P2B-Trace: Privacy-Preserving Blockchain-based Contact Tracing to Combat Pandemics

Zhe Peng, Cheng Xu, Haixin Wang, Jinbin Huang, Jianliang Xu, and Xiaowen Chu

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.
Type
Conference paper
Publication
In Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data (SIGMOD ’21)
Date
June 2021
DOI
10.1145/3448016.3459237
Note
Short Paper, accepted to appear
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