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International Conference on Big Data Intelligence and Computing

DataCom 2022: Big Data Intelligence and Computing pp 142–154Cite as

Privacy-Protective Distributed Machine Learning Between Rich Devices and Edge Servers Using Confidence Level

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13864))

Abstract

Many recent studies have focused on using the personal information collected by edge devices for machine learning on servers while protecting privacy. In most cases, a high-performance server aggregates and manages all data. However, users may refuse to pass on their personal information to an external server owing to the risk of information leakage. To address this concern, we propose a distributed machine learning model that does not extract any sensitive data from the edge device. In the proposed model, edge devices also run machine learning and integrates learning results obtained at edge servers with those obtained at edge devices by comparing their confidence levels. To validate the proposed model, we performed experiments on facial image recognition using Jetson Nano. Experimental results confirm that the proposed model enables the use of personal data for machine learning without transferring any sensitive information to the edge server. With this learning model, each user could use personal information securely and could receive the best match results.

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Author information

Authors and Affiliations

  1. Ochanomizu University, 2-1-1 Otsuka, Tokyo, Japan

    Saki Takano & Masato Oguchi

  2. The University of Tokyo, 7-3-1-1 Hongo, Bunkyo-ku, Tokyo, Japan

    Akihiro Nakao

  3. Kogakuin University, 1-24-2 Nishi-shinjuku, Suhinjuku-ku, Tokyo, Japan

    Saneyasu Yamaguchi

Authors
  1. Saki Takano
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  2. Akihiro Nakao
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  3. Saneyasu Yamaguchi
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  4. Masato Oguchi
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Corresponding author

Correspondence to Saki Takano .

Editor information

Editors and Affiliations

  1. Asia University, Taichung, Taiwan

    Ching-Hsien Hsu

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Mengwei Xu

  3. University of New Brunswick, Fredericton, NB, Canada

    Hung Cao

  4. Asia University, Taichung, Taiwan

    Hojjat Baghban

  5. University of Fiji, Samabula, Fiji

    A. B. M. Shawkat Ali

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Takano, S., Nakao, A., Yamaguchi, S., Oguchi, M. (2023). Privacy-Protective Distributed Machine Learning Between Rich Devices and Edge Servers Using Confidence Level. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_10

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  • DOI: https://doi.org/10.1007/978-981-99-2233-8_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2232-1

  • Online ISBN: 978-981-99-2233-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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