification. The evaluation results demonstrate the effectiveness of our proposed system.",
keywords = "blockchain, contact tracing, integrity, privacy-preserving",
author = "Zhe Peng and Cheng Xu and Haixin Wang and Jinbin Huang and Jianliang Xu and Xiaowen Chu",
note = "Funding Information: This work was supported by Hong Kong RGC projects 12201520, 12200819, and Guangdong Basic and Applied Basic Research Foundation 2020A1515111070. ; ACM SIGMOD International Conference on Management of Data, SIGMOD 2021 ; Conference date: 20-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
doi = "10.1145/3448016.3459237",
language = "English",
isbn = "9781450383431",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery (ACM)",
pages = "2389--2393",
booktitle = "SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data",
address = "United States",
}