Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Efficient Multi-Party Contact Tracing
Mathieu de GoyonAtsuko MiyajiYangguang Tian
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JOURNAL FREE ACCESS

2022 Volume 30 Pages 878-887

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Abstract

Since the beginning of the Covid-19 Pandemic, Contact Tracing Apps have been implemented in many countries as a way to detect if someone has been in contact with a patient within a minimum amount of time. However, most existing solutions only consider users in pairs. Since many people meet at the same time in real-life scenarios, those applications aren't able to accurately reflect the situation. Moreover, extending current schemes to a multi-party setting could cause scaling problems and place a heavier load on the device. In this paper, we propose a new Contact Tracing protocol that works in a multi-party setting. We evaluate our scheme to show its efficiency.

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© 2022 by the Information Processing Society of Japan
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