BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing
Zhe Peng, Jinbin Huang, Haixin Wang, Shihao Wang, Xiaowen Chu, Xinzhi, Zhang, Li Chen, Xin Huang, Xiaoyi Fu, Yike Guo, Jianliang Xu

TL;DR
BU-Trace is a decentralized, permissionless mobile contact tracing system that uses QR and NFC technologies to ensure privacy and ease of use, validated through real-world experiments.
Contribution
The paper introduces BU-Trace, a novel privacy-preserving contact tracing system that operates without location permissions and incorporates intelligent behavior detection.
Findings
Achieves privacy preservation without location permissions
Demonstrates effectiveness in real-world scenarios
Supports user acceptance through a user study
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users' requirements on privacy and convenience. In this paper, we propose BU-Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU-Trace and conduct extensive experiments in several…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCOVID-19 Digital Contact Tracing · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection
