Privacy-Preserving Proximity Tracing System for Large-Scale Public Health Events

With the acceleration of globalization and the increase in population mobility, the spread of diseases has become one of the major challenges in global public health. In the face of possible future outbreaks, how to conduct real-time tracing and response has become an urgent problem for today's society. Existing solutions are mainly based on Bluetooth and Global Positioning System (GPS) technologies, which suffer from physical environment limitation, low transmission efficiency, honesty of participants, and sensitive information leakage. How to balance privacy, security and usability remains a challenge. In this paper, we design a privacy-preserving proximity tracing system, which supports real-time monitoring of data. Specifically, a hashed lightweight token based on the user's identity is first generated, and the multi-factor authentication is proposed during NFC data transmission. Next, classification of health status is made through monitoring and screening of users' risk data. Finally, equipping with Huffman tree and differential privacy technique, hierarchical protection based on location access frequency is achieved. The proposed system fully protects the privacy and security of users in the process of data tracking without hindering the analysis of big data for decision-making, and support cross-region interactive operation with high accuracy, security and practicality.