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International Conference on Information Integration and Web

iiWAS 2022: Information Integration and Web Intelligence pp 409–414Cite as

An Accurate, Flexible and Private Trajectory-Based Contact Tracing System on Untrusted Servers

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13635))

Abstract

Infections by the Covid-19 coronavirus have proliferated since the end of 2019, and many privacy-protective contact tracing systems have been proposed to limit infections from spreading. However, the existing Bluetooth-based contact tracking systems lack accuracy and flexibility. In addition, it is desirable to have a contact tracing system that, in the future, can contribute to limiting the proliferation of new coronaviruses and as yet unknown viruses. In this study, we propose a method to extend a contact tracing system to be more flexible, accurate, and capable of dealing with unknown viruses by using trajectory data and infection factor information while protecting privacy. We also implemented the proposed extension method and measured its execution time and confirmed its practicality.

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Notes

  1. 1.

    We note that an exhaustive list of infection factor information or a discussion of the availability of such information is beyond the scope of this paper. Our focus is to confirm that when some or all of such information is available it can be used to efficiently calculate the probability of infection using PCT-TEE. If only partial infection factor information is available, we calculate the probability of infection assuming the worst case scenario.

  2. 2.

    These human flow datasets are synthetically derived from actual trajectories, but for this study, they are considered to be actual datasets. More details on the specific process of data creation can be found here http://pflow.csis.u-tokyo.ac.jp/data-service/pflow-data/.

  3. 3.

    https://www.csis.u-tokyo.ac.jp.

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Acknowledgements

We thank Professor Miki Nagao of Kyoto University Hospital for her thoughtful responses to the authors’ questions about the infection probability calculation model. This work was partially supported by JST CREST JPMJCR21M2, JST SICORP JPMJSC2107, JST SICORP JPMJSC2006, Grant-in-Aid for Scientific Research 22H03595, 21K19767, 19K20269, and the KDDI Foundation Research Grant.

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Authors and Affiliations

  1. Kyoto University, Yoshida-Honmachi Sakyo-ku Kyoto, Japan

    Ruixuan Cao, Fumiyuki Kato, Yang Cao & Masatoshi Yoshikawa

Authors
  1. Ruixuan Cao
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  2. Fumiyuki Kato
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  3. Yang Cao
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  4. Masatoshi Yoshikawa
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Corresponding author

Correspondence to Masatoshi Yoshikawa .

Editor information

Editors and Affiliations

  1. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  2. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  3. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

  4. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

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Cao, R., Kato, F., Cao, Y., Yoshikawa, M. (2022). An Accurate, Flexible and Private Trajectory-Based Contact Tracing System on Untrusted Servers. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_35

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  • DOI: https://doi.org/10.1007/978-3-031-21047-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21046-4

  • Online ISBN: 978-3-031-21047-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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