Existing Bluetooth-based Private Contact Tracing (PCT) systems can privately
detect whether people have come into direct contact with COVID-19 patients.
However, we find that the existing systems lack functionality and flexibility,
which may hurt the success of the contact tracing. Specifically, they cannot
detect indirect contact (e.g., people may be exposed to coronavirus because of
used the same elevator even without direct contact); they also cannot flexibly
change the rules of ”risky contact”, such as how many hours of exposure or
how close to a COVID-19 patient that is considered as risk exposure, which may
be changed with the environmental situation. In this paper, we propose an
efficient and secure contact tracing system that enables both direct contact
and indirect contact. To address the above problems, we need to utilize users’
trajectory data for private contact tracing, which we call trajectory-based
PCT. We formalize this problem as Spatiotemporal Private Set Intersection. By
analyzing different approaches such as homomorphic encryption that could be
extended to solve this problem, we identify that Trusted Execution Environment
(TEE) is a proposing method to achieve our requirements. The major challenge is
how to design algorithms for spatiotemporal private set intersection under
limited secure memory of TEE. To this end, we design a TEE-based system with
flexible trajectory data encoding algorithms. Our experiments on real-world
data show that the proposed system can process thousands of queries on tens of
million records of trajectory data in a few seconds.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Kato_F/0/1/0/all/0/1">Fumiyuki Kato</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_Y/0/1/0/all/0/1">Yang Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Masatoshi_Y/0/1/0/all/0/1">Yoshikawa Masatoshi</a>

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