Privacy-preserving contact tracing using homomorphic encryption: Poster abstract

Digital contact tracing is an essential countermeasure for an epidemic as a society, and balancing the surveillance resolution and user privacy for contact tracing remains an open challenge. Existing contact tracing schemes are primarily based on proximity tracing, which uses Bluetooth to detect coexistence. Proximity tracing has a strong advantage in anonymizing the users, but shows low epidemiological resolution and lacks the flexibility to be integrated with other data sources. To address this problem, we propose an alternative scheme we phrase as context tracing. Our scheme achieves strong performance in both surveillance resolution and user privacy protection by integrating multi-modal sensor fusion and homomorphic encryption. While this advantage comes at the cost of high computational overhead, we discuss possible optimization strategies for reducing energy consumption on mobile devices.