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CoCoT: Collaborative Contact Tracing
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AI
DOI：10.1145/3626232.3653254.png)
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Abstract
En 中文
Contact tracing can limit the spread of infectious diseases by notifying people of potential exposure to disease. Manual contact tracing is resource-intensive, but much of it can be automated using mobile phones, which are ubiquitous and can detect and record nearby contacts. Two major problems arise with automated contact tracing (ACT): preventing abuse for mass surveillance and accurately determining contacts. For example, the most widely adopted solution-Google and Apple's Exposure Notification (GAEN)-protects user privacy but suffers from inaccurate distance measurements that result in poor risk assessments. We propose to use collaboration among nearby devices to increase distance estimation accuracy, and therefore risk assessment accuracy, while minimizing the loss of user privacy. Our protocol, CoCoT, extends GAEN, a proximity-based, distributed ACT protocol, by adding an additional broadcast to share locally-derived distance estimates. To evaluate CoCoT, we develop a method for merging phone sensor datasets with human interaction datasets to approximate realistic scenarios and test our protocol. CoCoT improves distance estimate accuracy by 28% over the current best distance estimators and we analytically show impact on privacy, security, and battery consumption are minimal.
Keywords:
Automatic Contact Tracing
Distributed Optimization
Network Localization
Smartphone Privacy
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