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.
|Title of host publication||SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - 2020 Nov 16|
|Event||18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020 - Virtual, Online, Japan|
Duration: 2020 Nov 16 → 2020 Nov 19
|Name||SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems|
|Conference||18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020|
|Period||20/11/16 → 20/11/19|
Bibliographical noteFunding Information:
This research was supported by the Yonsei University Research Fund of 2019 (2019-22-0180).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computer Networks and Communications